www.ciat.cgiar.org Eco-Efficient Agriculture for the Poor
Predicting crop yield and response to nutrients from
soil spectr...
Introduction
• Generalized management recommendations that need to be refined
• Rapid development and utilization of infra...
The key approach
Soil spectra
Soil
parameters
Crop
response
??
Appropriately
characterize
soils
Appropriately
characterize...
Key objectives
• Determine variance in yield from spectra alone and
from soil parameters alone
• Determine additional vari...
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...
Yield and response contrasts
• Yields with NPK• Control yields
Soil and topographic characterization
₊
Soil infra-red spectra (0-20 cm depth) Aster DEM-derived topographic covariates (3...
Statistical approaches
• Samples selected ~67% of
total
• Remainder=out-of-bag
samples
• Variables selected =sqrt(total
no...
Ordinary least squares regression
• Principal components
extracted from soil
spectra
• Collinearity between
additional cov...
Partial least squares regression
• Principal components
extracted from soil
spectra, maximizing
variations in yield
• Cont...
Further results with partial least squares
regression and random forest
Variance explained RMSE No. of PCs
Partial least s...
Refining predictions
• ~7% more variance in yield explained with
spectra than soil parameters
• Topographic and climate da...
Thank you
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.

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

  1. 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. 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. 3. The key approach Soil spectra Soil parameters Crop response ?? Appropriately characterize soils Appropriately characterize topographic features Appropriately characterize seasonal weather ₊ ₊
  4. 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. 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. 6. Yield and response contrasts • Yields with NPK• Control yields
  7. 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. 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. 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. 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. 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. 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. 13. Thank you

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