Sampling Methodology for Energy - ICAR

Sampling Methodology for Energy Audit Survey

(Energy Management in Agriculture component of the ICAR-All India Coordinated Research Project on Energy in Agriculture and Agro-based Industries)

Hukum Chandra Pradip Basak

ICAR-Indian Agricultural Statistics Research Institute, New Delhi

P. Subramanian R. Mahendiran

Agricultural Engineering College and Research InstituteTamil Nadu Agricultural University, Coimbatore

K. C. PandeyICAR-Central Institute of Agricultural Engineering, Bhopal, Madhya Pradesh

January 2021

(i)

ForewordThe pattern of utilizing the energy inputs in farm production is closely linked with cropping pattern and cropping intensities. The choice of energy resources has been dynamic due to preferences to chemical inputs and electro-mechanical power sources for agriculture. The regular and timely flow of quality primary data is desirable to understand the pattern of energy use in agricultural production system. All India Coordinated Research Project on Energy in Agriculture and Agro based Industries (ICAR-AICRP on EAAI), ICAR-Central Institute of Agricultural Engineering (ICAR-CIAE), Bhopal has encompassed a new component on Energy Management in Agriculture (EMA) in its ambit with objective of enhancing energy use efficiency in Indian agricultural sector. The ICAR-AICRP on EAAI has, therefore, initiated energy audit survey to identify

energy intensive operations for further energy conservation and efficiency improvement. The ICAR-Indian Agricultural Statistics Research Institute (ICAR-IASRI), New Delhi was associated with EMA component of ICAR-AICRP on EAAI and played a lead role in finalization of sampling methodology for energy audit survey. The ICARI-IASRI, New Delhi has also been associated with ICAR-AICRP on Energy Requirement in Agricultural Sector, a predecessor of EMA component of ICAR-AICRP on EAAI and provided methodologies for estimating/projecting the energy requirement in agricultural sector, maximization of yield subject to the constraints on the availability of energy from different sources and minimization of total energy for obtaining a given level of yield.

I am happy to note that ICARI-IASRI, New Delhi, Co-ordination Unit, ICAR-AICRP on EAAI, ICAR-CIAE, Bhopal and Co-operating centre of the ICAR-AICRP on EAAI, Tamil Nadu Agricultural University (TNAU), Coimbatore have prepared the manual on Sampling Methodology for Energy Audit Survey for the implementation of energy audit survey by different co-operating centres of the ICAR-AICRP on EAAI. This document consists of sampling methodology including sample size, allocation of sample sizes in different strata (agro-climatic zones) and sub-strata (land holding categories), selection of sample in different stages (e.g. first stage as villages and second stage as farm households), estimation procedure and schedules for data collection. Further, the authors have also provided step by step illustration with examples which would make this manual user friendly for the practitioners. I take this opportunity to congratulate the Project Coordinator, ICAR-AICRP on EAAI, ICAR-CIAE, Bhopal and the team of scientists from ICARI-IASRI, New Delhi and TNAU, Coimbatore for their sincere collective efforts in bringing out this important document. I am sure that readers will find this document informative and useful.

28.01.2021 Rajender Parsad)

PrologueAgricultural system requires energy (direct as well as indirect) as an input at all stages of agricultural production. Direct energy requirements refer to the gross energy of the fuels directly used in the production process. Indirect energy requirements include all remaining processes needed for the production of the input and its use at the farm, including fuel production and transport, raw materials production and transport, energy embedded in equipment and transport of finished products up to the farm. Estimation of direct and indirect energy use is mandatory when elaborating on energy use and energy efficiency in production agriculture, to avoid

sub-optimization and incorrect recommendations on technology use in farming operations.

Securing agricultural production with maximum net energy productivity and minimized impact is a key issue in agricultural systems. In this regard, energy audit in production agriculture is very much important to achieve higher efficiency with minimum energy input. Energy audit in farm operations will provide the benefit of minimum use of energy sources and likelihood of installing energy savings recommendations.

The ICAR-IASRI, New Delhi; Co-ordinating Cell, ICAR-AICRP on EAAI, ICAR-CIAE, Bhopal and Tamil Nadu Agricultural University, Coimbatore have jointly prepared a manual on “Sampling Methodology for Energy Audit Survey” to address the sampling methodology (protocol) for conducting energy audit in production agriculture. It covers sampling design, sampling size, estimation formula, data analysis procedure, schedules and instruction manual. The contents of the manual are updated and have been presented lucidly with interesting illustrations. The contents will be very useful to analyse the energy consumption in various crops production in the respective regions. This manual can effectively be used to reduce the energy consumption and improve the energy use efficiency in production agriculture and agro-based industries.

I congratulate the authors for their sincere collective efforts in compiling and publishing this important document. I am sure readers will find this document informative and useful.

(C.R. Mehta)28/01/2021 Director

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ICAR-CENTRAL INSTITUTE OF AGRICULTURAL ENGINEERING Nabibagh, Berasia Road, Bhopal - 462038

Fax No. 0755-2734016, Gram: KRIYANTRA, Phone: 0755-2521001 E-mail: [email protected], [email protected], https//:www.ciae.nic.in

PROLOGUE

Agricultural system requires energy (direct as well as indirect) as an input at all stages of agricultural production. Direct energy requirements refer to the gross energy of the fuels directly used in the production process. Indirect energy requirements include all remaining processes needed for the production of the input and its use at the farm, including fuel production and transport, raw materials production and transport, energy embedded in equipment

and transport of finished products up to the farm. Estimation of direct and indirect energy use is mandatory when elaborating on energy use and energy efficiency in production agriculture, to avoid sub-optimization and incorrect recommendations on technology use in farming operations. Securing agricultural production with maximum net energy productivity and minimized impact is a key issue in agricultural systems. In this regard, energy audit in production agriculture is very much important to achieve higher efficiency with minimum energy input. Energy audit in farm operations will provide the benefit of minimum use of energy sources and likelihood of installing energy savings recommendations. The ICAR-IASRI, New Delhi; Co-ordinating Cell, ICAR-AICRP on EAAI, ICAR-CIAE, Bhopal and Tamil Nadu Agricultural University, Coimbatore have jointly prepared a manual on “Sampling Methodology for Energy Audit Survey” to address the sampling methodology (protocol) for conducting energy audit in production agriculture. It covers sampling design, sampling size, estimation formula, data analysis procedure, schedules and instruction manual. The contents of the manual are updated and have been presented lucidly with interesting illustrations. The contents will be very useful to analyse the energy consumption in various crops production in the respective regions. This manual can effectively be used to reduce the energy consumption and improve the energy use efficiency in production agriculture and agro-based industries. I congratulate the authors for their sincere collective efforts in compiling and publishing this important document. I am sure readers will find this document informative and useful.

