Spatial sampling of weather data for regional crop yield simulations

Bussel, L.G.J. van; Ewert, Frank; Zhao, Gang; Hoffmann, Holger; Enders, Andreas; Wallach, Daniel; Asseng, Senthold; Baigorria, Guillermo A.; Basso, Bruno; Biernath, Christian; Cammarano, Davide; Chryssanthacopoulos, James; Constantin, Julie; Elliott, Joshua; Glotter, Michael; Heinlein, Florian; Kersebaum, Kurt Christian; Klein, Christian; Nendel, Claas; Priesack, Eckart; Raynal, Hélène; Romero, Consuelo C.; Rötter, Reimund P.; Specka, Xenia; Tao, Fulu

doi:10.1016/j.agrformet.2016.01.014

Cited by 38 publications

(27 citation statements)

References 45 publications

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“…Simulation results were up‐scaled using a stratified sampling method, a guided sampling method to improve the scaling quality (van Bussel et al, ), with several points per wheat mega region when necessary (Gbegbelegbe et al, ). During the up‐scaling process, the simulation result of a location was weighted by the production the location represents as described below (Asseng et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…Asseng et al (2018) recently demonstrated that a multi-model ensemble could also simulate the impact of heat shocks and extreme drought on wheat yield. Global warming will also affect weeds, pests, and diseases, which are not considered in our analysis, but could significantly impact crop production (Jones et al, 2017;Juroszek & von Tiedemann, 2013;Stratonovitch, Storkey, & Semenov, 2012). Possible agricultural land use changes were not considered here, which could increase production (Nelson et al, 2014), but also accelerate further greenhouse gas emissions (Porter, Howden, & Smith, 2017), adding to the uncertainty of future impact projections.…”

Section: Uncertaintiesmentioning

confidence: 99%

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Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Liu

Martre

Ewert

et al. 2019

Global Change Biology

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126

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Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade.

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Section: Methodsmentioning

confidence: 99%

Section: Uncertaintiesmentioning

confidence: 99%

Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Liu

Martre

Ewert

et al. 2019

Global Change Biology

Self Cite

126

View full text Add to dashboard Cite

show abstract

“…In this context, it is possible to consider irrigation only being applied during the crop phases in which the crop is most sensitive, reducing the water requirement without drastically reducing yield for soybeans . Nov for all scenarios, while under rainfed conditions, the yield differences between sowing dates were smaller, when compared to irrigated conditions (Figure 2 VAN BUSSEL et al, 2016) and also the yield variability attributed to the rainfall distribution and soil water availability in Southern Brazil . Based on the results from the ensemble of models, the tendency was similar between climate scenarios, as demonstrated through relative soybean yields ( Figure 3).…”

Section: Crop Managements and Theirs Interactionsmentioning

confidence: 99%

Calibration, uncertainties and use of soybean crop simulation models for evaluating strategies to mitigate the effects of climate change in Southern Brazil

Battisti¹

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“…Moreover, the WOFOST model can be applied in two modes: the potential mode, in which crop growth is determined by temperature, day length, solar radiation and genetic characteristics assuming absence of any water or other stress factors; the water-limited mode, in which crop growth is influenced by rainfall, soil type and field topography. In both modes, we assume that the nutrient supply is optimal, and other yield-limiting factors such as pests, weeds, and farm management are not considered [7]. LAI is one of the most important state variables in the WOFOST model, and it can indicate the potential grain yield.…”

Section: Introductionmentioning

confidence: 99%

“…The crop growth simulation model WOFOST is a member of family of Wageningen crop models [2]. It is a mechanistic process-based model which applies generic process description assuming that weather, soil and crop data are homogeneous [5]. Specifically, the growth-driving processes describe the plant growth based on light interception and CO 2 assimilation; the growth-controlling processes describe the crop phenological development [6].…”

Section: Introductionmentioning

confidence: 99%

Study of growth and yield of winter wheat using WOFOST model based on wireless sensor network data

Wu¹,

Zhu²,

Zhang³

et al. 2016

Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)

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Abstract. This study aims to research the applicability of the WOFOST model and predict winter wheat yield on a regional scale. The automatic information in study area could be achieved from the wireless sensor network (WSN) deployed in cropland. The observed results including the phenological dates, LAI and the yield are compared with the simulated results in WOFOST. It is found that the maximum deviation of observed and simulated days taken to anthesis is seven days, and the maximum deviation of observed and simulated days taken to maturity is five days. The simulated days to anthesis and days to maturity indicate that WOFOST model underestimates the phenological period of wheat in both years. Through the analysis of LAI, it can be seen that simulated results are agree well with the observed results, except the early stage of growth. The phenomenon can be attributed to the simulated growth period is earlier than the actual test. Moreover, the observed mean yields are 5410 and 5536 kg ha -1 during 2006-2007 and 2007-2008, respectively. The simulated mean yields are 5936 and 6027 kg ha -1 during 2006-2007 and 2007-2008, respectively. Based on the relative root mean square error, the modified WOFOST model presented a very good to fair estimation of wheat yields. The results of this study provide an important theoretical reference for WOFOST model monitoring and estimating winter wheat growth status on a regional scale. It indicates that WOFOST model is a useful tool in yield forecasting.

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Spatial sampling of weather data for regional crop yield simulations

Cited by 38 publications

References 45 publications

Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Calibration, uncertainties and use of soybean crop simulation models for evaluating strategies to mitigate the effects of climate change in Southern Brazil

Study of growth and yield of winter wheat using WOFOST model based on wireless sensor network data

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