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

Dataset’s Files

maize_production_8_17.R
MD5: 08b6a030afe39e34eb9cb63ff5ba573b


maize_production_input_table.tab
MD5: 5f169572820479d66c83ed15f8f6c428


Maize_production_legend.tab
MD5: 4158dccff5cfe9de9da9030bf8cbca88


SoilTissueGrainYieldHS4_Soil agronomy.tab
MD5: 047715b461b9689ea0f99a3f33d2ce70


Variables Description_Soil agronomy.tab
MD5: a4848118915eb562980c16d4d9c114ec


Terms of use
This dataset is made available under the Creative Commons Attribution 4.0 International license (CC-BY-4.0). The license allows you, the user, to copy and redistribute the material in any medium or format and/or transform, and build upon the material for any purpose, even commercially.
Creative Commons License.
Authors

Tamba, Yvonne; Chacha, Robin; Mboi, Damaris; Aynekulu, Ermias; Luedeling, Eike; Shepherd, Keith

Keywords

crop modelling, decision analysis, agronomics

Publisher

ICRAF Soil and Land Health Theme

Publication date

25 Jul 2022

DOI

https://doi.org/10.34725/DVN/KKHVOF