Maximizing dataset variability in agricultural surveys with spatial sampling based on MaxVol matrix approximation

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Precision Agriculture Pub Date : 2024-12-13 DOI:10.1007/s11119-024-10197-y

Anna Petrovskaia, Mikhail Gasanov, Artyom Nikitin, Polina Tregubova, Ivan Oseledets

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Abstract

Soil sampling is crucial for capturing soil variability and obtaining comprehensive soil information for agricultural planning. This article evaluates the potential of MaxVol, an optimal design method for soil sampling based on selecting locations with significant dissimilarities. We compared MaxVol with conditional Latin hypercube sampling (cLHS), simple random sampling (SRS) and Kennard-Stone algorithm (KS) to evaluate their ability to capture soil data distribution. We modeled spatial distributions of soil properties using simple kriging (SK) and regression kriging (RK) interpolation techniques and assessed the interpolation quality using Root Mean Square Error. According to the results, MaxVol performs similarly or better than popular sampling designs in describing soil distributions, particularly with a smaller number of points. This is valuable for costly and time-consuming field surveys. Both MaxVol and Kennard-Stone are deterministic algorithms, unlike cLHS and random sampling, providing a reliable sampling scheme. Thus, the proposed MaxVol algorithm enables obtaining soil property distributions based on environmental features.

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基于MaxVol矩阵近似的空间采样最大化农业调查数据变异性

土壤取样对于捕获土壤变异和获得农业规划所需的全面土壤信息至关重要。本文对MaxVol的潜力进行了评价，MaxVol是一种基于选择显著差异位置的土壤采样优化设计方法。我们将MaxVol与条件拉丁超立方体采样（cLHS）、简单随机采样（SRS）和Kennard-Stone算法（KS）进行了比较，以评估它们捕获土壤数据分布的能力。采用简单克里格（SK）和回归克里格（RK）插值技术对土壤性质的空间分布进行了建模，并利用均方根误差对插值质量进行了评价。根据结果，MaxVol在描述土壤分布方面的表现与流行的采样设计相似或更好，特别是在点数较少的情况下。这对于昂贵且耗时的现场调查来说是很有价值的。MaxVol和Kennard-Stone都是确定性算法，不像cLHS和随机抽样，提供了可靠的抽样方案。因此，所提出的MaxVol算法能够基于环境特征获得土壤性质分布。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.