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Dong Z, Wang N, Xie J, Ke X. Coupled Vis-NIR spectroscopy with chemometrics strategy for soil organic carbon prediction in the Agro-pastoral Transitional zone of northwest China. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 318:124496. [PMID: 38796895 DOI: 10.1016/j.saa.2024.124496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 05/09/2024] [Accepted: 05/19/2024] [Indexed: 05/29/2024]
Abstract
Rapidly and accurately grasp the change of soil organic carbon content in farmland, which is of great significance in guiding the timely and effective mastery of farmland soil fertility and improvement of soil physical properties. In this study, an ASD FieldSpec 4 spectrometer was used to collect spectral reflectance data on 128 agricultural soil samples taken from Jingbian County, Yulin City, Shaanxi Province, China. Firstly, descriptive statistics of the SOC in the study area were performed, and secondly, after 10 spectral transformations were performed, the correlation analysis and the Boruta algorithm were used to extract the characteristic wavebands of soil organic carbon, respectively, in order to reduce the redundancy of the data. Finally, by comparing the accuracies of different strategies, we constructed a spectral prediction model of soil organic carbon in farmland of the Northwest Agricultural and Animal Husbandry Intertwined Zone that integrates the optimal preprocessing, feature selection strategy and modelling method. The results indicate that: 1) The mean SOC content of the farmland in the study area was low and at the nutrient deficient level, with the standard errors and coefficients of variation for the modelling and validation sets were 1.596 g kg-1, 1.457 g kg-1, 54 % and 52 %, respectively; 2) The shape and trend of spectral special curves with different SOC contents show consistency, and the SOC content is negatively correlated with spectral reflectance; 3) CA selects more feature bands, but the feature bands are more homogeneous, while the Boruta algorithm can effectively remove irrelevant variables and improve the SOC feature selection effect; 4) The SOC prediction model based on Boruta-FD-RF can be better for soil organic carbon estimation, with R2 of 0.899 and 0.748 for the training set and validation set, respectively, RMSE of 1.432 g kg-1 and 1.967 g kg-1, and RPD of 2.557 and 1.647, respectively. The results show that the SOC model established by integrating optimal spectral pre-processing, feature selection strategy and chemometrics strategy has obvious improvement in prediction accuracy and stability, and this study provides an important reference for the fast and accurate estimation of SOC content in farmland of Agro-pastoral Transitional zone in northwest China.
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Affiliation(s)
- Zhenyu Dong
- Institute of Water Resources and Hydro-electric Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China; State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an 710048, China
| | - Ni Wang
- Institute of Water Resources and Hydro-electric Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China; State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an 710048, China.
| | - Jiancang Xie
- Institute of Water Resources and Hydro-electric Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China; State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an 710048, China
| | - Xinyue Ke
- Institute of Water Resources and Hydro-electric Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China; State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an 710048, China
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Wang G, Miao X, Xu B, Tian D, Ren J, Li Z, Li R, Zheng H, Wang J, Tang P, Feng Y, Zhou J, Xu Z. Exploring the Water-Soil-Crop Dynamic Process and Water Use Efficiency of Typical Irrigation Units in the Agro-Pastoral Ecotone of Northern China. PLANTS (BASEL, SWITZERLAND) 2024; 13:1916. [PMID: 39065443 PMCID: PMC11280002 DOI: 10.3390/plants13141916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 06/30/2024] [Accepted: 07/04/2024] [Indexed: 07/28/2024]
Abstract
Groundwater resources serve as the primary source of water in the agro-pastoral ecotone of northern China, where scarcity of water resources constrains the development of agriculture and animal husbandry. As a typical rainfed agricultural area, the agro-pastoral ecotone in Inner Mongolia is entirely dependent on groundwater for agricultural irrigation. Due to the substantial groundwater consumption of irrigated farmland, groundwater levels have been progressively declining. To obtain a sustainable irrigation pattern that significantly conserves water, this study faces the challenge of unclear water transport relationships among water, soil, and crops, undefined water cycle mechanism in typical irrigation units, and water use efficiency, which was not assessed. Therefore, this paper, based on in situ experimental observations and daily meteorological data in 2022-2023, utilized the DSSAT model to explore the growth processes of potato, oat, alfalfa, and sunflower, the soil water dynamics, the water balance, and water use efficiency, analyzed over a typical irrigation area. The results indicated that the simulation accuracy of the DSSAT model was ARE < 10%, nRMSE/% < 10%, and R2 ≥ 0.85. The consumption of the soil moisture during the rapid growth stage for the potatoes, oats, alfalfa, and sunflower was 7-13% more than that during the other periods, and the yield was 67,170, 3345, 6529, and 4020 kg/ha, respectively. The soil evaporation of oat, potato, alfalfa, and sunflower accounted for 18-22%, 78-82%; 57-68%, and 32-43%, and transpiration accounted for 40-44%, 56-60%, 45-47%, and 53-55% of ETa (333.8 mm-369.2 mm, 375.2 mm-414.2 mm, 415.7 mm-453.7 mm, and 355.0 mm-385.6 mm), respectively. It was advised that irrigation water could be appropriately reduced to decrease ineffective water consumption. The water use efficiency and irrigation water use efficiency for potatoes was at the maximum amount, ranging from 16.22 to 16.62 kg/m3 and 8.61 to 10.81 kg/m3, respectively, followed by alfalfa, sunflowers, and oats. For the perspective of water productivity, it was recommended that potatoes could be extensively cultivated, alfalfa planted appropriately, and oats and sunflowers planted less. The findings of this study provided a theoretical basis for efficient water resource use in the agro-pastoral ecotone of Northern China.