(C.R. Mehta)

28/01/2021 Director

PrefaceIn production agriculture, crop yield could be maximized through optimum use of various inputs and maintaining the timeliness of agricultural operations. Various types of energy such as direct and indirect; renewable and nonrenewable; commercial and non-commercial energy are being used in agriculture. Direct energy use in agriculture are predominantly petroleum-based fuels involved in the operation of tractor, power tiller, combine harvester, self-propelled machineries etc., and machinery for land preparation,irrigation, weeding, harvesting,application of fertilizer and plant protection chemicals, transplantation etc. Indirect energy sources are seeds, fertilizers and pesticides. Generally, fertilizers and fuel energy are the major energy consuming sources in production agriculture.

Energy assessment and identification of energy intensive operations are the prime techniques in finding out possible solutions to energy management in agriculture. Increasing energy efficiency and reduction of energy intensity are the prime factors to utilize available energy in agriculture. These lead to improved energy efficiency and reduced cost of production without sacrificing the crop yield. Application of renewable energy systems are one of the effective solutions in reducing energy consumption and pollution in production agriculture. The optimum energy use in agricultural production provides the higher crop yield, cost savings, fossil fuel conservation and also reduces air pollution.

Energy audit is the first step to use of available energy in efficient manner. For carrying out energy audit and management in production agriculture, the systematic methodology is required to identify energy intensive operation and exploring the energy conservation measures. For conducting energy audit in industrial sector, optimized set procedures are available by BIS and BEE but the same are neither optimized nor standardized for conducting energy audit in agriculture production system. Hence, there is urgent need to formulate adequate methodology for conducting energy audit survey in agro-industrial system. In order to carry out energy audit survey, methodologies are customized for sampling design, sample size, allocation of sample sizes in different strata and sub-strata, selection of sample in different stages, listing exercise as well as schedules, and finally estimation of parameters. In view of the facts stated above an effort has been made in this manual to clearly elaborate the procedure of conducting energy audit survey in agricultural sector. The manual on Sampling Methodology for Energy Audit Survey is prepared for adoption among the centres of ICAR-AICRP on Energy in Agriculture and Agro-based Industries (EAAI) so that results may be compared logically. It is hoped that the content of this manual will be very useful in conducting energy audit survey of the allocated crops for various cooperating centres systematically.

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Authors thankfully acknowledge the valuable guidance and suggestions provided by the eminent experts especially Professor B.S. Pathak, Dr. S. Kamaraj, Dr. T.K. Bhattacharya and other user experts from cooperating centres of ICAR-AICRP on EAAI for their input and feedback. The authors express their sincere thanks to the Deputy Director General (Engg), Assistant Director General (Farm Engg), Director, ICAR-IASRI, New Delhi and Director, ICAR-CIAE, Bhopal, Madhya Pradesh for providing the necessary support and encouragement in bringing out this methodological manual.

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Authors

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Foreword i

Prologue ii

Preface iii-iv

1 Background 1-2

2 Sampling Design and Sample Size 2-8

2.1 Sample size 2

2.2 Allocation of sample size 2-4

2.3 Selection of villages from Agro climatic zones and 5 households from villages

2.3.1 Sample selection using Random number table 5-6

2.3.2 Sample selection using Excel worksheet 6

2.4 Collection of data 7

3 Estimation Formula and Data Analysis 8-13

4 Schedules and Instruction Manual 14-22

Annexure I : Random Number Table 23-24

Contents

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1Sampling Methodology for Energy Audit Survey

1. BACKGROUND

A new component on “Energy Management in Agriculture” was included in the ICAR- All India Coordinated Research Project on “Energy in Agriculture and Agro based Industries” (ICAR-AICRP on EAAI) with effect from 12th Five Year Plan approval with basic motto of reducing energy intensity and increasing energy efficiency in Indian agricultural sector. The ICAR-AICRP on EAAI has therefore decided to conduct energy audit in agricultural production systems and agro-industrial sector by the cooperating centres, considering the most prominent crops grown in the respective regions. The EMA activities started with the crop, which occupy the maximum cropping area in the state where ICAR-AICRP on EAAI cooperating centres exist. To carry out energy auditing in crop production system, it was proposed to conduct energy audit survey of the selected crops. The objective of the survey was to identify energy intensive operations in the selected crops for further energy conservation and efficiency improvement (i.e. loss minimization). For instance, Tamil Nadu Agricultural University, Coimbatore centre was assigned to carry out the energy audit for sugarcane crop. In order to implement this energy audit survey and data collection on energy use in agriculture, an appropriate sampling strategy is inevitable.

In the expert group meetings organized at TNAU, Coimbatore and annual workshops of the ICAR-AICRP on EAAI, it was suggested to utilize the sampling expertise available at the ICAR-Indian Agricultural Statistics Research Institute (ICAR-IASRI), New Delhi for the energy audit survey. Accordingly, ICAR-IASRI, New Delhi was associated by ICAR-AICRP on EAAI to chock out the sampling design and data collection procedures to be adopted in the “Energy Management in Agriculture” component during 2018. The ICAR-IASRI, New Delhi was entrusted a lead role in finalization of sampling methodology for Energy Audit Survey. The sampling methodology was also discussed in expert group meetings and annual workshops and a training programme was organized at the ICAR-IASRI, New Delhi in close leadership of Dr. Hukum Chandra of ICAR-IASRI, New Delhi. The inputs and feedback from several experts and stockholders were incorporated in the finalization of sampling methodology. A two days training cum interaction programme on “Sampling Design and Schedules for Implementation of Energy Audit Survey” for the scientists involved in the ICAR-AICRP on EAAI was also organized at ICAR-IASRI, New Delhi during November 1-2, 2019. The main aim of this programme was to impart training on sampling design and schedules for implementation of energy audit survey. Professor B.S. Pathak,

2 Sampling Methodology for Energy Audit Survey

an Eminent Energy Expert and Dr. K. C. Pandey, Project Coordinator of ICAR-AICRP on EAAI shared their expertises on energy auditing during this training programme.

