Spatial and temporal variability of productivity of coffee plants grown in an experimental field located in Três Pontas, Brazil
¹Federal University of Lavras, Department of Agricultural Engineering, Trevo Rotatório Professor Edmir Sá Santos, BR37200-900, Lavras, Brazil
²Agricultural Research Company of Minas Gerais, Av. José Cândido da Silveira 1647, Bairro União Belo Horizonte, BR31170-495 Belo Horizonte, Brazil
³University of Florence, Department of Agricultural, Food and Forestry Science and Technology, Piazza S. Marco 4, IT055 27571, Florence, Italy
*Correspondence: sthefany.santos1@estudante.ufla.br
Abstract:
The coffee grower seeks to increase productivity, as well as reduce the operating costs of his crop. Precision Agriculture (PA) is composed of a cycle of tools and technologies that can bring a good return to coffee growers, seeking to optimize production processes, bringing better yields and minimizing costs. Therefore, the objective of this research is to evaluate the space-time behavior of productivity in a coffee plantation, aiming to apply AP techniques. The study was carried out in a coffee plantation of the species (Coffea arabica), cultivar Topázio MG1190, located in the municipality of Três Pontas, Brazil, with an area of 1.2 ha. With the aid of a GNSS RTK, 30 plants were georeferenced, from which their yields were later sampled in the years 2020, 2021 and 2022. The collected data were evaluated in two statistical processes in the RStudio software. The first stage consisted of a one-way analysis of variance with repeated measures, from the results it is concluded that there are differences between the production averages when buying the productivity of the years 2020, 2021 and 2022 and, in addition, the coefficient of variation for the three sets of samples was quite high (CV > 30%) indicating a heterogeneity between the data. The second stage consisted of a geostatistical analysis the data were fitted in a model and interpolated by ordinary kriging; the result was maps of spatial variability. Through these maps it was possible to evaluate the behavior of productivity spatially and temporally, as well as to quantify areas that had higher and lower levels of this attribute. It is concluded that productivity, even in the case of such a small productive area, can vary substantially in space and time, and the use of PA can help producers in decision making regarding management.
Key words:
biennial, geoestatistics, precision agriculture in coffee trees