Megan Sheahan and Christopher B. Barrett Cornell University

1. Understanding the existing agricultural input landscape in sub-Saharan Africa: Recent field, household, and community-level evidence Megan Sheahan and Christopher B. Barrett Cornell University Presented at the International Livestock Research Institute, Nairobi, Kenya, 10 April 2014 AGRICULTUREIN AFRICA TELLING FACTSFROM MYTHS

2. Broader Motivation: LSMS-ISA data Current challenges The world in which African farmers operate has changed: • High and more volatile food price environment • Africa is growing and urbanizing quickly • Production environment changes due to climate change and soil erosion Concurrent renewed investment in agricultural sector However, knowledge base is grounded in “old ideas” about African agriculture or inappropriate data Salient case studies, purposively selected samples, agricultural statistics of unknown quality

3. Broader Motivation: LSMS-ISA data An opportunity! • Collecting household survey data with focus on agriculture in 8 SSA countries  Burkina Faso, Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania, Uganda • Improving methodologies in data collection, producing best practice guidelines and research • Documenting and disseminating micro data for policy research • Building capacity in national institutions

4. Broader Motivation: LSMS-ISA data Survey features • Nationally representative (rural and urban, and various administrative levels) • 4 I’s • Integrated: multi-topic and geo-referenced (link with eco-systems) • Individual: gender/plot • Inter-temporal:panels with tracking • Information technology: concurrent data entry (CAPI, GPS) • Open data access policy http://www.worldbank.org/lsms-isa

5. Broader Motivation: LSMS-ISA data Survey instruments

6. Broader Motivation: LSMS-ISA data Data collection schedule for panel rounds

7. Broader Motivation: LSMS-ISA data Data collection schedule for panel rounds

8. Broader Motivation: “Myths and Facts” Project Project objectives • Provide a solid, updated, and bottom-up picture of Africa’s agriculture and farmers’ livelihoods • Create a harmonized and easy-to-use database of core agricultural variables for tabulation and regional cross-country benchmarking • Build a community of practice • Partnering institutions: World Bank, African Development Bank, Cornell University, Food and Agriculture Organization, Maastricht School of Management, Trento University, University of Pretoria, Yale University • Mentorship program for young African scholars from US and African institutions AGRICULTUREIN AFRICA TELLING FACTSFROM MYTHS Project led by Luc Christiaensenlchristiaensen@worldbank.org

9. Broader Motivation: “Myths and Facts” Project Common wisdoms revisited 9) Women perform the bulk of Africa’s agricultural tasks 10) Seasonality continues to permeate rural livelihoods • Smallholder market participation remains limited • Post harvest losses are large • Droughts dominate Africa’s risk environment • African farmers are increasingly diversifying their incomes • Agricultural commercialization and diversification improves nutritional outcomes • Use of modern inputs remains dismally low • Land, labor and capital markets remain largely incomplete • Agricultural labor productivity is low • Land is abundant and land markets are poorly developed • Rural entrepreneurs largely operate in survival mode. • Extension services are poor • Agroforestry is gaining traction • African agriculture is intensifying

10. Broader Motivation: “Myths and Facts” Project Common wisdoms revisited • Use of modern inputs remains dismally low

11. Motivation Why is it important to explore input use? • Increase in agricultural productivity necessary for agricultural transformation and poverty reduction • Expanded use of modern inputs, embodying improved technologies, is often seen as a prerequisite to increasing agricultural productivity • Common wisdoms (“stylized facts”): • African farmers use few modern inputs • Input provision systems remain poor • Those stylized facts have helped spur the new government input subsidy paradigm in SSA, although little cross-country, nationally representative, and recent evidence exists to support those stylized facts • Use LSMS-ISA data to describe “input landscape” related to fertilizer, modern seed varieties, agro-chemicals (pesticides, herbicides), irrigation, mechanized inputs (animal traction, farm machinery)

12. Structure of Paper Meticulously assembled data set but simple descriptive methodology • From where do common conceptions on input use currently come? • Macro-statistics: FAOStat, World Bank’s World Development Indicators, CGIAR’s Diffusion and Impact of Improved Varieties in Africa project • Micro-statistics: Literature review of studies on input use from household level data with large samples by country and input • With the newest available round of LSMS-ISA data in each country: • Who uses modern inputs and in what amounts? • What is the input provisioning situation? • What is the main source of variation in binary input use decision? • 10 most striking and important findings presented here

13. Sample and data considerations Households that cultivate at least one field in main ag season Sample includes over 22,000 households and 62,000 plots across 6 countries

14. (1) Input use is not uniformly low, especially with respect to percentage of cultivating households using inputs but also rates of use. Most true of inorganic fertilizer and agro-chemical use • Near-perfect match with macro-stats on inorganic fertilizer use rates across four countries Ethiopia, Niger, Tanzania, Uganda • Largest discrepancies in 2 of 3 countries with fertilizer subsidy programs Malawi and Nigeria • Relatively high shares of households use inorganic fertilizer, with 3 of 6 countries > 40 percent • Where > 30 percent of households use agro-chemicals, any implications for human health? • Uganda has lowest input use prevalence of 6 included countries

