NATIONAL LIVESTOCK CENSUS - International Programsand Prof Fred Dahm (Texas A&M University, College...

NATIONALLIVESTOCK CENSUS2002-2003

AFGHANISTAN

FINAL REPORT 2006

Cover page photos: Reynolds

AFGHANISTAN

NATIONALLIVESTOCK CENSUS

2002-2003

OSRO/AFG/212/AFG

FINAL REPORT

FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONSROME, 2008

The designations employed and the presentation of material in this information product do not implythe expression of any opinion whatsoever on the part of the Food and Agriculture Organization of theUnited Nations (FAO) concerning the legal or development status of any country, territory, city or areaor of its authorities, or concerning the delimitation of its frontiers or boundaries. The mention of specificcompanies or products of manufacturers, whether or not these have been patented, does not imply thatthese have been endorsed or recommended by FAO in preference to others of a similar nature that arenot mentioned.

The views expressed in this information product are those of the authors and do not necessarily reflectthe views of FAO.

ISBN 978-92-5-105950-0

All rights reserved. Reproduction and dissemination of material in this information product foreducational or other non-commercial purposes are authorized without any prior written permissionfrom the copyright holders provided the source is fully acknowledged. Reproduction of material in thisinformation product for resale or other commercial purposes is prohibited without written permissionof the copyright holders.Applications for such permission should be addressed to:ChiefElectronic Publishing Policy and Support BranchCommunication DivisionFAOViale delle Terme di Caracalla, 00153 Rome, Italyor by e-mail to:[email protected]

© FAO 2008

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CONTENTS

1. ACKNOWLEDGEMENTS 1

2. EXECUTIVE SUMMARY 3

3. BACKGROUND 7

4. ACTIVITIES 94.1. Organisation and Planning 9

4.2. Recruitment and Training 10

4.3. Data Collection 10

4.4. Data Entry and Checking 11

4.5. Data Analysis 11

5. RESULTS 135.1. Census Data Summaries (Level 1 Census) 13

5.1.1. Districts 135.1.2. Villages, communities and families 135.1.3. Livestock Numbers 165.1.4. Herd Structures 235.1.5. Changes in Livestock Ownership 26

5.2. Regional Production System Characteristics (Level 2 Census) 285.2.1. Forage Production and Feeding Situation 31

5.2.1.1. Types of forages used 315.2.1.2. Preferences for feeds and feed supply 32

5.2.2. Production Calendar 365.2.3. Livestock Species, Numbers, and Demography 43

5.3. Producer Characteristics (Level 2 Census) 465.3.1. Livestock Wealth Distribution 465.3.2. Livestock Management 52

5.3.2.1. Age structure 525.3.2.2. Livestock performance characteristics 545.3.2.3. Culling reasons 555.3.2.4. Livestock herd management and sales policy 56

5.3.3. Markets 595.3.3.1. Livestock sales – priority categories and reasons 595.3.3.2. Trading partners for farmers 595.3.3.3. Distance to markets 595.3.3.4. Sales decisions 60

5.4. Women Livestock Survey 625.4.1. Introduction 625.4.2. Work Responsibility 65

5.4.2.1. Cattle 655.4.2.2. Sheep 675.4.2.3. Goats 69

5.4.3. Decision Making 715.4.3.1. Cattle 715.4.3.2. Sheep 735.4.3.3. Goats 755.4.3.4. Graphical Summaries 77

5.4.4. Goals and Preferences 805.4.4.1. Preferred livestock species for herd expansion 80

5.4.4.1.1. Cattle 805.4.4.1.2. Poultry 815.4.4.1.3. Sheep 825.4.4.1.4. Goats 835.4.4.1.5. Summary of goals and preferences 84

5.4.5. Improvements and Problems 865.4.5.1. Problems identified for cattle production 865.4.5.2. Desired improvements in cattle production 885.4.5.3. Problems identified for sheep production 905.4.5.4. Desired improvements in sheep production 925.4.5.5. Problems identified for goat production 945.4.5.6. Desired improvements for goat production 965.4.5.7. Summary 98

5.5. Karakul Survey 1025.5.1. Introduction 1025.5.2. Flock Size and Distribution of Colour Types 1035.5.3. Pelt Production 1055.5.4. Markets 108

5.5.4.1. Returns from pelts and wool 1085.5.4.2. Market opportunities 109

6. DISCUSSION 1136.1. Census Data Summaries 113

6.2. Regional Production Systems and Producer Characteristics 114

6.3. Women Survey 116

6.4. Karakul Survey 116

7. METHODS 1197.1. Survey Design 119

7.2. Analysis Methodology 120

7.3. Data Consistency 120

7.4. Key Lessons 125

8. ANNEX (ONLY AVAILABLE ON CD) 1298.1. District Naming Comparisons and Explanations 129

8.2. Tabular Summary Statistics Level 1 146

8.3. Tabular Summary Statistics Level 2 173

8.4. Tabular Summary Statistics Women Livestock Survey 178

8.5. Tabular Summary Statistics Data Consistency 261

8.6. Maps 264

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FOREWORDThis report provides the analysis of the four separate surveys conducted under the 2003 LivestockCensus.

Livestock plays a fundamental role in Afghan economy and livelihoods. It has been accorded one ofthe highest priorities in the Master Plan of the Ministry of Agriculture, Irrigation and Livestock (MAIL).Over the past 30 years livestock population has fluctuated due to insecurity and drought in thecountry. Reliable, updated information and data on livestock numbers, products and productionsystem of the country are scanty. Such data are needed for the appraisal, formulation andimplementation of livestock development programmes and projects.

The 2003 Afghanistan National Livestock Census conducted by the Food and AgricultureOrganization of the United Nations (FAO) and MAIL aimed to narrow the data and information gapon livestock. The census programme was designed by Professor Wolfgang Pittroff, University ofCalifornia, Davis, United States of America and Dr Olaf Thieme, FAO. Census enumeration andassessment as well as data processing were done by FAO and MAIL staff. Fieldwork was supervisedand managed by Dr Len Reynolds and the FAO national team.

My heartiest thanks are due to all institutions and persons involved in the conduct of the survey andin production of its results. First and foremost, I would like to thank the farmers and the villagers whoprovided data and information for the survey. The census would not have been successful withouttheir wholehearted support. My special thanks are due to staff of FAO and MAIL for the questionnairedesign, survey enumeration and assessment. I should like to acknowledge the financial supportprovided by the Government of Italy for this important activity through the Italian voluntarycontribution to ITAP 2002/2003 in Afghanistan project (OSRO/AFG/212/ITA). This work would nothave been possible without FAO's technical assistance and operational support.

H.E. Obaidullah Ramin Minister of Ministry of Agriculture, Irrigation and Livestock (MAIL)

Kabul, AfghanistanJanuary 2008

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1. ACKNOWLEDGEMENTSThis work was made possible thanks to the help of many diverse groups including the Government ofItaly through the Italian Cooperation Fund, the Afghanistan Ministry of Agriculture and AnimalHusbandry (MAAH), the Afghanistan Ministry of Interior (MoI), the Central Statistical Office (CSO) inKabul, the Dutch Committee for Afghanistan, Oxfam (UK) and the University of Kabul. Assistance andcooperation was gratefully received from many staff in the MAAH including Mr. Jawat, DeputyMinister of MAAH, Dr Hanif, General President of Animal Husbandry, and Dr Raufi, General Presidentof Veterinary Services. Supervision of fieldwork would not have been possible without the enthusiasticsupport of many staff from the MAAH, the Emergency Operations and Rehabilitation Division (TCE) ofFAO, the University of Kabul, the Dutch Committee for Afghanistan and Oxfam UK. Fieldwork wasdependant upon the conscientious attention of almost 900 staff, mainly from Veterinary Field Unitswho visited villages and collected the data. A concentrated period of data entry and checking wasundertaken by a small group of hard working young staff.

Clarification of district names and locations was undertaken with assistance from the MoI, the CSOand Dr Pinney from the Afghanistan Ministry of Reconstruction and Rural Development. Assistancefrom UN personnel across diverse projects in Afghanistan and Rome is acknowledged, includingparticularly Dr Majok, Dr Favre, Dr Crowley, Mr. Mack and Mr. Miagostovich.

The Afghanistan National Livestock Census was designed by Prof Wolfgang Pittroff, University ofCalifornia, Davis, and Dr Olaf Thieme, FAO. Field work was managed by Dr Len Reynolds and DrHabib Nawroz.

Data were analysed by Prof Wolfgang Pittroff (University of California, Davis), Dr Olaf Thieme (FAO)and Prof Fred Dahm (Texas A&M University, College Station, United States of America).

The preliminary report was published in 2003. A long period of in-depth analysis of the massiveamounts of data collected in the census followed, especially focusing on Level 2 and WomenLivestock Resources Surveys. Data analysis iterated in numerous rounds with post-hoc data clean-upand clarification for all Levels and Surveys. Particularly important was a comparative analysis of Level 1and Level 2 data using advanced statistical methods. Given the need for a reliable base line data setand the enormous logistical demands faced by nation-wide census operations in Afghanistan, thisextraordinary additional effort seemed to be well justified. Some minor modifications of certainsummary statistics published in the preliminary report resulted, but all conclusions remained the same.

The final report was written by Prof Wolfgang Pittroff and Dr Olaf Thieme, assisted by Dr LenReynolds, Dr Nawroz and Mr. Haroon Nessar.

The project team hopes that the hard work of nearly a thousand people summarized in this reportwill contribute to the reconstruction of Afghanistan.

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2. EXECUTIVE SUMMARY1. After decades of warfare and a devastating drought it was reported that the numbers of livestock

in Afghanistan had fallen dramatically. In early 2002, the Ministry of Agriculture and AnimalHusbandry of the Transitional Islamic State of Afghanistan requested FAO to organise a nationallivestock census.

2. The work started in Afghanistan in October 2002 with funding from the Government of Italy andfield work was completed by April 2003. Every village in Afghanistan was visited, with theexception of Barmal District in Paktika Province because of insecurity, and parts of Ghor Provincebecause of lack of accessibility over the winter period.

3. A team of 28 supervisors, 24 female surveyors and 821 enumerators were employed for datacollection; 14 data entry personnel and 10 data checkers undertook data entry and proofing.

4. Livestock numbers, limited herd structure data and change in ownership during drought weredetermined by total enumeration in Level 1 of the census. Total enumeration was conducted atthe community level in every village in Afghanistan (with the exceptions mentioned above).

5. Level 2 of the Census consisted of a detailed production system analysis designed to survey arepresentative cross-section of Afghan livestock producers. These data were collected bysupervisors from 1 284 selected representative households in randomly chosen villages. The datagive detailed information about production practices, production calendars, key productionbottlenecks, and suggestions for interventions.

6. In order to ascertain the role of women in livestock production in Afghanistan, a detailed surveyfocusing on gender role in work responsibility, decision making and producer goals wasconducted by female enumerators interviewing 2 899 women in livestock producer households.The data give a detailed picture of gender role in livestock production in Afghanistan, and inaddition to Level 2 data, show where successful interventions for livestock development shouldbe directed.

7. Given the historic significance and current potential for value-added livestock production, a surveywas conducted to gather information specific to the Karakul sector. Detailed information onKarakul sheep production systems was collected from 132 producers. The data are a snapshot ofcurrent production practices and provide, for the first time, background information about thispotentially important value-added sector. It should be followed up by a market analysis.

8. The collected data covered 3 044 670 families in 53 214 communities across 36 724 villages.Some communities were unable to state the number of existing families.

9. The total number of cattle in Afghanistan was 3.72 million, and there were 8.77 million sheep,7.28 million goats, 1.59 million donkeys, 0.18 million camels, 0.14 million horses, 12.16 millionchicken, 0.42 million ducks and 0.60 million turkeys.

10. The number of cows kept per family was low, with only Khost, Kunar, Laghman and NuristanProvince reporting more than 1.5 cows per family. The data on number of calves suggests that inmany areas, restocking of depleted cattle herds would not be possible at the current reproductionrates.

11. Similarly in many areas reported numbers of young sheep and goats appear low and rebuilding ofherds will be slow.

12. The numbers of families without livestock have increased during the years of drought from 11.4families to 14.4 families per community. However, at the same time numbers of family percommunity has increased

13. There are no pre-drought livestock census data which could be used for direct comparisons.However, earlier survey results combined with the information from the present census indicatethat stock holding per family have decreased sharply.

14. Information from the Level 2 and Women Surveys clearly show that feed and forage productionare the major bottleneck for livestock production development in Afghanistan. Future livestockdevelopment activities should fully consider these findings and make fodder production anintegral part. There is also an urgent need for projects that integrate crop production andlivestock development and further animal health programs must be evaluated and planned inconjunction with interventions that are aimed at improving the feed situation.

15. Generally, the farmers’ responses after the drought broke in 2003 indicated an optimistic outlook.It was also the time when the new Government took office which might have partly influenced

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the positive views about the future. However, Afghanistan is part of the largest drought-proneregion in the world and drought will remain a recurring phenomenon. Watershed rehabilitationand drought preparedness must accompany the reconstruction of the Afghan livestock sector tomake it more resilient in the future.

16. Three areas in livestock production appear to be most relevant for interventions: Forageproduction, dairy production, and poultry production. Clearly, cattle are the livestock specieswhich is most important to farmers, and dairy cows are their most important animals. The biggesteffect on increased production, improved livelihoods and more food security could be achieved byhelping farmers with suitable forage production, ideally well integrated into field crop rotationschemes. Farmers are keenly interested in market integration with dairy products – the potentialbenefits for small scale commercial dairy development appear to be substantial. Further, a largeproportion of women surveyed reported a keen interest in poultry production with a clear focuson egg sales. This seems to be the most effective intervention directly benefiting women in ruralAfghanistan, but also in urban centres and should receive appropriate attention by developmentworkers.

17. Wealth distribution data show that Afghan livestock producers are extremely poor byinternational standards. However, social stratification exists and is regionally differentiated. Theregional differentiation is also a result of the 1998-2002 drought, which was of variable severityin different parts of the country. The census shows that the western region has the highestnumber of farmers with no livestock at all. This was the region most affected by the drought.

18. There appears to be potential for income generation from intensified small ruminant, especiallysheep production. Most settled owners of small ruminants have cattle as well and often belongto the wealthier part of the population. The nomadic Kuchi population who keeps a large part ofthe small ruminants was not included in the present surveys but pre-drought information existsfrom another FAO (1999) study. Opportunities to develop sheep production are more promisingfor farmers with access to irrigated land supporting greatly improved forage production. Furtheranalysis of the sheep sector seems indicated, since opportunities for value-added, possiblystratified production appear to exist.

19. Level 2 surveys on feed supply and problems and constraints faced by producers provided muchinsight into the state of natural resources supporting livestock production in Afghanistan.Livestock owners make substantial efforts to supplement the feeding from natural pastures andcrop aftermath with cultivated fodder crops. Feeding of concentrates is very common, but thequantities are usually low and fed mainly during the winter months.

20. The length of the feeding period in the harsh climate of Afghanistan commonly exceeds sixmonths in most locations. Thus, available resources for supplemental feeding and winter foragedetermine the number of animals that can be kept. This ‘Winter Feed Gap’, the major constraintof livestock production in most parts of Afghanistan, must be addressed with great care,however. Although no systematic data are available, the aspect of rangeland conditions appearsto be poor to catastrophic in many areas of the country. An expansion of livestock numbers,especially small ruminants, facilitated by improved forage resources for the winter feeding periodwill likely further increase pressure on already stressed rangelands. For small ruminants foragesharvested or grazed from pastures and rangelands appear to be quantitatively the most importantfeed resource. Rangeland conservation, accordingly, is a task of national priority.

21. The average time to reach markets was about two hours, a surprisingly low figure considering theroad conditions in Afghanistan. It is possible though that more villages were selected for the Level2 survey which was nearby the market centres. Farmers did not seem to be overly concernedabout access to markets. In light of the other critical issues identified (nutrition, low reproduction,farmer concerns about veterinary health care) markets are not an issue of highest priority.

22. The analysis of distribution of work load and decision making clearly suggests that women areresponsible for most livestock-related work at the homestead, while children and to a lesserdegree men handle livestock tasks outside the home compound. Since dairy animals are mostlyfed at the homestead, cattle forage programs should consider gender in their design. Decisionmaking in livestock production is the domain of men, but for some of the important decisions(purchase or sale of cattle) women share responsibility in the decision making process. Womendecide milk and wool sales; therefore, milk collection schemes must explicitly consider the viewsand involvement of women.

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23. Women from rural households provided a wealth of answers on questions related to problems oflivestock production and desirable improvements. They overwhelmingly selected cattle as theirmost important species, and opted for milk production for sale as the most importantdevelopment activity for cattle production. For women the second most important species waschicken, with the aim of egg production for sale. Future survey work in preparation of livestockinterventions should further pursued these questions by asking the questions to both men andwomen.

24. Karakul sheep production for pelts was in the past an economically very important livestockproduction in Afghanistan and is still practised. Farmers reported lower sales in pelts and woolcompared to the previous year, but expected to sell more the following year. Much, if not most ofthe Karakul output enters the international markets. More research on the international marketsfor Astrakhan pelts is therefore needed to judge the potential future of Karakul sheep productionin Afghanistan.

25. The census was designed with two levels of sampling intensity. Level 1 as total enumerationcensus, designed to determine the total number of livestock in the country, and to provide anassessment of livestock losses caused by a catastrophic drought. Level 2 was a survey designed todescribe production systems and markets, based on a detailed questionnaire to selected farmers.The data analysis process revealed that sufficient well-trained personnel for checking of dataconsistency during the census are the key to timely and accurate census execution and analysis.Only stringent quality control exercised during the census allows the use of sophisticatedstatistical methods for in-depth consistency checking.

photo by: Thieme

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3. BACKGROUNDLivestock play a fundamental role in Afghan agriculture, but existing information on the numbers ofanimals in the country and their distribution predates many years of warfare and a devastating fouryear long drought. Field reports indicated that animal numbers have fallen sharply since the earliersurveys, but there are no statistics to substantiate the claim. Farmers are aware of the need forvaccination against animal disease but animal numbers are required for planning veterinarycampaigns. Reliable statistics as well as comprehensive production system information are needed toguide the design and implementation of livestock development programs carried out in the rebuildingof the Afghan agricultural sector.

During 2002, the Italian Government agreed to contribute funds for the agricultural sub-sectors ofthe Intermediate and Transitional Assistance for the Afghan people (ITAP), including the provision offunds for a National Livestock Census.

An interim report with the core of the census data was published in 2003. This final report includesthe expanded analysis and summary of four separate surveys: Levels 1 and 2 of the livestock census(Level 2 included a detailed production system appraisal), the Women Livestock Owner Survey, andthe Karakul Survey.

photo by: Reynolds

4. ACTIVITIES4.1 ORGANISATION AND PLANNING

Work in Afghanistan started in September 2002. An International Livestock Consultant developed thecensus and data analysis design and provided operational guidelines and training manuals. TheCensus Manager planned and supervised the administration of the field work. Census design andmethodology are described in full detail in Section 6. Briefly, the census was conducted on two levelsof intensity. Level 1 constituted the complete enumeration of all livestock numbers, including limitedinformation on livestock demography and recent changes in livestock wealth. These data werecollected at the community level, with a community typically representing a mosque assembly withina village or town. A much more detailed Level 2 census was administered by Supervisors to individualhouseholds in randomly selected districts and villages. Level 2 covered detailed information on animalhusbandry, feed management and markets. In addition to the census, two separate surveys wereadministered. One survey specifically addressed livestock production issues from the perspective ofwomen. This survey was administered by female enumerators. The second additional surveyspecifically addressed Karakul sheep production as a livestock activity of historic significance andcurrent potential for valued-added production.

Work plans were drawn up by early October, with the intention of completing data collection beforelivestock moved out of their winter quarters with the onset of spring weather. The start of work wasdivided so that provinces with predominantly mountainous areas would start earlier than loweraltitude Provinces where winter would be less of a hindrance to movement of data collectors.

Two levels of management were established. The Census Manager and an Assistant Manager basedin Kabul were responsible for nation-wide management of activities. Supervisors, covering 2-3Provinces provide the second management level, with Data Collectors (Field Staff) based in eachdistrict are responsible for visiting all villages in their District, and collecting information on animalnumbers.

A national data base kept by the Afghanistan Information Management System (AIMS) under the UNprovided a list of Provinces, districts and villages. Afghanistan comprised 32 Provinces, 329 Districtsand 30 172 villages. Provinces varied from 4 Districts (Sari Pul) to 22 (Nangarhar). Rural districts variedfrom 4 villages (Andkhoy in Faryab Province) to 620 (Daykundi in Uruzgan Province). Based onpopulation estimates from AIMS, and assuming seven persons per family, village size varied from sixfamilies per village in Wormamay district (Paktika Province) to 3 016 in Andkhoy district (FaryabProvince). However, much of the village data was derived from information compiled in the 1970s.This material was taken as the planning base for the census.

A complication arose from variations in the number of districts between the pre-2000 situationrecognised by AIMS as the authorised situation and the number of districts recognised officially by theMinistry of the Interior. A further complication arose from the administrative situation on the groundas a result of the creation of new districts by Provincial administrations, which have not been officiallyrecognised by Central Government.

It was decided that the locally accepted name for a district would be used in the Livestock Census -based on the answers from respondents. Names were taken and recorded in Dari or Pashtu. Thesurvey forms and training manuals were prepared in English and translated into two local languages,Dari and Pashtu. Training for Supervisors was held in English, translated into Dari by an interpreter.Field staff training, provided by the Supervisors was held in Dari or Pashtu.

It was estimated that each pre-2000 district would require an average of 4.5 man months (mm) ofwork to collect the Level 1 census data, giving a national total of 1500 mm. Less time would berequired for smaller districts or districts with easier travel conditions; larger or more difficult districtswould require more time. It was also recognised that flexibility was required, and a time planconceived in Kabul could only be taken as guidance. Supervisors were allowed, within limits, to vary

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the manpower allowances to suit conditions on the ground. Considerable responsibility thereforerested on Supervisors and their judgement of the situation.

Orders were placed for vehicles and computer equipment during October. Computer equipmentarrived during January. Registration of the first vehicle occurred during February, and the secondvehicle was registered during March.

4.2 RECRUITMENT AND TRAINING

Twenty five supervisors were selected and trained by the Census Manager and his Assistant. Elevenpersons came from the Ministry of Agriculture and Animal Husbandry (MAAH), ten from previousFAO projects, one from the University of Kabul, one from an NGO and two from the private sector. Supervisor training, lasting three days, covered the purpose of the survey, the use of Level 1 and Level2 forms, and organisation and administration of Supervisor duties. During the course, the Level 1 andLevel 2 forms were field-tested by the Supervisors in a nearby village. Lessons learnt during fieldtesting were incorporated into the training. Supervisors also received guidance on organisation andimplementation of the training courses to be held in the Provinces for the Field staff.

Pairs of Supervisors were then allocated to cover 2-3 Provinces, given a list of the suggested man-months needed for data collection for each district, and an upper limit of the total man- monthsavailable for the group of Provinces under their control. They were given authority to vary the manmonths used for any one district, as long as the total man-months remained with in the limit for theirgroup of Provinces.

The country was divided into higher and lower attitude Provinces. Supervisors in higher altitudelocations started work recruitment and training of Field staff at the end of October. Selection andtraining in lower altitude areas started in late November.

In the Provinces the Supervisors visited Province Ministries to introduce themselves and inform theauthorities of their activities. In some locations it was possible to have broadcast interviews on localradio stations to publicise the livestock census. In these areas, villages then knew of the census beforethe enumerators arrived.

Data collectors were recruited by the Supervisors at District level, from local people with knowledgeof the area and of the livestock sector. These predominantly were Veterinary staff, originally part ofthe Ministry of Agriculture, but recently in a private sector environment. Other suitable local personswere recruited in Districts where no veterinary field unit existed. Training courses were organised bythe Supervisors at suitable locations around their group of Provinces, with around 30 potential fieldstaff attending each course. Attendance at a training course, and a demonstrable understanding ofthe proposed activities and duties was a requirement before contracts were offered to the field staff.

4.3 DATA COLLECTION

Field data was largely numeric. A record field remained blank where the respondents were unable toanswer the question. All names and dates were recorded in local script, Dari or Pashtu. It was decidedto record “perceived” names of the District in which the community understands itself to be located.

Information was collected at the village level. However, it was believed that information on totalanimal numbers would be more reliable from groups of a limited number of respondents, and that itwould be difficult to obtain reliable data from a single meeting in a large village. Therefore, duringthe planning period the collection unit was defined as a community group within a village.Community groups were based on mosque assemblies. A small village might have a single assembly,whereas a large village would have several.

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Enumerators made two visits to each mosque assembly. On the first visit the reason for the censuswas explained to prevent any misconception over its potential tax raising implications, and anexplanation was given of the data required. The Shura (council of elders) was requested to organisecollection of information from village families by the return date of the enumerator. A second visitwas made a few days later and information collected from a representative group from the mosqueassembly.

In addition, data was collected from all urban centres. Kabul city was treated as a separate Unit fromKabul Province for data collection. One supervisor and 30 data collectors were involved in the censusof Kabul City, taking one month to complete. Enumerators visited Local Area representatives, who inturn contacted street representatives to organise data collection. Kabul City data collectors wererecruited from the Departments of Animal Science and Veterinary Science in the University of Kabul.Field staff in the Provinces were derived predominantly from the staff of Veterinary Field Units (VFU).The majority of VFUs are independent entities, although some are still formally in contract with NGOs.FAO received regular information about approximately 230 VFU, but in some regions of Afghanistanthese linkages were lacking. Letters of Agreement were prepared with two NGOs, covering areas inthe west, west–central and southwest regions of Afghanistan to assist with identification andselection of suitable Field staff in those areas where they have links to VFUs, and where FAO was notrepresented. Visits were made by census management and supervisors to the field to oversee thework, and ensure data quality.

Data collection was competed in all Provinces by the end of March, with the exception of GhorProvince. During the winter and early spring the road access to Ghor is virtually impossible because ofsnow and mud. The main access road was only declared open by the Government on 13 April. Thesurvey was undertaken in Ghor using large numbers of data collectors and completed by the end ofApril. Insecurity prevented data collection in only Barmal District of Paktika Province, adjacent to thePakistan border.

4.4 DATA ENTRY AND CHECKING

Ten data entry personnel were recruited in early February for training by the Survey Design andAnalysis consultant, who visited Kabul again during mid February. Data were entered onto computerby two teams of five staff, on a tailored form to simplify transfer of information from paper tocomputer. Data were entered in duplicate, once by each team. Each team worked on data from oneProvince each at a time, with single districts being allocated to a single operator. Data entry wascompleted by early June.

Duplicate entry by different teams simplified the task of checking the accuracy of entry, bycrosschecking the two files relating to a single district. Differences were identified and then correctedby reference to the original data form received from the field. Data checking and correction of thebasic data set was completed by the end of July

In total more than 53 000 records for Level 1 were entered by each team, each record containing 73fields. Level 2 data comprised 1 285 data records each containing 207 fields. The Women’s surveyproduced 2508 records with 170 data fields each. The Karakul survey material consisted of 133records with 63 data fields each.

4.5 DATA ANALYSIS

Data were analysed using standard software for descriptive statistics and the statistical analysispackage SAS for analytical statistics. For total livestock numbers, records without information about number of families were included. Forall computed variables on family basis, all records with missing information about number of familieswithin the community for which the data were enumerated were omitted. Due to many missing valuecells for small stock numbers, it was decided on an individual record basis whether the missing entry

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was truly a missing value, or conversely, indicated zero. The criterion used was overall number ofanimals in the corresponding species. Where this decision was not possible, the record waseliminated.

For all computed variables involving animal ratios, records containing zeroes for the numerator wereeliminated. For records with missing values for the numerator variable, it was decided on an individualrecord basis if the missing entry denoted zero, or was truly a missing value.

The issues of data consistency encountered in this survey are reflective of general problems faced bysurveys and are further addressed in the Methods section. In the Results section we report summariesbased on Provinces and/or Agro-ecological zones. Detailed tables with all district level data are listedin the Annex sections.

photo by: Thieme

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5. RESULTS5.1 CENSUS DATA SUMMARIES5.1.1 Districts

Information from field staff employed to collect census data revealed that the FAO baselineinformation on the number of Districts was at variance with the de facto situation on the ground.Under the many shifting political administrations, new districts were established by dividing thosealready in existence. While the legal status of the new districts is uncertain and their boundariesunclear, it was decided that location identification data recorded on the survey sheet would reflectthe District names in current use by the local population.

The Ministry of the Interior (MoI) had an official list of districts, based on the 1991 situation, but thiswas different from the list used by the Central Statistical Organisation (CSO). The AIMS list of districtsderives from 1979 when the only human population census occurred. The field data collected by thecurrent Livestock Census, shows significant variation from the official government list and from theAIMS list (see Annex). The census was able to identify the relationship between districts on thedifferent lists, allowing partial comparisons to be drawn between the results of the current censusand earlier FAO work. However, since the only available computerised maps of Afghanistan have beenorganised by AIMS, and the maps presented in this report are based on the AIMS maps, the resultsshown here must be viewed keeping in mind these potential divergences.

At the provincial level, there are a number of differences between the official list and AIMS data.Some districts are allocated to a different district in the AIMS list, and some districts have beencompletely omitted. Within Provinces there are many districts that do not appear on the AIMS list,and some that have disappeared.

The AIMS data base, from pre-2000 data included 329 Districts in 32 Provinces. Areas in whichProvincial capitals are sited are counted by the MoI as “Administrative Units” rather than Districts,e.g. the capital areas of Ghazni, Qalat, and Qalay-I-Naw. The CSO uses the title “Minor Civil Division”for all Provincial Centres and Districts. There are 351 Districts plus 32 Provincial administrative Areason the MoI list, but 357 plus 32 Provincial Centres in the Minor Civil Divisions of the CSO.Information collected from the field has shown that there are 397 de facto districts (countingProvincial Administrative Units as districts), plus up to 7 more in Ghor Province, and Barmal District inPaktika Province that were not accessed by the Census (Table 1). Some of the Districts on the MoI andCSO lists are not recognised by name by the people of the ground. These were Rashidan and WaliMohd Shaheed in Ghazni Province, Taraw in Paktika Province and Firoz Nakhchir in SamanganProvince. Full details are discussed in the Annex.

The project collected data from 16 urban districts (Nahya) in Kabul City. These areas are not countedas separate districts by the MoI, but are called Districts by the CSO.

5.1.2 Villages, communities and families

The Census data showed that there were more villages than registered in the current AIMS data base.The Census recorded 36 724 villages in the districts surveyed, compared to 30 690 villages recordedin the AIMS data base, and around 36 000 villages known to the Ministry of the Interior. The numberof families recorded in the Census was 3 044 670, suggesting 83 families per village. However, thetrue number of families will be even greater because some communities were unable to state thenumber present. An attempt was made to determine the number of Kuchi (nomadic livestockproducer) families separately from resident families. The overall total of 23 949 Kuchi familiesrecorded in the census understates the true position as normal migration patterns have beendisturbed by insecurity along the Pakistan border.

Human population can be estimated, based on the numbers of families and family size. Recentestimates of rural family size (FAO, 2003) at 11.6 persons per household are higher than the figure of7.6 used in official calculations. The Livestock Census has not formally distinguished between urbanand rural families outside the capital. Assuming that 25 percent of families are urban-based and 75percent rural, and an urban family size of 7.6 people per household, the national human populationis at least 32 274 000. The Central Statistical Office (CSO, 2003) estimated the human population at22.2 million.

14

Province MoI #2003

CSO2003

AIMS2001

LivestockCensus2003

Badakhshan 27 27 13 30Badghis 6 6 7 9Baghlan 11 14 11 15Balkh 15 14 14 15Bamyan 6 6 5 7Farah 10 10 11 11Faryab 13 13 12 11Ghazni 18 18 16 17Ghor 9 9 7 (3) **Helmand 11 12 13 15Hirat 15 16 16 16Jawzjan 9 9 9 14Kabul 14 14 14 15Kandahar 15 15 12 17Kapisa 5 6 6 8Khost 12 11 12 13Kunar 14 14 12 15Kunduz 6 6 7 7Laghman 4 4 5 5Logar 6 6 5 7Nangahar 21 21 20 25Nimroz 5 4 5 6Nuristan 7 7 6 7Paktika 18 18 15 (17) *Paktya 9 10 12 14Parwan 13 13 12 14Samangan 6 6 5 9Sari Pul 5 5 6 7Takhar 16 16 12 17Uruzgan 8 8 10 9Wardak 8 8 8 10Zabul 9 9 9 12

Total 351 355 329 397# MoI data base excludes 31 Provincial capital administrative areas* Only 17 districts surveyed out of 18 districts in Paktika** Only 3 districts surveyed in Ghor Province

Table 1 Summary of districts recognised by the Ministry of the Interior (MoI), the Central Statistical Office (CSO), AIMS and reported to the Livestock Census

15

Table 2 Number of villages and families on the AIMS database dated 2001 and reported to the 2003 Livestock Census

Province AIMS2001

Livestock Census

2003

Livestock Census2003

Villages Villages FamiliesBadakhshan 1,820 1,633 111,874Badghis 639 919 94,815Baghlan 895 1,259 107,231Balkh 771 1,349 157,230Bamyan 1,543 1,712 54,169Farah 818 1,263 80,829Faryab 634 970 135,537Ghazni 2,680 2,336 97,471Ghor 1,714 815 25,867Hilmand 1,136 2,297 119,188Hirat 1,435 2,686 332,893Jawzjan 254 513 96,796Kabul 690 987 70,840Kabul City 420 289,964Kandahar 1,853 2,662 86,206Kapisa 399 582 52,059Khost 519 1,526 39,219Kunar 475 911 52,661Kunduz 377 898 63,777Laghman 464 590 49,839Logar 394 1,110 51,780Nangarhar 971 1,688 136,804Nimroz 427 611 24,969Nuristan 168 365 23,835Paktika 981 1,734 38,725Paktya 707 1,215 33,068Parwan 980 1,203 88,993Samangan 471 513 81,989Sari Pul 556 434 58,177Takhar 664 1,523 151,157Uruzgan 2,491 2,239 97,120Wardak 1,347 2,072 87,067Zabul 1,427 2,173 52,521

Total 30,700 36,724 3,044,670* Only 17 districts surveyed out of 18 districts in Paktika** Only 3 districts surveyed in Ghor Province

5.1.3 Livestock numbers

Nationally the total number of cattle was 3.7 million. Provincial summary figures are shown below.The most important cattle owning Provinces were Badakhshan and Nangarhar, each holding over 0.3million cattle, and Takhar and Uruzgan with over 0.2 million each. Nationally, there were 8.8 millionsheep and 7.3 million goats. Almost 0.8 million sheep were in Hirat Province, and over 0.5 million inBadghis, Faryab, Hilmand and Kandahar. Out of 7.3 million goats, over 0.5 million occurred in Hirat,Hilmand and Nuristan. Afghanistan has 1.6 million donkeys, with over 0.1 million in Badakhshan,Hirat and Takhar each. With less than 175 000 camels, only Hilmand and Kandahar held more than20 000. Horses occurred in the smallest numbers (140 000), with more than 20 000 only in Kunduz.

16

17

These numbers constitute a snapshot in time whose most valuable function is that of a nationalbaseline inventory. Combined with data on herd structure and reproductive performance levels, anassessment of the recovery and development potential of the Afghan livestock sector is possible. Basicherd structure data were collected in Level 1. More detailed herd structure information and data onreproductive performance became available through the Level 2 surveys. These data can be used inherd dynamics models to project growth and off take potential of the national inventory. The contribution of livestock to livelihoods is an important factor in assessing the importance of thelivestock sector. Hence, animal numbers per household must also be considered. These numbers arepresented below, summarised on the basis of province and agro-ecological region. More informationon agro-ecological regions is given in Section 5.2 of this report.

Only families in Badakhshan, Khost, Kunar, Laghman and Nuristan own on average more than threeor more cattle. Families own more sheep than goats in most Provinces, but Nuristan families haveseven times as many goats as sheep. In general, goats are more numerous than sheep in provincesalong the border with Pakistan. Poultry are kept in greatest numbers by families in the east andsouth-east.

For most Afghan farmers, animals are the only source for power for cultivation and transport. Thepreferred animal for draft power is the oxen; however, many farmers are too poor to own oxen. Thenext most frequently used animal for draft is the donkey. The statistics for draft animals summarisedthree categories: oxen (taken from the answers for this category in the Level 1 survey), cattle anddonkeys available for draft (taken from the corresponding answer categories in the Level 1 survey)and animals potentially available for draft (a computed category that included all cattle, donkeys,camels and horses older than two years). The following table presents these numbers as averages perprovince and summarised for agro-ecological regions. A very small number of records of the Level 1census did not list the number of families present in the community. Accordingly, all statisticscomputed on a per family basis were calculated on a filtered data set that was created after furtherconsistency checking.

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Table 3 Summary of total livestock on the basis of province

Province Cattle Sheep Goats Donkeys Camels Horses

Badakhshan 317,120 400,521 402,658 107,336 208 12,996Badghis 40,873 630,896 275,430 93,857 9,829 3,190Baghlan 168,170 332,665 236,127 73,653 770 19,457Balkh 74,976 479,323 147,483 58,932 7,474 9,796Bamyan 77,893 227,650 60,143 48,905 0 2,691Farah 78,525 164,559 493,029 39,848 6,490 1,849Faryab 74,967 634,855 353,179 79,228 12,215 4,394Ghazni 84,795 246,083 76,266 39,087 721 840Ghor 40,354 104,636 40,822 17,415 1 1,922Hilmand 184,866 596,074 583,178 52,536 20,247 2,775Hirat 185,785 790,708 696,894 155,211 14,088 4,977Jawzjan 32,669 440,338 125,146 29,689 15,171 8,305Kabul 57,713 91,994 97,140 16,300 356 491Kabul City 14,728 10,994 11,534 2,346 0 1,050Kandahar 70,286 605,049 390,156 42,756 31,229 1,263Kapisa 141,736 90,266 72,270 14,000 270 928Khost 164,426 79,924 167,300 30,726 3,040 365Kunar 196,990 104,007 433,008 29,815 46 216Kunduz 157,888 328,231 49,462 51,721 12,219 20,012Laghman 158,359 161,097 163,306 19,831 1,762 246Logar 58,748 41,610 30,044 14,436 149 216Nangarhar 304,892 267,749 238,991 63,378 3,322 3,362Nimroz 11,448 65,978 126,315 19,330 9,872 662Nuristan 95,892 75,480 559,898 12,821 0 3,074Paktika 52,265 104,444 160,663 13,977 4,018 226Paktya 85,670 41,292 137,071 16,103 553 291Parwan 121,194 149,197 169,947 42,918 137 2,505Samangan 42,121 323,416 124,377 67,106 3,247 5,259Sari Pul 63,187 240,893 85,822 60,703 3,198 4,914Takhar 236,194 324,031 231,552 139,314 912 14,718Uruzgan 229,956 362,503 332,365 74,796 10,967 7,613Wardak 56,428 93,937 55,139 31,958 60 978Zabul 34,295 161,951 154,151 27,562 2,699 513

Total 3,715,409 8,772,351 7,280,866 1,587,594 175,270 142,094

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Table 4 Summary of total poultry on the basis of province

Province Chickens Ducks Turkeys

Badakhshan 314,992 2,897 1,793

Badghis 243,141 113 2,650

Baghlan 280,234 13,889 69,876

Balkh 287,895 2,036 3,677

Bamyan 123,432 1,324 6,227

Farah 438,934 10,763 40,431

Faryab 187,457 314 2,905

Ghazni 321,420 1,625 5,696

Ghor 71,391 287 622

Hilmand 850,020 82,262 89,815

Hirat 691,101 4,632 22,744

Jawzjan 155,470 72 560

Kabul 345,497 1,505 3,342

Kabul City 584,833 2,557 4,743

Kandahar 579,870 2,943 12,373

Kapisa 336,556 12,724 1,891

Khost 613,025 3,769 41,816

Kunar 567,032 17,278 53,298

Kunduz 236,551 11,589 15,019

Laghman 391,365 118,245 28,999

Logar 198.365 3,099 6,316

Nangarhar 1,046,032 45,327 86,156

Nimroz 136,657 5,596 21,809

Nuristan 281,504 216 672

Paktika 287,518 405 2,484

Paktya 472,394 2,478 13,852

Parwan 307,186 8,462 4,336

Samangan 118,862 209 288

Sari Pul 124,831 222 1,575

Takhar 345,947 7,182 6,718

Uruzgan 656,214 56,201 43,210

Wardak 268,652 1,312 2,918

Zabul 290,467 1,000 907

Total 12,155,846 422,533 599,718

20

Table 5 Livestock owned per family in 2002-2003

Agro-Ecological Region Province Cattle Sheep Goats Chickens Donkeys Horses Camels Oxen

Draft Animals (total

-

potential)*

Draft Animals

(reportedin

survey)Badakhshan 3.05 4.31 4.23 2.93 0.96 0.15 0.00 0.66 2.98 1.50

East Khost 4.49 2.71 5.33 16.77 0.90 0.01 0.11 0.07 3.16 0.80

Kunar 4.32 2.23 10.65 12.75 0.65 0.01 0.00 0.59 3.77 1.29

Laghman 3.76 7.03 4.10 10.20 0.65 0.01 0.08 0.31 3.13 0.91

Nangarhar 2.66 3.67 3.31 9.65 0.57 0.02 0.06 0.33 2.43 0.85

Nuristan 4.20 4.40 31.04 15.24 0.69 0.17 0.00 0.52 3.85 1.28

Paktika 1.34 4.19 6.63 7.83 0.37 0.01 0.15 0.02 1.10 0.33

Paktya 2.76 1.95 4.94 15.11 0.49 0.01 0.02 0.03 1.84 0.49

Average 3.06 3.47 6.78 12.06 0.60 0.02 0.08 0.20 2.45 0.75

Centre- Kabul 0.86 2.14 2.24 5.30 0.28 0.01 0.01 0.06 0.62 0.05

EastKabul City 0.08 0.25 0.07 2.44 0.02 0.00 0.00 0.00 0.07 0.02

Kapisa 2.89 2.21 1.82 6.64 0.32 0.02 0.00 0.12 1.29 0.68

Logar 1.31 1.30 0.98 4.71 0.31 0.01 0.00 0.09 0.96 0.38

Parwan 1.52 2.03 2.33 3.75 0.49 0.03 0.00 0.10 0.83 0.44

Wardak 0.87 1.83 0.84 3.32 0.48 0.01 0.00 0.11 0.73 0.52

Average 1.16 1.71 1.35 4.11 0.37 0.01 0.00 0.09 0.76 0.38

Centre Bamyan 1.75 6.13 1.55 2.85 0.90 0.07 0.00 0.39 1.94 1.22

Ghazni 1.02 3.26 1.00 3.93 0.47 0.01 0.00 0.06 0.68 0.46

Ghor 1.94 5.63 1.75 3.36 0.72 0.09 0.00 0.66 2.39 1.25

Uruzgan 2.73 4.72 4.20 9.70 0.92 0.05 0.13 0.54 2.90 1.28

Zabul 0.69 4.40 4.60 6.75 0.62 0.01 0.07 0.10 0.98 0.62

Average 1.61 4.57 2.85 5.97 0.72 0.04 0.05 0.30 1.70 0.91

North Baghlan 1.88 4.26 2.96 3.22 0.75 0.23 0.01 0.39 1.37 1.00

Balkh 0.58 3.99 1.16 2.08 0.44 0.07 0.06 0.13 0.75 0.50

Faryab 0.55 4.80 2.62 1.37 0.60 0.03 0.09 0.22 1.02 0.77

Jawzjan 0.35 4.80 1.34 1.66 0.31 0.09 0.16 0.06 0.67 0.34

Kunduz 2.56 5.57 0.75 4.15 0.81 0.28 0.17 0.60 2.92 1.15

Samangan 0.49 3.53 1.47 1.38 0.80 0.06 0.03 0.14 0.78 0.77

Sari pul 1.12 4.42 1.60 2.30 1.05 0.09 0.07 0.32 1.49 1.07

Takhar 1.83 2.50 1.90 2.79 1.00 0.11 0.01 0.54 2.34 1.33

Average 1.22 4.12 1.88 2.42 0.70 0.13 0.06 0.30 1.38 0.87

West Badghis 0.55 9.94 4.37 3.08 1.10 0.05 0.15 0.21 1.39 1.16

Farah 1.15 2.70 8.77 6.51 0.62 0.03 0.14 0.06 1.12 0.62

Hilmand 1.68 6.90 6.55 8.62 0.57 0.04 0.32 0.06 1.56 0.50

Hirat 0.70 4.55 4.43 2.80 0.67 0.03 0.10 0.18 1.13 0.73

Kandahar 0.92 8.12 5.75 7.44 0.59 0.02 0.34 0.03 1.31 0.53

Nimroz 0.60 4.35 8.70 7.76 0.86 0.03 0.54 0.00 1.47 0.26

Average 1.04 6.15 6.11 6.19 0.66 0.03 0.25 0.09 1.32 0.62

Overall AVG 1.60 4.28 3.91 5.87 0.64 0.05 0.10 0.22 1.56 0.75* A computed category that include all cattle, donkeys, camels and horses older than two years

23

5.1.4 Herd Structures

Female cattle older than 2 yearsAlthough most cows do not enter lactation at 2 years in Afghanistan, for the simplification of theLevel 1 survey, this age threshold was chosen to provide an upper limit to numbers in meaningfulstock classes, in particular cattle available for dairy production and recruitment (see below). Onlyfamilies in Khost, Kunar, Laghman and Nuristan owned more than 1.5 cows per family. Note that allstatistics in this section not referenced on the basis of family were computed from the full data set(which included also records not listing the number of families present in the community surveyed).

Young stockA simple approach to summarising the state of reproduction is to compute a ratio of young stock tonumber of reproductive females. If data are of acceptably accuracy (this can be only expected if thesimplest of numbers are used, e.g. two age classes), this ratio can provide some insight as to whetherpopulations are stable, declining or have potential to increase. Of course there will be effects such astime of the year, regional markets and so forth that will influence the numbers, since not all data canbe collected simultaneously. Therefore, these data, on their own, are not sufficient to suggest highpriority areas nor do they give reason for complacency. The data are computed as the sum ofcategories (younger than 2 years) and current year’s young (calves, lambs, kids, foals). Thesecategories were kept clearly separate in the census. In many cases, no-entry cells were interpreted astrue zeroes. This may not always be correct. Therefore, the values reported in Table 7 must be viewedas a lower limit.

CattleThe data indicate that restocking of depleted herds in many areas will not be possible with currentreproduction rates. This is further substantiated by Level 2 data and will be discussed below. Theavailable information suggests that cattle reproduction in the east-central area (Ghazni, Wardak,Logar, Parwan, Kapisa Kunar) and in Kunduz and Badghis at 0.7 calves per adult female per yearindicates a calving interval of > 15 months after allowing for 10 percent mortality. At the other endof the scale, the calving interval in Jawzan appears to be 3.5 years or greater. Note that thesecalculations do not consider female sterility. Given the extreme poverty of farmers in Afghanistan,rigorous culling based on deficient reproductive performance is unlikely. Depending on results fromLevel 2, more in-depth analysis of this critical problem is clearly indicated, for example by way of spotsurveys and market surveys. It is also important to compare these data with available informationabout regional extent and severity of drought.

SmallstockThe basic data in Level 1 do not differentiate between Karakul and other sheep breeds. Provinceswith higher levels of Karakul could expect lower proportions of two year old young stock. The datashown below suggests lamb numbers/ewe/year of 1.5 available for restocking and above only inLogar, Nangarhar and Wardak, allowing for a mortality rate over two years of 25 percent subject tothe same caveats as the cattle values. Similarly for goats, 1.5 kids/doe/year is only reached in Badghis,Nangarhar and Nuristan.

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Table 6 Cows owned per family in 2002-2003

Agro-Ecological Region

Province Average Standard Deviation

Badakhshan 1.08 0.88

East Khost 2.35 1.56Kunar 1.86 4.72Laghman 1.90 1.70Nangarhar 1.32 2.51Nuristan 1.87 1.15Paktika 0.65 0.55Paktya 1.33 0.97Average 1.49 2.25

Centre-East Kabul 0.35 0.38Kabul City 0.05 0.10Kapisa 0.83 0.44Logar 0.65 0.55Parwan 0.44 0.40Wardak 0.39 0.35Average 0.45 0.44

Centre Bamyan 0.56 0.39Ghazni 0.47 0.45Ghor 0.60 0.35Uruzgan 0.97 0.91Zabul 0.32 0.45Average 0.60 0.65

North Baghlan 0.70 0.78Balkh 0.26 0.35Faryab 0.13 0.13Jawzjan 0.18 0.26Kunduz 1.11 1.28Samangan 0.18 0.27Sari pul 0.37 0.48Takhar 0.76 0.69Average 0.48 0.71

West Badghis 0.14 0.21Farah 0.52 0.68Hilmand 0.77 0.62Hirat 0.27 0.36Kandahar 0.47 0.44Nimroz 0.35 0.44Average 0.47 0.54

Overall Average 0.68 1.12

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Table 7 Ratio of young per adult for cattle, sheep, goats and donkeysYoung per

CowYoung per

EweYoung per

DoeYoung per

Jenny


Province AVG SD AVG SD AVG SD AVG SD

Badakhshan 1.3 0.8 1.3 1.2 1.3 0.9 1.4 1.6

East Khost 0.9 0.7 0.7 0.8 0.9 2 0.5 0.6

Kunar 1.4 0.9 1.4 1 1.3 1 1.3 1.4

Laghman 1.1 1 1.3 3.1 1.2 1.1 0.7 1

Nangarhar 1.2 1 2.1 3.7 1.5 3.1 0.6 0.9

Nuristan 1.3 0.8 1.4 0.9 1.9 11.4 1.3 1

Paktika 1.1 0.6 1.2 1 1 1.2 0.3 0.5

Paktya 1.1 0.6 1.3 1.6 1.1 1.7 0.3 0.5

Centre- Kabul 1.2 1.0 1.5 1.9 1.3 3.2 0.8 1.5

East Kabul City 1 0.8 1.7 2.1 1.2 0.9 0.8 1.5

Kapisa 1.6 0.8 1.8 1.5 1.3 1.2 0.8 0.8

Logar 1.3 0.9 2.3 2.8 1.3 1.3 0.2 0.4

Parwan 1.5 1.0 1.4 2.1 1.2 1.3 0.8 0.9

Wardak 1.5 0.8 1.9 3 1.4 1.2 0.4 0.6

Centre Bamyan 1.0 0.6 0.9 0.6 1.0 0.7 0.6 0.6

Ghazni 1.4 1.1 1.5 1.6 1.3 1.3 0.3 0.9

Ghor 1.3 1.1 1.2 0.9 1.3 1.0 0.9 0.9

Uruzgan 1.1 1.0 1.2 2.3 0.9 0.9 0.7 1.1

Zabul 0.8 0.7 1.3 2.6 0.9 1.0 0.5 0.9

North Baghlan 0.9 0.8 0.4 0.8 0.5 0.9 0.4 0.6

Balkh 0.6 0.8 0.5 0.6 0.6 0.9 0.4 0.7

Faryab 0.7 0.8 0.8 1.7 1 1.7 0.4 0.7

Jawzjan 0.5 0.4 0.6 0.9 0.7 0.9 0.3 0.3

Kunduz 1.5 0.8 1.5 1.5 1.4 1.1 1.1 0.9

Samangan 0.7 0.9 0.5 1 0.5 0.9 0.7 1

Sari Pul 0.7 0.6 0.4 0.3 0.5 0.5 0.3 0.5

Takhar 1.2 0.6 1.1 0.7 1 0.6 1.1 0.6

West Badghis 1.7 1.9 1.6 1.5 1.7 1.4 1 2.7

Farah 1 1.3 1.5 2.4 1.4 1.7 1.4 1.6

Hilmand 1.1 1.1 1.5 2 1.1 1 0.9 1.1

Hirat 1.1 1.1 0.9 1.2 0.9 4.7 0.7 0.9

Kandahar 0.8 0.6 1 1.5 0.9 0.9 0.4 0.8

Nimroz 0.7 1.4 0.5 0.7 0.6 0.6 0.3 0.8

Overall Average 1.1 0.9 1.2 1.9 1.1 2.1 0.7 1.1

5.1.5 Changes in Livestock Ownership

Preliminary data and evidence suggested a massive and widespread reduction in livestock numbersdue to a 4 year long drought. Lacking dependable survey data prior to the onset of that drought, it isnot easily possible to obtain an accurate estimate of the extent of the impact of the drought. It wasdecided to ask in all communities the numbers of families without livestock now, and the number offamilies without livestock 4 years before the interviews took place (a time that corresponds to pre-drought conditions). In addition to drought, two additional factors must be considered: (i) Theappreciation of the Afghan currency after the ousting of the Taliban regime forced many to sell stockin order to finance loans, and (ii) the substantial number of refugees returning home to Afghanistan.This migration impacted the number of families in the communities, a distortion compounded by thefact that many returnees initially left their livestock behind outside Afghanistan. This is particularlyobvious in the case of Kabul City. Data for Kabul City were excluded from the calculation of theaverage of the Centre-East region

The following province-level summary shows that overall there was a substantial reduction in numberof families owning livestock. However, this was not the case for all provinces. At the time the censuswas taken, many returned refugees had probably still part or all of their livestock holdings left at theirhomes in exile. Many refugees needed to settle property claims and were unsure about security, sothis attitude is understandable. While we have verbal evidence for this behaviour, we cannotsubstantiate this speculation with numbers, however.

