This diagnostic analysis examines Senegal's agrifood system structure, growth, and future drivers of transformation. Key findings include:
1) Senegal's agrifood system has been transforming, with off-farm components growing more rapidly than primary agriculture and accounting for over half of agrifood GDP by 2019.
2) Growth has been driven by both export-oriented and domestic market-oriented value chains, with less-traded value chains making the largest contribution due to their size.
3) Moving forward, jointly promoting value chains like fish, horticulture, groundnuts, rice, and maize could effectively achieve multiple development outcomes like reducing poverty and hunger, improving diets, boost
Niger's agrifood system grew rapidly from 2009-2019 but lacked transformation. While GDP grew at 6.2% annually, the off-farm sector did not expand fast enough and its share of total agrifood GDP barely changed. Value chains oriented toward the domestic market drove most growth. Looking ahead, no single value chain can drive all development outcomes effectively, but jointly promoting millet, root crops, small ruminants, fisheries, and horticulture offers an approach to achieve multiple goals like reducing poverty and improving diets.
Mali's agrifood system lacks transformation and is dominated by agriculture.
Agricultural GDP and employment shares have barely changed between 2009-2019, with agriculture making up over half of GDP and two-thirds of employment. Growth has been driven by exportable and less-traded value chains, with oilseeds, rice, maize, pulses and root crops growing above average. Looking forward, jointly promoting oilseeds, pulses, sorghum and cattle/dairy could effectively achieve multiple development outcomes like reducing poverty and improving diets.
This document summarizes the key findings from a diagnostic analysis of Sudan's agrifood system conducted by IFPRI. It finds that:
1) Sudan's agrifood system lacked transformation from 2011-2019, with agricultural GDP share barely changing and off-farm GDP growing modestly. The system remains dominated by primary agriculture.
2) Growth has been driven by less-traded value chains oriented toward the domestic market, like livestock and fruits. Domestic consumption patterns are important drivers of agricultural transformation.
3) Moving forward, jointly promoting value chains like fruits, root crops, rice and wheat could effectively achieve multiple development outcomes like reducing poverty and improving diets.
Zambia's agrifood system has performed poorly in recent years, with slow or negative growth in key food crops and livestock that are important for food security and nutrition. While off-farm components of the agrifood system GDP grew modestly from 2010-2019, primary agriculture lacked growth. Looking ahead, promoting value chains like horticulture, maize, cereals and export crops could effectively achieve development outcomes like reducing poverty and improving diets, though each value chain has different strengths. Joint promotion of multiple value chains is needed to drive inclusive agricultural transformation in Zambia.
Niger's agrifood system grew rapidly from 2009-2019 but lacked transformation. While GDP grew at 6.2% annually, the off-farm sector did not expand fast enough and its share of total agrifood GDP barely changed. Value chains oriented toward the domestic market drove most growth. Looking ahead, no single value chain can drive all development outcomes effectively, but jointly promoting millet, root crops, small ruminants, fisheries, and horticulture offers an approach to achieve multiple goals like reducing poverty and improving diets.
Mali's agrifood system lacks transformation and is dominated by agriculture.
Agricultural GDP and employment shares have barely changed between 2009-2019, with agriculture making up over half of GDP and two-thirds of employment. Growth has been driven by exportable and less-traded value chains, with oilseeds, rice, maize, pulses and root crops growing above average. Looking forward, jointly promoting oilseeds, pulses, sorghum and cattle/dairy could effectively achieve multiple development outcomes like reducing poverty and improving diets.
This document summarizes the key findings from a diagnostic analysis of Sudan's agrifood system conducted by IFPRI. It finds that:
1) Sudan's agrifood system lacked transformation from 2011-2019, with agricultural GDP share barely changing and off-farm GDP growing modestly. The system remains dominated by primary agriculture.
2) Growth has been driven by less-traded value chains oriented toward the domestic market, like livestock and fruits. Domestic consumption patterns are important drivers of agricultural transformation.
3) Moving forward, jointly promoting value chains like fruits, root crops, rice and wheat could effectively achieve multiple development outcomes like reducing poverty and improving diets.
Zambia's agrifood system has performed poorly in recent years, with slow or negative growth in key food crops and livestock that are important for food security and nutrition. While off-farm components of the agrifood system GDP grew modestly from 2010-2019, primary agriculture lacked growth. Looking ahead, promoting value chains like horticulture, maize, cereals and export crops could effectively achieve development outcomes like reducing poverty and improving diets, though each value chain has different strengths. Joint promotion of multiple value chains is needed to drive inclusive agricultural transformation in Zambia.