This document deliberates a sampling methodology (protocol) including sample size, allocation of sample sizes in different strata and sub-strata, selection of sample in different stages and listing exercise as well as schedules, instruction manual and estimation formula for the implementation of energy audit survey by different cooperating centres of the ICAR-AICRP on EAAI.

2. SAMPLING DESIGN AND SAMPLE SIZE

In consultation with experts in the field of Energy Management in Agriculture, Project Coordinator of ICAR-AICRP on EAAI, cooperating centres working on energy management and also considering the resources available including budget, manpower and time, a sampling strategy (protocol) has been finalized for the energy audit survey. The sampling design and other details are described below.

In the energy audit survey, a stratified two-stage sampling design has been adopted. Different agro-climatic zones (ACZs) in the state having significant area of the crop under study are considered as strata. If some of the ACZs are not cultivating the selected crop and/or having negligible crop area, the ACZ need not be considered as strata. For instance, Tamil Nadu has seven agro-climatic zones and two agro-climatic zones do not have significant sugarcane growing area. Hence, the remaining five ACZs are considered as strata for energy audit survey of sugarcane crop. The villages within the strata (ACZs) are the first stage unit (FSU) and the farm households are the second stage unit (SSU) of selection.

2.1 Sample size

Based on the resource availability and other aspects deliberated in several meetings, it has been decided to select a sample of 30 villages for a major crop of the state. From each of the selected village, 20 eligible farm households are selected. It is also to be considered that these 20 farm households are to be evenly distributed among the three land holding categories.

2.2 Allocation of sample size

In this survey, 600 farm households will be selected for energy auditing in the selected major crop of the state. The sample of 600 farm households will be allocated in different stages (i.e. FSUs and SSUs) as follows:


Allocation of number of villages in Agro-climatic zones

The selected sample of 30 villages will be allocated to different strata (ACZs) in proportion to the area of cultivation under the selected crop. For example, out of seven agro-climatic zones in Tamil Nadu, 4 agro-climatic zones have major area of sugarcane cultivation and another one agro-climatic zone has minimum sugarcane cropping area. Hence, 30 villages will be distributed among these 5 strata (ACZs), using proportional allocation methodology as described below.

Let, n is the total number of villages to be selected from the state. Here, n = 30 for this energy auditing survey. Let, H is the total number of ACZs (strata) in the state having significant cropped area under the identified crop.

Let Ah is the area of the selected crop in the hth ACZ (stratum). The number of villages (FSUs) to be allocated in the hth stratum is calculated as

For example out of 7 ACZs in Tamil Nadu, sugarcane crop is prevalent (in terms of sugarcane growing area) in 5 ACZs (4 major and 1 minor ACZ). Therefore, number of ACZs (strata) for sugarcane crop in Tamil Nadu is H = 5 (Not 7) and sample size of 30 villages will be allocated in H = 5 ACZs (strata) in proportion to area under sugarcane in these ACZs (strata). The area Ah ( h = 1, ...., 5) under sugarcane crop in these 5 ACZs are reported as 160552, 39950, 63575, 64023, and 13641 ha, respectively. Thus, total area

under sugarcane in all the five ACZs is ha.

The number of villages to be selected from the first ACZ (stratum) is

Similarly, for the second ACZ, number of villages to be selected is

Hence, the number of villages to be selected from the remaining ACZs 3rd ,4th and 5th are 6, 6, and 1, respectively. The number of villages to be selected from the ACZs need to be rounded off to the nearest integer in such a way that the total number of selected villages in the state equals the sample size of the state in terms of villages.


Allocation of number of households in land holding categories in villages

In each of the selected village, a sample of 20 eligible farm households will be selected. These 20 farm households will be selected from three landholding categories (i.e. Marginal holdings: land size 1 ha or less; Small holdings: land size 1 to 2 ha; Other holdings: land size greater than 2 ha) in the village. These three landholding categories are known as sub-strata. Allocation of 20 farm households in these three land holding categories (sub-strata) will be done based on the proportion of number of farm households in these categories (sub-strata) in the selected village. The information on number of eligible farm households in three categories (sub-strata) in the selected villages will be available from the listing exercise where information will be collected in schedule-1 (see next section).

Let Mhi is the total number of eligible farm household in all the land categories (sub-strata) in the ith selected village of the hth stratum. Let m is the sample size, i.e., number of farm households to be selected in the selected village (here, m = 20). The number of farm households to be allocated in the jth (j = 1,2,3) sub-stratum (land holding category) is calculated as

where Mhij is the total number of eligible farm household in jth category or sub-stratum in the ith selected village of the hth stratum.

Example: Let, in a selected village, total number of eligible farm household is Mhi = 100 and among those, Mhi1 = 30 households belong to the marginal land holding category (Category-1), Mhi2 = 60 households to the small land holding category (Category-2), and Mhi3 = 10 households to the other land holding category (Category-3). By applying proportional allocation process, the total number of sample farm households (m = 20) to be allocated in the Category-1 is given by

Likewise, the number of eligible farm households to be allocated in the second and third

land holding categories are calculated as and .

Note: If there is no farm households in any of the three land holding categories in the selected village, remaining sample household will be allocated in the next land holding category.