It goes without question, however, that drought and war substantially reduced the overall livestockpopulation and the average livestock holdings per family.

26

photo by: Thieme

27

Table 8 Summary of changes in livestock holdings (families owning no livestock)


ProvinceFamilies without

livestock 1998

Families without

livestock 2002/2003

Percent Changeduring

Drought

Families withonly poultry

2002/2003

Badakhshan 11,274 14,522 28.81 9,156

East Khost 633 1,008 59.24 882

Kunar 1,781 2,453 37.73 1,447

Laghman 3,314 4,777 44.15 3,644

Nangarhar 1,491 26,720 52.76 19,705

Nuristan 1,422 2,290 61.04 1,735

Paktika 6,623 7,612 14.93 5,971

Paktya 2,959 4,093 38.32 3,690

Total 34,223 48,953 43.04 37,074

Centre- Kabul 17,730 14,994 -15.43 10,419

East Kabul City 24,739 265,401 972.80 163,682

Kapisa 2,818 4,258 51.10 3,666

Logar 14,908 15,927 6.84 14,293

Parwan 11,197 16,206 44.74 8,447

Wardak 26,452 32,466 22.74 25,466

Total 97,844 349,252 14.7* 225,973

Centre Bamyan 9,383 12,874 37.21 4,260

Ghazni 38,724 33,083 -14.57 30,297

Ghor 5,298 8,006 51.11 3,346

Uruzgan 16,440 15,366 -6.53 8,523

Zabul 18,467 21,575 16.83 11,703

Total 88,312 90,904 2.94 58,129

North Baghlan 25,644 32,319 26.03 9,611

Balkh 47,752 66,182 38.60 20,789

Faryab 42,955 49,366 14.92 16,054

Jawzjan 34,304 43,412 26.55 9,970

Kunduz 6,816 8,506 24.79 4,614

Samangan 22,016 28,277 28.44 7,422

Sari Pul 9,609 14,859 54.64 2,793

Takhar 27,852 24,147 -13.30 16,406

Total 216,948 267,068 23.10 87,659

West Badghis 26,207 31,354 19.64 11,041

Farah 19,712 23,144 17.41 11,584

Hilmand 14,245 15,222 6.86 10,888

Hirat 75,183 136,168 81.12 57,469

Kandahar 21,278 28,114 32.13 17,354

Nimroz 10,599 13,051 23.13 5,230

Total 167,224 247,053 47.74 113,566

Overall Sum 615,825 1,017,752 65.27 53,1557*Data for Kabul City were excluded from Centre-East for the calculation of the average change in numbers of families without livestock (see text)

5.2 REGIONAL PRODUCTION SYSTEM CHARACTERISTICS

In order to structure the results of the survey, especially the detailed Level 2 analysis, distinct agro-ecological zones were defined based on regional production characteristics including climatic andtopographic aspects. The following table summarises the classification of provinces into these regionsand the corresponding number of respondents for each region in the Level 2 survey. Some provinceswere not visited with the Level 2 survey. Badakhshan, for its unique properties, constitutes an agro-ecological region of its own.

28

photo by: Thieme

29

Some key statistics from Level 1 are presented as graphical summaries, indicating regional differencesapparent in the most basic indicators. Numerical differences between regions and response variablesare represented proportionally by the diameter of the circles.

Table 9 Classification of provinces into agro-ecological regionsAgro-ecologicalRegion Province

Number of Villages

Interviewed farmers

East Khost 6 30Kunar NV* NVLaghman 4 29Nangarhar 16 130Nuristan NV NVPaktika 6 30Paktya 7 32All 39 251

Centre-East Kabul 13 40Kabul City 33Kapisa 3 10Logar 7 50Parwan 12 42Wardak 6 30All 41 205

Centre Bamyan 18 76Ghazni 9 57Ghor 5 25Uruzgan NV NVZabul 10 54All 42 212

North Baghlan 21 97Balkh 5 32Faryab 4 28Jawzjan 4 29Kunduz 16 30Samangan 8 64Sari Pul 5 39Takhar 10 20All 73 339

West Badghis 5 25Farah 16 77Hilmand 4 20Hirat 15 136Kandahar NV NVNimroz 4 19All 44 277

Badakhshan NV NV

All 239 1284*NV: not visited

30

Figure 1 Livestock per family across regions

Livestock Class

Cattle Sheep Goats Chicken Donkeys Horses Oxen Draft

East

Centre-East

Centre

North

West

Badakshan

Figure 2 Cows and draft animals across regions

Livestock Class

Cows OxenDraft Animals

Potential Draft Animals

East

Centre-East

Centre

North

West

Badakshan

Agr

o-ec

olog

ical

Reg

ion

Agr

o-ec

olog

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Reg

ion

5.2.1 Forage Production and Feeding Situation

The following tables summarise the use of different types of feedstuff, their source (on farmproduction or purchase), preferences of use and sufficiency of feed supplies. Detailed summaries withindividual and total numbers of respondents are listed in Appendix 5.

5.2.1.1 Types of forages used

The following table summarises the percentages of farmers using the feedstuffs listed, specified foreach agro-ecological region. Wheat straw was the most commonly used feed from farm productionin all regions and Maize Straw is also widely used except in the Centre Region where little Maize isgrown. Shaftal and Lucerne are the most popular grown and used fodder crops. Shaftal as annualfodder crop has a relatively higher importance in Eastern Afghanistan while in the other regionsLucerne is the more important fodder crop. Hay from natural grassland has the greatest importance inthe Central Highlands. Maize is the most important farm produced concentrate in all regions exceptin the West is where Barley is more important as concentrate feed.

A large proportion of farmers in all regions purchase both concentrates and roughage for feedingtheir livestock. In addition to their own production approximately 40 percent of the interviewedfarmers still have to purchase additional straw to feed their livestock which by quantity is most likelythe most important purchased feedstuff. Although not specifically asked in the survey one canassumed that the Shaftal and Lucerne where purchased for feeding as fresh fodder crop. ManyAfghan farmers also purchase concentrates with Cotton seed cake, Wheat Bran, Maize and Barley inthe North and West being the more commonly purchased feed types.

31

photo by: Thieme

32

5.2.1.2 Preferences for feeds and feed supply

In addition to the actual use of feedstuffs the interviewed farmers were also asked for their mostpreferred feedstuffs, either purchased or produced on their own farm. In addition to the pre-definedtypes of feedstuffs of the previous chapter a number of other categories were hereby mentioned bythe interviewed farmers. The frequency of answers was used to establish a list of the most preferredfeedstuffs in the different Agro-ecological Regions.

Table 10 Types of forages used in the agro -ecological region (percentage of respondents)

Agro-ecological Region

East Centre-East

Centre North West

Own Production

Wheat Straw 71.7 88.8 67.5 75.8 74.8

Shaftal*) 64.5 64.4 31.1 8.0 48.2

Lucerne 10.8 66.3 49.1 22.1 54.0

Maize Straw 56.6 48.3 9.0 15.9 21.2

Maize 37.5 52.2 8.5 13.0 18.0

Hay 5.2 3.9 38.2 25.4 27.0

Wheat Bran 7.2 10.2 5.2 27.1 5.8

Barley 0.4 1.5 3.3 2.1 19.1

Sorghum 2.4 2.4 0.9 7.7 0.0

Millet 0.0 6.3 2.8 0.0 0.7

Cotton Seed Cake 0.4 2.9 1.9 3.5 1.1

Purchased Feed

Cotton Seed Cake 57.0 86.3 52.4 83.2 6.1

Wheat Straw 40.6 35.1 42.9 44.2 38.8

Wheat Bran 21.1 82.0 36.8 38.3 6.5

Lucerne 6.8 45.9 34.4 16.2 24.5

Barley 8.0 7.3 6.1 26.8 77.6

Maize 15.1 33.2 26.9 21.5 7.6

Shaftal 10.0 37.6 23.6 9.7 14.4

Maize Straw 28.3 5.9 2.4 5.9 1.8

Hay 3.2 3.4 10.4 10.9 2.2

Sorghum 0.4 5.4 0.9 3.5 0.7

Millet 1.2 2.9 0.5 0.3 0.7*Persian Clover, Trifolium resupinatum, but Trifolium clusii (annual Strawberry clover,less productive) is also called ‘Shaftal’.

33

Clearly, farmers prefer concentrates when buying feeds. Oil cakes are the most preferred concentratefeed, but that may be simply a reflection of availability and price. Purchase of Shaftal and Lucerne isespecially popular in the Centre and Centre-East regions, presumably as winter fodder.

Table 11 Preferences for purchased feeds (percentage of respondents)

Agro-ecological region

East Centre-East Centre North West

Forage 4.7 26.6 27.7 8.7 32.0

Lucerne 1.1 10.9 12.5 3.0 7.0

Persian clover 3.4 7.1 8.9 1.4 7.2

Hay 0.3 3.7 2.1 2.7 12.9

Green Plants 4.6 1.4 0.2 4.5

Mountain Grass 0.3 2.8 1.2 0.4

Various Crop Aftermath 27.3 11.2 19.9 10.6 17.6

Wheat Straw 17.3 8.9 12.4 5.1 14.4

Mung Bean Straw 0.8 5.3 0.3

Rice Straw 0.8 0.2 4.9 0.2

Maize Husks 5.9

Barley Straw 0.1 1.7 2.9

Maize Stalks 2.0 0.9

Maize Aftermath 2.4 0.1

Concentrates 67.7 59.4 49.5 75.9 47.6

Oil Cakes 10.3 21.3 15.0 35.5 5.6

Barley 10.3 2.2 7.3 26.5 30.2

Vetch (Lathyrus sativus) 11.8 9.3 9.1

Maize 9.4 4.4 7.9 2.8 5.0

Wheat Bran 0.7 7.8 8.5 7.4 0.9

Wheat 14.5 0.3 0.2 0.4 1.4

Grains 5.8 0.6 3.6

Bread 5.6 0.2 2.8 0.3

Soybean 8.2

Mung Bean (Vigna radiata) 2.8 0.6 0.5

Flour 2.4 0.3 0.2

Others 0.3 2.8 2.9 4.9 2.8

34

In terms of cultivated forage production, there seems to be considerable interest in Persian clover andLucerne. Especially Persian clover can be integrated well into crop rotations and should be considereda priority in livestock development programs. Noteworthy is that the preferences expressed for ‘Mountain Grass’ (harvested rangeland vegetation)corresponds with higher availability of this type of forage in higher elevation sites. At the same time,this regional differentiation reflects on the state of rangelands. There is simply no surplus to beharvested on rangelands in most of Afghanistan.

Table 12 Preferences for on-farm feeds (percentage of respondents)



Forage 33.0 55.5 66.3 24.9 43.0

Persian clover 27.8 21.9 18.0 2.2 7.7

Lucerne 1.2 21.7 28.5 4.9 10.1

Mountain Grass 0.3 1.1 14.2 8.4 7.3

Green Plants 3.1 3.6 2.5 1.4 12.9

Hay 0.2 4.8 3.0 6.4 4.8

Weeds 0.3 2.3 0.1

Green Barley 0.2 0.2 1.5

Crop Aftermath 53.7 23.2 29.3 35.8 18.8

Wheat Straw 28.9 21.2 26.8 22.3 17.9

Maize Husks 12.4 0.2 0.9

Maize Aftermath 8.1 0.5 0.2

Rice Straw 3.9 0.1 2.0 0.9

Khasha (crop aftermath) 6.1

Mung Bean Straw 0.1 5.1 0.1

Crop Aftermath 0.7 1.5

Tree leaves 0.3 0.8

Concentrates 9.8 19.4 4.5 30.2 37.7

Maize 5.9 10.9 1.7 5.1 6.5

Barley 1.0 0.2 1.5 13.3 11.6

Grains 0.7 3.5 10.2

Wheat 0.3 2.0 8.3

Sorghum 1.4 5.3 0.2

Oil Cakes 2.5 0.7 1.3 0.2

Wheat Bran 0.5 0.6 0.7 1.9 0.6

Cotton Seed 1.7

Barley flour 1.3

Others 3.5 1.9 0.0 9.1 0.5

35

These answers provide a clear indication that in 2003 the drought effects began to subside. Thesedata are valuable as baseline data for future programs in drought preparedness.

Table 14 Length of feeding periods (months)

Agro-ecological Region

EastCentre-

East Centre North West

Pasture grazing 8.76 6.86 6.01 5.90 7.44

Stubble period 2.16 2.77 2.05 3.06 2.34

Supplementation period 6.64 8.22 6.29 4.15 4.45

Table 13 Feed supplies (percentage of respondents)



Enough Feed 2002* 44.6 23.9 17.0 3.5 5.0

Enough Feed 2003 45.4 26.8 25.5 47.8 41.7

Pasture Sufficient 2002 16.3 6.3 29.7 11.5 6.8

Pasture Sufficient 2003 17.9 5.4 29.7 36.6 34.2

Purchased Supplements 2002 73.7 93.7 85.8 57.2 61.5

Purchased Supplements 2003 72.9 86.8 80.7 28.9 47.1

Produced Supplements 2002 25.5 15.1 9.4 5.0 2.2

Produced Supplements 2003 27.1 20.0 12.3 14.5 15.8

Feed Prices Increased 2002 80.9 95.1 87.3 96.2 85.3

Feed Prices Increased 2003 74.5 94.1 73.6 17.1 43.5* For all subjects percentage of respondents

36

5.2.2 Production CalendarThe production calendar is summarised graphically by region. Lambing and kidding, timing of use offeed resources and sales are considered. The area of the circles in the graphs corresponds to numberof respondents. Total number of respondents varied between 828 (‘Supplemental Feeding’) and 1114(‘Best Time to Sell Cattle’). The detailed figures are provided in the Tables A5-A10 of the Annex.

photo by: Thieme

37

Figure 3 Lambing distribution by region (percent respondents represented in proportion)

Month

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

East

Centre-East

Centre

North

West

Figure 4 Kidding distribution by region (percent respondents represented in proportion)

Month


East

Centre-East

Centre

North

West

Agr

o-ec

olog

ical

Reg

ion

Agr

o-ec

olog

ical

Reg

ion

38

Figure 5 Begin of grazing season by region(percent respondents represented in proportion)

Month


East

Centre-East

Centre

North

West

Figure 6 End of grazing season by region(percent respondents represented in proportion)

Month


East

Centre-East

Centre

North

West

Agr

o-ec

olog

ical

Reg

ion

Agr

o-ec

olog

ical

Reg

ion

39

Figure 7 Begin of stubble grazing by region(percent respondents represented in proportion)

Month


East

Centre-East

Centre

North

West

Figure 8 End of stubble grazing by region (percent respondents represented in proportion)

Month


East

Centre-East

Centre

North

West

Agr

o-ec

olog

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Reg

ion

Agr

o-ec

olog

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Reg

ion

40

Figure 9 Begin of supplementary feeding by region (percent respondents represented in proportion)

Month


East

Centre-East

Centre

North

West

Figure 10 End of supplementary feeding by region(percent respondents represented in proportion)

Month


East

Centre-East

Centre

North

West

Agr

o-ec

olog

ical

Reg

ion

Agr

o-ec

olog

ical

Reg

ion

41

Figure 11 Best time to sell cattle by region(percent respondents represented in proportion)

Month


East

Centre-East

Centre

North

West

Figure 12 Best time to sell sheep by region (percent respondents represented in proportion)

Month


East

Centre-East

Centre

North

West

Agr

o-ec

olog

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Reg

ion

Agr

o-ec

olog

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Reg

ion

42

Figure 13 Best time to sell goats by region(percent respondents represented in proportion)

Month


East

Centre-East

Centre

North

West

Figure 14 Best time to sell surplus by region(percent respondents represented in proportion)

Month


East

Centre-East

Centre

North

West

Agr

o-ec

olog

ical

Reg

ion

Agr

o-ec

olog

ical

Reg

ion

43

5.2.3 Livestock Species, Numbers and DemographyThe following summary graphs depict the age structure for major livestock species, differentiated byagro-ecological region. Data for camels and horses are summarised for the whole survey becausethere were not enough respondents to justify breakdown by agro-ecological zone. Note that differentage categories were enumerated for different species. Data are also presented in tabular form forfurther reference in Section 5.3.2 ‘Livestock Management’.

Figure 15 Composition of cattle herds by sex and age groups in different regions

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

East

Centre-East

Centre

North

West

Agr

o-ec

olog

ical

Reg

ion

Young Female1-2Years Female2-3Years Female3-4Years Female>4Years Male Castrated

Figure 16 Composition of sheep flocks by sex and age groups in different regions

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

East

Centre-East

Centre

North

West

Agr

o-ec

olog

ical

Reg

ion


44

Figure 17 Composition of goat flocks by sex and age groups in different regions

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

East

Centre-East

Centre

North

West

Agr

o-ec

olog

ical

Reg

ion


Figure 18 Composition of donkey herds by sex and age groups in different regions

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

East

Centre-East

Centre

North

West

Agr

o-ec

olog

ical

Reg

ion


45

Figure 19 Composition of camel herd by sex and age groups (all regions)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

YCamels FCamels12 FCamels23 FCamels34 FCamels>4 MCamels CCamels

Figure 20 Composition of horse herd by sex and age groups (all regions)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Females < 1 Year Males < 1 Year Females > 1 Year Males > 1 Year

46

5.3 PRODUCER CHARACTERISTICS5.3.1 Livestock Wealth Distribution

From the detailed Level 2 data, information about distribution of livestock ownership could becomputed. Four livestock species were used to classify wealth distribution: cattle, sheep, goats andchicken. This information is presented on the basis of province and agro-ecological region.

Table 15 Livestock wealth distribution classificationsClass Cattle Sheep Goats Chicken1 0 0 0 02 1-2 1-49 1-49 1-103 3-4 50-99 50-99 11-204 5-10 100-499 100-499 21-505 > 10 > 499 > 499 > 50

photo by: Park

47

Table 16 Cattle ownership distribution (percentages)

Herd size (number of cattle)

Region Province 0 1-2 3-4 5-10 > 10Total

Respondents

East Khost 16.67 10.00 10.00 40.00 23.33 30

Laghman 17.24 48.28 34.48 29

Nangarhar 2.31 21.54 35.38 37.69 3.08 130

Paktika 20.00 20.00 16.67 33.33 10.00 30

Paktya 3.13 18.75 28.13 21.88 28.13 32

All 5.98 19.12 30.68 35.06 9.16 251

Centre-East

Kabul 7.50 30.00 37.50 25.00 40

Kabul City

24.24 24.24 30.30 18.18 3.03 33

Kapisa 60.00 30.00 10.00 10

Logar 10.00 28.00 22.00 34.00 6.00 50

Parwan 30.95 45.24 21.43 2.38 42

Wardak 6.67 43.33 40.00 10.00 30

All 8.78 32.20 34.15 22.44 2.44 205

Centre Bamyan 15.87 46.03 19.05 17.46 1.59 63

Ghazni 35.09 26.32 28.07 10.53 57

Ghor 24.00 40.00 16.00 16.00 4.00 25

Zabul 18.52 25.93 31.48 22.22 1.85 54

All 23.12 34.17 24.62 16.58 1.51 199

North Baghlan 10.31 20.62 24.74 37.11 7.22 97

Balkh 9.38 43.75 46.88 32

Faryab 29.63 33.33 29.63 7.41 27

Jawzjan 48.28 34.48 3.45 13.79 29

Kunduz 3.33 23.33 23.33 30.00 20.00 30

Samangan 18.75 37.50 23.44 14.06 6.25 64

Sari Pul 15.38 30.77 25.64 25.64 2.56 39

Takhar 5.00 20.00 20.00 30.00 25.00 20

All 16.27 29.59 24.85 22.49 6.80 338

West Badghis 36.00 32.00 12.00 20.00 25

Farah 28.57 45.45 16.88 9.09 77

Hilmand 15.00 30.00 25.00 20.00 10.00 20

Hirat 39.85 36.09 15.79 7.52 0.75 133

Nimroz 15.00 55.00 10.00 15.00 5.00 20

All 32.73 39.27 16.00 10.55 1.45 275

Overall AVG/Total 17.67 30.76 25.55 21.45 4.57 1,268

48

Table 17 Sheep ownership distribution (percentages)

Flock size (number of sheep)

Region Province 0 1-49 50-99 100-499 > 499 Total

Respondents

East Khost 20.00 43.33 36.67 30

Laghman 48.28 48.28 3.45 29

Nangarhar 78.46 21.54 130

Paktika 73.33 10.00 16.67 30

Paktya 31.25 59.38 3.13 6.25 32

All 52.59 38.25 1.99 7.17 251

Centre-East

Kabul 67.50 32.50 40

Kabul City

42.42 57.58 33

Kapisa 30.00 70.00 10

Logar 56.00 44.00 50

Parwan 69.05 30.95 42

Wardak 56.67 43.33 30

All 57.56 42.44 205

Centre Bamyan 41.27 50.79 7.94 63

Ghazni 43.86 56.14 57

Ghor 52.00 48.00 25

Zabul 33.33 66.67 54

All 41.21 56.28 2.51 199

North Baghlan 44.33 39.18 8.25 6.19 2.06 97

Balkh 3.13 68.75 18.75 9.38 32

Faryab 14.81 77.78 7.41 27

Jawzjan 10.34 58.62 13.79 10.34 6.90 29

Kunduz 3.33 63.33 10.00 13.33 10.00 30

Samangan 29.69 64.06 4.69 1.56 64

Sari Pul 10.26 69.23 17.95 2.56 39

Takhar 25.00 55.00 15.00 5.00 20

All 23.67 57.99 9.76 6.21 2.37 338

West Badghis 8.00 64.00 16.00 12.00 25

Farah 48.05 49.35 1.30 1.30 77

Hilmand 60.00 40.00 20

Hirat 48.87 41.35 7.52 2.26 133

Nimroz 15.00 70.00 10.00 5.00 20

All 43.27 47.64 6.18 2.91 275

Overall AVG/Total 41.88 49.05 4.73 3.71 0.63 1268

49

Table 18 Goat ownership distribution (percentages)

Flock size (number of goats)

Region Province 0 1-49 50-99 100-499 Total Respondents

East Khost 6.67 83.33 6.67 3.33 30

Laghman 79.31 20.69 29

Nangarhar 72.31 27.69 130

Paktika 50.00 46.67 3.33 30

Paktya 15.63 53.13 9.38 21.88 32

All 55.38 39.04 1.99 3.59 251

Centre-East

Kabul 75.00 25.00 40

Kabul City 66.67 33.33 33

Kapisa 100 10

Logar 80.00 20.00 50

Parwan 97.62 2.38 42

Wardak 90.00 10.00 30

All 82.93 17.07 205

Centre Bamyan 52.38 47.62 63

Ghazni 63.16 36.84 57

Ghor 20.00 76.00 4.00 25

Zabul 57.41 40.74 1.85 54

All 52.76 46.23 1.01 199

North Baghlan 37.11 58.76 3.09 1.03 97

Balkh 6.25 90.63 3.13 32

Faryab 25.93 74.07 27

Jawzjan 13.79 82.76 3.45 29

Kunduz 33.33 66.67 30

Samangan 50.00 43.75 4.69 1.56 64

Sari Pul 10.26 89.74 39

Takhar 40.00 55.00 5.00 20

All 30.47 66.27 2.07 1.18 338

West Badghis 92.00 4.00 4.00 25

Farah 20.78 71.43 2.60 5.19 77

Hilmand 60.00 40.00 20

Hirat 31.58 63.16 3.76 1.50 133

Nimroz 95.00 5.00 20

All 25.45 68.73 2.91 2.91 275

Overall AVG/Total 46.29 50.32 1.74 1.66 1268

50

Table 19 Chicken ownership distribution (percentages)

Ownership class (number of chicken)

Region Province 0 1-10 11-20 21-50 > 50Total

Respondents

East Khost 13.33 43.33 23.33 16.67 3.33 30

Laghman 6.90 68.97 17.24 6.90 29

Nangarhar 3.85 40.77 31.54 20.00 3.85 130

Paktika 3.33 56.67 33.33 6.67 30

Paktya 3.13 37.50 34.38 12.50 12.50 32

All 5.18 45.82 29.48 15.54 3.98 251

Centre-East

Kabul 7.50 47.50 32.50 12.50 40

Kabul City

21.21 42.42 27.27 9.09 33

Kapisa 10.00 40.00 40.00 10.00 10

Logar 36.00 44.00 12.00 8.00 50

Parwan 14.29 47.62 33.33 2.38 2.38 42

Wardak 16.67 53.33 30.00 30

All 19.51 46.34 26.83 6.83 0.49 205

Centre Bamyan 47.62 47.62 4.76 63

Ghazni 22.81 59.65 14.04 3.51 57

Ghor 24.00 76.00 25

Zabul 16.67 51.85 27.78 3.70 54

All 29.15 55.78 13.07 2.01 199

North Baghlan 17.53 60.82 13.40 8.25 97

Balkh 3.13 50.00 21.88 25.00 32

Faryab 29.63 66.67 3.70 27

Jawzjan 58.62 34.48 6.90 29

Kunduz 13.33 30.00 33.33 20.00 3.33 30

Samangan 42.19 42.19 15.63 64

Sari Pul 33.33 64.10 2.56 39

Takhar 20.00 35.00 35.00 10.00 20

All 25.74 49.70 15.09 8.58 0.89 338

West Badghis 16.00 76.00 8.00 25

Farah 2.60 32.47 42.86 22.08 77

Hilmand 45.00 50.00 5.00 20

Hirat 39.85 52.63 4.51 3.01 133

Nimroz 25.00 60.00 10.00 5.00 20

All 21.45 46.55 22.91 8.73 0.36 275

Overall AVG/Total 20.27 48.66 21.21 8.68 1.18 1268

51

In order to visualize the regional difference in livestock wealth distribution, summary data arepresented graphically by agro-ecological region.

Figure 21 Wealth distribution cattle(percent respondents represented in proportion)

Wealth Class

0 head 1-2 head 3-4 head 5-10 head > 10 head

East

Centre-East

Centre

North

West

Figure 22 Wealth distribution sheep(percent respondents represented in proportion)

Wealth Class

0 head 1-49 head 50-99 head 100-499 head > 499 head

East

Centre-East

Centre

North

West

Agr

o-ec

olog

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Reg

ion

Agr

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Reg

ion

52

5.3.2 Livestock ManagementLivestock management comprised the following responses: age structures, performance characteristicfor cattle, sheep and goats, herd and flock composition (from Level 2 data) by agro-ecological region,and the culling and herd management policy in these three species.

5.3.2.1 Age structures

photo by: Guerne Bleich

53

Table 20 Age structure in herds and flocks surveyed in Level 2 by Agro-ecological region (percentages of the total herd or flock)

Region Females1-2 years

Females2-3 years

Females 3-4 years

Females > 4 years

Adultmale

Castrated male

Cattle

East 22.6 17.1 9.2 14.3 29.0 5.6 5.9Centre-East 18.1 14.6 11.5 18.5 19.7 3.5 5.9

Centre 18.4 12.6 7.2 15.9 27.8 15.0 7.4

North 22.4 9.3 5.4 11.8 26.2 4.4 19.3

West 22.5 7.9 20.7 22.0 14.4 6.9 17.6

Sheep

East 29.4 16.3 21.5 21.7 11.0 2.0 0.6Centre-East 24.8 9.7 25.5 21.4 8.0 9.2 0.7Centre 24.8 12.6 21.4 21.7 11.1 4.1 6.3North 19.4 11.9 23.2 16.0 13.0 2.5 0.8West 28.3 18.8 21.5 19.4 6.5 2.5 4.0

Goats

East 39.4 17.8 18.2 16.9 7.6 1.7Centre-East

18.4 25.2 28.2 21.8 2.0 5.1 0.3

Centre 33.0 11.9 25.8 18.4 11.6 2.0

North 29.6 12.2 23.6 16.7 6.2 5.2 1.1

West 33.4 15.2 22.5 17.2 6.3 2.7 4.0

Donkeys

East 17.6 5.7 10.8 26.5 18.7 24.9 3.4Centre-East 4.5 0.8 4.5 17.5 41.7 3.0

Centre 4.1 1.6 4.2 15.7 17.2 46.4 4.7

North 6.3 4.2 4.2 11.7 35.4 34.8 0.9

West 8.2 3.8 5.9 15.0 27.1 18.9 22.0

Camels*

4.3 11.0 9.3 10.4 31.7 28.0 2.7

Horses* Males Females Males Females

Younger than 1 year Older than 1 year

4.51 7.09 38.81 49.59

*Averages for all regions

Younger< 1 year

54

5.3.2.2 Performance characteristics

Table 21 Livestock performance characteristics


East East Centre North WestPerformance Characteristics for Cattle

Percentage producers owning bull 13.55 23.41 27.83 32.15 14.03

Percentage cows calving each year 43.03 80.49 35.69 55.76 49.57

Keep cattle (years) 9.75 9.74 9.32 9.44 8.73

Performance Characteristics for Sheep

Ewes per ram 43.01 13.22 20.92 61.57 47.40

Lambs born (per 10) 8.45 10.10 9.04 8.31 8.24

Lambs weaned (per 10) 6.90 7.57 6.82 6.30 7.14

Age weaning ( months) 4.12 4.28 4.40 4.36 3.08

Age lambs sold (months) 6.64 9.21 6.58 5.84 5.56

Keep sheep (years) 5.41 4.91 5.22 5.69 5.64

Performance Characteristics for Goats

Does per buck 42.30 8.19 18.54 39.17 46.77

Kids born (per 10) 9.70 13.45 10.69 9.56 9.96

Kids weaned (per 10) 7.69 9.79 7.96 7.21 8.82

Age weaning (months) 4.28 4.25 4.15 4.23 3.11

Age kids sold (months) 7.10 8.56 6.41 6.34 5.50

Centre-

It is very obvious that these reproduction data indicate the most important problem: insufficientnutrition impinging upon reproduction. Strategic supplementation during breeding periods is animportant and probable high-impact strategy, especially for small ruminants.

55

5.3.2.3 Culling Reasons

Table 22 Culling reasons (percentages)


Reasons fur Culling East Centre-East Centre North West

Cows

Old age 15.9 17.9 29.7 58.9 54.4

Unproductive 1.0 12.1 13.9 13.8 3.1

Infertile 1.9 3.6 3.7 1.3

No milk 6.3 6.1

Sick 0.5 9.2 4.8 1.7 14.6

Feed shortage 40.4 42.0 35.2 18.5 15.9

Drought 1.9Cash need 35.1 9.7 6.7 3.4 10.6

Inadequate breed 5.3 1.0

Ewes

Old age 34.7 21.8 36.6 61.2 60.8

Unproductive 2.0 7.5 10.4 12.3 3.2

Infertile 2.0 2.2 3.7 1.6

No milk 4.1 4.5 0.4

Sick 1.0 10.9 6.0 0.7 10.4

Feed shortage 16.3 41.5 32.8 14.6 12.4

Drought 1.0 0.7

Cash need 44.9 8.2 7.5 7.1 11.6

Inadequate breed 3.4

Feed shortage is the most frequently cited reason for culling of cattle, and old age dominates thereasons given for sheep.

56

5.3.2.4 Livestock herd management and sales policy

Table 23 Livestock which are sold first (percentages)

Agro-ecological regionEast Centre-East Centre North West

Species

Cattle 40.0 62.4 41.4 36.5 30.1

Sheep 7.1 2.5 17.7 30.9 19.9

Goat 2.9 4.5 3.9 24.4 10.5

Other 3.3 1.6

Not specified 50.0 30.7 37.0 4.9 37.9

Sex

Male 75.8 41.1 20.4 7.5 18.4

Female 3.8 4.5 6.6 17.9 21.1

Not specified 20.4 54.5 72.9 74.6 60.5

Age

Sub Adult 5.4 18.3 16.6 8.8 3.9

Adult 16.3 27.7 38.7 48.9 71.5

Not specified 78.3 54.0 44.8 42.3 24.6

Performance

Producing 1.7 5.0 8.3 3.3 8.2

Not producing 7.5 37.1 34.3 1.3 5.9

Not specified 90.8 57.9 57.5 95.4 85.9

57

Table 24 Livestock which are kept under all circumstances (percentages)


Species

Cattle 49.8 80.8 72.7 60.3 27.0

Sheep 5.1 0.5 2.2 32.3 6.6

Goat 2.5 3.5 4.9 1.3 12.7

Other 0.4 4.2 16.6

Not specified 42.2 15.2 20.2 1.9 37.1

Sex

Male 0.4 1.5 12.6 8.7 2.7

Female 97.5 97.5 82.0 56.5 52.9

Not specified 2.1 1.0 5.5 34.8 44.4

Age

Sub adult 4.2 6.0 0.3 31.3

Adult 94.1 98.5 89.1 65.2 46.7

Not specified 1.7 1.5 4.9 34.5 22.0

Performance

Producing 39.7 66.7 66.7 32.6 42.5

Not producing 0.8 0.5

Not specified 59.5 32.8 33.3 67.4 57.5


58

These data appear at first sight contradictory. Cattle are listed both as animals sold first and asanimals kept under all circumstances. The explanation is that cattle must frequently be sold becauseof feed shortages. When farmers expect to not have sufficient feed, they will sell cows and not takeany chances, because of the substantial value of these animals. At the same time, they are the mostvalued livestock species and only sold when it is certain that they cannot be kept. On the other hand,farmers will not necessarily sell all sheep, even when feed shortages are imminent, but rather gamblethat somehow small ruminants will survive anyway. Farmers are prepared to accept small ruminantlosses over the winter feeding period.

photo by Pittroff

59

5.3.3 MarketsIn this segment, data on comparative advantages of species and animal categories in the market,reasons for sale (prices, buyer behaviour), distance to market, and buyer properties are summarised.

5.3.3.1 Livestock sales – priority categories and reasons

Table 25 Sale of livestock (percentages)


EastCentre-


Sell calves* 33.1 75.4 39.9 35.3 60.0

Sell cattle* 27.1 70.1 43.8 31.4 58.9

Sell animals for feed 19.1 32.7 25.5 37.2 48.6

Sell animals for cash 26.3 50.2 50.0 59.0 60.1

Priority of Selling for:

Feed purchase 45.1 90.6 64.2 72.9 61.6

Cash 54.9 9.4 35.8 27.1 38.4

* Only responses from cattle owners were considered

5.3.3.2 Trading partners for farmers

Table 26 Livestock trading partners for farmers (percentages)


EastCentre-


Local trader 72.5 60.4 83.8 74.1 82.6

Regional trader 18.4 23.4 7.0 17.0 9.1

Foreign trader 0.4 4.3

Butcher 3.4 4.9

Bazaar 1.1 0.3

Different types 8.6 16.2 3.8 5.1 3.4

5.3.3.3 Distance to markets

Table 27 Distance to markets (hours)


EastCentre-


Selling animal 2.11 1.90 2.86 2.28 1.90

Selling wool 1.84 1.96 2.62 1.83 1.77

Selling milk 1.06 1.81 2.46 1.45 1.43

Buying feed 2.18 2.03 2.93 2.09 1.81

60

5.3.3.4 Sales decisions

Livestock producers were asked about current market experiences compared with the year before.Note that the question on prices was triangulated, asking the same question once in the animalcategory (‘Prices Higher’), and once in the price category (‘Price Increased’). There are some numericaldifferences in the answers, but it is obvious that all responses were optimistic and indicative ofstrongly improved market prospects.

Table 28 Market changes from 2001/2 to 2002/3 (percentages)


EastCentre-


Cattle

Prices higher 67.4 94.7 93.5 88.3 88.1

Earned more money 27.1 42.2 28.8 77.4 55.7

Sold more animals 14.4 34.2 35.9 11.0 11.9

Price Increased 94.9 98.4 98.0 95.1 98.4

More buyers 23.0 21.8 36.5 65.0 15.1

Sheep

Prices higher 73.1 85.1 88.0 85.3 87.8



Price Increased 94.1 89.7 94.9 90.7 98.7

More buyers 55.4 21.5 48.6 58.2 17.9

Goats

Prices higher 82.1 85.7 91.5 87.7 87.8



Price Increased 96.4 85.7 92.6 91.9 98.5

More buyers 37.6 12.9 30.7 55.0 18.4

61

Table 29 Which category sells best? (percentages)



Cattle Calves 39.0 14.4 19.4 37.7 77.2

Cows 56.0 83.9 74.8 26.2 12.7

Males 4.0 0.6 3.9 34.6 7.6

Total (n) 100 174 155 260 197

Sheep Lambs 23.1 0.7 2.2 3.7 10.0

Females 33.9 60.7 59.0 21.9 38.1

Males 18.2 10.7 3.6 26.3 1.3

Big Size 24.0 26.4 33.1 46.7 48.5

Total (n) 121 140 139 270 239

Goats Lambs 19.8 1.0 0.9 1.9 38.0

Females 48.4 81.0 66.1 60.1 34.1

Males 26.4 1.0 0.0 10.8 0.5

Big Size 4.4 15.0 30.4 24.7 25.0

Total (n) 91 100 112 158 208

photo by Pittroff

5.4 WOMEN LIVESTOCK SURVEY5.4.1 Introduction

A total of 2.899 interviews were conducted by a female enumerator team assembled and trained bythe FAO Mission and the Ministry of Agriculture and Livestock. Interviews were conducted in Marchand April of 2003. The interviewed females from the rural households identified themselves mostly as‘Housewife’ and ‘Mother’, but also as ‘Head of Household’, ‘Mother in law’, or ‘Daughter in law’. Agroup of enumerators operated in each of the provinces visited. Enumerators did not work in morethan one province; however, they worked in several districts within their province. Some problemswith data quality assurance have to be suspected for a subsection of the survey in Kandahar province.Data from Kandahar province substantially diverging from national averages should be treated withcaution.

The survey comprises three sections: livestock statistics, information on division of labour in the familyand decision making, and inquiries into problems, possible improvements and perceived constraints tothe improvement of the livestock production enterprise of the respondents. The surveyed populationincludes mostly married women and, in a much smaller proportion, female heads of household. Themajority of respondents were the decision making female members of the household and a smallernumber those with less authority (daughters in law, daughters). The information about decisionmaking and work responsibilities was considered to be of highest importance. There is currently noreliable information available how work and decision making is divided between men, women andchildren. The data reported here are unique and of great relevance for developing interventionsspecifically designed to address gender issues in Afghanistan.

It was important to obtain a picture of the wealth distribution for women’s livestock resources.Accordingly, after determination of true zero responses, the complete data set was filtered toeliminate all respondents that did not have any animals of the species for which the followingstatistics are computed. That is, the following data do not represent global averages, but describestock distribution for those respondents that did indeed own stock of the species in question.However, global averages including zero stock respondents from this survey are used elsewhere in theanalysis of the Afghan Livestock Census data.

The absolute numbers and proportion of interviewed female farmers owning stock was summarisedfor all livestock species.

62

photo by Pittroff

63

* Tutum Dara is part of the Chaharikar district** Zhiray is part of the Panjwayi district

Table 30 Family size in the women livestock survey

Province District Average STD

Nangarhar Bati Kot 11.92 6.44

Bihsud 11.06 5.49

Shewa 9.61 3.95

All 10.90 5.49

Kabul Bagrami 10.51 4.69

Musayi 10.66 4.40

Paghman 9.83 4.85

All 10.35 4.66

Logar Puli Alam 11.86 8.14

Parwan Chaharikar 8.36 3.17

Tutum Dara* 8.58 3.53

All 8.46 3.35

Badakhshan Argo 8.30 2.94

Baharak 8.28 2.52

All 8.29 2.73

Balkh Chimtal 8.16 3.71

Dihdadi 9.04 3.45

Nahri Shahi 9.25 5.13

All 8.61 3.99

Kandahar Arghandab 12.68 5.09

Daman 13.35 4.43

Dand 12.60 4.58

Panjwayi 13.91 5.40

13.63 4.28

All 13.33 4.89

Overall Average 10.18 5.00

Zhiray**

64

In order to understand the following analyses, it is important to summarize ownership of importantlivestock species per province. Of the small ruminant owners, only minorities had either goats but nocattle (185, or 27 percent) or sheep but no cattle (163, or 18.9 percent). Although the differencebetween sheep and goat ownership for those who did not own cattle was small, goats are morefrequent among those who do not own cattle. The results confirm Level 1 and Level 2 data: the mostimportant livestock species in Afghanistan is cattle.

Table 31 Distribution of respondents in the women livestock survey by family status

Household Position Number Percentage

Housewife 1735 59.85

Mother 422 14.56

Mother in Law 172 5.93

Grandmother 12 0.41

Head of family 186 6.42

Widow 1 0.03

Daughter in Law 227 7.83

Daughter 117 4.04

Unknown 27 0.93

Total 2899

Information about work responsibility and decision making authority was asked separately for thethree main livestock species, cattle, sheep and goats. Similar information about chicken had beenearlier collected with larger number of village women and was therfore not included in the questions.Answers were only analysed for those respondents who actually owned the concerned livestockspercies.

