Adjusting the Main Cropping Types in Mollisol Regions Could Improve the Net Primary Productivity of Low‐Producing Areas by 20%–30% Under Future Climate Change
Abstract Rationalizing site‐specific crop types is an effective strategy for ensuring food security under climate change. This study employed environmental covariates representing climate, soil, and vegetation, combined with a hybrid convolutional neural network ‐ Long Short‐term Memory‐self‐attenti...
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Wiley
2025-07-01
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| Series: | Earth's Future |
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| Online Access: | https://doi.org/10.1029/2025EF006074 |
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| author | Yilin Bao Xiangtian Meng Huanjun Liu Mingchang Wang Fengmei Yao Abdul Mounem Mouazen |
| author_facet | Yilin Bao Xiangtian Meng Huanjun Liu Mingchang Wang Fengmei Yao Abdul Mounem Mouazen |
| author_sort | Yilin Bao |
| collection | DOAJ |
| description | Abstract Rationalizing site‐specific crop types is an effective strategy for ensuring food security under climate change. This study employed environmental covariates representing climate, soil, and vegetation, combined with a hybrid convolutional neural network ‐ Long Short‐term Memory‐self‐attention (CNN‐LSTM‐SA) model to predict net primary productivity (NPP) of the Northeast China (NEC) and the Mississippi River Basin (MRB) Mollisol regions. The analysis covered the periods from 2001 to 2020, and 2021 to 2040 under two Shared Socioeconomic Pathways (SSPs): SSP245 and SSP585. Subsequently, areas requiring crop type adjustments were identified, and appropriate crops were assigned to each growth site. Our results elucidate that: (a) During 2021–2040, a general increase in temperature and minor fluctuations in precipitation were observed across the study area. In the NEC, crop NPP initially increases before decreasing, whereas in the MRB, it consistently decreases. (b) Both vegetation and soil covariates explained 75.6% of NPP variability in the NEC, while in the MRB, climate factors, particularly precipitation, accounted for 18.4% of the variability. (c) The proportion of area requiring adjustment in the NEC ranged from 4.45% to 5.13% (SSP245) to 5.05%–5.77% (SSP585), while in the MRB, it varied from 4.92% to 7.54% (SSP245) to 6.49%–9.10% (SSP585), suggesting a necessity for more substantial cropping type adjustments under the SSP585 climate scenario. (d) In the NEC, the area cultivated with corn, soybean, and other crops will decrease, while rice cultivation will increase. Conversely, a decrease in wheat and pasture, and an increase in corn and soybean cultivation are suggested in the MRB. (e) Following crop type adjustments, the average NPP enhancements for corn, soybean, rice, and other crops in unsuitable areas of the NEC were 22.85%, 22.2%, 17.35%, and 20.5%, respectively, In the MRB, the average NPP enhancements for corn, soybean, wheat, and pasture were 28.5%, 26.9%, 32.4%, and 21.1%, respectively. Our research provides valuable insights into predicting future NPP changes, and develops effective crop adjustment strategies to address global food security challenges. |
| format | Article |
| id | doaj-art-c2d82a6110fb4acd9d18b17b51ae02e7 |
| institution | Matheson Library |
| issn | 2328-4277 |
| language | English |
| publishDate | 2025-07-01 |
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| series | Earth's Future |
| spelling | doaj-art-c2d82a6110fb4acd9d18b17b51ae02e72025-07-29T02:12:53ZengWileyEarth's Future2328-42772025-07-01137n/an/a10.1029/2025EF006074Adjusting the Main Cropping Types in Mollisol Regions Could Improve the Net Primary Productivity of Low‐Producing Areas by 20%–30% Under Future Climate ChangeYilin Bao0Xiangtian Meng1Huanjun Liu2Mingchang Wang3Fengmei Yao4Abdul Mounem Mouazen5College of Geoexploration Sciences and Technology Jilin University Changchun ChinaDepartment of Environment Ghent University Gent BelgiumNortheast Institute of Geography and Agroecology Chinese Academy of Sciences Changchun ChinaCollege of Geoexploration Sciences and Technology Jilin University Changchun ChinaCollege of Earth and Planetary Sciences