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Crop modelling in data-poor environments – A knowledge-informed probabilistic approach to appreciate risks and uncertainties in flood-based farming systems

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  • Liman Harou, Issoufou
  • Whitney, Cory
  • Kung'u, James
  • Luedeling, Eike

Abstract

Crop models can support agricultural decisions, yet their reliability is necessarily limited when they do not sufficiently represent the complexity and specific circumstances of the target system. In some cases, models have such prohibitively high data requirements that they are only applicable with far-reaching and often questionable assumptions. In this paper, we demonstrate a customizable solution-oriented approach for crop modelling in situations where data and resources are limited. To address system complexity and produce a probabilistic crop model that does not depend on precise data, we used participatory analysis to describe system components using individual Bayesian networks that formalize expert knowledge into probabilistic causal relationships among important variables. We then used these Bayesian networks to generate inputs for a Monte Carlo model that illustrates the determinants of crop growth and simulates plausible ranges of expected grain and biomass yields at various stages of crop development. The resulting model accounts for all important variables and their interactions, as examined by local and foreign experts and described in relevant literature. We describe how to develop and customize such a model to specific situations based on case studies related to flood-based farming systems in Ethiopia and Kenya. The model assesses the performance of cropping systems and individual crops, and identifies factors of high importance for system outcomes. This approach to crop modelling paves the way for new opportunities to support agricultural decisions, since it does not require perfect information and can accommodate system complexity and uncertainty in data-poor environments.

Suggested Citation

  • Liman Harou, Issoufou & Whitney, Cory & Kung'u, James & Luedeling, Eike, 2021. "Crop modelling in data-poor environments – A knowledge-informed probabilistic approach to appreciate risks and uncertainties in flood-based farming systems," Agricultural Systems, Elsevier, vol. 187(C).
  • Handle: RePEc:eee:agisys:v:187:y:2021:i:c:s0308521x20308751
    DOI: 10.1016/j.agsy.2020.103014
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    References listed on IDEAS

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    1. Paul Gallagher, 1987. "U.S. Soybean Yields: Estimation and Forecasting with Nonsymmetric Disturbances," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 69(4), pages 796-803.
    2. Erkossa, Teklu & Hagos, Fitsum & Lefore, Nicole, 2014. "Proceedings of the Workshop on Flood-based Farming for Food Security and Adaption to Climate Change in Ethiopia: Potential and Challenges, Adama, Ethiopia, 30-31 October 2013," IWMI Conference Proceedings 208911, International Water Management Institute.
    3. Gallagher, Paul W., 1987. "U.S. Soybean Yields: Estimation and Forecasting with Non-Symmetric Disturbances," Staff General Research Papers Archive 10779, Iowa State University, Department of Economics.
    4. Octavio A. Ramirez & Sukant Misra & James Field, 2003. "Crop-Yield Distributions Revisited," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(1), pages 108-120.
    5. Erkossa, Teklu & Langan, Simon J. & Hagos, Fitsum, 2014. "Constraints to the development, operation and maintenance of spate irrigation schemes in Ethiopia," Conference Papers h046925, International Water Management Institute.
    6. Issoufou Liman Harou & Cory Whitney & James Kung’u & Eike Luedeling, 2020. "Mapping Flood-Based Farming Systems with Bayesian Networks," Land, MDPI, vol. 9(10), pages 1-29, October.
    7. Marie Laure Delignette-Muller & Christophe Dutang, 2015. "fitdistrplus : An R Package for Fitting Distributions," Post-Print hal-01616147, HAL.
    8. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
    9. Delignette-Muller, Marie Laure & Dutang, Christophe, 2015. "fitdistrplus: An R Package for Fitting Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i04).
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