Table 32 Ownership of livestock species in the women livestock survey(numbers of respondents owning the species listed)

Livestock speciesProvince Cattle Sheep Goats Poultry Donkeys Camel BuffalosNangarhar 608 47 126 584 276 55Kabul 403 101 62 367 244Logar 130 60 13 113 57 1Parwan 361 51 20 225 261Badakhshan 262 178 201 199 262 1Balkh 442 180 156 431 385 46 1Kandahar 302 244 111 378 145 26 1Total 2508 861 689 2297 1630 74 57

65

5.4.2 Work Responsibilities5.4.2.1 Cattle

Table 33 Work responsibility – feeding cattle (percentages of respondents )

Province No Response

Women Men Children Womenand Men

Women/ Children

Total Respondents

Nangarhar 0.33 73.85 18.09 7.40 0.16 0.16 608

Kabul 65.51 14.89 19.60 403

Logar 1.54 49.23 16.15 33.08 130

Parwan 1.39 63.16 14.96 20.50 361

Badakhshan 53.44 41.60 4.96 262

Balkh 0.90 65.61 29.86 2.94 0.45 0.23 442

Kandahar 0.33 99.01 0.66 0.33 302Overall Average/Total 0.56 69.14 19.46 10.65 0.12 0.08 2508

Table 34 Work responsibility – grazing cattle (percentages of respondents )



Women/Children

Total Respondents

Nangarhar 58.22 4.28 10.03 27.30 0.16 608

Kabul 7.44 11.91 13.90 66.75 403

Logar 10.00 20.77 3.85 65.38 130

Parwan 1.66 16.07 13.57 68.70 361

Badakhshan 5.73 41.98 52.29 262

Balkh 1.58 24.43 45.70 27.83 0.23 0.23 442

Kandahar 0.33 1.66 21.85 76.16 302Overall Average/Total

16.39 11.44 21.89 50.16 0.08 0.04 2508

Table 35 Work responsibility – watering cattle (percentages of respondents )

ProvinceNo

ResponseWomen Men Children Women

and MenWomen/ Children

Total Respondents

Nangarhar 0.66 75.33 14.64 9.05 0.16 0.16 608

Kabul 0.25 56.82 13.40 29.53 403

Logar 0.77 41.54 6.92 50.77 130

Parwan 1.39 60.11 12.47 26.04 361

Badakhshan 27.10 40.08 32.82 262

Balkh 0.90 65.84 29.64 3.17 0.45 442

Kandahar 0.99 88.74 5.30 4.97 302Overall Average/Total 0.72 63.32 17.90 17.90 0.12 0.04 2508

66

Table 36 Work responsibility – tending young cattle (percentages of respondents )


Women Men Children Women and Men

Women/ Children

Total Respondents

Nangarhar 4.77 90.30 3.45 1.32 0.16 608

Kabul 2.98 95.29 0.74 0.74 0.25 403

Logar 20.00 61.54 3.08 15.38 130

Parwan 41.55 52.63 1.39 4.43 361

Badakhshan 3.82 93.13 2.67 0.38 262

Balkh 10.18 70.81 18.33 0.68 442

Kandahar 2.65 97.02 0.33 302Overall Average/Total

11.16 81.86 4.86 2.03 0.04 0.04 2508

Table 37 Work responsibility – milking cattle (percentages of respondents )



Total Respondents

Nangarhar 6.58 91.28 1.97 0.16 608

Kabul 5.46 94.29 0.25 403

Logar 11.54 86.92 0.77 0.77 130

Parwan 46.26 53.74 361

Badakhshan 3.44 95.42 1.15 262

Balkh 12.44 80.54 6.79 0.23 442

Kandahar 13.25 86.75 302Overall Average/Total

13.88 84.13 1.87 0.04 0.08 2508

Table 38 Work responsibility – treating cattle (percentages of respondents )

ProvinceNo

Response Women Men ChildrenWomen

and Men

Women/ Children

Total Respondents

Nangarhar 1.32 4.61 92.76 1.15 0.16 608

Kabul 0.50 11.17 86.35 1.99 403

Logar 3.08 36.92 60.00 130

Parwan 1.39 20.50 72.30 5.26 0.28 0.28 361

Badakhshan 22.90 75.95 1.15 262

Balkh 1.13 10.86 86.88 0.68 0.45 442

Kandahar 0.66 88.41 10.93 302Overall Average/Total 1.04 22.73 74.44 1.59 0.16 0.04 2508

67

5.4.2.2 Sheep

Table 39 Work responsibility – feeding sheep (percentages of respondents )

ProvinceNo


and Men

Women/Children

Menand

Children

Total Respondents

Nangarhar 6.38 57.45 19.15 14.89 2.13 47

Kabul 0.99 58.42 13.86 25.74 0.99 101

Logar 41.67 11.67 46.67 60

Parwan 9.80 64.71 13.73 11.76 51

Badakhshan 38.76 56.74 4.49 178

Balkh 8.33 38.89 48.89 3.89 180

Kandahar 0.41 94.67 1.23 2.87 0.41 0.41 244Overall Average/Total 2.90 59.70 26.60 10.34 0.23 0.12 0.12 861

Table 40 Work responsibility – grazing sheep (percentages of respondents )


Women Men Children Men andChildren

Total Respondents

Nangarhar 57.45 6.38 36.17 47

Kabul 6.93 15.84 17.82 59.41 101

Logar 3.33 13.33 5.00 78.33 60

Parwan 9.80 7.84 19.61 62.75 51

Badakhshan 2.81 57.30 39.89 178

Balkh 9.44 17.22 56.67 16.11 0.56 180


7.20 7.67 32.17 52.85 0.12 861

Table 41 Work responsibility – watering sheep (percentages of respondents )

ProvinceNo Response Women Men Children Total

Respondents

Nangarhar 6.38 59.57 14.89 19.15 47

Kabul 0.99 49.50 13.86 35.64 101

Logar 30.00 6.67 63.33 60

Parwan 9.80 62.75 13.73 13.73 51

Badakhshan 16.29 57.30 26.40 178

Balkh 9.44 37.78 48.33 4.44 180

Kandahar 0.41 88.52 2.46 8.61 244

Overall AVG/Total 3.14 51.22 26.36 19.28 861

68

Table 42 Work responsibility – tending young sheep(percentages of respondents )

ProvinceNo Response Women Men Children Women/

ChildrenTotal

Respondents

Nangarhar 34.04 63.83 2.13 47

Kabul 21.78 75.25 0.99 0.99 0.99 101

Logar 28.33 48.33 1.67 21.67 60

Parwan 70.59 27.45 1.96 51

Badakhshan 0.56 53.37 33.71 12.36 178

Balkh 10.00 46.67 41.67 1.67 180


20.33 58.54 16.49 4.53 0.12 861

Table 43 Work responsibility – milking sheep (percentages of respondents )



Total Respondents

Nangarhar 57.45 42.55 47

Kabul 20.79 78.22 0.99 101

Logar 28.33 71.67 60

Parwan 70.59 29.41 51

Badakhshan 0.56 97.19 2.25 178

Balkh 22.22 56.11 20.00 0.56 1.11 180

Kandahar 73.36 26.23 0.41 244Overall Average/Total 37.28 57.49 4.88 0.12 0.23 861

Table 44 Work responsibility – treating sheep(percentages of respondents )

Province No Response Women Men Children Total Respondents

Nangarhar 8.51 4.26 82.98 4.26 47

Kabul 0.99 15.84 79.21 3.96 101

Logar 3.33 33.33 63.33 60

Parwan 9.80 37.25 50.98 1.96 51

Badakhshan 0.56 21.35 77.53 0.56 178

Balkh 10.00 10.56 78.89 0.56 180

Kandahar 0.82 88.11 11.07 244Overall Average/Total 3.83 38.21 56.91 1.05 861

69

5.4.2.3 Goats

Table 45 Work responsibility – feeding goats (percentages of respondents )

Province

No Response

Women Men Children Women/ Children

Men andChildren

Total Respondents

Nangarhar 7.94 77.78 7.14 7.14 126

Kabul 11.29 56.45 9.68 20.97 1.61 62

Logar 15.38 46.15 15.38 23.08 13

Parwan 25.00 50.00 10.00 15.00 20

Badakhshan 0.50 59.20 35.32 4.98 201

Balkh 6.41 44.23 44.23 4.49 0.64 156


5.37 63.86 23.37 7.11 0.15 0.15 689

Table 46 Work responsibility – grazing goats (percentages of respondents )


Women Men Children Men andChildren

Total Respondents

Nangarhar 60.32 3.97 4.76 30.16 0.79 126

Kabul 12.90 17.74 9.68 59.68 62

Logar 15.38 30.77 7.69 46.15 13

Parwan 25.00 10.00 25.00 40.00 20

Badakhshan 0.50 5.97 39.30 54.23 201

Balkh 6.41 23.72 55.13 14.74 156

Kandahar 2.70 2.70 7.21 87.39 111Overall Average/Total 15.24 10.74 27.72 46.15 0.15 689

Table 47 Work responsibility – watering goats (percentages of respondents )

Province No Response Women Men Children Men andChildren

Total Respondents

Nangarhar 7.94 73.81 4.76 12.70 0.79 126

Kabul 11.29 58.06 9.68 20.97 62

Logar 15.38 38.46 7.69 38.46 13

Parwan 25.00 45.00 10.00 20.00 20

Badakhshan 0.50 24.38 39.30 35.82 201

Balkh 6.41 44.87 43.59 5.13 156

Kandahar 1.80 85.59 1.80 10.81 111Overall Average/Total 5.37 51.81 23.80 18.87 0.15 689

70

Table 48 Work responsibility – tending young goats (percentages of respondents )


Women Men Children Total Respondents

Nangarhar 20.63 76.98 0.79 1.59 126

Kabul 12.90 83.87 3.23 62

Logar 30.77 53.85 7.69 7.69 13

Parwan 60.00 40.00 20

Badakhshan 0.50 72.64 13.93 12.94 201

Balkh 10.26 50.64 37.82 1.28 156

Kandahar 9.01 88.29 1.80 0.90 111Overall Average/Total 11.18 70.68 13.50 4.64 689

Table 49 Work responsibility – milking goats (percentages of respondents )

Province No Response Women Men Children Total Respondents

Nangarhar 30.95 67.46 0.79 0.79 126

Kabul 11.29 88.71 62

Logar 46.15 53.85 13

Parwan 60.00 40.00 20

Badakhshan 0.50 98.01 1.49 201

Balkh 25.00 56.41 18.59 156


Table 50 Work responsibility – treating goats (percentages of respondents )

Province No Response Women Men Children Women

and MenWomen/ Children

TotalRespondents

Nangarhar 10.32 1.59 85.71 2.38 126

Kabul 11.29 16.13 70.97 1.61 62

Logar 15.38 46.15 38.46 13

Parwan 25.00 15.00 60.00 20

Badakhshan 0.50 42.79 56.72 201

Balkh 7.05 16.67 74.36 1.28 0.64 156


5.95 33.82 59.22 0.58 0.29 0.15 689

71

5.4.3 Decision Making5.4.3.1 Cattle

Table 51 Decision making cattle – Purchasing animals(percentages of respondents )

Province No Response Women Men Women and Men

TotalRespondents

Nangarhar 0.16 30.26 69.57 608

Kabul 0.25 51.36 48.39 403

Logar 1.54 48.46 50.00 130

Parwan 0.55 46.81 52.63 361

Badakhshan 48.47 51.53 262

Balkh 2.04 5.88 91.86 0.23 442


0.68 33.65 65.63 0.04 2508

Table 52 Decision making cattl e – Purchasing feed(percentages of respondents )


Total Respondents

Nangarhar 0.16 24.51 75.33 608

Kabul 0.25 46.15 53.60 403

Logar 0.77 49.23 50.00 130

Parwan 0.55 46.54 52.91 361

Badakhshan 32.82 67.18 262

Balkh 2.04 5.88 91.86 0.23 442


0.64 28.95 70.37 0.04 2508

Table 53 Decision making cattle – Selling animals(percentages of respondents )

Province No Response Women Men Total Respondents

Nangarhar 0.16 30.26 69.57 608

Kabul 0.74 41.69 57.57 403

Logar 50.00 50.00 130

Parwan 1.11 45.71 53.19 361

Badakhshan 72.90 27.10 262

Balkh 2.04 5.43 92.53 442

Kandahar 0.66 48.34 50.99 302

Overall Average/Total 0.76 37.60 61.64 2508

72

Table 54 Decision making cattle – Selling milk(percentages of respondents )


Total Respondents

Nangarhar 41.28 44.08 14.64 608

Kabul 8.68 86.10 5.21 403

Logar 66.92 30.00 3.08 130

Parwan 44.88 49.58 5.54 361

Badakhshan 98.47 1.53 262

Balkh 9.50 21.04 69.23 0.23 442


28.59 52.31 19.06 0.04 2508

Table 55 Decision making cattle – Treating animals (percentages of respondents )



Total Respondents

Nangarhar 1.64 22.86 75.16 0.33 608

Kabul 0.74 36.72 62.53 403

Logar 0.77 58.46 40.77 130

Parwan 0.55 25.21 73.96 0.28 361

Badakhshan 42.37 57.63 262

Balkh 2.26 5.88 91.86 442


1.16 34.09 64.63 0.04 0.08 2508

photo by Reynolds

73

5.4.3.2 Sheep

Table 56 Decision making sheep – Purchasing animals(percentages of respondents )


Nangarhar 6.38 25.53 68.09 47

Kabul 0.99 51.49 47.52 101

Logar 1.67 43.33 55.00 60

Parwan 1.96 52.94 45.10 51

Badakhshan 50.56 49.44 178

Balkh 7.78 5.00 87.22 180

Kandahar 0.82 12.70 86.48 244


Table 57 Decision making sheep – Purchasing feed(percentages of respondents )


Nangarhar 6.38 19.15 74.47 47

Kabul 0.99 53.47 45.54 101

Logar 1.67 45.00 53.33 60

Parwan 1.96 52.94 45.10 51

Badakhshan 32.58 67.42 178

Balkh 7.78 4.44 87.78 180

Kandahar 0.82 11.07 88.11 244


Table 58 Decision making sheep – Selling animals(percentages of respondents )


Nangarhar 6.38 29.79 63.83 47

Kabul 1.98 45.54 52.48 101

Logar 3.33 40.00 56.67 60

Parwan 1.96 52.94 45.10 51

Badakhshan 65.73 34.27 178

Balkh 7.78 3.89 88.33 180

Kandahar 2.05 28.28 69.67 244


74

Table 59 Decision making sheep – Selling milk (percentages of respondents )

Province No Response Women Men Womenand Men

Total Respondents

Nangarhar 80.85 6.38 12.77 47

Kabul 30.69 65.35 2.97 0.99 101

Logar 83.33 16.67 60

Parwan 66.67 25.49 7.84 51


Balkh 19.44 8.33 72.22 180


46.34 35.89 17.65 0.12 861

Table 60 Decision making sheep – Selling wool (percentages of respondents )


Nangarhar 74.47 2.13 23.40 47

Kabul 46.53 37.62 15.84 101

Logar 91.67 6.67 1.67 60

Parwan 41.18 25.49 33.33 51


Balkh 7.78 7.22 85.00 180

Kandahar 70.90 19.26 9.84 244


Table 61 Decision making sheep – Treating animals


Total Respondents

Nangarhar 12.77 12.77 72.34 2.13 47

Kabul 0.99 52.48 46.53 101

Logar 3.33 51.67 45.00 60

Parwan 3.92 35.29 60.78 51

Badakhshan 0.56 37.64 61.80 178

Balkh 7.78 4.44 87.78 180


(percentages of respondents )

75

5.4.3.3 Goats

Table 62 Decision making goats – Purchasing animals


Nangarhar 15.87 15.87 68.25 126

Kabul 8.06 59.68 32.26 62

Logar 7.69 61.54 30.77 13

Parwan 10.00 50.00 40.00 20

Badakhshan 54.23 45.77 201

Balkh 5.77 6.41 87.82 156

Kandahar 2.70 36.04 61.26 111



Table 63 Decision making goats– Purchasing feed


Nangarhar 15.87 11.90 72.22 126

Kabul 8.06 56.45 35.48 62

Logar 7.69 53.85 38.46 13

Parwan 10.00 50.00 40.00 20

Badakhshan 38.31 61.69 201

Balkh 5.77 7.05 87.18 156

Kandahar 2.70 29.73 67.57 111



Table 64 Decision making goats – Selling animals

Province No Response Women MenTotal

Respondents

Nangarhar 16.67 18.25 65.08 126

Kabul 8.06 51.61 40.32 62

Logar 7.69 53.85 38.46 13

Parwan 10.00 50.00 40.00 20

Badakhshan 76.62 23.38 201

Balkh 5.77 5.77 88.46 156

Kandahar 2.70 54.95 42.34 111



76

Table 65 Decision making goats – Selling milk


Nangarhar 81.75 11.90 6.35 126

Kabul 19.35 80.65 62

Logar 92.31 7.69 13

Parwan 65.00 35.00 20


Balkh 19.87 8.97 71.15 156

Kandahar 55.86 36.94 7.21 111



Table 66 Decision making goats – Selling fibre


Nangarhar 88.89 5.56 5.56 126

Kabul 51.61 33.87 14.52 62

Logar 100 13

Parwan 60.00 20.00 20.00 20


Balkh 10.26 8.33 81.41 156

Kandahar 67.57 29.73 2.70 111



Table 67 Decision making goats – Treating animals

Province No Response Women Men Womenand Men

Total Respondents

Nangarhar 17.46 8.73 73.02 0.79 126

Kabul 11.29 48.39 40.32 62

Logar 7.69 46.15 46.15 13

Parwan 15.00 30.00 55.00 20

Badakhshan 52.24 47.76 201

Balkh 5.77 5.77 88.46 156


6.68 38.61 54.57 0.15 689


77

5.4.3.4 Graphical Summaries

In order to facilitate a comparative view of these findings, summary graphics were compiled.

Figure 24 Work responsibilities in cattle management

0% 20% 40% 60% 80% 100%

Milking Cattle

Tending Young Cattle

Cattle Feeding

Watering Cattle

Treating Cattle

Cattle Grazing

Women Men Children Combined Responsibility No Response

Figure 25 Work responsibilities in sheep management

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Sheep Feeding

Tending Young Sheep

Milking Sheep

Watering Sheep

Treating Sheep

Sheep Grazing


78

Figure 26 Work responsibilities in goat management

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Milking Goat

Tending Young Goat

Goat Feeding

Watering Goat

Treating Goat

Goat Grazing


Figure 27 Decision making for cattle management

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Selling Milk

Selling Animals

Treating Animals

Purchasing Animals

Purchasing Feed

Women Men Both No Response

79

Figure 28 Decision making for sheep management

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Treating Animals

Selling Milk

Selling Animals

Selling Wool

Purchasing Animals

Purchasing Feed


Figure 29 Decision making for goat management

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Selling Milk

Selling Animals

Selling Fibre

Treating Animals

Purchasing Animals

Purchasing Feed


80

5.4.4 Goals and Preferences

The Women Livestock Survey attempted to evaluate the most important problems perceived andimprovements desired by the respondents. All women participating in the survey were given theoption to rank specific problems and desired improvements.

In the first question of this Section the respondents were asked from which livestock species theywould like to have more animals, and for what purpose of production (milk, meat, wool and work,where applicable, either for household consumption or for sale). Only one preferred species could belisted, but multiple answers for preferred uses were possible. Respondents were required to decideexclusively whether household consumption or production for sale was the most important use. In allbut some cases for poultry these restrictions were successfully maintained. The answers fromrespondents who selected chickens as their most preferred species for herd expansion were alsomaintained in those cases which were ambiguous for preferred use.

5.4.4.1 Preferred livestock species for herd expansion

5.4.4.1.1 Cattle

The number of respondents who selected cattle as their preferred species for owning more animalswas 1,655. Among these respondents 261 did not presently own cattle.

Table 68 Cattle preferred use: milk (percentages of respondents)

Province No Response (No Preference)

Household Consumption

Sale Total Respondents

Nangarhar 1.53 45.09 53.37 326

Kabul 0.75 26.42 72.83 265

Logar 3.66 74.39 21.95 82

Parwan 16.77 20.00 63.23 155


Balkh 2.83 3.89 93.29 283

Kandahar 1.29 33.59 65.12 387


Table 69 Cattle preferred use: work/draft

Province No Response (No Preference) Home Use Rent Total

Respondents

Nangarhar 100 326

Kabul 100 265

Logar 97.56 2.44 82

Parwan 83.23 10.97 5.81 155

Badakhshan 55.41 44.59 157

Balkh 17.31 65.72 16.96 283

Kandahar 99.74 0.26 387



81

A clear majority of respondents selected cattle as their most preferred species for herd expansion, andof these, most opted for milk. Multiple selections of preferred use were possible; the distribution ofthe answers shows that milk is what the respondents need most. Further, the majority of therespondents are interested in milk for sale – this clearly illustrates the potential of market-integrateddairy production.

5.4.4.1.2 Poultry

The second most preferred animal species was poultry (chicken). Out of 775 respondents selectingchicken as their most coveted species for herd expansion, 185 actually did not own any chickens.

Table 71 Chicken preferred use: meat (percentages of respondents )



SaleHousehold

Consumption and Sale

Total Respondents

Nangarhar 84.82 3.63 11.55 303

Kabul 92.68 2.44 4.88 82

Logar 46.15 53.85 13

Parwan 96.45 0.71 2.84 141

Balkh 33.19 9.36 55.32 2.13 235

Kandahar 100 1Overall AVG/Total

71.35 5.55 22.45 0.65 775

Table 70 Cattle preferred use: meat (percentages of respondents )




Nangarhar 99.69 0.31 326

Kabul 99.62 0.38 265

Logar 93.90 1.22 4.88 82

Parwan 100 155

Badakhshan 10.83 89.17 157

Balkh 13.07 2.12 84.81 283

Kandahar 99.22 0.78 387


The vast majority of the women chose market-oriented egg production as the goal for chicken flockexpansion.

Table 72 Chicken preferred use: eggs (percentages of respondents)


Household Consumption Sale


and Sale

Total Respondents

Nangarhar 16.50 27.39 56.11 303

Kabul 14.63 31.71 53.66 82

Logar 15.38 30.77 53.85 13

Parwan 13.48 5.67 80.85 141

Balkh 11.06 6.81 81.70 0.43 235

Kandahar 100 1Overall AVG/Total

14.19 17.68 68.00 0.13 775

82

5.4.4.1.3 Sheep

Approximately 14.4 percent of all respondents (417) selected sheep as their preferred species forexpansion of livestock production. Of these, 244 actually did not own sheep at the time of theinterviews, but all respondents selecting sheep as the species preferred for herd expansion ownedcattle. This observation again demonstrates that sheep ownership in Afghanistan seems to beconcentrated among wealthier farmers.

Table 73 Sheep preferred use: milk (percentages of respond ents)

Province No Response(No Preference)

Household Consumption Sale Total

Nangarhar 100 4

Kabul 77.42 8.60 13.98 93

Logar 84.09 15.91 44

Parwan 69.70 9.09 21.21 66

Balkh 27.27 3.03 69.70 66


Kandahar 86.21 6.90 6.90 29


Respondents

Table 74 Sheep preferred use: meat (percentages of respondents )



Nangarhar 75.00 25.00 4

Kabul 30.11 53.76 16.13 93

Logar 13.64 77.27 9.09 44

Parwan 46.97 51.52 1.52 66

Badakhshan 13.04 86.96 115

Balkh 7.58 7.58 84.85 66

Kandahar 20.69 79.31 29


Respondents

Table 75 Sheep preferred use: wool (percentages of respondents)




Nangarhar 100 4

Kabul 97.85 2.15 93

Logar 100 44

Parwan 96.97 3.03 66

Badakhshan 0.87 79.13 20.00 115

Balkh 28.79 13.64 57.58 66

Kandahar 100 29


The majority of respondents are interested in an expansion of sheep husbandry because of market-oriented meat production. Wool and milk production from sheep was much less important to therespondents.

83

5.4.4.1.4 Goats

Table 76 Goats preferred use: milk (percentages of respondents )



Respondents

Nangarhar 40.00 40.00 20.00 5

Kabul 87.50 12.50 8

Parwan 100 1

Badakhshan 100 13

Balkh 100 3

Kandahar 66.67 33.33 3


Table 77 Goats preferred use: meat (percentages of respondents )

Province No Response(No Preference) Sale Total Respondents

Nangarhar 100 5

Kabul 87.50 12.50 8

Parwan 100 1

Badakhshan 100 13

Balkh 33.33 66.67 3

Kandahar 100 3

Overall Average/Total 51.52 48.48 33

Table 78 Goats preferred use: fibre (percentages of respondents )




Nangarhar 100 5

Kabul 87.50 12.50 8

Parwan 100 1


Balkh 33.33 33.33 33.33 3

Kandahar 100 3Overall Average/Total

51.52 39.39 9.09 33

Goats are clearly not a species considered especially desirable by female Afghan livestock producers.Those that opted for goats were mostly interested in milk production. Given that milk is so importantin Afghanistan, it would be interesting to find out more about the fact that the potentially mostefficient dairy animal receives so little attention by livestock owners. One possible reason could bethat there are no productive dairy goats present in the country. A pilot project for extensive dairy goatproduction might be a worthwhile consideration.

5.4.4.1.5 Summary of Goals and Preferences:The key aspects of the preceding section are summarised graphically. Since the clear emphasis in theanswers was on cattle and poultry production, results were graphed for only these subsectors.

84

Figure 30 Preferred Livestock Species

Cattle58%

Sheep14%

Goat1%

Poultry27%

Cattle Sheep Goat Poultry

Milk Use24.8%

Milk Sale41.0%

Meat Use1.0%

Meat Sale15.9%

Own Work12.0%

Work Rent5.2%

Milk Use Milk Sale Meat Use Meat Sale Own Work Work Rent

Figure 31 Priorities for cattle production

85

It is very obvious that cattle are the preferred species, and that milk production for sale is the mostimportant priority for livestock development for Afghan women livestock producers. The second mostimportant priority is poultry development, again with the aim to earn money by selling products onthe market.

Figure 32 Priorities for poultry production

Meat Use5% Meat Sale

20%

Egg Use15%

Egg Sale60%

Meat Use Meat Sale Egg Use Egg Sale

photo by Thieme

86

5.4.5 Problems and Improvements

The survey collected considerable amounts of information on problems and opportunities perceivedby female livestock owners. All women participating in the survey were given the option to rankspecific problems and desired improvements. It was considered to be necessary to summarise thesedata in both graphical and tabular form. Summary tables in this section do not contain data ondistrict level. These can be found in Annex 8.4.

5.4.5.1 Problems identified for cattle production

Table 79 Problems cattle production - Not enough feed(percentages of respondents )

Province Most important

Second most important

Third most important

Not Mentioned

Total Respondents

Nangarhar 77.12 13.48 3.76 5.64 319

Kabul 49.54 18.58 20.43 11.46 323

Logar 50.94 8.49 8.49 32.08 106

Parwan 28.10 18.57 9.52 43.81 210

Badakhshan 62.96 32.10 1.85 3.09 162

Balkh 68.86 11.40 6.36 13.38 456


66.05 13.53 7.32 13.11 1900

Table 80 Problems cattle production - Animal disease(percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 12.54 54.55 25.71 7.21 319

Kabul 24.15 54.18 14.86 6.81 323

Logar 11.32 45.28 16.98 26.42 106

Parwan 64.29 28.10 4.29 3.33 210

Badakhshan 32.72 62.35 4.32 0.62 162

Balkh 20.39 38.16 28.51 12.94 456


21.68 49.37 21.16 7.79 1900

Table 81 Problems cattle production - Too far to the market(percentages of respondents )




Not Mentioned

Total Respondents

Badakhshan 0.62 1.23 42.59 55.56 162

Balkh 1.54 23.90 29.82 44.74 456

Kabul 1.55 3.10 95.36 323

Kandahar 0.31 0.93 7.10 91.67 324

Logar 5.66 14.15 12.26 67.92 106

Nangarhar 1.88 21.63 25.08 51.41 319

Parwan 0.95 38.57 30.95 29.52 210Overall Average/Total

1.21 14.95 20.84 63.00 1900

87

Table 82 Problems cattle production - Not enough buyers(percentages of respondents )




NotMentioned

Total Respondents

Nangarhar 1.25 5.96 8.78 84.01 319

Kabul 0.31 2.79 3.41 93.50 323

Logar 1.89 3.77 14.15 80.19 106

Parwan 1.43 5.71 10.48 82.38 210

Badakhshan 0.62 0.62 1.85 96.91 162

Balkh 0.22 14.04 11.40 74.34 456


0.63 5.79 6.95 86.63 1900

Table 83 Problems cattle production - Too much work(percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 6.90 3.45 18.50 71.16 319

Kabul 8.05 4.64 11.76 75.54 323

Logar 12.26 4.72 6.60 76.42 106

Parwan 2.86 2.38 6.67 88.10 210

Badakhshan 2.47 6.17 91.36 162

Balkh 9.21 1.32 0.88 88.60 456


6.05 2.37 7.84 83.74 1900

Table 84 Problems cattle production - Cost of labor(percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 0.63 1.57 97.81 319

Kabul 3.10 13.00 30.34 53.56 323

Logar 17.92 23.58 33.02 25.47 106

Parwan 0.95 3.33 9.52 86.19 210

Badakhshan 0.62 3.70 43.21 52.47 162

Balkh 0.44 0.22 0.88 98.46 456


1.79 4.47 12.89 80.84 1900

88

5.4.5.2 Desired Improvements in Cattle production

Table 85 Desired improvements cattle production - Better feeding (percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 66.47 6.04 13.90 13.60 331

Kabul 63.61 21.41 7.65 7.34 327

Logar 70.00 7.27 3.64 19.09 110

Parwan 27.01 11.37 9.95 51.66 211

Badakhshan 62.73 29.81 2.48 4.97 161

Balkh 77.32 15.33 3.89 3.46 463


69.65 12.63 6.13 11.59 1924

Table 86 Desired improvements cattle production - Better health care (percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 6.34 58.91 8.46 26.28 331

Kabul 16.21 40.67 23.24 19.88 327

Logar 12.73 22.73 17.27 47.27 110

Parwan 11.85 29.38 29.38 29.38 211

Badakhshan 35.40 58.39 4.97 1.24 161

Balkh 10.58 77.32 7.13 4.97 463

Kandahar 0.31 61.68 36.14 1.87 321Overall Average/Total 11.43 55.35 17.78 15.44 1924

Table 87 Desired improvements cattle production - Better water access (percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 0.91 1.21 0.60 97.28 331

Kabul 8.87 22.32 14.07 54.74 327

Logar 2.73 7.27 1.82 88.18 110

Parwan 2.84 4.74 4.27 88.15 211

Badakhshan 0.62 8.07 91.30 161

Balkh 7.99 1.94 29.16 60.91 463


4.11 5.77 11.59 78.53 1924

89

Table 88 Desired improvements cattle production - Easier market access (percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 1.81 3.63 7.25 87.31 331

Kabul 1.22 3.98 94.80 327

Logar 0.91 18.18 13.64 67.27 110

Parwan 2.37 11.85 15.17 70.62 211

Badakhshan 1.86 19.88 78.26 161

Balkh 1.94 2.16 31.10 64.79 463


1.09 3.90 13.77 81.24 1924

Table 89 Desired improvements cattle production - Better knowledge about animal husbandry (percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 9.67 18.43 27.49 44.41 331

Kabul 2.45 9.17 33.03 55.35 327

Logar 1.82 27.27 19.09 51.82 110

Parwan 20.38 27.01 17.06 35.55 211

Badakhshan 4.97 12.42 82.61 161

Balkh 0.22 1.94 14.25 83.59 463


4.47 16.06 26.87 52.60 1924

Table 90 Desired improvements cattle production - Access to credit (percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 14.80 11.18 40.48 33.53 331

Kabul 8.56 4.89 14.68 71.87 327

Logar 10.91 15.45 43.64 30.00 110

Parwan 33.18 15.17 23.22 28.44 211

Badakhshan 1.24 3.73 52.17 42.86 161

Balkh 1.51 1.30 11.45 85.75 463


8.73 5.93 22.04 63.31 1924

90

5.4.5.3 Problems identified for sheep production

Table 91 Problems sheep production - Not enough feed(percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 50.00 50.00 2

Kabul 75.00 13.89 2.78 8.33 36

Logar 60.00 10.00 5.00 25.00 20

Parwan 50.00 8.33 16.67 25.00 12

Badakhshan 73.45 20.35 2.65 3.54 113

Balkh 55.36 14.29 7.14 23.21 112


73.20 12.66 3.72 10.42 403

Table 92 Problems sheep production - Animal disease(percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 50.00 50.00 2

Kabul 8.33 63.89 22.22 5.56 36

Logar 15.00 50.00 5.00 30.00 20

Parwan 25.00 33.33 16.67 25.00

Badakhshan 23.89 72.57 3.54 113

Balkh 26.79 27.68 29.46 16.07 112


17.12 56.58 17.62 8.68 403

Table 93 Problems sheep production - Too far to market(percentages of respondents)




Not Mentioned

Total Respondents

Nangarhar 100 2

Kabul 5.56 2.78 5.56 86.11 36

Logar 5.00 15.00 15.00 65.00 20

Parwan 25.00 41.67 33.33 12

Badakhshan 0.88 14.16 84.96 113

Balkh 25.00 17.86 57.14 112


0.74 8.93 14.64 75.68 403

91

Table 94 Problems sheep production - Not enough buyers(percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 50.00 2

Kabul 2.78 11.11 86.11 36

Logar 15.00 85.00 20

Parwan 16.67 83.33 12

Badakhshan 2.65 4.42 92.92 113

Balkh 0.89 8.93 8.93 81.25 112

Kandahar 100 108Overall Average/Total

0.25 3.97 5.71 90.07 403

Table 95 Problems sheep production - Too much work(percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 100 2

Kabul 5.56 2.78 11.11 80.56 36

Logar 10.00 10.00 10.00 70.00 20

Parwan 8.33 16.67 75.00 12

Badakhshan 2.65 1.77 18.58 76.99 113

Balkh 16.07 2.68 1.79 79.46 112


6.95 3.23 10.92 78.91 403

Table 96 Problems sheep production - Cost of labor(percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 100 2

Kabul 8.33 19.44 72.22 36

Logar 10.00 15.00 45.00 30.00 20

Parwan 8.33 25.00 66.67 12

Badakhshan 1.77 56.64 41.59 113

Balkh 0.89 2.68 3.57 92.86 112


0.99 2.73 24.07 72.21 403

92

5.4.5.4 Desired improvements for sheep production

Table 97 Desired improvements sheep production - Better feeding(percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 50.00 50.00 2

Kabul 88.24 5.88 2.94 2.94 34

Logar 60.00 10.00 30.00 20

Parwan 60.00 10.00 10.00 20.00 10

Badakhshan 76.11 20.35 1.77 1.77 113

Balkh 77.97 12.71 4.24 5.08 118


82.09 11.19 2.24 4.48 402

Table 98 Desired improvements sheep production - Better health care (percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 50.00 50.00 2

Kabul 2.94 55.88 11.76 29.41 34

Logar 30.00 30.00 10.00 30.00 20

Parwan 10.00 20.00 20.00 50.00

Badakhshan 23.89 71.68 3.54 0.88 113

Balkh 11.02 75.42 7.63 5.93 118


12.69 67.91 11.69 7.71 402

Table 99 Desired improvements sheep production - Better water access(percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 100 2

Kabul 14.71 14.71 70.59 34

Logar 5.00 20.00 75.00 20

Parwan 100 10

Badakhshan 2.65 30.09 67.26 113

Balkh 4.24 2.54 33.05 60.17 118


1.49 3.98 22.39 72.14 402

93

Table 100 Desired improvements sheep production - Easier market access (percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 50.00 50.00 2

Kabul 2.94 11.76 85.29 34

Logar 15.00 20.00 65.00 20

Parwan 10.00 30.00 60.00 10


Balkh 3.39 3.39 22.03 71.19 118


1.24 2.24 11.94 84.58 402

Table 101 Desired improvements sheep production - Better knowledge about animal husbandry (percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 100 2

Kabul 11.76 32.35 55.88 34

Logar 20.00 20.00 60.00 20

Parwan 50.00 10.00 40.00 10

Badakhshan 1.77 12.39 85.84 113

Balkh 0.85 4.24 21.19 73.73 118


0.25 11.44 28.86 59.45 402

Table 102 Desired improvements sheep production - Access to credit(percentages of respondents )




Not Mentioned

Total Respondents

Nangarhar 100 2

Kabul 5.88 8.82 23.53 61.76 34

Logar 5.00 10.00 40.00 45.00 20

Parwan 30.00 10.00 30.00 30.00 10

Badakhshan 3.54 45.13 51.33 113

Balkh 2.54 1.69 9.32 86.44 118


2.24 3.23 21.39 73.13 402

94

5.4.5.5 Problem identified for goat production

Table 103 Problems goat production - Not enough feed(percentages of respondents )




Not Mentioned

Total Respondents

Badakhshan 54.55 36.36 9.09 11

Balkh 40.28 22.22 4.17 33.33 72

Kabul 16.67 50.00 33.33 6

Kandahar 58.33 33.33 8.33 12

Nangarhar 50.00 50.00 2

Parwan 100 2Overall Average/Total 41.90 26.67 6.67 24.76 105

Table 104 Problems goat production - Animal disease(percentages of respondents )




Not Mentioned

Total Respondents


Balkh 34.72 23.61 23.61 18.06 72

Kabul 50.00 33.33 16.67 6

Kandahar 41.67 50.00 8.33 12

Nangarhar 50.00 50.00 2


Table 105 Problems goat production - Too far to the market (percentages of respondents )




Not Mentioned

Total Respondents


Balkh 18.06 18.06 63.89 72 18.06

Kabul 100 6

Kandahar 100 12

Nangarhar 50.00 50.00 2 50.00

Parwan 100 2 100Overall Average/Total 15.24 15.24 69.52 105 15.24

95

Table 106 Problems goat production - Not enough buyers(percentages of respondents )




Not Mentioned

Total Respondents

Badakhshan 100 11

Balkh 1.39 2.78 1.39 94.44 72

Kabul 100 6

Kandahar 8.33 8.33 83.33 12

Nangarhar 100 2


Table 107 Problems goat production - Too much work(percentages of respondents )




Not Mentioned

Total Respondents


Balkh 22.22 2.78 75.0 72

Kabul 33.33 66.7 6

Kandahar 41.67 25.00 33.3 12

Nangarhar 100 2


Table 108 Problems goat production - Cost of labor(percentages of respondents )




Not Mentioned

Total Respondents

Badakhshan 9.09 63.64 27.27 11

Balkh 1.39 4.17 11.11 83.33 72

Kabul 16 .67 83.33 6

Kandahar 16.67 83.33 12

Nangarhar 100 2


96

5.4.5.6 Desired improvements for goat production

Table 109 Desired improvements goat production - Better feeding (percentages of respondents )




Not Mentioned

Total Respondents


Balkh 76.74 9.30 8.14 5.8 86

Kabul 80.00 20.00 5

Kandahar 100 4

Nangarhar 100 2


Table 110 Desired improvements goat production - Better health care(percentages of respondents )




Not Mentioned

Total Respondents


Balkh 11.63 72.09 6.98 9.3 86

Kabul 20.00 40.00 40.0 5

Kandahar 50.00 50.00 4

Nangarhar 100 2

Parwan 100 2Overall Average/Total

12.73 67.27 10.91 9.1 110

Table 111 Desired improvements goat production - Better water access (percentages of respondents )




Not Mentioned

Total Respondents


Balkh 5.81 2.33 36.05 55.8 86

Kabul 20.00 20.00 60.0 5

Kandahar 100 4

Nangarhar 100 2


97

Table 112 Desired improvements goat production - Easier market access (percentages of respondents )




NotMentioned

Total Respondents


Balkh 2.33 6.98 17.44 73.3 86

Kabul 100 5

Kandahar 100 4

Nangarhar 50.00 50.0 2


Table 113 Desired improvements goat production - Knowledge about animal husbandry (percentages of respondents )




Not Mentioned

Total Respondents


Balkh 2.33 4.65 22.09 70.9 86

Kabul 20.00 20.00 60.0 5

Kandahar 50.00 25.00 25.0 4

Nangarhar 100 2


Table 114 Desired improvements goat production - Access to credit (percentages of respondents )




Not Mentioned

Total Respondents


Balkh 1.16 1.16 3.49 94.2 86

Kabul 20.00 20.00 60.0 5

Kandahar 25.00 75.0 4

Nangarhar 50.00 50.0 2


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5.4.5.7 SummaryThe following graphs summarise the preceding sections.

Figure 33 Problems of cattle production

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

FeedShortage

Disease MarketDistance

No Buyers WorkLoad

LabourCost

First Priority Second Priority Third Priority Not Mentioned

Figure 34 Priorities for improvement of cattle production

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

Feed Health Water Market Husbandry Credit


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Figure 35 Problems of sheep production

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

FeedShortage


No Buyers WorkLoad

LabourCost


Figure 36 Priorities for improvement of sheep production

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%



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Figure 37 Problems of goat production

0.0%5.0%

10.0%

15.0%20.0%25.0%30.0%35.0%

40.0%45.0%50.0%

FeedShortage


No Buyers Work Load LabourCost


Figure 38 Priorities for improvement of goat production

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%



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Figure 39 First priorities for improving cattle production

Feed, 70.0%

Water, 4.1%

Health, 11.5%

Market, 1.1%

Husbandry, 4.5% Credit, 8.8%


Figure 40 First priorities for improving sheep production

Feed, 82.1%

Credit, 2.2%

Husbandry, 0.2%

Water, 1.5%Market, 1.2%

Health, 12.7%


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5.5 KARAKUL SURVEY5.5.1 Introduction

A survey was carried out to collect data and information on Karakul production. This sector has beentraditionally of great significance in Northern Afghanistan. Because it is a value added sector, it offersopportunities for income generation as an alternative to the production of illicit drugs. Very little isknown about production figures and producers of Karakul in Central and South Asia in general.During the 90’s, Karakul went into a deep decline in former Soviet Central Asia. This decline has beendriven largely by two factors: (1) the disruption of Soviet-internal and export market channels afterthe break-up of the Soviet Union, and (2) the general decline in international demand for Karakulpelts, a development that parallels the decline in demand for pelts in the western industrialized worldin general. However, anecdotal evidence from Central Asia suggests that in recent years, demand, inparticular from countries of the former Soviet Union has been increasing again. Unfortunately, thereare no reliable data on the Karakul sector available at all.

As a first step in the development of a Karakul research and development program, basic productioninformation and producer perceptions need to be collected. The Karakul Survey was conducted in thenorthern provinces of Kunduz, Balkh, Faryab, Samangan, Sari Pul and Jawzjan. A total of 131 Karakulproducers were surveyed.

The results also include information about the coefficient of variation because we felt that thisadditional parameter was useful, given the relatively small sample size and the fact that this is firstrecent published evaluation of Afghan Karakul production.

photo by Thieme

5.5.2 Flock Size and Distribution of Colour Types

In some areas, notably Balkh, answers to the questions about colour distribution in flocks were notanswered. In order to ascertain colour distribution, the data set was filtered for each colour type toinclude only those records that were true zeros or above. Therefore, the colour type distributionstatistics could not be combined into one table.

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Table 115 Size of karakul flocks in different provinces

Province Average Flock Size STD of Flock Size

Balkh 168 161.69

Faryab 45.32 48.07

Jawzjan 197.27 339.84

Kunduz 181.61 142.22

Samangan 14.22 12.09

Saripul 101.67 113.68

Overall 133.95 189.87

Table 116 Number and proportion of GREY colour pelts in Karakul flocks fromdifferent provinces

Province Average Flock Size Average No of Grey Colour

Percentages of Grey Colour

Faryab 45.35 23.76 52.40

Jawzjan 96.50 43.14 44.71

Kunduz 181.61 129.10 71.09

Samangan 14.22 6.11 42.97

Saripul 36.25 21.25 58.62

Table 117 Number and proportion of SUR (golden-brown) colour pelts in karakul flocks from different provinces

Province Average Flock Size Average No of Sur Colour

Percentages of Sur Colour

Faryab 43.86 0.57 1.30

Jawzjan 96.50 0.29 0.30

Kunduz 182.80 17.13 9.37

Samangan 14.35 0.00 0.00

Saripul 36.25 0.00 0.00

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Since the Sur (Golden-Brown) colour achieves the highest price on the market, higher proportion ofSur type animals would probably increase income for Karakul farmers. The association of high-pricedcolour types with lethal factors requires careful consideration, however.

Wealth distribution was defined by delineating 5 ownership classes:

These ownership classes differ from the ones defined above for Level 2 summaries because onlyproducers actually owning sheep were interviewed.

Table 119 Ownership classes for karakul flocksOwnership Class Flock Size1 1-152 16-493 50-994 100-4995 > 499

Table 118 Number and proportion of BLACK colour pelts in karakul flocks from different provinces

Province Average Flock Size

Average No.of Black Colour

Percentages of Black colour

Faryab 45.35 21.41 47.21

Jawzjan 96.50 53.07 55.00

Kunduz 181.61 35.95 19.80

Samangan 14.22 8.11 57.03

Saripul 36.25 15.00 41.38

Table 120 Distribution of karakul flock sizes in different provinces

Ownership class

Province 1 2 3 4 5 Total Respondents

Balkh 4.35 30.43 56.52 8.70 23

Faryab 26.32 47.37 10.53 15.79 19

Jawzjan 9.09 18.18 36.36 27.27 9.09 22

Kunduz 2.44 17.07 14.63 60.98 4.88 41

Samangan 83.33 11.11 5.56 18

Saripul 33.33 44.44 22.22 9Overall Average

17.42 19.70 21.21 37.12 4.55 132

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These data are graphically summarised as follows:

Figure 41 Karakul sheep wealth distribution(percent respondents represented in proportion)

1-15 head 16-49 head 50-99 head 100-499 head > 499 head

Balkh

Faryab

Jawzjan

Kunduz

Samangan

Saripul

There are several conclusions from these numbers. First, there is a very large variation in average flocksize both between and with provinces (see coefficient of variation). Second, the SUR colour type israre and sometimes absent. This is the colour that achieves the highest prices in the market. Clearly,there is considerable scope for production improvement. In Karakul sheep, the most expensive coloursand curl types are associated with lethal factors. Therefore, the selection to increase Percentages andNumbers of these high-value pelt types is not a trivial undertaking and would benefit considerablyfrom support by research and extension entities. Third, there are quite large flocks in Afghanistan,suggesting the potential to rebuild an industry that formerly was economically very important in theNorthern provinces.

5.5.3 Pelt Production

According to Central Asian folklore, Karakul pelt production developed out of the practice to kill onelamb if a ewe had twins, especially in a bad forage year, because the milk production capacity of theKarakul ewe under range conditions suffices only for the successful rearing of one lamb. One shouldexpect therefore a relationship between forage year and decision to pelt lambs rather than rearingthem. This decision is based on rainfall. In Northern Afghanistan, most of the precipitation falls in thewinter months. At lambing time in early spring, producers therefore have a good sense of expectedforage production. Accordingly, the survey asked for typical pelt harvest averages in a bad and in agood forage year.

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Table 121 Performance characteristics of karakul flocks in different provinces

Province Performance Characteristics Average STD CV

Balkh Lambing Rate 85.22 9.26 10.87

Weaning Rate 65.22 8.78 13.47

Pelting Rate (Good Forage Year) 34.00 28.71 84.43

Pelting Rate (Bad Forage Year) 56.00 8.79 15.70

Pelting Rate 2001 39.44 23.68 60.04

Pelting Rate 2002 39.38 30.10 76.44

Faryab Lambing Rate 96.84 17.49 18.06

Weaning Rate 66.84 20.79 31.10



Pelting Rate 2001 31.54 26.27 83.28

Pelting Rate 2002 63.33 100.03 157.94

Jawzjan Lambing Rate 90.48 19.87 21.97

Weaning Rate 65.24 15.92 24.41



Pelting Rate 2001 40.00 48.99 122.47

Pelting Rate 2002 44.44 49.69 111.80

Kunduz Lambing Rate 69.02 20.93 30.32

Weaning Rate 53.50 27.44 51.28



Pelting Rate 2001 40.73 45.82 112.49

Pelting Rate 2002 45.00 63.36 140.81

Samangan Lambing Rate 77.78 10.83 13.92

Weaning Rate 60.56 11.29 18.64



Pelting Rate 2001 35.56 20.61 57.96

Pelting Rate 2002 25.56 18.32 71.71

Sari Pul Lambing Rate 97.78 11.33 11.59

Weaning Rate 75.56 16.41 21.71



Pelting Rate 2001 13.33 19.44 145.77

Pelting Rate 2002 NA NA NAOverall Averages Lambing Rate

82.52 20.05 24.30

Weaning Rate 61.92 20.68 33.40



Pelting Rate 2001 36.24 36.64 101.12

Pelting Rate 2002 42.19 59.16 140.24

Interestingly, the results are not consistent and may be conditioned by differences between provincesin the general forage situation and feed availability. Usually, Karakul producers pelt most of theirlambs in a bad forage year, and rear as many as possible (pelting only male) in a good year. This wasthe case in the provinces of Balkh, Faryab, Jawzjan, and Samangan, but not in Kunduz and Sari Pul. Lambing Percentages and Numbers was quite low in Kunduz and Samangan. This indicatesconsiderable potential for improvement in nutrition and reproduction. This is even more evident forthe weaning rate data. These performance data demonstrate the benefits that a developmentprogram could realize by improving nutrition and health services for Karakul production. On the otherhand, these reproductive figures are too low for a speedy recovery of sheep flocks after the drought.Interventions in the Karakul sheep sector are necessary.

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photo by Pittroff

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5.5.4 Markets5.5.4.1 Returns from pelts and wool

In order to gather information about recent developments, producers were asked if their returns forwool and pelts were better or worse than the previous year.

Table 122 Returns from the sale of pelts in 2002 compared to 2001

Province Lower Returns

Higher Returns

NA Number of Respondents

Percentages with lower returns

Balkh 23 23 100

Faryab 19 19 100

Jawzjan 21 1 22 95.45

Kunduz 28 12 1 41 68.29

Samangan 6 2 10 18 33.33

Sari Pul 9 9 100

All 106 14 12 132 80.30

On average, producers reported lower returns, with the notable exception of Samangan. This maysuggest regional differences in marketing. Unfortunately, there are no marketing studies available forKarakul pelt production. Most likely, the market is dominated by a few traders that buy directly fromproducers at lambing time. This may explain why in one province apparently better prices were paid.A better understanding of the Karakul marketing channels in Afghanistan, and indeed Uzbekistanand Turkmenistan (all these countries are likely served by the same traders) is required to developinterventions that help producers directly. The total absence of any quality control and any assistancein training in pelt treatment and conservation must play a role in the economic returns for producers.This could be improved.

Table 123 Returns from the sale of wool in 2002 compared to 2001

Province Lower Returns

Higher Returns NA Number of

Respondents

Percentages with lower

returns

Balkh 23 23 100

Faryab 19 19 100

Jawzjan 10 11 1 22 45.45

Kunduz 29 11 1 41 70.73

Samangan 7 1 10 18 38.89

Sari Pul 9 9 100

All 97 23 12 132 73.48

The situation for wool sales mirror the results obtained for the pelt market. Again Samangan was theexception. The fact that most of the carpet wool in Afghanistan is imported would suggest goodmarket potential for home-grown wool. However, there is not a single mill in Afghanistan;consequently, wool produced in Afghanistan is hand-spun and can likely not compete with woolpurchased at low prices from New Zealand and Europe. Again, an intervention program should lookat the market and intermediary processing. Investing in a wool mill in Afghanistan could be aprofitable enterprise, if initially some regulation of the wool market was enacted by the Government.Definitely, this could help Afghan sheep producers in rebuilding their flocks and businesses.

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Overall, sheep producers had a somewhat optimistic view of the pelt market.

Table 126 Interest of customers for buying more or less pelts

Province Less More NA Number of Respondents

Percentages of Respondents who

expected to sell less

Balkh 11 12 23 47.83

Faryab 12 7 19 63.16

Jawzjan 19 2 1 22 86.36

Kunduz 1 40 41 2.44

Samangan 2 7 9 18 11.11

Sari Pul 9 9 100

All 54 68 10 132 40.91

5.5.4.2 Market Opportunities

The farmers were asked about current and expected market opportunities. First it was of interested tosee if pelts and wool sales differed from the previous year.

Table 124 Market opportunities for karakul pelts

Sale of pelts in 2002 compared to 2001


Percentagesof Respondents

who sold less

Balkh 14 8 1 23 60.87

Faryab 17 2 19 89.47

Jawzjan 17 4 1 22 77.27

Kunduz 23 18 41 56.10

Samangan 12 1 5 18 66.67

Sari Pul 9 9 100

All 92 33 7 132 69.70

The reasons for lower sales could be manifold, including the desire to rebuild flocks. Therefore, thesenumbers do not reflect future intentions of producers.

The wool market seemed to have been better than the pelt market, but still more farmers reportedless wool sales than those reporting higher wool sales. Without more detailed information aboutregional and international markets, these data are difficult to interpret.

Table 125 Market opportunities for karakul wool

Sale of karakul wool in 2002 compared to 2001


Percentagesof Respondents

who sold less

Balkh 5 18 23 21.74

Faryab 12 7 19 63.16

Jawzjan 10 10 2 22 45.45

Kunduz 23 14 4 41 56.10

Samangan 13 2 3 18 72.22

Sari Pul 9 9 100

All 72 51 9 132 54.55

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Table 127 Interest of customers for buying more or less wool



expected to sell less

Faryab 12 7 19 63.16

Jawzjan 12 9 1 22 54.55

Kunduz 1 34 6 41 2.44

Samangan 8 7 3 18 44.44

Sari Pul 9 9 100

All 51 71 10 132 38.64

The same result was obtained for wool. Again, farmers in Kunduz were the most optimistic.

Finally, farmers were asked if they would be interested in producing more pelts, and which colourobtains the best price for pelts.

With the exception of Samangan, farmers were clearly interested in Karakul production. It woulddefinitely be of interest to conduct follow up work in Samangan to clarify.

Table 128 Interest of customers for producing more or less pelts



are interested in producing more

Balkh 2 21 23 91.30

Faryab 7 12 19 63.16

Jawzjan 10 11 1 22 50.00

Kunduz 39 2 41 95.12

Samangan 9 9 18 0.00

Sari Pul 3 5 1 9 55.56

All 31 88 13 132 66.67

In international markets, the SUR colour usually achieves the highest price. The fact that with theexception of Samangan province most farmers listed GREY as the colour fetching the best pricesuggests perhaps a preference for that colour that is typical for the market in Afghanistan itself. Thisfurther demonstrates the need for market studies to design interventions that help revitalize theKarakul sheep sector in Afghanistan.

Table 129 Colour of pelts for which best price was obtained

Province Grey Sur NA Number of Respondents

Percentages of Grey

Balkh 23 23 100

Faryab 19 19 100

Jawzjan 22 22 100

Kunduz 34 7 41 82.93

Samangan 3 12 3 18 16.67

Sari Pul 9 9 100

All 110 19 3 132 83.33

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photos by Pittroff

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6. DISCUSSION6.1 CENSUS DATA SUMMARIES

Previous data on livestock numbers can be found in FAO surveys undertaken since 1995 (1995,1997/98 and 2000). Some areas covered in 1995 were omitted from the larger coverage of 1998,because of security and accessibility problems. Previous surveys, due to numerous constraints, couldnot attempt a systematic estimation of comprehensive livestock numbers. Further, in the absence ofreliable human demographics and consistent information about location and administrative settingsof human settlements (as explained above, there is no consistent district database), it was impossibleto develop a sampling frame that would have allowed for truly randomized sampling on either of thetwo levels. Consequently, the method of total enumeration was used for Level 1.

No reliable data exist on which to base estimates of the proportion of families or communitiesincluded in prior surveys. Accordingly, a comparison of the Afghanistan National Livestock Censusdata to total numbers reported in previous surveys is not sensible, but nevertheless the numbers perhousehold may give a general indication of livestock numbers pre-drought, together with the changesthat have occurred between 1995 and today.

Livestock owned per family, and sheep numbers in particular have clearly fallen sharply. Although thedecline between 1998 and 2003 appears steeper than between 1995 and 1998, changes werealready underway during the earlier period. There are reports of trans- border movements of livestockby emigrating families, and of returnees leaving stock behind while they investigated the situation inAfghanistan for themselves.

The time of year of the surveys is also important because the Kuchi herds may or may not be present.The Kuchi population in the south and south-east of Afghanistan spend the winter in Pakistan or indistricts of Afghanistan close to the border (FAO, 1999). With the current security situation along theborder area, it is probable that many would have chosen to remain in Pakistan. During the 1995 survey, the average sheep flock for Kuchis was 100 head, and nationally the totallivestock owned by Kuchis was equal to the number owned by the resident population. The presentcensus only captured records from 29,000 Kuchi families, far fewer than were recorded by the limitedsurveys during 1995 (59,000 families) and 1998 (75,000 families).

Given that a far smaller Kuchi population was captured by the 2003 Census, a comparison would bebetter made with the stock holdings of resident families in previous years. On this basis it is still clearthat livestock holdings have declined and that sheep have been particularly hard hit by adverseconditions.Limited livestock data can be found in the Agricultural surveys undertaken by FAO twice yearly, winterand summer. In the current census, livestock numbers per family have been shown to be considerablyreduced from pre-drought values. The survey undertaken in May-June 2002 collected data from 5000farms in 540 villages across all 32 Provinces and agro-ecological zones. The results indicated that onaverage a typical farm owned two cattle, four sheep, four goats and six birds. Compared to theprevious year, livestock numbers and the proportion of farms keeping livestock had decreased. Surveydata over the years 1998-2002 indicated livestock numbers had fallen faster in the later years of thedrought.