Ghana’s agrifood system has been transforming, with agricultural GDP declining as a share of the total economy between 2009-2019. Growth has mainly been driven by domestic-oriented value chains, indicating changing diets are important drivers. Looking forward, promoting a joint approach across key value chains like maize, horticulture, pulses, rice and livestock could effectively achieve multiple development outcomes like reducing poverty and improving employment and nutrition, though no single value chain is optimal for all goals.
Madagascar's agrifood system performed poorly from 2009-2019, with stagnant growth in agriculture GDP and agrifood system GDP. The domestic market-oriented value chains, particularly rice, were the main cause of poor performance. Looking forward, jointly promoting the growth of rice, livestock, and horticulture value chains could effectively achieve multiple development outcomes like reduced poverty and improved nutrition, as these value chains rank highly in their ability to drive inclusive agricultural transformation. However, no single value chain is optimal for achieving all development goals.
Malawi’s agrifood system has been gradually transforming, with off-farm components growing faster than primary agriculture GDP between 2009-2019. The system remains dominated by primary agriculture and employment. Growth has been driven by domestic market-oriented value chains like maize, cattle and dairy, and horticulture. Looking forward, jointly promoting value chains like cattle and dairy, horticulture, and fisheries could effectively achieve multiple development outcomes like reducing poverty and improving diets.
Burkina Faso's agrifood system grew rapidly from 2009-2019 but lacked transformation. Agricultural GDP fell modestly as a share of total GDP while the off-farm agrifood share barely changed. Growth was driven by less-traded value chains due to their large size and exportable value chains with the fastest growth. Looking forward, promoting cattle & dairy, horticulture, and root crops could effectively achieve multiple development outcomes like reducing poverty and improving diets through employment and growth effects, though no single value chain is optimal for all outcomes.
This document provides a summary of a diagnostic analysis of the Democratic Republic of Congo's agrifood system conducted by IFPRI. It is divided into four parts:
1) The current structure of DRC's agrifood system, which shows that agriculture makes up over 30% of GDP and 70% of employment.
2) An analysis of value chains, which finds that import-oriented value chains dominate GDP but domestic consumption is also an important driver of transformation.
3) Growth trends from 2009-2019, which lacked structural transformation as the off-farm share of GDP did not change and growth was mainly in import-oriented livestock and roots crops value chains.
4) A modeling of faster growth
Xinshen Diao, Mia Ellis, Karl Pauw, Gracie Rosenbach, Serge Mugabo, Karl Pauw, David Spielman, and James Thurlow
International Food Policy Research Institute
Kenya's agrifood system has been transforming with faster growth in off-farm components than primary agriculture. However, primary agriculture still dominates in value terms, comprising over 20% of GDP. Growth has been driven mainly by less-traded value chains oriented toward the domestic market, as opposed to export-oriented value chains. Looking forward, promoting value chains like pulses & oilseeds, fruits & nuts, and cattle & dairy could effectively achieve multiple development outcomes like reducing poverty, improving diets, and stimulating employment and economic growth.
Ethiopia's agrifood system has undergone rapid transformation between 2009 and 2019, with agricultural GDP declining as a share of total GDP while off-farm components increased. Growth has been driven by domestic market-oriented value chains like cereals, roots, and livestock. Looking forward, jointly promoting high-growth value chains like horticulture, wheat and barley, maize, and livestock could effectively achieve multiple development outcomes like poverty reduction, improved nutrition, employment, and economic growth. No single value chain maximizes all outcomes, so a balanced approach is needed.
