Are soybean models ready for climate change food impact assessments?

Kothari, Kritika; Battisti, Rafael; Boote, Kenneth J.; Archontoulis, Sotirios; Confalone, Adriana; Constantin, Julie; Cuadra, Santiago Viana; Debaeke, Philippe; Faye, Babacar; Grant, Brian; Hoogenboom, Gerrit; Jing, Qi; Laan, M. van der; Silva, Fernando Antônio Macena da; Marin, Fábio Ricardo; Nehbandani, Alireza; Nendel, Claas; Purcell, Larry C.; Qian, Budong; Ruane, Alex C.; Schoving, Céline; Silva, Evandro H. F. M.; Smith, Ward; Soltani, Afshin; Srivastava, Amit Kumar; Vieira, Nilson Aparecido; Slone, Stacey; Salmerón, Montserrat

doi:10.1016/j.eja.2022.126482

Cited by 35 publications

(21 citation statements)

References 49 publications

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“…Calibration and testing results for the individual models are displayed in Figure 1; the respective performance indicators are displayed in Table 2. The performance indicators show an RSME ranging from 467 to 1274 kg ha −1 (90% quantiles) for soybean dry matter seed yield across all models and MGs using the independent test data set, which is considered a good result for the range of soybean varieties considered (compare Kothari et al, 2022). Applying four suitable models in ensemble mode further reduces the prediction error considerably (Martre et al, 2015;Wallach et al, 2018).…”

Section: Model Calibrationmentioning

confidence: 86%

“…In our study, we, therefore, collated data sets from various biophysical conditions and maturity groups to achieve a high accuracy of predictions (Table 2). Kothari et al (2022) found considerable variability among models in simulating soybean yield responses to increasing temperature and CO 2 . Heat stress, however, was not investigated in their study, and it seems that suitable data sets for calibrating heat stress algorithms are lacking, especially for European soybean varieties.…”

Section: Discussionmentioning

confidence: 99%

“…The high uncertainty in model responses indicated the limited applicability of individual models for climate change food projections, suggesting the use of an ensemble mean of simulations across models. The ensemble is a useful approach to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrisation (Kothari et al, 2022; Martre et al, 2015). This approach was, therefore, also applied in our study, with four simulation models that are known for their ability to closely reproduce the effects of water supply, temperature and atmospheric CO 2 concentration of crop growth and yield formation (Table 1).…”

Section: Discussionmentioning

confidence: 99%

“…STICS (Brisson et al, 2003) has been calibrated and tested for soybean MGs 00 to II in Canada (Jégo et al, 2010) and a range of MGs in southern France (Schoving et al, 2022). APSIM, MONICA and STICS also underwent joint testing against further data sets within an AgMIP model intercomparison exercise (Kothari et al, 2022).…”

Section: Model Calibrationmentioning

confidence: 99%

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Future area expansion outweighs increasing drought risk for soybean in Europe

Nendel

Reckling

Schulz

et al. 2023

Global Change Biology

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The European Union is highly dependent on soybean imports from overseas to meet its protein demands. Individual Member States have been quick to declare selfsufficiency targets for plant-based proteins, but detailed strategies are still lacking.Rising global temperatures have painted an image of a bright future for soybean production in Europe, but emerging climatic risks such as drought have so far not been included in any of those outlooks. Here, we present simulations of future soybean production and the most prominent risk factors across Europe using an ensemble of climate and soybean growth models. Projections suggest a substantial increase in potential soybean production area and productivity in Central Europe, while southern European production would become increasingly dependent on supplementary irrigation. Average productivity would rise by 8.3% (RCP 4.5) to 8.7% (RCP 8.5) as a result of improved growing conditions (plant physiology benefiting from rising temperature and CO 2 levels) and farmers adapting to them by using cultivars with longer phenological cycles. Suitable production area would rise by 31.4% (RCP 4.5) to 37.7% (RCP | 1341 NENDEL et al.