Table 130 A comparison of livestock numbers per family from FAO surveys and census results between 1995 and 2003Species 1995 Survey 1998 Survey 2003 Census

All Resident All Resident AllCattle 3.66 3.75 2.51 2.75 1.22Sheep 21.93 12.59 14.20 7.93 2.88Goats 9.40 5.67 5.76 4.38 2.40Donkey 1.09 0.96 0.71 0.62 0.52Camels 0.41 0.12 0.23 0.07 0.06Poultry 11.56 11.86 6.84 7.01 4.00

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Preliminary results from the Agricultural survey in winter 2002 indicted that both livestock numbersand proportion of farmers with livestock had started to increase compared to earlier in the year. Moreinformation is needed on the marketing patterns for livestock to understand the significance of theseobservations.

The present Census shows that over the country as a whole, livestock numbers per family are lowerthan found in the surveys.

6.2 REGIONAL PRODUCTION SYSTEM AND PRODUCER CHARACTERISTICS

The classification of provinces into agro-ecoregions was based on expert opinion. Provinces that aresimilar in terms of production calendar, natural resource endowment and climatic conditions weregrouped together. Badakhshan is different enough from all other provinces to merit definition as aunique agro-ecological region. From Level 1 data, several summary statistics were computed thatdemonstrated regional differences in livestock wealth, including species-specific differences. Thesedata help understand subsequent summaries on the basis of agro-ecological region.

Forage productionThe forage production identified wheat straw, Shaftal (Persian clover), Lucerne, maize straw andmaize as the important cultivated and crop-aftermath forages produced on farm. There wereconsiderable differences between agro-ecological regions in some forages, but clearly the mostimportant on-farm feed resource across the whole country is wheat straw. Among the purchasedfeeds that are currently used, cotton seed cake, wheat straw, wheat bran and Lucerne are the mostpopular. When asked about preferences for purchased feeds, farmers tended to favour concentrate feed.

When planning interventions for on-farm forage production, the differences between regions shouldbe taken into account. Except for the northern region, farmers seem to favour forages and cropaftermath. Shaftal was particularly popular in the East and Centre-East regions.

Length of feeding periodOn average livestock is out on pastures for more than half of the year. Animals need to be fed in thewinter between 4 and 8 months, depending on region. The very long period for Centre-East is clearlyconditioned by Kabul (see Appendix for province-level data), due to the limited grazing capacityaround Kabul City. Information about grazing periods was not asked separately for the differentlivestock species. However, it is clear that large numbers of the lactating cattle, especially in the Eastand Centre-East, are also stall-fed during most of the spring and summer months.

The length of the winter feeding period compounds the generally extremely low forage productioncapacity to create a 'winter feed gap', probably the most critical constraint for livestock production inmost parts of Afghanistan. This is definitely the most critical intervention needed. However, it must berecognized that this constraint limits the number of animals grazing rangelands the rest of the year.Given the extremely deteriorated condition of rangelands in Afghanistan (appropriate quantitativedata are lacking and should be urgently procured), the 'winter feed gap' may currently be animportant safety mechanism preventing further, perhaps irreversible destruction of rangelands. Thisimplies that in locations, were grazing of livestock is important, forage interventions must not takeplace outside a watershed rehabilitation context, lest incentives be created to even further increasepressure on already overstressed rangeland grazing resources.

Production calendarThe forage year appears to begin in March for most of the country. This coincides with the maincalving and lambing periods and the begin of lactation for a large proportion of the livestock. Theaverage early turnout in the northern and western regions is somewhat surprising. Certainly, thebegin of the forage year or grazing season in higher elevations occurs much later.The distribution pattern for end of grazing season was much less clear. For most of the country, thegrazing season ends in September - November. The western region is relatively late with mostrespondents indicating November as the end of the grazing period. However, Kuchi nomads beginleaving higher elevation pastures in some areas as soon as late August.

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Supplementary feeding can begin as early as September; in the northern and western areas, farmerstart supplementation in November, which coincides with the end of the forage year. The end of thesupplementary feeding period again mirrors the answers given for the grazing period.

Markets, Trading Partners and Sales DecisionsMarch and September are the months most livestock are sold. In March, farmers have cash needs forinputs for crop production. In the autumn, farmers typically sell animals either ready for market(lambs/kids) or sell those animals in excess of available feed resources. Autumn markets, therefore, aregood indicators of the forage supply situation. Fluctuations in numbers of animals sold on autumnmarkets and associated price changes are a good indication of the severity of the 'winter feed gap',especially if high numbers of draft animals are sold. These dynamics should be monitored as they area good indicator of the ability of farmers to successfully plant their spring crops.

Farmers were asked what type of animal they sell first, and which type of livestock they will try tokeep under all circumstances. The species most often mentioned in both categories was cattle. Whatappears to be contradictory at first sight makes prefect sense when considering the dynamics of theforage situation. Farmers with low winter feed supplies will often take chances and try keeping smallstock. However, they plan carefully their available forage supply for cattle and when insufficient, theywill sell their most valuable animals rather than risking losing them. Although autumn market pricesare lower, draft animals are still needed until November for ploughing in most places and will getacceptable prices. These answers are consistent with the general conclusion that cattle are the mostimportant livestock species in Afghanistan.

Priority categories for livestock sales were further broken down. Multiple answers could be given tothese questions. Across the entire country, the most frequently given answer for selling cattle was topurchase feed. There were some differences between regions in terms of the age category sold first,but not within region. The average distance to markets was about two hours.

Local traders were the most important business contact for most farmers, followed by regionaltraders. This suggests that butchers seem to rely on traders when procuring animals for slaughter.Farmers generally reported an increase in livestock prices in 2003, which is reflective of the fact thatthe drought had broken and demand for animals was higher. Most farmers reported that they hadsold more animals than in the year before, and that they earned more money. Cows sold best in most locations.

Wealth distributionThe data on wealth distribution show a rather clearly regionally differentiated picture. Ghazni,Jawzjan, Badghis and Hirat are cattle-poor, the three last provinces especially because they had beenhit hard by the drought. In the case of Jawzjan, traditionally a sheep production region, this may haveto do more with the agro-ecological conditions than true poverty, as sizeable sheep holdings can befound in this province. However, in Jawzjan there is also a higher than average number of farmers notowning any livestock at all. The data provide clear evidence of a correlation of drought effects withlivestock wealth. The western region had the highest number of farmers with no cattle at all, and thelowest number of farmers with more than 10 head of cattle. The central region was also cattle-poor,a consequence of the past drought and years of conflict. The eastern region, on the other hand, isrelatively cattle-rich. Numbers for sheep and goats reflect regional characteristics discussed before(most larger sheep holdings in the north, most large goat holdings in the east.

Age structureThe Level 2 age structure data are similar to Level 1 although they indicate a more positive situation.It must be taken into account that no truly randomized sampling frame could be developed for theLevel 2 surveys, which is discussed in more detail in the chapter about Methods. Accordingly,comparisons between Levels have inherent shortcomings. The conclusion that the off take potentialof Afghan livestock populations is generally low due to inherently low reproductive performance isupheld, however. Livestock dynamics models (for example BAPTIST, 1992) can be parameterized withLevel 2 and more detailed off take scenarios can be simulated on the basis of these data. The reasons given for culling support the interpretation of the market data given above. Whileobviously the most important reason to sell an animal is old age, feed shortage is the second mostfrequently cited reason to sell animals.

6.3 WOMEN SURVEY

The women survey focused on three areas: work distribution between men, women and children,decision making authority, and goals, preferences and problems formulated by the women farmers.Questions about work distribution and decision making were by livestock species. The results indicatethat the responsibility for specific tasks is distributed among genders and age groups in characteristicways independent of livestock species. Activities that would require work outside the homesteadcompound are mostly handled by men and children. Feeding livestock (at the homestead) is typically atask for which women are responsible, whereas herding grazing animals is a task perfumed bychildren and to a lesser degree by men. Watering animals, tending young and milking animals is theresponsibility of women. Treating sick animals is a task performed mostly by men; however, for smallruminants, the involvement of women in animal treatment is larger. These clear patterns of division oflabour suggest the need to consider gender in interventions. For example, tuberculosis preventionshould specifically target women since they are most exposed to lactating animals. Likewise, they areresponsible for feeding animals on farm. Feeding improvement interventions targeting especially dairyanimals should be aware of the need to consider special training needs for women, but also the factthat decisions about input supply are largely made by men. Since the herding of grazing animals islargely a task of children, interventions increasing work load due to herding will likely impact schoolenrolment and should be carefully monitored.

Decision making in livestock production is mostly a domain of men, but for some important activities,such as purchasing cattle, or selling livestock in general, more than a third of the respondentsreported that women make these decisions. Women also decide milk and wool sales. Women play animportant role in decision making about treating sick animals and this should be reflected in thedesign of veterinary programs.

The vast majority of women selected cattle as the species preferred for expansion of their livestockholdings. The most frequently cited intention of using cattle was milk production for sale. The nextmost important species for women was poultry; the most frequent reason given was egg productionfor sale. Sheep were selected only by about 15 percent of all respondents as the livestock specieswhich they preferred for future investments. More than half of those respondents preferring sheepdid not actually own sheep, but all of the women who wanted to have (more) sheep, also ownedcattle. Preferences for sheep were explained by milk and meat, and to a lesser degree by woolproduction. Goats were not a preferred livestock species for women farmers, and proposals to expandgoat production or introduce specific goat systems such as dairy-emphasis goat production should becarefully piloted and evaluated for adoption.

Most women named problems with feeding as the most important for cattle, sheep and goatproduction, followed by better veterinary health care. Answers for other possible problems wereheterogeneous and should be consulted for the planning of specific improvement interventions.

Answers given for desired improvements largely reflected the corresponding answers for the mainproblems. Improvement of feed supply and veterinary health care were the most frequently requestedinterventions. In general, the respondents did not seem to perceive labour, market access or lack ofknowledge as burning problems. Especially the answers regarding the lack of knowledge suggestpotential difficulties in implementing farmer training programs.

6.4 KARAKUL SURVEY

The Karakul survey produced some limited information about production parameters and marketopportunities. However, the high variability of the given answers precludes strong conclusions. It isalso not clear from the survey how much Karakul sheep production in general is contributing to thelivelihoods of the farmers. Large Karakul sheep herds exist and most interviewed farmers expected tosell more, and were interested in producing more. Karakul production was traditionally an importantlivestock sector in Afghanistan; considerable market research is necessary to determine its futurepotential.

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photos by Pittroff

7. METHODS7.1 SURVEY DESIGN

From the outset, the census was designed to take place on two levels. Level 1 was planned as acomplete enumeration. There were no reliable baseline data, and such an effort is required in order tobegin a systematic census program needed for designing sector policies, development interventions,and national vaccination programs.

Typically, in national censuses the approach is to develop an appropriate sampling frame for randomsampling that allows the creation of 'gold standard' data against which total enumeration data arethen evaluated. This was not possible in Afghanistan. Although the comparison was attempted (seeSection 7.2), neither level could, in fact, provide 'gold standard' data quality data. However, theresults of this census allow the definition of an appropriate sampling frame for the future.Accordingly, the methodology described in Section 7.2 can be considered a reference for future work.

The design of the census on two levels was motivated primarily by two considerations: first, acquiredetailed production system data, and second, create a data set that allows consistency checking ofLevel 1 data where feasible. While the comparison, as discussed, suffered from inherent samplingdesign problems impossible to avoid, the analysis of production system characteristics was verysuccessful and further supported by data derived in the survey of female livestock owners.

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As this report amply documents, data from both levels are intrinsically consistent and provide anexcellent summary picture of the Afghan livestock sector. The key problems and most promisingintervention routes were identified. Level 2 results allow the application of livestock herd dynamicmodels (for example Baptist, 1992) to extrapolate herd growth and potential off take data. Theimpact of the drought on animal numbers was identified by the survey; however, the reasons for thedecline of animal numbers are manifold. For example, many farmers in Afghanistan sell livestockwhen their wheat crop fails. While often this coincides with forage shortages caused by drought, thesale of the animals does not necessarily coincide with the worst state of the pastures in a drought.

photo by Rlung

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7.2 ANALYSIS METHODOLOGY

Most of the analysis effort was invested in data cleaning. The most critical problem encountered wasthe separation of true zero answers from missing answers. Many statistics required the computation ofratios (for example, number of certain livestock per species, or number of young per dam, etc.). Thefrequent inconsistency of data entries as a result of unsatisfactory distinction between true zero andmissing answer required considerable verification effort. With the exception of the Level comparisonstatistics, only basic univariate summary statistics were employed. Additional analysis is conceivable, forexample discriminate analysis verifying that the definition of agro-ecological zones adequately reflectsimportant production system characteristics. Further, reproduction data could be used for livestockpopulation dynamics modelling.

Most of the analysis effort was invested in data cleaning. The most critical problem encountered was the separation of true zero answers from missing answers. Many statistics required the computation of ratios (for example, number of certain livestockper species, or number of young per dam, etc.). The frequent inconsistency of data entries as a result of unsatisfactory distinction between true zero and missing answer required considerable verification effort. With the exception of the Level comparison statistics, only basic univariate summary statistics were employed. Additional analysisis conceivable, for example discriminate analysis verifying that the definition of agro-ecological zones adequately reflects important production system characteristics. Further, reproduction data could be used for livestock population dynamics modelling.

7.3. Data consistency

Assumptions and Notation Comparison of Level I and Level II observations were made at the district level. Level I observations are regarded as census data. We denote ratios of number of livestock per family in a district by:

cattle the number of cattle per family,

sheep the number of sheep per family,

goats the number of goats per family,

donkeys the number of donkeys per family,

camels the number of camels per family.

In our an alysis, we take as our null hypothesis that these ratios computed from Level I (census) data are the true district values. For example, if:

,cattle ijX the nu mber of cattle owned by the thj family in the

thi village of the district ,

and there are N villages in the district and iR families in the thi village, then:

cattle,1 1

1

iRNcattle iji j

Nii

X

R .

The four other ratios are computed in the same manner.

Level II observations are obtained from sub-samples of families that have been randomly selected from

each of n villages. Let ir denote the number of families selected from the thi village. The n villages

were randomly selected from the district. Thus a Level II observation, ijY , is represented as:

ij i ijY , 1, 2, , i n , 1, 2, , ij r ,

where

the mean of all ijY ís in the district,

i the deviation of the mean of all ijY ís in the thi village from ,

and

ij the deviation of ijY from the mean of all ijY ís in the thi village.

Because both the number of villages and the number of families in the Level II data are generally qui te small relative to the corresponding numbers in the population (Level I data), we ignore the use of finite

7.3 DATA CONSISTENCY

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population correction factors in our analysis and regard i ís and ij ís as random components in our

representation of ijY . [Asid e: Use of finite population correction factors in our analysis likely would

have the effect of slightly decreasing our standard error estimates fo r Level II estimates of district means.This would result in slight strengthening of the conc lusions that we present.] Thus, we assume that:

i ís iid 20,. and ij ís iid

20,. ,

Comparisons of Level I and Level II Observations

We outline the basic analysis for cattle data only, and then present results of our analyses for catt le,sheep, goat, donkey and camel data. Under the null hypothesis that

cattle,1 1

1

iRNcattle iji j

Nii

X

R

is the true ratio of cattle per family in a given district, we compute:

cattle cattlecattle

cattle

ˆt

ˆs.e.,

where

cattleˆ,1 1

1

irncattle iji j

nii

Y

r,

is an unbiased estimator of cattle computed from the Level II data. Derivation and computation of

the standard error of cattleˆ , cattleˆs.e. , follows.

Derivation of Standard Erro r :

Letting 1n

iim r (and dropping the subscript ìcattleî for notational convenience), we have

that:

cattleˆ 1 1

1

irniji j

nii

Y

r 11 ni.im Y ,

where replac ement of a subscript by a dot indicates summation over that subscript. Becauseobservations from different villages are independent due to the random sampling of villages:

cattle ..ˆVar Var Y 211 n

i.im Var Y 211 n

i.im Var Y .

Using our mixed model representation of ijY :

1 1 1 ,i i ir r ri. ij ij iji j j

j j

Var Y Var Y Cov Y Y

2 2 21i i ir r r

2 2 2i ir r .

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Therefore:

121 n

.. i.iVar Y Var Ym

2 2 212

1 ni ii r r

m2

2 212

1nii r

mm

We note that for the special case in which equal numbers of families are sub-sampled in each

village, i.e.,

(say)1 2 nr r r r , so that m nr , then:

..Var Y 2 21 1n nr

.

Restricted maximum likelihood (REML) estimates of 2

and2

, which we denote by 2ˆ and

2ˆ respectively, were obtained using the VARCOMP Procedure of SAS Version 9.0. The

estimated variance of ..Y (or cattleˆ ) was computed as:

22 21

21n

ii.. cattle

rˆ ˆ ˆVar Y Var

mm ,

and the standard error of ..Y (or cattleˆ ) then was computed as:

22 21

21n

ii.. cattle

rˆ ˆ ˆs.e. Y s.e.

mm

Example Calculation of cattlet :

We use observations from the Qadi s district of Ba dghis provinc e to illustrate the calculation of

cattlet . The Level I (census) value for cattle in the Qadis district is:

0 5105cattle . .

Families from six ( n 6) villages in the Qa dis district were randomly sub-sampled for Level IIobservation. The numbers of families selected were (in no particular order):

1 5r , 2 4r , 3 1r , 4 5r , 5 5r and 6 5r .

The Level II estimated ratio of cattle per family was 2 4000cattleˆ . . REML estimates of the

variance components were:

2 1 35743ˆ . and 2 6 41973ˆ . ,

so that the standard error of cattleˆ was computed as:

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Conclusions and DiscussionIf Level 2 data are randomly selected sub-samples from districts, then the distributions of the valuesare inconsistent with the hypothesis that Level 1 (census) values are true values. However, Level 2observations were sampled mostly from families with livestock, and no sampling frame could bedeveloped based on the distribution of livestock wealth from Level 1 data. In total, only 29respondents in Level 2 (of 1284) had no livestock at all. Therefore, we should expect the distributionsof the values to exhibit the tendencies noted in the table above. This, of course, is because Level 2ratios are estimating the ratios of number livestock to number of families, given that the family haslivestock, whereas Level 1 data are global averages of number of animals per family, definitelyincluding all families that had no livestock. Thus, there are three potential sources of bias in the Level2 data: one is the fact that the number of families without livestock in the sample is likely notrepresentative of the number of families without livestock in the population. This bias could becorrected if the number of families without livestock (for each specific species in question) could bereliably estimated from Level 1 data. However, this is not possible since the questions about numberof families without livestock in Level 1 were not species-specific. The next source of bias is wealthdistribution. Level 1 data are global community averages per family – not allowing to derive afrequency distribution of livestock ownership according to wealth classes as discussed above. If weknow the proportion of families in each district who possess no livestock of a given type, then bias-correction adjustments can be made to our Level 2 ratios. The bias-corrected Level 2 ratios can thenbe reanalyzed using the procedures outlined herein to assess accuracy of the Level 1 (census)observations. The third source of bias is the selection of villages for Level 2 sampling. Althoughenumerators were asked to not intentionally select villages for sampling, systematic sampling basedon convenience criteria is of course likely. However, since no village list was available at the start ofLevel 2, randomized sampling of villages was not possible.

22 21

21n

ii.. cattle

rˆ ˆ ˆs.e. Y s.e.

mm

2 2 2 2 2 2

25 4 1 5 5 5 11 35743 6 41973

2525. .

0 71477. .

Finally, we compute cattlet as:

0 51050 2 4000 2 6440 71477cattle

. .t .

. .

Summary of Results :

The following table presents values of cattlet ( sheept , goatst and donkeyst are given in the Tables A -

A). If the ratios cattle , sheep , goats and donkeys computed using the Level 1 (census) data are

the true district values, th en corresponding values of cattlet , sheept , goatst and donkeyst shou ld

behave approximately like independent standard normal random variables based on Central Limi t Theorem arguments. As a rule of thumb, approximately 95 percent of the values should fall betweenminus two and plus two, and essentially all values should fall betwee n minus three and plus thre e. Further, values should be distributed somewhat symmetric ally about zero. All four tables exhibit, to varying degrees, the following tendencies:

1. An excess (relative to our rule of thumb) of large t values.

2. Most t values are positive, i.e., values tend to be greater than zero.

Conclusions and DiscussionIf Level 2 data are randomly selected sub-samples from districts, then the distributions of the t values are inconsistent with the hypothesis that Level 1 (census) values are true values. However, Level 2 observations were sampled mostly from families with livestock,and no sampling frame could be developed based on the distribution of livestock wealthfrom Level 1 data. In total, only 29 respondents in Level 2 (of 1284) had no livestock atall. Therefore, we should expect the distributions of the t values to exhibit the tendencies noted in the table above. This, of course, is because Level 2 ratios are estimating the ratiosof number livestock to number of families, given that the family has livestock, whereas Level 1 data are global averages of number of animals per family, definitely including all families that had no livestock. Thus, there are three potential sources of bias in the Level 2 data: one is the fact that the number of families without livestock in the sample is likely not representative of the number of families without livestock in the population. This biascould be corrected if the number of families without livestock (for each specific species in question) could be reliably estimated from Level 1 data. However, this is not possible since the questions about number of families without livestock in Level 1 were not species-specific. The next source of bias is wealth distribution. Level 1 data are global community averages per family – not allowing to derive a frequency distribution of livestockownership according to wealth classes as discussed above. If we know the proportion offamilies in each district who possess no livestock of a given type, then bias-correction adjustments can be made to our Level 2 ratios. The bias-corrected Level 2 ratios can then be reanalyzed using the procedures outlined herein to assess accuracy of the Level 1 (census) observations. The third source of bias is the selection of villages for Level 2 sampling. Although enumerators were asked to not intentionally select villages for sampling, systematic sampling based on convenience criteria is of course likely.

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Table 131 Comparison of cattle numbers in selected districts from Level 1 and Level 2 observations

Obs Province District cattleπ ˆ cattleπ s.e.( ˆ cattleπ ) cattlet

1 Badghis Qadis 0.511 2.400 0.715 2.644

2 Baghlan Baghlan 1.805 3.849 1.045 1.956

3 Baghlan Dushi 1.718 4.344 0.493 5.325

4 Baghlan Khinjan 2.477 5.563 0.675 4.571

5 Balkh Dawlat Abad 0.350 2.250 0.201 9.463

6 Bamyan Kamhard 2.001 2.571 0.869 0.656

7 Bamyan Saighan 1.534 2.321 0.437 1.804

8 Bamyan Shibar 1.050 1.100 0.307 0.164

9 Bamyan Yakawlang 1.708 3.214 1.120 1.345

10 Farah Anar Dara 0.446 1.432 0.352 2.802

11 Faryab Dawlat Abad 0.291 1.821 0.345 4.434

12 Ghazni Dih Yak 0.997 2.231 0.320 3.855

13 Ghazni Zana Khan 0.429 1.936 0.690 2.184

14 Ghor Tulak 0.851 2.640 0.668 2.680

15 Hilmand Nad Ali 1.961 4.000 0.811 2.514

16 Hirat Ghoryan 0.380 1.600 0.537 2.270

17 Hirat Kohsan 0.289 0.611 0.224 1.441

18 Hirat Obe 0.936 2.449 0.317 4.774

19 Jawzjan Qurghan 0.105 1.379 0.445 2.864

20 Kabul Chahar Asyab 1.121 4.067 0.496 5.939

21 Kabul City Nahya 14 0.060 6.375 1.235 5.113

22 Kapisa Hisa Awal Kohistan 3.020 4.200 1.321 0.894

23 Kunduz Dasht Archi 1.628 8.100 3.065 2.112

24 Kunduz Imam Sahib 3.764 15.900 6.623 1.832

25 Laghman Alingar 2.211 4.036 0.314 5.812

26 Logar Baraki Barak 1.256 4.807 1.234 2.877

27 Logar Charkh 0.725 4.474 0.589 6.362

28 Nangarhar Kama 5.053 4.515 0.554 -0.970

29 Nangarhar Khogyani 2.235 3.278 0.531 1.965

30 Nangarhar Muhmand Dara 2.654 5.359 0.460 5.887

31 Nangarhar Pachir Wa Agam 2.515 4.191 0.746 2.246

32 Nimroz Khash Rod 0.667 2.600 0.568 3.403

33 Paktika Mata Khan 2.345 5.917 1.965 1.818

34 Paktika Sharan 2.214 3.000 1.025 0.767

35 Paktya Chamkani 3.409 4.923 0.970 1.560

36 Paktya Dandi Patan 3.432 8.526 3.359 1.516

37 Parwan Bagram 1.404 4.037 0.405 6.505

38 Parwan Jabalusaraj 1.306 5.200 1.126 3.457

39 Samangan Kaldar 1.078 3.750 0.751 3.555

40 Samangan Khulm 0.416 3.875 2.048 1.689

41 Sari Pul Sayed Abad 1.750 2.000 0.730 0.342

42 Takhar Ishkamish 1.561 7.800 2.681 2.327

43 Takhar Yangi Qala 2.087 7.100 2.029 2.471

44 Zabul Shahjoy 0.777 3.148 0.695 3.413

45 Zabul Shahr-e-Safa 0.965 3.087 0.789 2.688

While the exact comparison between Levels 1 and 2 ultimately proved impossible, we shouldcomment on general tendencies exhibited in the data. The production system data from Level 2appear to be plausible and coherent. Likewise, summary statistics from Level 1, for example on agestructure, are compatible with expert opinion about levels of reproduction typical for the extreme lowinput conditions of Afghanistan. No systematic bias could be detected. Thus, we arrived at theconclusion that the census data provide a reliable picture of the Afghan livestock sector.

7.4. KEY LESSONS LEARNED

“Statistical inference, properly interpreted, can be misleading. But the nature of statistical evidence issuch that we cannot observe strong misleading evidence very often.” (Chambers and Skinner, 2003;p. 60)

Important lessons were learned in the Afghan National Livestock Census. They fall into the categoriesof Methodology, Organization and Supervision, Logistics, and Analysis.

MethodologyThe Census was designed to be conducted on two levels of sampling intensity (in terms of amount ofinformation asked from farmers). Level 1, or total enumeration, had the explicit objective of visitingevery community in Afghanistan and enumerate livestock numbers by species, relating it to numberof families. Level 2, or sub sampling, had the objective to gather detailed production systeminformation and data that could assist in the interpretation of the general stock inventories gatheredin Level 1 and inform the design of interventions in the livestock sector.A survey requires a sampling frame. A frame is an operational representation of the population unitsof interest (in the case of the Afghanistan National Livestock Census, all farmers). A frame could befor example a list of all farmers. Typically, there would be various steps involved in developing asampling frame for general populations. In any case, at some point a list of all objects in the area tobe sampled must be available. This was obviously not possible for the Census. Accordingly, the onlyfeasible solution for the estimation of livestock populations in Afghanistan was total enumeration.However, information on a more detailed level was required, given the near-total lack of informationabout production system characteristics, markets, and producer problems and preferences.Accordingly, a much more detailed questionnaire had to be designed and administered to a subsample of farmers in Afghanistan. However, since it was not possible to develop a sampling framebecause of the total enumeration survey being conducted almost concurrently, certain aspects of theLevel 2 survey data may be biased. Sources of bias include omission of provinces (due to security andlogistics issues), non-random selection of villages within districts and provinces and non-randomselection of farmers (because no frame was available). Accordingly, Level 2 violated one of Dalenius’srequired prerequisites of a survey (as cited and discussed in Biemer and Lyberg, 2003, p. 4) – thesampling of units from the frame in accordance with a sampling design specifying a probabilitymechanism and a sample size. This critical issue affected certain aspects of Level 2 results, inparticular composite statistics on number of livestock per family, as discussed above. However, theconsistency of data on production system properties and farmer preferences clearly demonstrates thateven a survey impacted by (in our case unavoidable) design deficiencies can produce valuable andtrustworthy data, if conducted by a responsible field team. In fact, given the enormous logisticsproblems typically faced by survey teams in developing countries, it is arguable if the rigorousconditions theoretically required are ever met. What is needed, however, is an assessment anddiscussion of possible sources of error. Unfortunately, this seems to be very rarely accomplished inpractice. As the key lesson learned we conclude that a more rigorous description of methods forsurvey design and analysis of data gathered under conditions not meeting ‘ideal’ requirements wouldbe useful for practitioners.

The methodology problems encountered in the Afghanistan National Livestock Census had two majorsources:

(1) The time restrictions implied by the donor (2) The security problems restricting access to parts of the country

125

The implications of these restrictions, which seem to be frequently encountered in similar surveyscenarios, will be discussed next.

LogisticsOne of the most critical problems encountered when reviewing previous FAO livestock surveys inAfghanistan was the proper identification of location. As detailed in this report, considerableconfusion persists about delineation of districts. As a result, unique identification of sampling units(typically mosque communities within village) for Level 1 (total enumeration) data collection wasexpected to be difficult. To remedy the problem of non-unique identification of sampling units, it wasproposed to equip all surveying teams with GPS units for the recording of geographical coordinatesthat would ensure unique identification. However, the timeline set by the donor made it impossible towait until all equipment had arrived. As a result, a major effort had to be invested in uniqueidentification of locations (communities visited for total enumeration). This was a key problem inpreparing the data for comparison with Level 2 data. Obviously, the translation from and into Dariand operational difficulties in maintaining translation standards compounded the problem.Nevertheless, the key lesson learned is that census quality is a direct function of available time forpreparation and execution. In the preparation of a livestock census operation, all logistical problemsmust be analysed carefully and their impact on data quality assessed as conservatively as possible. It iscritically important to maintain good and direct communications between the donor and the censusteam to ensure that spending deadlines do not impinge upon survey quality. If a census beginswithout the necessary preparation, delays in data analysis are unavoidable if minimal standards ofdata quality, integrity and utility are to be maintained.

Organization and SupervisionThe number of people employed in the Afghanistan National Livestock Census indicates the scopeand intensity of a task of this magnitude. Given the extraordinary difficulties accessing communities inAfghanistan, the extent to which farmers were reached constitutes a major achievement by itself. Theorganization scheme employing numerator teams and supervisors in all provinces worked well for thedata collection process. However, a critical problem that could have been prevented to a certaindegree by reallocation of personnel resources became apparent during the data analysis process. Asdescribed above, one of the most difficult, and in some cases almost intractable problems is thedifferentiation between true zero responses, and no or missing responses. In particular in thecalculation of summary statistics such as livestock per family, or number of young per adult femalethe confounding of true zeros and missing observations causes enormous problems that requireconsiderable time to resolve. An important lesson learned in the Afghanistan National LivestockCensus, therefore, is that consistency checking of data sheets and data entry should begin as soon asthe first field data arrive at the census headquarters. As long as enumerators are in the field,corrections are possible with manageable effort. Post hoc data corrections are very difficult, timeconsuming and costly. Thus, not only is it necessary to employ a data entry team that facilitatesconsistency checking, there should be an additional team that interfaces between the enumerator –supervisor teams and the data entry teams. The task of this team should be the immediate checkingof data sheets and identification of all instances of inconsistent information. It is necessary to developa special manual for this team that facilitates this task. This manual should be developed during thetesting phase of the survey.

Although an effort was made to ensure spelling consistency, the level of preparation of the data entryteam was not consistent, especially in terms of knowledge of English. If language problems of thelevel of complexity encountered in Afghanistan are to be expected, data files should be checked byan additional group of personnel trained specifically to identify spelling consistency problems.Especially if multi-level surveys are conducted, comparisons between survey levels will be extremelytime-consuming or even impossible if spelling problems have been resolved post-hoc.

Investment in adequate personnel support during data collection and data entry is the key factor intimely execution and analysis of livestock surveys.

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127

AnalysisMost problems encountered in the analysis of the Afghanistan National Livestock Census data werecaused by a few, well defined issues. Chief among them were: differentiation between true zeros andno/missing response, proper identification of location, and spelling inconsistency.

While the methods needed for census data analysis are not very sophisticated, comparison betweenlevels or analysis of triangulated questions can be very involved, as the above section on comparisonmethodology suggests. More sophisticated methods will be useful only, however, if the quality of datamatches their requirements. The most critical issue in survey statistics is the definition of the samplingframe and the appropriate definition of sampling size. As discussed above, this was not possible forLevel 2 within the time frame set by the donor.

Livestock surveys are often necessary in post crisis situations, where very little if any tangibleinformation needed for the construction of a sampling frame is available. In such cases, totalenumeration should be considered as a first step, focusing on livestock numbers, number of familiesand, if it all possible, data that allow to derive a distribution of livestock wealth classes so that thisimportant factor can be considered in subsequent survey work. After the analysis of totalenumeration data, the next step for the development of a sampling frame for more detailed analyseswould be the selection of villages/communities within district and province. For detailed Level 2 typesurveys, a sampling frame can be developed based on the randomized selection of villages. In otherwords, the selection of survey units (farmers) following a national census would be a two stepprocess, first developing a sampling frame for villages, and then for farmers (respondents) withinvillages.

The analysis of Level 2 data produced evidence of some questions not properly understood byrespondents and enumerators. Although training was conducted, a manual was written and thesurvey was tested, few survey results are free of such problems. Accordingly, the important lessonlearned is that extensive testing and in-depth analysis of test survey data pays high dividends innational census programs.

REFERENCES:

Baptist, R. 1992. Derivation of steady state herd productivity. Agricultural Systems 39:253-272

Biemer, P.P. & L.E. Lyberg. 2003. Introduction to Survey Quality. Wiley Interscience, 402 p.

Chambers, R.L. & C.J. Skinner. 2003. Analysis of Survey Data. Wiley, London 376 p.

CSO. 2003. Estimated population of Afghanistan 2003-2004, Transitional Islamic State ofAfghanistan, Kabul, Afghanistan

FAO. 1999. Activities of the Kuchi Working Team. Working Paper 1/99. FAO Kabul, Afghanistan

FAO. 2003. National Crop Output Assessment. FAO Kabul, Afghanistan

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photo by: Pittroff

8. ANNEX8.1 DISTRICT NAMING COMPARISONS AND EXPLANATIONSThe capital cities of Provinces and the geographic areas in which they are located are not officiallydesignated as Districts. They are called Administrative Units. However, there is no differentiation madeby the people on the ground. The Ministry of the Interior therefore omits these Administrative unitsfrom their list of Districts.

During the recent periods of insecurity, additional districts have been formed in some locationsunofficially by local area commanders, and in some cases pre-existing Districts have been unofficiallymoved from one Province to another. Examples are especially found in the northern provinces ofFaryab, Jawzjan, and Balkh.

The district list used by AIMS dates from 1979 when the first and last complete human populationcensus was taken. The list of official districts provided by the Ministry of the Interior originates from1991, but unfortunately the Ministry of the Interior has no maps showing the boundaries of theseofficial districts. The Central Statistical Organisation has hand drawn maps with district boundariesand village location, which remain to be computerised. However, some official district names are notin use by the local population. The Livestock Census therefore relied on information provided to thedata collectors by village representatives, together with their own local knowledge in recordingDistrict names.

The following is an attempt to reconcile different sources of information.

129

130

B a d a k hs ha n P r o v ince

FAO LIVESTOCK CENSUS AIMS DATA MOI DISTRICTS

ARGHONJ KHWA [FAYZABAD] ARGHON J KHWAARGO [FAYZABAD] ARGO

BAHARAK BAHARAK BAHARAKDARAEEM [FAYZABAD] DARAEEM

DARWAZ BALA DARWAZ DARWAZ BALAFAYZABAD FAYZABAD (PROVINCIAL CAPITAL)ISHKASHIM ISHKASHIM ISHKASHIM

JURM JURM JURMKARAN WA MANJAN KURAN WA MUNJAN KARAN WA MUNJAN

KHASH [JURM] KHASHKHWAHAN KHWAHAN KHWAKAN

KISHIM KISHIM KISHIMKOHISTAN [RAGH] KOHISTAN

KOHISTAN RAGH [RAGH]KOOF [DARWAZ] KOOFAB

NASI DARWAZ [DARWAZ] DARWAZRAGHISTAN [RAGH] RAGHISTAN

SHAHRI BUZURG SHA HRI BUZURG SHAHRI BUZURGSHIGHNAN SHIGHNAN SHIGHNAN

SHIKI DARWAZ [DARWAZ] SHIKI (DARWAZ )SHOHADA [BAHARAK] SHOHADA

TAGAB KISHIM [KISHIM] TAGAB (KISHIM?)TESHKAN [KISHIM] TESHKANWAKHAN WAKHAN WAKHANWARDOOJ [BAHARAK] WARDOOJ

YAFTA L BALA [FAYZABAD]YAFTAL - i - PAYAN [FAYZABAD] YAFTA SUFLA

YAMGAN [JURM] YAMGANYAWAN RAGH RAGH YAWAN (GIROWAN)

ZEBAK ZEBAK ZEBAK

• Arghonj Khwa, Daraeem, Yaftal Payan, Yaftal Bala and Argo were all part of FazyabadDistrict

• Tagab Ki shim and Teshkan (FAO and MoI) were part of Kishim District • Yangam and Khash (FAO and MoI) were part of Jurm District • Wardooj and Shohada (FAO and MoI) were part of Baharak District • Nasi Darwaz, Shiki Darwaz and Koof (FAO and MoI) were part of Darwaz Distri ct – the

remaining portion is now Darwaz Bala District • Kohistan, Kohistan Ragh, Yawan Ragh and Ragistan (FAO and MoI) were part of Ragh

District

131

B a d g is P r ov ince

FAO LIVESTOCK CENSUS AIMS DATA MOI DISTRICTS AB KAMARI AB KAMARI

BALA MURGHAB [MURGHAB]

DARA IBOUM [QADIS] GHORMACH GHORMACH GHORMACH

JAWAND JAWAND JAWAND

MUQUR MUQUR MUQUR

MURGHAB MURGHAB MURGHAB

QADIS QADIS QADIS

QALAY i NAW QALAY I NAW (PROVINCIAL CAPITAL)SANGE AATASH [AB KAMARI]

• Ab Kamari, Sange Aatash (FAO and/or MoI) were part of Qalay i Naw District• Dara i boum (FAO and/or MoI) is part of Qadis District• Bala Murghab (FAO and/or MoI) is part of Murghab District

B a g hla n P r ov ince


[KHOST WA FIRING] FIRING WA GHAROO

[KHOST WA FIRING] GUZARGAH I NUR

ANDARAB MARKAZ [DEHSALAH (ANDARAB)] ANDARAB

BAGHLAN MARKAZ BAGHLAN

BAGHLAN QADEEM [BAGHLANI JADEED]

BAGHLANI JADEED BAGHLANI JADEED BAGHLAN JADEED

BANOO ANDARAB [DEHSALAH (ANDARAB)]

BURKA BURKA

DAHANI-I-GHORI DAHANI-I-GHORI DAHAN I GHORI

DEH SALAH ANDARAB DEHSALAH (ANDARAB) DEH SALAH

DUSHI DUSHI

KHINJAN KHINJAN KHINJAN

KHOST WA FIRING KHOST WA FIRING KHOST WA FIRING

NAHRIN NAHRIN NAHRIN

PULI HISARI ANDARAB [DEHSALAH (ANDARAB)] PULI HISARI (ANDARAB ?)

PULI KHUMRI PULI KHUMRI (PROVINCIAL CAPITAL)

TALA WA BURFUK TALA WA BARFAK TALA WA BARFAK

• Puli Khumri (FAO)is the same as Baghlan Markaz (MoI)• Baghlan Qadeem (FAO) is officially part of Baghlan Jadeed District• Deh Salah Andarab (AIMS) has officially been divided into Deh Salah Andarab, and Pui Hisari

Andarab District (FAO and MoI).• Banoo Andarab (FAO) was part of Andarab district• Kahmard District (AIMS) is officially part of Bamyan Province• Khwaja Hijran was part of Nahrin District• Guzargah i Nur and Firing wa Gharoo were part of Khost Wa Firing District

132

B a lk h P r ov ince


KALDAR KALDAR

KHULM KHULM

BALKH BALKH BALKH

CHAHAR BOLAK CHAHAR BOLAK CHARBOLAK

CHAHAR KINT CHAHAR KINT CHARKINT

CHAI [DAWLATABAD]

CHIMTAL CHIMTAL CHEMTAL

DAWLATABAD DAWLATABAD DAWLATABAD

DIHDADI DIHDADI DIHDADI

KISHINDIH KISHINDIH KISHINDEH

KOHI ALBARZ [NAHRI SHAH] KOHI ALBARZ

MARMUL MARMUL MARMUL

MAZAR ISHARIF MAZARI SHARIF (PROVINCIAL CAPITAL)

NAHRI SHAHI NAHRI SHAH NAHRI SHAH

SHOLGARA SHOLGARA SHOLGARA

SHORTEPA SHORTEPA SHORTEPA

ZAREH [KISHINDIH] ZAREH

• Kaldar and Khulm (AIMS and MoI)Districts are officially in Balkh Province• Chai (FAO)was part of Dawlatabad• Zareh (FAO and MoI) was part of Kishindih District

B a m ya n P r ov ince


BAMYAN BAMYAN (PROVINCIAL CAPITAL)

KAMHARD [SE E BAGHLAN PROV] KAHMARD

PANJAB PANJAB PANJAB

SAIGHAN [KAHMARD] SAIGHAN

SHIBAR SHIBAR SHIBAR

WARAS WARAS WARAS

YAKAWLANG YAKAWLANG YAKAWANG

• Kamhard District (FAO and MoI) has been officially reallocated to Bamyan Province from Baghlan Province (AIMS)

• Saighnan (FAO and MoI) District was part of Kahmard District

133

F a r a h P r ov ince


ANAR DARA ANAR DARA ANAR DARA

BAKWA BAKWA BAKWA

BALA BULUK BALA BULUK BALA BULUK

FARAH MARKAZ FARAH (PROVINCIAL CAPITAL)

GULISTAN GULISTAN GULISTAN

KHAKI SAFED KHAKI SAFED KHAKI SAFED

LASH WA JUWAYN LASH WA JUWAYN LASH WA JUWAYN

PURCHAMAN PURCHAMAN PURCHAMAN

PUSHTE ROD PUSHTE ROD PUSHTE ROD

QALAY I KAH QALAY I KAH QALAY I KAH

QALAY I KAH PUSHT KOH QALAY I KAH PUSHT KOH QALAY I KAH PUSHT KOH

SHIB KOH QALAY I KAH SHIB KOH QALAY I KAH SHIB KOH QALAY I KAH

F a r ya b P r ov ince


QARAMQOL QARAMQOL

KHANI CHAHAR BAGH KHANI CHARBAGH

ANDKHOY ANDKHOY

[ANDKHOY] QURGHAN

ALMAR ALMAR ALMAR

BILCHIRAGH BILCHIRAGH BILCHIRAGH

DAWLAT ABAD DAWLAT ABAD DAWLAT ABAD

GURZIWAN [BILCHIRAGH] GURZIWAN

KHWAJA MOSA [PASHTUN KOT]

KHWAJA SABZ POSH KHWAJA SABZ POSH KHWAJA SABZ POSH

KOHISTAN KOHISTAN KOHISTAN

MAYMANA MAYMANA (PROVINCIAL CAPITAL)

PASHTUN KOT PASHTUN KOT PASHTUN KOT

QAYSAR QAYSAR QAYSAR

SHIRIN TAGAB SHIRIN TAGAB SHIRIN TAGAB

• Gurziwan (FAO and MoI) was part of Bilchiragh District.• Khwaja Mosa (FAO)was part of Pashtun Kot District• Qurghan (MoI) was part of Andkhoi

134

Gha z n i P r ov ince

FAO LIVESTOCK CENSUS AIMS DATA MOI DISTRICTS BAHRAMI SHAHID

(JAGHATU) WALI MOHD SHAHEED

[JAGHATU] RASHIDAN

(DEYA)AJRISTAN AJRISTAN AJRISTAN

AB BAND AB BAND AB BAND

ANDAR ANDAR ANDAR

DIH-YAK DIH YAK DIH YAK

GELAN GELAN GELAN

GHAZNI MARKAZ GHAZNI (PROVINCIAL CAPITAL)

GIRO GIRO GIRO

JAGHATU JAGHATU JAGHATU

JAGHURI JAGHURI JAGHURI

KHWAJA OMARY [JAGHATU] KHWAJA OMARY

MALISTAN MALISTAN MALISTAN

MUQUR MUQUR MUQUR

NAWA NAWA NAWA

NAWUR NAWUR NAWUR

QARABAGH QARABAGH QARABAGH

WAGHAZ [JAGHATU] WAGHAZ

ZANA KHAN ZANA KHAN ZANA KHAN

• Waghaz and Khwaja Omari (FAO and MoI) were part of Jaghatu District• Rashidan (MoI) Districts was Bahrami Shahid plus part of Jaghatu District• Wali Mohd Shaheed (MoI) was part of Ghazni Markaz

Ghor P r ov ince


CHAGHCHARAN (PROVINCIAL CAPITAL)

[CHAGHCHARAN] DAWLAT YAR

DO LAINA

SAGHAR SAGHAR

TAYWARA TAYWARA

PASABAND PASABAND

[MURGHAB,BADGHIS PROVINCE] CHAR SADRA

LAL WA SARJANGAL LAL WA SARJANGAL LAL WA SARJANGAL

SHAHRAK SHAHRAK SHAHRAK

TULAK TULAK TULAK

• Char Sadra (MoI)was part of Murghab District (Badghis Prov), and Chagcharan District• Dawlat Yar (MoI) was part of Chagcharan District• Do Laina (MoI) was part of Shahrak District• Only 3 districts were covered by the Livestock census survey

135

H i lm a nd P r ov ince


BAGHRAN BAGHRAN BAGHRAN

DISHU DISHU DISHU

GARMSER GARMSER GARMSER

GERISHK NAHRI SARRAJ [LASHKAR GAR] GERISHK

KAJAKI KAJAKI KAJAKI

KHANSHEEN [REG]

LASHKARGAH LASHKARGAH (PROVINCIAL CAPITAL)

MARJA [NAD ALI]

MUSA QALA MUSA QALA MUSA QALA

NAD ALI NAD ALI NAD ALI

NAW ZAD NAW ZAD NAW ZAD

NAWA NAWA NAWA

REG REG REG

SANGIN SANGIN SANGIN

WASHER WASHER WASHER

• Geriskh (FAO and MoI) was part of Lashkar Gar.• Marja (FAO) is of Nad Ali District.• Khansheen is part of Reg District.

H ir a t P r ov ince


ADRASKAN ADRASKAN ADRASKAN

CHISHTE SHARIF CHISTI SHARIF CHISHTE SHARIF

FARSI FARSI FARSI

GHORYAN GHORYAN GHORYAN

GULRAN GULRAN GULRAN

GUZARA GUZARA GUZARA

HIRAT HERAT (PROVINCIAL CAPITAL)

INJIL INJIL INJIL

KARUKH KARUKH KARUKH

KOHSAN KOHSAN KOHSAN

KUSHKE KUHNA KUSHKI KUHNA KUSHKE KOHNA

OBE OBE OBE

PASHTUN ZARGHUN PASHTUN ZARGHUN PASHTUN ZARGHUN

RABAT ISANGY KUSHK KUSHK

SHINDAND SHINDAND SHINDAND

ZINDA JAN ZINDA JAN ZINDA JAN

136

K a bu l P r ov ince


BAGRAMI BAGRAMI BAGRAMI

CHAHAR ASYAB CHAHAR ASYAB CHAHAR ASYAB

DIH SABZ DIH SABZ DEH SABZ

FARZA [MIR BACHA KOT] FARZA

GULDARA GULDARA GUDARA

ISTALIF ISTALIF ISTALIF

KABUL CITY KABUL (PROVINCIAL CAPITAL)

KALAKAN KALAKAN KALAKAN

KHAKI JABBAR KHAKI JABBAR KHAKI JABBAR

MIR BACHA KOT MIR BACHA KOT MIR BACHA KOT

MUSAYI MUSAYI MUSAYI

PAGHMAN PAGHMAN PAGHMAN

QARABAGH QARABAGH QARABAGH

SAROBI SUROBI SAROBI

SHAKADARA SHAKARDARA SHAKARDARAH

• Farza District (FAO and MoI) was part of Mir Bacha Kot District

Ja w z ja n P r ov ince


ANDKHOI [ANDKHOI,FARYAB PROVINCE]

AQCHA AQCHA AQCHA

DARZAB DARZAB DARZAB

FAZYABAD FAZYABAD FAYZABAD

KHAMYAB KHAMYAB KHAMYAB

KHANI CHARBAGH[KHANI CHARBAGH,FARYAB

PROVINCE]KHWAJA DUKOH KHWAJA DUKOH KHWAJA DUKOH

MARDYAN MARDYAN MARDYAN

MINGAJIK MINGAJIK MINGAJIK

QARAMQOL [QARAMQOL,FARYAB PROVINCE]

QARQIN QARQIN QARQEEN

QOSHTEPA [DARZAB] QOSHTEPA

QURGHAN [QURGHAN,FARYAB PROVINCE]

SHIBIRGHAN SHIBIRGHAN (PROVINCIAL CAPITAL)

• Andkhoi, Qaramqol, Khani Charbagh and Qurghan Districts (FAO) are officially part of Faryab Province

• Qoshtepa (FAO and MoI) was part of Darzab

137

K a p is a P r ov ince


ALASAI ALASAY ALASAI

HISA I AWAL KOHISTAN [KOHISTAN] HISA AWAL KOHISTAN

HISA I DOUM KOHISTAN [KOHISTAN]

KOHBAND KOHBAND KOHBAND

KOHISTAN KOHISTAN

MAHMUD RAQI MAHMUD RAQI (PROVINCIAL CAPITAL)

NIJRAB NIJRAB NIJRAB

TAGAB TAGAB TAGAB

• Officially the Districts of Kohistan, Hiasa Awal-i-Kohistan and Hisa Doum Kohistan (FAO and/or MoI) are one district – Hisa Awal-i- Kohistan District

K a nd a ha r P r ov ince


ARGHANDAB ARGHANDAB ARGHANDAB

ARGHISTAN ARGHISTAN ARGHISTAN

DAMAN DAMAN DAMAN

GHORAK GHORAK GHORAK

JARRI [ARGHANDAB,MAYWAND AND JARRI

KANDAHAR (DAND) KANDAHAR (DAND) (PROVINCIAL CAPITAL)

KHAKREZ KHAKREZ KHAKREZ

MARUF MARUF MARUF

MAYWAND MAYWAND MAIWAND

MEENASHEEN [SHAH WALI KOT] MEENASHIN

NESH [NESH,URUZGAN PROVINCE] NESH

PANJWAYI PANJWAYI PANJWAYI

REG [SHORABAK] REG

SHAH WALI KOT SHAH WALI KOT SHAHWALI KOT

SHIGAH [DAMAN]

SHORABAK SHORABAK SHORABAK

SPIN BOLDAK SPIN BOLDAK SPIN BOLDAK

• Jarri District (FAO and MoI) was formed from parts of Arghandab, Maywand and Panjwayi Districts

• Reg (FAO and MoI) was part of Shorabek District• Nesh (FAO and MoI) was shown on the AIMS database as part of Uruzgan Province• Shigah (FAO) is part of Daman

138

K una r P r ov ince


[BAR KUNAR] GHAZI ABAD

ASADABAD MARKAZ ASAD ABAD (PROVINCIAL CAPITAL)

ASMAR BAR KUNAR BAR KUNAR

CHAPA DARA CHAPA DARA (GULSALIK) CHAPADARAH

CHAWKAY CHAWKAY CHAWKAY

DANGAM DANGAM DANGAM

KHAS KUNAR KHAS KUNAR KHAS KUNAR

MARAWARA MARAWARA MANAWARA

MANOGUY PECH DARAE PECH

NARANG NARANG NARANG

NARI NARI NARI

NURGAL NURGAL NURGAL

SHIGAL [ DANGAM] SHIGAL WA SHILTON

SIRKANAY SIRKANAY (PASHIT) SIRKANI

WATA PUR [PECH] WATA PUR

• Asmar (FAO) was part of Bar Kunar District• Shigal (FAO and MoI)was part of Dangam District• Wata Pur (FAO and MoI) was part of Pech District• Ghazi Abad (MoI)was formed from parts of Bar Kunar and Kamdesh (Nuristan Province)

K hos t P r ov ince


[SHAMAL,PAKTYA PROVINCE] SHAMAL

ALISHIR TERE ZAYI TERE ZAI

BAK BAK BAK

GURBUZ GURBUZ GURBUZ

ISMAIL KHIL MANDO ZAYI MANDO ZAI

JAJI MAYDAN JAYI MAYDAN JAJI MAYDAN

KHOST MARKAZ KHOST (MATUN) (PROVINCIAL CAPITAL)

MUSA KHIL MUSA KHEL MUSA KHEL

NADIR SHAH KOT NADIR SHAH KOT NADIR SHAH KOT

QALANDAR QALANDAR QALANDAR

SABRI YAQUBI [SABARI]

SPERA SPERA SPERA

TANI TANI TANI

YAQUBI SABARI SABARI

• Alishir (FAO) is an alternative name for Tere Zai District• Shamal (MoI) District has been officially reallocated to Khost from Paktya Province• Yaqubi and Sabri Yaqubi (FAO and/or MoI) were Sabari District• Ismail Khel (FAO) is an alternative name for Mando Zayi

139

L a g hm a n P r ov ince


ALINGAR ALINGAR ALINGAR

ALISHING ALISHING ALISHING

DAWLAT SHAH DAWLAT SHAH DAWLAT SHAH

MIHTERLAM MIHTARLAM (PROVINCIAL CAPITAL)

QARGHAYI QARGHAYI (SURKHAKAN) QARGHAYEE

• Nuristan District is now in Nuristan Province

K und uz P r ov ince


ALI ABAD ALI ABAD ALI ABAD

CHAHAR DARA CHAHAR DARA CHAHAR DARA

DASHT ARCHI DASHT ARCHI DASHT ARCHI

IMAM SAHIB IMAM SAHIB HAZRAT IMAM SAHIB

KHAN ABAD KHAN ABAD KHAN ABAD

KUNDUZ MARKAZ KUNDUZ MARKAZ (PROVINCIAL CAPITAL)

QALA ZAL QALA ZAL QALAI ZAL

L og a r P r ov ince


AZRA AZRA

BARAKI BARAK BARAKI BARAK BARAKI BARAK

CHARKH CHARKH CHARKH

KHARWAR [CHARKH] KHARWAR

KHUSHI KHUSHI KHOSHI

MUHAMMAD AGHA MUHAMMAD AGHA MOHAMAD AGHA

PULI ALAM PULI ALAM (PROVINCIAL CAPITAL)

• Azra District (FAO and MoI) has officially been moved from Paktya Province.

