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1 Introduction to yield gap analysis

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1 Introduction to yield gap analysis

  1. 1. Yield Gap Analysis and Crop Modeling Workshop Nairobi, Kenya RESEARCH PROGRAMS ON Climate Change, Agriculture and Food Security POTATO YIELD GAP ANALYSIS: A REVIEW Integrated Systems for the Humid Tropics Roots, Tubers and Bananas International Potato Center Sub-program: Production Systems and Environment
  2. 2. POTATO YIELD GAP ANALYSIS: A REVIEW Masai Lodge, 24-29 June 2013 D. Harahagazwe, R. Quiroz, B. Condori, C. Barreda and F. de Mendiburu
  3. 3. GYGA Workshop, Kenya 2012
  4. 4. WHY YIELD GAP ANALYSIS MATTERS?
  5. 5. WHY YIELD GAP ANALYSIS MATTERS? • SSA will account for one half of the world population increment by 2050 • Continued increased demand for agricultural products (food, feed and biofuels): – agricultural food demand is expected to increase by 50% by 2050 (Tilman et al., 2001) – The feed grain demand in developing countries is expected to increase by 84% by 2020 (1997’s baseline – Delgado et al., 1999) • Unfortunately the maximum possible yields achieved in farmers’ fields might level off or even decline in many regions over the next few decades (Lobell et al., 2009) – plateau theory
  6. 6. • Business as usual will not meet projected global food demand in the coming years due to various factors
  7. 7. Three broad options to face the global food demand (Licker et al., 2010): –Expand the area of croplands at the expense of other ecosystems; –Increase the yields on the existing croplands (i.e. closing the yield gaps) –Reallocate current agricultural production to more productive uses
  8. 8. • Yp analysis provides a measure of untapped food production capacity • Also, knowledge of yield gaps (importance, magnitudes and causes) helps in better orienting investments in agricultural research R&D as it is a good management decision tool for improved resource-use efficiency (land, fertilizers, water, etc..)
  9. 9. Examples of yield gaps at global level (Neumann et al., 2010) Based on frontier yield (source: – Wheat: 36 % – Rice: 36% – Maize: 50 % (c. 80% in Africa)
  10. 10. POTATO PRODUCTION AND PRODUCTIVITY IN SSA Source: FAOSTAT, 2013
  11. 11. Annual Production in SSA Eastern and Central Africa 3500 Annual Production (x1000 t) 3000 Burundi DR Congo Ethiopia Kenya Rwanda Tanzania Uganda 2500 2000 1500 1000 500 0 1960 1970 1980 1990 2000 2010 2020 Year Source: D. Harahagazwe (FAOSTAT datasets)
  12. 12. Annual Production in Southern Africa Annual Production (x1000t) 4000 Angola Madagascar Malawi Mozambique 3000 2000 1000 0 1960 1970 1980 1990 Year 2000 2010 2020
  13. 13. West Africa 1200 Annual Production (x1000t) 1000 Nigeria 800 600 400 200 0 1960 1970 1980 1990 2000 2010 2020 Year Source: D. Harahagazwe (FAOSTAT datasets)
  14. 14. Annual Production in ECA region Annual Production (x1000t) 8000 6000 Burundi DR Congo Ethiopia Kenya Rwanda Tanzania Uganda 4000 2000 0 1960 1970 1980 1990 2000 2010 Year Source: D. Harahagazwe (FAOSTAT datasets)
  15. 15. YIELD GAP CONCEPT
  16. 16. Yield Gap •Yg = Yp – Ya • “The difference between Yp and average farmers’ yields over some specified spatial and temporal scale of interest” (Lobell et al., 2009)
  17. 17. Conceptual framework of various Yg (Source: Lobell et al., 2009) YGF<YGE<YGM
  18. 18. • Yg can be defined and measured in a variety of ways: Lack of consistency in Yg analysis in literature • Normally developed countries have low yield gaps for some crops like maize, wheat, potato and rapeseed (Licker et al., 2010) • Yield gaps across Africa are on the higher end of the spectrum for many crops
  19. 19. Yield gaps estimated at 2 levels •Local focus (site-based approach) •Upscaling approach (region, national, global)
  20. 20. Assessment of Yp and Yg (Lobell et al., 2009) 3 methods: 1) Model simulations 2) Field experiments and yield contests 3) Historical maximum farmer yields
  21. 21. Attributes of Best Crop Models used in Yg analysis (van Ittersum et al., 2013)  Daily step simulation  Flexibility to simulate management practices  Simulation of fundamental physiological processes  Crop specificity  Minimum requirement of crop “genetic” coefficients Validation against data from field crops that approach Yp (Yw) User friendly Full documentation of model parameterization and availability
  22. 22. But the best assessment of Yg SHOULD BE an integration of (Lobell et al., 2009): a) b) c) d) e) Remote sensing Geospatial analysis Simulation models, Field experiments and On-farm validation
  23. 23. POTENTIAL YIELD
