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Projections Report

Information Products for Nile Basin Water Resources Management

NILE BASIN INITIATIVEInitiative du Bassin du Nil

Agricultural Water Use Projectionsin the Nile Basin 2030: Comparison with the Food for Thought (F4T) Scenarios

Projections Report

FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS

Rome, 2011

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Projections Report i

Contents

List of tables iii

List of figures iii

List of Acronyms iv

Acknowledgements v

1. Background and introduction 1

Background 1

Report structure 1

2. Agriculture towards 2030/2050 and the Food for Thought (F4T) scenarios 2

Relationship between the AT2030 projection and the F4T scenarios 2

Scenario 1 – Nile on its Own (NO) 2

Scenario 2 – Joint Effort (JE) 3

Scenario 3 – Unintended Consequences (UC) 4

Scenario 4 – Double Burden (DB) 4

3. Data consolidation and the projection protocol 6

Data reconciliation and consolidation 6

Projection protocol 8

4. The basic water use results 10

2005 Baseline 10

Basic water use projections under AT2030/50 assumptions 11

Projections Reportii

5. Moving from water use to water productivity - an analytical framework 15

The analytical framework 15

Indicator crops 17

The water productivity model 18

6. Results 20

AT2030 projection 20

Variable assumptions for the Food for Thought (F4T) scenarios 22

The scenario analyses 22 Scenario 1 – Nile on its Own. 22 Scenario 2 – Joint Effort 29 Scenario 3 – Unintended Consequences 32 Scenario 4 – Double Burden 33 Sugar only 33 Rainfed only vs irrigated only 37

7. Discussion and recommendations 43

Discussion 43

Recommendations 44

References 46

Annex 1: The Excel files 47

Annex 2: Estimated agricultural water use and withdrawals in the Nile Basin 48

Projections Report iii

List of Figures

Figure 1: The scenario framework 3Figure 2: The analytical framework 16Figure 3: Architecture of “the model” 19Figure 4: AT2030 – AWP exceedence percent’s by district 21Figure 5: AT2030 - AWP exceedence percent’s by area 21Figure 6: Nile on its Own – AWP exceedence percent’s by district 28Figure 7: Nile on its Own - AWP exceedence percent’s by area 29Figure 8: Joint Effort – AWP exceedence percent’s by district 30Figure 9: Joint Effort - AWP exceedence percent’s by area 31Figure 10: Sudan’s AWP exceedence percent’s by area 31Figure 11: Unintended Consequences – AWP exceedence percent’s by district 32Figure 12: Unintended Consequences - AWP exceedence percent’s by area 33Figure 13: Double Burden – AWP exceedence percent’s by district 34Figure 14: Double Burden - AWP exceedence percent’s by area 34Figure 15: Sugar only – AWP exceedence percent’s by district 36Figure 16: Sugar only - AWP exceedence percent’s by area 36Figure 17: Rainfed AWP compared with irrigated AWP - Entire basin 39Figure 18: Rainfed AWP compared with irrigated AWP - Eastern Nile 40Figure 19: Rainfed AWP compared with irrigated AWP - Equatorial lakes 41Figure 20: Comparison of rainfed and irrgated areas in the entire Basin, the Eastern Nile and the Equatorial Lakes sub-basins in 2005 (Consolidated project data) 42

List of Tables

Table 1: Districts covered in the analysis 6Table 2: Evapotranspiration for rainfed production 10Table 3: Irrigation water requirements in the Nile Basin 2005 (Project baseline) 11Table 4: AT2030/50 Projections of harvested areas in the Nile Basin (ha) 12Table 5: AT2030/50 projections for rainfed production Et in the Nile Basin (km3) 12Table 6: AT2030/50 projections of irrigation water use requirements in the Nile Basin (km3) 14Table 7: Hydrological affordability of the projections 15Table 8: Indicator crop clusters 18Table 9: Variables 23Table 10: Weighted average national AWP’s implicit in the projections data (cal/m3) 26Table 11: Projected harvested areas for sugar 35

Projections Reportiv

List of Acronyms

AWP Agricultural Water ProductivityEN Eastern Nile DistrictsEL Equatorial LakesNB Nile BasinEt EvapotranspirationETa Evapotranspiration under non irrigated conditionsETc Evapotranspiration of the irrigated areaF4T Food for ThoughtOECD Organisation for Economic Cooperation and DevelopmentNBI Nile Basin Initiative

Projections Report v

Acknowledgements

This report was compiled by FAO consultant Philip Riddell together with inputs from FAO staff, Jacob Burke, Jean-Marc Faurès and Jippe Hoogeveen. Overall design of the reports and information products was managed by Nicoletta Forlano, James Morgan, and Gabriele Zanolli.

Projections Reportvi

Projections Report 1

1. Background and introduction

Background

FAO project GCP/INT/945/ITA developed a set of information products to serve as the basis for decisions on water policy and water resources management in the Nile Basin. This required the consolidation of a wide array of natural resource and remote-sensing data across the Nile Basin. Key information products included a suite of agricultural water use and farming systems data compiled at district level for the ten countries in the basin. In addition a component of the project was concerned with the crafting of four possible water use scenarios, the so-called Food for Thought (or F4T) scenarios. The analysis described here compares the allocation and productivity projections suggested by the F4T scenarios with the situation in 2030 as anticipated by the study “World Agriculture towards 2030/2050” (AT2030/2050-FAO, 2006) which compiled data for 93 developing countries, including all the riparian countries of the Nile Basin.

Report structure

This document presents the results of the study and has seven sections including this one.

Section 2 briefly introduces the AT2030/2050 baseline and the F4T scenarios.

Section 3 describes the work necessary to construct a single set of cropping calendars by consolidating data from the various available sources before defining the data reconciliation and projection protocol that was adopted for the study.

Section 4 presents the 2005 baseline adopted for the report and their basic water use projections under AT2030/50 assumptions.

Section 5 begins by establishing the analytical framework for the study and thereafter describes and justifies the use of generic indicator crops before closing with an introduction to the water productivity model that was constructed for the purposes of the study.

Section 6 presents the results of the analysis in terms of the agricultural productivity of water and the water allocation implications of the baseline situation and the four scenarios.

Section 7, which closes the report, presents a brief discussion of the analysis while suggesting possible next steps.

The main report is supported by two annexes, Annex 1 with the excel files that comprise the model introduced in Section 5.3; and Annex 2 other showing estimated agricultural water withdrawals by district for the baseline year 2005.

Projections Report2

Relationship between the AT2030 projection and the F4T scenarios

The FAO AT2030/2050 interim report projections for developing countries are based on a basket of 35 crops divided in four categories1. The projections offer “a comprehensive and consistent picture of the food and agricultural situation in 2030 and 2050” (Bruinsma, 2009) in 187 countries2. However, the projections do not deal with a number of important global Issues such as the impact on agriculture of demand for biofuels, or the consequences of climate change, or again the elimination of hunger and undernourishment by 2050. The AT2030/2050 projections are derived from an analysis of existing trends and expert judgements with respect to how the national supply utilization accounts are closed for each of the threshold years.

The scenarios on the other hand are “stories about the external environment that show how important events might evolve over time, and describe the logic behind these possible developments. They do not predict what will happen but identify what might happen. Although they are based on the best available knowledge and insights, the rationale for using scenarios is that the

future is fundamentally uncertain and that multiple futures are possible. In a scenario development process, participants address the question of how uncertainties might affect their collective futures. The process promotes the joint exploration of possibilities, facilitates alignment of views, encourages ‘what if’ questions, and allows the collective discovery of what some appropriately call a ‘common ground’” (FAO F4T report). Used in this way, scenarios help prepare the FAO companion report F4T for the uncertain futures that lie ahead.

The scenarios emerge within four spaces determined by the crossing of two axes which capture the factors considered by the scenario builders to be the most relevant uncertainties – in this case governance and international trade as shown in Figure 1.

The resulting scenarios are briefly defined in the following sub-sections – for a more detailed description, reference can be made to the F4T Scenarios Report prepared by the project.

Scenario 1 – Nile on its Own

Simply stated, this scenario defines a region that has overcome major barriers to good governance but is nonetheless stifled by

2. Agriculture towards 2030/2050 and the Food for Thought (F4T) scenarios

1 Cereals, non-cereal staple food crops, other food crops and beverage/industrial crops. 2 Of which 29 are agglomerated into four regional clusters.

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unfavourable international trade conditions. The governments of the region continue to pursue appropriate plans and policies for economic prosperity.

But due to the failure of the WTO Doha Round the basin’s international trade prospects are greatly handicapped by continuing overprotection in the Organization for Economic Cooperation and Development (OECD) markets in the form of tariff and non-tariff barriers and production subsidies.

Eventually, the combination of improved governance and high food import prices stabilizes higher farmgate prices and promotes investment in agricultural modernization, all of which contributes to a gradual increase in the basin’s wealth.

Scenario 2 – Joint Effort

Under this scenario, the Nile Basin states begin to benefit from a positive combination of good governance (resulting from both domestic and international pressure) and favourable terms of trade. The latter stem mainly from a major paradigm shift in the OECD countries, which abandon their subsidy structures and trade barriers and negotiate trade and capacity-building agreements with the countries of the Nile Basin.

Benefits would include a very significant improvement in the delivery of services such as extension, seeds, fertilizer and credit. These, along with new markets and intra-regional trade in turn facilitate a change away from a political economy of self-sufficiency towards comparative advantage and agriculture-led economic growth and diversification.

Figure 1: The scenario framework

Accountable, legitimate, enabling

Unfavourable business environment, stifling, chaos

1 2

4 3

Dis

tort

ions

, une

ven

play

ing

field

s

Favo

urab

le te

rms

of tr

ade,

fair

com

petit

ion

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Moreover, dismantling of the tariff structures takes place along the entire value chain, allowing added value to accrue increasingly closer to the point of production, generating rapid growth in food-processing industries and hence demand for more raw materials. This in turn allows more people to enter the formal economy, propelled not only by rising educational standards but also by the jobs created by economic diversification. As a result, agriculture becomes more professional and increasingly moves from subsistence to large-scale, commercial production. Some challenges could, however, emerge with respect to natural resource allocation and exploitation.

Scenario 3 – Unintended Consequences

This scenario assumes the much-needed breakthrough in the Doha trade round, with subsidy and tariff structures dismantled in a similar fashion to Scenario 2. Unfortunately however, the trade accord is not accompanied by improvements in the governance of the Nile states. The new markets opened up by completion of the Doha round takes them by surprise. Poor, ineffective agriculture policies or poor implementation of good policies severely constrain efforts to take advantage of the new opportunities. Potential benefits of the OECD’s good intentions are lost under the weight of poor governance with all its institutional and service delivery shortcomings.

To make matters worse, the regional policy framework is fraught with inconsistencies, while land tenure issues remain unresolved. Investment in vital post-harvest infrastructure is inadequate and mechanisms to stabilize prices are missing. As a result, production

remains constrained and it is difficult to transport food to urban areas, which continue to be given greater priority by governments. Food policies are further compromised by the imposition of irrational import tariffs by the Nile Basin countries.

Lastly, despite the trend for global prices of agricultural commodities to increase, local farmers’ response is constrained by the lack of services and infrastructure. Price volatility and other factors resulting from poor governance also reduce the competitiveness of Nile Basin farmers in the new export markets.

Even so, given the existence of the new markets and the greater entrepreneurship of the commercial growers, some investments are nonetheless made. But these tend to be concentrated on large estate-type operations in order to mitigate investors’ perceived risk.

Most people remain poor, yet unremitting population growth exerts pressure on a deteriorating natural resource base.

Scenario 4 – Double Burden

This scenario depicts the worst of both worlds, poor governance and unfavourable trading regimes. This results in a system that is not only unaccountable to the population but is also characterized by financial and human resources limitations.

Unclear or non-existent land tenure arrangements constrain investment in both production enhancement and sustainability, while deterioration of already dilapidated infrastructure, increasing social disconnection and chaotic, unregulated markets all tend to push farmers towards subsistence production.

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To make matters worse, local prices are kept low by the dumping on the world markets of subsidy-driven surpluses in the OECD countries. This further accelerates the shift away from commercialized production towards subsistence systems which themselves are characterized by low yields and extension into already fragile ecosystems.

