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      Refining soil nutrient assessment: Incorporating land use boundaries for precision agriculture

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      PLOS ONE
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          Abstract

          Soil nutrient levels play a crucial role in determining crop yield. A comprehensive understanding of the spatial distribution patterns and evaluation grades of soil nutrients is of significant practical importance for informed fertilization practices, enhancing crop production, and optimizing agricultural land utilization. This study focuses on the urban area of Kashi Prefecture in Xinjiang as a case study. Utilizing soil sample data, GIS spatial interpolation analysis was conducted, incorporating plot boundary information to propose a comprehensive evaluation method for assessing soil nutrient levels at the plot level. Experimental findings revealed the following: (1) The average values of soil organic matter (SOM), total nitrogen (AN), total potassium (AK), and total phosphorus (AP) in the study area were determined to be 13.3 g/kg, 0.74 g/kg, 0.33 g/kg, and 0.03 g/kg, respectively. Among these, AN and SOM were classified as the fourth grade, indicating relatively deficient levels, while AK and AP were classified as the first and second grade, indicating relatively abundant levels. (2) The comprehensive evaluation of soil nutrient grades in the study area primarily fell within the third, fourth, and second grades, representing areas of 29.08 km 2, 25 km 2, and 4.05 km 2, accounting for 50.03%, 43%, and 6.97% of the total area, respectively. (3) The evaluation results of soil nutrient levels at the plot level emphasized the boundary characteristics and provided a more refined assessment grade. This evaluation method is better suited to meet the practical production requirements of farmers and is considered feasible. The outcomes of this study can serve as a reference for precision agriculture management.

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          Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry

          Soil nutrient is one of the most important properties for improving farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. This study aims to explore the preprocessing and modeling methods of hyperspectral images obtained from an unmanned aerial vehicle (UAV) platform for estimating the soil organic matter (SOM) and soil total nitrogen (STN) in farmland. The results showed that: (1) Multiplicative Scattering Correction (MSC) performed better in reducing image scattering noise than Standard Normal Variate (SNV) transformation or spectral derivatives, and it yielded a result with higher correlation and lower signal-to-noise ratio; (2) The proposed feature selection method combining Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling algorithm (CARS), could provide selective preference for hyperspectral bands. Exploiting this method, 24 and 22 feature bands were selected for SOM and STN estimation, respectively; (3) The particle swarm optimization (PSO) algorithm was employed to obtain optimized input weights and bias values of the extreme learning machine (ELM) model for more accurate prediction of SOM and STN. The improved PSO-ELM model based on the selected preference bands achieved higher prediction accuracy (R 2 of 0.73 and RPD of 1.91 for SOM, R 2 of 0.63, and RPD of 1.53 for STN) than support vector machine (SVM), partial least squares regression (PLSR), and the ELM model. This study provides an important guideline for monitoring soil nutrient for precision agriculture with imaging spectrometry.
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            Ecological Stoichiometric Characteristics of Plant–Soil–Microorganism of Grassland Ecosystems under Different Restoration Modes in the Karst Desertification Area

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              Delineating suitable zones for solar-based groundwater exploitation using multi-criteria analysis: A techno-economic assessment for meeting sustainable development goals (SDGs)

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: SupervisionRole: ValidationRole: Writing – original draft
                Role: Data curationRole: Formal analysisRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                9 September 2024
                2024
                : 19
                : 9
                : e0308423
                Affiliations
                [001] China Geological Survey Urumqi Comprehensive Survey Center on Natural Resources, Urumqi, China
                Central South University of Forestry and Technology, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-8087-424X
                Article
                PONE-D-24-18114
                10.1371/journal.pone.0308423
                11383206
                39250506
                49ddbed7-983e-45c4-abf8-701dcc600585
                © 2024 Xu, He

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 6 May 2024
                : 23 July 2024
                Page count
                Figures: 6, Tables: 2, Pages: 14
                Funding
                Funded by: Science and Technology Innovation Fund Project of Natural Resources Integrated Survey Command Center
                Award ID: KC20230015, KC20220007
                Award Recipient :
                Funded by: China Geological Survey Project
                Award ID: DD20230484
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: U2003109
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100009110, Natural Science Foundation of Xinjiang Uygur Autonomous Region;
                Award ID: 2022D01A149
                Award Recipient :
                Quan Xu was supported by the Science and Technology Innovation Foundation of the Command Center for Comprehensive Survey of Natural Resources (KC20230015), the Geological Survey of China (DD20230740), and the National Natural Science Foundation of China (U2003109). He played a role in data collection, methodology, and writing – original draft. Junling He was supported by the Science and Technology Innovation Fund of the Command Center for Integrated Survey of Natural Resources (KC20220007) and the Geological Survey of China (DD20230484). He played a role in writing – review & editing.
                Categories
                Research Article
                Biology and Life Sciences
                Agriculture
                Agricultural Soil Science
                Earth Sciences
                Soil Science
                Agricultural Soil Science
                Physical Sciences
                Mathematics
                Numerical Analysis
                Interpolation
                Biology and Life Sciences
                Agriculture
                Crop Science
                Crops
                People and Places
                Population Groupings
                Professions
                Agricultural Workers
                Biology and Life Sciences
                Agriculture
                Agrochemicals
                Fertilizers
                Computer and Information Sciences
                Geoinformatics
                Geographic Information Systems
                Earth Sciences
                Geography
                Geoinformatics
                Geographic Information Systems
                Biology and Life Sciences
                Agriculture
                Agricultural Methods
                Sustainable Agriculture
                Ecology and Environmental Sciences
                Sustainability Science
                Sustainable Agriculture
                Physical Sciences
                Mathematics
                Probability Theory
                Statistical Distributions
                Custom metadata
                The data are available from the Open Science Framework database. https://osf.io/a9ezj/ DOI 10.17605/OSF.IO/A9EZJ.

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