Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Scaling Agronomy for Smallholder Recommendation Systems


Published on

Scaling Agronomy for Smallholder Recommendation Systems using Mid-Infrared and Total X-Ray Fluorescence Spectroscopy for Rapid, Low Cost Soil-Plant Analysis

Published in: Science
  • Be the first to comment

  • Be the first to like this

Scaling Agronomy for Smallholder Recommendation Systems

  1. 1. 86: Symposium – Smallholders Managing Soil Health for Climate Resilience 2018 ASA, CSSA, and CSA Annual Meeting (Nov. 4-7) in Baltimore, MD, USA Scaling Agronomy for Smallholder Recommendation Systems using Mid-Infrared and Total X-Ray Fluorescence Spectroscopy for Rapid, Low Cost Soil- Plant Analysis Keith Shepherd, Erick Towett, Andrew Sila Africa Soil Information Service
  2. 2. Improving relevance of soils information for users Limitations • Inference space of recommendations not known • Uncertainty not represented or communicated • Soil science knowledge not integrated into economic decision making Shepherd KD. How soil scientists can do a better job of making their research useful. The Conversation (Science & Technology) 14 August 2018.
  3. 3. Africa Soil Information Service Statistically sound sampling schemes Sample diversity Unbiased prevalence data Shepherd et al. (2015). Land health surveillance and response: A framework for evidence-informed land management. Agricultural Systems 132: 93–106
  4. 4. Africa Soil Information Service
  5. 5. 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
  6. 6. AfSIS – national level sampling & mapping EthioSIS, GhaSIS, NiSIS, TanSIS Soils, Crop trials
  7. 7. Soil-Plant Spectral Technology Mid-infrared spectrometer Handheld x-ray fluorescence •Soils properties •Plant macro & micro nutrients •Compost quality •Fertilizer certification •Digital mapping of soil properties •Plant nutrition monitoring; large n trials •Soil carbon inventory •Agro-input and output quality screening •Mining reclamation →
  8. 8. Spectral Shape Relates to Basic Soil Properties: • Mineral composition • Iron oxides • Organic matter • Carbonates • Soluble salts • Particle size distribution These properties are the determinants of most functions! MIR spectral fingerprints
  9. 9. On-line Spectral Prediction Engine Bayesian Additive Regression Trees AfSIS
  10. 10. MIR soil spectral profiling 0.0 0.5 1.0 6 7 8 pH density Clu 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 Clu Machakos County, Kenya (Technoserve Ltd)
  11. 11. NIR Plant N calibration in yam trials YAMSYS Plant N calibration
  12. 12. Plant N calibration applied to treatments
  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. 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
  15. 15. 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
  16. 16. SpecWeb software • Load spectral files • Display spectra • Monitor standards • Select calibration samples • Perform calibrations • Perform predictions
  17. 17. Represent & communicate uncertainty • Use distributions not averages • Communicate uncertainty to users • Maintain links to original data • Validate recommendations • Focus further measurement on areas of uncertainty that matter
  18. 18. 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
  19. 19. Decision-focused agricultural research • Identify the decision goals & alternatives • Risk-Return analysis of intervention options • Holistic - all relevant factors considered • Quantifies uncertainties and risks; combines expert knowledge with data • Quantifies trade-offs - $ • Value-of-information analysis • Where to measure & how much to spend on it • Guides adaptive monitoring Tools developed • Monte Carlo simulation R package • Bayesian Networks with value-of-information analysis Using uncertainty and value-of-information analysis to define data needs Luedeling E and Shepherd KD. 2016. Decision-Focused Agricultural Research. The Solutions Journal 7: 46-54
  20. 20. Examples •Shepherd K, Hubbard D, Fenton N, Claxton K, Luedeling E, De Leeuw J, 2015. Development goals should enable decision-making. Nature 523, 152-154. •Luedeling E and Shepherd KD. 2016. Decision-Focused Agricultural Research. The Solutions Journal 7: 46-54. •Yet, B., Constantinou, A., Fenton, N., Neil, M., Luedeling, E. and Shepherd, K. 2016. A Bayesian Network Framework for Project Cost, Benefit and Risk Analysis with an Agricultural Development Case Study. Expert Systems With Applications 60: 141–155. •Rosenstock,T.S., Mpanda, M., Rioux J., Aynekulua, E., Kimaro, A.A., Neufeldt, H., Shepherd. K.D., Luedeling. E. 2014. Targeting conservation agriculture in the context of livelihoods and landscapes. Agriculture, Ecosystems and Environment 187: 47–51 •Luedeling, E., Oord, A., Kiteme, B., Ogalleh, S., Malesu, M., Shepherd, K. D., De Leeuw, J. (2015). Fresh groundwater for Wajir – ex-ante assessment of uncertain benefits for multiple stakeholders in a water supply project in Northern Kenya. Frontiers in Environmental Science 3: 16. •Favretto, N., Luedeling, E., Stringer, L. C., & Dougill, A. J. (2017). Valuing ecosystem services in semi-arid rangelands through stochastic simulation. Land Degradation and Development 28, 65–73. •Tamba Y, Muchiri C, Shepherd K, Muinga G, Luedeling E. 2017. Increasing DryDev’s Effectiveness and Efficiency through Probabilistic Decision Modelling. ICRAF Working Paper No 260. Nairobi, World Agroforestry Centre. •Tamba Y, Muchiri C, Luedeling E, Shepherd K. 2018. Probabilistic decision modelling to determine impacts on natural resource management and livelihood resilience in Marsabit County, Kenya. ICRAF Working Paper No 281. Nairobi, World Agroforestry Centre •Wafula J, Karimjee Y, Tamba Y, Malava G, Muchiri C, Koech G, De Leeuw J, Nyongesa J, Shepherd K and Luedeling E. (2018) Probabilistic assessment of investment options in honey value chains in Lamu County, Kenya. Frontiers in Applied Mathematics and Statistics 4: 6- 11 •Whitney CW, Lanzanova D, Muchiri C, Shepherd KD, Rosenstock TS, Krawinkel M, Tabuti JRS, & Luedeling E. (2018).Probabilistic decision tools for determining impacts of agricultural development policy on household nutrition. Earth’s Future 6: 359–372.