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Su Z, Zhao J, Zhuang M, Liu Z, Zhao C, Pullens JWM, Liu K, Harrison MT, Yang X. Climate-adaptive crop distribution can feed food demand, improve water scarcity, and reduce greenhouse gas emissions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 944:173819. [PMID: 38857807 DOI: 10.1016/j.scitotenv.2024.173819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/17/2024] [Accepted: 06/04/2024] [Indexed: 06/12/2024]
Abstract
Optimizing crop distribution stands as a pivotal approach to climate change adaption, enhancing crop production sustainability, and has been recognized for its immense potential in ensuring food security while minimizing environmental impacts. Here, we developed a climate-adaptive framework to optimize the distribution of staple crops (i.e., wheat, maize, and rice) to meet the multi-dimensional needs of crop production in China. The framework considers the feasibility of the multiple cropping systems (harvesting more than once on a cropland a year) and adopts a multi-dimensional approach, incorporating goals related to crop production, water consumption, and greenhouse gas (GHG) emissions. By optimizing, the total irrigated area of three crops would decrease by 7.7 % accompanied by a substantial 69.8 % increase in rain-fed areas compared to the baseline in 2010. This optimized strategy resulted in a notable 10.0 % reduction in total GHG emissions and a 13.1 % decrease in irrigation water consumption while maintaining consistent crop production levels. In 2030, maintaining the existing crop distribution and relying solely on yield growth would lead to a significant maize production shortfall of 27.0 %, highlighting a looming challenge. To address this concern, strategic adjustments were made by reducing irrigated areas for wheat, rice, and maize by 2.3 %, 12.8 %, and 6.1 %, respectively, while simultaneously augmenting rain-fed areas for wheat and maize by 120.2 % and 55.9 %, respectively. These modifications ensure that production demands for all three crops are met, while yielding a 6.9 % reduction in GHG emissions and a 15.1 % reduction in irrigation water consumption. This optimization strategy offers a promising solution to alleviate severe water scarcity issues and secure a sustainable agricultural future, effectively adapting to evolving crop production demands in China.
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Affiliation(s)
- Zheng'e Su
- College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China; Sanya Institute of China Agricultural University, Sanya 572025, China
| | - Jin Zhao
- College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China.
| | - Minghao Zhuang
- College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China
| | - Zhijuan Liu
- College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China
| | - Chuang Zhao
- College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China
| | - Johannes W M Pullens
- Department of Agroecology, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
| | - Ke Liu
- Tasmanian Institute of Agriculture, University of Tasmania, Newnham Drive, Launceston, Tasmania 7248, Australia
| | - Matthew Tom Harrison
- Tasmanian Institute of Agriculture, University of Tasmania, Newnham Drive, Launceston, Tasmania 7248, Australia
| | - Xiaoguang Yang
- College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China
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Liu L, Xu H, Liu S, Liu X. China's response to extreme weather events must be long term. NATURE FOOD 2023; 4:1022-1023. [PMID: 38030889 DOI: 10.1038/s43016-023-00892-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Affiliation(s)
- Lei Liu
- State Key Laboratory of Nutrient Use and Management, Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, China.
- College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China.
| | - Hang Xu
- College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China
| | - Sheng Liu
- College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China
| | - Xuejun Liu
- State Key Laboratory of Nutrient Use and Management, Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, China
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Xu FL, Hu PM, Wan X, Harrison MT, Liu K, Xiong QX. Crop sensitivity to waterlogging mediated by soil temperature and growth stage. FRONTIERS IN PLANT SCIENCE 2023; 14:1262001. [PMID: 37965002 PMCID: PMC10642075 DOI: 10.3389/fpls.2023.1262001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 10/09/2023] [Indexed: 11/16/2023]
Abstract
Waterlogging constrains crop yields in many regions around the world. Despite this, key drivers of crop sensitivity to waterlogging have received little attention. Here, we compare the ability of the SWAGMAN Destiny and CERES models in simulating soil aeration index, a variable contemporaneously used to compute three distinct waterlogging indices, denoted hereafter as WI Destiny, WIASD1, and WIASD2. We then account for effects of crop growth stage and soil temperature on waterlogging impact by introducing waterlogging severity indices, WI Growth, which accommodates growth stage tolerance, and WI Plus, which accounts for both soil temperature and growth stage. We evaluate these indices using data collected in pot experiments with genotypes "Yang mai 11" and "Zheng mai 7698" that were exposed to both single and double waterlogging events. We found that WI Plus exhibited the highest correlation with yield (-0.82 to -0.86) suggesting that waterlogging indices which integrate effects of temperature and growth stage may improve projections of yield penalty elicited by waterlogging. Importantly, WI Plus not only allows insight into physiological determinants, but also lends itself to remote computation through satellite imagery. As such, this index holds promise in scalable monitoring and forecasting of crop waterlogging.
