Seasonal Predictability of Four Major Crop Yields Worldwide by a Hybrid System of Dynamical Climate Prediction and Eco-Physiological Crop-Growth Simulation
The purpose of this study was to evaluate the prediction accuracy of a newly developed crop yield prediction system, composed of a dynamical seasonal climate prediction model (SINTEX-F2) and an eco-physiological process-based crop growth model (PRYSBI2). We explored the 3-months lead prediction accu...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2020-06-01
|
| Series: | Frontiers in Sustainable Food Systems |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fsufs.2020.00084/full |
| _version_ | 1818352469252505600 |
|---|---|
| author | Takeshi Doi Gen Sakurai Toshichika Iizumi |
| author_facet | Takeshi Doi Gen Sakurai Toshichika Iizumi |
| author_sort | Takeshi Doi |
| collection | DOAJ |
| description | The purpose of this study was to evaluate the prediction accuracy of a newly developed crop yield prediction system, composed of a dynamical seasonal climate prediction model (SINTEX-F2) and an eco-physiological process-based crop growth model (PRYSBI2). We explored the 3-months lead prediction accuracy of year-to-year variations in yield of four major crops (maize, rice, wheat, and soybean) in global regions and evaluated for which crops and in which areas the system performs well. The results indicated the system is more accurate for wheat relative to the other crops. Also, we found that different strategies would be useful in improving the system, depending on the crop. For winter wheat and rice, we need to improve the temperature predictions, particularly over the mid-latitudes, whereas improving rainfall predictions was more important for maize. For spring wheat and soybeans, the crop growth simulation itself should be improved. Although this study is only a first step, we believe that additional efforts to improve the system by understanding and incorporating processes of climate and crop growth will potentially provide useful prediction information to big stakeholders like global agribusiness companies and countries for improving food security in regions where crop yield is vulnerable to extreme climate shocks and where food markets are isolated from international trade. |
| first_indexed | 2024-12-13T18:54:08Z |
| format | Article |
| id | doaj.art-88e83e5598364117928e6ebb9985a51d |
| institution | Directory Open Access Journal |
| issn | 2571-581X |
| language | English |
| last_indexed | 2024-12-13T18:54:08Z |
| publishDate | 2020-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Sustainable Food Systems |
| spelling | doaj.art-88e83e5598364117928e6ebb9985a51d2022-12-21T23:34:52ZengFrontiers Media S.A.Frontiers in Sustainable Food Systems2571-581X2020-06-01410.3389/fsufs.2020.00084521221Seasonal Predictability of Four Major Crop Yields Worldwide by a Hybrid System of Dynamical Climate Prediction and Eco-Physiological Crop-Growth SimulationTakeshi Doi0Gen Sakurai1Toshichika Iizumi2Application Laboratory, Research Institute for Value-Added-Information Generation (VAiG), Japan Agency for Marine-Earth Science and Technology, Yokosuka, JapanInstitute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, Tsukuba, JapanInstitute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, Tsukuba, JapanThe purpose of this study was to evaluate the prediction accuracy of a newly developed crop yield prediction system, composed of a dynamical seasonal climate prediction model (SINTEX-F2) and an eco-physiological process-based crop growth model (PRYSBI2). We explored the 3-months lead prediction accuracy of year-to-year variations in yield of four major crops (maize, rice, wheat, and soybean) in global regions and evaluated for which crops and in which areas the system performs well. The results indicated the system is more accurate for wheat relative to the other crops. Also, we found that different strategies would be useful in improving the system, depending on the crop. For winter wheat and rice, we need to improve the temperature predictions, particularly over the mid-latitudes, whereas improving rainfall predictions was more important for maize. For spring wheat and soybeans, the crop growth simulation itself should be improved. Although this study is only a first step, we believe that additional efforts to improve the system by understanding and incorporating processes of climate and crop growth will potentially provide useful prediction information to big stakeholders like global agribusiness companies and countries for improving food security in regions where crop yield is vulnerable to extreme climate shocks and where food markets are isolated from international trade.https://www.frontiersin.org/article/10.3389/fsufs.2020.00084/fullseasonal predictioncrop yieldsprocess-based predictiondynamical seasonal climate predictioneco-physiological crop-growth simulation |
| spellingShingle | Takeshi Doi Gen Sakurai Toshichika Iizumi Seasonal Predictability of Four Major Crop Yields Worldwide by a Hybrid System of Dynamical Climate Prediction and Eco-Physiological Crop-Growth Simulation Frontiers in Sustainable Food Systems seasonal prediction crop yields process-based prediction dynamical seasonal climate prediction eco-physiological crop-growth simulation |
| title | Seasonal Predictability of Four Major Crop Yields Worldwide by a Hybrid System of Dynamical Climate Prediction and Eco-Physiological Crop-Growth Simulation |
| title_full | Seasonal Predictability of Four Major Crop Yields Worldwide by a Hybrid System of Dynamical Climate Prediction and Eco-Physiological Crop-Growth Simulation |
| title_fullStr | Seasonal Predictability of Four Major Crop Yields Worldwide by a Hybrid System of Dynamical Climate Prediction and Eco-Physiological Crop-Growth Simulation |
| title_full_unstemmed | Seasonal Predictability of Four Major Crop Yields Worldwide by a Hybrid System of Dynamical Climate Prediction and Eco-Physiological Crop-Growth Simulation |
| title_short | Seasonal Predictability of Four Major Crop Yields Worldwide by a Hybrid System of Dynamical Climate Prediction and Eco-Physiological Crop-Growth Simulation |
| title_sort | seasonal predictability of four major crop yields worldwide by a hybrid system of dynamical climate prediction and eco physiological crop growth simulation |
| topic | seasonal prediction crop yields process-based prediction dynamical seasonal climate prediction eco-physiological crop-growth simulation |
| url | https://www.frontiersin.org/article/10.3389/fsufs.2020.00084/full |
| work_keys_str_mv | AT takeshidoi seasonalpredictabilityoffourmajorcropyieldsworldwidebyahybridsystemofdynamicalclimatepredictionandecophysiologicalcropgrowthsimulation AT gensakurai seasonalpredictabilityoffourmajorcropyieldsworldwidebyahybridsystemofdynamicalclimatepredictionandecophysiologicalcropgrowthsimulation AT toshichikaiizumi seasonalpredictabilityoffourmajorcropyieldsworldwidebyahybridsystemofdynamicalclimatepredictionandecophysiologicalcropgrowthsimulation |