  24. 24. Yield Potential vs. Potential Yield Definition 1 (Evans and Fischer, 1999): Yield potential: “yield of a cultivar when grown in environments to which it is adapted, with nutrients and water non-limiting and with pests, diseases, weeds, lodging, and other stresses effectively controlled”. Potential yield: “the maximum yield which could be reached by a crop in given environments, as determined, for example, by simulation models with plausible physiological and agronomic assumptions”.
  25. 25. Definition 2 (GYGA project): Yield potential = Potential yield: “yield of a crop cultivar when grown with water and nutrients non-limiting and biotic stress effectively controlled”(van Ittersum et al., 2013 - GYGA group http://www.yieldgap.org/ ).
  26. 26. Hierarchy of Yield Drivers and Associated Yield Levels Crop Traits Germplasm Defining factors Potential yield (Yp) CO2 Dry Matter Yield Radiation Limiting factors Attainable yield Climate Temperatu re Reducing factors Water Actual yield (Ya) Nutrients Soils Weeds Pests Source: R. Quiroz (Modified from Penning de Vries & Rabbinge, 1995) Diseases
  27. 27. Measuring yield potential: a mission impossible? • A concept rather than a quantity: quid estimation? – perfection! (Lobell et al., 2009) • Well-managed field studies in which all growth factors are eliminated • Replicated over a number of years and sites to obtain a reliable average Yp • Representative of the dominant cropping system in the region of interest (planting date, spacing, cultivar maturity, etc..) Source: GYGA, 2012
  28. 28. ACTUAL YIELD
  29. 29. Actual Yield (Ya) (Source: van Ittersum et al., 2013) • Working definition: “The yield actually achieved in a farmer’s field” • Time and space dimension: – The average yield (in space and time) achieved by farmers in the region under the most widely used management
  30. 30. Actual Potato Yield at Global Level Source: D. Harahagazwe (datasets from Monfreda et al., 2008)
  31. 31. ZOOMING IN – AFRICA (Source: D. Harahagazwe, datasets from Monfreda et al., 2008)
  32. 32. Tuber Yield in SSA Eastern and Central Africa 25 Burundi DR Congo Ethiopia Kenya Rwanda Tanzania Uganda Tuber Yield (t.ha-1) 20 15 10 5 0 1960 1970 1980 1990 Year 2000 2010 2020 Source: D. Harahagazwe (FAOSTAT datasets)
  33. 33. Southern and West Africa 18 Angola Madagascar Malawi Mozambique Nigeria 16 -1 Tuber Yield (t.ha ) 14 12 10 8 6 4 2 1960 1970 1980 1990 2000 2010 2020 Year Source: D. Harahagazwe (FAOSTAT datasets)
  34. 34. Sources of Actual Yields • Preferably at site level (as defined by selected weather station and dominant soil types): mean and spatial/temporal variation • High quality sub-national data (county, district, village, municipality level) • Last option (coarse resolutions): Global gridded yield datasets/maps like Monfreda et al., 2008 (best available global crop yield datasets) or SPAM Source: GYGA, 2012
  35. 35. EXAMPLE OF YIELD GAP
  36. 36. Potential Yield, Attainable Yield and Actual Yield Ex: Ndinamagara (Cruza 148) Gisozi, 2007 50 Fresh Tuber Yield (t.ha-1) 44 40 30 Yield Gap (41 t.ha-1) Yield Gap Fraction (0.93) 20 10 3 0 Potential Yield Actual Yield
  37. 37. REFERENCES • • • • • • • • FAOSTAT. 2013. URL: http://faostat3.fao.org/home/index.html Evans, L. T. and Fischer, R. A. 1999. Yield Potential: Its Definition, Measurement, and Significance. Crop Sci. 39 (6) 1544-1551. Ittersum, M. K. van, Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P. A. and Hochman, Z. 2013. Yield gap analysis with local to global relevance-A review. Field Crops Research 143, 4-17. GYGA. 2012. Global Yield Gap and water Productivity Atlas (GYGA) Workshop Training Materials. 6-8 June 201, Naivasha, Kenya. GYGA. 2013. Global Yield Gap Atlas web site. URL: http://www.yieldgap.org/ Lobell, D.B., Cassman, K.G., Field, C.B. 2009. Crop Yield gaps: their importance, magnitudes, and causes. Ann. Rev. Environ. Resour. 34, 179-204. Van Wart, J., Van Bussel, L.G.J., Wolf, J., Licker, R., Grassini, P., Nelson, A., Boogaard, H., Gerber, J., Muelle, N.D., Classens, L., Cassman, K.G., Van Ittersum, M.K. 2013. Use of agroclimatic zones to upscale simulated crop yield potential. Field Crops Res. 143. 44-55. Monfreda, C., Ramankutty, N., Foley, J.A. 2008. Farming the planet: 2. geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochem. Cy. 22, 1-19.
  38. 38. • MapSpaM. SPAM data Download. URL: http://mapspam.info/download/ accessed on 19 June 2013 • Neumann, K., Verburg, P.H., Stehfest, E., Müller, C. 2010. The yield gap of global grain production: a spatial analysis. Agric. Syst. 103, 316-326. • Tilman, D., Fargione, J., Wolf, B., D’Antonio, C., Dobson, A., Howarth, R., Schindler, D., Schlesinger, W.H., Simberloff, D. & Swackhammer, D. Forecasting agriculturally driven global environmental change. Science, 292, 281-284.
  39. 39. ASANTE SANA! THANKS A LOT! MERCI BEAUCOUP! MUCHAS GRACIAS! MUITO OBRIGADO! MURAKOZE!

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