The nutritional and productive attributes of Arabica coffee (Coffea arabica L.) can vary spatially within cultivated areas. Precision farming techniques applied to coffee plantations can diagnose this spatial variability and propose solutions to correct this unevenness. The objective of this study was to characterize the distribution and spatial dependence of leaf macronutrient concentration and fruit production in an Arabica coffee plantation, in Barra do Choça, Bahia, northeastern Brazil, at two sampling densities. The concentrations of leaf macronutrients (N, P, K, Ca, Mg, and S) in 2019 and coffee production in the 2018/2019 and 2019/2020 agricultural years were evaluated at sampling densities of 2 and 5 points ha^–1. The data were subjected to descriptive and geostatistical analyses. The results showed that the sampling density directly interferes in the identification of spatial dependence for the leaf macronutrient concentrations and fruit production in Arabica coffee plantations. While the sampling of 2 points ha⁻¹ revealed a "weak" spatial dependence index for Mg and fruit production in the 2018/2019 agricultural year, in addition to the occurrence of a pure nugget effect for the other macronutrients and the 2019/2020 agricultural year, the sampling of 5 points ha⁻¹ was able to identify "strong" spatial dependence for P, K, Ca, and Mg; "moderate" for N and fruit production in both agricultural year; and "weak" only for S. The analysis under higher sampling density revealed nutritional imbalance in the coffee plantation, with N deficiency in 44.8% and P defficiency in 36.1% of the sampling area. Adequate K, Ca, and Mg concentrations were indentified only in 40.2%, 35.4% and 45.5% of the area, respectively. These data showed that sampling density of 5 points ha⁻¹ is more favorable for identifying patterns of dependence on leaf macronutrients and yield of Arabica coffee, favoring the mapping of its distribution and consequent identification of management zones. A positive spatial correlation was also found between the leaf concentration of some macronutrients and the fruit production of Arabica coffee at the highest sampling density.

阿拉比卡咖啡 ( Coffea arabica L.)的营养和生产属性在种植区域内可能存在空间差异。应用于咖啡种植园的精准农业技术可以诊断这种空间变异性，并提出纠正这种不均匀性的解决方案。本研究的目的是描述位于巴西东北部巴伊亚州巴拉杜乔卡的阿拉比卡咖啡种植园中叶片常量营养素浓度和果实产量的分布和空间依赖性，采用两种采样密度。2019 年叶片常量营养素（N、P、K、Ca、Mg 和 S）浓度以及 2018/2019 和 2019/2020 农业年的咖啡产量在 2 和 5 点 ha ^{–1的采样密度下进行了评估}. 对数据进行了描述性和地质统计分析。结果表明，采样密度直接干扰了阿拉比卡咖啡种植园叶片常量营养素浓度和果实产量的空间依赖性识别。虽然 2 个点 ha ^-1的抽样显示 2018/2019 农业年度 Mg 和水果产量的空间依赖性指数“弱”，但其他常量营养素和 2019/2020 农业年度出现纯金块效应年，5 个点的抽样 ha ^-1能够识别 P、K、Ca 和 Mg 的“强”空间依赖性；两个农业年度的氮和水果产量“中等”；在较高采样密度下的分析显示咖啡种植园营养不平衡，44.8% 的采样区域缺氮，36.1% 的区域缺磷。仅在 40.2%、35.4% 和 45.5% 的区域分别确定了足够的 K、Ca 和 Mg 浓度。这些数据表明，采样密度为 5 个点 ha ^-1更有利于确定阿拉比卡咖啡对叶片常量营养素和产量的依赖模式，有利于绘制其分布图并随后确定管理区域。在最高采样密度下，一些常量营养素的叶子浓度与阿拉比卡咖啡的果实产量之间也存在正空间相关性。