Populations continue to rise nonetheless, but there is no meaningful employment opportunity for large segments of the community. Entire economies perform hugely below potential and poverty remains pervasive and persistent.

Projections Report6

Data reconciliation and consolidation

Before the water use projections could be distributed across the Nile Basin districts on a month-by-month basis, it was first necessary to compile a suite of rainfed and irrigated, district-level cropping calendars, and thereafter to reconcile the results with the crops assumed in the projections file. Cropping calendars for rainfed crops were

compiled from project data collected at district level for each country, while for irrigated crops, cropping calendars were derived from FAO’s AQUASTAT database, related country reports and other sources where necessary. The results were compiled in a set of spreadsheets (see Annex 1) detailing irrigated and rainfed crop production at district level across the whole basin. Data was compiled for 216 districts in all, as shown in Table 1.

3. Data consolidation and the projection protocol

Table 1: Districts covered in the analysis

Country Province or region

Districts

Egypt Frontier Governorates

Al Wadi/Al JadidMatruth

Shamal Sina

Lower Egypt Al BahayrahAl DaqahliyahAl Gharbiyah

Al MinufiyahAl QalyubiyahAs Ismailiyah

Ash SharqiyahDumyatKafr-El-Sheikh

Upper Egypt Al FayyumAl JizahAl Minya

AswanAsyiutBeni Suwayf

QinaSuhaj

Urban governorates

Al IskandariyahAl Qahirah

As SuwaysBur Said

Sudan Bahr Al Ghazal North Bahr Al Ghazal

Central Al JazeeraBlue Nile

Sennar White Nile

Darfur North Darfur South Darfur West Darfur

Eastern Gadaref Kassala

Equatoria East Equatoria

Khartoum Khartoum

Kordofan North Kordofan South Kordofan West Kordofan

Northern Northern River Nile

(Continued)

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Table 1: (Continued)


Districts

Sudan US Bahr Al Ghazal Lakes

Upper Nile Jonglei Unity Upper Nile

Eritrea Not applicable Gash-Barka

Ethiopia Amhara Agew Awi E.Gojam N.Gonder

N.Shewa N.Wello S.Gonder

S.Wello W.Gojam W.Hamra

Gambella Gambella

Oromiya E.Wellega Illubabor

Jimma S.W. Shewa

W.Shewa W.Wellega

SNNPR Bench Maji Keffa Sheka

Tigray Bahr el Gazal – Lakes Central

Eastern Western

Uganda Central Uganda

KalangalaKampalaKayungaKibogaLuwero

MasakaMpigiMubendeMukono Nakasongola

RakaiSsembabuleWakiso

Eastern Uganda

BugiriBusiaIgangaJinjaKaberamaidoKamuli

KapchorwaKatakwiKumi Mayuge MbaleNamutumba

PallisaSironkoSorotiTororo

Northern Uganda

AdjumaniApacAruaGulu

KitgumKotidoLiraMoroto

MoyoNakapiripiritNebbiPader

WesternUganda

BuliisaBundibugyoBushenyiHoimaIbandaIsingiro Kabale

KabaroleKamwengeKanunguKaseseKibaale KiruhuraKisoro

KyenjojoMasindiMbararaNtungamoRukungiri

Kenya Nyanza BondoGuchaHoma BayKisii

KisumuKuriaMigoriNyamira

NyandoRachuonyoSiayaSuba

(Continued)

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Table 1: (Continued)


Districts

Kenya Rift Valley BometBuretKeiyoKericho

Marakwet NakuruNandiNarok

Transmara Transzoia Uasin Gishu

Western BungomaBusiaButere Mumias

KakamegaLugariMt. Elgon

TesoVihiga

United Rep.Tanzania

Kagera BiharamuloBukoba Rural Bukoba Urban

KaragweMuleba Ngara

Mara BundaMusoma

Musoma UrbanSerengeti

Tarime

Mwanza GeitaKwimba Magu

MissungwiMwanza Sengerema

Ukerewe

Shinyanga BariadiBukombeKahama

KishapuMaswaMeatu

Shinyanga RuralShinyanga Urban

Tabora Nzenga

Rwanda Not applicable ButareByumbaCyanguguGikongoro

Gisenyi GitaramaKibungoKibuye

Kigali RuhengeriUmutara

Burundi Not applicable BubanzaBujumbura RuralBururiCankuzoGitega Karuzi

KayanzaKirundoMakambaMuramvyaMuyinga Mwaro

NgoziRutanaRuyigi

Projection protocol

Cropping calendars compiled by the project from national reports were reconciled with those used in the AT2030/50 projection files. However, given that the AT2030/50 projections data are agglomerated in terms of annual totals at country level, they do not as such apply to specific river basins or individual districts. In some cases moreover, the projections data included crops that

were grown in regions that did not lie within the basins – coconut in United Republic of Tanzania for example. In addition, since the AT2030/50 projection data concerns annual harvested areas, they could not be used directly for the calculation of monthly water requirements. Accordingly it was necessary to craft a protocol to guide the use of ‘expert judgement’ in distributing country projections among the Nile Basin districts.

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But since the projections, when distributed at district level, are intended to form part of a water accounting tool, they must have a temporal as well as spatial component. In other words they must be based on cropping data that indicate not only how much of a particular crop is grown at district level, but also when it is grown.

Therefore the projection protocol assumes that the cropping calendars (derived as much as possible from project sources) determine the quantity of evapotranspiration from rainfed crops and the quantity of irrigation water withdrawn from surface and groundwater sources.

The protocol also assumes that a 2005 baseline3 can be set for all districts in the

basin and used for national projections in harvested areas and yields to 2030. Hence the cropping calendars remain fixed for the 2005 baseline and the subsequent projections to 2030.

But since projections can only be made in respect of crops appearing in the projections data; and since the projections can only be distributed, and their distributed water requirements estimated in respect of crops appearing in the available cropping calendars data, the crops themselves have to be common to both model and scenario. This was often not the case. Therefore, as with the consolidation of project data this too required ‘expert judgment’ to produce a conformable set of crops.

3 It should be understood that there is no direct quantitative significance in this baseline since its use is only to drive the pro-rata distribution of the national projections at the district level. Nonetheless unless otherwise stated, from here on, the term “baseline” refers to that suggested by the farming system/cropping calendars and not the projection data. The percentage increases are self-correcting in that if the district baseline has been underestimated, the percentage changes will be larger, and vice-versa.

Projections Report10

2005 Baseline

The evapotranspiration (Et) demand for rainfed production is calculated for the harvested areas 2005 baseline and is summarized in Table 2. The calculations assume that reference Et (ETo) for specific crops is applied to the harvested areas. While it is accepted that this will be an overestimate of actual Et in any one year, it sets an upper limit for the basin. The overall volumes indicate the dominance of rainfed production in equatorial zones.

An important point to emphasize is that Et from cultivated land under rainfed conditions will be close to Et under climax or secondary vegetation established when land is uncultivated. Hence, the impact of rainfed cultivation on the overall water balance of the Nile is considered negligible.

The water withdrawals for irrigation for the basin in 2005 were estimated using the cropping calendar data compiled at district level throughout the basin and consolidated for the purpose of this study as explained in the previous chapter. The results are presented at country level in Table 3, and at district level in Annex 1. It is must be stressed that the results calculate annual demand for evapotranspiration for baseline rainfed production and irrigated production based on distributed demand in the basin. This Et demand can be translated into actual water withdrawals by using the national “Water requirement ratio” calculated in FAO AQUASTAT (http://www.fao.org/nr/water/aquastat/water_use/index5.stm). On the assumption that this ratio applies equally across the whole country, including the portion of the Nile Basin, it can be used to calculate the volume of water withdrawn from the Nile system for each country. As with the rainfed calculation, this estimate gives an upper limit to agricultural water withdrawals based on reference Et. Actual Et and actual withdrawals will always be below this limit.

To be consistent with the groupings of the Nile Basin Initiative (NBI), the Eastern Nile sub-basin is taken to be the portion of the basin occupied by Egypt, Sudan, Eritrea and Ethiopia, while the Equatorial Lakes sub-basin comprises the area occupied by Uganda, Kenya, United Republic of Tanzania, Rwanda and Burundi (as well as the Democratic Republic of the Congo for which no comparable information was available).

4. The basic water use results

Table 2: Evapotranspiration for rainfed production

Country Estimated rainfed Et (km3)

Egypt

Sudan 52.110

Eritrea 0.209

Ethiopia 14.881

Eastern Nile Total 67.199

Uganda 65.625

Kenya 18.163

United Rep. Tanzania

13.887

Rwanda 8.820

Burundi 4.153

Equatorial Lakes Total

110.647

Nile Basin Total 177.847

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The difference between the two sub-basins is clear. Agricultural water use in the Eastern Nile is dominated by irrigation, while the impact of irrigation in the Equatorial Lakes is minimal. This will be shown to have significant implications on the agricultural water productivity projections analysed in Section 6. But of equal interest are the high irrigation abstractions in Egypt and Sudan, both being greater than their allocations under the 1959 Nile Waters Agreement which apportioned 55.5 km3 and 18.5 km3

respectively. This was based on the population at the time and based on a mean annual flow of 84 km3 at Dongola less 10 km3 of reservoir evaporation losses. Egypt’s abstractions in fact are more or less equal to the long-term flow into the country (Abu Zeid et-al 2007) and the irrigation withdrawals can be explained by the high rates of re-use known to characterise Egypt’s highly productive irrigation sector. Sudan also has limited levels of re-use in the Blue and White Nile sub-basins..

Basic water use projections under AT2030/50 assumptions

The AT2030/50 projections provide a basic reference point for the scenario work, but they are limited by their macro assumption that the entire country is subject to the same trend in population, income growth and improvements in water requirement ratios that can be anticipated over time as countries respond to water scarcity and adopt improved methods of irrigation water management. Nonetheless these macro-assumptions can be used to provide estimates of what the AT2030/50 projections assume in terms of harvested areas and overall water use when no changes in governance and terms of trade occur. Table 4 therefore applies the national changes in harvested areas projected by AT2030/50 to the portions of each country lying within the Nile Basin.

Table 5 applies weighted mean unit water use values to each country’s rainfed areas

Table 3: Irrigation water requirements in the Nile Basin 2005 (Project baseline)

Country Water used/withdrawn km3

Irrigated

Crop water requirements

Water Use Requirement Ratio

Irrigation withdrawal (km3)

Egypt 36.461 53% 68.795

Sudan 11.004 40% 27.511

Eritrea 0.041 32% 0.127

Ethiopia 0.106 22% 0.483

Eastern Nile Total 47.612 96.916

Uganda 0.249 30% 0.829

Kenya 0.323 30% 1.076

Tanzania 0.001 30% 0.003

Rwanda 0.095 30% 0.317

Burundi 0.014 30% 0.048

Equatorial Lakes Total 0.682 2.274

Nile Basin Total 99.190

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Table 5: AT2030/50 projections for rainfed production Et in the Nile Basin (km3)

Country Weighted mean unit Et (m3/ha)

2005 2030 2050

rainfed Et (km3) Et (km3) Et (km3)

Egypt - - - -

Sudan 3 710 52.110 63.721 71.258

Eritrea 3 552 0.209 0.204 0.182

Ethiopia 4 996 14.881 19.202 24.207

Eastern Nile Total - 67.199 83.127 95.646

Uganda 8 014 65.625 96.753 123.769

Kenya 8 237 18.163 19.027 20.455

Tanzania 7 045 13.887 17.048 18.903

Rwanda 7 608 8.820 10.308 11.204

Burundi 7 389 4.153 5.535 7.014


- 110.647 148.672 181.346

Nile Basin Total - 177.847 231.798 276.992

Table 4: AT2030/50 Projections of harvested areas in the Nile Basin (ha)

Country 2005 Baseline 2030 2050

Harvested Areas

Rainfed

Harvested Areas

Irrigated

Harvested Areas

Rainfed

Harvested Areas

Irrigated

Harvested Areas

Rainfed

Harvested Areas

Irrigated

Egypt - 3 927 039 - 4 713 319 - 5 075 778

Sudan 14 044 805 1 156 747 17 174 350 1 364 266 19 205 528 1 820 407

Eritrea 58 715 4 143 57 387 7 238 51 131 8 270

Ethiopia 2 978 340 14 171 3 843 100 19 462 4 844 934 31 764

Eastern Nile Total 17 081 860 5 102 100 21 074 837 6 104 285 24 101 593 6 936 218

Uganda 8 188 584 33 203 12 072 721 90 612 15 443 741 111 407

Kenya 2 204 922 41 693 2 309 804 59 377 2 483 212 75 701

Tanzania 1 971 035 130 2 419 828 197 2 683 097 266

Rwanda 1 159 197 15 637 1 354 825 18 800 1 472 641 22 796

Burundi 562 104 3 158 749 155 5 813 949 287 8 654


14 085 842 93 821 18 906 334 174 799 23 031 978 218 825

Nile Basin Total 31 167 702 5 195 921 39 981 171 6 279 083 47 133 571 7 155 043

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for 2030 and 2050 (the 2005 values being those developed in Table 2). The anticipated expansion of rainfed agriculture in the Equatorial Lakes sub-basin will account for a near 70 percent increase in overall Et demand by crops but only some 30 percent in the Eastern Nile. Clearly for rainfed agriculture these volumetric increases will not impact overall hydrological balances since they will not exceed Et from natural vegetation.

