Adapting and evaluating the CROPGRO-peanut model for response to phosphorus on a sandy-loam soil under semi-arid tropical conditions

Abstract

Phosphorus (P) deficiency is a major constraint to crop production in many agricultural systems globally. Application of phosphorus fertilizer is essential for optimal crop yields when soils are P limiting. Crop simulation models can provide an alternative, less time consuming and inexpensive means of determining the optimum crop P requirements under varied soil and climatic conditions. The CROPGRO-peanut model is capable of simulating the growth and yield of peanut in response to weather, soil, water, nitrogen and management practices but its capability in predicting crop responses to soil and fertilizer P needs to be established. Our objective was to adapt and evaluate the CROPGRO-peanut model within the DSSAT system to simulate the growth and yield of peanut (Arachis hypogaea) in response to soil and fertilizer P. Data from four P fertilizer treatments (0, 13, 26 and 39kgPha−1) and two cropping seasons (2002 and 2003) experiments on an Alfisol were used to calibrate the model. The model was tested using two data sets from a P fertilizer×cultivar trial conducted on-station in 1997 and 1998 and P fertilizer×fungicide trials on-farm in 2002. The limited testing showed that the P module accurately simulated the seasonal patterns of aboveground biomass and pod yield in the on-farm trials. Averaged across sampling dates RMSEs in the on-farm trials ranged from 100 to 398kgha−1 (d≥0.99) for total biomass and from 97 to 263kgha−1 (d values≥0.98) for pod yield. At final harvest, the variability of simulated biomass and pod yield was about 5.4 and 10.0% of the observed biomass and pod yield respectively. In the P fertilizer×cultivar trial, the variability of simulated biomass and pod yield were 22 and 13% for cv. F-Mix and 19 and 5% for cv. Chinese respectively. The model simulated the seasonal patterns of vegetative P content in the four farmers’ fields fairly well with RMSEs ranging from 0.28 to 1.29kgha−1 when averaged across measurements dates. The model outputs were sensitive to soil P test values, the method of P fertilizer application and to plant P uptake factors, such as root P extraction radius and root length density. There were differences in the sensitivity of the simulations to changes in target tissue P concentrations. Increasing or decreasing the optimum P concentration of seed and minimum P concentrations of leaf and stem had the greatest effects on pod yield compared to other plant parts. We conclude that the generic soil and plant P model in DSSAT 4.5 is capable of simulating peanut growth and yield in response to soil P levels or fertilizer application on an Alfisol.