These set of slides were presented at the BEP Seminar "Targeting in Development Projects: Approaches, challenges, and lessons learned" held last Oct. 2, 2023 in Cairo, Egypt
Caitlin Welsh
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Bofana, Jose. 2023. Mapping cropland extent over a complex landscape: An assessment of the best approaches across the Zambezi River basin. PowerPoint presentation given during the Project Inception Workshop, VIP Grand Hotel, Maputo, Mozambique, April 20, 2023
Mananze, Sosdito. 2023. Examples of remote sensing application in agriculture monitoring. PowerPoint presentation given during the Project Inception Workshop, VIP Grand Hotel, Maputo, Mozambique, April 20, 2023
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International Food Policy Research Institute (IFPRI). 2023. Statistics from Space: Next-Generation Agricultural Production Information for Enhanced Monitoring of Food Security in Mozambique. PowerPoint presentation given during the Project Kickoff Meeting (virtual), January 12, 2023
International Food Policy Research Institute (IFPRI). 2023. Statistics from Space: Next-Generation Agricultural Production Information for Enhanced Monitoring of Food Security in Mozambique. Component 1. Stakeholder engagement for impacts. PowerPoint presentation given during the Project Inception Workshop, VIP Grand Hotel, Maputo, Mozambique, April 20, 2023
Centro de Estudos de Políticas e Programas Agroalimentares (CEPPAG). 2023. Statistics from Space: Next-Generation Agricultural Production Information for Enhanced Monitoring of Food Security in Mozambique. Component 3. Digital collection of groundtruthing data. PowerPoint presentation given during the Project Inception Workshop, VIP Grand Hotel, Maputo, Mozambique, April 20, 2023
ITC/University of Twente. 2023. Statistics from Space: Next-Generation Agricultural Production Information for Enhanced Monitoring of Food Security in Mozambique. Component 2. Enhanced area sampling frames. PowerPoint presentation given during the Project Inception Workshop, VIP Grand Hotel, Maputo, Mozambique, April 20, 2023
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Senegal’s Agrifood System Structure and Drivers of Transformation
1. Senegal’s Agrifood System
Structure and Drivers of Transformation
Xinshen Diao, Mia Ellis, Karl Pauw, Josee Randriamamonjy, and James Thurlow
International Food Policy Research Institute
This diagnostic analysis was conducted by IFPRI with financial support from USAID.
July 2023
2. Four Parts to the Diagnostics
• Current structure
What does Senegal’s food system look like today?
• Decomposing value chains
How are different products contributing to the broader agrifood system?
• Growth and market structure
How is Senegal’s agrifood system growing and transforming?
• Future drivers of inclusive agricultural transformation
Which value chains could be most effective?
2019
2009-2019
2019+
3. Summary
Senegal’s agrifood system (AFS) diagnostic results
Senegal’s AFS has been transforming
• The off-farm components of AgGDP+ have grown more rapidly than primary agriculture GDP
• More than half of AgGDP+ was off-farm in 2009, and the off-farm share of AgGDP+ further rose during 2009–2019
• The agricultural employment share fell significantly, an indication of rapid structural change in the economy and rising agricultural labor
productivity
AFS growth has been driven by value chains oriented both toward the domestic market and for export
• Less-traded value chains dominated the AFS growth with their large size
• Exportable value chains made an important contribution to AFS growth with above-average growth
Looking forward, the structure of AFS growth will be crucial in driving development outcomes…
(e.g., poverty, dietary improvement, employment creation, and growth)
…but no single value chain is the most effective at driving all these development outcomes
• Root crops and cotton are most effective at reducing poverty; horticulture and cattle & dairy are best for improving diet quality; and rice
and fish have strong employment effects and large growth multipliers
Jointly promoting fish, horticulture, groundnuts, rice, and maize would offer an effective way to achieve
multiple development outcomes
4. Framework | Agrifood Systems (AFS)
Primary agriculture
Agroprocessing
Trade and transport
Food services
Trade and transport
Input supply Demand
Consumption of own-
produced goods
Purchase of primary
agricultural goods
Purchase of processed
agrifood goods
Purchase of ready-made
foods outside of home
Imports
A
C
B
D
E
Includes agriculture, plus all upstream/downstream sectors
• Five major components (A to E)
• Same format as standard economywide datasets (e.g., national accounts)
• Allows us to measure AFS structure and performance using actual data
Agrifood System GDP (AgGDP+)
Total value added generated by all agricultural
value chains (in constant dollars)
Agrifood System Employment (AgEMP+)
Total number of workers who are primarily
employed in an agricultural value chain
5. Structure2019 | Senegal’s Agrifood System Today
GDP and employment in Senegal’s agrifood system (2019)
• Part 1 focuses on the current size and
structure of the national agrifood system
• Latest AgGDP+ and AgEMP+ estimates
• Decomposed into five AFS components
• Situates AFS within the broader economy
• Uses official data sources
• GDP from national accounts
• Employment from various sources (i.e., population
census, labor force surveys, ILO, etc.)