It will be seen that the water used in Eritrea’s rainfed sub-sector diminishes by 2030 and again by 2050 – this however, could be misleading. It is because the AT2030/50 projections indicate a national reduction in the area planted to crops in the part of the country lying within the Nile Basin.

A similar calculation is made for irrigated agriculture in Table 6. Water use requirement ratios for 2030 and 2050 are calculated on the basis of FAO AT2030/50 projection

assumptions and are assumed to change from the 2005 baseline presented in Table 2. The 2005 ratios are established on the basis of calculated crop water requirements over the known cropped areas as part of reported withdrawals. However, for the 2030 and 2050 projections, the ratios are modelled on the basis of projected responses to water scarcity and the capacity to adopt more progressive irrigation technology and management. The 15 percent increase in agricultural water withdrawals from the 2005 baseline to 2050 is in line with global projections (FAO, 2006; Bruinsma, 2009) assuming that current macroeconomic trends in the Nile Basin countries can be applied.

The sobering conclusion to take from this projection is that water requirement ratios for irrigated agriculture will have to improve significantly to stay within overall limits of water resource availability in the Eastern Nile Basin.

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Tabl

e 6:

AT2

030/

50 p

roje

ctio

ns o

f irr

igat

ion

wat

er u

se r

equi

rem

ents

in th

e N

ile B

asin

(km

3 )

Cou

ntry

Wei

ghte

d m

ean

unit

wat

er

wit

hdra

wal

s (m

3 /ha

)

2005

2030

2050

Irri

gate

dW

ater

Use

R

equi

rem

ent

Rat

io

Irri

gati

on W

ater

W

ithd

raw

als

(km

3 )

Wat

er U

se

Req

uire

men

t R

atio

Irri

gati

on

Wit

hdra

wal

s (k

m3 )

Wat

er U

se

Req

uire

men

t R

atio

Irri

gati

on

Wit

hdra

wal

s (k

m3 )

Egyp

t9

285

53%

68.7

9561

%71

.740

64%

73.6

36

Suda

n9

513

40%

27.5

1143

%30

.182

50%

34.6

35

Eritr

ea9

847

32%

0.12

733

%0.

216

33%

0.24

7

Ethi

opia

7 49

822

%0.

483

22%

0.66

322

%1.

083

East

ern

Nile

To

tal

--

96.9

1610

2.80

210

9.60

0

Uga

nda

7 49

330

%0.

829

30%

2.26

331

%2.

693

Ken

ya7

746

30%

1.07

631

%1.

484

31%

1.89

2

Tanz

ania

8 07

130

%0.

003

31%

0.00

530

%0.

007

Rw

anda

6 07

630

%0.

317

30%

0.38

131

%0.

447

Bur

undi

4 55

730

%0.

048

31%

0.08

530

%0.

131

Equa

tori

al

Lake

s To

tal

--

2.27

4-

4.21

8-

5.17

0

Nile

Bas

in T

otal

-99

.190

10

7.02

0

114.

770


The analytical framework

To examine the quantitative implication of the F4T scenarios, the most obvious water use parameter is the amount of water withdrawn from watercourses and aquifers for irrigated agriculture and the Et used by rainfed crops. But for several reasons such an approach would be limited in value.

First, as there is no direct connection between volumes of water and scenarios that are defined by governance and terms of trade. The AT2030/50 projections are constrained

by land and water limits at national level. Table 7 compares the current equipped areas with the 1997 FAO estimates (FAO, 1997) of irrigation potential (equipped areas). In fact water requirements constraints bring the basin total to less than 8 000 000 ha. But within this envelope there is room for growth, particularly in the EL region.

Second – the Et used by rainfed crops has no impact on overall basin water balances since the Et difference between climax or rangeland vegetation and annual cropping patterns is negligible at basin level.

Table 7: Hydrological Affordability of the Projections

CountryEquipped Irrigated Area (ha) Estimated Potential Equipped Irrigated Area (ha) *

Egypt 3 401 717 4 420 000

Sudan 1 830 908 2 750 000

Eritrea 5 865 150 000

Ethiopia 88 024 2 220 000

EN Total 5 326 514 9 540 000

Uganda 9 063 202 000

Kenya 14 501 180 000

United Rep. Tanzania 935 30 000

Rwanda 7 885 150 000

Burundi 32 12 80 000

Dem. Rep. Congo 0 10 000

EN Total 35 596 652 000

Nile Basin Total 5 362 109 10 192 000Taking water constraints into account d/s

of Sudan <8 000 000 ha.

Note: * From FAO (1997) Table 28.

5. Moving from water use to waterproductivity - an analytical framework

Projections Report

5. Moving from water use to water productivity - an analytical framework

16

Third, the F4T scenarios are concerned with results or outcomes. In this context therefore, it is not the water withdrawals themselves that are of interest, but rather the productive impact of those withdrawals.

Therefore it was decided to determine a key parameter that captures the productive rather than quantitative aspect of water allocation. The ideal parameter would be economic productivity (USD/m3) which would be particularly useful because various studies suggest or confirm a link between high economic water allocation efficiencies and increased socio-economic and environmental benefits (Cai et.al. 2001; Keller et.al. 1996). The socio-economic benefits in particular are relevant to the scenarios.

However, to use economic productivity would require projections for each crop, not only in terms of its specific commodity price,

but also of any specific added value, which itself may involve the allocation of additional water that would need to be accounted for.

There would also be the matter of the costs of capital investments necessary to secure a given level of productivity and of stepped tariffs which could apply in respect of in-country added value for export crops. Given that some 34 different crops are common to both the projections and project data, each of which could in theory be included in this analysis, the development of an economic indicator would be beyond the time and resources available. Accordingly, it has been necessary to work with a proxy and this required crafting of a qualitative analytical framework as indicated in Figure 2.

Although presented as a two-dimensional figure, the analytical framework displays four different parameters. The first and second

Figure 2: The analytical framework

high economic mobility of water

(diverse intersectoral allocation of water)

low economicmobility of water

(local foodself-sufficiency focus)

key variable is the agricultural productivity of water

Double Burden

Unintended Conseguences

AT2030 Projection

Nile on its Own

Join Effort

key variable is cropping system diversity

local self-sufficiency (farming systems dominated by subsistence crops)

an efficient agricultural sector in a diverse economy

Projections Report


17

comprise the key variables while the third and fourth illustrate the impact that changes in these variables represent in terms of dimensionless basin characteristics. Thus:

Parameter 1 is the diversity of the cropping systems. For the purposes of this study it is assumed that low diversity is associated with a predominance of subsistence crops (rather than for instance an industrial mono crop such as wheat on the North American prairies); while high diversity assumes that agriculture is largely an economic rather than a subsistence activity, pursued on the basis of an economically advantageous allocation of all factors of production.

Parameter 2 is the agricultural water productivity (AWP) which when strictly defined is taken as crop production per unit of water (kg/m3). However, in this context a broader definition is assumed and water productivity is taken as a ratio of agricultural product output (goods and services) over water input. The output can be determined in terms of biological goods or products such as crops (grain, fodder) or livestock (meat, fish) and can be expressed in term of yields, nutritional value or economic value. For this analysis, nutritional value of the crops grown is used and AWP is therefore expressed in terms of calories per m3 of water.

Parameter 3 is the economic efficiency of water allocation in the basin and is illustrated by the size of the ‘bubbles’ representing each of the four scenarios and AT2030/2050 baseline. The position and size of these bubbles as illustrated in the graphic reflect the result of joint scoring.

Parameter 4 is ‘basin welfare’, which can be thought of as an integration of social, economic and environmental benefits at basin level. The analytical framework

illustrates this in terms of the intensity of the blue background in the space between the axes.

The approach uses indicative cropping systems for the AT2030 baseline and for each of the scenarios at the levels of the Eastern Nile and Equatorial Lakes sub-basins and for the Nile Basin as a whole. It considers the implications of each in terms of agricultural water productivity. Because the AWP of a particular crop does not depend on how the crop is used, both food and non-food uses are included.

Indicator crops

Thirty four different crops are common to both the AT2030/2050 projections and project data and therefore eligible for inclusion in this analysis. In addition to the reasons given above concerning the difficulties of including them all individually, there is also the fact that the crops themselves fall into several categories (e.g basic foods, other foods, industrial, fodder, biofuels etc.) each of which would require separate analysis and commentary.

The analysis is limited to indicator crop clusters, one of which captures staple food crops and as such comprises a surrogate for subsistence farming. The other captures selected oil crops, sugar cane and sugar beet (the latter in Egypt only) and therefore represents a surrogate for cash cropping. If the productivity of the two clusters is expressed in common units, then it will be clear that the ratio between them defines a point on the horizontal axis of the analytical framework and provides the means to estimate the agricultural productivity of water – which is the key indicator. The common unit used for quantifying productivity is calories per unit of water withdrawn to irrigate the crop.

Projections Report


18

The two crop clusters are set out in Table 8 along with the calorific values assumed for each. But it should be noted that the use of calories to compare different crops prevents the inclusion of fodder crops in the study. These are unquestionably cash crops, and can be expected to become increasingly important in scenarios 1 and 2. They are already very significant in Egypt, with a harvested area around 20 percent of the country’s total. But they are difficult to represent in calorific terms and in any case, the AT2030 data is very unspecific in respect of such crops which are assumed to be subsumed in “other lands” for which neither yield nor production projections are provided.

The water productivity model

The consolidated, reconciled cropping calendars and the AT2030/2050 projections data have been combined with a scenario builder and a calculations platform to form

a Nile Basin agricultural water productivity model which estimates the total agricultural productivity in terms of calories/m3 for different cropping profiles agglomerated at district level through the country, and plots the results in terms of exceedence for both district and area. In Figure 3 it should be noted that the changes to the cropping profiles refer to the changed percentages of the total cropped area occupied by a given irrigated or rainfed indicator crop, not the change in the total area. The model does not forecast how much is withdrawn under a particular scenario, but rather the calorific productivity of each m3 withdrawn. The model can do this for:

• rainfed and irrigated crops combined; • rainfed and irrigated crops separately; • for the basin as a whole, or • broken down into:

- sub-basins (Eastern Nile and Equatorial Lakes); and - individual countries; - specific crops.

Note: * calorific values are taken from AT2030/2050 source files.

