Use of CERES-Wheat model for wheat yield forecast in central Indo-Gangetic Plains of India

Abstract

A methodology was developed for large area yield forecast using a crop simulation model and a discrete technology trend, and was applied to the coherent wheat yield variability zones of Eastern Uttar Pradesh, India. The approach consisted of three major steps: (a) prediction of technology trend yield using historical yield series of the region; (b) prediction of weather-induced deviation in wheat yield using CERES-Wheat simulation model and relating weather-induced deviation in simulated yield to deviation in observed yield deviations from technology trend; and (c) final yield forecast by incorporating predicted yield deviation in trend predicted yield. The regression coefficients for step (b) were generated using 10 years' data (1984/85–1994/95) and the reliability of the approach was tested on a data set of 5 years' independent data (1995/96–1999/2000). The results showed that this approach could capture year-to-year variability in large area wheat yield with reasonable accuracy. The Root Mean Square Error (RMSE) between observed and predicted yield was reported as 0·098 t/ha for the mean yield of 2·072 t/ha (4·72%). However, the RMSE was slightly higher in the forecasting period in comparison to the calibration period. The use of this methodology for issuing the pre-harvest forecast and the effect of upgrading the technology trend were also studied. The pre-harvest forecasts were made using in-season weather data up to the end of February and climatic-normal for the rest of the wheat-growing season, which showed good agreement with observed wheat yields. The forecasts of wheat yield for the season 1999/2000 were made using the technology trend up to 1994/95 and the updated technology trend up to 1998/99, which showed that the RMSE fell in the latter case, from 4·10 to 2·50%.

Estimating wheat yield and quality by coupling the DSSAT-CERES model and proximal remote sensing

“…Our results demonstrated reasonable accuracy for yield simulation even over several wheat growth seasons. This result was consistent with Nain et al (2004), Ahmad et al (2012), and Thorp et al (2012). The DSSAT-CERES model is a process-based model that can capture the nonlinear interannual variability in crop yield, due to the consideration of interannual variations in weather conditions, management strategies, and the interactions between the two (Nain et al, 2004).…”

“…For wheat, the AgMIP (Agricultural Model Intercomparison and Improvement Project) listed 27 representative wheat models around the world (Asseng et al, 2013). The decision support system for agrotechnology transfer (DSSAT) including the CERES-Wheat model has been widely used over the last 25 years by many researchers for many different applications, and it has been shown to be capable of robust yield predictions across various environmental, soil, and management strategy conditions (Landau et al, 1998;Jones et al, 2003;Nain et al, 2004;Fang et al, 2008;Thorp et al, 2010;Liu et al, 2011;Huffman et al, 2015). When applied over a large-scale area, however, the model has shown low prediction performance because the boundary conditions (soil, management) are often poorly known and model parameters are uncertain (Moulin et al, 1998;Dorigo et al, 2007).…”

Identification of Most Important Weeks on Minimum Temperature for Wheat Crop

“…The value of R 2 was 80.60% which is significant at 1% level of significance. Nain et al, (2004) also reported that the differences between observed and predicted wheat yield were, on an average, 4.72% by using weather variables for IndoGangetic plains. Likewise, yield of groundnut was significantly correlated with growing season rainfall in southern India Gadgil et al, (2002).…”

Maize yield forecasting for Zimbabwe farming sectors using satellite rainfall estimates

“…The use of this technique is not unique to southern Africa as the model has been used in many parts of the world to assess national food security and provide early food shortage warning (Reynolds et al 2000). Early assessment of yield helps in strategic planning and decision making (Nain et al 2002, Nain et al 2004Domenikiotis et al 2004), and the longer the lead time for the postplanting yield forecasts the better.…”