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Predicting crop yield and response to Nutrients from soil spectra at WCSS 2014 Kihara et al.
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Predicting crop yield and response to Nutrients from soil spectra at WCSS 2014 Kihara et al.

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Predicting crop yield and response to Nutrients from soil spectra at WCSS 2014 Kihara et al.

Predicting crop yield and response to Nutrients from soil spectra at WCSS 2014 Kihara et al.

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  • 1. www.ciat.cgiar.org Eco-Efficient Agriculture for the Poor Predicting crop yield and response to nutrients from soil spectra: example from sub-Sahara Africa Job Kihara et al.
  • 2. Introduction • Generalized management recommendations that need to be refined • Rapid development and utilization of infra-Red spectra technology, libraries growing, potential for cheap assessments • Good predictions of soil parameters from soil spectra • Similarly, revolution in statistical approaches for data mining • These present opportunity to take, scan a soil sample and predict yield • On-going and pioneering work, future to inform on specific nutrients a farmer can apply, and cheaply. • No known direct linkage between crop response and soil spectra • Low productivity, high yield gap among smallholder farmers • Majority cannot afford conventional soil testing
  • 3. The key approach Soil spectra Soil parameters Crop response ?? Appropriately characterize soils Appropriately characterize topographic features Appropriately characterize seasonal weather ₊ ₊
  • 4. Key objectives • Determine variance in yield from spectra alone and from soil parameters alone • Determine additional variance in yield explained by topographic and weather variables • Find out to what extent soil spectra can predict response to fertilizer.
  • 5. 5http://afsis-dt.ciat.cgiar.org/ No. Responses 1 Control/ unfertilized yield 2 NPK (100kg N, 30 kg P and 60 kg K/ha) yield 3 Change in yield (NPK-control) Kiberashi, Tanzania. Fertile site newly converted; 29 plots Mbinga, Tanzania. Bread basket for the country; 35 plots Nkhata Bay, Malawi. Acidity problems in some parts 21 plots
  • 6. Yield and response contrasts • Yields with NPK• Control yields
  • 7. Soil and topographic characterization ₊ Soil infra-red spectra (0-20 cm depth) Aster DEM-derived topographic covariates (30m) (slope, elevation, flow accumulation, wetness index, aspect) Seasonal weather • Total seasonal rainfall • Number of rainy days
  • 8. Statistical approaches • Samples selected ~67% of total • Remainder=out-of-bag samples • Variables selected =sqrt(total no. of variables) Source: Touw et al. 2013: Briefings in Bioinformatics; 14(3): 315–326. • Principal component analysis and OLS • Partial least squares regression • Random forest Contribution to variance explained by spectra, and by additional covariates
  • 9. Ordinary least squares regression • Principal components extracted from soil spectra • Collinearity between additional covariates checked • Important variables identified • OLS model created • 3-fold cross-validation undertaken Cross-validated R2= 0.67 RMSEP= 1.29 t/ha Maize grain yield (Control)~ 10 PCs + Seasonal Rainfall
  • 10. Partial least squares regression • Principal components extracted from soil spectra, maximizing variations in yield • Contribution of spectra alone revealed • Important covariates added in an OLS model • 3-fold cross-validation undertaken Cross-validated R2= 0.60 RMSEP= 1.41 t/ha Maize grain yield~Comp1+Comp2+Flow Accumulation, with 3-fold cross-validation
  • 11. Further results with partial least squares regression and random forest Variance explained RMSE No. of PCs Partial least squares regression Control grain yield 60.5 1.7 2 NPK grain Yield 65.8 1.39 10 Response 30.6 1.8 2 Random forest Control grain yield 62.1 1.65 18 NPK grain Yield 49.4 1.43 21 • Important additional covariates: Flow accumulation, slope and elevation, explaining only 1-2% additional variance in yield • Response to yield is varied, low at poor and fertile fields
  • 12. Refining predictions • ~7% more variance in yield explained with spectra than soil parameters • Topographic and climate data explain more variance in yield with soil parameters than with spectra • Need to expand the initial library for spectra- crop response linkages • Include additional depths in spectra analysis, and depth restrictions
  • 13. Thank you