Evaluating CERES-Maize Model Using Planting Dates and Nitrogen Fertilizer in Zambia

Abstract

) assigned to sub-plot. Phenological stages and aboveground biomass were used for model evaluation and these were observed at vegetative and reproductive stages. Soil water profiles were monitored using the Diviner 2000 Probe. Planting date significantly affected grain and biomass yield at P < 0.05. The coefficients of variation for grain and biomass yield were below 12% and considered efficient. The Generalized Likelihood Uncertainty Estimation (GLUE) programme was used to estimate the genetic coefficients for the CERES-maize model. The model's prediction of plant emergence (±1 days), time to anthesis (≥ −3 ≤ ±1 days) and maturity (≥ −4 ≤ 6 days) was good. Simulation of biomass (RMSE=1135 kg/ha, d=0.96, EF=0.86) was reasonably accurate while leaf area index (d = 0.54, EF = −0.65) was simulated with less accuracy due to poor d-stat and forecasting efficiency. The model's simulation of grain yield was fair (NRMSE = 21.4%) while soil root water availability demonstrated that substantial potential yield may have been lost due to water stress. The results showed that the model can be used to accurately determine optimum planting date, biomass yield and nitrogen fertilizer rates with reasonable accuracy.

Climate change impact on maize (Zea mays L.) yield using crop simulation and statistical downscaling models: A review

“…The model is able to accurately predict yield variability, nitrogen uptake and maize growth response to nitrogen (Pang et al, 1997). It can also be used to explore the potential of new cultivars for new areas before establishing costly field experiments (Bationo et al, 2012b) and to determine the optimum planting dates (Soler et al, 2005;Chisanga et al, 2015). The APSIM, on the other hand, is a modular modeling framework that runs at a daily time-step and mimics crop growth and development, yield, soil water and nitrogen dynamics either for single crop or crop rotations in response to climatic and management scenarios (He et al, 2015).…”

“…Study results showed that the CERES-maize model could be used as a decision support system for soil specific nitrogen leaching characterization and to increase food production while using the fertilizers and water efficiently (Sarkar, 2009) in both developed and developing countries. The CERES-maize model has been evaluated in Zimbabwe, Malawi and South Africa (Tsimba, 2011;Tsimba et al, 2013) and Zambia (Chinene, 1985;Chisanga et al, 2015). It has also been extensively tested in Kenya and under tropical conditions in Hawaii, Indonesia, and the Philippines.…”

“…The CERES-maize model has been used in Zambia by researchers such as Chinene (1985), GRZ and UNDP (2007) and Chisanga et al (2015) in simulating maize yield. Chinene (1985) also evaluated the effect of water and nitrogen on grain yield on a clayey, kaolinitic isohyperthermic oxic paleausalf in Zambia.…”

“…Lengthy and costly agronomic and modelling field trials with a high number of treatments, could be pre-evaluated by conducting, in minutes, experiments on a desktop computer or laptop (Steduto et al, 2009). Crop simulation modeling can be used to decide on the optimum plant densities and dates of planting for maize crop (Soler et al, 2005;Chisanga et al, 2015).…”

Thin Layer Mathematical Modelling of Cob Maize in a Natural Convection Solar Drier

“…Maize is Zambia's staple food and is grown widely throughout the country and provides 60 percent of all calories consumed in Zambia (Chisanga et al, 2015). Jazayeri and Amani (2004) reported that maize accounts for over 30 % of lower-income households and contributes 50 % of protein intake.…”