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Item Mechanized and irrigated coffee cultivation promotes physical subsurface constraints in Oxisols(Sociedade Brasileira de Ciência do Solo, 2025-06-16) Escobar, Katherine Martinez; Silva, Laís Maria Rodrigues; Morais, Keise Duarte Bacelar de; Neves, Júlio César Lima; Oliveira, Teogenes Senna deSoils of the Cerrados (Brazilian Savanna) are deep, well-structured, and well-drained, with flat to gently undulating terrain that favors mechanization for coffee cultivation. However, these soils are susceptible to compaction. This study aimed to assess the effect of mechanization on the physical characteristics of an Oxisol under irrigated coffee cultivation in the Alto Paranaíba-Minas Gerais State. We selected eight areas with different cultivars and years of Arabica coffee plantation, sampling five positions: right soil under the tree crown (RSC), right tractor lines (RTL), interrows (IR), left tractor lines (LTL), and left soil under the tree crown (LSC) at layers of 0.00-0.10, 0.10-0.20, 0.20-0.30, and 0.30-0.40 m. We conducted principal component analysis (PCA) and analysis of variance, comparing means through Tukey’s test (p<0.05). The PCA selected three principal components (PC1, PC2, and PC3) composed of 12 physico-chemical properties from a total of 27 evaluated. Total porosity (TP), mean penetration resistance (PRmean), volumetric moisture (θ) at 100 kPa (θ 100 kPa) and 300 kPa (θ 300 kPa) tensions, particle density (PD), and granulometric fractions (clay, fine sand, and coarse sand) were among the most influential attributes. Total porosity and PRmean demonstrated the existence of compaction in the tractor wheel tracks, particularly in the 0.00-0.20 m layer. The 3.5-year-old plantation did not show significant variations in these properties. The θ 100 kPa and θ 300 kPa were higher in the compacted areas, indicating increased water retention but potentially limiting aeration. Clay content increased with depth, while sand fractions decreased, influencing the soil susceptibility to compaction. The vigor of coffee plants, as identified by satellite images (NDVI), could not be fully associated with the physical constraints of the subsurface, as even areas with low vigor did not consistently correlate with poor physical properties in laboratory analyses. These findings highlight the complex interplay between soil physical properties and coffee plant performance, emphasizing the need for comprehensive management strategies in mechanized coffee cultivation.Item Multitemporal variables for the mapping of coffee cultivation areas(Empresa Brasileira de Pesquisa Agropecuária - Embrapa, 2019) Souza, Carolina Gusmão; Arantes, Tássia Borges; Carvalho, Luis Marcelo Tavares de; Aguiar, PolyanneThe objective of this work was to propose a new methodology for mapping coffee cropping areas that includes multitemporal data as input parameters in the classification process, by using the Landsat TM NDVI time series, together with an object-oriented classification approach. The algorithm BFAST was used to analyze coffee, pasture, and native vegetation temporal profiles, allied to a geographic object-based image analysis (GEOBIA) for mapping. The following multitemporal variables derived from the R package greenbrown were used for classification: mean, trend, and seasonality. The results showed that coffee, pasture, and native vegetation have different temporal behaviors, which corroborates the use of these data as input variables for mapping. The classifications using temporal variables, associated with spectral data, achieved high-global accuracy rates with 93% hit. When using Only temporal data, ratings also showed a hit percentage above 80% accuracy. Data derived from Landsat TM time series are efficient for mapping coffee cropping areas, reducing confusion between targets and making the classification process more accurate, contributing to a correct characterization and mapping of objects derived from a RapidEye image, with a high spatial solution.Item Relationship between coffee crop productivity and vegetation indexes derived from oli / landsat-8 sensor data with and without topographic correction(Associação Brasileira de Engenharia Agrícola, 2018-05) Nogueira, Sulimar M. C.; Moreira, Maurício A.; Volpato, Margarete M. L.The reflectance values