Table 8: Indicator crop clusters

Subsistence crops Calorific value (Cal/Kg)* Cash crops Calorific value (Cal/Kg)1

wheat 2 904 sesame 574

rice 2 408 sunflower 284

maize 3 148 unspecified oil crops 9 586

barley 2 563 sugar (beet and cane) 436

millet 2 831

sorghum 2 880

other cereals 3 253

potatoes 713

sweet potatoes 991

cassava 968

other root crops 1 156

pulses 3 375

Projections Report


19

Figure 3: Architecture of the model

MODULE 2: Scenario builder

Variables

MODULE 3: Data and projections

OPERATOR

Scenario cropping profilesPhysical irrigation water use ŋ's

Sucrose recovery rates

AT baseline and projectionsRainfed awp by crop and districtIrrigated awp by crop and district

MODULE 3: Calculatios platforms

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

% o

f are

a ac

hiev

ing

a gi

ven

leve

l of a

wp

productivity of total water supplied (calories/m3)

2005 - Eastern Nile2005 - Entire Basin2005 - Equatorial LakesAT2030 - Eastern NileAT2030 - Entire BasinAT2030 - Equatorial Lakes

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10

0 20

0 30

0 40

0 50

0 60

0 70

0 80

0 90

0 10

00

1100

12

00

1300

14

00

1500

16

00

1700

18

00

1900

20

00

2100

22

00

2300

24

00

2500

26

00

2700

28

00

2900

30

00

3100

32

00

3300

34

00

3500

36

00

3700

38

00

3900

40

00

4100

42

00

4300

44

00

4500

46

00

4700

48

00

4900

50

00 %

of d

istr

icts

ach

ievi

ng a

giv

en le

vel o

f aw

p



AWP %> by district

AWP %> area

OUTPUTS

Basic water useThe AT2030 projections

The F4T scenariosSugar only

Comparison between irrigated and rainfedIndividual countries


Before discussing the results it is important to establish some key assumptions. First, the scenarios themselves are only a description of likely changes in terms of two key variables, governance and terms of trade; they are not projection estimates. Therefore, the agricultural water allocation assumptions that have been made for each scenario only represent possible responses. There is no direct cause-and-effect relationship between a given scenario and a specific water allocation profile. However, the assumptions do represent expert judgement and have produced results that lie within credible limits. These results confirm the value of the model as an information product.

Second, it is important that the discussion presented here is limited to the 2005 baseline as assumed by the FAO AT2030/2050 files and the situation in 2030 derived from the AT projections and the four F4T scenarios. No attempt has been made to deal with 2050 as the scenarios are specifically concerned with 2030 outcomes. The results for 2030 projections alone are presented and discussed in three parts.

The first presents and discusses the AT2030 projection in terms of AWP exceedence percentages by district and by area for the two indicator crop clusters in the two sub-basins and the basin as a whole for 2005 (i.e. the baseline year) and 2030 (the scenario year).

The second defines the matrix of variables assumed to reflect agricultural water allocation under the four scenarios.

The third presents and discusses the results of the ensuing analyses, not only in terms of the scenarios at basin level, but also with respect to i) the specific case of sugar; and ii) rainfed and irrigated agriculture separately.

AT2030 projection

The results of the model are expressed in ‘exceedence plots’ where the percentage of districts and the percentage of the area obtaining a given level of AWP for the indicator crops are plotted against water productivity expressed in calories/m3. These plots simply indicate the shifts that can be expected as production expands and intensifies in line with the projected changes in demand for agricultural production at national level.

Projected agricultural water productivity (AWP) exceedence plots by district and by area for the entire basin, plus the two sub-basins (Eastern Nile and Equatorial Lakes) are shown in Figures 4 and 5 respectively. Figure 4 presents a baseline situation where the percentage of districts having i) low (ie < around 300 cal/m3) are similar and ii) high (> 2 000 cal/m3) are more or less the same in both basins. But between those values, it is clear that AWP is significantly higher in the Eastern Nile districts (EN) than in the Equatorial Lakes (EL), but with the entire basin, being slightly more aligned with the EL than with the EN. By 2030 however, the AT projections indicate a significantly improved EL situation – especially between 900 and 1 300 cal/m3 where it equals the EN districts.

6. Results

Projections Report

6. Results

21

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

% o

f are

a ac

hiev

ing

a gi

ven

leve

l of A

WP



0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 4 500 5 000

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0

200

400

600

800

1 00

0

1 20

0

1 40

0

1 60

0

1 80

0

2 00

0

2 20

0

2 40

0

2 60

0

2 80

0

3 00

0

3 20

0

3 40

0

3 60

0

3 80

0

4 00

0

4 20

0

4 40

0

4 60

0

4 80

0

5 00

0

% o

f dis

tric

ts a

chie

ving

a g

iven

leve

l of A

WP



The EN districts nonetheless remain more productive in the mid-range, up to 2 400 cal/m3,above which there is once more almost no discernable distinction between the two sub-basins.

By way of comparison, when the same analysis is carried out using exceedence percentages by cropped areas (Figure 5), the situation is very different. First, in 2005 the EL region presented a noticeably

Figure 4: AT2030 – AWP exceedence percentage by district

Figure 5: AT2030 - AWP exceedence percentage by area

Projections Report

6. Results

22

4 Although in theory it is possible to change rainfall use ratios by improved land management and cultivation practices, it was decided to keep rainfall efficiencies constant for the purpose of this analysis.

better situation than the EN. This is explained by the overwhelming dominance of highly productive rainfed farming in the EL sub-basin. But the differences begin to decrease once AWP values rise above around 600 cal/m3 and above 1 000 cal/m3, there is almost no difference between the two. Nonetheless, by 2030, and consistent with Figure 4, EL will have improved at a significantly faster rate than EN, except for the percentage of the total areas achieving more than around 2 400 cal/m, i.e. some 6 percent of the total area, which are more or less the same whether for the entire basin or the two sub-basins.

Variable assumptions for the Food for Thought (F4T) scenarios

The ratio of subsistence crops to cash crops indicates the position of a particular scenario along the horizontal axis of the analytical framework. Alone however, these are not enough to define a particular scenario. Also required are the crop yields; cropping patterns; irrigation water requirement ratios and, in the case of sugar cane, sucrose recovery rates. The crop yield growth from the 2005 baseline to 2030 is already assumed in the AT2030 projections for each crop and are hence given data; but, as was shown in Figure 3, each of the other variables can be changed by the operator of the model4.

The values for these variable are indicated in Table 9, where it should be noted that the percentage changes in the cropping pattern lines signify changes in the share planted to the crop in question and not the actual

changes in area. Altogether however, these individual crop changes represent an overall change in the cropping profile across the area being considered.

It should also be noted that the analysis assumes irrigated areas change in line with the percentage changes to cropped area and are not reallocated, accelerated or decelerated inconsistently with the assumptions in the AT2030/50 projections. Any inaccuracies that this assumption introduces can be expected to be of secondary importance in relation to the nature and objectives of the analyses, because increases in area irrigated for one crop will be adequately balanced by decreases in another. Although not ideal, the alternative would have been a far riskier set of assumptions concerning local irrigation expansion and investment policies.

The scenario analyses

Before discussing the AWP forecasts for the four scenarios and other analyses, it is helpful to have to hand an indication of the AWP of the various indicator crops as they are encountered in the riparian countries. Table 10 therefore presents the national weighted average AWP (irrigated and rainfed separately and combined) as suggested by the yields assumed for 2030 in the projections file.

Scenario 1 – Nile on its OwnThis scenario is concerned with the situation where terms of trade remain unfavourable, but with the basin characterized nonetheless by improved governance. As far as agricultural water allocation is concerned, this facilitates

Projections Report

6. Results

23

Tabl

e 9:

Subs

iste

nce

crop

sC

ash

crop

s

Wat

er

requ

irem

ent

rati

o

Sucr

ose

re

cove

ry

rate

C

ount

ryC

ase

wheat

rice

maize

barley

millet

sorghum

other cereals

potatoes

sweet potatoes

cassava

other root crops

pulses

sugar beet

sugar cane

sesame

sunflower

unspecified oil crops

Egyp

t%

base

line

100%

100%

100%

100%

10

0%

100%

100%

10

0%10

0%10

0%10

0%10

0%10

0%

53%

12%

%sc

enar

io 1

100%

100%

100%

100%

10

0%

100%

100%

10

0%10

0%10

0%10

0%10

0%10

0%

53%

14%

%sc

enar

io 2

50%

50%

50%

25%

25

%

150%

100%

10

0%20

0%15

0%15

0%15

0%15

0%

55%

14%

%sc

enar

io 3

150%

100%

150%

100%

10

0%

100%

100%

15

0%20

0%75

%75

%10

0%10

0%

53%

12%

%sc

enar

io 4

200%

50%

200%

200%

20

0%

75%

75%

75

%15

0%75

%50

%75

%75

%

53%

12%

Suda

n%

base

line

100%

100%

100%

10

0%10

0%

100%

10

0%

100%

100%

100%

40

%12

%

%sc

enar

io 1

100%

200%

100%

10

0%10

0%

150%

15

0%

125%

125%

125%

45

%14

%

%sc

enar

io 2

100%

200%

100%

75

%75

%

200%

20

0%

200%

200%

200%

50

%14

%

%sc

enar

io 3

100%

75%

150%

15

0%15

0%

75%

10

0%

100%

75%

75%

35

%12

%

%sc

enar

io 4

75%

50%

150%

20

0%20

0%

50%

75

%

100%

50%

50%

30

%12

%

Eritr

ea%

base

line

100%

10

0%10

0%

10

0%

32

%

no

suga

r

%sc

enar

io 1

150%

10

0%10

0%

15

0%

35

%

%sc

enar

io 2

200%

50

%50

%

20

0%

40

%

%sc

enar

io 3

75%

12

5%12

5%

50

%

32

%

%sc

enar

io 4

50%

20

0%20

0%

10

%

32

%

(Con

tinue

d)

Vari

able

s

Projections Report

6. Results

24

Tabl

e 9:

Subs

iste

nce

crop

sC

ash

crop

s

Wat

er

requ

irem

ent

rati

o

Sucr

ose

re

cove

ry

rate

C

ount

ryC

ase

wheat

rice

maize

barley

millet

sorghum

other cereals

potatoes

sweet potatoes

cassava

other root crops

pulses

sugar beet

sugar cane

sesame

sunflower


Ethi

opia

%ba

selin

e10

0%

100%

100%

100%

100%

100%

100%

10

0%

10

0%22

%12

%

%sc

enar

io 1

150%

15

0%10

0%12

5%10

0%10

0%

15

0%

150%

125%

30%

14%

%sc

enar

io 2

200%

15

0%10

0%15

0%10

0%10

0%

20

0%

200%

200%

35%

14%

%sc

enar

io 3

100%

10

0%10

0%12

5%12

5%10

0%

75

%

75%

75%

22%

12%

%sc

enar

io 4

100%

10

0%10

0%20

0%20

0%10

0%

50

%

75%

75%

22%

12%

Uga

nda

%ba

selin

e10

0%10

0%10

0%

100%

100%

10

0%10

0%10

0%

100%

10

0%10

0%10

0%

30%

12%

%sc

enar

io 1

150%

150%

150%

10

0%10

0%

150%

150%

100%

12

5%

150%

150%

150%

35

%14

%

%sc

enar

io 2

200%

200%

200%

10

0%10

0%

200%

200%

100%

15

0%

200%

200%

200%

40

%14

%

%sc

enar

io 3

100%

100%

100%

12

5%12

5%

75%

75%

125%

75

%

75%

75%

75%

30

%12

%

%sc

enar

io 4

75%

75%

125%

15

0%15

0%

50%

50%

125%

50

%

50%

50%

50%

25

%12

%

Ken

ya%

base

line

100%

100%

100%

100%

100%

100%

10

0%10

0%10

0%

100%

10

0%10

0%10

0%

30%

12%

%sc

enar

io 1

150%

150%

150%

125%

75%

100%

12

5%12

5%10

0%

125%

15

0%15

0%15

0%

35%

14%

%sc

enar

io 2

150%

200%

150%

125%

50%

100%

15

0%15

0%15

0%

150%

20

0%20

0%20

0%

40%

14%

%sc

enar

io 3

100%

100%

100%

100%

125%

125%

75

%75

%12

5%

75%

75

%75

%75

%

30%

12%

%sc

enar

io 4

75%

50%

125%

125%

150%

150%

50

%50

%12

5%

50%

50

%50

%50

%

25%

12%

(Con

tinue

d)

(Con

tinue

d)

Projections Report

6. Results

25

Tabl

e 9:

Subs

iste

nce

crop

sC

ash

crop

s

Wat

er

requ

irem

ent

rati

o

Sucr

ose

re

cove

ry

rate

C

ount

ryC

ase

wheat

rice

maize

barley

millet

sorghum

other cereals

potatoes

sweet potatoes

cassava

other root crops

pulses

sugar beet

sugar cane

sesame

sunflower


Tanz

ania

%ba

selin

e

100%

100%

10

0%10

0%

100%

100%

100%

100%

100%

10

0%10

0%10

0%

30%

12%

%sc

enar

io 1

15

0%15

0%

75%

100%

12

5%12

5%10

0%10

0%12

5%

150%

150%

150%

35

%14

%

%sc

enar

io 2

20

0%15

0%

50%

100%

15

0%15

0%15

0%10

0%15

0%

200%

200%

200%

45

%14

%

%sc

enar

io 3

10

0%10

0%

125%

125%

75

%75

%12

5%10

0%75

%

75%

75%

75%

30

%12

%

%sc

enar

io 4

50

%12

5%

150%

150%

50

%50

%12

5%10

0%50

%

50%

50%

50%

25

%12

%

Rw

anda

%ba

selin

e10

0%10

0%10

0%

10

0%

100%

100%

100%

100%

100%

30

%

no

suga

r

%sc

enar

io 1

100%

125%

100%

75%

15

0%15

0%10

0%10

0%12

5%

35%

%sc

enar

io 2

100%

150%

100%

50%

20

0%20

0%10

0%10

0%15

0%

40%

%sc

enar

io 3

75%

75%

125%

150%

75

%75

%12

5%10

0%75

%

30%

%sc

enar

io 4

75%

50%

150%

200%

50

%50

%15

0%10

0%50

%

30%

Bur

undi

%ba

selin

e10

0%10

0%10

0%

10

0%

100%

100%

100%

100%

100%

30

%12

%

%sc

enar

io 1

100%

125%

100%

75%

15

0%15

0%10

0%10

0%12

5%

35%

14%

%sc

enar

io 2

100%

150%

100%

50%

20

0%20

0%10

0%10

0%15

0%

40%

14%

%sc

enar

io 3

75%

75%

125%

150%

75

%75

%12

5%10

0%75

%

30%

12%

%sc

enar

io 4

75%

50%

150%

200%

50

%50

%15

0%10

0%50

%

30%

12%

(Con

tinue

d)

Projections Report

6. Results

26

Tabl

e 10

:COUNTRY

REGIME

wheat

rice

maize

barley

millet

sorghum

other cereals

potatoes

sweet potatoes

cassava

other root crops

pulses

sugar beet

sugar cane

sesame

sunflower


Egyp

tar

ithm

etic

mea

n

irri

gate

d2

265

1 64

22

687

658

1

379

1

549

3 15

6

2 33

71

040

1 66

51

827

113

85

Suda

n

arith

met

ic m

ean

rain

fed

2 21

9

463

1 73

9

1 28

6

10

613

0

arith

met

ic m

ean

irri

gate

d71

861

639

4

69

1

763

85

0

1 22

5

wei

ghte

d m

ean

both

1 45

3

15

89

1 21

0

Eritr

ea

arith

met

ic m

ean

rain

fed

1 08

9

836

1 01

9

70

arith

met

ic m

ean

irri

gate

d

523

wei

ghte

d m

ean

both

1

017

Ethi

opia

arith

met

ic m

ean

rain

fed

1 17

5

1 75

675

41

132

918

883

866

1 29

2

arith

met

ic m

ean

irri

gate

d

62

6

1

857

wei

ghte

d m

ean

both

1 63

0

Uga

nda

arith

met

ic m

ean

rain

fed

1 48

167

11

497

1

713

1 01

0

1 68

688

71

894

80

2

3 44

316

066

arith

met

ic m

ean

irri

gate

d

851

1 13

1

6

357

108

wei

ghte

d m

ean

both

77

01

497

4 19

115

9

(Con

tinue

d)

Wei

ghte

d av

erag

e na

tion

al A

WP

’s im

plic

it in

the

proj

ecti

ons

data

(cal

/m3 )

Projections Report

6. Results

27

Tabl

e 10

:COUNTRY

REGIME

wheat

rice

maize

barley

millet

sorghum

other cereals

potatoes

sweet potatoes

cassava

other root crops

pulses

sugar beet

sugar cane

sesame

sunflower


Ken

ya

arith

met

ic m

ean

rain

fed

1 73

2

733

2 66

245

270

7

1 55

01

948

1 42

8

449

1

790

9697

arith

met

ic m

ean

irri

gate

d

1 28

178

3

1

835

wei

ghte

d m

ean

both

732

1 81

5

Uni

ted

Rep

. Ta

nzan

ia

arith

met

ic m

ean

rain

fed

65

487

4

550

625

82

594

31

294

2 86

347

3

65

24

arith

met

ic m

ean

irri

gate

d

1 23

21

599

2 16

1

wei

ghte

d m

ean

both

70

294

5

Rw

anda

arith

met

ic m

ean

rain

fed

1 90

782

755

6

1

031

1

088

2 44

292

11

365

423

arith

met

ic m

ean

irri

gate

d

706

wei

ghte

d m

ean

both

66

5

Bur

undi

arith

met

ic m

ean

rain

fed

1 32

01

152

818

1 25

2

744

3 34

91

520

1 34

086

6

arith

met

ic m

ean

irri

gate

d

1 41

0

wei

ghte

d m

ean

both

(Con

tinue

d)

Projections Report

6. Results

28

an increase in irrigation application efficiencies, improved sucrose recovery rates and a slight commercialisation of the sector as represented by a shift away from the more low yielding, but drought tolerant food crops towards better yielding, but more drought sensitive alternatives. There is also a proportionally larger area under cash crops. In other words, this scenario assumes a rightward shift along the horizontal axis of the analytical framework (Figure 2). The results are shown in Figures 6 and 7.

In terms of districts, the differences between the F4T Scenario result and the WT2030 projection in the upper AWP range (i.e. > 2 900 cal/m3) are negligible. The negligible scale of the changes possibly arises because at such high levels, there is little further scope for improving the productivity of already highly productive crops. Below the 2 900 cal/m3 mark, districts in EN, tend to have higher AWP than those in EL down to around 1 200 cal/m3, below

which the EL districts are more productive until 400 cal/m3, below which there is once again no discernible difference between the two. The range 1 200 to 2 900 cal/m3 is more interesting however, in that whereas the EL districts vary slightly on both sides of the AT2030 projection, the EN districts are consistently more productive.

Despite being less productive than those in EN in the mid-range, the EL districts nonetheless do exhibit some slight improvements as compared with the AT2030 case, at least above 1 600 cal/m3. Below, that change is also negligible except for a very slight tendency in some places for productivity to fall below the AT2030 projections. This tendency becomes rather more apparent when the same analysis is carried out in terms of areas – see Figure 7, which shows that between 800 and 2 200 cal/m3 (EL) AWP are expected to fall below the levels indicated in the AT2030 projections. This may at first glance seem counterintuitive because it

Figure 6: Nile on its Own – AWP exceedence percentage by district

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AT2030 - Eastern NileAT2030 - Entire BasinAT2030 - Equatorial LakesNile on its Own - Eastern NileNile on its Own - Entire BasinNile on its Own - Equatorial Lakes

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may be expected that an improving situation would automatically involve an increase in AWP over the AT2030 projections – which is by definition policy-neutral. But in fact, if an increasing proportion of the area is allocated to higher value, but less productive (in terms of AWP) cash crops (such as sesame and sunflower which increase in Sudan5, Eritrea and Ethiopia) then it would be reasonable to see a decrease in water productivity in parts of the region (which explains the reduction in AWP for EN below 400 cal/m3), especially if yields (tonnes/ha) of food crops do not increase very much: this of course would be different if the productivities were analysed in economic terms, in which case there would be a noticeable improvement in productivity where land has been allocated to higher value crops. In the context of this study however, the trend in question is most clearly demonstrated in the case of the Sudan – see figure 10.

As with the analysis by district however, in the case of areas, there is once again uniformity at the higher end of the range (i.e. AWP > around 2 400 cal/m3). This again is most likely due to there being less scope for improvement with already productive crops.

Scenario 2 – Joint EffortThis scenario is concerned with the situation where both governance and terms of trade have become favourable. This facilitates a further increase in irrigation application efficiency, larger areas under cash crops and greater volumes of regional trade based on comparative advantage in terms of crop production. Equally, reliance on drought- tolerant crops is further reduced, while EL riparians take advantage of the expected export market for basic foods that South Asia is expected to have become by then. In other words, this scenario assumes an additional

Figure 7: Nile on its Own - AWP exceedence percentage by area

% o

f are

a ac

hiev

ing

a gi

ven

leve

l of a

wp


AT2030 - Eastern NileAT2030 - Entire BasinAT2030 - Equatorial LakesNile on its own - Eastern NileNile on its own - Entire BasinNile on its own - Equatorial Lakes

0%

10%

20%

30%

40%

50%

60%

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80%

90%

100%

0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 4 500 5 000

5 Sudan already has very large areas under such crops, hence the effect on the area plot.

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6. Results

30

rightward shift along the horizontal axis of the analytical framework.

The results are shown in Figures 8 and 9 which in terms of districts shows a continuation of the trend already established under Scenario 1 with both sub-basins performing very much the same as per the AT2030 projections in the lower part of the range (< 1 100 cal/m3). Thereafter, AWP in EN remains greater than in EL, until 3 300 cal/m3. Above that range, the two sub-basins show no discernible difference. Also, in the mid-range, i.e. 1 200 - 2 600 cal/m3 in EN and 1 200 - 2 600 cal/m3 in EL, there are improvements, not only because of increased production of crops such as sweet potatoes which are characterised by high levels of AWP (at least in Kenya, Rwanda and Burundi) – but also because of the comparatively greater improved yields (kg/ha) assumed in the projections files which will contribute to this trend. Where there are mid-range increases in EN AWP they can also be

explained by the increased areas assumed for potatoes, pulses and sugar (both beet and cane) in Egypt, pulses in Sudan, and wheat maize, pulses and sugar cane in Ethiopia.

The trend towards lower AWP in area terms already noted in the context of areas in Scenario 1 (Nile on its Own) becomes very much more apparent under this scenario, as indicated in Figure 9, where the AWP actually fall significantly below those suggested by the AT2030 projections for almost the entire range in the Eastern Nile and between 800 and 2 100 cal/m3 in the Equatorial Lakes sub-basin. It is therefore interesting to take a closer look at this by considering the specific case of the Sudan where its large areas under sesame and sunflower dominate statistically, as is clearly demonstrated in Figure 10 which presents the projections by area for Sudan alone. The large areas under oil crops in Scenario 2 (Joint Effort almost 30 percent of the baseline harvested area)

Figure 8: Joint Effort – AWP exceedence percentage by district

0

0.1

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AT2030 - Eastern NileAT2030 - Entire BasinAT2030 - Equatorial LakesJoin Effort - Eastern NileJoin Effort - Entire BasinJoin Effort - Equatorial Lakes

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31

not only pull AWP even below 2005 levels but also are enough to influence the chart for much of the range for the entire basin because of the scales. But the effect of low AWP in cash cropping actually persists much further up the range, to some 2 100 cal/m3 (EL) and 2 400 cal/m3 (EN). It will be noticed

nonetheless that under all projections, even the least ‘favourable’ (in terms of AWP) scenario (Double Burden), there will be increases in AWP at the very lowest end of the range – this is due to increased physical yields (kg/ha) forecast for sesame in the AT2030 projections (more than 300 percent).

Figure 9: Joint Effort - AWP exceedence percentage by area

Figure 10: Sudan’s AWP exceedence percentage by area


AT2030 - Eastern NileAT2030 - Entire BasinAT2030 - Equatorial LakesJoint Effort - Eastern NileJoint Effort - Entire BasinJoint Effort - Equatorial Lakes

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 4 500 5 000

% o

f are

a ac

hiev

ing

a gi

ven

leve

l of a

wp


2005AT2030 ProjectionNile on its OwnJoint EffortUnintended ConsequencesDouble Burden

% o

f are

a ac

hiev

ing

a gi

ven

leve

l of a

wp

0%

10%

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30%

40%

50%

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100%

500 1 000 1 500 2 000 2 5000

Projections Report

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32

Scenario 3 – Unintended ConsequencesThis scenario is concerned with the situation where terms of trade have become favourable, but governance remains poor. Cash cropping remains, especially sugar in EN, but is generally reduced in proportion to food crops which begin to include greater areas under drought-tolerant crops such as cassava, millet and sorghum.