• Kharwar District (FAO and MoI) was part of Charkh District.

140

N a ng a r ha r P r ov ince


ACHIN ACHIN ACHIN

BATIKOT BATI KOT BATIKOT

BIHSUD [JALALABAD] BIHSUD

CHAPARHAR CHAPARHAR CHAPARHAR

DAR INUR DARA-I-NUR DARA-I-NUR

DEH BALA DIH BALA DEH BALA

DUR BABA DUR BABA DUR BABA

GOSHTA GOSHTA GHOSTA

HISARAK HISARAK HISARAK

JALALABAD MARKAZ JALALABAD (PROVINCIAL CAPITAL)

GHANI KHEL SHINWAR SHINWAR SHINWAR

KAMA KAMA KAMA

KHOGYANI KHOGYANI KHOGYANI

KHOGYANI WAZIR [KHOGYANI]

KOT [RODAT] KOT

KUZ KUNAR (SHEWA) KUZ KUNAR (SHEWA) KUZ KUNAR

LAL PUR LAL PUR LA PUR

MUHMAND DARAH MUHMAND DARA MOHMAND DARA

NAZIAN NAZYAN NAZIAN

PACHIR WA AGAM PANCHIR WA AGAM PACHA WA AGAM

RODAT RODAT RODAT

SHERZAD SHERZAD SHERZAD

SHEWA [KUZ KUNAR (SHEWA)]

SURKH ROD SURKH ROD SURKH ROD

• Khogyani and Khogyani Wazir (FAO) are all one district – Khogyani.• Bihsud (FAO and MoI) was part of Jalalabad District.• Kuz Kunar and Shewa (FAO and/or MoI) are one district – Kuz Kunar District.• Kot (FAO and MoI) was part of Rodat District.

N im r oz P r ov ince


CHAHAR BURJAK CHAHAR BURJAK CHAHAR BURJAK

CHAKHANSUR CHAKHANSUR CHAKHANSUR

DELARAM [KHASH ROD /CHAKANSUR] DELARAM

KANG KANG KANG

KHASH ROD KHASH ROD KHASH ROD

ZARANJ MARKAZ ZARANJ (PROVINCIAL CAPITAL)

• Delamaram (FAO and MoI) was part of Khashrod and Chakansur Districts.

141

N ur is ta n P r ov ince


BARGI MATAL BARGI MATAL BARGI MATAL

DO AAB [MANDOL] DO AAB

KAMDESH KAMDESH KAMDESH

MANDOL MANDOL MANDOL

NURISTAN NURISTAN NURISTAN

WAMA WAMA WAMA

WAYGAL WAYGAL WIGAL

• Nooristan District (AIMS) is officially in Nuristan Province.• Do Aib was part of Mandol District

P a k t ik a P r o v ince


ZARGHUN SHAHR ZARGHUM SHAHR ? TARAW

(BARMAL) BARMAL BARMAL DILA KHOSHMAND DILA DILA

GAYAN GAYAN GAYAN GOMAL GOMAL GOMAL

JANI KHIL [ZARGHUN SHAHR] JANI KHEL KHIR KOT [ZARGHUN SHAHR]

MATA KHAN MATA KHAN MATA KHAN NIKA NIKA NIKA

OMNA OMNA OMNA SAR HAWZA SAR HAWZA SAR HAWZA

SAROBI SAROBI SAROBI SHARANA MARKAZ SHARAN (PROVINCIAL CAPITAL)

URGUN URGUN URGUN WAZA KHAWA WAZA KHWA WAZA KHWA WOR MAMAY WOR MA MAY WORMAMAY YAHYA KHIL [ZARGHUN SHAHR] YAHYA KHEL YUSUF KHIL [ZARGHUN SHAHR] YUSUF KHEL

ZIRUK ZIRUK ZIRUK

• Yahya Khel, Yusuf Khel, Jani Khel (FAO and MoI) were part of Zarghun Shahr District.• Khir Kot (FAO) is part of Jani Khil and is not a separate d istrict.

142

P a k tya P r ov ince


AZRA

ADMA [ZURMAT]

AHMAD KHEIL {JAJI} LIJA AHMAD KHEL

CHAMKANI CHAMKANI CHAMKANI

DAND WA PATAN DAND WA PATAN DAND WA PATAN

DOHMANDA SHAMAL [SHAMAL]

GARDEZ GARDEZ (PROVINCIAL CAPITAL)

JAJI JAJI JAJI

JANI KHEL JANI KEL JANI KHEL

LIJA MANGAL LIJA MANGAL

SAYED KARAM SAYID KARAM SAYED KARAM

SHAMAL SHAMAL

SHWAK SHWAK SHWAK

WAZA JADRAN JADRAN WAZA JADRAN

ZURMAT ZURMAT ZURMAT

• Adma and Zurmat (FAO) are one District – Zurmat District.

• Dohmanda Shamal and Shamal (FAO) are one District – Shamal, and is now part of KHOST

Province.

• Admad Khel and Lija Mangal (FAO) are one district – Lija Ahmed Khel (MoI), which was

originally part of Jaji District.

• Azra District (AIMS) is officially part of Logar Province.

P a r w a n P r ov ince


BAGRAM BAGRAM BAGRAM

CHAHARIKAR CHAHARIKAR (PROVINCIAL CAPITAL)

HISA AWA PANJSHIR PANJSHER HISA AWA PANJSHIR

HISA CHAORUM PANJSHIR HISA CHAORUM PANJSHIR

HISA DOUM PANJSHIR HISA IAWALI PANJSHER HISA DOUM PANJSHIR

HISA SOUM PANJSHIR HISA IDUWUMI PANJSHER HISA SOUM PANJSHIR

JABALUSARAJ JABALUSARAJ JABALSARAJ

KOHI SAFI KOHI SAFI KOHI SAFI

SALANG SALANG SALANG

SAYED KHIL [CHARIKAR, AND JABALUSARAJ] SAYED KHEL

SHIKH ALI SHEKH ALI SHIKH ALI

SHINWARI SHINWARI SHINWARI

SIAGIRD (GHORBAND) GHORBAND GHORBAND

SURKH IPARSU SURKHI PARSA SURKHI PARSA

• Sayed Khil (FAO and MoI) is located between Charikar and Jabalusaraj Districts.• Siagird (FAO) is offically called Ghorband District.• The three parts of Panjsher (AIMS) and been divided into four districts (FAO and MoI).

143

S a m a ng a n P r o v ince


[KHULM,BALKH PROVINCE] FIROZ NAKHCHIRAYBAK (MARKAZ

SAMANGAN) AYBAK (PROVINCIAL CAPITAL)

DARA-I-SUF DARA-I-SUFDARA-I-SUF SUFLA [DARA-I-SUF] DARA ISUF PAYEENDARA-I-SUF-BALA [DARA-I-SUF] DARA ISUF BALAHAZRATI SULTAN HAZRATI SULTAN HAZRATI SULTAN

KALDAR [KALDAR,BALKH PROVINCE]KHORAM WA SARBAGH KHURAM WA SARBAGH KHURAN SARBAGH

KHULM [KHULM,BALKH PROVINCE]RUYI DU AB RUYI DU AB RUYI DU AB

• Dara I Suf (AIMS) has been officially divided into Dara I Suf Bala and Dara I Suf Sufla (FAO

and or MoI) Districts.

• Khulm and Kaldar (FAO) Districts are officially part of Balkh Province.

• Firoz Nakhchir (MoI) was part of Khulm District.

S a r ipu l P r ov ince


BALKHAB BALKHAB BALKHAB

KOHISTANAT KOHISTANAT KOHISTANAT

SANGCHARAK SANGCHARAK SANG CHARAK

SARI PUL SARI PUL (PROVINCIAL CAPITAL)

SAYED ABAD [SAYYAD]

SAYYAD SAYYAD SAYYAD

SOZMA QALA SOZMA QALA SUZMA QAA

• Sayed Abad (FAO) was part of Sayyad

144

T a k ha r P r ov ince


BAHARAK [TALUQAN] BAHARAK

BANGI BANGI BANGI

CHA AAB CHA AAB CHA AAB

CHAL CHAL CHAL

DARQAD DARQAD DARQAD

DASHTE QALA [YANQI QALA] DASHTE QALA

FARKHAR FARKHAR FARKHAR

HAZAR SOMOCH [TALUQAN] HAZAR SOMOCH

ISHKAMISH ISHKAMISH ISHKAMISH

KALAFGAN KALAFGAN KALAFGAN

KHWAJA BAHWADDIN [YANQI QALA] KHWAJA BAHWADDIN

KHWAJA GHAR KHWAJA GHAR KHWAJA GHAR

NAMAK AAB [TALUQAN /BANGI] NAMAK AAB

RUSTAQ RUSTAQ RUSTAQ

TALUQAN TALUQAN (PROVINCIAL CAPITAL)

WARSAJ WARSAJ WARSAJ

YANGI QALA YANGI QALA YANGI QALA

• Baharak (FAO and MoI) was part of Taluqan District.• Namak Aab (FAO and MoI) was part of Taluqan and Bangi District.• Dashte Qala (FAO and MoI) was part of Yanqi Qala District.• Khwaja Bahwaddin (FAO and MoI) was part of Yanqi Qala District.• Hazar Somoch (FAO and MoI) was part of Taluqan District.

U r uz g a n P r ov ince


NESH

CHAR CHEENAH SHAHIDI HASSAS SHAHID IHASSAS

CHORA CHORA CHORA

DAYKUNDI DAYKUNDI DAYKUNDI

DIHRAWUD DIHRAWUD DIHRAWUD

GIZAB GIZAB GIZAB

KHAS URUZGAN KHAS URUZGAN KHAS URUZGAN

KIJRAN KIJRAN KIJRAN

SHAHRISTAN SHAHRISTAN SHAHRISTAN

TIRIN KOT TIRIN KOT (PROVINCIAL CAPITAL)

• Char Cheenah (FAO) is officially called Shahid I Hassas District.• Nesh (AIMS) District is officially in Kandahar Province.

145

W a r d a k P r ov ince


BEHSUD MARKAZ BIHSUD MARKAZI BIHSUD MARKAZ

CHAK CHAKI WARDAK CHAK

DAY MIRDAD DAY MIRDAD DAYMERDAD

HISA-i-AWALI BEHSUD HISA-I-AWALI BIHSUD HISA AWALI BIHSUD

JAGHATU [JAGHATU,GHAZNI PROVINCE] JAGHATU

JALREZ JALREZ JAREZ

KOTI ASHRO MAYDAN SHAHR (KOTI ASHRO) (PROVINCIAL CAPITAL)

MARKAZ [MAYDAN SHAHR (KOTI ASHRO)]

MAYDAN SHAHR [MAYDAN SHAHR (KOTI ASHRO)]

MAYDEN SHAHR /KOTI ASHRO [MAYDAN SHAHR (KOTI ASHRO)]

NIRKH NIRKH NIRKH

SAYED ABAD SAYD ABAD SAYED ABAD

• Maydan Shahr, Maydan Shahr/ Koti Ashro and Koti Ashro (FAO) are all one district – Maydan

Shahr District (FAO).

• Jaghatu District is shown by AIMS as part of Jaghatu in Ghazni Province (FAO).

Za bu l P r ov ince


ARGHANDAB ARGHANDAB ARGHANDAB

ATGHAR ATGHAR ATGHAR

DAYCHOPAN DAYCHOPAN DAYCHOPAN

KHAKI AFGHAN [DAYCHOPAN] KAKAR

MIZAN MIZAN MIZAN

NAWBAHAR [SHAMALZAI AND SHAHJOY]

QALAT QALAT (PROVINCIAL CAPITAL)

SHAHJOY SHAHJOY SHAHJOY

SHAHR SAFAH TARNAK WA JALDAK TARNAK WA JALDAK

SHAMULZAYI SHAMULZAYI SHAMALZAI

SHINKAY SHINKAY SHINKAY

SEYOURAY [SHINKAY]

• Khaki Afghan (FAO) and Karar (MoI) are part of Day Chopan District.• Seyouray (FAO) was part of Shinkay.• Nawbahar (FAO) is part of Shamalzai and Shahjoy Districts. • Shahr Safah is officially called Tarnak wa Jaldak District.

146

Table A1 Total number of selected lives tock species on district basis

Province District Cattle Sheep Goats Donkeys Camels Horses Chicken

Badakhshan Arghonj Khwa 4,037 15,399 7,733 1,163 0 105 2,063

Argo 35,107 45,179 44,745 15,041 0 1,497 35,207

Baharak 16,725 25,863 11,005 3,821 0 268 12,596

Daraeem 14,850 27,574 28,425 7,329 0 1,138 19,366

Darwaz Bala 6,938 8,411 12,076 1,778 0 358 6,444

Fayzabad 8,057 11,405 11,354 3,800 0 162 13,113

Ishkashim 6,683 11,517 7,847 1,186 44 554 2,790

Jurm 11,330 16,412 16,848 4,018 0 201 9,583

Karan Wa Manjan 2,999 3,630 4,549 715 0 324 1,430

Khash 4,636 14,704 5,841 2,006 0 133 5,240

Khwahan 9,595 7,026 8,955 2,446 0 352 5,776

Kishim 68,906 40,595 45,927 21,227 0 1,085 58,855

Kohistan 5,923 9,361 7,450 2,270 0 321 2,875

Kohistan Ragh 1,652 2,315 1,906 646 0 96 774

Koof 6,837 5,720 9,824 1,858 0 224 2,609

Nasi Darwaz 9,044 11,655 19,292 2,129 0 348 6,390

Raghistan 12,817 14,404 13,842 2,278 0 1,357 6,950

Shahri Buzurg 5,367 6,538 9,075 5,877 48 963 5,095

Shighnan 8,355 11,784 11,408 1,534 0 377 1,900

Shiki Darwaz 5,281 3,257 11,014 1,657 0 74 5,608

Shohada 9,534 16,581 13,050 2,636 0 218 5,220

Tagab Kishim 7,141 11,339 19,854 2,890 0 500 10,382

Teshkan 3,715 4,515 10,799 2,771 0 257 6,342

Wakhan 5,146 9,660 4,467 991 115 291 1,092

Wardooj 8,952 14,926 19,018 2,277 0 138 4,795

Yaftal Bala 1,619 1,395 1,265 580 0 72 1,853

Yaftal-i-Payan 12,750 19,558 16,579 5,626 0 413 15,213

Yamgan 8,260 15,013 8,470 2,146 0 372 5,315

Yawan Ragh 11,998 10,141 15,473 3,793 0 421 6,651

Zebak 2,866 4,644 4,567 847 1 377 822

All 317,120 400,521 402,658 107,336 208 12,996 262,349

Badghis Bala Murghab 919 7,770 6,364 2,453 206 26 6,928

Dara-i-Boum 626 21,633 13,093 2,838 280 137 6,229

Ghormach 6,690 200,693 49,555 13,241 2,337 426 16,519

Jawand 3,132 39,188 15,790 5,348 394 206 12,117

Muqur 424 7,977 7,341 4,808 358 73 5,932

Murghab 14,942 216,937 62,850 17,411 2,830 778 54,475

Qadis 8,244 48,431 41,418 16,208 88 548 38,627

Qala-i-Naw 5,651 40,616 46,561 23,179 416 765 42,336

Sange Aatash 245 47,651 32,458 8,371 2,920 231 10,758

All 40,873 630,896 275,430 93,857 9,829 3,190 193,921

8.2. TABULAR SUMMARY STATISTICS LEVEL 18.2.1 Total Livestock on District Basis

147

Baghlan Andarab 4,825 4,892 6,081 1,241 0 516 6,615

Baghlan 6,139 15,018 1,231 1,146 115 375 10,045

Baghlan Jadid 15,543 52,861 11,339 6,140 299 1,851 16,357

Baghlan Qadeem 8,353 17,131 2,660 1,968 45 1,047 17,784

Banoo Andarab 8,322 9,748 19,865 3,164 0 628 9,095

Burka 5,593 16,783 15,273 8,690 0 849 18,826

Dahana-i-Ghori 10,274 38,732 15,068 11,257 60 1,251 20,506

Deh Salah Andarab 4,803 7,254 7,674 1,944 0 744 5,586

Dushi 16,699 31,987 31,054 7,723 12 600 24,228

Khinjan 9,211 9,076 16,379 2,479 0 361 6,606

Khost Wa Firing 32,968 30,970 47,800 6,869 0 4,424 24,812

Nahrin 11,779 31,112 29,979 9,486 224 1,748 42,995

Puli Hisari Andarab 3,902 7,046 6,827 783 0 2,532 1,763

Puli Khumri 17,034 27,317 2,990 4,834 15 285 22,275

Tala Wa Burfuk 12,725 32,738 21,907 5,929 0 2,246 9,077

All 168,170 332,665 236,127 73,653 770 19,457 236,570

Balkh Balkh 12,790 46,998 11,490 4,164 311 1,326 26,564

Chahar Bolak 8,968 60,199 30,103 4,293 2,025 1,241 47,647

Chahar Kint 2,362 25,761 13,429 4,886 0 640 6,586

Chai 1,151 10,535 4,272 1,042 618 435 15,096

Chimtal 4,012 36,548 7,959 2,853 431 460 13,218

Dawlat Abad 3,437 46,742 11,295 2,436 1,113 1,389 19,391

Dihdadi 5,038 17,641 2,982 1,890 149 249 16,080

Kishindih 3,238 10,871 9,399 7,262 0 180 656

Kohi Albarz 680 28,583 6,553 4,562 644 162 5,556

Marmul 281 7,661 3,362 1,519 0 120 270

Mazar-i-Sharif 5,833 27,579 4,794 1,573 180 1,043 36,788

Nahri Shahi 3,058 47,507 10,196 2,341 386 883 10,396

Sholgara 11,418 79,054 13,999 9,665 1,206 790 24,042

Shortepa 6,974 9,612 5,810 3,071 388 332 15,265

Zareh 5,736 24,032 11,840 7,375 23 546 11,485

All 74,976 479,323 147,483 58,932 7,474 9,796 249,040

Bamyan Bamyan 6,585 16,464 3,301 8,348 0 201 9,127

Kamhard 13,196 16,151 7,063 8,410 0 192 23,711

Panjab 15,522 39,951 10,781 6,000 0 391 20,852

Saighan 5,593 13,267 5,864 4,783 0 196 6,134

Shibar 5,354 18,821 5,968 5,016 0 226 7,366

Waras 14,395 81,242 16,912 6,985 0 451 29,846

Yakawlang 17,248 41,754 10,254 9,363 0 1,034 11,401

All 77,893 227,650 60,143 48,905 0 2,691 108,437



148

Farah Anar Dara 2,123 6,297 17,245 1,994 420 0 10,758

Bakwa 5,134 13,481 8,039 979 389 25 14,825

Bala Buluk 8,374 20,036 36,226 3,551 1,992 831 33,307

Farah Markaz 9,894 11,476 31,492 4,475 122 37 56,753

Gulistan 5,623 7,509 70,536 3,892 221 18 21,672

Khaki Safed 6,370 21,433 24,641 3,352 982 33 19,891

Lash Wa Juwayn 3,227 23,301 39,614 2,353 2,059 49 43,933

Purchaman 22,358 25,072 183,913 10,018 111 804 68,318

Pushte Rod 6,573 9,618 15,104 3,186 133 17 26,264

Qala i Kah 673 4,977 6,105 982 1 2 7,662

Qala-i-Kah Pusht Koh 4,706 10,940 32,018 2,770 35 7 30,893

Shib Koh Qalay-i-Kah 3,470 10,419 28,096 2,296 25 26 20,653

All 78,525 164,559 493,029 39,848 6,490 1,849 354,929

Faryab Almar 12,964 85,378 35,865 13,355 2,027 420 43,628

Bilchiragh 6,228 18,007 13,820 7,346 22 842 5,980

Dawlat Abad 1,624 91,412 7,464 3,760 2,639 87 2,198

Gurziwan 7,810 33,108 16,036 8,369 70 596 10,824

Khwaja Mosa 4,519 41,339 11,075 5,549 2,233 56 3,592

Khwaja Sabz Posh 4,309 16,549 9,906 4,569 364 58 2,226

Kohistan 4,709 68,770 52,180 4,530 0 796 11,548

Maymana 5,949 10,146 11,347 3,896 97 108 16,793

Pashtun Kot 10,753 35,845 28,187 10,398 27 436 8,491

Qaysar 13,509 192,833 154,646 11,519 3,432 926 43,577

Shirin Tagab 2,593 41,468 12,653 5,937 1,304 69 8,046

All 74,967 634,855 353,179 79,228 12,215 4,394 156,903



149

Ghazni Ab Band 2,623 18,541 5,558 3,051 263 47 16,166

Ajristan 4,752 6,919 4,412 1,190 97 334 3,482

Andar 17,422 26,079 1,696 2,643 7 72 18,312

Dih Yak 3,817 9,879 1,933 1,448 0 14 15,915

Gelan 2,748 15,790 800 1,706 3 33 29,811

Ghazni Markaz 3,876 8,234 563 513 11 2 17,438

Giro 3,524 13,291 3,829 2,025 12 32 12,160

Jaghatu 4,471 11,204 2,818 2,588 0 20 13,021

Jaghuri 953 9,024 4,108 827 0 1 6,988

Khoja Omary 1,738 2,301 730 471 0 1 2,900

Malistan 11,104 33,881 13,682 5,119 4 39 16,871

Muqur 3,723 12,968 5,433 3,364 6 25 27,470

Nawa 1,906 19,957 13,167 2,289 316 42 17,739

Nawur 6,090 12,784 4,523 4,424 0 117 13,844

Qara Bagh 13,108 30,131 6,749 5,266 0 46 31,834

Waghaz 2,130 11,097 2,577 951 2 12 8,344

Zana Khan 845 4,248 3,768 1,266 0 3 6,016

All 84,830 246,328 76,346 39,141 721 840 258,311

Ghor Lal Wa Sarjangal 29,585 88,043 18,170 10,227 0 966 53,875

Shahrak 5,548 7,707 8,779 2,526 0 559 6,234

Tulak 5,221 8,886 13,873 4,662 1 397 7,628

All 40,354 104,636 40,822 17,415 1 1,922 67,737

Hilmand Baghran 15,932 84,761 198,812 6,645 210 637 85,170

Dishu 1,580 9,068 14,906 1,122 1,482 87 15,012

Garmser 19,050 81,217 25,434 5,966 4,235 104 50,629

Gerishk 17,571 49,283 18,559 4,907 1,555 363 58,318

Kajaki 17,908 21,727 12,437 5,920 304 82 46,006

Khanshen 3,337 9,974 13,440 874 2,052 97 16,222

Lashkar Gah 8,530 26,379 7,648 3,302 1,014 245 35,406

Marja 31,256 55,034 31,505 2,318 1,006 221 100,655

Musa Qala 6,143 22,240 32,934 1,868 803 163 39,189

Nad Ali 12,463 45,769 18,263 1,714 849 101 25,908

Naw Zad 6,054 51,063 42,114 5,013 2,719 287 41,599

Nawa 22,106 65,912 20,588 3,315 1,958 68 74,313

Reg 6,600 30,138 89,086 2,724 0 0 28,578

Sangin 12,788 14,679 4,817 2,456 171 218 22,929

Washer 3,548 28,830 52,635 4,392 1,889 102 22,241

All 184,866 596,074 583,178 52,536 20,247 2,775 662,175



150

Hirat Adraskan 8,641 22,166 80,703 7,643 1,636 259 17,957

Chishte Sharif 4,817 8,641 19,516 3,256 756 209 6,287

Farsi 3,960 5,579 6,227 3,915 0 326 3,192

Ghoryan 6,337 86,652 62,503 5,417 387 152 23,456

Gulran 8,447 175,077 59,388 21,210 682 650 47,720

Guzarah 15,323 26,740 20,807 5,203 496 126 57,162

Injil 45,318 50,207 26,682 14,008 179 575 66,176

Karukh 11,580 38,848 53,136 9,824 98 272 36,819

Kohsan 3,061 50,987 31,175 9,940 141 92 26,023

Kushke Kuhna 8,053 35,799 25,210 13,193 1,407 485 19,386

Obe 14,222 37,499 53,572 10,602 975 414 35,175

Pashtun Zarghun 12,256 29,636 37,095 6,339 851 272 20,295

Rabat-i-Sangy 14,791 73,505 42,204 20,280 2,393 792 47,070

Shindand 19,527 111,413 135,965 11,959 3,850 226 87,170

Zinda Jan 9,452 37,959 42,711 12,422 237 127 42,386

All 185,785 790,708 696,894 155,211 14,088 4,977 536,274

Jawzjan Andkhoi 1,222 32,878 4,249 870 693 488 4,974

Aqcha 3,136 44,869 15,563 2,430 1,118 1,117 17,188

Darzab 4,185 14,984 9,672 5,461 2 283 3,491

Fayz Abad 2,317 33,346 13,454 2,282 1,305 568 9,846

Khamyab 3,040 8,870 867 456 187 674 5,184

Khani Chaharbagh 416 12,908 2,765 1,027 777 252 1,920

Khwaja Dukoh 773 24,556 3,535 1,546 974 238 6,297

Mardyan 1,610 33,511 14,610 1,451 667 1,300 35,288

Mingajik 1,421 38,441 15,680 3,112 1,045 1,099 21,077

Qaramqol 308 23,188 2,648 821 947 121 1,902

Qarqin 5,211 3,767 2,572 984 178 503 3,673

Qoshtepa 1,781 63,708 13,392 4,562 4,849 599 2,763

Qurghan 548 18,400 3,531 565 589 335 3,170

Shibirghan 6,853 86,912 22,608 4,122 1,840 728 20,708

All 32,821 440,338 125,146 29,689 15,171 8,305 137,481



151

Kabul Bagrami 7,453 2,031 937 2,635 0 271 23,781

Chahar Asyab 3,344 1,565 1,262 424 2 7 16,977

Dih Sabz 3,188 8,420 7,788 1,103 0 50 61,201

Farza 3,255 15,476 18,577 2,184 120 5 13,342

Guldara 294 924 433 259 0 0 1,412

Istalif 370 2,973 1,464 391 0 38 4,538

Kalakan 1,006 1,133 421 277 8 28 3,893

Khaki Jabbar 1,057 3,127 4,743 821 0 4 7,570

Mir Bacha Kot 699 913 415 123 0 0 8,270

Musayi 4,210 2,212 2,499 949 0 0 14,072

Paghman 5,817 8,349 3,891 1,397 2 22 34,603

Qara Bagh 3,152 15,270 5,631 896 80 58 19,310

Sarobi 15,462 27,704 48,109 4,283 144 0 81,580

Shakardara 8,406 1,897 970 558 0 8 11,430

All 57,713 91,994 97,140 16,300 356 491 301,979

Kabul City Nahya 1 10 44 61 14 0 0 17,621

Nahya 2 76 128 334 23 0 1 3,851

Nahya 3 89 125 181 63 0 4 18,720

Nahya 4 919 871 432 38 0 38 26,364

Nahya 5 1,199 1,870 684 138 0 87 74,229

Nahya 6 799 1,835 360 122 0 87 24,220

Nahya 7 1,527 602 707 181 0 112 33,077

Nahya 8 1,162 486 449 137 0 47 90,030

Nahya 9 2,094 429 518 504 0 435 17,268

Nahya 10 1,268 539 606 187 0 29 20,657

Nahya 11 447 612 1,827 54 0 12 17,942

Nahya 12 275 180 591 26 0 0 13,812

Nahya 14 1,337 1,945 1,092 221 0 3 31,612

Nahya 15 2,189 1,092 3,351 167 0 64 88,590

Nahya 16 1,337 236 341 471 0 131 32,401

All 14,728 10,994 11,534 2,346 0 1,050 510,394



152

Kandahar Arghandab 8,441 17,733 4,866 3,273 429 125 65,239

Arghistan 4,416 31,386 19,476 4,835 517 162 18,094

Daman 3,905 28,619 15,521 1,908 435 34 21,225

Dand 3,489 5,523 2,893 1,033 87 101 11,095

Ghorak 3,075 31,485 22,446 2,111 864 19 17,267

Jarri 8,543 16,542 3,499 1,161 372 135 74,065

Kandahar 998 776 640 269 0 216 4,667

Khakrez 5,191 25,219 38,009 3,658 277 7 24,593

Maywand 7,337 64,755 19,967 3,906 1,726 7 60,067

Meenasheen 2,447 11,561 11,096 1,357 412 67 6,401

Nesh 3,037 11,565 16,597 2,949 403 8 18,788

Panjwayi 6,591 33,348 9,159 1,450 1,026 16 44,353

Reg 398 57,204 39,822 1,480 11,209 114 12,033

Shah Wali Kot 4,490 21,589 12,869 1,839 673 18 22,354

Shigah 1,028 21,066 8,211 1,679 194 5 8,958

Shorabak 320 164,399 117,341 3,312 11,208 6 28,036

Spin Boldak 980 33,395 9,254 1,981 1,365 42 7,987

All 70,286 605,049 390,156 42,756 31,229 1,263 463,855

Kapisa Alasay 6,247 4,549 3,156 502 53 95 10,289Hisa-i-Awal Kohistan 18,151 5,979 8,335 2,216 0 24 28,518Hisa-i-Doum Kohistan 27,940 6,593 993 1,903 5 196 63,246

Kohband 13,576 15,428 17,457 2,701 0 0 18,699

Mahmudraqi 19,155 15,213 3,509 2,257 128 149 31,947

Nurab 40,496 20,662 17,137 2,866 16 449 79,944

Tagab 16,171 21,842 21,683 1,555 68 15 49,811

All 141,736 90,266 72,270 14,000 270 928 282,454

Khost Alishir 14,960 33,108 19,472 4,912 1,378 131 49,120

Bak 10,581 3,662 3,129 2,138 37 33 26,626

Gurbuz 11,943 10,107 6,045 2,962 89 11 15,019

Ismail Khil 12,891 2,444 3,852 753 0 7 46,355

Jaji Maydan 15,573 5,011 15,285 2,319 35 133 23,345

Khost Markaz 25,820 4,283 4,929 2,180 166 20 70,536

Musa Khil 10,665 3,214 32,325 2,319 140 3 33,188

Nadir Shah Kot 13,183 4,055 9,426 2,535 0 0 74,854

Qalandar 6,495 1,899 25,000 1,191 34 0 20,571

Sabri Yaqubi 17,403 3,100 2,672 2,754 8 13 46,814

Spera 9,281 1,426 26,725 2,076 737 0 32,674

Tani 10,102 7,294 18,122 3,952 416 10 33,013

Yaqubi 5,529 321 318 635 0 4 16,648

All 164,426 79,924 167,300 30,726 3,040 365 488,763

Maruf 5,600 28,884 38,490 4,555 32 181 18,633



153

Kunar Asadabad Markaz 10,029 7,343 11,235 1,288 8 0 21,380

Asmar 13,899 8,980 45,192 959 0 0 30,486

Chapa Dara 11,057 2,240 21,769 318 0 0 13,618

Chawkay 22,164 6,746 32,341 3,659 0 2 67,471

Dangam 1,078 645 9,425 281 0 10 4,400

Khas Kunar 20,470 11,640 10,713 3,990 0 2 40,510

Manawara 20,254 2,206 36,446 1,130 0 3 37,987

Manoguy 6,895 3,410 23,720 1,337 0 0 35,440

Narang 11,655 8,522 17,264 3,578 15 86 22,510

Nari 18,952 25,143 96,765 4,600 0 62 57,600

Nurgal 24,215 12,775 34,470 3,483 23 1 32,540

Shigal 16,790 5,931 52,719 1,218 0 0 28,225

Sirkanay 7,242 2,716 9,679 2,199 0 50 17,680

Wata Pur 12,290 5,710 31,270 1,775 0 0 31,650

All 196,990 104,007 433,008 29,815 46 216 441,497

Kunduz Ali Abad 14,726 34,140 5,504 7,444 146 968 20,704

Chahar Dara 22,182 38,189 3,438 4,707 834 1,975 56,536

Dasht Archi 13,064 63,826 5,674 7,529 1,573 833 15,321

Imam Sahib 47,603 71,710 22,834 18,002 7,069 12,666 26,849

Khan Abad 11,162 12,493 5,339 6,661 75 639 20,013

Kunduz Markaz 32,046 40,319 551 3,420 328 1,850 33,988

Qala Zal 17,105 67,554 6,122 3,958 2,194 1,081 21,780

All 157,888 328,231 49,462 51,721 12,219 20,012 195,191

Laghman Alingar 26,559 15,737 51,971 2,140 110 19 59,895

Alishing 22,612 21,115 26,043 5,044 44 6 46,489

Dawlat Shah 24,685 4,575 51,718 823 0 2 53,429

Mihterlam Markaz 41,084 78,781 24,075 5,896 1,001 147 88,242

Qarghayi 43,419 40,889 9,499 5,928 607 72 87,043

All 158,359 161,097 163,306 19,831 1,762 246 335,098

Logar Azra 1,423 1,326 10,335 163 1 20 7,913

Baraki Barak 18,928 7,205 2,283 4,022 8 74 39,795

Charkh 5,641 1,599 3,250 1,632 0 21 16,717

Kharwar 7,591 5,943 3,179 2,727 79 87 16,786

Khushi 711 5,201 2,138 414 1 0 9,781

Muhammad Agha 8,739 10,048 4,634 2,265 60 5 33,238

Puli Alam 15,715 10,288 4,225 3,213 0 9 43,434

All 58,748 41,610 30,044 14,436 149 216 167,664



154

Nangarhar Achin 11,391 10,979 37,022 6,335 10 10 48,070

Bati Kot 27,094 9,026 1,884 5,043 192 16 58,830

Bihsud 13,950 22,560 4,093 2,520 74 55 29,750

Chaparhar 8,117 16,675 3,415 2,473 113 24 50,765

Dara-i-Nur 13,390 2,732 21,509 320 0 6 25,563

Deh Bala 10,798 15,847 9,366 2,604 0 1 50,280

Dur Baba 5,339 3,833 14,056 4,939 576 2,125 12,100

Ghani Khel Shinwar 15,366 6,218 5,088 2,124 86 0 34,680

Goshta 8,402 2,972 4,602 2,402 0 0 22,329

Hisarak 9,152 12,860 16,809 1,604 99 23 21,948

JalalAbad Markaz 7,013 2,262 1,311 663 8 260 40,279

Kama 48,313 12,893 13,245 4,017 97 5 75,440

Khogyani 19,826 23,105 10,688 3,072 284 159 70,996

Khogyani Wazir 8,989 13,272 7,487 1,461 32 31 29,063

Kot 6,301 5,736 3,696 1,100 0 3 33,980

Kuz Kunar Shewa 11,330 23,711 5,650 1,964 539 26 21,514

Lal Pur 7,754 2,705 11,818 3,974 5 17 18,803

Muhmandara 13,374 34,496 7,873 6,031 998 16 34,868

Nazian 4,644 3,584 19,501 1,830 7 91 16,520

Pachir Wa Agam 8,102 8,954 8,536 2,000 82 254 19,375

Rodat 6,616 3,992 2,911 1,640 0 10 20,380

Sherzad 22,201 13,236 24,133 3,361 0 58 42,325

Shewa 1,871 816 628 233 18 0 2,055

Surkh Rod 15,559 15,285 3,670 1,668 102 172 60,283

All 304,892 267,749 238,991 63,378 3,322 3,362 840,196

Nimroz Chahar Burjak 2,480 13,318 9,136 2,982 1,584 0 29,817

Chakhansur 1,746 23,106 17,414 1,215 4,438 190 16,066

Delaram 452 5,820 13,480 299 1,034 80 6,785

Kang 2,511 8,207 7,853 9,067 157 182 16,164

Khash Rod 3,585 12,099 68,804 2,322 1,880 204 33,719

Zaranj Markaz 674 3,428 9,628 3,445 779 6 11,430

All 11,448 65,978 126,315 19,330 9,872 662 113,981

Nuristan Bargi Matal 12,749 7,475 23,536 409 0 696 7,801

Do Aab 2,058 2,374 28,259 480 0 12 15,570

Kamdesh 11,567 10,556 47,392 849 0 180 11,109

Mandol 14,659 22,780 143,095 4,478 0 1,836 104,930

Nuristan 9,438 16,227 116,474 2,577 0 3 19,196

Wama 22,473 9,376 80,441 1,409 0 345 17,129

Waygal 22,948 6,692 120,701 2,619 0 2 33,586

All 95,892 75,480 559,898 12,821 0 3,074 209,321



155

Paktika

Dila Khoshmand 3,738 20,685 6,038 2,128 177 51 16,165

Gayan 3,991 331 4,182 1,111 102 0 36,636

Gomal 3,689 9,403 30,256 1,365 1,869 13 18,707

Jani Khil 3,364 9,050 6,362 973 64 17 9,359

Khir Kot 2,918 4,767 3,278 336 85 27 8,330

Mata Khan 3,177 5,585 962 464 0 7 14,525

Nika 1,829 178 8,466 734 260 0 13,333

Omna 1,030 1,717 9,205 516 375 2 3,603

Sar Hawza 1,120 2,012 4,761 702 78 4 6,830

Sarobi 2,103 1,529 6,622 651 127 1 10,923

Sharana Markaz 6,280 8,942 1,629 244 16 20 19,273

Urgun 4,142 1,790 7,466 1,286 19 4 24,304

Waza Khwa 1,942 20,132 27,966 1,300 302 8 18,899

Wor Mamay 2,255 6,599 25,939 502 193 50 13,548

Yahya Khil 3,291 5,886 2,375 216 5 14 5,826

Yusuf Khil 2,913 5,565 3,006 366 16 8 6,029

Ziruk 4,483 273 12,150 1,083 330 0 15,859

All 52,265 104,444 160,663 13,977 4,018 226 242,149

Paktya Adma 207 1,409 1,777 186 29 2 735

Ahmad Khel 3,208 587 13,447 1,168 33 25 25,483

Chamkani 10,746 1,478 14,933 1,974 5 11 48,912

Dand Wa Patan 7,335 2,670 21,224 1,388 97 36 53,983

Dowamanda Shamal 1,442 259 4,174 260 14 0 7,265

Gardez 6,747 5,288 1,570 754 0 5 36,159

Jaji 7,733 2,722 11,836 1,912 24 154 48,153

Jani Khel 14,419 2,743 36,219 2,100 178 4 46,022

Lija Mangal 1,918 754 9,946 728 26 0 14,740

Sayed Karam 10,836 3,883 1,402 2,693 1 3 51,696

Shwak 841 188 7,014 574 4 0 4,695

Waza Jadran 1,644 101 7,771 498 93 0 3,953

Zurmat 18,594 19,210 5,758 1,868 49 51 43,188

All 85,670 41,292 137,071 16,103 553 291 384,984



156

Parwan Bagram 11,067 11,780 3,682 1,435 75 69 17,002

Chaharikar 15,915 5,356 6,902 4,745 6 248 31,201

Hisa Awa Panjshir 14,305 21,325 23,463 3,189 0 1,550 15,416Hisa Chaorum Panjshir 8,887 10,419 24,457 2,497 0 48 22,755

Hisa Doum Panjshir 6,687 5,851 10,633 828 0 98 11,761

Hisa Soum Panjshir 5,326 2,588 5,610 642 0 60 15,138

Jabalusaraj 7,850 7,432 7,320 1,850 0 28 17,367Kohisafi 4,193 5,704 13,862 3,108 28 20 16,567

Salang 2,130 10,488 16,556 790 0 81 6,774

Sayed Khel 9,715 1,035 114 1,056 0 85 20,070

Shikh Ali 5,847 12,999 7,707 3,341 0 60 8,556

Shinwari 13,038 22,700 27,340 8,109 28 60 40,401

Siagird Ghorband 5,455 13,399 13,229 5,787 0 58 22,662

Surkhi Parsa 10,779 18,121 9,072 5,541 0 40 14,711

All 121,194 149,197 169,947 42,918 137 2,505 121,194

Samangan Aybak 6,801 63,333 18,404 5,928 469 554 16,210

Dara-i-Suf 7,799 16,859 12,183 10,299 0 776 10,302

Dara-i-Suf Sufla 3,150 10,610 9,704 13,735 22 509 3,526

Hazrati Sultan 5,505 82,106 25,292 11,380 854 852 9,222

Kaldar 3,068 6,949 5,762 1,617 426 113 7,376

Khoram wa Sarbagh 4,677 40,772 20,597 8,265 0 544 18,988

Khulm 3,384 51,167 14,575 3,672 1,474 646 19,731

Ruyi Du Ab 7,737 51,620 17,860 12,210 2 1,265 15,370

All 42,121 323,416 124,377 67,106 3,247 5,259 100,725

Sari Pul Balkhab 11,819 19,918 16,920 8,118 0 824 25,749

Kohistanat 13,409 28,742 7,228 12,506 45 1,355 28,042

Sang Charak 10,921 64,457 26,642 17,599 1,172 639 22,179Sari Pul 9,065 49,297 11,283 7,398 463 799 14,128

Sayed Abad 4,724 39,835 9,869 3,415 1,180 278 3,515

Sayyad 3,598 16,799 5,501 3,255 125 458 2,061

Sozma Qala 9,651 21,845 8,379 8,412 213 561 8,845

All 63,187 240,893 85,822 60,703 3,198 4,914 104,519



157

Takhar Baharak 15,572 25,074 15,060 8,747 68 615 20,877

Bangi 4,518 4,947 6,075 4,529 0 301 7,344

Cha Aab 10,272 15,852 18,188 9,662 3 640 20,308

Chal 7,474 14,170 14,179 5,439 0 374 5,624

Darqad 15,657 4,617 5,554 4,374 93 1,541 12,007

Dashte Qala 8,610 10,590 2,707 4,068 95 388 9,342

Farkhar 5,701 6,077 19,168 4,088 0 71 4,525

Hazar Somoch 1,301 3,042 2,256 2,064 0 71 4,805

Ishkamish 16,954 19,049 18,098 12,637 2 1,319 9,900

Kalafgan 15,024 24,385 26,472 11,707 0 567 23,793

Khwaja Bahwaddin 10,607 21,627 7,462 5,489 310 1,054 17,837

Khwaja Ghar 11,503 25,046 9,883 8,358 319 1,075 15,925

Namak Aab 772 570 783 1,015 0 32 2,295

Rustaq 38,025 55,172 40,812 26,943 18 2,640 39,737

Taluqan 42,900 52,126 14,649 17,296 2 1,491 48,130

Warsaj 14,726 22,218 21,061 3,336 0 184 12,099

Yangi Qala 16,578 19,469 9,145 9,562 2 2,355 28,257

All 236,194 324,031 231,552 139,314 912 14,718 282,805

Uruzgan Char Cheenah 9,631 17,293 15,622 3,362 330 489 24,900

Chora 11,578 26,270 22,434 6,005 932 208 44,460

Day Kundi 9,142 29,615 16,914 3,949 0 317 20,742

Dihrawud 54,686 35,919 38,842 8,728 4,410 5,393 99,008

Gizab 19,771 31,473 58,591 7,469 696 65 26,859

Khas Uruzgan 24,898 57,085 40,200 10,132 16 300 28,311

Kijran 19,420 20,095 22,230 6,503 408 226 43,516

Shahristan 31,810 80,261 73,833 9,731 40 73 64,734

Tirin Kot 49,020 64,492 43,699 18,917 4,135 542 136,747

All 229,956 362,503 332,365 74,796 10,967 7,613 489,277

Wardak Bihsud Markaz 6,818 20,776 5,255 3,362 12 252 14,248

Chak 8,658 9,778 10,513 5,294 0 24 26,952

Day Mirdad 4,445 6,427 6,098 3,338 0 33 13,459

Hisa Awali Bihsud 6,238 19,207 4,226 4,319 0 43 5,136

Jaghatu 2,126 7,692 3,316 3,257 0 3 15,534

Jalrez 5,461 4,379 4,983 2,588 0 14 20,548

Maydan Shahr 4,888 2,701 2,248 1,033 24 5 17,537

Nirkh 5,761 9,889 10,119 2,751 24 0 44,005

Sayed Abad 12,033 13,088 8,381 6,016 0 604 50,340

All 56,428 93,937 55,139 31,958 60 978 207,759



158

Zabul Arghandab 4,899 12,161 20,137 2,986 73 51 42,217

Atghar 1,487 5,163 7,790 1,488 38 15 7,143

Daychopan 3,257 9,194 14,809 2,816 420 94 8,828

Khaki Afghan 3,277 7,921 13,360 2,264 30 79 11,851

Mizan 1,290 12,785 10,244 1,341 703 13 6,064

Nawbahar 1,752 8,414 12,034 2,135 7 27 11,875

Qalat 6,289 34,907 15,039 3,932 546 28 27,079

Seyouray 1,650 6,079 8,263 1,332 5 32 12,634

Shahjoy 5,920 23,724 15,611 3,494 67 62 56,334

Shahr Safa 2,210 8,705 7,397 1,403 321 18 9,497

Shamulzayi 724 22,651 20,471 2,928 430 79 21,886

Shinkay 1,540 10,247 8,996 1,443 59 15 2,537

All 34,295 161,951 154,151 27,562 2,699 513 217,945

Afghanistan All 3,715,596 8,772,596 7,280,946 1,587,648 175,270 142,094 9,865,064



159

Table A2 Average and Standard Deviation of Total Animal Numbers per Family for Selected Species

