Eficiência do controle de nematóides, ferrugem e bicho mineiro em cafeeiros

Pós-graduação em Agronomia (Produção Vegetal) - FCAV

Qualidade da pulverização em volume reduzido para o controle do bicho-mineiro Leucoptera coffeella (Guérin-Mèneville & Perrottet, 1842) (Lepidoptera: Lyonetiidae)

Pós-graduação em Agronomia (Entomologia Agrícola) - FCAV

O trabalho rural e a sazonalidade do café: um estudo sobre assalariados do município de Campo Belo - MG

Pós-graduação em Serviço Social - FCHS

Produção de café sombreado

As concern about the environment and demand for special coffees, this review aimed to gather information about the effects of shading on the coffee crop, whereas its origin in the African's understory. Among the effects discussed are the increase in organic matter and improving of the soil fauna, nutrient cycling, decrease of soil erosion, environmental contamination, greenhouse gases, biodiversity conservation, light availability, temperature and wind mitigation, incidence of pests, plant diseases and weeds, production of the shade species and, finally, how all of these factors together have an effect on the phenology, yield and quality of coffee.

QuMinS: fast and scalable querying, mining and summarizing multi-modal databases

Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user’s attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) – given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing – in the same setting, find clusters, the top-'N IND.O' outlier images, and the 'N IND.R' images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method’s practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images.

Eficiência de fontes, dinâmica do magnésio no solo sob a cultura do café no Brasil e comparação da análise de K, Ca e Mg em solos do Arkansas-EUA seco em estufa e úmido de campo

A cultura do café no Brasil tem apresentado frequente deficiência de magnésio (Mg) limitando sua produtividade, portanto faz-se necessário o estudo de fontes que contenham Mg para essa cultura. Por outro lado, o estudo das metodologias de análise de K, Ca e Mg no solo é um outro ponto que precisa ser estudado para melhor manejo da fertilidade do solo e recomendação de adubações. Objetivou-se com o primeiro experimento avaliar a eficiência de fontes de magnésio para a cultura do café e a dinâmica deste nutriente no perfil do solo. E com o experimento desenvolvido em Arkansas-EUA, avaliar as correlações entre as concentrações de nutrientes do solo seco em estufa e úmido de campo extraídos com Mehlich-3 e 1 mol L-1 NH4OAc. Observou-se que o óxido e oxissulfato de Mg elevaram os valores de pH e CTC e diminuíram a concentração de H + Al do solo. As fontes diminuíram a disponibilidade de K e Ca, e aumentaram o Mg no solo. Na planta, óxido e sulfato de Mg proporcionaram maior concentração de Mg foliar. Apenas no segundo ano de avaliação houve aumento de produtividade do café. Os fertilizantes óxido e oxissulfato de Mg obtiveram o maior índice de eficiência agronômica em relação ao carbonato de Mg. No segundo experimento, K, Ca e Mg extraíveis com Mehlich-3 e NH4OAc foram altamente correlacionados (r2> 0,95) tanto para solo úmido de campo quanto para o seco em estufa. A relação entre as concentrações de K no solo seco em estufa e úmido de campo para Mehlich-3 e NH4OAc foram muito semelhantes e altamente correlacionados (r2 = 0,92). A secagem do solo em estufa teve efeito mínimo sobre as concentrações de Ca e reduziu a concentração de Mg tanto para Mehlich-3 quanto para NH4OAc. Entre os nutrientes estudados, a concentração de K foi a mais afetada pela secagem em estufa, necessitando de pesquisas de campo para correlacionar e calibrar novas recomendações agronômicas.

Resposta do cafeeiro arábica ao magnésio em solos fertilizados com altas doses de potássio

O aumento das doses de potássio (K) para atender a demanda nutricional desse nutriente em cafezais produtivos induz a deficiência de Mg. Esta pesquisa foi realizada com o objetivo de compreender como a classe de solo interfere na interação entre esses nutrientes (K e Mg), de maneira que a aplicação de alta dose de K não afete a nutrição da planta em relação ao Mg. Dois experimentos foram conduzidos em duas classes de solo: Latossolo, com textura muito argilosa, em Machado-MG, e Argissolo, com textura media sobre argilosa, em Monte Santo de Minas-MG. Adotou-se delineamento fatorial com três doses de K (110, 260 e 390 kg ha-1 K2O) x cinco doses de Mg (0, 81, 162, 324 e 405 kg ha-1 MgO), com três repetições. No Argissolo, a lixiviação de K impediu a redução da concentração foliar de Mg, independentemente da dose de K. No Latossolo, a concentração de Mg foliar variou com a dose de K, e apresentou ajuste quadrático. A concentração foliar de Mg aumentou linearmente com a dose desse nutriente, independentemente da classe de solo e da dose de K.