University of Chinese Academy of Sciences Beijing ChinaDepartment of Environment Ghent University Gent BelgiumAbstract Rationalizing site‐specific crop types is an effective strategy for ensuring food security under climate change. This study employed environmental covariates representing climate, soil, and vegetation, combined with a hybrid convolutional neural network ‐ Long Short‐term Memory‐self‐attention (CNN‐LSTM‐SA) model to predict net primary productivity (NPP) of the Northeast China (NEC) and the Mississippi River Basin (MRB) Mollisol regions. The analysis covered the periods from 2001 to 2020, and 2021 to 2040 under two Shared Socioeconomic Pathways (SSPs): SSP245 and SSP585. Subsequently, areas requiring crop type adjustments were identified, and appropriate crops were assigned to each growth site. Our results elucidate that: (a) During 2021–2040, a general increase in temperature and minor fluctuations in precipitation were observed across the study area. In the NEC, crop NPP initially increases before decreasing, whereas in the MRB, it consistently decreases. (b) Both vegetation and soil covariates explained 75.6% of NPP variability in the NEC, while in the MRB, climate factors, particularly precipitation, accounted for 18.4% of the variability. (c) The proportion of area requiring adjustment in the NEC ranged from 4.45% to 5.13% (SSP245) to 5.05%–5.77% (SSP585), while in the MRB, it varied from 4.92% to 7.54% (SSP245) to 6.49%–9.10% (SSP585), suggesting a necessity for more substantial cropping type adjustments under the SSP585 climate scenario. (d) In the NEC, the area cultivated with corn, soybean, and other crops will decrease, while rice cultivation will increase. Conversely, a decrease in wheat and pasture, and an increase in corn and soybean cultivation are suggested in the MRB. (e) Following crop type adjustments, the average NPP enhancements for corn, soybean, rice, and other crops in unsuitable areas of the NEC were 22.85%, 22.2%, 17.35%, and 20.5%, respectively, In the MRB, the average NPP enhancements for corn, soybean, wheat, and pasture were 28.5%, 26.9%, 32.4%, and 21.1%, respectively. Our research provides valuable insights into predicting future NPP changes, and develops effective crop adjustment strategies to address global food security challenges.https://doi.org/10.1029/2025EF006074net primary productivityconvolutional neural network‐long short‐term memory‐self‐attention modelinterrelationshipscrop adjustmentMollisol regions |
| spellingShingle | Yilin Bao Xiangtian Meng Huanjun Liu Mingchang Wang Fengmei Yao Abdul Mounem Mouazen Adjusting the Main Cropping Types in Mollisol Regions Could Improve the Net Primary Productivity of Low‐Producing Areas by 20%–30% Under Future Climate Change Earth's Future net primary productivity convolutional neural network‐long short‐term memory‐self‐attention model interrelationships crop adjustment Mollisol regions |
| title | Adjusting the Main Cropping Types in Mollisol Regions Could Improve the Net Primary Productivity of Low‐Producing Areas by 20%–30% Under Future Climate Change |
| title_full | Adjusting the Main Cropping Types in Mollisol Regions Could Improve the Net Primary Productivity of Low‐Producing Areas by 20%–30% Under Future Climate Change |
| title_fullStr | Adjusting the Main Cropping Types in Mollisol Regions Could Improve the Net Primary Productivity of Low‐Producing Areas by 20%–30% Under Future Climate Change |
| title_full_unstemmed | Adjusting the Main Cropping Types in Mollisol Regions Could Improve the Net Primary Productivity of Low‐Producing Areas by 20%–30% Under Future Climate Change |
| title_short | Adjusting the Main Cropping Types in Mollisol Regions Could Improve the Net Primary Productivity of Low‐Producing Areas by 20%–30% Under Future Climate Change |
| title_sort | adjusting the main cropping types in mollisol regions could improve the net primary productivity of low producing areas by 20 30 under future climate change |
| topic | net primary productivity convolutional neural network‐long short‐term memory‐self‐attention model interrelationships crop adjustment Mollisol regions |
| url | https://doi.org/10.1029/2025EF006074 |
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