2.3 Selection of villages from Agro-climatic zones and households from villages

For the selection of villages in an Agro-climatic zone (Strata), villages having no area under the selected crop should be removed from the villages list (i.e. sampling frame of FSUs). From the remaining villages, selection of villages has to be done. Let us assume that hth (h = 1, ...., H) ACZ (stratum) comprises of Nh villages (FSUs) having the selected crop and nh villages (number determined on the basis of village allocation formula given in previous section) are to be selected. Simple random sampling without replacement (SRSWOR) design is used for the selection of villages from each of the ACZs. To achieve this, a list of all the villages in the ACZ is to be prepared which may be given serial number from 1 to Nh for ACZ (stratum) h.

The selection of villages from the list of villages (i.e. sampling frame of FSUs) is done either by the random number table (see Annexure I) or by RANDBETWEEN function in the Excel worksheet. Steps for selection of random number using Random number table is given below.

2.3.1 Sample selection using random number table

A random number table is an arrangement of digits 0 to 9, in either a linear or rectangular pattern, where each position is filled with one of these digits. A table of random number is so constructed that all numbers, 0, 1, 2, . . . , 9 appear independent of each other. Some random number tables in common use are:

i. Tippette’s random number tables

ii. Fisher and Yates tables

A practical method of selecting a random sample is to choose units one-by-one with the help of a table of random numbers. By considering two-digit numbers, we can obtain numbers from 00 to 99, all having the same frequency. Similarly, three or more digit numbers may be obtained by combining three or more rows or columns of these tables.

Direct approach

The simplest way of selecting a sample of the required size is by selecting a random number from 1 to N and then taking the unit bearing that number. This procedure involves a number of rejections since all numbers greater than N appearing in the table are not considered for selection. The use of random number is, therefore, modified and the common modified procedure is ‘Remainder approach’.


Remainder approach

Let N be an r-digit number and let its r-digit highest multiple be N1. In this approach a random k is first chosen from 1 to N1 and then the unit with the serial number equal to the remainder obtained on dividing k by N is selected. If the remainder is zero, the last unit is selected. As an illustration, let N = 123, the highest three-digit multiple of 123 is 984. For selecting a unit, one random number from 001 to 984 has to be selected. Let the random number selected be 287. Dividing 287 by 123, the remainder is 41. Hence, the unit with serial number 41 is selected in the sample.

Village selection example: Select a random sample of 5 villages from a list of 112 villages in an ACZ.

Direct approach: Here, total number of villages in the ACZ is 112 which is a three digit number. In the random number Table A1 given in Annexure I, we first to choose a random column and row number (called Random Starting Place). In the random number Table A1, with eyes closed, drop a pencil anywhere on the page to indicate a starting place in the Table A1. Let us assume you dropped your pencil in Table A1 and it has indicated a starting place at column 14 and row 11. This number is 3. But, here total number of villages are 112 (i.e. a three digit number) so we have to use 3-digit number. Therefore, we have to use the 3-digit random numbers from columns 14 to 16, 17 to 19 and so on of the random number Table A1 in Annexure I and rejecting the numbers greater than 112 (also the number 000). In the column number 14-16 and starting from row number 11 (which is randomly chosen row), 3-digit numbers are 385, 242, 999, 101, 898, 872, 830,…,974, 104,., 071 and so on. Hence, we have the sample of size 05 bearing serial number 101, 104, 71, 038 and 044.

Remainder approach: In the above procedure, large number of random numbers is rejected. Hence a commonly used device, i.e. remainder approach, is employed to avoid the rejection of such large numbers. The greatest 3-digit multiple of 112 is 896. By using 3-digit random numbers as above, the sample has 05 villages with serial numbers 049 (385-3x112), 018 (242-2x112), 104, 088 (872-7x112) and 046 (830-7x112).

2.3.2 Sample selection using Excel worksheet

Without using any random number table, sample selection can be done with MS-Excel workbook using the procedure provided in the following flowchart.


2.4 Collection of data

The random numbers represent the corresponding village’s serial number which are to be selected in the sample. There are three stages of data collection in the survey as given below.

Schedule-I: Listing/Enumeration of Selected Villages

In this schedule, we list all the eligible farm households of the selected villages. Eligible farm households would be those who are growing that identified crop of the centre/state. This information will then be used for the preparation of land holding category-wise list of the eligible farm household in the selected village. The primary data will be collected with village, taluk/block, district, agro-climatic zone, farmer’s land holding area as per data questionnaire in Schedule 1.

Suppose, village number 104 is selected from ACZ-1, listing of eligible farm households in the selected village is to be done with the following information

a) Basic information about the selected village, and

b) Household wise information.

If the selected village consists of more than 200 households, creation of hamlet-groups may be considered in order to list the eligible households. When a large village is divided into a certain number of sub-divisions, these sub-divisions are known as hamlet-groups (hg's). These sub-divisions are created based on the natural geographical boundaries of the village. When there are more than 2 hamlet-groups in the selected village, we may select 2 hamlet-groups in order to list the eligible households. Select the bigger hamlet-group and from the remaining hamlet-groups, one may be selected. If the selected village is bigger, the selection of bigger hamlet-group may be done. For instance, if the number of households in the selected village is 400, the village may be divided into three hamlet-groups based on the natural geographical boundaries of the village. Select the bigger hamlet-group and out of the other two hamlet-groups, one may be selected. In questionnaire, schedule-I and II, mention the hamlet-group of the selected eligible farm household and total number of hamlet-groups in the village.

hg = hamlet-group


Schedule-II: List of operational holdings in selected village (Re-tabulation from Schedule-I)

This is the list of eligible farm households, categorized into three landholding categories as per size of the operational holdings recorded in Schedule-I.

From each category, allocated number of households are selected using Simple random sampling without replacement (SRSWOR) design. To achieve this, households within each category are given serial number from 1 to Mhij. The selection of households is done either by the random number table or by RANDBETWEEN function in the Excel worksheet. A total of mhij random numbers are selected from each category which represents the selected households. Schedule-II provides the list of eligible households to be surveyed for the assessment of energy consumption data on both source wise and operation wise for the identified crop.

