Probabilistic simulations of crop yield over western India using the DEMETER seasonal hindcast ensembles

By Andrew J. Challinor, J. Slingo, Tim Wheeler, Francisco J. Doblas-Reyes. 2005

123 citations recorded by the source corpus.

Process-based integrated modelling of weather and crop yield over large areas is becoming an important research topic. The production of the DEMETER ensemble hindcasts of weather allows this work to be carried out in a probabilistic framework. In this study, ensembles of crop yield (groundnut, Arachis hypogaea L.) were produced for 10 2.5° × 2.5° grid cells in western India using the DEMETER ensembles and the general large-area model (GLAM) for annual crops. Four key issues are addressed by this study. First, crop model calibration methods for use with weather ensemble data are assessed. Calibration using yield ensembles was more successful than calibration using reanalysis data (the European Centre for Medium-Range Weather Forecasts 40-yr reanalysis, ERA40). Secondly, the potential for probabilistic forecasting of crop failure is examined. The hindcasts show skill in the prediction of crop failure, with more severe failures being more predictable. Thirdly, the use of yield ensemble means to predict interannual variability in crop yield is examined and their skill assessed relative to baseline simulations using ERA40. The accuracy of multimodel yield ensemble means is equal to or greater than the accuracy using ERA40. Fourthly, the impact of two key uncertainties, sowing window and spatial scale, is briefly examined. The impact of uncertainty in the sowing window is greater with ERA40 than with the multi-model yield ensemble mean. Subgrid heterogeneity affects model accuracy: where correlations are low on the grid scale, they may be significantly positive on the subgrid scale. The implications of the results of this study for yield forecasting on seasonal time-scales are as follows. (i) There is the potential for probabilistic forecasting of crop failure (defined by a threshold yield value); forecasting of yield terciles shows less potential. (ii) Any improvement in the skill of climate models has the potential to translate into improved deterministic yield prediction. (iii) Whilst model input uncertainties are important, uncertainty in the sowing window may not require specific modelling. The implications of the results of this study for yield forecasting on multidecadal (climate change) time-scales are as follows. (i) The skill in the ensemble mean suggests that the perturbation, within uncertainty bounds, of crop and climate parameters, could potentially average out some of the errors associated with mean yield prediction. (ii) For a given technology trend, decadal fluctuations in the yield-gap parameter used by GLAM may be relatively small, implying some predictability on those time-scales.

Subfields: Atmospheric Science, Ecology, Evolution, Behavior and Systematics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/2f1aaeeb4955c3bcdbdcfb3c79939333c90b55d0

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Citations in the corpus

Use of vegetation index and meteorological parameters for the prediction of crop yield in India (2007, background): “…have been made in crop yield prediction based on probabilistic methods due to the availability of data from Development of a European Multi-Model Ensemble System for Seasonal and Inter-Annual Prediction (DEMETER), but inherent uncertainties remain due to climatic timescales (Challinor et al. 2005).”
The rationale behind the success of multi-model ensembles in seasonal forecasting — II. Calibration and combination (2005, background): “…and/or combination are beneficial, need to be considered in the context of the application to end-user models (e.g. Cantelaube and Terres 2005; Challinor et al., 2005; Marletto et al., 2005; Morse et al., 2005) and of the decision-making process of users (Sarewitz et al., 2000; Jochec et al.,…”
Crops and climate change: progress, trends, and challenges in simulating impacts and informing adaptation. (2009, methodology): “However, model error may not be overly problematic: Challinor et al. (2005c) found predictability in crop yields using climate model output both with and without correction of bias in the simulation of mean climate.”