Avaliação da variabilidade do estado nutricional e produtividade de café por meio da análise de componentes principais e geoestatística

Abstract

Assessment of the variability of the nutritional status and yield of coffee by principal component analysis and geostatisticsEvaluation of plant nutritional status brings several contributions to the coffee crop, especially when considering the spatial variation in the production fields. This experiment aimed to analyze the spatial variability in the nutritional status and yield of arabic coffee, using the principal component analysis (PCA) and geostatistics. The experiment was conducted in a grid of 50 sampling points in a field of Coffea arabica L., cv. Catuaí. Leaf samples were taken for macro and micronutrient analyses, and fruit were collected for yield evaluation. Data were subjected to PCA for transformation of nutrient values into new variables and then submitted to geostatistics to quantify the degree of spatial dependence of the main components and yield. The combined use of PCA and geostatistics allowed the efficient study of the nutritional status of the plants. The crop showed nutritional imbalance, with excess N and Cu limiting yield even in areas with adequate leaf concentration of the other nutrients. Determinar o estado nutricional das plantas traz diversas contribuições à cafeicultura, principalmente quando esse estado é avaliado, considerando-se a variação espacial existente nos campos de produção. Este experimento objetivou analisar a variabilidade espacial do estado nutricional e da produtividade de café arábica, utilizando técnicas de análise de componentes principais (ACP) e geoestatística. O experimento foi desenvolvido em uma malha de 50 pontos amostrais, montada em uma lavoura de Coffea arabica L., variedade Catuaí. Realizaram-se amostragens foliares para avaliação dos macro e micronutrientes e coletas de frutos para o levantamento da produtividade. Os dados foram submetidos à ACP, para transformação dos valores de nutrientes em novas variáveis e, em seguida, submetidos à geoestatística, para quantificar o grau de dependência espacial dos componentes principais e da produtividade. A aplicação conjunta de ACP e geoestatística permitiu estudar de forma eficiente o estado nutricional das plantas. A lavoura estudada apresenta desequilíbrio nutricional, sendo o excesso de N e de Cu limitantes à produtividade da cultura, mesmo em locais com adequada concentração foliar dos demais nutrientes.Palavras-chave: análise multivariada, nutrição vegetal, análise foliar, Coffea arabica L.

Geostatistical analysis of Arabic coffee yield in two crop seasons

“…Hence, plants will find a favorable environment for recovery, promoting stability in the production. However, as the data evaluated in the present study, variables of soil fertility and leaf nutrition may also exhibit spatial dependence (Silva et al, 2010;Silva & Lima, 2015), thus requiring knowledge on them to perform a localized and efficient management in the coffee plantation.…”

Evaluation of Slow Release Fertilizer on the Initial Development and Coffee Production

“…Studies and tools that describe the sustainable development practices of coffee growing, nutrient effectiveness in relation to productivity increases with economic return are essential. Nutritional imbalance has a direct impact on crop yields, being one of the major contributors to production declines (Silva & Lima, 2012).…”

Spatial distribution of physiological quality of Arábica coffee seeds to cultivate Catuai

“…Due to the major economic importance of the coffee culture in Brazil, the study and development of new techniques of dealing with the coffee culture are fundamental for reducing losses arising from inappropriate handling, which leads to productivity breakdown or even to values under the productive potentials of the currently grown varieties (Silva and Lima, 2012).…”

“…A study on ground fertility under the Arabica coffee cultivar was performed by Silva and Lima (2012) using geostatistics techniques and correlating the textural fractions of the ground with chemical features of the ground, finding significant and negative correlation of K with total and positive sand of Mg, with the silt in a Red-Yellow humic Latosoil.…”