• Senegal estimates indicate that
• AFS makes up one-third of GDP
($7.4 billion AgGDP+) …
• … and 40% of total employment
(2.0 million AgEMP+)
• Primary agriculture (A) is still large, but off-farm
components (B–E) have been catching up
(more than half of AgGDP+, 45% of AgEMP+)
GDP
($ billions)
Employment
(millions of workers)
Total economy 20.9 100% 2.5 100%
Agrifood system 7.4 35.5% 2.0 42.9%
Primary agric. (A) 3.5 16.7% 1.0 23.0%
Off-farm AFS 3.9 18.8% 0.9 20.0%
Processing (B) 1.9 8.9% 0.2 5.1%
Trade & transport (C) 1.5 7.4% 0.6 12.3%
Food services (D) 0.2 1.2% 0.1 1.6%
Input supply (E) 0.3 1.4% 0.0 1.0%
Rest of economy 13.5 64.5% 2.6 57.1%
6. Structure2019 | Comparing to Other Countries
• Importance and structure of the AFS varies at different stages of development
Senegal is a lower-middle-income country (LMIC)
• A: Senegal’s AgGDP+ share of total GDP lies between low-income countries (LICs) and LMICs
• B: Senegal’s off-farm components are larger than the LMIC average
• C: Senegal’s agro-processing sector is larger than expected
Share of total GDP (%) Share of AFS GDP (%) Share of off-farm AFS GDP (%)
LIC = low-income countries | LMIC = lower-middle income | UMIC = upper-middle-income | HIC = high-income Source: IFPRI Agri-Food System Database
A B C
4.2
26.4
16.9
7.1
1.2
16.7
8.2
13.4
11.9
10.6
6.6
18.8
All LIC LMIC UMIC HIC Senegal
Primary agriculture Off-farm AFS
34.0
66.2
58.6
40.2
15.6
46.9
66.0
33.8
41.4
59.8
84.4
53.1
All LIC LMIC UMIC HIC Senegal
Primary agriculture Off-farm AFS
33.7 37.8 38.4
46.9
26.1
47.1
31.7
42.8 38.6 21.4
35.9
39.3
23.1
13.7
11.2
18.2 27.8
6.1
11.4 5.8 11.8 13.5 10.3 7.5
All LIC LMIC UMIC HIC Senegal
Processing Trade and transport
Food services Input supply
7. Structure2019 | Supply vs. Demand Sides of the Agrifood System
Agrifood GDP vs. consumption
Primary, processed, and other product shares (%)
• AgGDP+ defines the AFS on the supply side
• Household demand and trade (imports) capture AFS structure on the demand side
• Agrifood processing is more important on the demand side than the supply side in the AFS
AgGDP+ Household demand
Agrifood exports vs. imports
Primary and processed product shares (%)
Exports ($1.34 bil.) Imports ($1.25 bil.)
46.9%
25.0%
28.1%
$0.39 bil.
29.2%
$0.95 bil.
70.8%
Primary agriculture
Agrifood processing
$1.11 bil.
88.9%
$0.14 bil.
11.1%
24.0%
67.7%
8.3%
Primary agriculture
Agroprocessing
Other off-farm
8. Value Chains2019 | Contributions & Trade Orientation
• Part 2 decomposes the AFS across broad value
chain groupings
• Classify value chains based on trade orientation
• Exportable value chains have above-average export-
output ratios (> 10.0%)
• Importable value chains have above-average import-
demand ratios (> 9.4%)
• Less-traded value chains make up the rest
• Strong comparative advantage in exports – four
exportable value chains; relatively smaller off-farm share
(21.2%) and larger on-farm (primary) share (33.3% of
total)
• Domestic market dominates AgGDP+ (52%) – eight less-
traded value chains; relatively smaller off-farm share
(46.8%) and larger on-farm share (57.8% of total) with
cattle & dairy and fish being exceptions
Promoting some exportable value chains, cattle & dairy,
and fish (less-traded) could be effective at driving
agricultural transformation by boosting value added and
employment in off-farm AFS
Share of total GDP (%) Exports /
output
(%)
Imports /
demand
(%)
Total
AFS
Primary
agric.
