Spatial variability of leaf macronutrient concentration and fruit production of an Arabica coffee plantation using two sampling densities - Ferreira, Gabriel Fernandes Pinto; Lemos, Odair Lacerda; Soratto, Rogério Peres [UNESP]; Perdoná, Marcos José (Springer Nature, 2022)
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.
Keywords: Geostatistics; Mapping; Precision coffee farming; Spatialization