15. (2) The incidence of irrigation and mechanization are really quite small. Micro-statistics similar to macro-statistics • Mechanization is proceeding slowly • Traction animal ownership above 20 percent in all countries except Malawi • 1-2 percent of households own a tractor • 1/4 of households in Nigeria used a mechanized input or animal power on their plots during main ag season

16. (3) Huge amount of variation within countries in the prevalence of input use and intensity. Example from Ethiopia • Most input use appears to be driven by certain regions and zones within countries

17. (4) Input use is as high on maize dominated plots as it is on average at the household level. Cash crops not driving input use? *Niger: millet/sorghum/millet/cowpea instead of maize (too few) • Commercially purchased maize seeds are used by 25-40 percent of maize cultivating households

18. (5) Consistent negative relationships between farm and plot sizes and input use intensity. Example from Nigeria • Local linear non-parametric regressions of unconditional inorganic fertilizer use rates • Shape differs by country, especially where ranges in size vary substantially  relatively flat for Malawiand different pattern for Ethiopia and Uganda • Negative relationship is even more pronounced at the plot level in all cases except Ethiopia  important policy implications! Nigeria – household level Nigeria – plot level

19. (6) Little variation in input use when households and plots are split by soil quality and erosion status, both farmer-perceived and geo-referenced. Moreover, few farmers consider their plots of ‘poor’ quality • Regression analysis reveals that ‘average’ and ‘poor’ plots significantly are more likely to receive inorganic fertilizer treatments than those categorized as ‘good’ • Knowledge gap among farmers? Weak evidence against ‘poor but efficient’ claim? • Implications for extension programs andthe need to invest in simple soil quality tests

20. (7) Surprisingly low correlation between the joint use of commonly ‘paired’ inputs. Especially apparent when moving from household to field level • Show correlation between two-way input use in paper • Can investigate three-way input use (fertilizer, seed, irrigation) for Ethiopia and Niger • Farmers may use >1 modern input on farm, but appear to be diversifying within farm rather than reaping output gains by pairing inputs together • Synergies from pairing inputs still yet to be exploited Ethiopia – household level Ethiopia – field level

21. (8) Fertilizer subsidies are not as universal as often believed or reported in government statistics. 3 of 6 current LSMS-ISA countries have government fertilizer subsidy programs • Mixed reviews by households on input market accessibility changes over time in Malawi, where fertilizer subsidies are most pervasive • > 50 percent of households receive a government fertilizer subsidy only in Malawi • Relatively low fertilizer subsidy occurrence in Nigeria alongside high rates of fertilizer use • Estimates of fertilizer subsidy coverage in LSMS-ISA data fall short of other estimates using government data • Could be issue of LSMS-ISA data collection timing or sluggish/failed distribution of planned vouchers by subsidy program implementers

22. (9) There is very low incidence of credit use for purchasing modern inputs. Statistics should capture both informal and formal credit types • < 1 percent of cultivating households used credit to purchase improved seed varieties, inorganic fertilizer, and agro-chemicals • True of all countries except Ethiopia, where the government issues input credit • (1) Country-averaged input-output price ratios imply that fertilizer is a good investment at aggregate levels + (2) Wealthier households more likely to use fertilizer • No or under-use may signal cash flow constraints, which could be aided by expanding credit options • Policy implication addressing rural financial market failures may be key for expanding input use

23. (10) Over half of the variation in inorganic fertilizer and agro-chemical use comes from the country level. Suggests that policy and institutional environment are very important • Ultimately interested to learn where most of the variation in input use comes from • Biophysical, infrastructure, market, socio-economic, or policy-specific variables? • Binary use at household level (avoids bias from survey design) • Also reported at field level under a number of different specifications (fewer field level characteristics match across surveys) • R2decomposition using Shapley-Owen values • > 50 percent of variation in fertilizer use can be explained by country level! • Suggests that geography, policy, and institutional environment are important for ushering a Green Revolution in Africa

24. Conclusions Main take-away messages • Input use is not always low  Much more heterogeneity in input use between and within countries than commonly assumed • - Varies by country, input, crop, and a large number of important covariates • - Micro-level statistics allow us to investigate this variation more fully • Scope for improvement remains • - Synergies in effectively combining inputs appropriately yet to be exploited • - Policy and institutions seem important for encouraging yield-enhancing input use • 10 findings presented here are only a small subset of what can be gleaned about input use from the LSMS-ISA surveys • Exploiting newly emerging panel data will allow us to provide greater nuance for guiding more intelligent policy design

25. Thank you! Contact: mbs282@cornell.edu