1
|
Wang D, Liang Y, Liu L, Huang J, Yin Z. Crop production on the Chinese Loess Plateau under 1.5 and 2.0 °C global warming scenarios. Sci Total Environ 2023; 903:166158. [PMID: 37574052 DOI: 10.1016/j.scitotenv.2023.166158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/06/2023] [Accepted: 08/07/2023] [Indexed: 08/15/2023]
Abstract
Global warming is a crucial factor affecting crop production in ecologically vulnerable areas. Warming-induced changes in the yields of different crops could pose significant challenges to food security and sustainability assessment. In this study, the World Food Studies model and a remote sensing product assimilation algorithm were used to develop a spatially explicit crop assimilation model applicable to the Loess Plateau of China. The model was used to simulate potential changes in actual yields and yield gaps for winter wheat and maize under three typical climate scenarios (Representative Concentration Pathways (RCPs): RCP 2.6, RCP 4.5, and RCP 8.5) from 2016 to 2060. Average yields increased in both winter wheat (2.38 %-4.96 %) and maize (5.41 %-6.85 %), with maize (RCP 4.5 > RCP 8.5 > RCP 2.6) more adapted to climate warming than winter wheat (RCP 2.6 > RCP 8.5 > RCP 4.5) in terms of yield increase rate. The yield increase and yield gap for winter wheat decreased most significantly in RCP2.6 (-2.28 %). Maize yield did not exceed 80 % of the potential yield in any scenario. The average phenological periods for winter wheat and maize are predicted be 2-4 and 9-16 days earlier, respectively. Crop yields were negatively correlated with radiation and yield gaps were positively correlated with precipitation. Future climate change will likely cause dramatic interannual crop yield fluctuations. Winter wheat is predicted to experience yield stagnation after 2050, whereas maize production potential will increase briefly before experiencing a long-term decline in growth. The results of this multi-scenario simulation assessment of crop production provide scientific support for implementing climate-adapted crop management strategies and integrated dry-crop-irrigated agriculture to meet food security objectives in this ecologically fragile area. We recommend integrated management measures to ensure regional food security through crop variety improvement, irrigation regulation, and planting structure optimization.
Collapse
Affiliation(s)
- Dan Wang
- School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
| | - Youjia Liang
- School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China.
| | - Lijun Liu
- College of Resources and Environment, Yangtze University, Wuhan 430100, China
| | - Jiejun Huang
- School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
| | - Zhangcai Yin
- School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
| |
Collapse
|
2
|
Liu J, Hou X, Chen S, Mu Y, Huang H, Wang H, Liu Z, Li S, Zhang X, Zhao Y, Huang J. A method for estimating yield of maize inbred lines by assimilating WOFOST model with Sentinel-2 satellite data. Front Plant Sci 2023; 14:1201179. [PMID: 37746025 PMCID: PMC10513754 DOI: 10.3389/fpls.2023.1201179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023]
Abstract
Maize is the most widely planted food crop in China, and maize inbred lines, as the basis of maize genetic breeding and seed breeding, have a significant impact on China's seed security and food safety. Satellite remote sensing technology has been widely used for growth monitoring and yield estimation of various crops, but it is still doubtful whether the existing remote sensing monitoring means can distinguish the growth difference between maize inbred lines and hybrids and accurately estimate the yield of maize inbred lines. This paper explores a method for estimating the yield of maize inbred lines based on the assimilation of crop models and remote sensing data, initially solves the problem. At first, this paper analyzed the WOFOST(World Food Studies)model parameter sensitivity and used the MCMC(Markov Chain Monte Carlo) method to calibrate the sensitive parameters to obtain the parameter set of maize inbred lines differing from common hybrid maize; then the vegetation indices were selected to establish an empirical model with the measured LAI(Leaf Area Index) at three key development stages to obtain the remotely sensed estimated LAI; finally, the yield of maize inbred lines in the study area was estimated and mapped pixel by pixel using the EnKF(Ensemble Kalman Filter) data assimilation algorithm. Also, this paper compares a method of assimilation by setting a single parameter. Instead of the WOFOST parameter optimization process, a parameter representing the growth weakness of the inbred lines was set in WOFOST to distinguish the inbred lines from the hybrids. The results showed that the yield estimated by the two methods compared with the field measured yield data had R2: 0.56 and 0.18, and RMSE: 684.90 Kg/Ha and 949.95 Kg/Ha, respectively, which proved that the crop growth model of maize inbred lines established in this study combined with the data assimilation method could initially achieve the growth monitoring and yield estimation of maize inbred lines.