The results are shown in Figures 11 and 12.

The first thing that becomes apparent in the case of districts is the remarkable consistency between the two sub-basins and the basin as a whole with the AT2030 projections throughout the range, with any changes concerning improvements in AWP – especially in the mid-range. But this is not surprising given the projection data’s focus on food security, which would be more strongly reflected in the cropping systems here, than in scenarios 1 and 2. Even so,

there are minor inconsistencies between the AT2030 and scenario projections.

However when it comes to areas there are

greater differences between the two sub-basins, especially in EN below 600 cal/m3, where the effects of reduced cash cropping (in particular of low AWP for oil crops) is very pronounced. Above that point however the EN remains very close to AT2030 projections, with only negligible increases where increases are encountered at all. On the other hand, EL demonstrates a significant increase between 500 cal/m3 (below which it is consistent with the AT2030 projections) and 2 000 cal/m3 (above which it is once again virtually indistinguishable from AT2030 projections). The increase in mid-range AWP arises from a fairly complex combination of reduced production of crops with low AWP and some increases in selected crops with higher AWP expectations (such as cassava in Uganda, Kenya, the United Republic of

Figure 11: Unintended Consequences – AWP exceedence percentage by district

0

0.1

0.2

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AT2030 - Eastern NileAT2030 - Entire BasinAT2030 - Equatorial LakesUnintended Consequences - Eastern NileUnintended Consequences - Entire BasinUnintended Consequences - Equatorial Lakes

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33

Tanzania and Burundi where it is widely produced – as is millet and sorghum in Uganda). In addition, even where food crops have lower than expected AWP, if these are higher than the cash crops (which they are in most cases) any shift in their favour will elevate the AWP across the range.

Scenario 4 – Double Burden Finally, there is F4T Scenario 4 in which governance and terms of trade both deteriorate, with the effect that marginalization of producer communities deepens and cropping patterns are increasingly characterized by subsistence and low-value commodities.The trend that emerged under F4T Scenario 3 continues here, but is more apparent and consistent once again with the food security oriented AT projections data along with increasing AWP as more land is allocated from low-productivity cash crops. Also, this is more apparent in EN where there was more cash cropping to begin with – especially at the lower end of the range where the effect

of the shift away from oil crops will be more keenly observed.

See Figures 13 and 14, where these trends are suggested in both the district and area charts and can be explained in much the same terms as F4T Scenario 3.

Sugar onlySugar is interesting because it is the only potential biofuel crop currently produced at any significant level in the Nile Basin, where it is grown under both irrigated and rainfed conditions. It is also of interest because of the involvement of the private sector, with the crop produced under a variety of models, including public-private partnerships and nucleus estate and outgrower programmes.

The AT2030/2050 projections suggest that the cropped areas under sugar will expand as shown in Table 11, which also indicates the areas assumed for the four scenarios.

Figure 12: Unintended Consequences - AWP exceedence percentage by area


AT2030 - Eastern NileAT2030 - Entire BasinAT2030 - Equatorial LakesUnintended consequences - Eastern NileUnintended consequences - Entire BasinUnintended consequences - Equatorial Lakes

0%

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0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 4 500 5 000

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Projections Report

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34

Since the scenario analyses concern the redistribution of a range of crops throughout district-level farming systems; and since sugar beet and sugar cane have the same calorific content, it would be meaningless to analyse the scenarios in respect of sugar

alone. Accordingly, Figures 15 and 16, simply plot the exceedence percentages of the AWP of sugar in the baseline year 2005, and for the areas projected by the AT2030 data. The results are plotted for the two sub-basins and the Basin as whole,

Figure 14: Double Burden - AWP exceedence percentage by area


AT2030 - Eastern NileAT2030 - Entire BasinAT2030 - Equatorial LakesDouble burden - Eastern NileDouble burden - Entire BasinDouble burden - Equatorial Lakes

0%

10%

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0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 4 500 5 000

% o

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Figure 13: Double Burden – AWP exceedence percentage by district

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AT2030 - Eastern NileAT2030 - Entire BasinAT2030 - Equatorial LakesDouble Burden - Eastern NileDouble Burden- Entire BasinDouble Burden - Equatorial Lakes

Projections Report

6. Results

35

in terms of districts (Figure 15) and areas (Figure 16).

Two main findings emerge. First, despite an overall increase in the AWP of sugar production in the Eastern Nile in the Equatorial Lakes sub-basin, in terms of area it actually falls in the 1 500 to 1 800 cal/m3 range. The data suggests that this so because a disproportionate percentage of the increase is expected in locations associated with lower yields.

Second, the AWP of sugar in the Equatorial Lakes sub-basin is higher, especially by 2030. This is explained by the generally higher cane weights achieved in the parts of the sub-basin,

especially Uganda as suggested by Table 10. Considering the possibility of higher production efficiencies in small farms, and also that outgrowers can outperform (nucleus) estates, a detailed check for any correlation between farm size, production arrangements and the higher AWP in the Equatorial Lakes region would be worthwhile.

In addition, it will be seen that there is a noticeable surge in Equatorial Lakes AWP in the range of 2 000 to some 3 300 cal/m3. This can be attributed in great part to the large percentage spatial increase of high-yielding cane in Uganda, and in lesser part to a large increase in the United Republic of Tanzania’s irrigated cane.

Table 11: Projected harvested areas for sugar

Country Crop/ Regime

Area (ha)

2005 AT2030 Scenario 1 Scenario 2 Scenario 3 Scenario 4

Egypt irrigated beet

42 919 42 472 42 472 63 708 31 854 31 854

irrigated cane

132 352 225 488 225 488 338 232 169 116 112 744

Sudanirrigated

cane69 747 95 288 119 110 190 576 95 288 95 288

Ethiopiairrigated

cane1 681 2 732 4 098 5 465 2 049 2 049

EN Total 246 699 365 980 391 169 597 981 298 307 241 935

Ugandarainfed

cane52 283 151 870 227 805 303 739 113 902 75 935

irrigated can

2 331 6 993 10 490 13 986 5 245 3 497

Kenyarainfed

cane157 196 157 196 235 794 314 393 117 897 78 598

irrigated cane

4 298 7 163 10 744 14 325 5 372 3 581

Tanzaniairrigated

cane20 31 47 63 24 16

Burundiirrigated

cane107 214 322 429 161 107

EL Total 216 235 323 467 485 202 646 935 242 601 161 734

Nile Basin Total 462 934 689 447 876 371 1 244 916 540 908 403 669

Projections Report

6. Results

36

Figure 16: Sugar only - AWP exceedence percentage by area

0%

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0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 4 500 5 000

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Figure 15: Sugar only – AWP exceedence percentage by district

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Projections Report

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37

Rainfed Only vs Irrigated OnlyFor a given crop grown under optimal growing conditions with respect to soil moisture, insolation, nutrient regime, pest and disease control etc, it should be self evident that rainfed agriculture will generally be associated with higher AWP’s than irrigated. This is because there are none of the distribution or application losses. But conditions are seldom optimal, and the advantages that accrue to irrigation are less concerned with theoretical levels of agricultural water productivity than overall agronomic performance. This includes: i) obviating the risks of inadequate or mis-timed rainfall; ii) justifying increased investments in farm inputs and more sustainable farm practices; iii) crop diversification; and iv) the concentration of service provision (information, extension, markets and communications etc). It is interesting therefore to examine what this means in the Nile Basin: Figures 17 (Entire Basin), 18 (Eastern Nile) and 19 (Equatorial Lakes) have been compiled for this purpose.

The first theme to emerge is that for the basin as a whole (and except at the very lowest levels), the AWP of the irrigated sub-sector is vastly greater than for rainfed until AWP levels begin to exceed 3000 cal/m3 (4000 for the 2005 figures), whereupon they become effectively equal. But this is just one story; whereas the data actually has several more useful ones to tell. To tell them, it is first necessary to note that for reasons which are made clear by Figure 20, the story of irrigation in the entire basin, is really that of Egypt; while to a lesser extent, for rainfed it is that of Sudan.

The enormous influence that Egypt’s irrigated area has, not only in the Eastern Nile, but actually in the entire basin is clear in Figure 20.

With this in mind, it is useful to recall from Table 10 that with the exception of barley, Egypt’s irrigated productivity is very high in comparison with rainfed elsewhere in the entire Nile Basin, which it even exceeds in the case of three crops: wheat, rice and maize. For the Eastern Nile sub-basin alone, for all crops except sorghum and pulses, Egypt’s irrigated AWP’s are higher than, or second ranked in comparison to the other riparians, regardless of whether their crops are irrigated or rainfed. It is these high irrigated yields over such a large proportion of the entire irrigated area (79.5% of the entire basin’s, and 80.7% of the Eastern Nile’s), along with the greater reliance on irrigation in the Eastern Nile that produce the results shown in the graphics.6 In the Equatorial Lakes however, the largest irrigated area by country is Kenya with just over 42% of the total, closely followed by Uganda with just over 33%, and then Rwanda with almost 21% and Tanzania with only 0.13%7.

By analysing each sub-basin separately therefore, it becomes immediately clear that the convergence of irrigated and rainfed AWPs above around 3000 cal/m3 mentioned above is only encountered in the Eastern Nile. There, the high irrigated AWPs accrue to high kg/m3

yields of sweet potatoes (at 991 cal/kg) in Egypt; whereas high rainfed AWPs accrue to good kg/m3 yields on small areas of cereal and pulses with high calorific values (between 2850 and 3500 cal/kg) in Sudan. Given the difference crop types, there is nothing to be gained by comparing them, and this is especially so given the insignificance of the areas involved.

Things are very different in the Equatorial Lakes sub-basin, and are different in several ways.

6 Although not strictly within the scope of this study, which for the moment is concerned with the agricultural productivity of water in the context of information productions for Nile Basin water resources management, it is important to note that Egypt’s dominance of the data occludes other strategic issues such as the low levels of irrigation investment in Ethiopia and the enormous costs of sediment removal in Sudan’s large public irrigation schemes.

7 Consolidated project data.

Projections Report

6. Results

38

First, with the exception of the 2005 figure, the analyses indicate a close similarity between irrigated and rainfed values at the lower end of the scale up to AWPs of around 500 cal/m3. This is the opposite of the Eastern Nile situation for two reasons. One is that the confluence occurs at the lower end rather than then upper. The other is that the low rainfed AWPs accrue largely to pulses which seem to underperform throughout the region, despite the significant area planted to them (almost 20% of the entire harvested area); whereas rainfed pulses are characterised by high AWPs in the Eastern Nile. Similarly, low irrigated AWPs in the Equatorial Lakes region accrue entirely to sesame grown in Uganda where it comprises some 25% of the non-rice irrigated area. What makes this particularly interesting is that both pulses and sesame are high value crops. If AWP was expressed in financial rather than calorific terms, the result could be expected to be rather different.

Second, the rapid reduction in cumulative exceedence areas at around 600 cal/m3 for 2005 and at around 1250 cal/m3 for each of the scenarios. This explained by the significant kg/m3 yield increases that both the AT2030 projects and scenarios assume for maize, rice (and to a much lesser extent, sorghum) throughout the EL region.

Third, the gradual divergence commencing at around the 2800 cal/m3 mark with irrigated AWPs remaining an order of magnitude greater than the rainfed. This is explained by the large increase of the irrigated sugar cane area anticipated by the AT2030 projections and is reflected in all four scenarios. Over 60% of this increase is expected to take place in Uganda where kg/m3 yields may be questionable. Although cane weights are known to be high in Uganda, it would be wise to regard them with a healthy degree

of caution. In fact, if they were rejected for analytical purposes, then the irrigated and rainfed productivities could be expected to converge once more at around 3500 cal/m3. It is interesting also that sugar remains more productive as an irrigated crop both for the AT2030 projections and for the scenarios – this is explained by the year round watering that irrigated cane receives as compared with the seasonal watering that purely rainfed cane receives.