Cows Donkeys ChickenPreferred

Ploughing AnimalsDraft

Animals

Province District AVG STD AVG STD AVG STD AVG STD AVG STD

Badakhshan Arghonj Khwa 1.41 0.95 1.16 0.49 2.60 1.20 0.83 0.41 2.12 0.87

Argo 0.95 0.81 1.25 0.98 3.80 3.08 0.66 0.36 1.93 3.09

Baharak 1.94 1.36 1.04 1.17 3.50 3.74 0.88 0.63 1.80 1.28

Daraeem 0.91 1.17 0.93 0.79 2.94 2.46 0.51 0.82 1.06 1.31

Darwaz Bala 0.90 0.32 0.74 0.26 3.87 1.62 1.16 0.45 1.84 0.53

Fayzabad 0.53 0.40 0.63 0.42 2.13 2.02 0.42 0.38 1.06 0.75

Ishkashim 1.90 0.72 1.07 0.41 3.50 1.53 1.39 0.67 2.22 0.61

Jurm 0.75 0.62 0.77 0.39 2.35 1.84 0.44 0.35 1.19 0.66

Karan Wa Manjan 2.12 1.74 1.50 0.66 8.82 21.15 0.50 0.52 2.10 0.84

Khash 0.94 0.43 0.96 0.17 3.22 1.58 0.72 0.38 1.41 0.49

Khwahan 0.79 0.61 0.71 0.28 2.82 3.42 1.36 0.94 2.13 2.79

Kishim 1.17 0.76 1.67 0.98 6.32 4.04 0.72 0.63 1.70 1.01

Kohistan 1.65 0.85 1.64 0.63 2.97 1.62 0.96 0.40 1.98 0.78

Kohistan Ragh 1.56 0.95 1.43 0.39 2.15 0.72 0.80 0.34 1.65 0.56

Koof 0.97 0.56 0.77 0.34 1.75 1.21 0.61 0.29 1.47 0.56

Nasi Darwaz 0.93 0.44 0.78 0.27 3.23 1.32 0.25 0.14 1.68 0.51

Raghistan 1.65 1.20 0.66 0.58 2.31 1.63 0.72 0.65 1.43 0.88

Shahri Buzurg 0.30 0.12 1.16 0.44 1.23 0.85 0.07 0.05 0.61 0.22

Shighnan 0.77 0.34 0.64 0.26 1.00 0.92 0.29 0.14 0.89 0.30

Shiki Darwaz 0.64 0.28 0.77 0.13 4.06 2.80 0.34 0.24 1.39 0.29

Shohada 1.01 0.68 0.73 0.55 2.30 4.74 0.84 0.73 1.37 0.92

Tagab Kishim 0.98 0.69 0.90 0.30 3.66 1.65 0.42 0.33 1.10 0.49

Teshkan 0.34 0.23 0.64 0.20 1.68 2.37 0.29 0.13 0.94 0.27

Wakhan 1.28 0.66 0.96 0.28 1.18 0.84 0.96 0.46 1.67 0.60

Wardooj 1.11 0.37 0.94 0.25 2.96 1.22 0.56 0.34 1.54 0.53

Yaftal Bala 0.77 0.33 0.84 0.18 4.07 2.10 1.17 0.76 2.12 0.77

Yaftal-i-Payan 1.05 0.73 1.13 0.70 3.66 3.58 0.70 0.45 1.59 0.92

Yamgan 1.17 0.76 0.71 0.28 2.37 1.54 0.80 0.66 1.37 0.58

Yawan Ragh 1.50 0.85 0.94 0.52 1.99 1.34 0.69 0.44 1.49 0.78

Zebak 1.38 0.46 1.35 0.51 1.70 1.20 0.98 0.39 1.66 0.52

All 1.08 0.88 0.96 0.68 2.93 3.42 0.66 0.58 1.50 1.32

160

Badghis Bala Murghab 0.24 0.17 1.52 0.58 5.97 5.53 0.19 0.14 1.25 0.46

Dara-i-Boum 0.09 0.08 1.16 0.51 3.12 1.48 0.08 0.08 0.89 0.37

Ghormach 0.10 0.15 1.17 0.52 2.30 2.73 0.33 0.26 1.33 0.66

Jawand 0.09 0.11 0.74 0.35 2.37 1.50 0.23 0.22 1.00 0.54

Muqur 0.02 0.06 1.06 1.12 1.39 1.32 0.07 0.15 0.94 0.99

Murghab 0.36 0.31 1.22 0.83 4.74 5.37 0.38 0.37 1.47 1.25

Qadis 0.14 0.20 1.17 0.73 3.73 3.19 0.25 0.29 1.24 0.85

Qala-i-Naw 0.07 0.09 1.08 0.81 2.74 3.19 0.09 0.14 1.10 0.88

Sange Aatash 0.01 0.02 0.85 0.50 1.21 1.18 0.00 0.02 0.79 0.47

All 0.14 0.21 1.10 0.72 3.08 3.58 0.21 0.28 1.16 0.89

Baghlan Andarab 0.58 0.35 0.51 0.29 3.06 2.45 0.42 0.28 0.63 0.39

Baghlan 0.88 0.56 0.59 0.90 4.12 2.79 0.35 0.31 0.83 0.80

Baghlan Jadid 1.10 0.94 0.91 0.76 3.59 2.83 0.50 0.38 1.20 0.74

Baghlan Qadeem 1.08 0.92 0.79 0.55 7.69 7.53 0.77 0.47 1.43 0.71

Banoo Andarab 1.38 2.24 1.04 0.46 3.45 3.02 0.50 0.23 1.42 0.55

Burka 0.29 0.37 1.32 0.83 3.52 3.08 0.39 0.35 1.38 0.83

Dahana-i-Ghori 0.57 0.67 1.14 0.66 2.70 2.86 0.38 0.40 1.41 0.85

Deh Salah Andarab 0.41 0.42 0.56 0.31 2.25 2.39 0.32 0.19 0.62 0.33

Dushi 0.76 0.59 0.85 0.58 4.17 6.36 0.21 0.23 0.76 0.52

Khinjan 1.10 0.65 0.69 0.38 3.96 2.12 0.25 0.27 0.83 0.47

Khost Wa Firing 0.79 0.59 0.39 0.24 1.71 2.19 0.32 0.24 0.63 0.36

Nahrin 0.30 0.43 0.55 0.37 3.85 4.83 0.16 0.21 0.73 0.45

Puli Hisari Andarab 0.16 0.32 0.33 0.34 1.01 1.90 0.26 0.26 0.46 0.44

Puli Khumri 0.75 0.75 0.52 0.37 2.58 2.51 0.58 0.79 1.14 1.10

Tala Wa Burfuk 0.99 0.31 1.46 0.50 3.50 2.20 1.14 0.73 2.38 1.00

All 0.70 0.78 0.75 0.62 3.22 3.87 0.39 0.43 1.00 0.78




Animals


161

Balkh Balkh 0.74 0.67 0.39 0.27 3.01 2.35 0.18 0.17 0.51 0.35

Chahar Bolak 0.37 0.31 0.33 0.25 4.08 3.25 0.10 0.13 0.36 0.26

Chahar Kint 0.09 0.09 0.55 0.31 1.02 1.15 0.17 0.14 0.71 0.36

Chai 0.12 0.09 0.16 0.12 2.42 3.89 0.00 0.00 0.14 0.12

Chimtal 0.31 0.25 0.45 0.29 2.30 1.80 0.23 0.22 0.58 0.39

Dawlat Abad 0.24 0.21 0.26 0.24 2.35 1.76 0.00 0.00 0.20 0.19

Dihdadi 0.20 0.23 0.25 0.18 2.54 1.36 0.17 0.13 0.33 0.19

Kishindih 0.04 0.04 0.54 0.33 0.05 0.22 0.19 0.27 0.56 0.31

Kohi Albarz 0.03 0.05 0.75 0.70 1.08 0.74 0.10 0.14 0.67 0.57

Marmul 0.08 0.06 0.87 0.26 0.21 0.21 0.06 0.09 0.93 0.29

Mazar-i-Sharif 0.11 0.10 0.05 0.06 1.26 1.44 0.00 0.02 0.05 0.07

Nahri Shahi 0.26 0.27 0.33 0.31 1.73 1.42 0.05 0.11 0.30 0.28

Sholgara 0.30 0.35 0.71 0.47 2.10 3.41 0.38 0.43 0.96 0.71

Shortepa 0.71 0.39 0.55 0.62 3.85 1.87 0.10 0.06 0.52 0.23

Zareh 0.38 0.30 1.32 0.65 3.01 1.96 0.04 0.12 1.40 0.82

All 0.26 0.35 0.44 0.47 2.08 2.45 0.13 0.23 0.50 0.53

Bamyan Bamyan 0.29 0.24 0.80 0.41 0.89 0.91 0.16 0.16 0.78 0.42

Kamhard 0.85 0.57 1.27 0.79 4.35 2.63 0.60 0.57 1.74 1.10

Panjab 0.72 0.38 0.88 0.34 3.18 1.81 0.72 0.42 1.36 0.56

Saighan 0.62 0.34 1.40 0.51 1.81 1.08 0.47 0.29 1.59 0.62

Shibar 0.46 0.26 1.14 0.48 1.97 1.53 0.42 0.24 1.35 0.55

Waras 0.42 0.23 0.73 0.33 4.52 2.23 0.04 0.07 1.07 0.48

Yakawlang 0.66 0.50 0.98 0.49 1.29 0.97 0.50 0.33 1.29 0.64

All 0.56 0.39 0.90 0.44 2.85 2.20 0.39 0.41 1.22 0.61

Farah Anar Dara 0.22 0.23 0.49 0.36 3.51 2.24 0.01 0.10 0.40 0.28

Bakwa 0.59 1.01 0.25 0.22 4.79 5.54 0.00 0.01 0.17 0.14

Bala Buluk 0.28 0.29 0.42 0.32 4.63 3.87 0.05 0.11 0.34 0.27

Farah Markaz 0.59 0.49 0.44 0.35 6.09 4.35 0.04 0.07 0.36 0.30

Gulistan 0.41 0.33 0.98 0.87 6.29 3.31 0.11 0.12 1.16 0.68

Khaki Safed 1.14 2.08 0.84 0.45 6.29 5.74 0.02 0.08 0.63 0.36

Lash Wa Juwayn 0.35 0.36 0.62 0.41 13.66 20.93 0.01 0.04 0.37 0.40

Purchaman 0.64 0.47 0.81 0.71 7.09 5.23 0.12 0.23 1.06 1.04

Pushte Rod 0.63 0.46 0.62 0.45 6.44 3.38 0.05 0.07 0.47 0.42

Qala i Kah 0.21 0.33 0.72 0.21 7.41 6.02 0.02 0.05 0.72 0.30

Qala-i-Kah Pusht Koh 0.75 0.92 0.59 0.30 7.50 6.22 0.02 0.05 0.37 0.33

Shib Koh Qalay-i-Kah 0.44 0.37 0.53 0.38 5.54 4.04 0.01 0.06 0.52 0.38

All 0.52 0.68 0.62 0.55 6.51 6.98 0.06 0.14 0.62 0.68




Animals


162

Faryab

Almar 0.12 0.15 1.00 0.50 3.32 3.25 0.21 0.25 1.04 0.70

Bilchiragh 0.22 0.16 0.79 0.36 0.76 0.52 0.35 0.17 1.09 0.44

Dawlat Abad 0.04 0.04 0.73 0.19 0.57 0.45 0.11 0.14 0.42 0.41

Gurziwan 0.18 0.13 0.57 0.28 0.86 0.68 0.27 0.17 0.79 0.37

Khwaja Mosa 0.17 0.25 0.75 0.56 0.59 0.52 0.29 0.23 1.04 0.66

Khwaja Sabz Posh 0.10 0.08 0.46 0.19 0.28 0.15 0.25 0.15 0.74 0.33

Kohistan 0.15 0.07 0.84 0.33 3.09 0.95 0.53 0.22 1.28 0.46

Maymana 0.15 0.11 0.27 0.25 1.34 0.90 0.11 0.13 0.38 0.36

Pashtun Kot 0.14 0.17 0.70 0.41 0.86 1.40 0.32 0.22 1.01 0.51

Qaysar 0.11 0.05 0.45 0.29 2.19 1.36 0.14 0.05 0.56 0.25

Shirin Tagab 0.07 0.06 0.53 0.28 0.88 0.59 0.06 0.06 0.58 0.29

All 0.13 0.13 0.60 0.39 1.37 1.59 0.22 0.20 0.77 0.50

Ghazni Ab Band 0.31 0.31 0.73 0.56 5.84 4.38 0.02 0.07 0.57 0.51

Ajristan 0.71 0.40 0.58 1.13 2.30 2.24 0.33 0.16 0.76 0.30

Andar 0.90 0.49 0.28 0.18 2.77 1.69 0.02 0.06 0.31 0.21

Dih Yak 0.44 0.32 0.39 0.26 5.52 3.95 0.06 0.07 0.42 0.28

Gelan 0.27 0.26 0.35 0.43 6.42 3.65 0.02 0.04 0.26 0.31

Ghazni Markaz 0.32 0.36 0.07 0.11 2.59 2.26 0.00 0.02 0.07 0.11

Giro 0.60 0.55 0.56 0.36 4.33 2.49 0.02 0.07 0.52 0.33

Jaghatu 0.43 0.41 0.56 0.33 3.82 2.02 0.03 0.11 0.65 0.46

Jaghuri 0.18 0.30 0.32 0.24 3.67 1.65 0.03 0.06 0.34 0.26

Khoja Omary 0.30 0.22 0.18 0.14 1.64 0.97 0.01 0.04 0.18 0.15

Malistan 0.54 0.37 0.65 0.30 2.68 1.50 0.14 0.12 0.71 0.35

Muqur 0.39 0.27 0.58 0.33 6.38 4.90 0.01 0.03 0.43 0.29

Nawa 0.15 0.14 0.43 0.46 4.28 2.00 0.01 0.05 0.37 0.42

Nawur 0.44 0.60 0.82 0.56 4.04 3.46 0.26 0.27 0.80 0.51

Qara Bagh 0.64 0.42 0.47 0.38 3.43 2.07 0.08 0.17 0.46 0.40

Waghaz 0.27 0.25 0.25 0.22 2.75 2.10 0.03 0.09 0.27 0.25

Zana Khan 0.24 0.26 0.61 0.28 5.34 2.61 0.00 0.01 0.59 0.27

All 0.47 0.45 0.47 0.44 3.93 3.02 0.06 0.14 0.46 0.41

Ghor Lal Wa Sarjangal 0.70 0.33 0.72 0.28 4.03 2.31 0.83 0.45 1.43 0.57

Shahrak 0.41 0.28 0.56 0.48 1.75 2.31 0.22 0.23 0.64 0.48

Tulak 0.34 0.32 0.85 0.40 1.81 1.52 0.32 0.22 0.94 0.45

All 0.60 0.35 0.72 0.34 3.36 2.42 0.66 0.47 1.25 0.62




Animals


Hilmand Baghran 0.64 0.31 0.52 0.41 8.64 6.91 0.10 0.14 0.50 0.27

Dishu 0.53 0.42 0.56 0.28 10.86 5.48 0.04 0.08 0.43 0.31

Garmser 1.04 0.85 0.51 0.50 8.59 6.19 0.02 0.08 0.48 0.44

Gerishk 0.91 0.69 0.56 0.68 10.50 16.82 0.14 0.30 0.64 0.72

Kajaki 0.93 0.53 0.64 0.39 7.39 6.21 0.10 0.22 0.54 0.37

Khanshen 1.17 1.03 0.61 0.51 15.17 7.05 0.01 0.05 0.52 0.47

Lashkar Gah 0.69 0.41 0.53 0.42 8.61 5.37 0.00 0.01 0.40 0.35

Marja 0.70 0.33 0.12 0.14 8.25 8.21 0.09 0.14 0.23 0.17

Musa Qala 0.67 0.81 0.35 0.42 9.12 18.56 0.05 0.11 0.35 0.46

Nad Ali 0.83 0.58 0.51 1.03 5.99 3.84 0.02 0.06 0.37 0.59

Naw Zad 0.31 0.30 0.77 0.89 7.08 8.84 0.03 0.07 0.50 0.59

Nawa 1.00 0.61 0.38 0.75 12.47 11.07 0.00 0.01 0.14 0.41

Reg 0.93 0.22 0.58 0.08 7.27 1.46 0.12 0.07 0.76 0.13

Sangin 0.92 0.36 0.48 0.38 5.06 4.47 0.04 0.11 0.50 0.39

Washer 0.34 0.39 1.71 1.33 7.24 10.11 0.01 0.04 1.31 1.03

All 0.77 0.62 0.57 0.69 8.62 10.34 0.06 0.16 0.50 0.56

Hirat Adraskan 0.27 0.44 0.56 0.39 1.80 2.46 0.12 0.20 0.58 0.45

Chishte Sharif 0.28 0.26 0.63 0.42 1.43 1.34 0.30 0.41 0.86 0.64

Farsi 0.27 0.19 0.79 0.37 1.10 0.97 0.13 0.17 1.04 0.46

Ghoryan 0.17 0.22 0.47 0.38 2.57 2.55 0.02 0.06 0.36 0.32

Gulran 0.27 0.35 1.51 1.00 4.91 3.77 0.10 0.20 1.29 0.87

Guzarah 0.32 0.47 0.28 0.30 2.37 1.32 0.23 0.30 0.44 0.45

Injil 0.43 0.42 0.30 0.33 1.65 1.43 0.26 0.32 0.54 0.54

Karukh 0.18 0.19 0.63 0.76 2.91 3.04 0.31 0.32 0.83 0.85

Kohsan 0.09 0.14 0.94 0.24 2.73 1.32 0.03 0.11 0.74 0.32

Kushke Kuhna 0.30 0.44 1.52 0.73 3.25 4.16 0.21 0.25 1.51 0.70

Obe 0.32 0.34 0.78 0.49 3.93 3.69 0.31 0.38 0.79 0.66

Pashtun Zarghun 0.22 0.21 0.38 0.30 1.64 1.08 0.23 0.22 0.56 0.31

Rabat-i-Sangy 0.26 0.26 0.75 0.43 2.19 1.89 0.17 0.33 0.77 0.52

Shindand 0.24 0.27 0.40 0.28 4.20 3.41 0.14 0.16 0.50 0.31

Zinda Jan 0.28 0.63 0.81 0.57 3.92 4.08 0.13 0.21 0.71 0.56

All 0.27 0.36 0.67 0.61 2.80 3.00 0.18 0.27 0.73 0.62




Animals


163

164

Jawzjan Andkhoi 0.11 0.07 0.12 0.11 0.91 0.70 0.00 0.02 0.12 0.11

Aqcha 0.15 0.13 0.18 0.17 1.41 1.26 0.03 0.05 0.21 0.18

Darzab 0.19 0.13 1.01 0.35 0.78 0.54 0.40 0.26 1.42 0.53

Fayz Abad 0.15 0.11 0.31 0.22 1.68 2.01 0.04 0.08 0.23 0.20

Khamyab 0.92 0.38 0.27 0.16 3.28 1.40 0.07 0.06 0.38 0.21

Khani Chaharbagh 0.08 0.06 0.42 0.20 1.03 1.03 0.04 0.09 0.39 0.22

Khwaja Dukoh 0.14 0.12 0.34 0.27 1.62 3.38 0.00 0.00 0.34 0.27

Mardyan 0.14 0.08 0.21 0.15 6.06 5.78 0.03 0.02 0.20 0.12

Mingajik 0.11 0.13 0.35 0.21 2.51 2.00 0.00 0.00 0.30 0.19

Qaramqol 0.07 0.07 0.28 0.14 0.82 1.02 0.01 0.03 0.28 0.14

Qarqin 1.26 0.30 0.36 0.13 1.49 1.04 0.02 0.03 0.40 0.10

Qoshtepa 0.07 0.08 0.80 0.43 0.61 0.61 0.19 0.17 0.86 0.44

Qurghan 0.07 0.07 0.12 0.10 0.81 1.07 0.00 0.00 0.11 0.09

Shibirghan 0.18 0.17 0.23 0.25 1.22 1.22 0.06 0.09 0.24 0.27

All 0.18 0.26 0.31 0.32 1.66 2.42 0.06 0.13 0.34 0.39

Kabul Bagrami 0.60 0.42 0.41 0.22 4.52 1.96 0.05 0.09 0.01 0.02

Chahar Asyab 0.58 0.35 0.18 0.16 6.97 4.43 0.10 0.18 0.00 0.02

Dih Sabz 0.28 0.21 0.30 0.24 14.29 10.70 0.08 0.11 0.01 0.06

Farza 0.49 0.40 0.55 0.80 5.15 3.50 0.02 0.04 0.01 0.03

Guldara 0.10 0.10 0.19 0.19 1.30 0.61 0.01 0.02 0.17 0.13

Istalif 0.07 0.05 0.14 0.14 2.13 1.20 0.00 0.01 0.09 0.09

Kalakan 0.08 0.07 0.05 0.03 1.04 0.88 0.01 0.03 0.04 0.04

Khaki Jabbar 0.26 0.35 0.64 0.36 7.03 4.74 0.08 0.11 0.25 0.36

Mir Bacha Kot 0.07 0.09 0.03 0.05 1.76 1.54 0.01 0.05 0.01 0.05

Musayi 0.84 0.43 0.46 0.25 7.82 3.97 0.15 0.13 0.01 0.06

Paghman 0.28 0.15 0.20 0.30 4.99 3.51 0.02 0.04 0.00 0.00

Qara Bagh 0.10 0.12 0.07 0.15 1.87 2.04 0.01 0.02 0.05 0.08

Sarobi 0.75 0.50 0.83 0.77 12.20 7.22 0.22 0.23 0.10 0.19

Shakardara 0.58 0.23 0.11 0.09 2.49 1.43 0.04 0.04 0.14 0.10

All 0.35 0.38 0.28 0.42 5.30 5.89 0.06 0.12 0.05 0.13




Animals


165

Kabul City Nahya 1 0.00 0.00 0.00 0.01 3.66 2.05 0.00 0.00 0.00 0.00

Nahya 2 0.01 0.00 0.00 0.01 0.49 0.17 0.00 0.00 0.00 0.00

Nahya 3 0.01 0.01 0.00 0.01 1.47 1.32 0.00 0.00 0.00 0.01

Nahya 4 0.02 0.07 0.00 0.00 1.09 1.25 0.00 0.00 0.00 0.01

Nahya 5 0.07 0.11 0.06 0.21 7.38 6.31 0.01 0.02 0.07 0.21

Nahya 6 0.03 0.02 0.01 0.01 1.78 0.80 0.00 0.01 0.01 0.01

Nahya 7 0.10 0.16 0.02 0.05 2.03 0.89 0.01 0.02 0.03 0.07

Nahya 8 0.03 0.04 0.01 0.02 3.20 1.63 0.00 0.00 0.01 0.01

Nahya 9 0.08 0.08 0.04 0.07 2.09 1.19 0.03 0.09 0.06 0.13

Nahya 10 0.05 0.11 0.01 0.02 0.96 1.00 0.00 0.00 0.01 0.02

Nahya 11 0.03 0.03 0.01 0.02 1.85 0.31 0.00 0.00 0.00 0.00

Nahya 12 0.03 0.03 0.01 0.01 2.80 0.54 0.00 0.00 0.01 0.01

Nahya 14 0.09 0.22 0.03 0.07 2.09 1.34 0.02 0.07 0.05 0.15

Nahya 15 0.02 0.02 0.00 0.00 2.09 0.41 0.00 0.00 0.00 0.00

Nahya 16 0.08 0.15 0.05 0.08 3.02 1.94 0.00 0.01 0.05 0.08

All 0.05 0.10 0.02 0.07 2.44 2.57 0.00 0.03 0.02 0.08

Kandahar Arghandab 0.78 0.82 0.46 0.70 11.44 11.72 0.00 0.01 0.43 0.68

Arghistan 0.49 0.44 1.22 0.59 5.86 5.42 0.06 0.12 1.16 0.62

Daman 0.51 0.54 0.44 0.64 6.74 6.15 0.00 0.04 0.45 0.64

Dand 0.36 0.30 0.19 0.27 2.66 1.63 0.00 0.01 0.18 0.26

Ghorak 0.54 0.49 1.56 1.89 9.77 6.85 0.01 0.04 1.23 1.46

Jarri 0.52 0.31 0.11 0.14 11.30 5.83 0.05 0.13 0.11 0.14

Kandahar 0.12 0.12 0.05 0.07 0.96 0.73 0.00 0.00 0.05 0.09

Khakrez 0.48 0.33 0.88 0.62 7.12 3.70 0.07 0.11 0.77 0.49

Maruf 0.42 0.45 0.83 0.47 5.13 5.98 0.05 0.14 0.67 0.45

Maywand 0.58 0.44 0.80 1.09 11.36 7.62 0.00 0.03 0.72 0.93

Meenasheen 0.40 0.21 0.59 0.35 3.59 1.98 0.15 0.12 0.62 0.34

Nesh 0.71 0.36 1.33 0.90 11.93 8.34 0.04 0.08 1.38 0.52

Panjwayi 0.57 0.33 0.21 0.25 10.38 4.64 0.00 0.01 0.20 0.23

Reg 0.05 0.09 0.89 1.13 7.32 6.63 0.02 0.07 0.59 0.59

Shah Wali Kot 0.62 0.48 0.54 0.56 7.02 6.49 0.05 0.10 0.58 0.56

Shigah 0.38 0.71 1.01 0.66 6.94 5.78 0.00 0.00 0.98 0.68

Shorabak 0.04 0.09 0.92 0.38 9.97 5.91 0.01 0.02 0.58 0.24

Spin Boldak 0.26 0.24 0.47 1.29 5.30 6.98 0.01 0.04 0.37 0.49

All 0.47 0.44 0.59 0.78 7.44 6.76 0.03 0.09 0.53 0.65




Animals


166

Kapisa Alasay 0.49 0.27 0.12 0.13 3.74 2.94 0.08 0.06 0.19 0.22

Hisa-i-Awal Kohistan 1.03 0.58 0.38 0.23 6.10 3.39 0.07 0.09 1.03 0.69Hisa-i-Doum Kohistan 0.96 0.33 0.25 0.16 8.93 4.88 0.03 0.13 1.01 0.35

Kohband 1.02 0.41 0.84 0.23 8.68 3.38 0.36 0.18 1.15 0.41

Mahmudraqi 1.10 0.33 0.38 0.32 6.21 1.54 0.00 0.00 0.95 0.41

Nurab 0.70 0.32 0.19 0.17 6.21 3.06 0.28 0.17 0.36 0.25

Tagab 0.47 0.29 0.30 0.59 6.02 2.60 0.02 0.05 0.18 0.23

All 0.83 0.44 0.32 0.36 6.64 3.61 0.12 0.17 0.68 0.54

Khost Alishir 2.26 1.26 1.27 1.08 15.96 9.22 0.00 0.00 0.91 0.91

Bak 3.09 1.30 1.15 0.62 15.30 5.87 0.06 0.09 0.93 0.59

Gurbuz 2.87 1.43 1.08 0.85 7.51 2.66 0.03 0.05 0.73 0.54

Ismail Khil 1.89 0.73 0.18 0.27 14.11 5.49 0.00 0.00 0.16 0.23

Jaji Maydan 4.74 2.79 1.26 0.45 15.51 5.80 0.33 0.41 1.41 0.66

Khost Markaz 1.90 0.91 0.37 0.57 12.16 6.75 0.00 0.06 0.30 0.50

Musa Khil 2.10 1.52 0.78 0.37 13.42 5.77 0.08 0.18 0.86 0.46

Nadir Shah Kot 2.28 0.70 0.90 0.32 36.19 14.04 0.11 0.09 0.87 0.34

Qalandar 2.29 1.10 0.97 0.54 27.85 14.54 0.04 0.17 1.02 0.55

Sabri Yaqubi 2.14 0.85 0.80 0.56 17.85 8.23 0.01 0.04 0.72 0.40

Spera 2.47 2.42 1.30 0.75 32.73 36.00 0.37 0.52 1.42 0.91

Tani 1.84 0.84 1.32 0.77 14.20 7.51 0.00 0.01 1.10 0.63

Yaqubi 2.35 0.74 0.57 0.29 18.83 6.02 0.01 0.03 0.56 0.29

All 2.35 1.56 0.90 0.78 16.77 13.67 0.07 0.22 0.80 0.72

Kunar Asadabad Markaz 1.04 0.43 0.67 0.68 12.56 14.10 0.44 0.40 1.10 0.71

Asmar 1.60 1.02 0.28 0.32 12.40 9.14 0.79 0.50 1.23 0.77

Chapa Dara 0.88 0.32 0.08 0.13 5.43 3.55 0.73 0.34 0.95 0.39

Chawkay 3.56 13.74 0.91 1.32 18.72 16.53 0.49 1.23 1.48 1.35

Dangam 1.10 0.28 0.58 0.36 14.49 9.25 0.48 0.10 1.26 0.43

Khas Kunar 2.76 1.94 1.31 0.43 21.89 14.45 1.35 0.99 3.32 1.78

Manawara 1.21 0.55 0.64 0.26 11.34 10.26 0.46 0.38 1.11 0.54

Manoguy 2.18 0.79 0.26 0.28 11.49 7.23 0.41 0.32 0.78 0.50

Narang 1.28 0.72 1.16 0.54 9.54 5.16 0.63 0.46 1.29 0.57

Nari 1.35 0.99 0.79 0.48 19.10 25.34 0.24 0.18 0.99 0.61

Nurgal 1.88 0.80 0.81 0.87 9.54 5.18 0.46 0.31 1.27 0.80

Shigal 2.54 1.13 0.48 0.69 10.41 8.18 0.80 0.49 1.35 0.82

Sirkanay 1.25 0.69 0.99 0.32 15.34 9.50 0.48 0.26 1.67 0.53

Wata Pur 1.42 1.07 0.60 0.38 10.09 9.51 0.45 0.63 1.07 1.09

All 1.86 4.72 0.65 0.73 12.75 12.89 0.59 0.64 1.29 0.99




Animals


167

Kunduz Ali Abad 1.13 1.47 1.09 0.80 3.40 3.09 0.59 0.60 1.39 1.03

Chahar Dara 1.71 1.02 0.63 0.42 8.43 6.24 0.48 0.32 0.96 0.49

Dasht Archi 0.81 1.12 0.99 1.27 2.90 2.88 0.31 0.87 0.44 0.97

Imam Sahib 1.63 2.24 1.41 1.08 2.78 4.93 0.95 0.54 2.23 1.24

Khan Abad 0.53 0.36 0.64 0.47 3.20 2.19 0.38 0.29 0.89 0.67

Kunduz Markaz 1.05 0.50 0.30 0.21 3.52 2.26 0.81 0.54 1.10 0.61

Qala Zal 0.86 0.44 0.62 0.32 4.18 1.99 0.99 1.25 1.36 0.68

All 1.11 1.28 0.81 0.84 4.15 4.35 0.60 0.69 1.15 1.00

Laghman Alingar 1.33 0.86 0.51 1.08 6.17 4.75 0.20 0.30 0.61 1.07

Alishing 1.63 1.03 0.95 1.67 11.29 7.83 0.29 0.21 1.35 1.58

Dawlat Shah 2.14 1.86 0.13 0.19 8.31 7.47 0.15 0.16 0.25 0.27

Mihterlam Markaz 2.08 2.29 0.70 1.24 10.46 11.09 0.40 0.31 1.05 1.09

Qarghayi 2.41 1.65 0.81 1.00 14.87 11.30 0.47 0.44 1.13 0.79

All 1.90 1.70 0.65 1.20 10.20 9.46 0.31 0.33 0.91 1.13

Logar Azra 0.50 0.38 0.14 0.17 7.37 4.66 0.06 0.14 0.28 0.32

Baraki Barak 0.81 0.55 0.35 0.23 4.23 3.15 0.14 0.14 0.43 0.32

Charkh 0.40 0.30 0.22 0.17 3.23 2.18 0.03 0.05 0.23 0.18

Kharwar 0.51 0.31 0.56 0.29 4.63 3.09 0.07 0.11 0.60 0.34

Khushi 0.18 0.18 0.26 0.37 6.13 3.29 0.01 0.03 0.27 0.37

Muhammad Agha 0.51 0.34 0.26 0.30 4.46 2.64 0.07 0.11 0.33 0.34

Puli Alam 0.90 0.73 0.30 0.29 5.32 3.14 0.11 0.17 0.37 0.35

All 0.65 0.55 0.31 0.28 4.71 3.18 0.09 0.13 0.38 0.34




Animals


168

Nangarhar Achin 0.60 0.33 0.57 0.46 5.94 2.03 0.12 0.14 0.68 0.49

Bati Kot 1.73 1.07 0.52 0.42 9.70 7.40 0.10 0.09 0.50 0.35

Bihsud 1.76 1.26 0.47 0.61 8.72 6.82 0.26 0.36 0.69 0.64

Chaparhar 0.62 0.44 0.34 0.37 10.39 5.23 0.15 0.18 0.46 0.40

Dara-i-Nur 1.34 0.58 0.09 0.15 6.40 3.82 0.41 0.31 0.43 0.24

Deh Bala 1.72 0.98 0.51 0.26 17.23 13.28 0.63 0.37 1.14 0.45

Dur Baba 3.53 14.61 1.36 1.02 6.62 3.73 0.06 0.10 1.45 1.00

Ghani khel 1.52 0.87 0.42 0.33 7.99 3.43 0.21 0.18 0.45 0.22

Ghani khel Shinwari 0.84 0.49 0.55 0.37 7.43 4.22 0.14 0.14 0.44 0.23

Goshta 2.21 1.32 1.20 0.51 12.31 7.18 0.20 0.23 1.31 0.53

Hisarak 1.94 0.59 0.88 0.28 17.31 5.50 0.95 0.54 1.82 0.70

JalalAbad Markaz 0.41 0.54 0.10 0.15 6.51 3.85 0.03 0.05 0.14 0.19

Kama 2.79 1.08 0.63 0.72 11.65 5.76 0.26 0.26 0.96 0.82

Khogyani 0.99 0.87 0.33 0.32 10.70 7.78 0.39 0.31 0.73 0.54

Khogyani Wazir 0.99 0.42 0.39 0.27 11.99 7.14 0.54 0.31 0.96 0.50

Kot 0.64 0.41 0.29 0.25 9.88 4.60 0.33 0.26 0.64 0.41

Kuz Kunar 1.72 1.12 0.44 0.58 8.44 6.72 0.36 0.39 0.84 0.62

Kuz Kunar Shewa 1.16 1.04 1.44 2.73 6.71 4.81 0.38 0.54 1.16 1.19

Lal Pur 1.51 1.00 1.16 0.67 8.09 4.46 0.13 0.12 1.24 0.66

Muhmandara 1.41 1.01 1.51 1.60 10.62 8.10 0.11 0.22 0.94 1.04

Nazian 0.98 0.77 0.80 0.53 8.46 3.75 0.23 0.45 1.03 0.61

Pachir Wa Agam 1.29 0.72 0.53 0.45 9.08 4.96 0.70 0.41 1.29 0.72

Rodat 0.66 0.43 0.30 0.17 5.12 3.93 0.10 0.10 0.36 0.20

Sherzad 1.79 0.98 0.62 0.35 10.27 5.39 0.67 0.57 1.40 0.73

Shewa 2.16 0.99 0.63 0.59 7.64 5.51 0.42 0.51 1.03 1.17

Surkh Rod 0.67 0.49 0.21 0.62 7.34 7.57 0.15 0.12 0.33 0.53

All 1.32 2.51 0.57 0.77 9.65 6.75 0.33 0.39 0.85 0.73

Nimroz Chahar Burjak 0.56 0.32 1.10 0.48 14.88 10.58 0.01 0.03 0.22 0.39

Chakhansur 0.49 0.51 0.55 0.45 8.09 5.85 0.00 0.03 0.05 0.13

Delaram 0.21 0.14 0.31 0.27 9.79 3.82 0.01 0.04 0.16 0.24

Kang 0.29 0.54 1.49 1.16 4.54 6.36 0.00 0.01 0.71 1.16

Khash Rod 0.46 0.40 0.54 0.47 8.99 6.90 0.01 0.05 0.09 0.17

Zaranj Markaz 0.10 0.19 0.65 0.57 4.08 4.56 0.00 0.02 0.12 0.45

All 0.35 0.44 0.86 0.81 7.76 7.79 0.00 0.03 0.26 0.68




Animals


169

Nuristan Bargi Matal 1.90 0.94 0.19 0.14 6.30 3.87 1.25 0.58 1.64 0.67

Do Aab 1.17 0.52 0.54 0.32 22.43 10.96 0.03 0.05 0.46 0.28

Kamdesh 1.36 0.49 0.23 0.19 5.31 3.62 0.49 0.30 0.74 0.33

Mandol 2.04 1.04 1.27 0.48 42.48 23.88 0.08 0.14 1.46 0.55

Nuristan 1.44 0.55 1.05 0.94 8.60 4.11 0.12 0.15 1.16 0.95

Wama 2.85 1.76 0.43 0.34 11.02 16.06 0.80 1.08 1.35 1.16

Waygal 1.83 1.07 0.59 0.42 8.84 11.64 0.84 0.59 1.49 0.78

All 1.87 1.15 0.69 0.66 15.24 19.19 0.52 0.68 1.28 0.84

Paktika Dila Khoshmand 0.62 0.35 0.64 0.33 6.08 2.49 0.01 0.07 0.50 0.26

Gayan 0.84 0.17 0.45 0.37 13.25 8.73 0.05 0.05 0.36 0.26

Gomal 0.49 0.32 0.50 0.61 8.99 5.63 0.01 0.04 0.43 0.52

Jani Khil 0.61 0.36 0.36 0.24 5.19 2.31 0.01 0.03 0.28 0.20

Khir Kot 0.48 0.39 0.13 0.18 3.44 2.71 0.04 0.09 0.14 0.22

Mata Khan 1.28 0.55 0.31 0.27 14.27 7.52 0.00 0.02 0.28 0.24

Nika 0.96 0.37 0.93 0.45 17.20 15.01 0.00 0.00 0.93 0.45

Omna 0.53 0.54 0.41 0.38 4.56 2.05 0.01 0.03 0.38 0.38

Sar Hawza 0.34 0.41 0.49 0.40 5.55 4.69 0.01 0.04 0.46 0.38

Sarobi 0.47 0.18 0.39 0.17 6.71 2.12 0.09 0.10 0.39 0.18

Sharana Markaz 1.25 0.67 0.11 0.24 9.68 5.87 0.01 0.04 0.08 0.15

Urgun 0.81 0.53 0.45 0.31 12.68 7.44 0.01 0.02 0.43 0.31

Waza Khwa 0.18 0.24 0.37 0.35 6.14 1.95 0.00 0.02 0.36 0.34

Wor Mamay 0.44 0.32 0.21 0.29 7.60 3.10 0.07 0.13 0.24 0.26

Yahya Khil 0.99 0.39 0.13 0.10 5.44 3.05 0.00 0.00 0.13 0.10

Yusuf Khil 0.65 0.41 0.16 0.23 3.28 1.32 0.00 0.00 0.15 0.21

Ziruk 1.48 0.97 0.86 0.36 14.21 11.00 0.01 0.07 0.86 0.36

All 0.65 0.55 0.37 0.39 7.83 5.90 0.02 0.07 0.33 0.35

Paktya Adma 0.67 0.39 1.32 0.65 7.18 2.08 0.06 0.09 1.27 0.82

Ahmad Khel 0.85 0.40 0.57 0.34 18.31 6.93 0.04 0.05 0.61 0.34

Chamkani 1.54 0.50 0.61 0.33 20.72 8.31 0.03 0.11 0.64 0.36

Dand Wa Patan 1.82 0.72 0.62 0.32 32.25 13.43 0.07 0.15 0.69 0.40

Dowamanda Shamal 1.08 0.44 0.43 0.40 13.07 4.57 0.00 0.00 0.42 0.41

Gardez 0.72 0.79 0.16 0.24 8.48 6.50 0.02 0.03 0.16 0.20

Jaji 1.06 0.68 0.53 0.37 17.19 13.79 0.07 0.11 0.60 0.42

Jani Khel 2.92 1.91 0.72 0.30 24.21 12.97 0.07 0.13 0.78 0.39

Lija Mangal 0.75 0.47 0.62 0.48 18.06 6.74 0.03 0.06 0.65 0.50

Sayed Karam 1.19 0.41 0.53 0.34 14.33 5.94 0.00 0.01 0.46 0.29

Shwak 0.62 0.26 0.82 0.40 8.61 3.59 0.03 0.07 0.86 0.41

Waza Jadran 0.96 0.41 0.64 0.28 6.60 2.22 0.03 0.06 0.67 0.30

Zurmat 1.59 0.80 0.35 0.47 10.37 5.97 0.02 0.08 0.29 0.42

All 1.33 0.97 0.49 0.42 15.11 10.77 0.03 0.09 0.49 0.44




Animals


170

Parwan Bagram 0.51 0.28 0.23 0.29 2.75 1.36 0.15 0.13 0.16 0.22

Chaharikar 0.27 0.26 0.31 0.26 2.43 1.69 0.11 0.13 0.06 0.24

Hisa Awa Panjshir 1.08 0.76 0.70 0.38 4.35 2.26 0.10 0.11 0.99 0.60Hisa Chaorum Panjshir 0.69 0.46 0.52 0.18 5.40 2.16 0.01 0.01 0.67 0.25

Hisa Doum Panjshir 0.61 0.27 0.23 0.22 4.57 3.49 0.04 0.03 0.40 0.24

Hisa Soum Panjshir 0.71 0.44 0.27 0.29 5.99 2.37 0.13 0.11 0.13 0.15

Jabalusaraj 0.36 0.26 0.32 0.19 3.47 3.40 0.11 0.13 0.21 0.35

Kohisafi 0.27 0.26 0.87 0.77 4.35 3.98 0.05 0.07 0.41 0.40

Salang 0.28 0.22 0.28 0.27 3.37 1.68 0.01 0.02 0.32 0.32

Sayed Khel 0.40 0.21 0.20 0.19 4.31 3.18 0.27 0.22 0.11 0.26

Shikh Ali 0.55 0.35 0.94 0.40 3.02 1.73 0.08 0.10 0.88 0.48

Shinwari 0.35 0.24 1.01 0.62 5.28 3.47 0.16 0.12 0.70 0.43

Shinwari Ghorband 0.33 0.27 1.05 0.73 6.65 11.57 0.18 0.32 1.07 0.61

Siagird Ghorband 0.16 0.14 0.55 0.28 2.67 1.28 0.02 0.04 0.64 0.36

Surkhi Parsa 0.55 0.39 0.87 0.53 3.16 2.11 0.04 0.05 1.05 0.54

All 0.44 0.40 0.49 0.47 3.75 3.90 0.10 0.15 0.44 0.52

Samangan Aybak 0.29 0.22 0.73 0.49 2.86 2.03 0.23 0.19 0.81 0.49

Dara-i-Suf 0.18 0.23 0.76 0.45 0.86 0.67 0.21 0.17 0.80 0.48

Dara-i-Suf Sufla 0.05 0.08 0.64 0.34 0.24 0.32 0.08 0.09 0.64 0.33

Hazrati Sultan 0.41 0.53 1.47 0.69 1.45 1.52 0.08 0.13 1.24 0.56

Kaldar 0.18 0.21 0.56 0.28 3.14 1.64 0.17 0.11 0.55 0.27

Khoram wa Sarbagh 0.20 0.18 0.93 0.47 2.32 1.42 0.23 0.23 1.04 0.53

Khulm 0.20 0.25 0.40 0.39 2.87 2.72 0.08 0.11 0.44 0.38

Ruyi Du Ab 0.49 0.30 1.64 0.54 2.82 1.41 0.24 0.10 1.21 0.45

All 0.18 0.27 0.80 0.55 1.38 1.76 0.14 0.15 0.77 0.48

Sari Pul Balkhab 0.44 0.18 0.86 0.23 3.56 2.05 0.48 0.19 1.28 0.35

Kohistanat 0.02 0.06 1.95 1.22 5.12 4.62 0.09 0.12 0.22 0.31

Sang Charak 0.19 0.18 0.87 0.49 1.28 1.11 0.20 0.15 0.95 0.52

Sari Pul 0.48 0.47 0.84 0.57 2.14 2.17 0.34 0.26 1.04 0.68

Sayed Abad 1.07 1.11 1.48 1.14 2.01 2.32 0.49 0.38 1.72 1.10

Sayyad 0.58 0.84 1.29 1.59 0.87 0.72 0.40 0.42 1.56 1.84

Sozma Qala 0.46 0.44 1.02 0.69 1.22 1.42 0.41 0.38 1.28 0.86

All 0.37 0.48 1.05 0.82 2.30 2.55 0.32 0.28 1.07 0.80




Animals


171

Takhar Baharak 1.01 0.60 1.16 0.45 3.69 1.75 0.63 0.34 1.51 0.59

Bangi 0.39 0.22 0.96 0.39 2.08 1.01 0.30 0.18 1.06 0.47

Cha Aab 0.39 0.32 0.83 0.26 2.45 2.20 0.32 0.24 0.85 0.43

Chal 0.51 0.24 0.74 0.39 1.04 0.67 0.25 0.20 0.83 0.42

Darqad 1.60 0.58 1.04 0.49 3.77 1.83 1.21 0.37 1.90 0.56

Dashte Qala 0.62 0.45 0.70 0.29 2.01 0.95 0.31 0.17 0.77 0.30

Farkhar 0.37 0.14 0.76 0.19 1.42 1.62 0.26 0.15 0.83 0.31

Hazar Somoch 0.28 0.16 1.27 0.57 3.19 1.09 0.37 0.17 1.42 0.50

Ishkamish 0.64 0.46 1.30 0.72 1.34 1.03 0.48 0.33 1.38 0.70

Kalafgan 0.72 0.46 1.56 0.80 4.01 2.62 0.87 0.56 2.15 1.17

Khwaja Bahwaddin 1.31 0.55 1.67 0.42 5.26 2.49 1.34 0.58 2.72 0.87

Khwaja Ghar 0.47 0.20 0.92 0.40 2.59 0.79 0.48 0.19 1.20 0.38

Namak Aab 0.31 0.17 1.14 0.64 3.42 1.77 0.30 0.17 1.18 0.67

Rustaq 0.62 0.50 0.89 1.05 1.84 1.90 0.44 0.46 1.17 1.33

Taluqan 0.91 0.75 0.87 0.73 3.56 4.44 0.71 0.68 1.52 1.14

Warsaj 1.43 1.08 0.59 0.34 2.60 1.68 0.35 0.28 0.92 0.51

Yangi Qala 1.16 1.22 1.51 1.83 4.91 7.84 0.64 0.58 1.79 1.70

All 0.76 0.69 1.00 0.80 2.79 3.19 0.54 0.50 1.33 1.01

Uruzgan Char Cheenah 0.95 0.41 1.06 0.90 9.40 5.81 0.46 0.24 1.17 0.53

Chora 1.00 0.82 1.21 1.14 12.48 18.54 0.69 0.59 1.79 1.06

Day Kundi 0.26 0.26 0.28 0.24 2.04 1.50 0.19 0.18 0.50 0.37

Dihrawud 1.21 0.86 0.65 0.54 13.04 8.21 0.51 0.50 0.94 0.58

Gizab 0.75 0.36 0.84 0.45 5.37 4.28 0.65 0.47 1.24 0.61

Khas Uruzgan 0.93 0.74 1.33 0.78 4.79 2.78 0.77 0.66 1.86 1.12

Kijran 0.85 1.13 0.74 0.42 6.93 9.56 0.62 0.55 1.49 1.09

Shahristan 0.56 0.65 0.51 0.46 4.18 3.39 0.30 0.26 0.71 0.46

Tirin Kot 1.68 1.05 1.51 1.41 18.81 16.47 0.77 0.69 1.91 1.01

All 0.97 0.91 0.92 1.00 9.70 12.50 0.54 0.55 1.28 0.96

Wardak Bihsud Markaz 0.37 0.28 0.46 0.24 1.65 8.23 0.22 0.28 0.67 0.95

Chak 0.34 0.31 0.37 0.22 2.85 1.94 0.06 0.09 0.38 0.25

Day Mirdad 0.36 0.32 0.72 0.33 3.42 2.65 0.15 0.16 0.78 0.38

Hisa Awali Bihsud 0.73 0.48 1.19 0.50 1.87 1.31 0.34 0.33 1.33 0.57

Jaghatu 0.18 0.17 0.46 0.42 3.00 2.28 0.01 0.03 0.44 0.44

Jalrez 0.49 0.33 0.52 0.34 5.30 3.86 0.07 0.12 0.55 0.40

Maydan Shahr 0.57 0.36 0.21 0.18 5.38 2.95 0.11 0.14 0.29 0.24

Nirkh 0.31 0.33 0.24 0.30 5.41 3.85 0.04 0.12 0.25 0.32

Sayed Abad 0.27 0.19 0.27 0.18 3.05 2.08 0.01 0.03 0.16 0.18

All 0.39 0.35 0.48 0.43 3.32 4.09 0.11 0.21 0.52 0.60




Animals


172

Zabul Arghandab 0.38 0.51 0.66 0.88 16.13 19.63 0.13 0.19 0.69 0.86

Atghar 0.27 0.24 0.78 0.47 4.52 2.11 0.09 0.16 0.83 0.47

Daychopan 0.30 0.33 0.55 0.40 2.92 3.90 0.17 0.20 0.61 0.44

Khaki Afghan 0.44 0.47 0.57 0.35 5.37 7.26 0.19 0.28 0.66 0.57

Mizan 0.25 0.25 0.52 0.79 3.45 2.16 0.14 0.18 0.60 0.63

Nawbahar 0.20 0.24 0.68 0.45 5.41 2.36 0.04 0.08 0.43 0.26

Qalat 0.42 0.54 0.80 0.74 6.89 5.04 0.15 0.25 0.86 0.67

Seyouray 0.21 0.32 0.68 0.67 7.94 4.37 0.27 0.31 0.94 0.67

Shahjoy 0.44 0.41 0.45 0.36 8.48 4.50 0.01 0.04 0.42 0.36

Shahr Safa 0.59 0.40 0.64 0.72 6.07 3.66 0.01 0.06 0.46 0.54

Shamulzayi 0.06 0.17 0.56 0.38 5.17 2.74 0.02 0.06 0.49 0.38

Shinkay 0.39 1.00 0.82 0.62 3.45 2.79 0.08 0.12 0.62 0.53

All 0.32 0.45 0.62 0.60 6.75 8.30 0.10 0.19 0.62 0.58

Afghanistan All 0.68 1.12 0.64 0.67 5.87 7.88 0.22 0.38 0.75 0.78




Animals


173

Table A3 Summary of forage use and feeding situationMaize

ProductionMaize

PurchasedSorghum

ProductionSorghum Purchased

Millet Production

Millet Purchased

Region AnswerNumber

Respondents %Number

Respondents %Number

Respondents %Number

Respondents %Number

Respondents %Number

Respondents %

East Total 251 251 251 251 251 251

No 157 62.55 213 84.86 245 97.61 250 99.60 251 100 248 98.80

Yes 94 37.45 38 15.14 6 2.39 1 0.40 0 0 3 1.20

Centre Total 205 205 205 205 205 205

East No 98 47.80 137 66.83 200 97.56 194 94.63 192 93.66 199 97.07

Yes 107 52.20 68 33.17 5 2.44 11 5.37 13 6.34 6 2.93

Centre Total 212 212 212 212 212 212

No 194 91.51 155 73.11 210 99.06 210 99.06 206 97.17 211 99.53

Yes 18 8.49 57 26.89 2 0.94 2 0.94 6 2.83 1 0.47

North Total 339 339 339 339 339 339

No 295 87.02 266 78.47 313 92.33 327 96.46 339 100 338 99.71

Yes 44 12.98 73 21.53 26 7.67 12 3.54 0 0 1 0.29

West Total 278 278 278 278 278 278

No 228 82.01 257 92.45 278 100 276 99.28 276 99.28 276 99.28

Yes 50 17.99 21 7.55 0 0 2 0.72 2 0.72 2 0.72

Total Total 1285 1285 1285 1285 1285 1285

No 972 75.64 1028 80.00 1246 96.96 1257 97.82 1264 98.37 1272 98.99

Yes 313 24.36 257 20.00 39 3.04 28 2.18 21 1.63 13 1.01

Table A3 Summary of forage use and feeding situationShaftal

ProductionShaftal

PurchasedLucerne

ProductionLucerne

PurchasedWheat

ProductionWheat

Purchased

Region AnswerNumber

Respondents %Number

Respondents %Number

Respondents %Number

Respondents %Number

Respondents %Number

Respondents %

East Total 251 251 251 251 251 251

No 89 35.46 226 90.04 224 89.24 234 93.23 71 28.29 149 59.36

Yes 162 64.54 25 9.96 27 10.76 17 6.77 180 71.71 102 40.64

Centre Total 205 205 205 205 205 205

East No 73 35.61 128 62.44 69 33.66 111 54.15 23 11.22 133 64.88

Yes 132 64.39 77 37.56 136 66.34 94 45.85 182 88.78 72 35.12

Centre Total 212 212 212 212 212 212

No 146 68.87 162 76.42 108 50.94 139 65.57 69 32.55 121 57.08

Yes 66 31.13 50 23.58 104 49.06 73 34.43 143 67.45 91 42.92

North Total 339 339 339 339 339 339

No 312 92.04 306 90.27 264 77.88 284 83.78 82 24.19 189 55.75

Yes 27 7.96 33 9.73 75 22.12 55 16.22 257 75.81 150 44.25

West Total 278 278 278 278 278 278

No 144 51.80 238 85.61 128 46.04 210 75.54 70 25.18 170 61.15

Yes 134 48.20 40 14.39 150 53.96 68 24.46 208 74.82 108 38.85

Total Total 1285 1285 1285 1285 1285 1285

No 764 59.46 1060 82.49 793 61.71 978 76.11 315 24.51 762 59.30

Yes 521 40.54 225 17.51 492 38.29 307 23.89 970 75.49 523 40.70

8.3. TABULAR SUMMARY STATISTICS LEVEL 2

174

Table A3 Summary of forage use and feeding situation

Hay Production

Hay Purchased

Wheat Bran Production

Wheat Bran Purchased

Cotton Seed CakeProduction

Cotton Seed CakePurchased

Region AnswerNumber

Respondents %Number

Respondents %Number

Respondents %Number

Respondents %Number

Respondents %Number

Respondents %

East Total 251 251 251 251 251 251

No 238 94.82 243 96.81 233 92.83 198 78.88 250 99.60 108 43.03

Yes 13 5.18 8 3.19 18 7.17 53 21.12 1 0.40 143 56.97

Centre Total 205 205 205 205 205 205

East No 197 96.10 198 96.59 184 89.76 37 18.05 199 97.07 28 13.66

Yes 8 3.90 7 3.41 21 10.24 168 81.95 6 2.93 177 86.34

Centre Total 212 212 212 212 212 212

No 131 61.79 190 89.62 201 94.81 134 63.21 208 98.11 101 47.64

Yes 81 38.21 22 10.38 11 5.19 78 36.79 4 1.89 111 52.36

North Total 339 339 339 339 339 339

No 253 74.63 302 89.09 247 72.86 209 61.65 327 96.46 57 16.81

Yes 86 25.37 37 10.91 92 27.14 130 38.35 12 3.54 282 83.19

West Total 278 278 278 278 278 278

No 203 73.02 272 97.84 262 94.24 260 93.53 275 98.92 261 93.88

Yes 75 26.98 6 2.16 16 5.76 18 6.47 3 1.08 17 6.12

Total Total 1285 1285 1285 1285 1285 1285

No 1022 79.53 1205 93.77 1127 87.70 838 65.21 1259 97.98 555 43.19

Yes 263 20.47 80 6.23 158 12.30 447 34.79 26 2.02 730 56.81

Table A3 Summary of forage use and feeding situation

Maize Stover Own Production

Maize Stover Purchased

Barley Own Production Barley Purchased

Region AnswerNumber

Respondents %Number

Respondents %Number

Respondents %Number

Respondents %East Total 251 251 251 251

No 109 43.43 180 71.71 250 99.60 231 92.03Yes 142 56.57 71 28.29 1 0.40 20 7.97

Centre Total 205 205 205 205East No 106 51.71 193 94.15 202 98.54 190 92.68

Yes 99 48.29 12 5.85 3 1.46 15 7.32

Centre Total 212 212 212 212

No 193 91.04 207 97.64 205 96.70 199 93.87

Yes 19 8.96 5 2.36 7 3.30 13 6.13

North Total 339 339 339 339

No 285 84.07 319 94.10 332 97.94 248 73.16

Yes 54 15.93 20 5.90 7 2.06 91 26.84

West Total 278 278 278 125

No 219 78.78 273 98.20 225 80.94 28 22.40

Yes 59 21.22 5 1.80 53 19.06 97 77.60

Total Total 1285 1285 1285 1285

No 912 70.97 1172 91.21 1214 94.47 1049 81.63Yes 373 29.03 113 8.79 71 5.53 236 18.37