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Affiliation(s)
- Guoshuai Wang
- Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, China; (G.W.); (J.R.); (P.T.)
- Institute of Water Resources for Pastoral Area, Ministry of Water Resources, Hohhot 010020, China
| | - Xiangyang Miao
- Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, China; (G.W.); (J.R.); (P.T.)
- Institute of Water Resources for Pastoral Area, Ministry of Water Resources, Hohhot 010020, China
- College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China;
| | - Bing Xu
- Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, China; (G.W.); (J.R.); (P.T.)
- Institute of Water Resources for Pastoral Area, Ministry of Water Resources, Hohhot 010020, China
- College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China;
| | - Delong Tian
- Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, China; (G.W.); (J.R.); (P.T.)
- Institute of Water Resources for Pastoral Area, Ministry of Water Resources, Hohhot 010020, China
| | - Jie Ren
- Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, China; (G.W.); (J.R.); (P.T.)
- Institute of Water Resources for Pastoral Area, Ministry of Water Resources, Hohhot 010020, China
| | - Zekun Li
- Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, China; (G.W.); (J.R.); (P.T.)
- Institute of Water Resources for Pastoral Area, Ministry of Water Resources, Hohhot 010020, China
| | - Ruiping Li
- College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China;
| | - Hexiang Zheng
- Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, China; (G.W.); (J.R.); (P.T.)
- Institute of Water Resources for Pastoral Area, Ministry of Water Resources, Hohhot 010020, China
- College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China;
| | - Jun Wang
- Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, China; (G.W.); (J.R.); (P.T.)
- Institute of Water Resources for Pastoral Area, Ministry of Water Resources, Hohhot 010020, China
| | - Pengcheng Tang
- Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, China; (G.W.); (J.R.); (P.T.)
- Institute of Water Resources for Pastoral Area, Ministry of Water Resources, Hohhot 010020, China
| | - Yayang Feng
- Farmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Xinxiang 453002, China
| | - Jie Zhou
- Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, China; (G.W.); (J.R.); (P.T.)
- Institute of Water Resources for Pastoral Area, Ministry of Water Resources, Hohhot 010020, China
- College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China;
| | - Zhiwei Xu
- Agriculture, Animal Husbandry and Water Resources Bureau of Saihan District, Hohhot 010018, China;
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Zhai J, Pu L, Lu Y, Huang S. Is the boom in staple crop production attributed to expanded cropland or improved yield? A comparative analysis between China and India. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 933:173151. [PMID: 38735335 DOI: 10.1016/j.scitotenv.2024.173151] [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: 02/29/2024] [Revised: 05/07/2024] [Accepted: 05/09/2024] [Indexed: 05/14/2024]
Abstract
The characteristics of cropland development and the dynamics of food production in China and India, the world's largest agricultural and most populous countries, are of great importance to global food security. However, there is a notable lack of a thorough comparison between China and India in this regard. Here, we systematically compare the differences between China and India using cropping intensity and crop production data, including cropland area, harvested area, total staple crop (i.e., cereal crops, tuber crops and pulse crops) production and yield capacity. The results are mainly as follows: (1) Both China and India experienced an increasing trend in cropland area and harvested area from 2001 to 2021, especially notable in India. In China, the cropland area and harvested area increased by 11.76 % and 14.36 %, respectively, while in India, they witnessed a more substantial increase of 31.10 % and 49.32 %, respectively. (2) The cropping intensity underwent significant transformations, primarily shifting between non-cropland, single-cropping, and double-cropping. Northwestern China exhibited a clear trend of non-cropland converting to single-cropping, whereas northeastern China showed a distinct pattern of single-cropping changing to non-cropland. The interconversion between single-cropping and double-cropping was also frequently observed in the main food-producing regions. In India, the cropland expansion and the adoption of double-cropping are highly pronounced, extending widely across most of the country. (3) From 2001 to 2021, the total staple crop production in China and India increased by 34.12 % and 55.81 %, respectively. Despite the rapid growth in India's total staple crop production, it still amounts to only about half of China's. The major crops production also showed different trends, China's cereal crops production increased significantly, while tuber and pulse crops production declined, and India's production of cereal, tuber, and pulse crops has all increased (4) China's yield capacity has increased by 17.28 %, while India's has only grown by 4.35 %. Despite the rapid increase in India's total staple crop production, the yield gap with China has widened. The boost in China's total staple crop production mainly resulted from improved yield capacity, whereas India relied more on the cropland area expansion, especially the increase in harvested area. Our comprehensive comparison of China and India in cropland development and staple crop production contributes to a deep understanding of the differences in agricultural production between the two countries, and provides lessons for global food security and sustainable agricultural development.