Analysis of classification algorithms for crop detection using LANDSAT 8 images

Remote sensing - the acquisition of information about an object or phenomenon without making physical contact with the object - is applied in a multitude of different areas, ranging from agriculture, forestry, cartography, hydrology, geology, meteorology, aerial traffic control, among many others. Regarding agriculture, an example of application of this information is regarding crop detection, to monitor existing crops easily and help in the region’s strategic planning. In any of these areas, there is always an ongoing search for better methods that allow us to obtain better results. For over forty years, the Landsat program has utilized satellites to collect spectral information from Earth’s surface, creating a historical archive unmatched in quality, detail, coverage, and length. The most recent one was launched on February 11, 2013, having a number of improvements regarding its predecessors. This project aims to compare classification methods in Portugal’s Ribatejo region, specifically regarding crop detection. The state of the art algorithms will be used in this region and their performance will be analyzed.

Irrigation management in pruned coffee tree crop

It was evaluated the effect of irrigation management on the production characteristics of coffee cultivar Acaiá MG-1474, planted in spacing of 3.00 m x 0.60 m, pruned in 2004, and irrigated by drip since the planting, in 1997. The experimental designed used was of randomized blocks with five treatments and four replications. The treatments consisted of irrigation management strategies, applying or not applying controlled moisture deficit in layer of 0 to 0.4m, in dry seasons of the year: A = no irrigation (control), B = irrigation during all year considering the factor of water availability in the soil (f) equal to 0.75, C = irrigation during all year considering f = 0.25, D = irrigation during all year, but in January /February /March /July /October /November /December with f = 0.25 and April /May /June /August /September with f = 0.75, E = irrigation only during April /May /June /August /September with f = 0.25. From July /2005 to June /2007 the applied water depth was defined based on Class A pan evaporation (ECA) and the period from July/2007 to June/2008 based on readings of matric potential of soil obtained from Watermark® sensors. Each plot consisted of three rows with ten plants per row, considering as useful plot five plants of center line. The results indicated that the E irrigation management was the most suitable for technical reasons.

Optimizing the width of strip weeding in arabica coffee in relation to crop age

The aim of this study was to determine the weed strip control (WSC) required for adequate coffee growth after transplanting. A non-irrigated, field-planted (spaced 3.80 x 0.70 m) crop was used. The experimental design was a randomized block, with four replicates. The treatments were arranged in a 9 x 18 split-plot design to test the WSC of 0, 15, 30, 45, 60, 90, 120, 150, and 190 cm, which involved continuously hand-weeding at each side of the coffee row, and 18 coffee growth measurements. Multiple regression analyses were carried out relating to growth-variables as a function of both WSC and growth-evaluation times. Brachiaria decumbens was the main weed accomplishing 88.5% of the total weed dry mass. The minimum width of the WSC increases as the crop ages after transplanting. Assuming reductions of 2% and 5% in the maximum coffee growth, the recommended WSC was 75 and 52 cm at 4 months after transplanting (MAT), 104 and 85 cm at 6 MAT, 123 and 105 cm at 9 MAT, 134 and 116 cm at 12 MAT, 142 and 124 cm at 15 MAT, and 148 and 131 cm at 18 MAT, respectively. It was concluded that integrated weed management in young coffee crops must focus on the weed control only in a minimum range along coffee rows, which increases with coffee plant age, keeping natural vegetation in the inter-rows.

Irrigation management in pruned coffee tree crop

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Higher yield and economic benefits are achieved in the macadamia crop by irrigation and intercropping with coffee

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.

A classification methodology for the risk of weed infestation using fuzzy logic

Despite modern weed control practices, weeds continue to be a threat to agricultural production. Considering the variability of weeds, a classification methodology for the risk of infestation in agricultural zones using fuzzy logic is proposed. The inputs for the classification are attributes extracted from estimated maps for weed seed production and weed coverage using kriging and map analysis and from the percentage of surface infested by grass weeds, in order to account for the presence of weed species with a high rate of development and proliferation. The output for the classification predicts the risk of infestation of regions of the field for the next crop. The risk classification methodology described in this paper integrates analysis techniques which may help to reduce costs and improve weed control practices. Results for the risk classification of the infestation in a maize crop field are presented. To illustrate the effectiveness of the proposed system, the risk of infestation over the entire field is checked against the yield loss map estimated by kriging and also with the average yield loss estimated from a hyperbolic model.