175

Table A4 General aspects of the feeding situation Enough Feed Pasture Sufficent

2001-2002 2002-2003 2001-2002 2002-2003

Region AnswerNumber

Respondents %Number

Respondents %Number

Respondents %Number

Respondents %

East Total 251 251 251 251

No 139 55.38 137 54.58 210 83.67 206 82.07

Yes 112 44.62 114 45.42 41 16.33 45 17.93

Centre Total 205 205 205 205

East No 156 76.10 150 73.17 192 93.66 194 94.63

Yes 49 23.90 55 26.83 13 6.34 11 5.37

Centre Total 212 212 212 212

No 176 83.02 158 74.53 149 70.28 149 70.28

Yes 36 16.98 54 25.47 63 29.72 63 29.72

North Total 339 339 339 339

No 327 96.46 177 52.21 300 88.50 215 63.42

Yes 12 3.54 162 47.79 39 11.50 124 36.58

West Total 278 278 278 278

No 264 94.96 162 58.27 259 93.17 183 65.83

Yes 14 5.04 116 41.73 19 6.83 95 34.17

Total Total 1285 1285 1285 1285

No 1062 82.65 784 61.01 1110 86.38 947 73.70

Yes 223 17.35 501 38.99 175 13.62 338 26.30

Table A4 General aspects of the feeding situation

Bought Supplements Produced Supplements Price for Supplements Up

2001-2002 2002-2003 2001-2002 2002-2003 2001-2002 2002-2003

Region AnswerNumber

Respondents %Number

%Number

%Number

%Number

%Number

%

East Total 251 251 251 251 251 251

No 66 26.29 68 27.09 187 74.50 183 72.91 48 19.12 64 25.50

Yes 185 73.71 183 72.91 64 25.50 68 27.09 203 80.88 187 74.50

Centre Total 205 205 205 205 205 205

East No 13 6.34 27 13.17 174 84.88 164 80.00 10 4.88 12 5.85

Yes 192 93.66 178 86.83 31 15.12 41 20.00 195 95.12 193 94.15

Centre Total 212 212 212 212 212 212

No 30 14.15 41 19.34 192 90.57 186 87.74 27 12.74 56 26.42

Yes 182 85.85 171 80.66 20 9.43 26 12.26 185 87.26 156 73.58

North Total 339 339 339 339 339 339

No 145 42.77 241 71.09 322 94.99 290 85.55 13 3.83 281 82.89

Yes 194 57.23 98 28.91 17 5.01 49 14.45 326 96.17 58 17.11

West Total 278 278 278 278 278 278

No 107 38.49 147 52.88 272 97.84 234 84.17 41 14.75 157 56.47

Yes 171 61.51 131 47.12 6 2.16 44 15.83 237 85.25 121 43.53

Total Total 1285 1285 1285 1285 1285 1285

No 361 28.09 524 40.78 1147 89.26 1057 82.26 139 10.82 570 44.36

Yes 924 71.91 761 59.22 138 10.74 228 17.74 1146 89.18 715 55.64

Respondents Respondents Respondents Respondents Respondents

176

Table A6 Monthly distribution of begin and end of pasture period by regions (number ofrespondents)

Begin of Pasture Period by Region End of Pasture Period by Region

Month East Centre-East

Centre North West All East Centre-East

Centre North West All

January 0 0 1 5 0 6 0 0 0 0 5 5

February 0 0 1 174 85 260 76 0 3 17 16 112

March 132 136 121 35 118 542 9 0 2 10 5 26

April 2 25 50 0 0 77 4 0 14 28 8 54

May 1 0 0 9 1 11 12 8 40 52 36 148

June 0 1 1 0 0 2 2 19 8 6 17 52

July 0 0 0 1 0 1 0 9 1 1 1 12

August 5 0 1 3 5 14 4 12 1 17 0 34

September 9 1 1 1 5 17 19 28 39 38 22 146

October 8 0 0 5 0 13 15 46 32 58 37 188

November 0 0 0 0 0 0 16 42 36 14 68 176

December 0 0 0 7 0 7 0 0 0 0 0 0

All 157 163 176 240 214 950 157 164 176 241 215 953

Table A5 Monthly distribution of lambing and kidding by regions (number of respondents)

Lambing by Region Kidding by Region




January 1 0 1 3 48 53 15 5 2 16 52 90

February 35 30 67 221 141 494 33 21 52 186 151 443

March 61 111 63 21 41 297 58 85 78 37 37 295

April 1 1 0 0 0 2 1 1 0 1 0 3

May 0 0 0 1 0 1 0 0 0 0 0 0

June 0 0 0 0 0 0 0 0 0 0 0 0

July 0 0 0 0 0 0 0 0 0 0 0 0

August 0 0 0 0 0 0 0 0 0 0 0 0

September 0 0 0 0 0 0 0 1 0 0 4 5

October 0 0 0 0 0 0 0 0 0 2 2 4

November 0 0 0 0 1 1 0 0 0 0 0 0

December 2 0 0 3 0 5 1 0 0 5 0 6

All 100 142 131 249 231 853 108 113 132 247 246 846

177

Table A7 Monthly distribution of begin and end of stubble grazing by regions (number of respondents)

Begin of Stubble Grazing by Region End of Stubble Grazing by Region




January 0 0 0 0 0 0 0 0 0 0 0 0

February 0 0 0 4 0 4 0 0 0 0 0 0

March 1 1 0 1 1 4 0 0 0 0 0 0

April 0 6 2 2 16 26 0 4 0 1 0 5

May 58 10 17 167 98 350 3 1 0 0 1 5

June 26 109 32 98 146 411 63 7 21 108 104 303

July 4 63 93 0 9 169 5 90 57 51 112 315

August 0 0 11 0 11 2 33 23 45 38 141

September 41 0 20 0 61 13 33 67 51 11 175

October 15 0 0 0 15 29 13 5 14 3 64

November 0 0 0 0 0 26 6 0 2 1 35

December 0 0 0 0 0 0 1 0 0 0 1

All 145 189 175 272 270 1051 141 188 173 272 270 1044

Table A8 Monthly distribution of begin and end of feed supplementation by regions (number of respondents)

Begin of Feed Supplementation by Region End of Feed Supplementation by Region




January 14 1 27 0 1 43 0 0 2 105 0 107

February 6 0 1 6 0 13 68 59 50 91 140 408

March 32 86 32 0 0 150 20 30 87 38 26 201

April 0 0 0 0 0 0 21 3 4 1 1 30

May 1 0 0 9 0 10 1 0 0 0 0 1

June 1 4 0 2 1 8 1 0 0 0 1 2

July 0 0 0 0 0 0 0 1 0 1 0 2

August 0 2 1 0 1 4 0 1 0 0 0 1

September 22 12 36 19 23 112 1 21 2 12 1 37

October 17 25 27 42 15 126 0 19 2 2 0 23

November 10 6 14 167 129 326 0 4 3 2 1 10

December 9 1 13 9 4 36 0 0 0 2 4 6

All 112 137 151 254 174 828 112 138 150 254 174 828

178

Table A10 Monthly distribution of selli ng goats and best time for selling by regions(number of respondents)

Selling goats by regions Best time for selling by regions




January 24 0 0 8 0 32 48 2 0 1 0 51

February 4 1 0 95 20 120 14 3 4 30 7 58

March 76 47 68 87 7 285 5 4 7 22 3 41

April 7 1 2 16 1 27 1 1 0 7 0 9

May 3 5 0 32 4 44 3 1 1 44 4 53

June 0 0 6 0 6 1 2 15 3 21

July 0 0 0 0 0 0 0 17 0 17

August 1 0 1 1 3 6 2 1 2 56 6 67

September 32 41 75 7 203 358 27 97 131 64 210 529

October 9 12 2 19 12 54 18 49 8 27 16 118

November 11 0 0 6 17 11 31 2 17 7 68

December 1 0 0 0 0 1 0 0 1 1 2

All 157 118 148 271 256 950 129 190 157 301 257 1034

Table A9 Monthly distribution of selling cattle and sheep by regions (number of respondents)

Selling Cattle by Regions Selling Sheep by Regions




January 80 0 0 1 0 81 28 0 0 8 0 36

February 11 6 3 109 39 168 4 4 2 108 21 139

March 107 119 92 92 20 430 81 35 48 89 6 259

April 7 10 2 12 2 33 7 2 1 18 1 29

May 1 0 0 0 1 4 3 1 30 5 43

June 1 1 0 2 0 4 0 1 7 0 8

July 0 0 1 0 1 0 0 0 0 0

August 1 1 40 0 42 1 1 2 1 3 8

September 4 24 75 35 157 295 39 50 100 15 194 398

October 4 17 1 3 9 34 7 34 11 1 14 67

November 10 0 11 2 23 15 1 0 3 19

December 1 0 0 1 0 2 1 0 0 0 0 1

All 215 189 174 307 229 1114 172 144 167 277 247 1007

179

Table A11 Work responsibility – Cattle feeding (percentages of respondents by district)

Province District No Response


Womenand

Children

Total Respondents

Badakhshan Argo 17.07 75.61 7.32 123

Baharak 85.61 11.51 2.88 139

All 53.44 41.60 4.96 262

Balkh Chimtal 0.93 56.07 38.79 3.27 0.47 0.47 214

Dihdadi 0.81 75.61 17.89 4.88 0.81 123

Nahri Shahi 0.95 73.33 25.71 105

All 0.90 65.61 29.86 2.94 0.45 0.23 442

Kabul Bagrami 60.84 26.51 12.65 166

Musayi 50.75 8.96 40.30 134

Paghman 92.23 3.88 3.88 103

All 65.51 14.89 19.60 403

Kandahar Arghandab 100 58

Daman 100 79

Dand 100 39

Panjwayi 98.82 1.18 85

Zhiray 2.44 95.12 2.44 41

All 0.33 302

Logar Puli Alam 1.54 49.23 16.15 33.08 130

Nangarhar Bati Kot 1.12 75.84 16.29 6.18 0.56 178

Bihsud 82.87 12.35 4.78 251

Shewa 59.22 27.93 12.29 0.56 179

All 0.33 608

Parwan Chaharikar 1.62 50.27 18.38 29.73 185

Tutum Dara 1.14 76.70 11.36 10.80 176

All 1.39 63.16 14.96 20.50 361

Overall Average/Total 0.56 69.14 19.46 10.65 0.12 0.08 2508

99.01 0.66

73.85 18.09 7.40 0.16 0.16

8.4. TABULAR SUMMARY STATISTICS WOMEN LIFESTOCK SURVEY

180

Table A12 Work responsibility – Cattle grazing (percentages of respondents by district )



Womenand

Children

Total Respondents

Badakhshan Argo 1.63 82.11 16.26 123

Baharak 9.35 6.47 84.17 139

All 5.73 41.98 52.29 262

Balkh Chimtal 1.87 24.77 52.80 20.09 0.47 214

Dihdadi 0.81 21.14 41.46 35.77 0.81 123

Nahri Shahi 1.90 27.62 36.19 34.29 105

All 1.58 24.43 45.70 27.83 0.23 0.23 442

Kabul Bagrami 8.43 6.02 26.51 59.04 166

Musayi 10.45 2.24 5.97 81.34 134

Paghman 1.94 33.98 3.88 60.19 103

All 7.44 11.91 13.90 66.75 403

Kandahar Arghandab 3.45 18.97 77.59 58

Daman 2.53 20.25 77.22 79

Dand 17.95 82.05 39

Panjwayi 1.18 25.88 72.94 85

Zhiray 2.44 24.39 73.17 41

All 0.33 1.66 21.85 76.16 302

Logar Puli Alam 10.00 20.77 3.85 65.38 130

Nangarhar Bati Kot 63.48 7.30 10.67 18.54 178

Bihsud 58.17 3.59 9.56 28.29 251

Shewa 53.07 2.23 10.06 34.64 179

All 58.22 4.28 10.03 27.30 0.16 608


Tutum Dara 1.70 9.09 11.36 77.84 176

All 1.66 16.07 13.57 68.70 361


181

Table A13 Work responsibility – Watering cattle (percentages of respondents by district )



Womenand

Children

Total Respondents

Badakhshan Argo 4.07 80.49 15.45 123

Baharak 47.48 4.32 48.20 139

All 27.10 40.08 32.82 262

Balkh Chimtal 0.93 57.01 38.32 2.80 0.93 214

Dihdadi 0.81 76.42 17.07 5.69 123

Nahri Shahi 0.95 71.43 26.67 0.95 105

All 0.90 65.84 29.64 3.17 0.45 442

Kabul Bagrami 0.60 54.82 22.89 21.69 166

Musayi 44.03 8.96 47.01 134

Paghman 76.70 3.88 19.42 103

All 0.25 56.82 13.40 29.53 403

Kandahar Arghandab 91.38 8.62 58

Daman 1.27 91.14 7.59 79

Dand 87.18 7.69 5.13 39

Panjwayi 87.06 5.88 7.06 85

Zhiray 4.88 85.37 4.88 4.88 41

All 0.99 88.74 5.30 4.97 302

Logar Puli Alam 0.77 41.54 6.92 50.77 130


Bihsud 0.80 80.48 10.36 7.97 0.40 251

Shewa 61.45 25.70 12.85 179

All 0.66 75.33 14.64 9.05 0.16 0.16 608


Tutum Dara 1.14 71.02 9.66 18.18 176

All 1.39 60.11 12.47 26.04

361


182

Table A14 Work responsibility – Tending young cattle (percentages of respondents by district)

Province DistrictNo

ResponseWomen Men Children

Womenand Men

Womenand

Children

Total Respondents

Badakhshan Argo 8.13 86.99 4.07 0.81 123

Baharak 98.56 1.44 139

All 3.82 93.13 2.67 0.38 262

Balkh Chimtal 14.49 60.28 25.23 214

Dihdadi 5.69 79.67 12.20 2.44 123

Nahri Shahi 6.67 81.90 11.43 105

All 10.18 70.81 18.33 0.68 442

Kabul Bagrami 3.01 94.58 1.20 0.60 0.60 166

Musayi 1.49 96.27 0.75 1.49 134

Paghman 4.85 95.15 103

All 2.98 95.29 0.74 0.74 0.25 403


Daman 2.53 96.20 1.27 79

Dand 2.56 97.44 39

Panjwayi 2.35 97.65 85

Zhiray 4.88 95.12 41

All 2.65 97.02 0.33 302

Logar Puli Alam 20.00 61.54 3.08 15.38 130


Bihsud 5.58 90.84 3.19 0.40 251

Shewa 7.26 83.24 6.15 3.35 179

All 4.77 90.30 3.45 1.32 0.16 608


Tutum Dara 58.52 39.20 1.14 1.14 176

All 41.55 52.63 1.39 4.43 361

Overall Average/Total

11.16 81.86 4.86 2.03 0.04 0.04 2508

183

Table A15 Work responsibility – Milking cattle (percentages of respondents by district )

Province District No Response Women Men Children Womenand Men

Total Respondents

Badakhshan Argo 7.32 91.87 0.81 123

Baharak 98.56 1.44 139

All 3.44 95.42 1.15 262

Balkh Chimtal 16.82 71.50 11.68 214

Dihdadi 8.94 87.80 2.44 0.81 123

Nahri Shahi 7.62 90.48 1.90 105

All 12.44 80.54 6.79 0.23 442

Kabul Bagrami 6.63 92.77 0.60 166

Musayi 0.75 99.25 134

Paghman 9.71 90.29 103

All 5.46 94.29 0.25 403


Daman 10.13 89.87 79

Dand 17.95 82.05 39

Panjwayi 17.65 82.35 85

Zhiray 9.76 90.24 41

All 13.25 86.75 302

Logar Puli Alam 11.54 86.92 0.77 0.77 130

Nangarhar Bati Kot 2.81 97.19 178

Bihsud 6.37 88.45 4.78 0.40 251

Shewa 10.61 89.39 179

All 6.58 91.28 1.97 0.16 608

Parwan Chaharikar 21.62 78.38 185

Tutum Dara 72.16 27.84 176

All 46.26 53.74 361

Overall Average/Total 13.88 84.13 1.87 0.04 0.08 2508

184

Table A16 Work responsibility – Treating cattle (percentages of respondents by district )



Men and Children

Total Respondents

Badakhshan Argo 19.51 78.86 1.63 123

Baharak 25.90 73.38 0.72 139

All 22.90 75.95 1.15 262

Balkh Chimtal 1.40 10.28 87.85 0.47 214

Dihdadi 0.81 8.94 87.80 1.63 0.81 123

Nahri Shahi 0.95 14.29 83.81 0.95 105

All 1.13 10.86 86.88 0.68 0.45 442

Kabul Bagrami 14.46 81.93 3.61 166

Musayi 0.75 9.70 88.06 1.49 134

Paghman 0.97 7.77 91.26 103

All 0.50 11.17 86.35 1.99 403


Daman 88.61 11.39 79

Dand 92.31 7.69 39

Panjwayi 84.71 15.29 85

Zhiray 2.44 87.80 9.76 41

All 0.66 88.41 10.93 302

Logar Puli Alam 3.08 36.92 60.00 130


Bihsud 0.80 5.18 92.83 0.80 0.40 251

Shewa 0.56 1.68 95.53 2.23 179

All 1.32 4.61 92.76 1.15 0.16 608

Parwan Chaharikar 1.62 12.43 77.30 8.11 0.54 185

Tutum Dara 1.14 28.98 67.05 2.27 0.57 176

All 1.39 20.50 72.30 5.26 0.28 0.28 361


185

Table A17 Work responsibility – Feeding sheep (percentages of respondents by district )

Province DistrictNo


and Men

Womenand

Children

Menand

Children

Total Respondents


Baharak 87.88 10.61 1.52 66

All 38.76 56.74 4.49 178

Balkh Chimtal 4.35 32.17 59.13 4.35 115

Dihdadi 20.59 52.94 23.53 2.94 34

Nahri Shahi 9.68 48.39 38.71 3.23 31

All 8.33 38.89 48.89 3.89 180

Kabul Bagrami 48.39 25.81 25.81 31

Musayi 4.35 21.74 8.70 60.87 4.35 23

Paghman 82.98 8.51 8.51 47

All 0.99 58.42 13.86 25.74 0.99 101


Daman 1.67 93.33 3.33 1.67 60

Dand 100.00 31

Panjwayi 96.05 1.32 2.63 76

Zhiray 86.21 6.90 3.45 3.45 29

All 0.41 94.67 1.23 2.87 0.41 0.41 244

Logar Puli Alam 41.67 11.67 46.67 60


Bihsud 11.11 77.78 11.11 9

Shewa 56.52 26.09 17.39 23

All 6.38 57.45 19.15 14.89 2.13 47


Tutum Dara 8.11 75.68 8.11 8.11 37

All 9.80 64.71 13.73 11.76 51


2.90 59.70 26.60 10.34 0.23 0.12 0.12 861

186

Table A18 Work responsibility – Grazing sheep (percentages of respondents by district)

Province DistrictNo

ResponseWomen Men Children

Men and Children

Total Respondents

Badakhshan Argo 0.89 86.61 12.50 112

Baharak 6.06 7.58 86.36 66

All 2.81 57.30 39.89 178

Balkh Chimtal 4.35 16.52 68.70 9.57 0.87 115

Dihdadi 20.59 17.65 32.35 29.41 34

Nahri Shahi 16.13 19.35 38.71 25.81 31

All 9.44 17.22 56.67 16.11 0.56 180

Kabul Bagrami 6.45 29.03 64.52 31

Musayi 17.39 4.35 13.04 65.22 23

Paghman 2.13 31.91 12.77 53.19 47

All 6.93 15.84 17.82 59.41 101


Daman 1.67 1.67 16.67 80.00 60

Dand 0.00 12.90 87.10 31

Panjwayi 1.32 23.68 75.00 76

Zhiray 6.90 10.34 82.76 29

All 1.64 0.82 15.98 81.56 244

Logar Puli Alam 3.33 13.33 5.00 78.33 60

Nangarhar Bati Kot 40.00 6.67 53.33 15

Bihsud 66.67 33.33 9

Shewa 65.22 8.70 26.09 23

All 57.45 6.38 36.17 47


Tutum Dara 8.11 8.11 13.51 70.27 37

All 9.80 7.84 19.61 62.75 51


7.20 7.67 32.17 52.85 0.12 861

187

Table A19 Work responsibility – Watering sheep(percentages of respondents by district )

Province District No Response Women Men Children Total Respondents

Badakhshan Argo 1.79 86.61 11.61 112

Baharak 40.91 7.58 51.52 66

All 16.29 57.30 26.40 178

Balkh Chimtal 5.22 32.17 58.26 4.35 115

Dihdadi 20.59 50.00 23.53 5.88 34

Nahri Shahi 12.90 45.16 38.71 3.23 31

All 9.44 37.78 48.33 4.44 180

Kabul Bagrami 48.39 25.81 25.81 31

Musayi 4.35 13.04 13.04 69.57 23

Paghman 68.09 6.38 25.53 47

All 0.99 49.50 13.86 35.64 101


Daman 1.67 91.67 3.33 3.33 60

Dand 93.55 3.23 3.23 31

Panjwayi 85.53 3.95 10.53 76

Zhiray 86.21 0.00 13.79 29

All 0.41 88.52 2.46 8.61 244

Logar Puli Alam 30.00 6.67 63.33 60


Bihsud 11.11 77.78 11.11 9

Shewa 56.52 21.74 21.74 23

All 6.38 59.57 14.89 19.15 47


Tutum Dara 8.11 72.97 8.11 10.81 37

All 9.80 62.75 13.73 13.73 51


3.14 51.22 26.36 19.28 861

188

Table A20 Work responsibility – Tending young sheep(percentages of respondents by district )

Province District No Response Women Men Children Women and

ChildrenTotal

Respondents

Badakhshan Argo 0.89 27.68 51.79 19.64 112

Baharak 96.97 3.03 66

All 0.56 53.37 33.71 12.36 178

Balkh Chimtal 6.09 40.87 52.17 0.87 115

Dihdadi 20.59 55.88 20.59 2.94 34

Nahri Shahi 12.90 58.06 25.81 3.23 31

All 10.00 46.67 41.67 1.67 180

Kabul Bagrami 96.77 3.23 31

Musayi 8.70 91.30 23

Paghman 42.55 53.19 2.13 2.13 47

All 21.78 75.25 0.99 0.99 0.99 101


Daman 23.33 71.67 5.00 60

Dand 35.48 64.52 31

Panjwayi 23.68 76.32 76

Zhiray 31.03 68.97 29

All 26.64 72.13 1.23 244

Logar Puli Alam 28.33 48.33 1.67 21.67 60


Bihsud 33.33 66.67 9

Shewa 26.09 69.57 4.35 23

All 34.04 63.83 2.13 47

Parwan Chaharikar 50.00 42.86 7.14 14

Tutum Dara 78.38 21.62 37

All 70.59 27.45 1.96 51


20.33 58.54 16.49 4.53 0.12 861

189

Table A21 Work responsibility – Milking sheep (Percentages of Respondents by District)

Province DistrictNo

ResponseWomen Men Children Women

and MenTotal

Respondents


Baharak 96.97 3.03 66

All 0.56 97.19 2.25 178

Balkh Chimtal 22.61 53.91 23.48 115

Dihdadi 26.47 58.82 8.82 5.88 34

Nahri Shahi 16.13 61.29 19.35 3.23 31

All 22.22 56.11 20.00 0.56 1.11 180


Musayi 8.70 91.30 23

Paghman 36.17 61.70 2.13 47

All 20.79 78.22 0.99 101


Daman 76.67 21.67 1.67 60

Dand 77.42 22.58 31

Panjwayi 69.74 30.26 76

Zhiray 68.97 31.03 29

All 73.36 26.23 0.41 244

Logar Puli Alam 28.33 71.67 60


Bihsud 44.44 55.56 9

Shewa 52.17 47.83 23

All 57.45 42.55 47


Tutum Dara 78.38 21.62 37

All 70.59 29.41 51


190

Table A22 Work responsibility – Treating sheep (percentages of respondents by district)

Province District No Response Women Men Children Total

Respondents

Badakhshan Argo 22.32 76.79 0.89 112

Baharak 1.52 19.70 78.79 66

All 0.56 21.35 77.53 0.56 178

Balkh Chimtal 4.35 10.43 85.22 115

Dihdadi 26.47 5.88 67.65 34

Nahri Shahi 12.90 16.13 67.74 3.23 31

All 10.00 10.56 78.89 0.56 180

Kabul Bagrami 25.81 67.74 6.45 31

Musayi 4.35 4.35 86.96 4.35 23

Paghman 14.89 82.98 2.13 47

All 0.99 15.84 79.21 3.96 101


Daman 1.67 86.67 11.67 60

Dand 90.32 9.68 31

Panjwayi 88.16 11.84 76

Zhiray 3.45 79.31 17.24 29

All 0.82 88.11 11.07 244

Logar Puli Alam 3.33 33.33 63.33 60


Bihsud 11.11 88.89 9

Shewa 4.35 4.35 86.96 4.35 23

All 8.51 4.26 82.98 4.26 47


Tutum Dara 8.11 48.65 43.24 37

All 9.80 37.25 50.98 1.96 51

OverallAverage/Total

3.83 38.21 56.91 1.05 861

191

Table A23 Work responsibility – Feeding goats (percentages of respondents by district)


Women Men ChildrenWomen

and Children

Men and Children

Total Respondents

Badakhshan Argo 33.02 59.43 7.55 106

Baharak 1.05 88.42 8.42 2.11 95

All 0.50 59.20 35.32 4.98 201

Balkh Chimtal 5.00 40.83 50.00 3.33 0.83 120

Dihdadi 10.00 55.00 25.00 10.00 20

Nahri Shahi 12.50 56.25 25.00 6.25 16

All 6.41 44.23 44.23 4.49 0.64 156

Kabul Bagrami 14.29 50.00 14.29 21.43 14

Musayi 9.52 33.33 9.52 42.86 4.76 21

Paghman 11.11 77.78 7.41 3.70 27

All 11.29 56.45 9.68 20.97 1.61 62


Daman 87.50 3.13 9.38 32

Dand 100 14

Panjwayi 100 30

Zhiray 16.67 75.00 8.33 12

All 1.80 92.79 1.80 3.60 111

Logar Puli Alam 15.38 46.15 15.38 23.08 13


Bihsud 7.69 86.15 6.15 65

Shewa 5.41 62.16 24.32 8.11 37

All 7.94 77.78 7.14 7.14 126


Tutum Dara 14.29 64.29 7.14 14.29 14

All 25.00 50.00 10.00 15.00 20


192

Table A24 Work responsibility – Grazing goats (percentages of respondents by district)


Women Men Children Men and Children

Total Respondents

Badakhshan Argo 5.66 67.92 26.42 106

Baharak 1.05 6.32 7.37 85.26 95

All 0.50 5.97 39.30 54.23 201

Balkh Chimtal 5.00 23.33 60.83 10.83 120

Dihdadi 10.00 25.00 40.00 25.00 20

Nahri Shahi 12.50 25.00 31.25 31.25 16

All 6.41 23.72 55.13 14.74 156

Kabul Bagrami 14.29 7.14 7.14 71.43 14

Musayi 14.29 9.52 9.52 66.67 21

Paghman 11.11 29.63 11.11 48.15 27

All 12.90 17.74 9.68 59.68 62


Daman 6.25 6.25 87.50 32

Dand 7.14 7.14 85.71 14

Panjwayi 3.33 6.67 90.00 30

Zhiray 16.67 8.33 75.00 12

All 2.70 2.70 7.21 87.39 111

Logar Puli Alam 15.38 30.77 7.69 46.15 13


Bihsud 60.00 1.54 4.62 33.85 65

Shewa 62.16 5.41 5.41 24.32 2.70 37

All 60.32 3.97 4.76 30.16 0.79 126


Tutum Dara 14.29 7.14 28.57 50.00 14

All 25.00 10.00 25.00 40.00 20


193

Table A25 Work responsibility – Watering goats (percentages of respondents by district )


Women Men Children Men and Children

Total Respondents

Badakhshan Argo 5.66 68.87 25.47 106

Baharak 1.05 45.26 6.32 47.37 95

All 0.50 24.38 39.30 35.82 201

Balkh Chimtal 5.00 42.50 49.17 3.33 120

Dihdadi 10.00 50.00 25.00 15.00 20

Nahri Shahi 12.50 56.25 25.00 6.25 16

All 6.41 44.87 43.59 5.13 156

Kabul Bagrami 14.29 64.29 7.14 14.29 14

Musayi 9.52 38.10 14.29 38.10 21

Paghman 11.11 70.37 7.41 11.11 27

All 11.29 58.06 9.68 20.97 62


Daman 84.38 3.13 12.50 32

Dand 100 14

Panjwayi 93.33 6.67 30

Zhiray 16.67 58.33 8.33 16.67 12

All 1.80 85.59 1.80 10.81 111

Logar Puli Alam 15.38 38.46 7.69 38.46 13


Bihsud 7.69 80.00 12.31 65

Shewa 5.41 64.86 16.22 10.81 2.70 37

All 7.94 73.81 4.76 12.70 0.79 126


Tutum Dara 14.29 57.14 7.14 21.43 14

All 25.00 45.00 10.00 20.00 20


194

Table A26 Work responsibility – Tending young goats(percentages of respondents by district )


Badakhshan Argo 51.89 23.58 24.53 106

Baharak 1.05 95.79 3.16 95

All 0.50 72.64 13.93 12.94 201

Balkh Chimtal 8.33 47.50 43.33 0.83 120

Dihdadi 15.00 55.00 25.00 5.00 20

Nahri Shahi 18.75 68.75 12.50 16

All 10.26 50.64 37.82 1.28 156


Musayi 9.52 90.48 21

Paghman 14.81 77.78 7.41 27

All 12.90 83.87 3.23 62


Daman 3.13 90.63 6.25 32

Dand 21.43 78.57 14

Panjwayi 13.33 86.67 30

Zhiray 16.67 83.33 12

All 9.01 88.29 1.80 0.90 111

Logar Puli Alam 30.77 53.85 7.69 7.69 13


Bihsud 24.62 73.85 1.54 65

Shewa 13.51 83.78 2.70 37

All 20.63 76.98 0.79 1.59 126


Tutum Dara 64.29 35.71 14

All 60.00 40.00 20


11.18 70.68 13.50 4.64 689

195

Table A27 Work responsibility – Milking goats(percentages of respond ents by district)


Badakhshan Argo 99.06 0.94 106

Baharak 1.05 96.84 2.11 95

All 0.50 98.01 1.49 201

Balkh Chimtal 24.17 54.17 21.67 120

Dihdadi 30.00 70.00 20

Nahri Shahi 25.00 56.25 18.75 16

All 25.00 56.41 18.59 156


Musayi 9.52 90.48 21

Paghman 11.11 88.89 27

All 11.29 88.71 62


Daman 9.38 87.50 3.13 32

Dand 21.43 78.57 14

Panjwayi 16.67 83.33 30

Zhiray 16.67 83.33 12

All 11.71 87.39 0.90 111



Bihsud 32.31 66.15 1.54 65

Shewa 27.03 70.27 2.70 37

All 30.95 67.46 0.79 0.79 126


Tutum Dara 64.29 35.71 14

All 60.00 40.00 20


16.98 77.94 4.93 0.15 689

196

Table A28 Work responsibility – Treating goats (percentages of respondents by district)

Province District No Response Women Men Children Women

and Men

Womenand

Children

Total Respondents


Baharak 1.05 24.21 74.74 95

All 0.50 42.79 56.72 201

Balkh Chimtal 5.83 15.83 76.67 1.67 120

Dihdadi 10.00 15.00 70.00 5.00 20

Nahri Shahi 12.50 25.00 62.50 16

All 7.05 16.67 74.36 1.28 0.64 156

Kabul Bagrami 14.29 14.29 64.29 7.14 14

Musayi 9.52 19.05 71.43 21

Paghman 11.11 14.81 74.07 27

All 11.29 16.13 70.97 1.61 62


Daman 96.88 3.13 32

Dand 85.71 14.29 14

Panjwayi 86.67 13.33 30

Zhiray 16.67 75.00 8.33 12

All 1.80 90.09 8.11 111

Logar Puli Alam 15.38 46.15 38.46 13


Bihsud 9.23 90.77 65

Shewa 10.81 81.08 8.11 37

All 10.32 1.59 85.71 2.38 126


Tutum Dara 14.29 21.43 64.29 14

All 25.00 15.00 60.00 20


5.95 33.82 59.22 0.58 0.29 0.15 689

197

Table A29 Decision making cattle – Purchasing animals(percentages of respondents by district )

Province DistrictNo

Response Women MenWomen and

MenTotal

Respondents


Baharak 17.27 82.73 139

All 48.47 51.53 262

Balkh Chimtal 2.80 5.61 91.59 214

Dihdadi 0.81 5.69 92.68 0.81 123

Nahri Shahi 1.90 6.67 91.43 105

All 2.04 5.88 91.86 0.23 442


Musayi 0.75 20.90 78.36 134

Paghman 76.70 23.30 103

All 0.25 51.36 48.39 403


Daman 22.78 77.22 79

Dand 20.51 79.49 39

Panjwayi 23.53 76.47 85

Zhiray 2.44 24.39 73.17 41

All 0.66 22.52 76.82 302

Logar Puli Alam 1.54 48.46 50.00 130


Bihsud 34.66 65.34 251

Shewa 0.56 20.67 78.77 179

All 0.16 30.26 69.57 608


Tutum Dara 56.25 43.75 176

All 0.55 46.81 52.63 361

Overall Average/Total 0.68 33.65 65.63 0.04 2508

198

Table A30 Decision making cattle – Purchasing feed(percentages of respondents by district)

Province District No Response Women Men Women and

MenTotal

Respondents


Baharak 10.79 89.21 139

All 32.82 67.18 262

Balkh Chimtal 2.80 5.14 92.06 214

Dihdadi 0.81 5.69 92.68 0.81 123

Nahri Shahi 1.90 7.62 90.48 105

All 2.04 5.88 91.86 0.23 442


Musayi 0.75 19.40 79.85 134

Paghman 80.58 19.42 103

All 0.25 46.15 53.60 403


Daman 15.19 84.81 79

Dand 12.82 87.18 39

Panjwayi 11.76 88.24 85

Zhiray 2.44 24.39 73.17 41

All 0.66 15.56 83.77 302

Logar Puli Alam 0.77 49.23 50.00 130


Bihsud 27.09 72.91 251

Shewa 0.56 15.64 83.80 179

All 0.16 24.51 75.33 608


Tutum Dara 56.25 43.75 176

All 0.55 46.54 52.91 361


0.64 28.95 70.37 0.04 2508

199

Table A31 Decision making cattle – Selling animals(percentages of respondents by district)

Province District No Response Women MenTotal

Respondents


Baharak 74.82 25.18 139

All 72.90 27.10 262

Balkh Chimtal 2.80 5.14 92.06 214

Dihdadi 0.81 4.88 94.31 123

Nahri Shahi 1.90 6.67 91.43 105

All 2.04 5.43 92.53 442


Musayi 1.49 17.16 81.34 134

Paghman 0.97 74.76 24.27 103

All 0.74 41.69 57.57 403


Daman 49.37 50.63 79

Dand 58.97 41.03 39

Panjwayi 44.71 55.29 85

Zhiray 2.44 58.54 39.02 41

All 0.66 48.34 50.99 302



Bihsud 32.67 67.33 251

Shewa 0.56 22.35 77.09 179

All 0.16 30.26 69.57 608


Tutum Dara 1.14 55.11 43.75 176

All 1.11 45.71 53.19 361


200

Table A32 Decision making cattle – Selling milk (percentages of respondents by district )

Province District No Response Women MenWomen and

MenTotal

Respondents


Baharak 99.28 0.72 139

All 98.47 1.53 262

Balkh Chimtal 14.49 14.49 71.03 214

Dihdadi 4.88 29.27 65.85 123

Nahri Shahi 4.76 24.76 69.52 0.95 105

All 9.50 21.04 69.23 0.23 442

Kabul Bagrami 6.63 91.57 1.81 166

Musayi 2.99 83.58 13.43 134

Paghman 19.42 80.58 103

All 8.68 86.10 5.21 403


Daman 51.90 39.24 8.86 79

Dand 53.85 41.03 5.13 39

Panjwayi 47.06 38.82 14.12 85

Zhiray 31.71 58.54 9.76 41

All 46.36 42.38 11.26 302

Logar Puli Alam 66.92 30.00 3.08 130


Bihsud 43.03 42.63 14.34 251

Shewa 45.25 31.84 22.91 179

All 41.28 44.08 14.64 608


Tutum Dara 74.43 24.43 1.14 176

All 44.88 49.58 5.54 361


201

Table A33 Decision making cattle – Treating animals ( percentages of respondents by district)

Province District No Response Women Men Children Women

and MenTotal

Respondents


Baharak 24.46 75.54 139

All 42.37 57.63 262

Balkh Chimtal 3.27 5.14 91.59 214

Dihdadi 0.81 6.50 92.68 123

Nahri Shahi 1.90 6.67 91.43 105

All 2.26 5.88 91.86 442


Musayi 2.24 11.94 85.82 134

Paghman 89.32 10.68 103

All 0.74 36.72 62.53 403


Daman 89.87 10.13 79

Dand 87.18 12.82 39

Panjwayi 85.88 14.12 85

Zhiray 2.44 85.37 12.20 41

All 0.99 87.42 11.59 302

Logar Puli Alam 0.77 58.46 40.77 130


Bihsud 1.99 25.50 72.51 251

Shewa 1.68 13.41 84.92 179

All 1.64 22.86 75.16 0.33 608


Tutum Dara 0.57 32.95 66.48 176

All 0.55 25.21 73.96 0.28 361


1.16 34.09 64.63 0.04 0.08 2508

202

Table A34 Decision making sheep – Purchasing animals(percentages of respondents by district)

Province District No Response Women Men Total Respondents


Baharak 12.12 87.88 66

All 50.56 49.44 178

Balkh Chimtal 4.35 2.61 93.04 115

Dihdadi 11.76 8.82 79.41 34

Nahri Shahi 16.13 9.68 74.19 31

All 7.78 5.00 87.22 180


Musayi 4.35 17.39 78.26 23

Paghman 74.47 25.53 47

All 0.99 51.49 47.52 101


Daman 1.67 16.67 81.67 60

Dand 12.90 87.10 31

Panjwayi 9.21 90.79 76

Zhiray 13.79 86.21 29

All 0.82 12.70 86.48 244

Logar Puli Alam 1.67 43.33 55.00 60


Bihsud 11.11 11.11 77.78 9

Shewa 39.13 60.87 23

All 6.38 25.53 68.09 47


Tutum Dara 64.86 35.14 37

All 1.96 52.94 45.10 51


203

Table A35 Decision making sheep – Purchasing feed(percentages of respondents by district )



Baharak 4.55 95.45 66

All 32.58 67.42 178

Balkh Chimtal 4.35 2.61 93.04 115

Dihdadi 11.76 5.88 82.35 34

Nahri Shahi 16.13 9.68 74.19 31

All 7.78 4.44 87.78 180


Musayi 4.35 17.39 78.26 23

Paghman 80.85 19.15 47

All 0.99 53.47 45.54 101


Daman 1.67 13.33 85.00 60

Dand 9.68 90.32 31

Panjwayi 5.26 94.74 76

Zhiray 13.79 86.21 29

All 0.82 11.07 88.11 244

Logar Puli Alam 1.67 45.00 53.33 60


Bihsud 11.11 11.11 77.78 9

Shewa 26.09 73.91 23

All 6.38 19.15 74.47 47


Tutum Dara 64.86 35.14 37

All 1.96 52.94 45.10 51


204

Table A36 Decision making sheep – Selling animals(percentages and numbers of districts)



Baharak 68.18 31.82 66

All 65.73 34.27 178

Balkh Chimtal 4.35 2.61 93.04 115

Dihdadi 11.76 2.94 85.29 34

Nahri Shahi 16.13 9.68 74.19 31

All 7.78 3.89 88.33 180


Musayi 4.35 17.39 78.26 23

Paghman 2.13 72.34 25.53 47

All 1.98 45.54 52.48 101


Daman 3.33 31.67 65.00 60

Dand 38.71 61.29 31

Panjwayi 22.37 77.63 76

Zhiray 48.28 51.72 29

All 2.05 28.28 69.67 244

Logar Puli Alam 3.33 40.00 56.67 60


Bihsud 11.11 22.22 66.67 9

Shewa 39.13 60.87 23

All 6.38 29.79 63.83 47


Tutum Dara 64.86 35.14 37

All 1.96 52.94 45.10 51


205

Table A37 Decision making sheep – Selling milk(percentages of respondents by district)

Province District No Response Women Men Women and Men

Total Respondents

Badakhshan Argo 100 112

Baharak 98.48 1.52 66

All 99.44 0.56 178

Balkh Chimtal 20.00 3.48 76.52 115

Dihdadi 17.65 14.71 67.65 34

Nahri Shahi 19.35 19.35 61.29 31

All 19.44 8.33 72.22 180


Musayi 21.74 65.22 8.70 4.35 23

Paghman 42.55 55.32 2.13 47

All 30.69 65.35 2.97 0.99 101


Daman 85.00 13.33 1.67 60

Dand 93.55 3.23 3.23 31

Panjwayi 86.84 7.89 5.26 76

Zhiray 82.76 10.34 6.90 29

All 86.48 10.25 3.28 244



Bihsud 88.89 11.11 9

Shewa 82.61 4.35 13.04 23

All 80.85 6.38 12.77 47


Tutum Dara 75.68 24.32 37

All 66.67 25.49 7.84 51


206

Table A38 Decision making sheep – Selling wool(percentages of respondents by district )



Baharak 98.48 1.52 66

All 99.44 0.56 178

Balkh Chimtal 4.35 6.09 89.57 115

Dihdadi 11.76 8.82 79.41 34

Nahri Shahi 16.13 9.68 74.19 31

All 7.78 7.22 85.00 180

Kabul Bagrami 25.81 45.16 29.03 31

Musayi 8.70 65.22 26.09 23

Paghman 78.72 19.15 2.13 47

All 46.53 37.62 15.84 101


Daman 71.67 18.33 10.00 60

Dand 80.65 12.90 6.45 31

Panjwayi 60.53 23.68 15.79 76

Zhiray 68.97 24.14 6.90 29

All 70.90 19.26 9.84 244

Logar Puli Alam 91.67 6.67 1.67 60


Bihsud 77.78 22.22 9

Shewa 82.61 17.39 23

All 74.47 2.13 23.40 47


Tutum Dara 54.05 27.03 18.92 37

All 41.18 25.49 33.33 51


207

Table A39 Decision making sheep – Treating animals(percentages of respondents by district )

Province District No Response Women Men Women and Men Total Respondents

Badakhshan Argo 0.89 49.11 50.00 112

Baharak 18.18 81.82 66

All 0.56 37.64 61.80 178

Balkh Chimtal 4.35 3.48 92.17 115

Dihdadi 11.76 2.94 85.29 34

Nahri Shahi 16.13 9.68 74.19 31

All 7.78 4.44 87.78 180


Musayi 4.35 8.70 86.96 23

Paghman 82.98 17.02 47

All 0.99 52.48 46.53 101


Daman 1.67 88.33 10.00 60

Dand 87.10 12.90 31

Panjwayi 88.16 11.84 76

Zhiray 86.21 13.79 29

All 0.82 88.11 11.07 244

Logar Puli Alam 3.33 51.67 45.00 60


Bihsud 22.22 77.78 9

Shewa 4.35 17.39 78.26 23

All 12.77 12.77 72.34 2.13 47


Tutum Dara 2.70 43.24 54.05 37

All 3.92 35.29 60.78 51


3.25 46.23 50.41 0.12 861

208

Table A40 Decision making goats – Purchasing animals(percentages of respondents by district)



Baharak 14.74 85.26 95

All 54.23 45.77 201

Balkh Chimtal 6.67 5.00 88.33 120

Dihdadi 5.00 10.00 85.00 20

Nahri Shahi 12.50 87.50 16

All 5.77 6.41 87.82 156

Kabul Bagrami 7.14 57.14 35.71 14

Musayi 4.76 33.33 61.90 21

Paghman 11.11 81.48 7.41 27

All 8.06 59.68 32.26 62


Daman 46.88 53.13 32

Dand 42.86 57.14 14

Panjwayi 23.33 76.67 30

Zhiray 16.67 33.33 50.00 12

All 2.70 36.04 61.26 111

Logar Puli Alam 7.69 61.54 30.77 13


Bihsud 15.38 15.38 69.23 65

Shewa 16.22 18.92 64.86 37

All 15.87 15.87 68.25 126


Tutum Dara 57.14 42.86 14

All 10.00 50.00 40.00 20


209

Table A41 Decision making goats – Purchasing feed(percentages of respondents by district)



Baharak 7.37 92.63 95

All 38.31 61.69 201

Balkh Chimtal 6.67 5.00 88.33 120

Dihdadi 5.00 10.00 85.00 20

Nahri Shahi 18.75 81.25 16

All 5.77 7.05 87.18 156

Kabul Bagrami 7.14 42.86 50.00 14

Musayi 4.76 33.33 61.90 21

Paghman 11.11 81.48 7.41 27

All 8.06 56.45 35.48 62


Daman 43.75 56.25 32

Dand 35.71 64.29 14

Panjwayi 20.00 80.00 30

Zhiray 16.67 25.00 58.33 12

All 2.70 29.73 67.57 111

Logar Puli Alam 7.69 53.85 38.46 13


Bihsud 15.38 10.77 73.85 65

Shewa 16.22 10.81 72.97 37

All 15.87 11.90 72.22 126


Tutum Dara 57.14 42.86 14

All 10.00 50.00 40.00 20


210

Table A42 Decision making goats – Selling animals(percentages of respondents by district)



Baharak 71.58 28.42 95

All 76.62 23.38 201

Balkh Chimtal 6.67 5.00 88.33 120

Dihdadi 5.00 5.00 90.00 20

Nahri Shahi 12.50 87.50 16

All 5.77 5.77 88.46 156

Kabul Bagrami 7.14 28.57 64.29 14

Musayi 4.76 28.57 66.67 21

Paghman 11.11 81.48 7.41 27

All 8.06 51.61 40.32 62


Daman 59.38 40.63 32

Dand 71.43 28.57 14

Panjwayi 53.33 46.67 30

Zhiray 16.67 50.00 33.33 12

All 2.70 54.95 42.34 111

Logar Puli Alam 7.69 53.85 38.46 13


Bihsud 16.92 15.38 67.69 65

Shewa 16.22 24.32 59.46 37

All 16.67 18.25 65.08 126


Tutum Dara 57.14 42.86 14

All 10.00 50.00 40.00 20


211

Table A43 Decision making goats – Selling milk(percentages of respondents by district)