Fourth, the fact that irrigated AWPs are closer to rainfed in the EL region. This is not because irrigation is underperforming there as compared to the Eastern Nile sub-basin. On the contrary, with the exception of scenario 2 (when irrigated AWPs in the Eastern Nile trend lower across the mid range values), irrigated AWPs are very similar for the two sub-basins, especially above around 1500 cal/m3. The closer relationship between rainfed and irrigation in the Equatorial Lakes sub-basin arises because the rainfed AWPs are much higher than for the Eastern Nile. This is in line with the intuitive expectation mentioned at the start of this section and is almost certainly due to the improved rainfall expectations in the sub basin. And the lower irrigated AWPs for the Eastern Nile than Equatorial Lakes under Scenario 2 can be explained by the greater relative shift towards low AWP, but high value cash crops on the large areas harvested in Egypt and Sudan (see Table 9).

Finally, is the “flat spot” between irrigated AWPs of 1250 to around 1800 cal/m3 which can be seen on both projection and scenario charts which indicates a significant increase of AWP on an largely unchanging area. In this case it is easily explained by the large increases in the AWP of irrigated rice expected to take place on a harvested area which itself will remain much the same in extent.

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Figure 17: Rainfed AWP compared with irrigated AWP - Entire basin

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Figure 18: Rainfed AWP compared with irrigated AWP - Eastern Nile

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Figure 19: Rainfed AWP compared with irrigated AWP - Equatorial lakes

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Figure 20: Comparison of rainfed and irrgated areas in the entire Basin, the Eastern Nile and the Equatorial Lakes sub-basins in 2005 (Consolidated project data)

Sudan

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Discussion

The projections exercise simply attempts to frame the F4T scenarios. In itself the exercise does not advocate any position or conclusion. The whole exercise was carried out in the context of producing information products for discussion and debate. The model is therefore open for such discussion.

Nonetheless the question of the productivity of water, agricultural or otherwise, is a very important parameter to model and monitor in a basin where agricultural water withdrawals are constrained and where downstream countries need to take decisions about inter-sectoral water allocations, rather than the intra-sectoral allocations that this model assesses. Decisions about inter-sectoral water allocation will be made on the basis of the economic productivity of water but for reasons explained above, this is a complex issue and as such has been beyond the scope and resources of thist analysis. Even so, the analysis carried out for the study, whether of the known situation in 2005, the AT2030 projections or the more speculative scenarios, do produce some interesting results. But before summarizing these results, it is necessary to stress that the AWP projections provide an answer to the question “what might happen to the agricultural productivity of water if governance changes this way, and the terms of trade change that way?” As such there is no right or wrong associated with the choice of component variables made for each of the analyses. Even so the use of indicator crop clusters and the selection of

the subsistence/cash crop ratio do produce results that can be explained rationally by both the raw data and ‘expert judgment’.

The first result that emerges from the analysis is the clear trend towards higher agricultural water productivity as cropping systems shift from subsistence towards cash crops when governance and trade become more favourable. In this sense economic productivity can increase even if water productivity is reduced by the import of water for irrigation. However, once land is irrigated, there are opportunities to increase water productivity through the application of more precision agriculture. This is an important finding, because when water becomes scarce intersectoral, demand is likely to intensify. Energy demand will increase for instance, in line with both increasing industrialisation and rising socio-economic conditions; industry itself is likely to become a larger user of water and agriculture may have to cut back – even with higher value cropping under way.

The second result concerns sugar, an important agro-industrial crop that is expected to expand considerably over the period studied, at least in some countries. It was shown that this is likely to reduce AWP because of low yield expectations at some new plantings; and this notwithstanding large productivity increases forecast for the Equatorial Lakes. If a significant amount of the sugar expansion is predicated on bioenergy demand, the question then arises as to whether or not the losses in productivity would be less if the same water

7. Discussion and recommendations

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were stored primarily for power generation, with irrigation being merely the residual, secondary benefit.

The final result concerns the difference between AWP rainfed and AWP irrigated not least with respect to their relative differences as they are encountered in the two sub-basins. This showed that water allocation and AWP is dominated, by Egypt in both the Eastern Nile in particular and the Basin as a whole. This does not mean however that it is irrigation or nothing throughout the basin. In the Equatorial Lakes, the model revealed a greater degree of similarity between the rainfed and irrigated AWP, although rainfed productivity remained greater than irrigated except at the lower and upper portions of the range. These similarities apply to the AT2030 baseline and projections as well as for each of the scenarios – although the similarity was less pronounced under scenarios 3 and 4, for which rainfed trended towards greater productivity than irrigated (but this is most likely due to the influence of highly productive rainfed maize and millet in Uganda and barley in Kenya). Nonetheless, the overall similarity is explained by better hydrological conditions in the sub-basin, the model points to the possibilities of a more heterogeneous approach to agricultural development and expansion than in the Eastern Nile.

But all this remains somewhat speculative, because the scenario ‘solutions’ themselves have been speculative. More time and different formulations of the scenario cropping systems can be expected to reveal less speculative narratives and these will be of interest to the Nile Basin’s water managers. As such the models’ relevance and utility is proven.

Recommendations

There are several ways in which the model can be improved or rendered more sophisticated. The most obvious of these has already been mentioned and concerns the economic productivity of water. Not only would this add value to the subsistence versus cash-cropping comparison, it would also inform intersectoral water allocation decision-making while allowing a different range of crops to be included. Mention has already been made of fodder crops which, although of great significance are difficult to capture in terms of calories, at least in any meaningful way. But an economic model would be very much more complex. For instance, while it would be a simple matter to substitute farmgate prices for calories in the model, this would have limited meaning without shadow prices, production budgets, the economic costs of asset creation, added value etc. Equally, input and output prices are neither fixed in time nor in space, so an economic model would ideally need to handle and project time-based data. Nonetheless, now that a basic distribution platform is actually in place for the basin, an economic model could be feasible.

Another enhancement would be to modify the model, so that variables currently treated in a linear fashion, such as percentage increases in area, or yields which are so far applied uniformly at a national level rather than specifically at district level, can be more finely applied. The projection distribution files (see Annex 1) already have shadow files for each country specifically in anticipation of this possibility, but no attempt has been made as yet to use them in this way. Equally, it may be decided to be advantageous for the model to undertake the analyses at catchment rather than

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district level. On a similar tack, it would be possible to manipulate the shadow files so as to incorporate a degree of non-linearity not just for the district changes, but also for irrigation expansion – but as indicated above, the value of this would be very much subject to the reliability of any assumptions made about growth of a particular country’s irrigated area, at district level.

Then, there is the matter of rainfall. The AWP estimated for rainfed conditions assume that rainfall is always adequate at every stage of each crop’s growth. This is

not likely to be the case in reality; but could be addressed statistically, perhaps using decadal rainfall exceedence probabilities as a first step before refining things further.

Next is the possibility of improving the model’s utility with respect to the functions it can already handle. The key issue is the model’s inability to chart more than one scenario simultaneously. At present, the charts comparing one scenario with another rely on data being saved as values before being processed as a spreadsheet chart. Automated chart production would help.


Abu Zeid, K., Tamrat, Hartveld, A., Rid-dell, P. and Seidelmann, R. 2007. Needs Assessment and Conceptual Design of the Nile Basin Decision Support System: Inception Report, Annex A, unpublished report prepared for the Nile Basin Initiative, Addis Ababa.

Bruinsma, 2009. The Resource Outlook to 2050 : by how much do land, water and crop yields need to increase by 2050? FAO Expert paper prepared for High-Level Expert Forum on How to Feed the World in 2050, March 12-13 2009. Rome. http://www.fao.org/wsfs/ forum2050/wsfs-background-documents/ wsfs-expert-papers/en/

Cai, X., Ringler, C., and Rosegrant, M., 2001. Does Efficient Water Management Matter? Physical And Economic Efficiency In The

References

River Basin, Environment and Production Technology Division, Discussion Paper No. 72, International Food Policy Research Institute, Washington DC.

FAO, 1997. Irrigation potential in Africa. A Basin approach. FAO Land and Water Bulletin No. 4. FAO, Rome, 177 pp.

FAO, 2009. Food for Thought; Demand for agricultural produce in the Nile Basin for 2030: four scenarios.

FAO, 2006. World Agriculture towards 2030/2050. Interim Report. Global Perspective Studies Unit. FAO Rome, 71pp.

Keller, A., Keller, J., and Seckler, D. 1996. Integrated Water Resource Systems, Theory And Policy Implications, Research Report NO 3, International Water Management Institute, Colombo.


Listed below are the Excel files which together comprise either the data sources or the water allocation model.

Annex 1: The Excel files

Description File

Source Files: between them, these files contained the district level cropping data collected by the project. There were many gaps and inconsistencies which were filled or resolved using “expert judgement” based wherever possible on other information sources, largely comprising the Nile Basin Initiative, or its projects such as the Conceptual Design for Decision Support, System, the Eastern Nile Irrigation and Drainage Study and the One System Inventory.

• awus-jippe egypt check.xls• Cropping Calendars for NileBasin Countries.xls• NileBasin_CropProductionData_

CroppingCalendars_JW_25Sep08.xls• Irrigation.xls• CropPat.xls• Revised Agric Water Use Data_2July9.xls• irrigation and drainage database for Kenya.xls• Nile Basin GIS Irrigation Database

2006-7Tanzania.xls• NileBasin_CropProductionData_IrrigatedOnly.xls

Module 1: data and projections these files distribute the AT2030 projections at district level for the irrigated and rainfed sub-sectors and do so for the cropping systems defined as per the projections protocol. The projections files also calculate unit water use values.

• Baseline and Projections.xls• Irrigated Agriculture Projections.xls• Rainfed Agriculture Projections.xls

Module 2: scenario builderthis file allows the user to change the ratio between subsistence and cash crops, physical irrigation water use efficiencies and sucrose recovery rates.

• Variables.xls

Module 3: calculation platformsthese files apply the scenarios to the distributions to the distributed projections

• Analysis 1 - basic water use results.xls• Analysis 2 – AT2030 projections.xls• Analysis 3a – F4T Scenario 1.xls• Analysis 3b – F4T Scenario 2.xls• Analysis 3c – F4T Scenario 3.xls• Analysis 3d – F4T Scenario 4.xls• Analysis 4 – sugar only.xls• Analysis 5a – irrigated only.xls• Analysis 5b – rainfed only.xls• Analysis 5c – irrigated and rainfed compared.xls• Projections Report Graphics.xls


The following table sets out the estimated district-level water uses and withdrawals taken by the Nile Basin agricultural sector.

Annex 2: Estimated agricultural water use and withdrawals in the Nile Basin


District Water used km3

Rainfed Irrigated Total

Croprequirement

Water userequirement

ration (η)

Irrigationwithdrawals

Egypt Frontier Governates

Al Wadi/Al Jadid

0.096 53% 0.180 0.180


Matruh 0.003 53% 0.006 0.006


Shamal Sina 0.000 53% 0.000 0.000

Egypt Lower Egypt Al Bahayrah 6.613 53% 12.477 12.477

Egypt Lower Egypt Al Daqahliyah 0.047 53% 0.088 0.088

Egypt Lower Egypt Al Gharbiyah 1.943 53% 3.667 3.667

Egypt Lower Egypt Al Minufiyah 2.218 53% 4.185 4.185

Egypt Lower Egypt Al Qalyubiyah 1.297 53% 2.448 2.448

Egypt Lower Egypt As Ismailiyah 0.823 53% 1.552 1.552

Egypt Lower Egypt Ash Sharqiyah 4.090 53% 7.717 7.717

Egypt Lower Egypt Dumyat 0.002 53% 0.003 0.003

Egypt Lower Egypt Kafr-El-Sheikh 3.004 53% 5.668 5.668

Egypt Upper Egypt Al Fayyum 2.352 53% 4.439 4.439

Egypt Upper Egypt Al Jizah 0.532 53% 1.004 1.004

Egypt Upper Egypt Al Minya 2.853 53% 5.384 5.384

Egypt Upper Egypt Aswan 1.177 53% 2.221 2.221

Egypt Upper Egypt Asyiut 2.310 53% 4.358 4.358

Egypt Upper Egypt Beni Suwayf 1.608 53% 3.034 3.034

Egypt Upper Egypt Qina 2.852 53% 5.381 5.381

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Croprequirement


ration (η)