Off-farm
AFS
Total 100 100 100 10.0 9.4
Exportable 26.9 33.3 21.2 33.4 2.6
Pulses 0.6 1.2 0.1 20.7
Groundnuts 15.1 17.8 12.8 33.2
Horticulture 8.2 10.1 6.5 26.4 8.2
Cotton 3.0 4.2 1.9 59.2
Importable 15.8 8.9 21.9 0.0 28.5
Rice 7.4 5.8 8.7 38.3
Other crops 8.4 3.0 13.2 0.1 18.8
Less traded 52.0 57.8 46.8 1.3 2.4
Maize 3.9 6.3 1.8 1.1
Sorghum & millet 11.1 11.2 11.0 0.8 5.5
Oher oilseeds 0.6 1.1 0.1 0.1
Roots 1.0 2.1 0.1 4.6
Cattle & dairy 13.7 11.1 16.0 0.0 1.2
Other livestock 6.6 12.8 1.2 0.4 0.2
Fish 11.2 9.9 12.3 4.2 1.7
Forestry 3.8 3.3 4.3 0.1 7.6
Breakdown of Senegal’s agrifood system (2019)
9. Growth2009-2019 | Agrifood System Performance
Senegal’s AFS has been transforming
• Agricultural share of total GDP fell during 2009-2019 (18.3% to 16.7%)
• Off-farm components accounted for more than half of AgGDP+
Agricultural employment share fell rapidly (39% to 23%)
• An indication of rapid structural change in the economy and rising agricultural labor productivity
Agricultural GDP, agrifood system GDP, and employment shares (2009–2019)
• Part 3 analyzes structural change in the AFS and the contribution of different value chains to AFS growth
18.3
38.4
52.5
38.9
16.7
35.5
53.1
23.0
Agricultural GDP share AgGDP+ share Off-farm share of AgGDP+ Agricultural employment
share
Share
(%)
2009 2019
10. Growth2009-2019 | Value Chain Performance
• AgGDP+ grew modestly during 2009–2019
(4.1% p.a.)
• Less-traded value chains dominated AFS growth
with their large size, contributing 47% of AFS
growth in 2009–2019
• Exportable value chains made an important
contribution to AFS growth with above-average
growth (4.5%), contributing 31% of AFS growth
• Value chains with above-average growth (*)
• Three exportable value chains – pulses,
groundnuts, and horticulture – of which pulses and
horticulture grew rapidly (8.5% and 5.7%
respectively)
• One importable value chain – rice – had highest
growth rate (8.5%)
• Three less-traded value chains – maize, other
oilseeds, and other livestock – all had rapid growth
• Off-farm and on-farm AFS have similar
growth rates for most fast-growing value
chains
Value chain growth in Senegal (2009-2019)
Average annual GDP growth rate (%)
Total
AFS
Primary
agric.
Off-farm
AFS
Process-
ing
Total AFS 4.1 3.9 4.2 3.9
Exportable 4.5 4.4 4.8 4.6
Pulses* 8.5 8.6 7.6
Groundnuts* 4.4 4.2 4.5 4.1
Horticulture* 5.7 6.2 5.1 4.0
Cotton 2.1 0.6 6.1 11.7
Importable 5.8 7.3 5.2 4.9
Rice* 8.5 9.4 8.0 8.5
Other crops 3.9 4.3 3.8 4.0
Less traded 3.4 3.3 3.5 3.4
Maize* 6.2 6.3 5.8 6.3
Sorghum & millet 1.3 1.1 1.5 2.0
Oher oilseeds* 6.3 6.4 5.9 4.5
Roots 3.4 3.4 3.2
Cattle & dairy 3.1 1.9 3.9 3.7
Other livestock* 5.5 5.6 4.8
Fish 3.9 3.0 4.6 3.9
Forestry 3.3 3.3 3.2 3.6
11. Future Drivers2019+ | Modeling Faster Growth
• IFPRI’s RIAPA model is used to analyze different sources of agricultural growth
• Expand production in different value chains
• Increase on-farm productivity growth rates in targeted value chains
• Achieve same overall growth in agriculture GDP (e.g., 1.0%)
• Track linkage effects within value chains and spillover effects to other value chains
• Assess outcomes
• Poverty – Poverty-growth elasticity in percentage points based on $2.15-a-day
• Hunger – Hunger-growth elasticity in percentage points based on prevalence of undernourishment
• Diet – Diet quality to growth elasticity in % derived from Reference Diet Deprivation index (REDD)
• Jobs – Employment multiplier in thousand employed persons associated with US$1 million growth in targeted value chain
• GDP – GDP growth multiplier in US$ millions associated with US$1 million growth in targeted value chain
• Average across outcomes
• The value of outcome indicators (elasticity or multiplier) is expected to differ across value chain growth; not all value chains are
equally effective at achieving all outcomes
• Normalizing the individual outcome scores
• The values of each outcome indicator are scaled so that the most effective value chain is given a score of one and the leasteffective is given a
score of zero. A value chain with adverse impact is also given a score of zero.
