Linking modelling and experimentation to better capture crop impacts of agroclimatic extremes—A review

Rötter, Reimund P.; Appiah, M. R.; Fichtler, Esther; Kersebaum, Kurt Christian; Trnka, Miroslav; Hoffmann, Munir P.

doi:10.1016/j.fcr.2018.02.023

Cited by 97 publications

(59 citation statements)

References 126 publications

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“…Although the algorithms behind crop models continue to be refined to improve the representation of biophysical pro-cesses, knowledge gaps still limit their use (Keating et al, 2003;Li et al, 2019;Rötter et al, 2018). This is particularly evident when predictions are made under extreme weather scenarios or in environments with specific characteristics such as shallow water tables or soil constraints (Wang & Smith, 2004).…”

Section: Introductionmentioning

confidence: 99%

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

et al. 2020

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We used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, USA. Historical, current and forecasted weather data were used to drive simulations, which were released in public four weeks after planting. In this paper, we (1) describe the methodology used to perform forecasts;(2) evaluate model prediction accuracy against data collected from 10 locations over four years; and (3) identify inputs that are key in forecasting yields and soil N dynamics. We found that the predicted median yield at planting was a very good indicator of end-of-season yields (relative root mean square error [RRMSE] of ∼20%). For reference, the prediction at maturity, when all the weather was known, had a RRMSE of 14%. The good prediction at planting time was explained by the existence of shallow water tables, which decreased model sensitivity to unknown summer precipitation by 50-64%. Model initial conditions and management information accounted for Abbreviations: APSIM, Agricultural Production Systems sIMulator; RRMSE, relative root mean square error.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. one-fourth of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R 2 = 0.88), root depth (R 2 = 0.83), biomass production (R 2 = 0.93), grain yield (R 2 = 0.90), plant N uptake (R 2 = 0.87), soil moisture (R 2 = 0.42), soil temperature (R 2 = 0.93), soil nitrate (R 2 = 0.77), and water table depth (R 2 = 0.41). We concluded that model set-up by the user (e.g. inclusion of water table), initial conditions, and early season measurements are very important for accurate predictions of soil water, N and crop yields in this environment. Neil Huth from CSIRO for their support with the APSIM model, Iowa State University students () for assistance with data collection and managing the field experiments. We also thank the APSIM Initiative for making the software publicly available and for ensuring software quality. ORCIDSotirios V. Archontoulis https://orcid.org/0000-0001-7595-8107 Mark A. Licht https://orcid.org/0000-0001-6640-7856 Kendall R. Lamkey

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Section: Introductionmentioning

confidence: 99%

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

et al. 2020

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“…climate, genotypes, management, and soil). Models can be used to understand the impact of climate change and extreme weather on cropping systems, including grain yield and soil carbon dynamics, and to assess the effectiveness of climate adaptation strategies [25,26]. The Systems Approach to Land Use Sustainability (SALUS) model [27] has been applied to assess the response of cereal and non-cereal crop production across the globe under different climate scenarios [28,29,30].…”

Section: Introductionmentioning

confidence: 99%

Impacts of climate variability and adaptation strategies on crop yields and soil organic carbon in the US Midwest

Liu

Basso

2020

PLoS ONE

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Climate change is likely to increase the frequency of drought and more extreme precipitation events. The objectives of this study were i) to assess the impact of extended drought followed by heavy precipitation events on yield and soil organic carbon (SOC) under historical and future climate, and ii) to evaluate the effectiveness of climate adaptation strategies (notillage and new cultivars) in mitigating impacts of increased frequencies of extreme events and warming. We used the validated SALUS crop model to simulate long-term maize and wheat yield and SOC changes of maize-soybean-wheat rotation cropping systems in the northern Midwest USA under conventional tillage and no-till for three climate change scenarios (one historical and two projected climates under the Representative Concentration Path (RCP) 4.5 and RCP6) and two precipitation changes (extreme precipitation occurring early or late season). Extended drought events caused additional yield reduction when they occurred later in the season (10-22% for maize and 5-13% for wheat) rather than in early season (5-17% for maize and 2-18% for wheat). We found maize grain yield declined under the projected climates, whereas wheat grain yield increased. No-tillage is able to reduce yield loss compared to conventional tillage and increased SOC levels (1.4-2.0 t/ha under the three climates), but could not reverse the adverse impact of climate change, unless early and new improved maize cultivars are introduced to increase yield and SOC under climate change. This study demonstrated the need to consider extreme weather events, particularly drought and extreme precipitation events, in climate impact assessment on crop yield and adaptation through no-tillage and new genetics reduces yield losses.

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“…Climate change is anticipated to increase the frequency of extreme weather events that threaten sustainable economic development and the resiliency of ecosystem services (Craig & Feng, ; Schirpke et al., ). The food system is particularly vulnerable because of shifting weather patterns such as extended droughts, flooding, frost, and heavy rain or hail (Intergovernmental Panel on Climate Change, ; Murray & Ebi, ; Rötter et al., ). Year 2050 climate change projections suggest that overall crop yields will decrease in most of the United States but particularly in the Midwest region (Wheeler & Braun, ).…”

Section: Introductionmentioning

confidence: 99%

Overcoming climate change adaptation barriers: A study on food–energy–water impacts of the average American diet by demographic group

Bozeman

Theis

2019

J of Industrial Ecology

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Effectively adapting to climate change involves overcoming social and ecological system barriers.The present study uses a three-phase adaptation framework to propose adaptation strategies aimed at overcoming socioecological barriers of the food-energy-water (FEW) nexus. Cradleto-farm-gate land, greenhouse gas (GHG), and water impacts-that derive from food consumption in the United States-are analyzed and differentiated by major demographic groups (Black, Latinx, and White). Results indicate that the White demographic yields the highest per capita GHG (680 kg of CO 2 eq⋅year −1 ) and water impacts (328,600 L⋅year −1 ) from food consumption, whereas the Black demographic yields the highest per capita land impacts (1,770 m 2 ⋅year −1 ) from food consumption. Our findings suggest that obtaining data with the intention of building consensus across sociodemographic lines overcomes barriers in the understanding phase, leading to increased social receptivity for many planning and managing phase processes. Specifically, we find that identifying and developing leaders who possess the cognitive and interpersonal capacity to manage many variables and stakeholders is key to assessing and selecting adaptation options in the planning phase. We also propose using government programming to encourage environmentally friendly food purchasing behavior. Then, we discuss how our proposals could be used in adaptation feasibility and evaluation activities in the managing phase. In all, these findings facilitate the development of improved climate change adaptation and policy by satisfying the understanding phase of the climate change adaptation framework, establishing a cross-disciplinary methodological approach to addressing socioecological problems, and providing useful FEW impact data for FEW nexus and climate change researchers.

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Linking modelling and experimentation to better capture crop impacts of agroclimatic extremes—A review

Cited by 97 publications

References 126 publications

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

Impacts of climate variability and adaptation strategies on crop yields and soil organic carbon in the US Midwest

Overcoming climate change adaptation barriers: A study on food–energy–water impacts of the average American diet by demographic group

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