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

World Agroforestry - Research Data Repository Dataverse OAI Archive

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Title Dataset for supporting the net agronomic assessment of yield limiting factors in maize production in Machakos county, Kenya
 
Identifier https://doi.org/10.34725/DVN/KKHVOF
 
Creator Tamba, Yvonne
Chacha, Robin
Mboi, Damaris
Aynekulu, Ermias
Luedeling, Eike
Shepherd, Keith
 
Publisher World Agroforestry (ICRAF)
 
Description 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
 
Subject Agricultural Sciences
Earth and Environmental Sciences
Social Sciences
Crop modelling
Decision analysis
Agronomics
 
Contributor Karari, Valentine
World Agroforestry (ICRAF)
CGIAR Research Program on Water, Land and Ecosystems
Bill and Melinda Gates Foundation
Technoserve- Innovations in Outcome Measurement
 
Type Biophysical and Social-economics data