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Precision Agriculture for smallholder farmers: Are we dreaming?


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Presentation delivered by Dr. Bruno Gerard (Global Conservation Agriculture Program, CIMMYT) at Borlaug Summit on Wheat for Food Security. March 25 - 28, 2014, Ciudad Obregon, Mexico.

Published in: Science

Precision Agriculture for smallholder farmers: Are we dreaming?

  1. 1. Precision Agriculture for smallholder farmers: Are we dreaming? Bruno Gerard and Francelino Rodrigues, International Maize and Wheat Improvement Center Kite aerial photography of Bagoua village, Niger, B. Gerard 1999
  2. 2. Kite aerial photography of Bagoua village, Niger, B. Gerard 1999
  3. 3. A system thinker and actor! “The greatest thing he [Norman Borlaug] did for the field of agronomy was to begin to show people that they had to think about multiple parts of the system… … If you think about what he did in the Green Revolution, it wasn’t about genetics, and it wasn’t about fertility, and it wasn’t about water. It was about all of those different things together.” Jerry Hatfield, lab director at the USDA-ARS in CSA March 2014 issue
  4. 4. Projected demand by 2050 (FAO) Linear extrapolations of current trends Potential effect of climate- change-induced heat stress on today’s cultivars (intermediate CO2 emission scenario)
  5. 5. Sustainable Intensification More than just sustaining yield increases, it is about economics and profitability, social equity and environmental friendliness Dealing with complex and heterogeneous systems
  6. 6. Source: Herrero et al. 2010
  7. 7. Technology generation Community to landscape system HH farming systemField Institutions & Markets Process research Enabling & analysis tools Output target - Water ‘Last mile providers’ Innovation systemsParticipatory co-innovation & learning - System interactions: - Livestock, cash crops; trees- Weeds - Pests & diseases - Soil health - Nutrients HH typologies (livelihood & biophysical) Trade-off analysis Bio-economic models Geospatial (domains, impact) - Knowledge products - Identify inefficiencies (markets, providers) Outcome Increased productivity & stability of farming systems Increased income of smallholder farmers Scale - Tillage - Rotation - Intercropping - Systems for the future Increased yield of maize/wheat for smallholder farmers - System impacts on NRM & ecosystem services - Mechanisation Business models - Communication products Sustainable Intensification Framework Courtesy: Peter Craufurd
  8. 8. “Sustainable Intensification” – producing more outputs with more efficient use of all inputs on a durable basis, while reducing environmental damage and building resilience, natural capital and the flow of environmental services – High PRODUCTIVITY Low Objective Time STABILITY Low High Time Objective Critical Variable RELIABILITY High Low Objective Time ADAPTABILITY High Low Objective Time Critical Variable RESILIENCE High Objective Time Low Critical Variable EFFICIENCY Courtesy: S. Lopez-Ridaura
  9. 9. Indicators must be integrated by multi-criteria methods for an overall evaluation of the main advantages and disadvantages of different solutions or scenarios (synergies and trade-offs) INTEGRATION OF INDICATORS Traditional System Conventional system Optimal 0.0 0.5 1.0 B/C ratio Food self sufficiency Erosion Soil Organic Matter Forage self sufficiency Yield variability with rainfall Vulnerability to changes in inputs and output prices Diversity of agricultural products Independence to external inputs Independence to hired labor Gross Margin Source: Lopez-Ridaura
  10. 10. Small farm 0 50 100 Gross Margin Return to labor Benefit/Cost Soil Carbon Balance Soil Nitrogen Balance Soil losses Gross margin variation with rainfall Gross Margin reduction in dry years Gross Margin variation with prices of outputs Gross margin reduction with low output prices Monetary Costs Dependence to external inputs 0 50 100 Gross Margin Return to labor Benefit/Cost Soil Carbon Balance Soil Nitrogen Balance Gross Margin variation with prices of outputs Gross margin reduction with low output prices Monetary Costs Dependence to external inputs Soil losses Gross margin variation with rainfall Gross Margin reduction in dry years Large farm Multi-criteria Farming systems analysis/ Recommendation domains Surveys (resource endowment, crops/animals, management, ….x…) Interviews (farm management, resource allocation, strategies) Modeling (MCDM, farm flows, optimization) FARMING SYSTEMS Courtesy: S. Lopez-Ridaura
  11. 11. MKT CSH CNS HOME LVSTK OE WOOD MKT CNS HOME LVSTK WOOD FOOD OFF-FARM CSH MKT CSH CNS HOME LVSTK WOOD FOOD MKT CNS HOME LVSTK WOOD FOOD OFF-FARM MKT CNS HOM E WOOD FOOD OFF-FARM CSH Type 1 Type 5 Type 4 Type 3 Type 2 Cash Labour Nutrients Resource allocation strategies Tittonell (2003) Farming Systems Typologies (Structural-functional) FARMING SYSTEMS
  12. 12. Mueller et al., Nature 2012
  13. 13. Year 1950 1960 1970 1980 1990 2000 2010 2020 Nitrogenefficiencyincerealproduction (megatonnescerealgrain/megatonnsfertilizerapplied) 20 30 40 50 60 70 80 Trends in N-fertilization efficiency in cereal production (annual global cereal production divided by annual global application of N-fertilizer) (Source: FAO 2012) Global food production has tripled during this period, but N-fertilizer applications have increased 10-fold (Tilman et al., 2001) Nitrogen application has reached a point of diminishing returns – i.e. we are applying more and more nitrogen to get similar yields and this may continue in future Courtesy: GV Subbaro, JIRCAS
