1. Byerlee’s Biases

2.  Accelerating agricultural growth from early 90s of about 4% annually Higher than Non-Agricultural Growth Positive per capita AgGDP growth and labor productivity growth  But decomposition analysis shows: Commodity prices Export crops Area expansion vs yield growth  And limits of time series statistical analysis  Bottom line--20% area MVs, 10 kg/ha fertilizer, 4% irrigated, low and stagnant yields

3.  Diverse rainfed systems Compare with similar systems in Asia and LA ? Labor constraint  Policies, infrastructure and institutions Macro and sectoral policies imposed high taxes Vacuum after structural adjustment--weak state capacity  Sequencing technology and complementary investments  High transactions costs and risks  Need for a value chain approach—input and output markets, and post-harvest

4.  Diversity and scale Small heterogeneous countries cutting across AEZs  Technologically distant from other regions  Late start on food crop research in Africa And then many false starts (maize, sorghum, rice) Low stock of knowledge and low R&D investment  Dealing with high climatic and price risks Climate and CC, small countries, nontradables, markets, policies  Sustainable soil fertility management (BR)  Still--High exploitable yield gaps in medium and high potential areas (Sasakawa etc)

5.  African maize revolutions up to 1990 Effects of structural adjustment, conflicts Policy discontinuities—e.g., Malawi Private sector vs subsidies  Rice Ofice du Niger, Mali NERICAs?  Cassava Biological control, MVs, post-harvest  Few sustained efforts on R&D Drought tolerant maize

6.  Production technologies MVs for local biotic and abiotic stresses Labor including gender roles Soil and water management technologies High risks  Research vs using existing technologies  Beyond technologies Production vs value chains Technology vs complementary interventions

8.  Defining and outlining a Green Revolution Rapid and sustained productivity gains for major food crops  Maize and rice.  Cassava?  Constrained optimization problem Adding value subject to time and data constraints Assets: Unique data sets—mainly cross-sectional, team with strong analytical skills, Asian and African experience, rice and maize focus

9.  No Green Revolution after 40 years of effort  Many promising starts died on the vine  Data and analysis Historical review of false starts in Africa with particular reference to rice and maize  Maize—Update Byerlee and Eicher, IFPRI  Rice—update Stanford, recent WARDA review

10.  Irrigation explains significant difference in African performance  But even after correcting for soils, climate etc, African farmers use low levels of MVs, inputs especially in med-high potential zones Is rainfall more risky in the same AEZ?  Data and analysis Use of climate change data sets, but within defined AEZs India district data

11.  Short-run rapid gains can be achieved by using current technologies Define cases such as hybrid maize, irrigated rice Provide economic returns (e.g., which fertilizer prices?)  Analyze role of other constraints Price risks and transactions costs Input markets and credit Post-harvest for rice  Data sets—Mozambique, Uganda, Kenya Descriptive statistics on prices etc from CC data

12.  Other technological constraints Soil and water management for local conditions Post-harvest  Data and analysis Mozambique, Ethiopia Good review of options to overcome the soil fertility problem in local context

13.  Knowledge stocks and investment in R&D over time Late start in Africa on food crops—review Efforts to catch up—CGIAR, Gates  Data and analysis Review of available sources

14.  Recognize Africa’s huge diversity All five hypotheses may hold at one time  Framing the context Kym Anderson study Competitive Commercialization of African Agriculture (Brazil, Thailand, Nigeria, Zambia, Mozambique) Michigan State University (esp. fertilizer) UN/Academies of Science Report