Egypt Upper Egypt Suhaj 1.883 53% 3.553 3.553

Egypt Urban Governates

Al Iskandariyah 0.466 53% 0.878 0.878


Al Qahirah 0.138 53% 0.260 0.260


As Suways 0.087 53% 0.163 0.163


Bur Said 0.068 53% 0.128 0.128

Sudan Bahr Al Ghazal

North Bahr Al Gh

0.155 0.005 40% 0.013 0.168

Sudan Central Al Jazeera 0.365 4.337 40% 10.843 11.208

Sudan Central Blue Nile 3.891 0.272 40% 0.679 4.571

Sudan Central Sennar 4.472 2.275 40% 5.688 10.160

Sudan Central White Nile 2.447 1.140 40% 2.850 5.297

Sudan Darfur North Darfur 0.273 0.019 40% 0.047 0.319

Sudan Darfur South Darfur 3.457 0.000 40% 0.000 3.457

Sudan Darfur West Darfur 0.013 0.013

Sudan Eastern Gadaref 9.626 0.504 40% 1.261 10.887

Sudan Eastern Kassala 0.994 0.842 40% 2.105 3.099

Sudan Equatoria East Equatoria 2.032 2.032

Sudan Khartoum Khartoum 0.033 0.333 40% 0.833 0.866

Sudan Kordofan North Kordofan 3.326 0.076 40% 0.191 3.517

Sudan Kordofan South Kordofan 4.663 4.663

Sudan Kordofan West Kordofan 6.638 6.638

Sudan Northern Northern 0.012 0.680 40% 1.700 1.711

Sudan Northern River Nile 0.037 0.503 40% 1.257 1.294

Sudan SU Bahr Al Ghazal

Lakes

Lakes 8.396 8.396

Sudan Upper Nile Unity 0.226 0.226

Sudan Upper Nile Upper Nile 1.054 0.018 40% 0.044 1.098

Eritrea Gash-Barka Gash-Barka 0.209 0.041 32% 0.127 0.336

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Croprequirement


ration (η)


Ethiopia Amhara Agew Awi 0.460 0.460

Ethiopia Amhara E.Gojam 1.583 0.009 22% 0.041 1.624

Ethiopia Amhara N.Gonder 2.651 0.001 22% 0.004 2.654

Ethiopia Amhara N.Shewa 0.229 0.003 22% 0.015 0.243

Ethiopia Amhara N.Wello 0.743 0.743

Ethiopia Amhara S.Gonder 1.715 1.715

Ethiopia Amhara S.Wello 1.489 1.489

Ethiopia Amhara W.Gojam 0.000 22% 0.002 0.002

Ethiopia Amhara W.Hamra 0.322 0.001 22% 0.004 0.326

Ethiopia Amhara W.Gojam 0.000 22% 0.002 0.002

Ethiopia Benishangul Gumuz

Benishangul 0.606 0.606

Ethiopia Gambella Gambella 0.059 0.071 22% 0.325 0.384

Ethiopia Oromiya E.Wellega 0.757 0.002 22% 0.011 0.768

Ethiopia Oromiya Illubabor 0.844 0.002 22% 0.009 0.853

Ethiopia Oromiya Jimma 0.319 0.319

Ethiopia Oromiya S.W. Shewa 0.000 0.000 22% 0.000 0.000

Ethiopia Oromiya W.Shewa 1.132 0.003 22% 0.012 1.144

Ethiopia Oromiya W.Wellega 0.006 22% 0.026 0.026

Ethiopia SNNPR Bench Maji 0.486 0.486

Ethiopia Tigray Central (Tigray) 0.001 22% 0.006 0.006

Ethiopia Tigray Eastern (Tigray)

0.002 22% 0.010 0.010

Ethiopia Tigray Southern (Tigray)

0.004 22% 0.017 0.017

Ethiopia Tigray Tigray 1.485 1.485

Ethiopia Tigray Western (Tigray)

0.000 22% 0.001 0.001


Kalangala 0.141 0.141


Kampala 0.176 0.176


Kayunga 0.837 0.837

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Croprequirement


ration (η)



Kiboga 1.057 1.057


Luwero (inc Nakaseke)

1.760 0.000 30% 0.001 1.761


Masaka 2.845 0.000 30% 0.000 2.846


Mpigi 1.484 0.004 30% 0.013 1.498


Mubende (inc Mityana)

1.637 1.637


Mukono 2.422 0.005 30% 0.017 2.439


Nakasongola 0.800 0.800


Rakai (inc Lyatonde)

1.218 1.218


Ssembabule 0.660 0.660


Wakiso 1.564 0.002 30% 0.005 1.569

Uganda Eastern Uganda

Bugiri 0.970 0.022 30% 0.072 1.042


Busia 1.235 0.005 30% 0.016 1.250


Iganga 2.848 0.013 30% 0.043 2.891


Jinja 0.776 0.032 30% 0.107 0.883


Kaberamaido 0.335 0.000 30% 0.000 0.335


Kamuli 2.857 0.114 30% 0.382 3.239


Kapchorwa 0.444 0.002 30% 0.005 0.449


Katakwi (inc Amuria)

1.054 1.054


Kumi 1.174 0.000 30% 0.001 1.175

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Croprequirement


ration (η)



Mayuge 0.718 0.000 30% 0.002 0.720


Mbale 1.894 0.000 30% 0.000 1.894


Pallisa 0.006 0.006


Pallisa (inc Budaka)

1.433 0.018 30% 0.059 1.492


Sironko 1.062 0.001 30% 0.002 1.064


Soroti 1.085 0.002 30% 0.006 1.091


Tororo (inc Butaleja)

2.369 0.011 30% 0.036 2.404

Uganda Northern Uganda

Adjumani 0.465 0.000 30% 0.000 0.465


Apac (inc Oyam)

1.215 1.215


Arua (inc Koboko, Maracha etc)

1.914 1.914


Gulu (inc Amuru)

0.719 0.719


Kitgum 0.331 0.331


Kotido (inc Abim)

0.405 0.405


Lira (inc Amolatai, Dokolo)

1.208 0.006 30% 0.020 1.228


Moroto 0.197 0.197


Moyo 0.535 0.535


Nakapiripirit 0.087 0.087


Nebbi 0.996 0.996


Pader 0.633 0.633

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Croprequirement


ration (η)


Uganda Western Uganda

Bundibugyo 0.239 0.239


Bushenyi 2.311 2.311


Hoima 0.773 0.773


Kabale 2.288 2.288


Kabarole 1.073 1.073


Kamwenge 0.902 0.902


Kanungu 0.845 0.845


Kasese 1.092 0.012 30% 0.041 1.133


Kibaale 1.551 1.551


Kisoro 0.980 0.980


Kyenjojo 1.101 1.101


Masindi, Buliisa

1.068 1.068


Mbarara 3.047 3.047


Ntungamo 1.319 1.319


Rukungiri 1.470 1.470

Kenya Nyanza Bondo 0.248 0.002 30% 0.005 0.254

Kenya Nyanza Gucha 0.816 0.008 30% 0.027 0.843

Kenya Nyanza Homa Bay 0.756 0.007 30% 0.022 0.778

Kenya Nyanza Kisii 0.826 0.018 30% 0.060 0.886

Kenya Nyanza Kisumu 0.224 0.031 30% 0.103 0.327

Kenya Nyanza Kuria 0.474 0.002 30% 0.006 0.480

Kenya Nyanza Migori 1.195 0.067 30% 0.223 1.418

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Croprequirement


ration (η)


Kenya Nyanza Nyamira 0.945 0.039 30% 0.130 1.075

Kenya Nyanza Nyando 0.749 0.018 30% 0.061 0.810

Kenya Nyanza Rachuonyo 0.776 0.010 30% 0.033 0.809

Kenya Nyanza Siaya 0.644 0.026 30% 0.087 0.732

Kenya Nyanza Suba 0.144 0.002 30% 0.007 0.150

Kenya Rift Valley Bomet 0.383 0.001 30% 0.003 0.387

Kenya Rift Valley Buret 0.711 0.002 30% 0.006 0.717

Kenya Rift Valley Kericho 0.647 0.002 30% 0.008 0.654

Kenya Rift Valley Nandi 0.811 0.003 30% 0.011 0.822

Kenya Rift Valley Narok 1.295 0.007 30% 0.025 1.320

Kenya Rift Valley Transmara 0.420 0.420

Kenya Rift Valley Transzoia 0.678 0.027 30% 0.091 0.769

Kenya Rift valley Uasin Gishu 0.388 0.000 30% 0.000 0.388

Kenya Rift Valley W. Pokot no data

Kenya Western Bungoma 1.038 0.014 30% 0.047 1.085

Kenya Western Butere Mumias 1.056 0.022 30% 0.072 1.128

Kenya Western Kakamega 0.739 0.004 30% 0.014 0.753

Kenya Western Lugari 0.301 0.001 30% 0.004 0.305

Kenya Western Mt. Elgon 0.200 0.001 30% 0.003 0.203

Kenya Western Teso 0.107 0.002 30% 0.008 0.115

Kenya Western Vihiga 0.355 0.001 30% 0.003 0.358


Kagera Biharamulo 0.349 0.349


Kagera Bukoba 0.000 30% 0.000 0.000


Kagera Bukoba Rural 0.813 0.813


Kagera Bukoba Urban 0.065 0.065


Kagera Karagwe 0.710 0.710


Kagera Muleba 0.672 0.672


Kagera Ngara 0.378 0.378

Projections Report

Annex 2

55




Croprequirement


ration (η)



Mara Bunda 0.298 0.000 30% 0.002 0.299


Mara Musoma 1.070 0.000 30% 0.001 1.072


Mara Musoma Urban 0.007 0.007


Mara Serengeti 0.471 0.000 30% 0.000 0.471


Mara Tarime 1.033 0.000 30% 0.001 1.034


Mwanza Geita 1.015 1.015


Mwanza Kwimba 0.836 0.836


Mwanza Magu 0.663 0.663


Mwanza Missungwi 0.520 0.520


Mwanza Mwanza 0.171 0.171


Mwanza Sengerema 1.567 1.567


Mwanza Ukerewe 0.326 0.326


Shinyanga Bariadi 1.483 1.483


Shinyanga Bukombe 0.008 0.008


Shinyanga Kahama 0.412 0.412


Shinyanga Kishapu 0.084 0.084


Shinyanga Maswa 0.374 0.374


Shinyanga Meatu 0.112 0.112


Shinyanga Shinyanga Rural

0.351 0.351


Shinyanga Shinyanga Urban

0.097 0.097

Projections Report

Annex 2

56




Croprequirement


ration (η)


Rwanda Butare Butare 0.965 0.028 30% 0.095 1.060

Rwanda Byumba Byumba 0.985 0.985

Rwanda Cyangugu Cyangugu 0.003 0.000 30% 0.000 0.004

Rwanda Gikongoro Gikongoro 0.575 0.575

Rwanda Gisenyi Gisenyi 0.504 0.504

Rwanda Gitarama Gitarama 1.412 0.011 30% 0.037 1.449

Rwanda Kibungo Kibungo 1.207 0.017 30% 0.056 1.263

Rwanda Kibuye Kibuye 0.321 0.321

Rwanda Kigali Kigali 1.197 0.026 30% 0.086 1.283

Rwanda Ruhengeri Ruhengeri 1.122 1.122

Rwanda Umutara Umutara 0.529 0.013 30% 0.042 0.571

Burundi Bubanza Bubanza no data

Burundi Bujumbura Rural

Bujumbura Rural

0.022 0.022

Burundi Bururi Bururi 0.067 0.067

Burundi Cankuzo Cankuzo 0.072 0.072

Burundi Gitega Gitega 0.647 0.001 30% 0.002 0.649

Burundi Kanyanza Kanyanza 0.005 30% 0.016 0.016

Burundi Karuzi Karuzi 0.273 0.000 30% 0.001 0.274

Burundi Kayanza Kayanza 0.668 0.668

Burundi Kirundo Kirundo 0.480 0.002 30% 0.007 0.486

Burundi Muramvya Muramvya 0.210 0.210

Burundi Muyinga Muyinga 0.559 0.002 30% 0.008 0.567

Burundi Mwaro Mwaro 0.168 0.168

Burundi Ngozi Ngozi 0.891 0.004 30% 0.015 0.906

Burundi Rutana Rutana 0.005 0.005

Burundi Ruyigi Ruyigi 0.091 0.091

Projections Report - Food and Agriculture Organization · Projections Report Information Products...

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