Improving Simulations of Rice in Response to Temperature and CO<sub>2</sub>
Crop models are frequently used to assess the impact of climate change responses. Evaluation of model performance against empirical data is crucial to establish confidence, particularly for rice (<i>Oryza sativa</i> L.), one of the world’s important cereal crops. Data from soil-plant-atm...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/12/12/2927 |
| _version_ | 1797461911390912512 |
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| author | Sanai Li David H. Fleisher Dennis Timlin Jinyoung Barnaby Wenguang Sun Zhuangji Wang V. R. Reddy |
| author_facet | Sanai Li David H. Fleisher Dennis Timlin Jinyoung Barnaby Wenguang Sun Zhuangji Wang V. R. Reddy |
| author_sort | Sanai Li |
| collection | DOAJ |
| description | Crop models are frequently used to assess the impact of climate change responses. Evaluation of model performance against empirical data is crucial to establish confidence, particularly for rice (<i>Oryza sativa</i> L.), one of the world’s important cereal crops. Data from soil-plant-atmosphere-research (SPAR) chambers and field plots were used to assess three versions of the ORYZA model to a range of climate conditions. The three versions were: V1–the original, V2–V1 plus a revised heat stress component, and V3–V2 plus a coupled leaf-level gas exchange algorithm. Comparison against SPAR datasets, which covered a range of temperatures at two CO<sub>2</sub> levels, indicated successive improvement in yield predictions with the model version. Root Mean Square Error (RMSE) decreased by 520 and 647 kg ha<sup>−1</sup> for V2 and V3, respectively, and Wilmott’s index of agreement improved by 10 and 12% compared with V1 when averaged across 20 treatments and three cultivars. Similar improvements were observed from 17 field dataset simulations with two additional varieties. These results indicated the importance of improving heat sterility functions and carbon assimilation methodologies that incorporate direct responses to air temperature and CO<sub>2</sub> concentration in rice models. Accounting for cultivar differences in thermal sensitivity is also an important consideration for climate assessments. |
| first_indexed | 2024-03-09T17:26:08Z |
| format | Article |
| id | doaj.art-90a693a0275e4e0caf743e1af93e77cf |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T17:26:08Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-90a693a0275e4e0caf743e1af93e77cf2023-11-24T12:43:25ZengMDPI AGAgronomy2073-43952022-11-011212292710.3390/agronomy12122927Improving Simulations of Rice in Response to Temperature and CO<sub>2</sub>Sanai Li0David H. Fleisher1Dennis Timlin2Jinyoung Barnaby3Wenguang Sun4Zhuangji Wang5V. R. Reddy6Adaptive Cropping Systems Laboratory, USDA-ARS, Beltsville, MD 20705, USAAdaptive Cropping Systems Laboratory, USDA-ARS, Beltsville, MD 20705, USAAdaptive Cropping Systems Laboratory, USDA-ARS, Beltsville, MD 20705, USAFloral and Nursery Plants Research, USDA-ARS National Arboretum, Beltsville, MD 20705, USANebraska Water Center, University of Nebraska Lincoln, Lincoln, NE 68588, USADepartment of Plant Science and Landscape Architecture, University of Maryland, College Park, MD 20742, USAAdaptive Cropping Systems Laboratory, USDA-ARS, Beltsville, MD 20705, USACrop models are frequently used to assess the impact of climate change responses. Evaluation of model performance against empirical data is crucial to establish confidence, particularly for rice (<i>Oryza sativa</i> L.), one of the world’s important cereal crops. Data from soil-plant-atmosphere-research (SPAR) chambers and field plots were used to assess three versions of the ORYZA model to a range of climate conditions. The three versions were: V1–the original, V2–V1 plus a revised heat stress component, and V3–V2 plus a coupled leaf-level gas exchange algorithm. Comparison against SPAR datasets, which covered a range of temperatures at two CO<sub>2</sub> levels, indicated successive improvement in yield predictions with the model version. Root Mean Square Error (RMSE) decreased by 520 and 647 kg ha<sup>−1</sup> for V2 and V3, respectively, and Wilmott’s index of agreement improved by 10 and 12% compared with V1 when averaged across 20 treatments and three cultivars. Similar improvements were observed from 17 field dataset simulations with two additional varieties. These results indicated the importance of improving heat sterility functions and carbon assimilation methodologies that incorporate direct responses to air temperature and CO<sub>2</sub> concentration in rice models. Accounting for cultivar differences in thermal sensitivity is also an important consideration for climate assessments.https://www.mdpi.com/2073-4395/12/12/2927crop modelphotosynthesishigh temperatureCO<sub>2</sub>spikelet fertilityheat stress |
| spellingShingle | Sanai Li David H. Fleisher Dennis Timlin Jinyoung Barnaby Wenguang Sun Zhuangji Wang V. R. Reddy Improving Simulations of Rice in Response to Temperature and CO<sub>2</sub> Agronomy crop model photosynthesis high temperature CO<sub>2</sub> spikelet fertility heat stress |
| title | Improving Simulations of Rice in Response to Temperature and CO<sub>2</sub> |
| title_full | Improving Simulations of Rice in Response to Temperature and CO<sub>2</sub> |
| title_fullStr | Improving Simulations of Rice in Response to Temperature and CO<sub>2</sub> |
| title_full_unstemmed | Improving Simulations of Rice in Response to Temperature and CO<sub>2</sub> |
| title_short | Improving Simulations of Rice in Response to Temperature and CO<sub>2</sub> |
| title_sort | improving simulations of rice in response to temperature and co sub 2 sub |
| topic | crop model photosynthesis high temperature CO<sub>2</sub> spikelet fertility heat stress |
| url | https://www.mdpi.com/2073-4395/12/12/2927 |
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