  14. 14. Our Precision Agriculture Principles • Precision agriculture for smallholder farmers should be seen at multiple scales: – Not only dealing with within field spatial variability but also intra-farm (and inter-farm) resource allocation – Precision Agriculture -> more precise agriculture (spatial and temporal dimension) – Where, when, what, how?
  15. 15. Why should new technologies not benefit smallholders farmers of the world? Penetration of cell phones in countries where we work is high ‘From the description of site-specific activities it is obvious that although precision agriculture, as seen in Europe and North America, is largely irrelevant in developing countries, the need for spatial information is actually greater, principally because of stronger imperative for change and lack of conventional support’ Cook et al., 2003.
  16. 16. 72.1 70 30 70.7 82.1 99 60 80 56.4 84.3 52.9 92.1 60 60 54.3 Cell phone Data Source: CCAFS Surveys 2012
  17. 17. Four building blocks of precision agriculture for smallholder farmers - Remote sensing and other monitoring tools (weather, soil monitoring ) -> diagnosis, spatial and temporal dimensions - Nutrient, water and disease management, crop modelling -> how you turn diagnosis into recommendations - Information and Communication Technologies -> how you get diagnosis from and provide recommendations to farmers (path for crowdsourcing) - Mechanization -> how you apply rec. in the field Articulation of those blocks are system specific and needs dvpt of specific business models
  18. 18. Connections of remote sensing products with (decision) support tools for farmers Field data base Recommendations Crop Mgr (IRRI/CIMMYT) Micro Credit Field boundaries Farmer information Crop management data Crop Insurance Irrigation scheduling Recommendation domains & Diagnostics for technology targeting Ground Cover Surface Soil Moisture Chlorophyll Key crop phenology Crop & fallow land Attainable Yield Actual Yield Yield gap Damage maps Surface water / flood Remote Sensing Digital elevation model Climate and weather Data
  19. 19. Fertility management practices • ‘Blanket’ recommendations for large areas • Based on old data • Developed on experiment stations, not farmers fields Recommendations that do not match local conditions cost farmers yield and profits – especially where fertilizers are $$
  20. 20. Embracing the promise of ICTs with accessible tools for site-specific nutrient management for rice, maize, and wheat in S. Asia Courtesy of Roland Buresh, IRRI 2. Compute field- specific guideline Model hosted on the cloud 1. Acquire field-specific information from farmers Web Smartphone 3. Provide customized field-specific guidelines in local language Multi-format output The architecture is in place
  22. 22. Precision nutrient management: Farmers Accessible Options • Decision Support Tools (Nutrient Expert for wheat) for SSNM+ • Handheld sensors • Band placement
  23. 23. Severe events (drought(s)) at different phenological stages of crop growth Extreme heat stress (wheat) -spikelet sterility and limited grain filling. CROPPING SYSTEMS Malik and M.L. Jat, et al
  24. 24. The combination and sequencing of crops with different management practices and under different environmental conditions Interaction occurring in crop rotations, intercropping, green manures and cover crops and their effect on the long term performance of the cropping systems CROPPING SYSTEMS Krupnik CIMMYT-GCAP
  25. 25. Amazing technological breakthrough More for less: better, easier, faster and cheaper Gerard et al. , Soil Sci. Plant Nutr.1997 CIMMYT 2013 Photo: J. Cairns
  26. 26. False color image of CIMMYT station at Obregon, Mexico acquired from multispectral camera at 1 m resolution on Feb. 15, 2013. Collaborative research with QuantaLab, Cordoba/Spain
  27. 27. Thermal image of CIMMYT station at Obregon, Mexico acquired from the thermal camera at 2 m resolution on Feb. 14, 2013. Well-watered (cooler) plots are shown in blue, while water-stressed (warmer) plots are shown in green and red Collaborative research with QuantaLab, Cordoba/Spain
  28. 28. Farm level benefits in RWCS of IGP • ~7 % gain in crop productivity • ~20 % (18 ha-cm yr-1) saving in irrigation water, • US$ 113 to 175 ha-1 higher system profitability • 10-13 % higher agronomic efficiency of nitrogen Laser land leveling is a precursor technology to CA A success story in India Source: Jat et al, 2005, 2006, 2009a,b,2011 Current # 25000
  29. 29. Mapping soil variability (EM38)
  30. 30. Priorities • Recommendation domains for intensification at different granularities (regional, national, landscape, farm, field) • Yield gap and risk assessment (link with crop insurance, credit) • Ex-ante assessment of information needs at extension and farmer levels • Improved management practices (water, nutrients, tillage, timing) and prototype site specific recommendations through ICT models
  31. 31. Priorities (cont.) • Upscaling/downscaling: On-farm trials - Proxi-sensors – UAV/airborne – spaceborne • Data articulation/fusion/assimilation –Vegetation, soil, climate/weather, socio- economic, markets • Cross-regional learning! • Additional partnership with ARIs • Public-private partnership (i.e BASF, Syngenta, crop ins., RS) • Capacity building of NARS and extension services
  32. 32.