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Affiliation(s)
- Jiahao Zhai
- School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; Key Laboratory of the Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Nanjing 210023, China
| | - Lijie Pu
- School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; Key Laboratory of the Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Nanjing 210023, China; School of Environment Engineering, Nanjing Institute of Technology, Nanjing 211167, China.
| | - Yumeng Lu
- School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; Key Laboratory of the Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Nanjing 210023, China
| | - Sihua Huang
- School of Environment Engineering, Nanjing Institute of Technology, Nanjing 211167, China; NJIT Research Center, The Key Laboratory of Carbon Neutrality and Territory Optimization, Ministry of Natural Resources, Nanjing 211167, China; International Joint Laboratory of Green & Low Carbon Development, Nanjing 211167, China
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Jin E, Du J, Bi Y, Wang S, Gao X. Research on Classification of Grassland Degeneration Indicator Objects Based on UAV Hyperspectral Remote Sensing and 3D_RNet-O Model. SENSORS (BASEL, SWITZERLAND) 2024; 24:1114. [PMID: 38400272 PMCID: PMC10892527 DOI: 10.3390/s24041114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 01/28/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024]
Abstract
Real-time and high-precision land cover classification is the foundation for efficient and quantitative research on grassland degradation using remote sensing techniques. In view of the shortcomings of manual surveying and satellite remote sensing, this study focuses on the identification and classification of grass species indicating grassland degradation. We constructed a UAV-based hyperspectral remote sensing system and collected field data in grassland areas. By applying artificial intelligence technology, we developed a 3D_RNet-O model based on convolutional neural networks, effectively addressing technical challenges in hyperspectral remote sensing identification and classification of grassland degradation indicators, such as low reflectance of vegetation, flat spectral curves, and sparse distribution. The results showed that the model achieved a classification accuracy of 99.05% by optimizing hyperparameter combinations based on improving residual block structures. The establishment of the UAV-based hyperspectral remote sensing system and the proposed 3D_RNet-O classification model provide possibilities for further research on low-altitude hyperspectral remote sensing in grassland ecology.
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Affiliation(s)
| | - Jianmin Du
- Mechanical and Electrical Engineering College, Inner Mongolia Agricultural University, Hohhot 010018, China; (E.J.); (Y.B.)
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Nie W, Yang F, Xu B, Bao Z, Shi Y, Liu B, Wu R, Lin W. Spatiotemporal Evolution of Landscape Patterns and Their Driving Forces Under Optimal Granularity and the Extent at the County and the Environmental Functional Regional Scales. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.954232] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Research on the evolution and driving forces of landscape patterns can provide important support for ecological governance decision-making. However, the heterogeneity of landscape patterns at the microscale (grain size and extent) and the enforceability of the zoning scale at the macroscale deserve more attention. The optimal grain size (30 ×30 m) and the extent (500 m) for landscape pattern research were obtained by analyzing the fluctuation of landscape metrics and semivariogram models in this study. The research area was divided into environmental functional regions (EFRs), which were defined according to the main ecological functions and protection objectives of each region. The analysis results of land use and land cover changes (LUCCs) showed that land use transfer in the past 20 years occurred mainly between woodland and cultivated land at the county scale, but this was not always the case in EFRs. The results of the landscape pattern analysis showed that landscape fragmentation, aggregation, and heterogeneity increased at the county scale during 1999–2020. Moreover, except within agricultural environmental protection areas (AEP) and living environment guaranteed areas (LEG), the degree and the speed of landscape damage decreased by 2020, and the turning point occurred in 2006–2013. The analysis results of geographical detectors showed that the digital elevation mode (DEM) and GDP were the main driving factors in most regions. At the county scale, the average explanatory power of the selected factors increased by 13.27% and 16.16% in 2006–2013 and 2013–2020, respectively. Furthermore, the study area was divided into three categories according to the intensity of human disturbance. The areas with high human disturbance need to focus on increasing land-use intensification and strengthening the development in low-slope hill regions. The areas of moderate human disturbance need to focus on improving the connectivity of ecological patches and optimizing industrial structures. Attention should be given to the monitoring of natural drivers and policy support for ecological governance in low human disturbance areas. The methods and findings in this study can provide a reference for decision-makers to formulate land-use policies, especially for integration into relevant urban planning, such as the spatial planning of national land that is being widely implemented in China.
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