Estimating sowing and harvest dates based on the Asian summer monsoon

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.

Spatial variations in crop growing seasons pivotal to reproduce global fluctuations in maize and wheat yields

“…S5). Crop calendar improvements at high spatial resolution, such as recently published for rice ( 50 ), are expected to further improve crop yield simulations—especially in regions where growing seasons are closely linked to the onset of the rainy season ( 51 , 52 ). There is no global information about temporal variations in growing season, but recent studies show that the implementation of high-quality crop development dates reported annually for the United States even allows for a historical reproduction of observed temporal trends in U.S. yields ( 11 , 12 ).…”

Developing a sequential cropping capability in the JULESvn5.2 land–surface model

“…2, Eq. 1 and Osborne et al (2015); Mathison et al (2018)). It is recommended for sequential cropping to prescribe a latest possible harvest date for those instances where the crop does not develop quickly enough and therefore does not reach maturity before the next crop in the rotation is due to be sown.…”

“…The India simulations focus on the north Indian states of Uttar Pradesh and Bihar. These states are key producers of rice and wheat in India and the use of a rice-wheat rotation is prevalent in this part of India (Mahajan and Gupta, 2009 not require a vernalization period which is important for winter wheat varieties (Griffiths et al, 1985;Robertson et al, 1996;Mathison et al, 2018). We select four points across these two states in order to gain understanding of the model response, particularly in terms of yield, to the variation in the conditions across the two states.…”

“…In this paper we describe and demonstrate the development and implementation of sequential cropping in JULES. This is part of a larger project to develop simulations for South Asia to understand the integrated impacts of climate change (Mathison et al, 2015(Mathison et al, , 2018 using state of the art RCM projections (Kumar et al, 2013;Mathison et al, 2013). This will improve understanding of the impacts of climate change and how they affect each other.…”

How Much Are Planting Dates for Maize Affected by the Climate Trend? Lessons for Scenario Analysis Using Land Surface Models

“…Thus, the planting date is one of main inputs as management information in process-based crop models [11][12][13]. There are many different methods to determine planting dates as an input to crop models [11][12][13][14][15][16]. One main method uses actual or simulated weather data to define rule-based planting dates [11,13,14].…”