ABSTRACT

Knowing the spatial distribution of nutritional status allows us to understand plants’ metabolic requirements and identify zones for differentiated management. Thus, the objective of this work was to use the fuzzy classification to standardize the values of macronutrients (N, P, K, Ca, Mg, and S) to construct the map of the average spatial distribution of nutritional status for Coffea canephora. A sample mesh of 80 georeferenced points was constructed to collect the leaves. A fuzzy controller, the Mamdani method, was used as linguistic variables the ranges of nutritional sufficiency: low, adequate, and high and the rules of inference. Geostatistical analysis was used to define semivariograms and perform interpolation by kriging and cokriging, having as covariates the fuzzy indexes for each macronutrient. The percentage of agreement between the maps was determined by correlation coefficients, confidence indexes, and the RMSE. The estimated maps for the macronutrients constructed by cokriging compared with the observed maps constructed by kriging presented spatial correlation coefficients (r_co) from 0.81 to 0.97, concordance indexes from 0.84 to 0.97 and confidence from 0.68 to 0.91 and RMSE from 0.01 to 0.23, showing high percentage of agreement between the maps in the use of fuzzy indexes as covariate of cokriging.

摘要

了解营养状况的空间分布使我们能够了解植物的代谢需求并确定差异化管理的区域。因此，这项工作的目的是使用模糊分类来标准化常量营养素（N、P、K、Ca、Mg 和 S）的值，以构建咖啡豆营养状况的平均空间分布图。. 构建了一个包含 80 个地理参考点的样本网格来收集叶子。一个模糊控制器，Mamdani 方法，被用作语言变量营养充足的范围：低、充足和高以及推理规则。地统计分析用于定义半变异函数并通过克里金法和协同克里金法进行插值，将每种常量营养素的模糊指数作为协变量。图之间的一致性百分比由相关系数、置信指数和 RMSE 确定。_协同克里金法构建的宏量营养素估计图与克里金法构建的观测图相比，呈现了空间相关系数（r _co) 从 0.81 到 0.97，一致性指数从 0.84 到 0.97，置信度从 0.68 到 0.91，RMSE 从 0.01 到 0.23，表明在使用模糊指数作为协同克里金法的协变量时，地图之间的一致性百分比很高。