Abstract
Crop models known to be based on the theory of crop physiology for describing the dynamic process of crop growth are recently explored for their uncertainties in model application under resource limited conditions. The aim of this study was to test Environmental Policy Integrated Climate (EPIC), on upland land rice production by taking into account seasonal variability in Guinean and Guinean-Sudanian zones in Benin and Nigeria (West Africa). A range of data available under farmer or experimental conditions in rainfed agriculture were measured or used from literature. The results show the accuracy of the model to simulate LAI, total above ground biomass and grain yield of upland rice for 2 NERICA rice cultivars. After calibration, the model showed average mean relative error between 0.06 and 0.15 with the model efficiency up to 0.98% in the case of LAI. The assessment of the model performances about sensitivity to N or P fertilizer application is also discussed under Ultisols. Large root mean square (RMSE) in calibration and the validation (>100) process suggested that robustness of the model became restrictive under severe environmental conditions such as in drought or flooding condition. Performance of the model at large scale should be executed of with land marginality classification. Key words: Environmental Policy Integrated Climate (EPIC), modeling, upland rice, West Africa.
Highlights
The operation of crop growth models is of interest for filling gap between information needed and that created by traditional experimental trials in soil and agronomic research or for extrapolating results gained on experimental stations leading to better integration of knowledge.Beside, simulation modeling represents a research tool for assessing climatic change patterns and their impacts on crop growth and yield
The results show the accuracy of the model to simulate LAI, total above ground biomass and grain yield of upland rice for 2 NERICA rice cultivars
Large root mean square (RMSE) in calibration and the validation (>100) process suggested that robustness of the model became restrictive under severe environmental conditions such as in drought or flooding condition
Summary
The operation of crop growth models is of interest for filling gap between information needed and that created by traditional experimental trials in soil and agronomic research or for extrapolating results gained on experimental stations leading to better integration of knowledge. Simulation modeling represents a research tool for assessing climatic change patterns and their impacts on crop growth and yield. The attempt to use crop growth models under extremely unfavourable growth conditions that is, water scarcity combined with low soil fertility or with indigenous management practices remains a. In rainfed low-input systems such as smallholder farms in West Africa, models developed for optimal management conditions fail to meet the needs of researchers and extension workers. That can be a key issue in Africa where about 80% of the rice production depends on rainfed conditions
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