Spatial and temporal patterns of cotton profitability in management zones based on soil properties and topography

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Precision Agriculture Pub Date : 2024-06-20 DOI:10.1007/s11119-024-10158-5

Jasmine Neupane, Chenggang Wang, Glen L. Ritchie, Fangyuan Zhang, Sanjit K. Deb, Wenxuan Guo

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Abstract

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.

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基于土壤特性和地形的管理区棉花收益的时空模式

目的了解田间绝对利润和相对利润的空间和时间变化，为对有限的农业投入（如水）进行因地制宜的管理提供依据。本研究的目的是评估棉花收益率的空间和时间变化模式，并评估根据土壤特性和地形建立的管理区（MZs）中收益的稳定性。每块田被划分为 30 米的网格，并收集了每个网格的土壤物理特性、地形和皮棉产量。根据投入成本和产出价格，还计算了每个网格的利润。根据土壤和地形属性，使用环中值分割（PAM）聚类算法为每块田创建了聚类或 MZ。结果在 8 块田地中，有 4 块田地的利润空间模式在多年中保持一致，这表明利用 MZs 进行特定地点投入管理具有潜力。结论土壤和地形特性的变化影响了田块内各管理区多年的收益率。因此，这项研究表明，了解管理区内收益模式的变化有助于确定针对具体田块和具体年份的精准投入管理的最佳策略。

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来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

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.