Jasmine Neupane, Chenggang Wang, Glen L. Ritchie, Fangyuan Zhang, Sanjit K. Deb, Wenxuan Guo
{"title":"基于土壤特性和地形的管理区棉花收益的时空模式","authors":"Jasmine Neupane, Chenggang Wang, Glen L. Ritchie, Fangyuan Zhang, Sanjit K. Deb, Wenxuan Guo","doi":"10.1007/s11119-024-10158-5","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Understanding spatial and temporal variability of absolute and relative profit within fields provides a basis for site-specific management of limited agricultural inputs such as water. The objectives of this study were to evaluate the pattern of spatial and temporal variation of cotton profitability and to assess the stability of profit in management zones (MZs) created based on soil properties and topography.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>This study analyzed profitability patterns in eight commercially managed fields in the Southern High Plains from 2000 to 2003. Each field was divided into 30 m grids and soil physical properties, topography, and lint yield were collected for each grid. Based on the input cost and output prices, profit was also calculated for each grid. Clusters or MZs based on soil and topographic properties were created for each field using the partitioning around medoids (PAM) clustering algorithm. ANOVA and Least Significant Difference tests were conducted to determine the difference in profit among the clusters over multiple years.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>In four of the eight fields, the spatial pattern of profit was consistent across multiple years, indicating the potential of using MZs for site-specific input management. For the rest of the fields, the profit pattern in clusters was inconsistent across multiple years, indicating the need for within-season dynamic MZs.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The variability in soil and topographic properties influenced the profitability of management zones within a field across multiple years. Hence, this study indicates that understanding the variability in profit patterns in management zones can help to determine the best strategy for field-specific and year-specific precision input management. </p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"57 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatial and temporal patterns of cotton profitability in management zones based on soil properties and topography\",\"authors\":\"Jasmine Neupane, Chenggang Wang, Glen L. Ritchie, Fangyuan Zhang, Sanjit K. Deb, Wenxuan Guo\",\"doi\":\"10.1007/s11119-024-10158-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Purpose</h3><p>Understanding spatial and temporal variability of absolute and relative profit within fields provides a basis for site-specific management of limited agricultural inputs such as water. The objectives of this study were to evaluate the pattern of spatial and temporal variation of cotton profitability and to assess the stability of profit in management zones (MZs) created based on soil properties and topography.</p><h3 data-test=\\\"abstract-sub-heading\\\">Methods</h3><p>This study analyzed profitability patterns in eight commercially managed fields in the Southern High Plains from 2000 to 2003. Each field was divided into 30 m grids and soil physical properties, topography, and lint yield were collected for each grid. Based on the input cost and output prices, profit was also calculated for each grid. Clusters or MZs based on soil and topographic properties were created for each field using the partitioning around medoids (PAM) clustering algorithm. ANOVA and Least Significant Difference tests were conducted to determine the difference in profit among the clusters over multiple years.</p><h3 data-test=\\\"abstract-sub-heading\\\">Results</h3><p>In four of the eight fields, the spatial pattern of profit was consistent across multiple years, indicating the potential of using MZs for site-specific input management. For the rest of the fields, the profit pattern in clusters was inconsistent across multiple years, indicating the need for within-season dynamic MZs.</p><h3 data-test=\\\"abstract-sub-heading\\\">Conclusion</h3><p>The variability in soil and topographic properties influenced the profitability of management zones within a field across multiple years. 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Spatial and temporal patterns of cotton profitability in management zones based on soil properties and topography
Purpose
Understanding spatial and temporal variability of absolute and relative profit within fields provides a basis for site-specific management of limited agricultural inputs such as water. The objectives of this study were to evaluate the pattern of spatial and temporal variation of cotton profitability and to assess the stability of profit in management zones (MZs) created based on soil properties and topography.
Methods
This study analyzed profitability patterns in eight commercially managed fields in the Southern High Plains from 2000 to 2003. Each field was divided into 30 m grids and soil physical properties, topography, and lint yield were collected for each grid. Based on the input cost and output prices, profit was also calculated for each grid. Clusters or MZs based on soil and topographic properties were created for each field using the partitioning around medoids (PAM) clustering algorithm. ANOVA and Least Significant Difference tests were conducted to determine the difference in profit among the clusters over multiple years.
Results
In four of the eight fields, the spatial pattern of profit was consistent across multiple years, indicating the potential of using MZs for site-specific input management. For the rest of the fields, the profit pattern in clusters was inconsistent across multiple years, indicating the need for within-season dynamic MZs.
Conclusion
The variability in soil and topographic properties influenced the profitability of management zones within a field across multiple years. Hence, this study indicates that understanding the variability in profit patterns in management zones can help to determine the best strategy for field-specific and year-specific precision input management.
期刊介绍:
Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming.
There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to:
Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc.
Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc.
Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc.
Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc.
Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc.
Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.