Schedule-III: Detailed operational holding survey - Farmer’s questionnaire

In each village, 20 eligible farm households will be selected from Schedule-II, and detailed operational holding survey is to be done using farmer’s questionnaire Schedule-III. Thus, a total of 600 farm households will be surveyed in the State. In these questionnaires, information on basic energy inputs and outputs, detailed energy consumption will be collected and verified with measurements for both source and operations wise. Data collection from the farm household has to be done two times, one at the beginning of the crop season and another at the harvesting time. For example, in case of sugarcane, the operations are land development, land preparation, seed bed preparation, planting, irrigation, weeding, fertilizer application, chemical application, earthing-up, de-trashing, harvesting, transportation and trash management. In addition to the energy consumption, information may be collected with cost details for the type of equipment/implements used, fuel/electricity consumption and man power used.

3. ESTIMATION FORMULA AND DATA ANALYSIS

Energy consumption will be calculated based on source-wise and operation-wise and also analysis will be carried out from the collected field data. Besides, estimates of different parameters, standard error and 95 per cent confidence interval will be calculated. From the analysis, energy intensive operation will be identified for better energy conservation and efficiency improvement. Recommendation with economic analysis may be given for effective implementation.


Estimation Formula

The components considered in the estimation are:

H is the total number of ACZs (strata) in the state for the selected crop,

Nh is the total number of villages in the ACZ,

nh is the number of villages to be selected out of Nh villages in the ACZ,

Mhij is the total number of eligible farm households in jth land holding category of the ith selected village in the hth ACZ, and

mhij is the number of eligible farm households to be selected in jth land holding category of the ith selected village in the hth ACZ.

Let yhijk be the values of the variables corresponding to kth household in the jth landholding category of the ith selected village belonging to the hth stratum, where k = 1, 2, ...., mhij, j = 1, 2,3, i = 1,2, ....., nh, and h = 1,2, ....., H . The estimated total of the variable in the selected state is given by

,

where, is the survey weight of the kth household in the

sample (k = 1,2,...., mhij) in the jth landholding category of the ith selected village belonging

to the hth stratum. Here, is the survey weight for selection of ith village in the

hth stratum (h=1,2,....,H), which is weight for first stage selection (i.e. FSU weights) and

is the survey weight for the households selected in the jth landholding category

of the ith selected village in the hth stratum, which is weight for second stage selection (i.e. SSU weights). Final weights for a household selected in the sample (whijk) is the product of FSU (wh) and SSU (whij) weights.

Calculation of survey weights

Let the number of ACZs (strata) for sugarcane crop in Tamil Nadu is H = 5 and sample size of n = 30 villages (to be selected) will be allocated in H = 5 ACZs (strata) in proportion to


area under sugarcane in these ACZs (strata). Suppose, the total number of villages in the ACZs are N1 = 140, N2 = 300, N3 = 400, N4 = 350, and N5 = 150, the number of villages to be selected from the ACZs are n1 = 14, n2 = 3, n3 = 6, n4 = 6 and n5 = 1. Here, out of total N = 1340 villages, a sample 30 villages are selected from H = 5 different ACZs (strata). The survey weights associated with villages in different strata (or ACZs) are calculated as in Table 1.

Table 1. Calculation of first stage weights

ACZs Total number of villages (Nh)

Number of villages selected in sample (nh)

Survey weight for villages

1 140 14 102 300 3 1003 400 6 66.74 350 6 58.35 150 1 150

It is noted that the survey weights associated with villages in various ACZs (strata) are different. Here, w1 (i.e. survey weight associated with villages selected in the first ACZ) implies that every sample village in the first ACZ presents 10 villages, whereas every sample village in the fourth ACZ presents 58.3 villages because survey weight associated with villages selected in the fourth ACZ is w4 = 58.3. This table indicates that the villages selected from ACZs have different survey weights and hence represents different number of villages.

Calculation of second stage weights

Let us assume that, for the first ACZ (i.e. h = 1), total number of eligible farm households in the second selected village (i.e. i = 2), belonging to the three land holding categories are 50, 150, and 60, respectively. Thus, M121 = 50, M122 = 150, and m123 = 60. Total number of eligible farm households in this village is 260. From the selected village, 20 eligible farm households will be selected for detailed energy consumption survey. The number of eligible farm households to be selected in the first, second and third land holding categories are allocated as m121 = 6, m122 = 12 and m123 = 2. For the first ACZ and the second selected village, the survey weights of the households selected in sample from the first,

second and third land holding categories (SSU weights) are

, and , respectively. For the first ACZ


and the second selected village, the survey weights associated with households selected from the first, second and third land holding categories are calculated as in Table 2.

Table 2. Calculation of second stage weights.

ACZs Village Number

Land holding

category

Total number of eligible farm households M12j

Number of eligible farm

households in sample m12j

Survey weight for farm households

1 2 1 50 6 8.331 2 2 150 12 12.51 2 3 60 2 30

The farm households selected from the first land holding category represent w121 = 8.33 households in the village whereas second land holding category represent w122 = 12.5 households in the village. In the third land holding category, each of the farm households in sample is representing w123 = 30 households in the village. It is evident that, all the farm households selected from the same village do not have equal weight or representation. In estimation, we should use the weighted mean (survey weight) instead of simple arithmetic mean. Similarly, survey weights can be calculated for the different land holding categories of the other villages.

Calculation of final weights

In each of the stratum (or ACZ), the final survey weight (or weight) of a farm household selected in the sample is the product of FSU and SSU weights. We multiply the survey weight associated with selection of village and selection of farm household. For the first ACZ and the second selected village, the final weights of the farm households selected in sample from the first, second and third land holding categories are calculated as

For the first ACZ and the

second selected village, the final weights associated with households selected from the first, second and third land holding categories are calculated as in Table 3.


Table 3. Calculation of final household survey weights.

