Future Crop Yield Projections Using a Multi-model Set of Regional Climate Models and a Plausible Adaptation Practice in the Southeast United States

Shin, Dong Jik; Cocke, S.; Baigorria, Guillermo A.; Romero, Consuelo C.; Kim, Baek-Min; Kim, Ki‐Young

doi:10.3390/atmos11121300

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…Hence, the optimized ensemble model results were selected for further temporal and spatial analyses. For reference, in crop modeling research, several investigators found that the multi-model average provides the best prediction of crop yields ( Asseng et al., 2014 ; Iizumi et al., 2018 ; Heino et al., 2020 ; Shin et al., 2020 ; Shahhosseini et al., 2021a ). It appears that the use of multi-model is a viable way toward increasing prediction in agriculture at the expense of additional model runs and time.…”

Section: Discussionmentioning

confidence: 99%

County-scale crop yield prediction by integrating crop simulation with machine learning models

et al. 2022

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Crop yield prediction is of great importance for decision making, yet it remains an ongoing scientific challenge. Interactions among different genetic, environmental, and management factors and uncertainty in input values are making crop yield prediction complex. Building upon a previous work in which we coupled crop modeling with machine learning (ML) models to predict maize yields for three US Corn Belt states, here, we expand the concept to the entire US Corn Belt (12 states). More specifically, we built five new ML models and their ensemble models, considering the scenarios with and without crop modeling variables. Additional input values in our models are soil, weather, management, and historical yield data. A unique aspect of our work is the spatial analysis to investigate causes for low or high model prediction errors. Our results indicated that the prediction accuracy increases by coupling crop modeling with machine learning. The ensemble model overperformed the individual ML models, having a relative root mean square error (RRMSE) of about 9% for the test years (2018, 2019, and 2020), which is comparable to previous studies. In addition, analysis of the sources of error revealed that counties and crop reporting districts with low cropland ratios have high RRMSE. Furthermore, we found that soil input data and extreme weather events were responsible for high errors in some regions. The proposed models can be deployed for large-scale prediction at the county level and, contingent upon data availability, can be utilized for field level prediction.

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Section: Discussionmentioning

confidence: 99%

County-scale crop yield prediction by integrating crop simulation with machine learning models

et al. 2022

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“…Crop growth simulation models (CGSMs) are considered valuable tools and have been used in the tactical and strategic decision support for crop productivity enhancement [13]. Crop models such as Cropping System Models (CSM) and Decision Support Systems for Agro-technology Transfer (DSSAT) can study cotton crop management, crop improvement, genotype, water management, and climate impact studies on cotton production [14][15][16][17][18]. Previous studies reported that these models are also used in studies of climate risk management and for enhancing the resilience in crops and cropping systems [8,19,20].…”

Section: Introductionmentioning

confidence: 99%

Building Climate-Resilient Cotton Production System for Changing Climate Scenarios Using the DSSAT Model

Awan¹,

Khaliq

Akhtar³

et al. 2021

Sustainability

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Cotton production is highly vulnerable to climate change, and heat stress is a major constraint in the cotton zone of Punjab, Pakistan. Adaptation is perceived as a critical step to deal with forecasted and unexpected climatic conditions. The objective of this study was to standardize and authenticate a cotton crop model based on climate and crop husbandry data in order to develop an adaptation package for cotton crop production in the wake of climate change. For the study, the data were collected from the cotton-growing areas of Punjab, viz. Bahawalpur and Khanewal. After the calibration and validation against field data, the Cropping System Model CSM–CROPGRO–Cotton in the shell of the decision support system for agro-technology transfer (DSSAT) was run with a future climate generated under two representative concentrations pathways (RCPs), viz. RCPs 4.5 and 8.5 with five global circulation models (GCMs). The whole study showed that a model is an artistic tool for examining the temporal variation in cotton and determining the potential impact of planting dates on crop growth, phenology, and yield. The results showed that the future climate would have drastic effects on cotton production in the project area. Reduction in seed cotton yield (SCY) was 25.7% and 32.2% under RCPs 4.5 and 8.5, respectively. The comparison of five GCMs showed that a hot/wet climate would be more damaging than other scenarios. The simulations with different production options showed that a 10% and 5% increase in nitrogen and plant population, respectively, compared to the present would be the best strategy in the future. The model further suggested that planting conducted 15 days earlier, combined with the use of water and nitrogen (fertigation), would help to improve yield with 10% less water under the future climate. Overall, the proposed adaptation package would help to recover 33% and 37% of damages in SCY due to the climate change scenarios of RCP 4.5 and 8.5, respectively. Furthermore, the proposed package would also help the farmers increase crop yield by 7.5% over baseline (current) yield.

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Impact of Climate Change on Integrated Pest Management Strategies

Gvozdenac¹,

Dedić²,

Mikić³

et al. 2022

Climate Change and Agriculture

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Future Crop Yield Projections Using a Multi-model Set of Regional Climate Models and a Plausible Adaptation Practice in the Southeast United States

Cited by 5 publications

References 36 publications

County-scale crop yield prediction by integrating crop simulation with machine learning models

County-scale crop yield prediction by integrating crop simulation with machine learning models

Building Climate-Resilient Cotton Production System for Changing Climate Scenarios Using the DSSAT Model

Impact of Climate Change on Integrated Pest Management Strategies

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