Collapse
Affiliation(s)
- Junyi Liu
- College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Xianpeng Hou
- College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Shuaiming Chen
- College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Yanhua Mu
- College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Hai Huang
- College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Hengbin Wang
- College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Zhe Liu
- College of Land Science and Technology, China Agricultural University, Beijing, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Shaoming Li
- College of Land Science and Technology, China Agricultural University, Beijing, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Xiaodong Zhang
- College of Land Science and Technology, China Agricultural University, Beijing, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yuanyuan Zhao
- College of Land Science and Technology, China Agricultural University, Beijing, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Jianxi Huang
- College of Land Science and Technology, China Agricultural University, Beijing, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China
| |
Collapse
|
3
|
Ren Y, Li Q, Du X, Zhang Y, Wang H, Shi G, Wei M. Analysis of Corn Yield Prediction Potential at Various Growth Phases Using a Process-Based Model and Deep Learning. Plants (Basel) 2023; 12:plants12030446. [PMID: 36771530 PMCID: PMC9920366 DOI: 10.3390/plants12030446] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/05/2023] [Accepted: 01/16/2023] [Indexed: 05/26/2023]
Abstract
Early and accurate prediction of grain yield is of great significance for ensuring food security and formulating food policy. The exploration of key growth phases and features is beneficial to improving the efficiency and accuracy of yield prediction. In this study, a hybrid approach using the WOFOST model and deep learning was developed to forecast corn yield, which analysed yield prediction potential at different growth phases and features. The World Food Studies (WOFOST) model was used to build a comprehensive simulated dataset by inputting meteorological, soil, crop and management data. Different feature combinations at various growth phases were designed to forecast yield using machine learning and deep learning methods. The results show that the key features of corn's vegetative growth stage and reproductive growth stage were growth state features and water-related features, respectively. With the continuous advancement of the crop growth stage, the ability to predict yield continued to improve. Especially after entering the reproductive growth stage, corn kernels begin to form, and the yield prediction performance is significantly improved. The performance of the optimal yield prediction model in flowering (R2 = 0.53, RMSE = 554.84 kg/ha, MRE = 8.27%), in milk maturity (R2 = 0.89, RMSE = 268.76 kg/ha, MRE = 4.01%), and in maturity (R2 = 0.98, RMSE = 102.65 kg/ha, MRE = 1.53%) were given. Thus, our method improves the accuracy of yield prediction, and provides reliable analysis results for predicting yield at various growth phases, which is helpful for farmers and governments in agricultural decision making. This can also be applied to yield prediction for other crops, which is of great value to guide agricultural production.
Collapse
Affiliation(s)
- Yiting Ren
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Qiangzi Li
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Xin Du
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Yuan Zhang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Hongyan Wang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Guanwei Shi
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Mengfan Wei
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China
| |
Collapse
|
4
|
Pan H, Chen Z, Allard DW, Ren J. Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation. Sensors (Basel) 2019; 19:s19143161. [PMID: 31323829 PMCID: PMC6679303 DOI: 10.3390/s19143161] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 07/16/2019] [Accepted: 07/17/2019] [Indexed: 11/16/2022]
Abstract
It is well known that timely crop growth monitoring and accurate crop yield estimation at a fine scale is of vital importance for agricultural monitoring and crop management. Crop growth models have been widely used for crop growth process description and yield prediction. In particular, the accurate simulation of important state variables, such as leaf area index (LAI) and root zone soil moisture (SM), is of great importance for yield estimation. Data assimilation is a useful tool that combines a crop model and external observations (often derived from remote sensing data) to improve the simulated crop state variables and consequently model outputs like crop total biomass, water use and grain yield. In spite of its effectiveness, applying data assimilation for monitoring crop growth at the regional scale in China remains challenging, due to the lack of high spatiotemporal resolution satellite data that can match the small field sizes which are typical for agriculture in China. With the