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Affiliation(s)
- Fu-Li Xu
- College of Agriculture, Yangtze University, Jingzhou, China
| | - Pei-Min Hu
- Meteorological Service Center, Jingzhou Meteorological Bureau, Jingzhou, China
| | - Xiao Wan
- College of Agriculture, Yangtze University, Jingzhou, China
| | - Matthew Tom Harrison
- Tasmanian Institute of Agriculture, University of Tasmania, Launceston, TAS, Australia
| | - Ke Liu
- College of Agriculture, Yangtze University, Jingzhou, China
- Tasmanian Institute of Agriculture, University of Tasmania, Launceston, TAS, Australia
| | - Qin-Xue Xiong
- College of Agriculture, Yangtze University, Jingzhou, China
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Wang W, Ji D, Peng S, Loladze I, Harrison MT, Davies WJ, Smith P, Xia L, Wang B, Liu K, Zhu K, Zhang W, Ouyang L, Liu L, Gu J, Zhang H, Yang J, Wang F. Eco-physiology and environmental impacts of newly developed rice genotypes for improved yield and nitrogen use efficiency coordinately. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 896:165294. [PMID: 37414171 DOI: 10.1016/j.scitotenv.2023.165294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/30/2023] [Accepted: 07/01/2023] [Indexed: 07/08/2023]
Abstract
Significant advancements have been made in understanding the genetic regulation of nitrogen use efficiency (NUE) and identifying crucial NUE genes in rice. However, the development of rice genotypes that simultaneously exhibit high yield and NUE has lagged behind these theoretical advancements. The grain yield, NUE, and greenhouse gas (GHG) emissions of newly-bred rice genotypes under reduced nitrogen application remain largely unknown. To address this knowledge gap, field experiments were conducted, involving 80 indica (14 to 19 rice genotypes each year in Wuxue, Hubei) and 12 japonica (8 to 12 rice genotypes each year in Yangzhou, Jiangsu). Yield, NUE, agronomy, and soil parameters were assessed, and climate data were recorded. The experiments aimed to assess genotypic variations in yield and NUE among these genotypes and to investigate the eco-physiological basis and environmental impacts of coordinating high yield and high NUE. The results showed significant variations in yield and NUE among the genotypes, with 47 genotypes classified as moderate-high yield with high NUE (MHY_HNUE). These genotypes demonstrated the higher yields and NUE levels, with 9.6 t ha-1, 54.4 kg kg-1, 108.1 kg kg-1, and 64 % for yield, NUE for grain and biomass production, and N harvest index, respectively. Nitrogen uptake and tissue concentration were key drivers of the relationship between yield and NUE, particularly N uptake at heading and N concentrations in both straw and grain at maturity. Increase in pre-anthesis temperature consistently lowered yield and NUE. Genotypes within the MHY_HNUE group exhibited higher methane emissions but lower nitrous oxide emissions compared to those in the low to middle yield and NUE group, resulting in a 12.8 % reduction in the yield-scaled greenhouse gas balance. In conclusion, prioritizing crop breeding efforts on yield and resource use efficiency, as well as developing genotypes resilient to high temperatures with lower GHGs, can mitigate planetary warming.
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Affiliation(s)
- Weilu Wang
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, The Ministry of Education of China, Institutes of Agricultural Science and Technology Development, Yangzhou University, Yangzhou 225009, China; Jiangsu Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China
| | - Dongling Ji
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China
| | - Shaobing Peng
- MARA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Irakli Loladze
- Bryan College of Health Sciences, Bryan Medical Center, Lincoln, NE 68506, USA
| | - Matthew Tom Harrison
- Tasmanian Institute of Agriculture, University of Tasmania, Newnham Drive, Launceston, Tasmania 7248, Australia
| | | | - Pete Smith
- School of Biological Sciences, University of Aberdeen, Aberdeen AB24 3UU, UK
| | - Longlong Xia
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
| | - Bin Wang
- Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Ke Liu
- Tasmanian Institute of Agriculture, University of Tasmania, Newnham Drive, Launceston, Tasmania 7248, Australia
| | - Kuanyu Zhu
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China
| | - Wen Zhang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100071, China
| | - Linhan Ouyang
- College of Economics and Management, Department of Management Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Lijun Liu
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China
| | - Junfei Gu
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China
| | - Hao Zhang
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China
| | - Jianchang Yang
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, The Ministry of Education of China, Institutes of Agricultural Science and Technology Development, Yangzhou University, Yangzhou 225009, China; Jiangsu Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China.
| | - Fei Wang
- MARA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.