• An average score with equal weights is used to measure the total impacts across all value chains
12. Future Drivers2019+ | Prioritizing Agricultural Growth
Poverty
(change in %-point)
Hunger
(change in %-point)
Jobs
(change in 1,000)
Diet quality
(change in %)
Average across outcomes
(averaged normalized scores, reordered)
GDP
(change in mil. $)
1.02
0.83
1.28
0.99
0.98
1.02
0.77
0.93
0.90
2.00
1.65
-0.34
1.18
0.54
0.26
0.26
0.52
0.17
-0.06
0.23
1.84
2.03
0.01
0.01
0.05
-0.02
0.75
0.15
0.07
0.16
0.05
0.12
-0.05
Root crops
Cotton
Maize
Sorghum
Horticulture
Groundnuts
Pulses
Cattle & dairy
Other livestock
Fish
Rice
-0.06
-0.04
-0.17
-0.29
-0.05
-0.06
-0.06
-0.04
0.00
0.01
-0.25
0.53
0.50
0.43
0.43
0.41
0.37
0.32
0.26
0.24
0.20
0.19
Fish
Horticulture
Rice
Maize
Cotton
Groundnuts
Root crops
Sorghum
Pulses
Cattle & dairy
Other livestock
Total
Fish
Horticulture
Rice
Maize
Cotton
Groundnuts
Root crops
Sorghum
Pulses
Cattle & dairy
Other livestock
Poverty Growth Jobs Diets
Individual outcomes
(per unit change in agriculture GDP, ordered by poverty outcome)
-0.19
-0.16
-0.13
-0.12
-0.11
-0.09
-0.06
-0.05
0.00
0.08
0.13
Root crops
Cotton
Maize
Sorghum
Horticulture
Groundnuts
Pulses
Cattle & dairy
Other livestock
Fish
Rice
13. Future Drivers2019+ | Key Messages from the Model
AFS growth is pro-poor
• Growth led by many value chains reduces poverty, but root crops and cotton are most effective
AFS growth is effective in improving food security (hunger) and diet quality
• Most value chains reduce hunger; the three cereal value chains – sorghum, maize, and rice – are most effective
• Most value chains improve diet quality; horticulture and cattle & dairy are most effective
Agricultural growth creates jobs but not necessarily on-farm
• Most value chains are associated with an increase in total employment, but AFS jobs are mainly created off-farm
• Rice and fish are the most effective value chains in creating jobs in the overall economy
Agricultural growth has strong growth multiplier effects that generate income beyond agriculture
• Fish, rice, and maize value chains have stronger multiplier effects for both AFS income and total GDP growth
In conclusion, promoting multiple value chains can achieve broad-based impact
• No single value chain group is the most effective in achieving all the outcomes we consider
• Fish, horticulture, and rice rank highly in the combined outcome scores, while maize and groundnuts have more balanced impacts on
poverty, diet, jobs, and growth
• Promoting these value chains jointly would offer an effective way to achieve broad-based outcomes
14. Note: Value Chain Groups and Agricultural Sectors in Individual VC Groups
Value chain group and their share
of AgGDP+
Individual products and their share of group’s agriculture GDP
Maize (3.9%) Maize 100%
Sorghum & millet (11.1%) Sorghum & millet 100%
Rice (7.4%) Rice 100%
Pulses (0.6%) Pulses 100%
Groundnuts (15.1%) Groundnuts 100%
Other oilseeds (0.6%) Other oilseeds 100%
Roots (1.0%) Cassava 51.1% | Irish potatoes 32.4% | Sweet potatoes 16.5%
Cotton (3.0%) Cotton 100%
Horticulture (8.2%)
Leafy vegetables 11.7% | Other vegetables 34.0% | Nuts 8.1% | | Bananas 4.2% | Other
fruits 41.0%
Other crops (8.4%) Other cereals 13.9% | Sugar 49.4% | Tobacco 21.3% | Other crops 15.4%
Cattle & dairy (13.7%) Cattle meat 52.6% | Raw milk 47.4%
Other livestock (6.6%) Poultry meat 46.1% | Eggs 11.2% | Small ruminants 37.4%| Other livestock 5.3%
Fish (11.2%) Aquaculture 0.4% | Capture fish 99.6%
Forestry (3.8%) Forestry 100%