ACZ Village Land holding

category

Farm household in sample

Survey weight for village (FSU weight) in the

first ACZ

Survey weight for farm

households (SSU weight)

Final survey weight

for farm households in the ACZ

1 2 1 1 10 8.33 83.331 2 1 2 10 8.33 83.331 2 1 3 10 8.33 83.331 2 1 4 10 8.33 83.331 2 1 5 10 8.33 83.331 2 1 6 10 8.33 83.331 2 2 1 10 12.5 1251 2 2 2 10 12.5 1251 2 2 3 10 12.5 1251 2 2 4 10 12.5 1251 2 2 5 10 12.5 1251 2 2 6 10 12.5 1251 2 2 7 10 12.5 1251 2 2 8 10 12.5 1251 2 2 9 10 12.5 1251 2 2 10 10 12.5 1251 2 2 11 10 12.5 1251 2 2 12 10 12.5 1251 2 3 1 10 30 3001 2 3 2 10 30 300

Similarly, survey weights can also be calculated for the different land holding categories of the villages and ACZs.

The estimate of average value of the variable in the selected ACZ is given by

Similarly, the estimate of average value of the variable in the state is given by


The estimate of variance of the estimate of average value of the variable in the state is given by

Hence, the standard error is . Finally 95% confidence interval of the

estimate of average value of the variable is calculated as :

.


4. SCHEDULES AND INSTRUCTION MANUAL

SCHEDULE-I

LISTING SCHEDULE– VILLAGE ENUMERATION QUESTIONNAIRE

A. Basic Information about the Selected Village

Village name and Code (1)

Taluk/Block (2)

District (3)

Agro-Climatic Zone (4)

B. Household-wise Information

S. No Farm household code no.

(5)

Name of the farmer

(6)

Fathers name and address

(7)

Mobile No.

(8)

Land holding area, ha

(9)

Signature of the Surveyor

Signature of Project Investigator


SCHEDULE - II

LIST OF SELECTED FARM HOUSEHOLDS IN SELECTED VILLAGE

Village and Code Taluk/Block District Agro-Climatic Zone

Sl. No.

Farm household code no.

(1)

Name of the farmer

(2)

Mobile No.

(3)

Land holding area, ha (4)

Category-1

1.

2.

3.

4.

5.

Category-2

6.

7.

8.

9.

10.

11.

12.

Category-3

13.

14.

15.

16.

17.

18.

19.

20.

Signature of Project Investigator


SCHEDULE -III

DETAILED OPERATIONAL HOLDING SURVEY – FARMER’S QUESTIONNAIRE

ENERGY INPUTS AND OUTPUTS IN AGRICULTURE PRODUCTION SYSTEM

Name of Crop: Centre:

Farm household code no.

Village and Code Taluk/Block District Agro-Climatic Zone

A. Farmer’s profile

Name and Address of the farmer

(1)

Mobile no.

(2)

Total area, ha (3)

Area under each crop (4)

Own Leased Crop ha

1.

2.

3.

4.

5.

B. ………..Crop name………………..Crop data

Variety name

(1)

Soil type

(2)

Planting Season (main/

special)

(3)

Type of cropping (Mono / Mixed/

Direct Seedling / Ratooning)

(4)

Inputs (5)

Yield, t/ha Proposed date of

harvesting

(8)

Source Quantity Crop name

(6)

Crop residue

(7)

1. Seedlings or Set

2. Irrigation Frequency : Depth (cm): Type : Drip/channel

3. Farm yard manure

4. Inorganic fertilizer 1. Urea 2. DAP 3. MoP 4. Others


C.

Ope

ratio

n w

ise

ener

gy c

onsu

mpt

ion

Farm

op

erat

ions

Type

of

equi

pmen

t /im

plem

ent

Cap

-ac

ity

kW/h

p

Mak

e &

M

odel

, si

ze

Wt,

kgW

ork-

ing

Hou

rs

Ener

gy

Rs.

Fuel

/Ele

ctric

ity c

onsu

mpt

ion

Labo

ur

Pet

rol,

lD

iese

l, l

Ele

ct

kWh

Ene

rgy,

R

sM

ale,

h

Fem

ale,

hE

nerg

y,

Rs

Ow

ned

Hire

dO

wne

dH

ired

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

1. L

and

deve

lop-

men

t

1. B

ull D

ozer

2. T

ract

or

3. O

ther

s

2. L

and

prep

ara-

tion

1. A

nim

al

draw

n

- Mou

ld

boar

d

- Cou

ntry

pl

ough

- O

ther

s2.

Tra

ctor

dr

awn

- R

otav

ator

- D

isc

plou

gh

-Dis

c-ha

rrow

-R

idge

s an

d fu

rrow

s

3. S

eed

bed

pre-

para

tion

1. M

anua

l

- S

pade

2. A

nim

al

-Cou

ntry

pl

ough

-M

ould

B

oard

3. P

ower

tille

r

-Rot

avat

or

4. T

ract

or

-R

otav

ator

5 O

ther

so

urce


4. P

lant

ing

1. M

anua

l2.

Oth

ers

5. Ir

rigat

ion

A.

Can

al

-Dep

th

-Mod

eB

. O

pen

wel

l/ bo

re w

ell

1. M

otor

2. D

iese

l en

gine

3. S

olar

pu

mp

4. O

ther

s

6. W

eedi

ng

1. M

anua

l2.

Tra

ctor

3.

Pow

er-ti

ller

4. S

elf

prop

elle

d

7. F

ertil

izer

A

pplic

at-

ion

1, M

anua

l2.

Mec

hani

cal

8. C

hem

i-ca

l app

li-ca

tion

1. S

pray

ers

- K

naps

ack

- H

and

spra

yer

2. O

ther

s

8. E

arth

ing

up

1 M

anua

l

•Spa

de

2. O

ther

s

9. D

e-tra

shin

gM

anua

l

1.S

ickl

e

2.

10. H

arve

s-tin

g1.

Man

ual

2. C

ombi

ne


11. T

rans

- po

rtatio

n1.

See

dlin

g/se

t Tr

acto

r/ot

hers

2. F

ertil

izer

/ch

emic

al

Trac

tor/

Oth

ers

3. H

arve

sted

ca

ne

Trac

tor

Lorr

y/tru

ck

4, O

ther

s

12.