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Chen F, Feng P, Harrison MT, Wang B, Liu K, Zhang C, Hu K. Cropland carbon stocks driven by soil characteristics, rainfall and elevation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 862:160602. [PMID: 36493831 DOI: 10.1016/j.scitotenv.2022.160602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 11/21/2022] [Accepted: 11/26/2022] [Indexed: 06/17/2023]
Abstract
Soil organic carbon (SOC) can influence atmospheric CO2 concentration and then the extent to which the climate emergency is mitigated globally. It follows the elucidation of the driving factors of cropland SOC stocks, which is fundamental to reducing soil carbon loss and promoting soil carbon sequestration. Here, we examined the influence of 16 environmental variables on SOC stocks and sequestration based on three machine learning soil mapping methods, i.e. multiple linear regression (MLR), random forest (RF) and extreme gradient boosting (XGBOOST), with 2875 observed soil samples from cropland topsoil across Hunan Province, China in 2010. We employed a structural equation model (SEM) to extricate the driving mechanisms of environmental variables on SOC stocks at the regional scale. Our results show that XGBOOST had the most reliable performance in predicting SOC stocks, explaining 66 % of the total SOC stock variation. Croplands with high SOC stocks were distributed in low-altitude and water-sufficient areas. The partial dependence of SOC on precipitation showed a trend of increasing and then slowly decreasing. In addition, the grid-based SEM results clearly presented the direct and indirect routes of environmental variables' impacts on cropland SOC stocks. Soil properties regulated by elevation, were the most influential natural factor on SOC stocks. Precipitation and elevation drove SOC stocks through direct and indirect effects respectively. Our SEM combined with machine learning approach can provide an effective explanation of the driving mechanism for SOC accumulation. We expect our proposed modelling approach can be applied to other regions and offer new insights, as a reference for mitigating cropland soil carbon loss under climate emergency conditions.
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Affiliation(s)
- Fangzheng Chen
- College of Land Science and Technology, China Agricultural University, Key Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, Beijing, PR China
| | - Puyu Feng
- College of Land Science and Technology, China Agricultural University, Key Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, Beijing, PR China.
| | - Matthew Tom Harrison
- Tasmanian Institute of Agriculture, University of Tasmania, Newnham, Launceston, Tasmania 7248, Australia
| | - Bin Wang
- New South Wales Department of Primary Industries, Wagga Wagga Agriculture Institute, Wagga Wagga, New South Wales 2650, Australia
| | - Ke Liu
- Tasmanian Institute of Agriculture, University of Tasmania, Newnham, Launceston, Tasmania 7248, Australia; Engineering Research Center of Ecology and Agricultural Use of Wetland, College of Agriculture, Yangtze University, Jingzhou 434025, Hubei, China
| | - Chenxia Zhang
- College of Land Science and Technology, China Agricultural University, Key Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, Beijing, PR China
| | - Kelin Hu
- College of Land Science and Technology, China Agricultural University, Key Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, Beijing, PR China
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Silver lining to a climate crisis in multiple prospects for alleviating crop waterlogging under future climates. Nat Commun 2023; 14:765. [PMID: 36765112 PMCID: PMC9918449 DOI: 10.1038/s41467-023-36129-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 01/16/2023] [Indexed: 02/12/2023] Open
Abstract
Extreme weather events threaten food security, yet global assessments of impacts caused by crop waterlogging are rare. Here we first develop a paradigm that distils common stress patterns across environments, genotypes and climate horizons. Second, we embed improved process-based understanding into a farming systems model to discern changes in global crop waterlogging under future climates. Third, we develop avenues for adapting cropping systems to waterlogging contextualised by environment. We find that yield penalties caused by waterlogging increase from 3-11% historically to 10-20% by 2080, with penalties reflecting a trade-off between the duration of waterlogging and the timing of waterlogging relative to crop stage. We document greater potential for waterlogging-tolerant genotypes in environments with longer temperate growing seasons (e.g., UK, France, Russia, China), compared with environments with higher annualised ratios of evapotranspiration to precipitation (e.g., Australia). Under future climates, altering sowing time and adoption of waterlogging-tolerant genotypes reduces yield penalties by 18%, while earlier sowing of winter genotypes alleviates waterlogging by 8%. We highlight the serendipitous outcome wherein waterlogging stress patterns under present conditions are likely to be similar to those in the future, suggesting that adaptations for future climates could be designed using stress patterns realised today.
