Soil sampling optimization using spatial analysis in irrigated mango fields under brazilian semi-arid conditions

Abstract Soil sampling is a fundamental procedure in the decision making regarding the management of the soil, thus, a sampling plan should represent as accurately as possible the evaluated crop field. Therefore, the objectives of this study were to suggest a soil sampling approach and soil sampling point allocation using spatial analyses and compare to the classic statistic method in irrigated mango orchards in the Brazilian semi-arid region. The experiment was carried out in three commercial mango orchards located in the region of the São Francisco Valley, Brazil. Soil samples were collected in 0-0.2 m and 0.2-0.4 m depths following regular grids where the number of samples varied from 50 to 56. Soil texture, soil bulk density, soil total porosity, microporosity, macroporosity, pH, Ca, Mg, Na, K, Al, P, potential acidity, and the sum of basis were evaluated. Classical and geostatistical statistics were used to determine the ideal number of soil samples. Fuzzy c-means clustering technique was used to separate the areas into homogeneous zones and to allocate the sampling points. The wide method of 20 individual soil samples proved to be inefficient. On the other hand, the use of geostatistics proved to be efficient and is required for each crop field. The c-means clustering was adequate to separate the areas into homogeneous zones and, thus, to assist the sampling point allocation.

摘要 土壤采样是土壤管理决策中的核心基础性环节，因此采样方案应尽可能精准地反映待评估农田的实际状况。为此，本研究旨在通过空间分析方法提出一套土壤采样方案与采样点布设策略，并与经典统计方法进行对比，研究区域为巴西半干旱地区的灌溉芒果园。本实验在巴西圣弗朗西斯科河谷区域的3个商业化芒果园中开展。按照规则网格布设采样点，分别在0~0.2 m与0.2~0.4 m土层深度采集土壤样品，每个果园的样品数量为50至56份。测定指标涵盖土壤质地、土壤容重、总孔隙度、微孔隙度、大孔隙度、pH值、钙（Ca）、镁（Mg）、钠（Na）、钾（K）、铝（Al）、磷（P）、潜在酸度以及盐基总量。采用经典统计与地统计方法确定最优土壤采样数量。采用模糊C均值聚类（Fuzzy c-means clustering）技术将研究区域划分为均质单元，并以此布设采样点。仅采集20份土壤样品的通用采样方法被证实效率低下。反之，地统计方法则被证实具备高效性，且适用于所有农田场景。模糊C均值聚类可有效实现区域均质化划分，进而辅助采样点的科学布设。