Any

ot

her

Sig

natu

re o

f far

mer

S

igna

ture

of s

urve

yor

Sig

natu

re o

f Pro

ject

Inve

stig

ator


INSTRUCTION MANUAL

SCHEDULE-I

PRELIMINARY SURVEY – VILLAGE ENUMERATION QUESTIONNAIRE

Column No. DescriptionA. Basic Information about the Selected Village 1. Write the Name and Code number of the village2. Write the name of the Taluk / Block3. Write the name of the District4. Write the name of the Agro climatic zoneB. Household-wise Information5. Serial number of farm household6. Give the Code number for the farm household, if it is available 7. Note down the farmer name with postal address8. Note down the Mobile number of farmer9. Note down the total land holding of the farmer in hectare

SCHEDULE-II

LIST OF SELECTED OPERATIONAL HOLDINGS IN SELECTED VILLAGE

This list provides the details of 30 selected operational holdings/farm households for the detailed survey. The selection of 30 operational holdings/farm households will be done from the list given in Schedule-I using the suggested random sample selection procedure. This selection will be done by respective AICRP Centre.

Column No. Description1. Note down the Code number for the farmer from Schedule-I 2. Note down the farmer name from Schedule-I 3. Note down the Mobile number of farmer from Schedule-I 4. Note down land holding area, ha from Schedule-I5. Note down the land holding category of the farmer using the predefined

classification rules


SCHEDULE-III

DETAILED OPERATIONAL HOLDING SURVEY –

FARMERS’ QUESTIONNAIRE

Column No. DescriptionA. Farmer Profile1. Note down the name and postal address of the farmer from Schedule-I (C.No.

B6 & B7)2. Note down the Mobile number of farmer from Schedule-I (C.No.B8)3. Note down the total land holding of the farmer in hectare with respect to owned

or leased. 4. Note down the crops under cultivation with operational area under each crop.B. ………..Write Crop Name………. Crop Data1. Note down the variety name of sugarcane under cultivation2. Note down the soil type in which sugarcane is cultivated3. Note down the planting season of sugarcane (Main/Special)4. Note down the …….(write crop name)……….. cropping type ie., Mono/Mixed/

Direct seedling/Ratooning/5. Note down the quantity of all the input source for sugarcane cultivation

• Seeding / set required • Irrigation (Frequency, depth in cm, type of the irrigation adopted)• Farm yard manure, vermicompost • Inorganic fertilizer (Urea, DAP, MoP, others)

6. Note down the sugarcane yield per hectare in tonnes (t/ha)7. Note down the crop residue produced per hectare in tonnes (t/ha)C. Operation Wise Energy Consumption1 & 2. The different operations of sugarcane cultivation are given as follows:

1. Land development includes the usage of bull dozer, tractor and other vehicles

2. Land preparation includes the usage of animal drawn (Mould plough, country plough or others), tractor drawn with various implements (rotavator, disc plough, disc harrow, ridges and furrows),

3. Seed bed preparation includes the usage of manual (spade), animal source (country plough, mould board plough), power tiller (rotavator), tractor (rotavator) and other source

4. Planting includes the usage of manual or other sources5. Irrigation source includes the usage of water from canal (depth and mode

to be included ) and open/bore well (Motor source or diesel engine or solar pump or others)


6. Weeding : the usage of weeding through manual, tractor and power operated and self propelled weeder.

7. Fertilizer application : The way of fertilizer application to the crop through manual or mechanical

8. Chemical application : The way of chemical application to the crop through sprayers (Knapsack, hand sprayer) or others

9. Earthing up : The process of earthing up by manual (spade) or other sources

10. Detrashing : The usage of manual (sickle) by the farmer

11. Harvesting : The usage of manual and combine harvester by the farmer

12. Transport : The transport facility used in various operations viz., Seedlings / set Tractor/others, Fertilizer / chemical Tractor / Others, Harvested cane Tractor/Lorry/truck or others are to be included

13. Collect the details if any others farm operations used by the farmer3. Note down the capacity of the equipment / implement used in various operations

for sugarcane cultivation4. Note down the make, model and size of the equipment / implement used in

various operations for sugarcane cultivation5. Note down the weight of the implement used in various operations for sugarcane

cultivation6. Note down the working hours of the implement/equipment used in various

operations for sugarcane cultivation7. Note down the total cost of the equipment/ implements used in various operations

for sugarcane cultivation in Rupees8. Note down the petrol consumption by the implements in litres9. Note down the diesel consumption by the implements in litres10. Note down the electricity consumption by the implements in kWh11. Note down the total cost of the fuel/electricity consumption by the implements in

Rupees12. Note down the working hours of owned male labour used in various operations

for sugarcane cultivation13. Note down the working hours of hired male labour used in various operations for

sugarcane cultivation14. Note down the working hours of owned female labour used in various operations

for sugarcane cultivation15. Note down the working hours of hired female labour used in various operations

for sugarcane cultivation16. Note down the total labour cost with respect to various operations for sugarcane

cultivation


Annexure IRandom Number Table A1

11164 36318 75061 37674 26320 75100 10431 20418 19228 9179221215 91791 76831 58678 87054 31687 93205 43685 19732 0846810438 44482 66558 37649 08882 90870 12462 41810 01806 0297736792 26236 33266 66583 60881 97395 20461 36742 02852 5056473944 04773 12032 51414 82384 38370 00249 80709 72605 67497

49563 12872 14063 93104 78483 72717 68714 18048 25005 0415164208 48237 41701 73117 33242 42314 83049 21933 92813 0476351486 72875 38605 29341 80749 80151 33835 52602 79147 0886899756 26360 64516 17971 48478 09610 04638 17141 09227 1060671325 55217 13015 72907 00431 45117 33827 92873 02953 85474

65285 97198 12138 53010 94601 15838 16805 61004 43516 1702017264 57327 38224 29301 31381 38109 34976 65692 98566 2955095639 99754 31199 92558 68368 04985 51092 37780 40261 1447961555 76404 86210 11808 12841 45147 97438 60022 12645 6200078137 98768 04689 87130 79225 08153 84967 64539 79493 74917

62490 99215 84987 28759 19177 14733 24550 28067 68894 3849024216 63444 21283 07044 92729 37284 13211 37485 10415 3645716975 95428 33226 55903 31605 43817 22250 03918 46999 9850159138 39542 71168 57609 91510 77904 74244 50940 31553 6256229478 59652 50414 31966 87912 87154 12944 49862 96566 48825

