RESPONSIVENESS OF OBAATANPA MAIZE GRAIN YIELD AND BIOMASS TO SOIL, WEATHER AND CROP GENETIC VARIATIONS

Atakora K. Williams, Fosu Mathias, Safo E. Yeboah, Tuffour O. Henry, Tetteh M. Francis

Abstract


Use of crop growth simulations models such as those incorporated into Decision Support System for Agro technology Transfer (DSSAT) are useful tools for assessing the impacts of crop productivity under various management systems. Maize growth model of DSSAT is Crop Environment Resource Synthesis (CERES) -Maize. To predict maize grain yield and biomass using CERES-maize under Guinea savanna agro ecological conditions with different weather scenarios, data on maize growth, yield and development as well as data on soil and weather was collected from field on-station experiment conducted during the 2010 growing season at Kpalesawgu, Tamale-Ghana. Twenty on-farm experiments were also conducted in the Tolon-Kunbungu and Tamale Metropolitan districts in Northern Ghana to determine the responsiveness of maize grain yield and biomass to soil, weather and crop genetic variations. The cultivar coefficient was however calibrated with data collected from the on-station field experiment at Kpalesawgu. The cultivar coefficient was however calibrated with data collected from the on-station field experiment at Kpalesawgu. Data on phenology, grain yield and biomass from the field experiment were used for model validation and simulations. Validation results showed good agreement between predicted and measured yields with a Normalized Random Square mean Error (NRSME) value of 0.181. Results of these sensitivity analysis results showed that the DSSAT model is highly sensitive to changes in weather variables such as daily maximum and minimum temperatures as well as solar radiation, however, the model was found to be least sensitive to rainfall.  The model also found to be sensitive to crop genetic and soil variations. Model predictions of the responsiveness of the yield and biomass to changes in soil, weather and crop genetic coefficients were found to be good with an r2 values between 0.95 to 0.99 except when predicting maize grain yield using changes in minimum temperature with an r2 value of 0.8577.


Keywords


Genetic variations, Grain yield, Soil, Biomass, Weather

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Copyright (c) 2014 Atakora K. Williams, Fosu Mathias, Safo E. Yeboah, Tuffour O. Henry, Tetteh M. Francis

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Journal of Plant Breeding and Genetics
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