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Deng J, Harrison MT, Liu K, Ye J, Xiong X, Fahad S, Huang L, Tian X, Zhang Y. Integrated Crop Management Practices Improve Grain Yield and Resource Use Efficiency of Super Hybrid Rice. FRONTIERS IN PLANT SCIENCE 2022; 13:851562. [PMID: 35432400 PMCID: PMC9007698 DOI: 10.3389/fpls.2022.851562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 02/07/2022] [Indexed: 06/14/2023]
Abstract
Super hybrid rice genotypes have transformed the rate of genetic yield gain primarily due to intersubspecific heterosis, although the physiological basis underpinning this yield transformation has not been well quantified. We assessed the radiation use efficiency (RUE) and nitrogen use efficiency (NUE) of novel hybrid rice genotypes under four management practices representative of rice cropping systems in China. Y-liangyou 900 (YLY900), a new super hybrid rice widely adopted in China, was examined in field experiments conducted in Jingzhou and Suizhou, Hubei Province, China, from 2017 to 2020. Four management practices were conducted: nil fertilizer (CK), conventional farmer practice (FP), optimized cultivation with reduced nitrogen (OPT-N), and optimized cultivation with increased nitrogen (OPT+N). Yield differences across the treatment regimens were significant (p < 0.05). Grain yield of OPT+N in Jingzhou and Suizhou were 11 and 12 t ha-1, which was 14 and 27% greater than yields obtained under OPT-N and FP, respectively. Relative to OPT-N and FP, OPT+N had greater panicle numbers (9 and 18%), spikelets per panicle (7 and 12%), spikelets per unit area (17 and 32%), and total dry weight (9 and 19%). The average RUE of OPT+N was 2.7 g MJ-1, which was 5 and 9% greater than that of OPT-N and FP, respectively, due to higher intercepted photosynthetically active radiation (IPAR). The agronomic efficiency of applied N (AEN) of OPT+N was 17 kg grain kg-1 N, which was 9 and 68% higher than that of OPT-N and FP. These results show that close correlations exist between yield and both the panicles number (R 2 = 0.91) and spikelets per panicle (R 2 = 0.83) in OPT+N. We conclude that grain yields of OPT+N were associated with greater IPAR, RUE, and total dry matter. We suggest that integrated cropping systems management practices are conducive to higher grain yield and resource use efficiency through expansion of sink potential in super hybrid rice production.
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Affiliation(s)
- Jun Deng
- Hubei Collaborative Innovation Center for Grain Industry, Agriculture College, Yangtze University, Jingzhou, China
| | - Matthew Tom Harrison
- Tasmanian Institute of Agriculture, University of Tasmania, Burnie, TAS, Australia
| | - Ke Liu
- Hubei Collaborative Innovation Center for Grain Industry, Agriculture College, Yangtze University, Jingzhou, China
- Tasmanian Institute of Agriculture, University of Tasmania, Burnie, TAS, Australia
| | - Jiayu Ye
- Hubei Collaborative Innovation Center for Grain Industry, Agriculture College, Yangtze University, Jingzhou, China
| | - Xin Xiong
- Hubei Collaborative Innovation Center for Grain Industry, Agriculture College, Yangtze University, Jingzhou, China
| | - Shah Fahad
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresource, College of Tropical Crops, Hainan University, Haikou, China
- Department of Agronomy, The University of Haripur, Haripur, Pakistan
| | - Liying Huang
- Hubei Collaborative Innovation Center for Grain Industry, Agriculture College, Yangtze University, Jingzhou, China
| | - Xiaohai Tian
- Hubei Collaborative Innovation Center for Grain Industry, Agriculture College, Yangtze University, Jingzhou, China
| | - Yunbo Zhang
- Hubei Collaborative Innovation Center for Grain Industry, Agriculture College, Yangtze University, Jingzhou, China
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