96155 95009 27429 72918 08457 78134 48407 26061 58754 0532629621 66583 62966 12468 20245 14015 04014 35713 03980 0302412639 75291 71020 17265 41598 64074 64629 63293 53307 4876614544 37134 54714 02401 63228 26831 19386 15457 17999 1830683403 88827 09834 11333 68431 31706 26652 04711 34593 22561

67642 05204 30697 44806 96989 68403 85621 45556 35434 0953264041 99011 14610 40273 09482 62864 01573 82274 81446 3247717048 94523 97444 59904 16936 39384 97551 09620 63932 0309193039 89416 52795 10631 09728 68202 20963 02477 55494 3956382244 34392 96607 17220 51984 10753 76272 50985 97593 34320

96990 55244 70693 25255 40029 23289 48819 07159 60172 8169709119 74803 97303 88701 51380 73143 98251 78635 27556 2071257666 41204 47589 78364 38266 94393 70713 53388 79865 9206946492 61594 26729 58272 81754 14648 77210 12923 53712 8777108433 19172 08320 20839 13715 10597 17234 39355 74816 03363

10011 75004 86054 41190 10061 19660 03500 68412 57812 5792992420 65431 16530 05547 10683 88102 30176 84750 10115 6922035542 55865 07304 47010 43233 57022 52161 82976 47981 4658886595 26247 18552 29491 33712 32285 64844 69395 41387 8719572115 34985 58036 99137 47482 06204 24138 24272 16196 04393

07428 58863 96023 88936 51343 70958 96768 74317 27176 2960035379 27922 28906 55013 26937 48174 04197 36074 65315 1253710982 22807 10920 26299 23593 64629 57801 10437 43965 1534490127 33341 77806 12446 15444 49244 47277 11346 15884 2813163002 12990 23510 68774 48983 20481 59815 67248 17076 78910

40779 86382 48454 65269 91239 45989 45389 54847 77919 4110543216 12608 18167 84631 94058 82458 15139 76856 86019 4792896167 64375 74108 93643 09204 98855 59051 56492 11933 6495870975 62693 35684 72607 23026 37004 32989 24843 01128 7465885812 61875 23570 75754 29090 40264 80399 47254 40135 69916


Random Number Table A240603 16152 83235 37361 98783 24838 39793 80954 76865 3271340941 53585 69958 60916 71018 90561 84505 53980 64735 8514073505 83472 55953 17957 11446 22618 34771 25777 27064 1352639412 16013 11442 89320 11307 49396 39805 12249 57656 8868657994 76748 54627 48511 78646 33287 35524 54522 08795 56273

61834 59199 15469 82285 84164 91333 90954 87186 31598 2594291402 77227 79516 21007 58602 81418 87838 18443 76162 5114658299 83880 20125 10794 37780 61705 18276 99041 78135 9966140684 99948 33880 76413 63839 71371 32392 51812 48248 9641975978 64298 08074 62055 73864 01926 78374 15741 74452 49954

34556 39861 88267 76068 62445 64361 78685 24246 27027 4823965990 57048 25067 77571 77974 37634 81564 98608 37224 4984816381 15069 25416 87875 90374 86203 29677 82543 37554 8917952458 88880 78352 67913 09245 47773 51272 06976 99571 3336533007 85607 92008 44897 24964 50559 79549 85658 96865 24186

38712 31512 08588 61490 72294 42862 87334 05866 66269 4315858722 03678 19186 69602 34625 75958 56869 17907 81867 1153526188 69497 51351 47799 20477 71786 52560 66827 79419 7088612893 54048 07255 86149 99090 70958 50775 31768 52903 2764533186 81346 85095 37282 85536 72661 32180 40229 19209 74939

79893 29448 88392 54211 61708 83452 61227 81690 42265 2031048449 15102 44126 19438 23382 14985 37538 30120 82443 1115294205 04259 68983 50561 06902 10269 22216 70210 60736 5877238648 09278 81313 77400 41126 52614 93613 27263 99381 4950004292 46028 75666 26954 34979 68381 45154 09314 81009 05114

17026 49737 85875 12139 59391 81830 30185 83095 78752 4089948070 76848 02531 97737 10151 18169 31709 74842 85522 7409230159 95450 83778 46115 99178 97718 98440 15076 21199 2049212148 92231 31361 60650 54695 30035 22765 91386 70399 7927073838 77067 24863 97576 01139 54219 02959 45696 98103 78867

73547 43759 95632 39555 74391 07579 69491 02647 17050 4986907277 93217 79421 21769 83572 48019 17327 99638 87035 8930065128 48334 07493 28098 52087 55519 83718 60904 48721 1752238716 61380 60212 05099 21210 22052 01780 36813 19528 0772731921 76458 73720 08657 74922 61335 41690 41967 50691 30508

57238 27464 61487 52329 26150 79991 64398 91273 26824 9482724219 41090 08531 61578 08236 41140 76335 91189 66312 4400031309 49387 02330 02476 96074 33256 48554 95401 02642 2911920750 97024 72619 66628 66509 31206 55293 24249 02266 3901028537 84395 26654 37851 80590 53446 34385 86893 87713 26842

97929 41220 86431 94485 28778 44997 38802 56594 61363 0420640568 33222 40486 91122 43294 94541 40988 02929 83190 7424741483 92935 17061 78252 40498 43164 68646 33023 64333 6408393040 66476 24990 41099 65135 37641 97613 87282 63693 5529976869 39300 84978 07504 36835 72748 47644 48542 25076 68626

02982 57991 50765 91930 21375 35604 29963 13738 03155 5991494479 76500 39170 06629 10031 48724 49822 44021 44335 2647452291 75822 95966 90947 65031 75913 52654 63377 70664 6008203684 03600 52831 55381 97013 19993 41295 29118 18710 6485158939 28366 86765 67465 45421 74228 01095 50987 83833 37216

Sampling Methodology for Energy - ICAR

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