accessibility of freely available images acquired by Sentinel satellites, it becomes possible to acquire data at high spatiotemporal resolution (10–30 m, 5–6 days), which offers attractive opportunities to characterize crop growth. In this study, we assimilated remotely sensed LAI and SM into the Word Food Studies (WOFOST) model to estimate winter wheat yield using an ensemble Kalman filter (EnKF) algorithm. The LAI was calculated from Sentinel-2 using a lookup table method, and the SM was calculated from Sentinel-1 and Sentinel-2 based on a change detection approach. Through validation with field data, the inverse error was 10% and 35% for LAI and SM, respectively. The open-loop wheat yield estimation, independent assimilations of LAI and SM, and a joint assimilation of LAI + SM were tested and validated using field measurement observation in the city of Hengshui, China, during the 2016–2017 winter wheat growing season. The results indicated that the accuracy of wheat yield simulated by WOFOST was significantly improved after joint assimilation at the field scale. Compared to the open-loop estimation, the yield root mean square error (RMSE) with field observations was decreased by 69 kg/ha for the LAI assimilation, 39 kg/ha for the SM assimilation and 167 kg/ha for the joint LAI + SM assimilation. Yield coefficients of determination (R2) of 0.41, 0.65, 0.50, and 0.76 and mean relative errors (MRE) of 4.87%, 4.32%, 4.45% and 3.17% were obtained for open-loop, LAI assimilation alone, SM assimilation alone and joint LAI + SM assimilation, respectively. The results suggest that LAI was the first-choice variable for crop data assimilation over SM, and when both LAI and SM satellite data are available, the joint data assimilation has a better performance because LAI and SM have interacting effects. Hence, joint assimilation of LAI and SM from Sentinel-1 and Sentinel-2 at a 20 m resolution into the WOFOST provides a robust method to improve crop yield estimations. However, there is still bias between the key soil moisture in the root zone and the Sentinel-1 C band retrieved SM, especially when the vegetation cover is high. By active and passive microwave data fusion, it may be possible to offer a higher accuracy SM for crop yield prediction.
Collapse
Affiliation(s)
- Haizhu Pan
- Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Science, Beijing 100081, China
| | - Zhongxin Chen
- Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Science, Beijing 100081, China.
| | - de Wit Allard
- Wageningen Environmental Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands
| | - Jianqiang Ren
- Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Science, Beijing 100081, China
| |
Collapse
|
5
|
Tian L, Liu X, Zhang B, Liu M, Wu L. Extraction of Rice Heavy Metal Stress Signal Features Based on Long Time Series Leaf Area Index Data Using Ensemble Empirical Mode Decomposition. Int J Environ Res Public Health 2017; 14:ijerph14091018. [PMID: 28878147 PMCID: PMC5615555 DOI: 10.3390/ijerph14091018] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Revised: 08/31/2017] [Accepted: 09/03/2017] [Indexed: 02/08/2023]
Abstract
The use of remote sensing technology to diagnose heavy metal stress in crops is of great significance for environmental protection and food security. However, in the natural farmland ecosystem, various stressors could have a similar influence on crop growth, therefore making heavy metal stress difficult to identify accurately, so this is still not a well resolved scientific problem and a hot topic in the field of agricultural remote sensing. This study proposes a method that uses Ensemble Empirical Mode Decomposition (EEMD) to obtain the heavy metal stress signal features on a long time scale. The method operates based on the Leaf Area Index (LAI) simulated by the Enhanced World Food Studies (WOFOST) model, assimilated with remotely sensed data. The following results were obtained: (i) the use of EEMD was effective in the extraction of heavy metal stress signals by eliminating the intra-annual and annual components; (ii) LAI df (The first derivative of the sum of the interannual component and residual) can preferably reflect the stable feature responses to rice heavy metal stress. LAI df showed stability with an R² of greater than 0.9 in three growing stages, and the stability is optimal in June. This study combines the spectral characteristics of the stress effect with the time characteristics, and confirms the potential of long-term remotely sensed data for improving the accuracy of crop heavy metal stress identification.
Collapse
Affiliation(s)
- Lingwen Tian
- School of Information Engineering, China University of Geoscience, Beijing 100083, China.
| | - Xiangnan Liu
- School of Information Engineering, China University of Geoscience, Beijing 100083, China.
| | - Biyao Zhang
- School of Information Engineering, China University of Geoscience, Beijing 100083, China.
| | - Ming Liu
- School of Information Engineering, China University of Geoscience, Beijing 100083, China.
| | - Ling Wu
- School of Information Engineering, China University of Geoscience, Beijing 100083, China.
| |
Collapse
|