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Section: Model Calibrationmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Model Calibrationmentioning

confidence: 99%

See 2 more Smart Citations

Future area expansion outweighs increasing drought risk for soybean in Europe

Nendel

Reckling

Schulz

et al. 2023

Global Change Biology

Self Cite

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“…Examples include changes in sowing dates, cultivars, irrigation, N fertilization, plant arrangements, and results are highly variable depending on the environment ( Bender et al, 2015 ; Moreira et al, 2015 ; Wegerer et al, 2015 ; Ortel et al, 2020 ; Zhao et al, 2020 ; de Borja Reis et al, 2021 ; Radzka et al, 2021 ). Projected soybean yield responses to climate change are highly variable depending on model assumptions and baseline climates ( Kothari et al, 2022 ). Some model-based climate change studies indicate soybean seed yields will decline in future climates scenarios ( Jin et al, 2017 ; Schauberger et al, 2017 ; Zabel et al, 2021 ) due to a 1.5°C temperature increase by 2050 ( Intergovernmental Panel on Climate Change [IPCC], 2018 ) and changes in precipitation patterns.…”

Section: Introductionmentioning

confidence: 99%

Climate Change and Management Impacts on Soybean N Fixation, Soil N Mineralization, N2O Emissions, and Seed Yield

et al. 2022

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Limited knowledge about how nitrogen (N) dynamics are affected by climate change, weather variability, and crop management is a major barrier to improving the productivity and environmental performance of soybean-based cropping systems. To fill this knowledge gap, we created a systems understanding of agroecosystem N dynamics and quantified the impact of controllable (management) and uncontrollable (weather, climate) factors on N fluxes and soybean yields. We performed a simulation experiment across 10 soybean production environments in the United States using the Agricultural Production Systems sIMulator (APSIM) model and future climate projections from five global circulation models. Climate change (2020–2080) increased N mineralization (24%) and N2O emissions (19%) but decreased N fixation (32%), seed N (20%), and yields (19%). Soil and crop management practices altered N fluxes at a similar magnitude as climate change but in many different directions, revealing opportunities to improve soybean systems’ performance. Among many practices explored, we identified two solutions with great potential: improved residue management (short-term) and water management (long-term). Inter-annual weather variability and management practices affected soybean yield less than N fluxes, which creates opportunities to manage N fluxes without compromising yields, especially in regions with adequate to excess soil moisture. This work provides actionable results (tradeoffs, synergies, directions) to inform decision-making for adapting crop management in a changing climate to improve soybean production systems.

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The role of crop simulation modeling in assessing potential climate change impacts

Timlin,

Paff,

Han

2024

Agrosystems Geosci & Env

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Agriculture is weather dependent, and changes in climate can have a drastic impact on our ability to feed, fuel, and clothe the world's population. Climate change is causing more frequent and unprecedented extreme weather events that are already negatively affecting agriculture. We need to assess the effects of extreme temperatures and rainfall on agriculture. Patterns of short‐term extreme weather events, such as elevated temperatures, flooding, and strong winds, are not predictable enough to design field experiments around. Process‐based crop and soil simulation models allow us to explore new management options and thus provide whole‐system‐based knowledge and management guides for different locations over variable climate conditions. By using crop simulation models, researchers can test different adaptation strategies and assess their effectiveness in reducing the impacts of climate change on agricultural production. In this paper, we discuss the development of crop models and how they have been used to assess the effects of a changing climate on agricultural productivity and propose methods for agriculture to adapt to those changes. We describe potential applications of crop models to assess regional issues such as irrigation demand, greenhouse gas emissions, and policy decisions. Better understanding of how weather and climate forecasts at various scales are provided and the reliability of these forecasts is important for using crop models as a planning tool. Different approaches for simulating long‐term climate change impacts on crop yield and seasonal yield forecasting are discussed. The use of ensemble models to better assess climate change impacts is also discussed.

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Are soybean models ready for climate change food impact assessments?

Cited by 35 publications

References 49 publications

Future area expansion outweighs increasing drought risk for soybean in Europe

Future area expansion outweighs increasing drought risk for soybean in Europe

Climate Change and Management Impacts on Soybean N Fixation, Soil N Mineralization, N2O Emissions, and Seed Yield

The role of crop simulation modeling in assessing potential climate change impacts

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