Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.

The Science of Agronomy to Scale

40 vues

Publié le

The Science of Agronomy to Scale

Publié dans : Sciences
  • Soyez le premier à commenter

  • Soyez le premier à aimer ceci

The Science of Agronomy to Scale

  1. 1. IS3.1 Soil fertility management and the African Green Revolution The Science of Agronomy to Scale Keith Shepherd
  2. 2. It’s all about reducing farmers’ decision risk • Input use (e.g. improved seed, fertilizer) is a big risky decision – high cost of being wrong, large uncertainty in outcome • Anything we can do to reduce this decision risk will have high information value • What is limiting? How much to put on? Maize yield (t/ha) Density 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0 2 4 6
  3. 3. What is the basis for reliable inference? • What is the region of interest? • What do we know about variation that matters? • Are populations or sub-populations defined and sampled in a statistically rigorous way? • What is the basis for inferring results from one location to another? Shepherd et al. (2015). Land health surveillance and response: A framework for evidence-informed land management. Agricultural Systems 132: 93–106 Convenience locations, few locations, don’t know what they represent?
  4. 4. Soil test calibration limitations • Soil tests require calibration using plant response trials on each soil type - not yet done systematically in Africa – wildly extrapolated • Single nutrient tests (e.g. P) need adjustment for soil type/properties (e.g. pH, organic matter, mineralogy) • No validation of recommendations = no learning
  5. 5. Further limitations of current soil testing approaches • Cost, speed • Surveillance approach requires large sample numbers – analytical costs prohibitive • Reproducibility • In Africa, labs face severe challenges (electricity, grade chemicals, water quality, gas quality, equipment servicing, etc)
  6. 6. 0 5 10 15 20 25 30 ATVC FELDA WAGENINGEN LAF BELFAST LAS EDAFONEI 974BRET WHAL XGCALAFIGA KSSL HILL CSS NEMALAB MARELl ARCWSG AECSAGRICS AARDVARKAA AGROLAB-SL IGEOLUNAM SPAL SOILLAB ERSAFVGSCA IRRI PASCAnalab LABAMB LSF RHODE AGROADGAZA EALG HCFR LASPEE WIKASO KARI-NARL BIOLAB CDAgrogand LABCAMPO Minus major outlier Soil test variation among labs Wageningen Evaluating Programs for Analytical Laboratories (WEPAL) Olsen P test
  7. 7. Soil test recommendations ignore risk •Performance of soil tests not stated or validated •Cannot trace data used to produce recommendations •No basis for learning & improvement
  8. 8. “It is not possible to review the response curves from field trials (relative yields vs. soil- P test) that form the basis of the fertilizer-P recommendation systems in each European country or region”. “Nor is it possible to identify the equations chosen for fitting the response curves” “There are almost as many types of calculations as there are countries” An overview of fertilizer-P recommendations in Europe: soil testing, calibration and fertilizer recommendations Jordan-Meille et al. Soil Use and Management, December 2012, 28, 419–435
  9. 9. E.g. Croplands GeoSurvey: probability cropland presence Define the region of interest Africa Soil Information Service (AfSIS) Ethiopia Soil Information System (EthiioSIS) Ghana Soil Information Service (GhaSIS) Nigeria Soil Information Service (NiSIS) Tanzania Soil Information Service (TanSIS)
  10. 10. Use sampling frames – soil, crop TanSIS Sampling locations (clusters) (red) and alternative sampling designs (green and blue). Valid inference
  11. 11. Soil-Plant Spectral Technology Mid-infrared spectrometer (MIR) Handheld x-ray fluorescence analyser (pXRF) •Soils properties •Plant macro & micro nutrients •Compost quality •Fertilizer certification Africa Soil Information Service (AfSIS) •Digital mapping of soil properties •Plant nutrition monitoring; large n trials •Soil carbon inventory •Agro-input and output quality screening •Mining reclamation pXRF allows rapid, low cost macro & micronutrient analysis
  12. 12. Spectral Shape Relates to Basic Soil Properties: • Mineral composition • Iron oxides • Organic matter • Carbonates • Soluble salts • Particle size distribution MIR spectral fingerprints
  13. 13. Foliar pXRF as diagnostic One Acre Fund trials in Western Kenya: Low P, K, S, Cu, Zn K P S Mg Ca Cu Zn Fe Mn
  14. 14. Digital mapping for spatial interpolation Low cost soil information through digital mapping
  15. 15. Hengl T, Leenaars JGB, Shepherd KD, Walsh MG, Heuvelink GBM, Mamo T, Tilahun H, Berkhout E, Cooper M, Fegraus E, Wheeler I, Kwabena NA. 2017. Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning. Nutrient Cycling in Agroecosystems 109:77–102. Digital soil mapping of soil nutrients
  16. 16. MIR soil spectral profiling 0.0 0.5 1.0 6 7 8 pH density Cluste A B 0.00 0.05 0.10 0.15 20 40 60 80 Clay (%) density Cluster A B 0.00 0.05 0.10 0.15 0 25 50 75 100 CEC (ECD) density Cluster A B 0.0 0.5 1.0 1.5 2.0 0.5 1.0 1.5 K (mg/kg) density Clust A B Machakos County, Kenya (Technoserve Ltd) • Response to applied nutrient • Fertiliser recovery fraction
  17. 17. Application levels for spectral technology • Digital mapping of soil constraints, crop nutritional deficiencies, spectral soil types • National scale • Refinement at county / district level • Local scale - UAV hyperspectral calibration / indices • Cost effective soil-plant testing services for farmers • National labs • Rural soil-plant spectral testing labs – walk-in service to farmers • Low cost sensors for community knowledge workers, private enterprises
  18. 18. Spectral lab network & capacity development Country Lab Benin AfricaRice Cameroon IITA; ICRAF Cote D’Ivoire CNRA; ICRAF Ethiopia ATA/NSTC (5); Mekelle Uni; Ghana CSRIO-SRI Kenya KARLO; One Acre Fund; CNLS, ICRAF Madagascar Antananarivo Uni (collaborative). Malawi CARS/ DARTS Mali IER Morocco Mohammed Vi Polytechnic /OCP (in progress) Mozambique IAMM Nigeria Obafemi Awolowo Un; IITA; IAR; FDMA&RD (2) South Africa KwaZulu-Natal Dept A Tanzania SARI; Min Ag (4); Sokoine Uni Outside Africa Australia (CSIRO); China (YPC); India (CIMMYT; ISSS-ICAR); Peru (IIAP); UK (Rothamsted) Soil archiving system Training courses; lab audits
  19. 19. Principles for taking agronomy to scale •Define the decision dilemma •Define the region of interest •Sample it to provide a sound basis for inference •Measure using rapid, low cost, reproducible methods •Represent & communicate the uncertainty in results •Validate recommendations using independent samples •Maintain the link to the original data •Focus further sampling to reduce uncertainty that matters

×