Dataset for supporting the net agronomic assessment of yield limiting factors in maize production in Machakos county, Kenya

This dataset is used for a holistic analysis of the costs, benefits, and risks of on-farm soil and plant health management. The dataset was produced in 2017 by a combination of field measurements and farmer surveys. It was collected for a research study aimed at identifying and testing accurate, consistent, and cost-effective measurement tools and methodologies for evaluating the outcomes of agricultural projects. Soils data was analysed by wet spectral methods to generate estimates of the Nitrogen (N), Phosphorus (K), and Potassium (P) levels in the soils which was then used as inputs for a stochastic crop production model. The decision model consisted of two main sections targeting interactions between biotic factors (rainfall variability, availability of soil nutrients, risk of drought and temperature) and abiotic factors (farm management practices/intensity of farm management). With the two datasets, we ran a risk-return model to project the productivity of maize production and highlight yield-limiting factors. The project was funded by Bill Melinda Gates Foundation and TechnoServe under the Innovation in Outcome Measurement (IOM) program

national Crop yield crop