Abstract
Abstract. Sowing and harvest dates are a significant source of uncertainty within crop models, especially for regions where high-resolution data are unavailable or, as is the case in future climate runs, where no data are available at all. Global datasets are not always able to distinguish when wheat is grown in tropical and subtropical regions, and they are also often coarse in resolution. South Asia is one such region where large spatial variation means higher-resolution datasets are needed, together with greater clarity for the timing of the main wheat growing season. Agriculture in South Asia is closely associated with the dominating climatological phenomenon, the Asian summer monsoon (ASM). Rice and wheat are two highly important crops for the region, with rice being mainly cultivated in the wet season during the summer monsoon months and wheat during the dry winter. We present a method for estimating the crop sowing and harvest dates for rice and wheat using the ASM onset and retreat. The aim of this method is to provide a more accurate alternative to the global datasets of cropping calendars than is currently available and generate more representative inputs for climate impact assessments. We first demonstrate that there is skill in the model prediction of monsoon onset and retreat for two downscaled general circulation models (GCMs) by comparing modelled precipitation with observations. We then calculate and apply sowing and harvest rules for rice and wheat for each simulation to climatological estimates of the monsoon onset and retreat for a present day period. We show that this method reproduces the present day sowing and harvest dates for most parts of India. The application of the method to two future simulations demonstrates that the estimated sowing and harvest dates are successfully modified to ensure that the growing season remains consistent with the internal model climate. The study therefore provides a useful way of modelling potential growing season adaptations to changes in future climate.
Highlights
Field studies dominate the modelling literature on crops and agriculture
The growing awareness of climate change and the likely impact this will have on food production has generated a demand for regional and global assessments of climate impacts on food security through, for example, projects such as the Agricultural Model Intercomparison and Improvement Project (AgMIP; Rivington and Koo, 2010; Rosenzweig et al, 2013, 2014), the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP; Warszawski et al, 2013, 2014), and the Global Gridded Crop Model Intercomparison (GGCMI; Elliott et al, 2015)
Sowing and harvest dates are an important input within crop models but are a source of considerable uncertainty
Summary
Field studies dominate the modelling literature on crops and agriculture. Many crop models are developed and applied at the field scale using site-specific observations to drive models and optimize outputs. The growing awareness of climate change and the likely impact this will have on food production has generated a demand for regional and global assessments of climate impacts on food security through, for example, projects such as the Agricultural Model Intercomparison and Improvement Project (AgMIP; Rivington and Koo, 2010; Rosenzweig et al, 2013, 2014), the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP; Warszawski et al, 2013, 2014), and the Global Gridded Crop Model Intercomparison (GGCMI; Elliott et al, 2015) Recent work in such climate–crop impact studies has sought to quantify uncertainty from the quality and scale of input data. A result from this work is that for global-scale simulations, planting dates are a significant source of uncertainty (Frieler et al, 2017; Elliott et al, 2015).
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