of a coffee crop are influenced by several factors such as planting direction, crop spacing, time of the year, plant age and topography which reduces the accuracy of the estimates derived from remote sensing data. In this context were evaluated the relationships between coffee productivity and values of NDVI, SAVI and NDWI vegetation indexes with and without topographic reflectance correction for different coffee phenological phases for the crop years 2013/2014 (low productivity) and 2014/2015 (high productivity). The evaluations were made through the standard deviation of vegetation indices (VIs), linear relationship between the cosine factor and the VIs and between VIs and coffee productivity. The best phenological phases of coffee to determine productivity from spectral indexes were the stages of dormancy and flowering. The results indicated that the NDVI was the best index to estimate the productivity of coffee trees with coefficient of determination (R2) that ranged from 0.58 to 0.90. There was an increase in R2 between productivity and NDVI with topographic correction in the dormancy phase in the year of low productivity; between productivity and NDVI with topographic correction in the flowering phase in the year of high productivity; and between productivity and SAVI and NDWI with topographic corrections in the flowering phase in the year of high productivity.Item Modis images for agrometeorological monitoring of coffee areas(Editora UFLA, 2013-04) Volpato, Margarete Marin Lordelo; Vieira, Tatiana Grossi Chquiloff; Alves, Helena Maria Ramos; Santos, Walbert Júnior Reis dosAgrometeorological monitoring of coffee lands has conventionally been performed in the field using data from land-based meteorological stations and field surveys to observe crop conditions. More recent studies use satellite images, which assess large areas at lower costs. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor of the Earth satellite provides free images with high temporal resolution and vegetation specific products, such as the MOD13, which provides the Normalized Difference Vegetation Index (NDVI) processed in advanced. The objective of this study was to evaluate the relation between the NDVI spectral vegetation index and the meteorological and water balance variables of coffee lands of the south of Minas Gerais in order to obtain statistical models of this relationship. The study area is located in the municipality of Três Pontas, Minas Gerais, Brazil. The statistical models obtained demonstrate a significant negative correlation between the NDVI and water deficit. NDVI values under 70% may represent a water deficit in the coffee plants. The models developed in this study could be used in the agrometeorological monitoring of coffee lands in the south of Minas Gerais.Item Imagens do sensor modis para monitoramento agrometeorológico de áreas cafeeiras(Editora UFLA, 2013-04) Volpato, Margarete Marin Lordelo; Vieira, Tatiana Grossi Chquiloff; Alves, Helena Maria Ramos; Santos, Walbert Júnior Reis dosO monitoramento agrometeorológico de áreas cafeeiras tem sido realizado convencionalmente em campo utilizando-se dados de estações meteorológicas terrestres e visitas à lavoura para se observar seu desenvolvimento. Estudos mais recentes utilizam imagens de satélite, que permitem avaliar grandes áreas a custos menores. O sensor Moderate Resolution Imaging Spectroradiometer (MODIS) do satélite Terra oferece gratuitamente imagens com alta resolução temporal e produtos voltados especialmente para vegetação como o MOD13, que fornece o índice de vegetação Normalized Difference Vegetation Index (NDVI) previamente processado. Objetivou-se, no presente estudo, avaliar a relação entre o índice de vegetação espectral NDVI e as variáveis meteorológicas e do balanço hídrico, em áreas cafeeiras do sul de Minas Gerais, visando à obtenção de modelos estatísticos dessa relação. A área de estudo localiza-se no município de Três Pontas, estado de Minas Gerais, Brasil. Os modelos estatísticos desenvolvidos demonstram a correlação significativa negativa entre o NDVI e déficit hídrico. Valores de NDVI menores que 70% podem indicar a deficiência hídrica de cafeeiros. Os modelos desenvolvidos no presente estudo poderão ser usados no monitoramento agrometeorológico de lavouras cafeeiras na região sul de Minas Gerais.