Baharak 98.95 1.05 95

All 99.50 0.50 201

Balkh Chimtal 22.50 5.00 72.50 120

Dihdadi 15.00 15.00 70.00 20

Nahri Shahi 6.25 31.25 62.50 16

All 19.87 8.97 71.15 156


Musayi 9.52 90.48 21

Paghman 33.33 66.67 27

All 19.35 80.65 62


Daman 53.13 37.50 9.38 32

Dand 50.00 42.86 7.14 14

Panjwayi 63.33 26.67 10.00 30

Zhiray 75.00 25.00 12

All 55.86 36.94 7.21 111



Bihsud 89.23 7.69 3.08 65

Shewa 75.68 10.81 13.51 37

All 81.75 11.90 6.35 126


Tutum Dara 78.57 21.43 14

All 65.00 35.00 20


212

Table A44 Decision making goats – Selling fibre(percentages of respondents by district)Province District No Response Women Men Total Respondents


Baharak 98.95 1.05 95

All 99.50 0.50 201

Balkh Chimtal 10.83 6.67 82.50 120

Dihdadi 5.00 10.00 85.00 20

Nahri Shahi 12.50 18.75 68.75 16

All 10.26 8.33 81.41 156

Kabul Bagrami 35.71 35.71 28.57 14

Musayi 4.76 71.43 23.81 21

Paghman 96.30 3.70 27

All 51.61 33.87 14.52 62


Daman 65.63 31.25 3.13 32

Dand 71.43 28.57 14

Panjwayi 66.67 26.67 6.67 30

Zhiray 75.00 25.00 12

All 67.57 29.73 2.70 111

Logar Puli Alam 100 13


Bihsud 89.23 4.62 6.15 65

Shewa 89.19 5.41 5.41 37

All 88.89 5.56 5.56 126


Tutum Dara 64.29 14.29 21.43 14

All 60.00 20.00 20.00 20


213

Table A45 Decision making goats – Treating animals(percentages of respondents by district)

Province District No Response Women Men Women/Men Total Respondents


Baharak 22.11 77.89 95

All 52.24 47.76 201

Balkh Chimtal 6.67 5.00 88.33 120

Dihdadi 5.00 5.00 90.00 20

Nahri Shahi 12.50 87.50 16

All 5.77 5.77 88.46 156

Kabul Bagrami 14.29 28.57 57.14 14

Musayi 9.52 14.29 76.19 21

Paghman 11.11 85.19 3.70 27

All 11.29 48.39 40.32 62


Daman 100 32

Dand 85.71 14.29 14

Panjwayi 86.67 13.33 30

Zhiray 16.67 75.00 8.33 12

All 3.60 89.19 7.21 111

Logar Puli Alam 7.69 46.15 46.15 13


Bihsud 18.46 6.15 73.85 1.54 65

Shewa 18.92 10.81 70.27 37

All 17.46 8.73 73.02 0.79 126


Tutum Dara 7.14 35.71 57.14 14

All 15.00 30.00 55.00 20


6.68 38.61 54.57 0.15 689

214

Table A46 Cattle preferred use: milk (percentages of respondents by district)

Province District No Response (No Preference)




Bihsud 1.77 48.67 49.56 113

Shewa 1.03 35.05 63.92 97

All 1.53 45.09 53.37 326


Musayi 2.27 13.64 84.09 88

Paghman 39.25 60.75 107

All 0.75 26.42 72.83 265

Logar Puli Alam 3.66 74.39 21.95 82

3.66 74.39 21.95 82


Tutum Dara 22.68 19.59 57.73 97

All 16.77 20.00 63.23 155


Baharak 99.05 0.95 105

All 99.36 0.64 157

Balkh Chimtal 2.38 2.38 95.24 168

Dihdadi 4.69 9.38 85.94 64

Nahri Shahi 1.96 1.96 96.08 51

All 2.83 3.89 93.29 283


Daman 1.05 36.84 62.11 95

Dand 34.78 65.22 46

Panjwayi 1.74 31.30 66.96 115

Zhiray 1.89 35.85 62.26 53

All 1.29 33.59 65.12 387


2.96 36.62 60.42 1655

215

Table A47 Cattle preferred use: work/draft (percentages of respondents by district)

Province DistrictNo Response (No

Preference)Household

Consumption SaleTotal

Respondents

Nangarhar Bati Kot 100 116

Bihsud100 113

Shewa100 97

All 100 326

Kabul Bagrami100 70

Musayi100 88

Paghman100 107

All 100 265


97.56 2.44 82


Tutum Dara 77.32 15.46 7.22 97

All 83.23 10.97 5.81 155


Baharak 36.19 63.81 105

All 55.41 44.59 157

Balkh Chimtal 15.48 77.38 7.14 168

Dihdadi 26.56 57.81 15.63 64

Nahri Shahi 11.76 37.25 50.98 51

All 17.31 65.72 16.96 283


Daman 98.95 1.05 95

Dand 100 46

Panjwayi 100 115

Zhiray 100 53

All 99.74 0.26 387


216

Table A48 Cattle preferred use: meat (percentages of respondents by district)





Bihsud 100 113

Shewa 100 97

All 99.69 0.31 326

Kabul Bagrami 100 70

Musayi 98.86 1.14 88

Paghman 100 107

All 99.62 0.38 265

Logar Puli Alam 93.90 1.22 4.88 82

93.90 1.22 4.88 82

Parwan Chaharikar 100 58

Tutum Dara 100 97

All 100 155


Baharak 15.24 84.76 105

All 10.83 89.17 157

Balkh Chimtal 14.29 1.19 84.52 168

Dihdadi 14.06 4.69 81.25 64

Nahri Shahi 7.84 1.96 90.20 51

All 13.07 2.12 84.81 283


Daman 100 95

Dand 100 46

Panjwayi 99.13 0.87 115

Zhiray 98.11 1.89 53

All 99.22 0.78 387


75.05 1.51 23.44 1655

217

Table A49 Poultry preference use: meat (percentages of respondents by district)



SaleHousehold

Consumption / Sale

Total Respondents


Bihsud 91.33 2.00 6.67 150

Shewa 80.95 7.14 11.90 84

All 84.82 3.63 11.55 303


Musayi 82.61 17.39 23

Paghman 100 13

All 92.68 2.44 4.88 82


46.15 53.85 13


Tutum Dara 96.15 3.85 26

All 96.45 0.71 2.84 141

Balkh Chimtal 34.21 1.75 63.16 0.88 114

Dihdadi 40.28 23.61 36.11 72

Nahri Shahi 20.41 6.12 65.31 8.16 49

All 33.19 9.36 55.32 2.13 235

Kandahar Zhiray 100 1


218

Table A50 Poultry preference use: eggs (percentages of respondents by district)

Province DistrictNo Response

(No Preference)Household

ConsumptionSale

Household Consumption /

Sale

Total Respondents


Bihsud 8.67 32.67 58.67 150

Shewa 23.81 33.33 42.86 84

All 16.50 27.39 56.11 303

Kabul Bagrami 10.87 52.17 36.96 46

Musayi 4.35 8.70 86.96 23

Paghman 46.15 53.85 13

All 14.63 31.71 53.66 82

Logar Puli Alam 15.38 30.77 53.85 13

15.38 30.77 53.85 13


Tutum Dara 19.23 11.54 69.23 26

All 13.48 5.67 80.85 141

Balkh Chimtal 9.65 6.14 84.21 114

Dihdadi 19.44 6.94 73.61 72

Nahri Shahi 2.04 8.16 87.76 2.04 49

All 11.06 6.81 81.70 0.43 235

Kandahar Zhiray 100 1


14.19 17.68 68.00 0.13 775

219

Table A51 Sheep preferred use: milk (percentages of respondents by district)

Province District No Response(No Preference)


Respondents


Bihsud 100 1

Shewa 100 1

All 100 4

Kabul Bagrami 77.36 11.32 11.32 53

Musayi 96.30 3.70 27

Paghman 38.46 15.38 46.15 13

All 77.42 8.60 13.98 93



Tutum Dara 82.61 8.70 8.70 46

All 69.70 9.09 21.21 66

Balkh Chimtal 33.33 3.33 63.33 30

Dihdadi 27.27 4.55 68.18 22

Nahri Shahi 14.29 85.71 14

All 27.27 3.03 69.70 66


Baharak 97.14 2.86 35

All 99.13 0.87 115


Daman 72.73 9.09 18.18 11

Dand 100 7

Panjwayi 100 7

Zhiray 100 2

All 86.21 6.90 6.90 29


220

Table A52 Sheep preferred use: meat (percentages of respondents by district)



Respondents


Bihsud 100 1

Shewa 100 1

75.00 25.00 4

Kabul Bagrami 35.85 62.26 1.89 53

Musayi 3.70 55.56 40.74 27

Paghman 61.54 15.38 23.08 13

30.11 53.76 16.13 93

Logar Puli Alam 13.64 77.27 9.09 44


Tutum Dara 32.61 67.39 46

46.97 51.52 1.52 66


Baharak 20.00 80.00 35

13.04 86.96 115

Balkh Chimtal 13.33 6.67 80.00 30

Dihdadi 4.55 4.55 90.91 22

Nahri Shahi 14.29 85.71 14

7.58 7.58 84.85 66


Daman 45.45 54.55 11

Dand 100 7

Panjwayi 100 7

Zhiray 100 2

20.69 79.31 29


221

Table A53 Sheep preferred use: wool (percentages of respondents by district)





Bihsud 100 1

Shewa 100 1

All 100 4


Musayi 100 27

Paghman 100 13

All 97.85 2.15 93

Logar Puli Alam 100 44


Tutum Dara 97.83 2.17 46

All 96.97 3.03 66


Baharak 2.86 31.43 65.71 35

All 0.87 79.13 20.00 115

Balkh Chimtal 36.67 13.33 50.00 30

Dihdadi 27.27 13.64 59.09 22

Nahri Shahi 14.29 14.29 71.43 14

All 28.79 13.64 57.58 66


Daman 100 11

Dand 100 7

Panjwayi 100 7

Zhiray 100 2

All 100 29


60.43 24.94 14.63 417

222

Table A54 Goats preferred use: milk (Percentages of respondents by district)

Province DistrictNo Response (No

Preference)Household

ConsumptionSale

Total Respondents


Bihsud 33.33 66.67 3

Shewa 100 1

All 40.00 40.00 20.00 5

Kabul Bagrami 100 5

Musayi 50.00 50.00 2

Paghman 100 1

All 87.50 12.50 8

Parwan Tutum Dara 100 1


Baharak 100 6

All 100 13

Balkh Chimtal 100 2

Dihdadi 100 1

All 100 3


Zhiray 100 1

All 66.67 33.33 3


6.06 75.76 18.18 33

223

Table A55 Goats preferred use: Meat milk(percentages of respondents by district)

Province District No Response(No Preference) Sale Total Respondents


Bihsud 100 3

Shewa 100 1

All 100 5

Kabul Bagrami 100 5

Musayi 50.00 50.00 2

Paghman 100 1

All 87.50 12.50 8



Baharak 100 6

All 100 13

Balkh Chimtal 50.00 50.00 2

Dihdadi 100 1

All 33.33 66.67 3


Zhiray 100 1

All 100 3

Overall Average/Total 51.52 48.48 33

224

Table A56 Goats preferred use: Fibre (percentages of respondents by district)





Bihsud 100 3

Shewa 100 1

All 100 5

Kabul Bagrami 100 5

Musayi 50.00 50.00 2

Paghman 100 1

All 87.50 12.50 8


Baharak 83.33 16.67 6

All 92.31 7.69 13


Dihdadi 100 1

All 33.33 33.33 33.33 3



Zhiray 100 1

All 100 3


51.52 39.39 9.09 33

225

Table A57 Problems of cattle production -'Not enough feed' - Feeding constraint(percentages of respondents by district)

Province District Most important



Not Mentioned

Total Respondents

Badakhshan Argo 45.65 47.83 4.35 2.17 46

Baharak 69.83 25.86 0.86 3.45 116

All 62.96 32.10 1.85 3.09 162

Balkh Chimtal 72.13 9.84 4.51 13.52 244

Dihdadi 71.68 13.27 10.62 4.42 113

Nahri Shahi 57.58 13.13 6.06 23.23 99

All 68.86 11.40 6.36 13.38 456

Kabul Bagrami 61.40 9.65 11.40 17.54 114

Musayi 20.56 32.71 39.25 7.48 107

Paghman 66.67 13.73 10.78 8.82 102

All 49.54 18.58 20.43 11.46 323


Daman 97.73 1.14 1.14 88

Dand 100 40

Panjwayi 100 87

Zhiray 100 44

All 98.77 0.62 0.62 324

Logar Puli Alam 50.94 8.49 8.49 32.08 106


Bihsud 83.04 14.29 0.89 1.79 112

Shewa 73.91 10.87 3.26 11.96 92

All 77.12 13.48 3.76 5.64 319


Tutum Dara 15.56 17.04 13.33 54.07 135

All 28.10 18.57 9.52 43.81 210


66.05 13.53 7.32 13.11 1900

226

Table A58 Problems of cattle production - Animal disease - Disease constraint(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 25.86 68.10 5.17 0.86 116

All 32.72 62.35 4.32 0.62 162

Balkh Chimtal 17.21 35.66 37.30 9.84 244

Dihdadi 18.58 34.51 24.78 22.12 113

Nahri Shahi 30.30 48.48 11.11 10.10 99

All 20.39 38.16 28.51 12.94 456

Kabul Bagrami 19.30 53.51 18.42 8.77 114

Musayi 36.45 41.12 14.02 8.41 107

Paghman 16.67 68.63 11.76 2.94 102

All 24.15 54.18 14.86 6.81 323


Daman 1.14 62.50 32.95 3.41 88

Dand 57.50 37.50 5.00 40

Panjwayi 67.82 31.03 1.15 87

Zhiray 61.36 36.36 2.27 44

All 0.31 63.89 33.33 2.47 324

Logar Puli Alam 11.32 45.28 16.98 26.42 106


Bihsud 9.82 55.36 28.57 6.25 112

Shewa 13.04 65.22 16.30 5.43 92

All 12.54 54.55 25.71 7.21 319


Tutum Dara 83.70 15.56 0.74 135

All 64.29 28.10 4.29 3.33 210


21.68 49.37 21.16 7.79 1900

227

Table A59 Problems of cattle production -'Too far to the market' –Market distance constraint (percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 0.86 1.72 59.48 37.93 116

All 0.62 1.23 42.59 55.56 162

Balkh Chimtal 0.41 26.64 31.56 41.39 244

Dihdadi 3.54 25.66 32.74 38.05 113

Nahri Shahi 2.02 15.15 22.22 60.61 99

All 1.54 23.90 29.82 44.74 456

Kabul Bagrami 1.75 2.63 95.61 114

Musayi 2.80 6.54 90.65 107

Paghman 100 102

All 1.55 3.10 95.36 323


Daman 1.14 1.14 7.95 89.77 88

Dand 10.00 90.00 40

Panjwayi 1.15 5.75 93.10 87

Zhiray 4.55 95.45 44

All 0.31 0.93 7.10 91.67 324

Logar Puli Alam 5.66 14.15 12.26 67.92 106


Bihsud 16.07 21.43 62.50 112

Shewa 3.26 18.48 15.22 63.04 92

All 1.88 21.63 25.08 51.41 319


Tutum Dara 51.85 30.37 17.78 135

All 0.95 38.57 30.95 29.52 210


1.21 14.95 20.84 63.00 1900

228

Table A60 Problems of cattle production -'Not enough buyers' - Sales constraint(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 0.86 1.72 97.41 116

All 0.62 0.62 1.85 96.91 162

Balkh Chimtal 13.93 6.56 79.51 244

Dihdadi 22.12 20.35 57.52 113

Nahri Shahi 1.01 5.05 13.13 80.81 99

All 0.22 14.04 11.40 74.34 456


Musayi 0.93 3.74 7.48 87.85 107

Paghman 1.96 2.94 95.10 102

All 0.31 2.79 3.41 93.50 323


Daman 100 88

Dand 2.50 97.50 40

Panjwayi 1.15 98.85 87

Zhiray 100 44

All 0.31 0.31 99.38 324

Logar Puli Alam 1.89 3.77 14.15 80.19 106


Bihsud 7.14 8.93 83.93 112

Shewa 2.17 3.26 7.61 86.96 92

All 1.25 5.96 8.78 84.01 319


Tutum Dara 8.15 13.33 78.52 135

All 1.43 5.71 10.48 82.38 210


0.63 5.79 6.95 86.63 1900

229

Table A61 Problems of cattle production -'Too much work' - Labor constraint(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 2.59 0.86 96.55 116

All 2.47 6.17 91.36 162

Balkh Chimtal 11.07 2.05 0.41 86.48 244

Dihdadi 5.31 0.88 1.77 92.04 113

Nahri Shahi 9.09 1.01 89.90 99

All 9.21 1.32 0.88 88.60 456

Kabul Bagrami 14.04 7.02 14.91 64.04 114

Musayi 5.61 4.67 15.89 73.83 107

Paghman 3.92 1.96 3.92 90.20 102

All 8.05 4.64 11.76 75.54 323

Kandahar Arghandab 3.08 1.54 9.23 86.15 65

Daman 1.14 5.68 93.18 88

Dand 100 40

Panjwayi 1.15 4.60 94.25 87

Zhiray 4.55 95.45 44

All 0.62 0.93 5.25 93.21 324

Logar Puli Alam 12.26 4.72 6.60 76.42 106


Bihsud 7.14 6.25 25.00 61.61 112

Shewa 7.61 30.43 61.96 92

All 6.90 3.45 18.50 71.16 319


Tutum Dara 100 135

All 2.86 2.38 6.67 88.10 210


6.05 2.37 7.84 83.74 1900

230

Table A62 Problems of cattle production - Cost of labor(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 0.86 3.45 31.90 63.79 116

All 0.62 3.70 43.21 52.47 162


Dihdadi 0.88 0.88 1.77 96.46 113

Nahri Shahi 1.01 98.99 99

All 0.44 0.22 0.88 98.46 456

Kabul Bagrami 5.26 25.44 52.63 16.67 114

Musayi 2.80 8.41 11.21 77.57 107

Paghman 0.98 3.92 25.49 69.61 102

All 3.10 13.00 30.34 53.56 323


Daman 1.14 7.95 90.91 88

Dand 2.50 5.00 92.50 40

Panjwayi 3.45 96.55 87

Zhiray 2.27 97.73 44

All 0.62 4.01 95.37 324

Logar Puli Alam 17.92 23.58 33.02 25.47 106


Bihsud 100 112

Shewa 1.09 2.17 96.74 92

All 0.63 1.57 97.81 319


Tutum Dara 0.74 4.44 9.63 85.19 135

All 0.95 3.33 9.52 86.19 210


1.79 4.47 12.89 80.84 1900

231

Table A63 Improvements for cattle production - Better feeding of cattle(percentages of respondents by district)

Province DistrictMost

importantSecond most

importantThird most important

Not Mentioned

TotalRespondents

Badakhshan Argo 52.17 26.09 6.52 15.22 46

Baharak 66.96 31.30 0.87 0.87 115

All 62.73 29.81 2.48 4.97 161

Balkh Chimtal 75.60 18.40 4.80 1.20 250

Dihdadi 77.39 11.30 4.35 6.96 115

Nahri Shahi 81.63 12.24 1.02 5.10 98

All 77.32 15.33 3.89 3.46 463

Kabul Bagrami 62.39 15.38 8.55 13.68 117

Musayi 48.15 36.11 12.96 2.78 108

Paghman 81.37 12.75 0.98 4.90 102

All 63.61 21.41 7.65 7.34 327


Daman 98.81 1.19 84

Dand 100 41

Panjwayi 100 88

Zhiray 100 44

All 99.38 0.62 321

Logar Puli Alam 70.00 7.27 3.64 19.09 110


Bihsud 75.65 5.22 5.22 13.91 115

Shewa 69.39 3.06 12.24 15.31 98

All 66.47 6.04 13.90 13.60 331


Tutum Dara 15.33 9.49 10.22 64.96 137

All 27.01 11.37 9.95 51.66 211


232

Table A64 Improvements for cattle production - Better health care of cattle(percentages of res pondents by district)




Not Mentioned

Total Respondents


Baharak 30.43 60.87 6.96 1.74 115

All 35.40 58.39 4.97 1.24 161

Balkh Chimtal 9.20 78.40 7.60 4.80 250

Dihdadi 13.91 72.17 7.83 6.09 115

Nahri Shahi 10.20 80.61 5.10 4.08 98

All 10.58 77.32 7.13 4.97 463

Kabul Bagrami 11.97 26.50 25.64 35.90 117

Musayi 28.70 29.63 26.85 14.81 108

Paghman 7.84 68.63 16.67 6.86 102

All 16.21 40.67 23.24 19.88 327

Kandahar Arghandab 59.38 35.94 4.69 64Daman 1.19 63.10 34.52 1.19 84

Dand 63.41 36.59 41

Panjwayi 62.50 35.23 2.27 88

Zhiray 59.09 40.91 44

All 0.31 61.68 36.14 1.87 321

Logar Puli Alam 12.73 22.73 17.27 47.27 110


Bihsud 5.22 65.22 10.43 19.13 115

Shewa 2.04 63.27 8.16 26.53 98

All 6.34 58.91 8.46 26.28 331


Tutum Dara 14.60 29.20 41.61 14.60 137

All 11.85 29.38 29.38 29.38 211


233

Table A65 Improvements for cattle production - Better water access for cattle(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 0.87 6.09 93.04 115

All 0.62 8.07 91.30 161

Balkh Chimtal 13.20 0.80 26.40 59.60 250

Dihdadi 4.35 20.87 74.78 115

Nahri Shahi 4.08 2.04 45.92 47.96 98

All 7.99 1.94 29.16 60.91 463

Kabul Bagrami 12.82 34.19 10.26 42.74 117

Musayi 6.48 25.93 28.70 38.89 108

Paghman 6.86 4.90 2.94 85.29 102

All 8.87 22.32 14.07 54.74 327


Daman 1.19 2.38 96.43 84

Dand 7.32 92.68 41

Panjwayi 3.41 4.55 92.05 88

Zhiray 2.27 97.73 44

All 0.31 1.87 4.98 92.83 321

Logar Puli Alam 2.73 7.27 1.82 88.18 110


Bihsud 100 115

Shewa 2.04 1.02 96.94 98

All 0.91 1.21 0.60 97.28 331


Tutum Dara 1.46 98.54 137

All 2.84 4.74 4.27 88.15 211


234

Table A66 Improvements for cattle production - Easier market access(percentages of respondents by district)




Not MentionedTotal

Respondents


Baharak 2.61 26.96 70.43 115

All 1.86 19.88 78.26 161

Balkh Chimtal 0.80 0.80 34.40 64.00 250

Dihdadi 5.22 5.22 33.04 56.52 115

Nahri Shahi 1.02 2.04 20.41 76.53 98

All 1.94 2.16 31.10 64.79 463

Kabul Bagrami 1.71 5.13 93.16 117

Musayi 1.85 98.15 108

Paghman 6.86 93.14 102

All 1.22 3.98 94.80 327


Daman 2.38 97.62 84

Dand 4.88 95.12 41

Panjwayi 1.14 1.14 97.73 88

Zhiray 100 44

All 0.31 1.56 98.13 321

Logar Puli Alam 0.91 18.18 13.64 67.27 110


Bihsud 0.87 3.48 6.96 88.70 115

Shewa 2.04 1.02 3.06 93.88 98

All 1.81 3.63 7.25 87.31 331


Tutum Dara 10.22 18.25 71.53 137

All 2.37 11.85 15.17 70.62 211


1.09 3.90 13.77 81.24 1924

235

Table A67 Improvements for cattle production - Better knowledge about animal husbandry(percentages of res pondents by district)




Not Mentioned

Total Respondents

Badakhshan Argo 15.22 26.09 58.70 46

Baharak 0.87 6.96 92.17 115

All 4.97 12.42 82.61 161

Balkh Chimtal 0.40 1.20 14.00 84.40 250

Dihdadi 4.35 16.52 79.13 115

Nahri Shahi 1.02 12.24 86.73 98

All 0.22 1.94 14.25 83.59 463

Kabul Bagrami 5.13 11.11 29.06 54.70 117

Musayi 1.85 2.78 20.37 75.00 108

Paghman 13.73 50.98 35.29 102

All 2.45 9.17 33.03 55.35 327


Daman 34.52 58.33 7.14 84

Dand 36.59 48.78 14.63 41

Panjwayi 32.95 56.82 10.23 88

Zhiray 40.91 54.55 4.55 44

All 35.51 54.52 9.97 321

Logar Puli Alam 1.82 27.27 19.09 51.82 110


Bihsud 6.09 18.26 32.17 43.48 115

Shewa 6.06 18.18 33.33 42.42 89

All 9.67 18.43 27.49 44.41 331


Tutum Dara 28.47 35.04 12.41 24.09 137

All 20.38 27.01 17.06 35.55 211


4.47 16.06 26.87 52.60 1924

236

Table A68 Improvements for cattle production - Access to credit(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 1.74 2.61 52.17 43.48 115

All 1.24 3.73 52.17 42.86 161

Balkh Chimtal 0.80 0.40 10.40 88.40 250

Dihdadi 2.61 3.48 12.17 81.74 115

Nahri Shahi 2.04 1.02 13.27 83.67 98

All 1.51 1.30 11.45 85.75 463

Kabul Bagrami 7.69 11.11 21.37 59.83 117

Musayi 14.81 2.78 10.19 72.22 108

Paghman 2.94 11.76 85.29 102

All 8.56 4.89 14.68 71.87 327


Daman 1.19 98.81 84

Dand 2.44 97.56 41

Panjwayi 2.27 97.73 88

Zhiray 2.27 97.73 44

All 2.49 97.51 321

Logar Puli Alam 10.91 15.45 43.64 30.00 110


Bihsud 13.04 7.83 44.35 34.78 115

Shewa 17.35 10.20 46.94 25.51 98

All 14.80 11.18 40.48 33.53 331


Tutum Dara 39.42 16.79 16.79 27.01 137

All 33.18 15.17 23.22 28.44 211


8.73 5.93 22.04 63.31 1924

237

Table A69 Problems of sheep production - Not enough feed –(percentages of respondents by district)




Not Mentioned

Total Respondents

Badakhshan Argo 67.44 24.42 3.49 4.65 86

Baharak 92.59 7.41 27

All 73.45 20.35 2.65 3.54 113

Balkh Chimtal 51.43 15.71 8.57 24.29 70

Dihdadi 75.00 8.33 8.33 8.33 24

Nahri Shahi 44.44 16.67 38.89 18

All 55.36 14.29 7.14 23.21 112


Musayi 57.14 28.57 14.29 7

Paghman 73.68 15.79 10.53 19

All 75.00 13.89 2.78 8.33 36


Daman 96.00 4.00 25

Dand 100 13

Panjwayi 94.59 5.41 37

Zhiray 100 11

All 96.30 3.70 108

Logar Puli Alam 60.00 10.00 5.00 25.00 20

Nangarhar Bati Kot

Bihsud

Shewa 50.00 50.00 2

All 50.00 50.00 2


Tutum Dara 42.86 14.29 28.57 14.29 7

All 50.00 8.33 16.67 25.00 12


73.20 12.66 3.72 10.42 403

238

Table A70 Problems of sheep production - Animal disease –(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 7.41 85.19 7.41 27

All 23.89 72.57 3.54 113

Balkh Chimtal 28.57 25.71 32.86 12.86 70

Dihdadi 16.67 20.83 33.33 29.17 24

Nahri Shahi 33.33 44.44 11.11 11.11 18

All 26.79 27.68 29.46 16.07 112

Kabul Bagrami 10.00 70.00 20.00 10

Musayi 14.29 42.86 28.57 14.29 7

Paghman 5.26 68.42 21.05 5.26 19

All 8.33 63.89 22.22 5.56 36


Daman 56.00 20.00 24.00 25

Dand 100 13

Panjwayi 5.41 67.57 27.03 37

Zhiray 100 11

All 1.85 71.30 21.30 5.56 108

Logar Puli Alam 15.00 50.00 5.00 30.00 20

Nangarhar Shewa 50.00 50.00 2


Tutum Dara 42.86 42.86 14.29 7

All 25.00 33.33 16.67 25.00 12


17.12 56.58 17.62 8.68 403

239

Table A71 Problems of sheep production - Too far to market–(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 3.70 55.56 40.74 27

All 0.88 14.16 84.96 113

Balkh Chimtal 24.29 15.71 60.00 70

Dihdadi 41.67 20.83 37.50 24

Nahri Shahi 5.56 22.22 72.22 18

All 25.00 17.86 57.14 112

Kabul Bagrami 100 10

Musayi 14.29 14.29 28.57 42.86 7

Paghman 5.26 94.74 19

All 5.56 2.78 5.56 86.11 36


Daman 8.00 92.00 25

Dand 7.69 92.31 13

Panjwayi 16.22 83.78 37

Zhiray 18.18 81.82 11

All 12.04 87.96 108

Logar Puli Alam 5.00 15.00 15.00 65.00 20

Nangarhar Shewa 100 2


Tutum Dara 28.57 71.43 7

All 25.00 41.67 33.33 12


0.74 8.93 14.64 75.68 403

240

Table A72 Problems of sheep production -Not enough buyers-(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 3.70 7.41 88.89 27

All 2.65 4.42 92.92 113

Balkh Chimtal 1.43 7.14 4.29 87.14 70

Dihdadi 20.83 25.00 54.17 24


All 0.89 8.93 8.93 81.25 112


Musayi 28.57 71.43 7

Paghman 10.53 89.47 19

All 2.78 11.11 86.11 36


Daman 100 25

Dand 100 13

Panjwayi 100 37

Zhiray 100 11

All 100 108


Nangarhar Shewa 50.00 50.00 2


Tutum Dara 14.29 85.71 7

All 16.67 83.33 12


241

Table A73 Problems of sheep production - Too much work-(Percentages of respondents by district)




Not Mentioned

Total Respondents

Badakhshan Argo 3.49 2.33 24.42 69.77 86

Baharak 100 27

All 2.65 1.77 18.58 76.99 113

Balkh Chimtal 18.57 2.86 1.43 77.14 70

Dihdadi 8.33 4.17 4.17 83.33 24

Nahri Shahi 16.67 83.33 18

All 16.07 2.68 1.79 79.46 112


Musayi 14.29 14.29 14.29 57.14 7

Paghman 5.26 94.74 19

All 5.56 2.78 11.11 80.56 36


Daman 4.00 4.00 24.00 68.00 25

Dand 7.69 92.31 13

Panjwayi 2.70 2.70 94.59 37

Zhiray 18.18 81.82 11

All 1.85 2.78 13.89 81.48 108

Logar Puli Alam 10.00 10.00 10.00 70.00 20



Tutum Dara 100.00 7

All 8.33 16.67 75.00 12


242

Table A74 Problems of sheep production -Cost of labor-(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 29.63 70.37 27

Àll 1.77 56.64 41.59 113

Balkh Chimtal 4.29 5.71 90.00 70

Dihdadi 100 24


All 0.89 2.68 3.57 92.86 112

Kabul Bagrami 20.00 40.00 40.00 10

Musayi 100 7

Paghman 5.26 15.79 78.95 19

All 8.33 19.44 72.22 36


Daman 8.00 92.00 25

Dand 23.08 76.92 13

Panjwayi 8.11 91.89 37

Zhiray 9.09 90.91 11

All 9.26 90.74 108

Logar Puli Alam 10.00 15.00 45.00 30.00 20



Tutum Dara 14.29 85.71 7

All 8.33 25.00 66.67 12


243

Table A75 Improvements for sheep production - Better feeding –(percentages of respondents by district)




Not Mentioned

Total Respondents

Badakhshan Argo 70.93 24.42 2.33 2.33 86

Baharak 92.59 7.41 27

All 76.11 20.35 1.77 1.77 113

Balkh Chimtal 77.50 15.00 3.75 3.75 80

Dihdadi 65.22 13.04 8.70 13.04 23

Nahri Shahi 100 15

All 77.97 12.71 4.24 5.08 118


Musayi 66.67 16.67 16.67 6

Paghman 94.74 5.26 19

All 88.24 5.88 2.94 2.94 34


Daman 100 24

Dand 100 13

Panjwayi 94.44 5.56 36

Zhiray 100 11

All 98.10 1.90 105

Logar Puli Alam 60.00 10.00 30.00 20


Shewa 100 1

All 50.00 50.00 2


Tutum Dara 33.33 16.67 16.67 33.33 6

All 60.00 10.00 10.00 20.00 10


82.09 11.19 2.24 4.48 402

244

Table A76 Improvements for sheep production - Better health care(percentages of respondents by district)




Not Mentioned

Total Respondents

Badakhshan Argo 29.07 68.60 1.16 1.16 86

Baharak 7.41 81.48 11.11 27

All 23.89 71.68 3.54 0.88 113

Balkh Chimtal 11.25 73.75 8.75 6.25 80

Dihdadi 17.39 65.22 8.70 8.70 23

Nahri Shahi 100 15

All 11.02 75.42 7.63 5.93 118

Kabul Bagrami 11.11 44.44 11.11 33.33 9

Musayi 66.67 33.33 6

Paghman 57.89 15.79 26.32 19

All 2.94 55.88 11.76 29.41 34


Daman 79.17 20.83 24

Dand 92.31 7.69 13

Panjwayi 5.56 61.11 33.33 36

Zhiray 81.82 9.09 9.09 11

All 1.90 71.43 24.76 1.90 105

Logar Puli Alam 30.00 30.00 10.00 30.00 20


Shewa 100 1

All 50.00 50.00 2


Tutum Dara 16.67 16.67 33.33 33.33 6

All 10.00 20.00 20.00 50.00 10


12.69 67.91 11.69 7.71 402

245

Table A77 Improvements for sheep production - Better access to water-(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 3.70 96.30 27

All 2.65 30.09 67.26 113

Balkh Chimtal 6.25 3.75 31.25 58.75 80

Dihdadi 21.74 78.26 23

Nahri Shahi 60.00 40.00 15

All 4.24 2.54 33.05 60.17 118

Kabul Bagrami 22.22 33.33 44.44 9

Musayi 16.67 16.67 66.67 6

Paghman 10.53 5.26 84.21 19

All 14.71 14.71 70.59 34


Daman 4.17 95.83 24

Dand 100 13

Panjwayi 11.11 88.89 36

Zhiray 9.09 9.09 81.82 11

All 0.95 11.43 87.62 105

Logar Puli Alam 5.00 20.00 75.00 20


Shewa 100 1

All 100 2


Tutum Dara 100 6

All 100 10


1.49 3.98 22.39 72.14 402

246

Table A78 Improvements for sheep production - Easier market access -(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 25.93 74.07 27

All 7.08 92.92 113

Balkh Chimtal 1.25 1.25 23.75 73.75 80

Dihdadi 13.04 13.04 26.09 47.83 23


All 3.39 3.39 22.03 71.19 118

Kabul Bagrami 100 9

Musayi 16.67 83.33 6

Paghman 21.05 78.95 19

All 2.94 11.76 85.29 34


Daman 4.17 95.83 24

Dand 100 13

Panjwayi 2.78 97.22 36

Zhiray 9.09 90.91 11

All 2.86 97.14 105

Logar Puli Alam 15.00 20.00 65.00 20


Shewa 100 1

All 50.00 50.00 2


Tutum Dara 33.33 66.67 6

All 10.00 30.00 60.00 10


1.24 2.24 11.94 84.58 402

247

Table A79 Improvements for sheep production - Better knowledge about animal husbandry- (percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 3.70 96.30 27

All 1.77 12.39 85.84 113

Balkh Chimtal 1.25 6.25 22.50 70.00 80

Dihdadi 17.39 82.61 23

Nahri Shahi 20.00 80.00 15

All 0.85 4.24 21.19 73.73 118

Kabul Bagrami 22.22 11.11 66.67 9

Musayi 16.67 83.33 6

Paghman 10.53 47.37 42.11 19

All 11.76 32.35 55.88 34


Daman 20.83 66.67 12.50 24

Dand 7.69 92.31 13

Panjwayi 33.33 44.44 22.22 36

Zhiray 9.09 72.73 18.18 11

All 24.76 56.19 19.05 105

Logar Puli Alam 20.00 20.00 60.00 20


Shewa 100 1

All 100 2


Tutum Dara 50.00 50.00 6

All 50.00 10.00 40.00 10


0.25 11.44 28.86 59.45 402

248

Table A80 Improvements for sheep production - Access to credit –(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 11.11 55.56 33.33 27

All 3.54 45.13 51.33 113

Balkh Chimtal 2.50 7.50 90.00 80

Dihdadi 4.35 8.70 13.04 73.91 23

Nahri Shahi 13.33 86.67 15

All 2.54 1.69 9.32 86.44 118

Kabul Bagrami 11.11 44.44 44.44 9

Musayi 16.67 50.00 33.33 6

Paghman 5.26 10.53 5.26 78.95 19

All 5.88 8.82 23.53 61.76 34


Daman 4.17 95.83 24

Dand 100 13

Panjwayi 8.33 91.67 36

Zhiray 100 11

All 0.95 4.76 94.29 105

Logar Puli Alam 5.00 10.00 40.00 45.00 20


Shewa 100 1

All 100 2


Tutum Dara 50.00 16.67 16.67 16.67 6

All 30.00 10.00 30.00 30.00 10


2.24 3.23 21.39 73.13 402

249

Table A81 Problems of goat production - Not enough feed –(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 25.00 75.00 4

All 54.55 36.36 9.09 11

Balkh Chimtal 42.59 20.37 5.56 31.48 54

Dihdadi 22.22 44.44 33.33 9

Nahri Shahi 44.44 11.11 44.44 9

All 40.28 22.22 4.17 33.33 72


Musayi 66.67 33.33 3

Paghman 100 1

All 16.67 50.00 33.33 6


Daman 57.14 28.57 14.29 7

Zhiray 100 1

All 58.33 33.33 8.33 12

Nangarhar Bihsud 50.00 50.00 2



41.90 26.67 6.67 24.76 105

250

Table A82 Problems of goat production -Animal disease –(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 75.00 25.00 4

All 45.45 54.55 11

Balkh Chimtal 31.48 22.22 25.93 20.37 54

Dihdadi 55.56 11.11 22.22 11.11 9

Nahri Shahi 33.33 44.44 11.11 11.11 9

All 34.72 23.61 23.61 18.06 72


Musayi 33.33 33.33 33.33 3

Paghman 100 1

50.00 33.33 16.67

6


Daman 57.14 28.57 14.29 7

Zhiray 100 1

41.67 50.00 8.33 12




34.29 28.57 23.81 13.33 105

All

All

251

Table A83 Problems of goat production -Too far to the market –(percentages of respondents by district)



importantNot Mentioned Total Respondents


Baharak 75.00 25.00 4

All 27.27 72.73 11

Balkh Chimtal 22.22 14.81 62.96 54

Dihdadi 11.11 44.44 44.44 9


All 18.06 18.06 63.89 72

Kabul Bagrami 100 2

Musayi 100 3

Paghman 100 1

All 100 6


Daman 100 7

Zhiray 100 1

All 100 12




15.24 15.24 69.52 105

252

Table A84 Problems of goat production - Not enough buyers-(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 100 4

All 100 11

Balkh Chimtal 1.85 1.85 96.30 54

Dihdadi 11.11 11.11 77.78 9

Nahri Shahi 100 9

All 1.39 2.78 1.39 94.44 72

Kabul Bagrami 100 2

Musayi 100 3

Paghman 100 1

All 100 6


Daman 14.29 14.29 71.43 7

Zhiray 100 1

All 8.33 8.33 83.33 12

Nangarhar Bihsud 100 2



0.95 2.86 1.90 94.29 105

253

Table A85 Problems of goat production -Too much work –(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 100 4

All 9.09 90.9 11

Balkh Chimtal 24.07 1.85 74.1 54

Dihdadi 22.22 77.8 9

Nahri Shahi 11.11 11.11 77.8 9

All 22.22 2.78 75.0 72


Musayi 33.33 66.7 3

Paghman 100 1

All 33.33 66.7 6


Daman 42.86 28.57 28.6 7

Zhiray 100 1

All 41.67 25.00 33.3 12




20.00 1.90 5.71 72.4 105

254

Table A86 Problems of goat production -Cost of labor -(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 25.00 75.00 4

All 9.09 63.64 27.27 11

Balkh Chimtal 1.85 3.70 12.96 81.48 54

Dihdadi 11.11 11.11 77.78 9

Nahri Shahi 100 9

All 1.39 4.17 11.11 83.33 72


Musayi 100 3

Paghman 100 1

All 16.67 83.33 6


Daman 28.57 71.43 7

Zhiray 100 1

All 16.67 83.33 12




0.95 3.81 17.14 78.10 105

255

Table A87 Improvements for goat production - Better feeding-(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 25.00 75.00 4

All 63.64 36.36 11

Balkh Chimtal 82.09 7.46 7.46 3.0 67

Dihdadi 37.50 25.00 25.00 12.5 8

Nahri Shahi 72.73 9.09 18.2 11

All 76.74 9.30 8.14 5.8 86

Kabul Bagrami 100 2

Musayi 66.67 33.33 3

All 80.00 20.00 5


Daman 100 1

Zhiray 100 1

All 100 4




256

Table A88 Improvements for goat production - Better health care -(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 75.00 25.00 4

All 36.36 63.64 11

Balkh Chimtal 7.46 79.10 5.97 7.5 67

Dihdadi 37.50 37.50 12.50 12.5 8

Nahri Shahi 18.18 54.55 9.09 18.2 11

All 11.63 72.09 6.98 9.3 86


Musayi 33.33 66.7 3

All 20.00 40.00 40.0 5


Daman 100 1

Zhiray 100 1

All 50.00 50.00 4




12.73 67.27 10.91 9.1 110

257

Table A89 Improvements for goat production - Better water access –(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 100 4

All 18.18 81.8 11

Balkh Chimtal 4.48 2.99 35.82 56.7 67

Dihdadi 12.50 87.5 8

Nahri Shahi 18.18 54.55 27.3 11

All 5.81 2.33 36.05 55.8 86

Kabul Bagrami 100 2

Musayi 33.33 33.33 33.3 3

All 20.00 20.00 60.0 5


Daman 100 1

Zhiray 100 1

All 100 4




4.55 2.73 30.91 61.8 110

258

Table A90 Improvements for goat production - Easier market access -(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 75.00 25.0 4

All 27.27 72.7 11

Balkh Chimtal 1.49 5.97 19.40 73.1 67

Dihdadi 12.50 12.50 25.00 50.0 8


All 2.33 6.98 17.44 73.3 86

Kabul Bagrami 100 2

Musayi 100 3

All 100 5


Daman 100 1

Zhiray 100 1

All 100 4




1.82 5.45 17.27 75.5 110

259

Table A91 Improvements for goat production - Knowledge about animal husbandry -(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 100 4

All 18.18 81.8 11

Balkh Chimtal 1.49 2.99 23.88 71.6 67

Dihdadi 12.50 25.00 25.00 37.5 8


All 2.33 4.65 22.09 70.9 86


Musayi 33.33 66.7 3

All 20.00 20.00 60.0 5


Daman 100 1

Zhiray 100 1

All 50.00 25.00 25.0 4




1.82 8.18 20.91 69.1 110

260

Table A92 Improvements for goat production - Access to credit –(percentages of respondents by district)




Not Mentioned

Total Respondents


Baharak 25.00 75.0 4

All 36.36 63.6 11

Balkh Chimtal 1.49 4.48 94.0 67

Dihdadi 100 8


All 1.16 1.16 3.49 94.2 86


Musayi 33.33 66.7 3

All 20.00 20.00 60.0 5


Daman 100 1

Zhiray 100 1

All 25.00 75.0 4




2.73 1.82 9.09 86.4 110

261

Table A93 Summary of the calculation of values of sheept by district

Obs Province District sheepπ π̂ s.e.( π̂ ) sheept

1 Badghis Qadis 2.999 4.080 14.081 2.917

2 Baghlan Baghlan 4.416 65.364 33.259 1.833

3 Baghlan Dushi 3.314 6.806 2.739 1.275

4 Baghlan Khinjan 2.441 56.969 27.280 1.999


6 Bamyan Kamhard 2.420 8.214 5.649 1.026

7 Bamyan Saighan 3.639 10.179 4.391 1.489

8 Bamyan Shibar 3.686 4.650 1.417 0.680


10 Farah Anar Dara 1.323 7.784 4.252 1.520


12 Ghazni Dih Yak 2.579 4.808 2.805 0.794

13 Ghazni Zana Khan 2.180 7.194 1.802 2.783

14 Ghor Tulak 1.448 7.520 3.535 1.718

15 Hilmand Nad Ali 7.211 1.600 0.499 -11.234

16 Hirat Ghoryan 5.195 18.320 4.676 2.807

17 Hirat Kohsan 4.807 21.778 6.193 2.741

18 Hirat Obe 2.469 11.667 3.021 3.044

19 Jawzjan Qurghan 3.522 149.517 58.110 2.512

20 Kabul Chahar Asyab 0.525 0.000 2.142 -0.245

21 Kabul City Nahya 14 0.087 2.000 0.998 1.916




25 Laghman Alingar 1.310 4.897 2.181 1.644


27 Logar Charkh 0.206 1.158 0.623 1.530

28 Nangarhar Kama 1.348 0.412 0.434 -2.156

29 Nangarhar Khogyani 2.605 0.694 0.225 -8.501

30 Nangarhar Muhmand Dara 6.844 0.179 0.160 -41.685

31 Nangarhar Pachir Wa Agam 2.655 1.571 0.576 -1.882

32 Nimroz Khash Rod 2.250 25.800 8.008 2.941


34 Paktika Sharan 3.152 65.556 27.604 2.261

35 Paktya Chamkani 0.469 11.308 4.383 2.473


37 Parwan Bagram 1.495 0.704 0.251 -3.158

38 Parwan Jabalusaraj 1.237 0.267 0.178 -5.465

39 Samangan Kaldar 2.443 7.719 2.078 2.539

40 Samangan Khulm 6.282 81.313 45.078 1.665




44 Zabul Shahjoy 3.115 8.679 2.778 2.002


sheepsheep

8.5. TABULAR SUMMARY STATISTICS DATA CONSISTENCY

262

Table A94 Summary of the calculation of values of goatst by district

Obs Province District goatsπ π̂ s.e.( π̂ ) goatst

1 Badghis Qadis 2.565 22.320 6.409 3.082

2 Baghlan Baghlan 0.362 14.970 4.682 3.120

3 Baghlan Dushi 3.208 9.281 4.894 1.241

4 Baghlan Khinjan 4.405 11.906 4.990 1.503


6 Bamyan Kamhard 1.065 1.571 1.425 0.356

7 Bamyan Saighan 1.608 2.893 0.896 1.433

8 Bamyan Shibar 1.171 1.650 0.483 0.993


10 Farah Anar Dara 3.623 28.351 8.574 2.884


12 Ghazni Dih Yak 0.505 1.692 0.847 1.402

13 Ghazni Zana Khan 1.878 3.000 1.208 0.929

14 Ghor Tulak 2.261 13.040 3.268 3.299

15 Hilmand Nad Ali 2.876 3.500 1.674 0.373

16 Hirat Ghoryan 3.747 10.120 2.341 2.723

17 Hirat Kohsan 2.939 28.528 8.127 3.149

18 Hirat Obe 3.528 13.041 2.522 3.772

19 Jawzjan Qurghan 0.676 11.517 3.542 3.061

20 Kabul Chahar Asyab 0.423 0.000 1.957 -0.216

21 Kabul City Nahya 14 0.049 0.250 0.250 0.804


25 Laghman Alingar 4.327 2.143 1.728 -1.264


28 Nangarhar Kama 1.385 0.412 0.199 -4.885

29 Nangarhar Khogyani 1.205 1.000 0.296 -0.694


31 Nangarhar Pachir Wa Agam 2.631 1.429 0.470 -2.559

32 Nimroz Khash Rod 12.796 23.250 6.475 1.614


34 Paktika Sharan 0.574 11.944 10.606 1.072

35 Paktya Chamkani 4.738 82.000 8.903 4.087


39 Samangan Kaldar 2.025 4.581 1.881 1.359

40 Samangan Khulm 1.785 16.313 7.936 1.831




44 Zabul Shahjoy 2.050 3.552 0.877 1.712


goatsgoats

263

Table A95 Summary of the calculation of values of donkeyst by district

Obs Province District donkeysπ π̂ s.e.( π̂ ) donkeyst

1 Badghis Qadis 1.004 2.640 0.395 4.146

2 Baghlan Baghlan 0.337 2.606 0.604 3.754

3 Baghlan Dushi 0.793 1.438 47.343 0.014

4 Baghlan Khinjan 0.667 1.344 100.385 0.007


6 Bamyan Kamhard 1.274 1.357 0.308 0.271

7 Bamyan Saighan 1.312 1.214 0.181 -0.540

8 Bamyan Shibar 0.985 1.100 0.204 0.564


10 Farah Anar Dara 0.419 0.811 0.128 3.072


12 Ghazni Dih Yak 0.378 0.385 0.112 0.059

13 Ghazni Zana Khan 0.630 0.452 0.118 -1.511

14 Ghor Tulak 0.760 1.560 0.201 3.988

15 Hilmand Nad Ali 0.269 0.800 0.138 3.855

16 Hirat Ghoryan 0.325 0.800 0.114 4.158

17 Hirat Kohsan 0.937 1.389 0.305 1.483

18 Hirat Obe 0.698 1.600 0.210 4.296

19 Jawzjan Qurghan 0.108 1.517 0.241 5.842

20 Kabul Chahar Asyab 0.142 0.400 0.184 1.401

21 Kabul City Nahya 14 0.010 1.750 0.398 4.376




25 Laghman Alingar 0.267 1.097 0.375 2.214


27 Logar Charkh 0.420 0.294 0.178 -0.707

28 Nangarhar Kama 0.346 0.278 0.148 -0.462

29 Nangarhar Khogyani 1.197 1.359 0.149 1.088


31 Nangarhar Pachir Wa Agam 0.432 1.750 0.108 12.223

32 Nimroz Khash Rod 0.342 0.333 0.228 -0.040


34 Paktika Sharan 0.626 1.077 0.307 1.467

35 Paktya Chamkani 0.650 0.421 0.143 -1.593


37 Parwan Bagram 0.308 0.200 0.238 -0.453

38 Parwan Jabalusaraj 0.568 0.938 0.107 3.453

39 Samangan Kaldar 0.449 1.969 0.163 9.322

40 Samangan Khulm 1.265 2.300 0.553 1.872




44 Zabul Shahjoy 0.613 0.696 0.346 0.240

donkeys donkeys

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NATIONAL LIVESTOCK CENSUS - International Programsand Prof Fred Dahm (Texas A&M University, College...

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Transcript of NATIONAL LIVESTOCK CENSUS - International Programsand Prof Fred Dahm (Texas A&M University, College...