Crop Modeling to Support Climate Change Resilience in Kenya
The adverse effects of climate change on agriculture are increasingly felt around the world, yet major knowledge gaps limit policymakers’ ability to predict and respond to food security threats caused by climate shocks. Policymakers need reliable data to develop strategies that address crop shortfalls which can lead to severe food insecurity.
To that end, AIR developed a crop modeling framework and applied it to the production of maize, a key food staple in Kenya, to help policymakers and leaders have a better understanding about the effects of climate change and make informed decisions as to when and where to plant crops. In addition, AIR is studying how crop modeling results compare to actual crop yields over time and how accurate the tool is when measured against traditional data collection surveys.
Crop models are essentially a mathematical representation of a cropping system that considers a variety of factors in predicting yields. AIR used a multistep process to run and validate its crop model, which considered multiple variables, including weather data, soil properties, and crop growing management.
Crop modeling is a tool that, for the time being, complements rather than replaces large-scale surveys on agricultural outcomes. AIR’s data scientists’ goal is to make crop modeling as reliable as traditional surveys, which can be costly and labor-intensive.