Spatio-temporal aspects of brown eye spot and nutrients in irrigated coffee

Silva, Marília Goulart da; Pozza, Edson Ampélio; Chaves, Eugênio; Neto, Helon Santos; Vasco, Gabriel Brandão; Paula, Paulo Victor Augusto Azevedo de; Dornelas, Gabriel Avelar; Alves, Marcelo de Carvalho; Silva, Mirian de Lourdes Oliveira e; Pozza, Adélia Aziz Alexandre

doi:10.1007/s10658-018-01611-z

Cited by 7 publications

(7 citation statements)

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“…This period was characterized by favorable conditions to the disease, with 22ºC average temperature, 69% relative humidity, low rainfall (1.6 mm) and high insolation (208.8 W m -2 ). It is known that the BES intensity varies according to climatic conditions, and that it is favored by high insolation, low water availability and temperatures between 10 and 25ºC (Pozza et al, 2010;Silva et al, 2016Silva et al, , 2019Chaves et al, 2018). This highest incidence in July may have occurred due to nutritional disorders as a consequence of low rainfall during this period, which caused the nutrient imbalance that favored the disease (Chaves et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

“…Possibly, these responses are related to a better use of water. In this sense, despite the higher BES incidence and severity on coffee trees covered with plastic film, there was a greater plant growth and leafing, due to the moisture in the soil and the more efficient absorption of nutrients, which favored the damage reduction caused by BES (Chaves et al, 2018;Silva et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

“…One of the coffee diseases favored by nutritional imbalance is brown eye spot (BES) (Pozza et al, 2010;Silva et al, 2019), whose causal agent is the fungus Cercospora coffeicola, Berk. & Cooke.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, more efficient fertilizers are applied, as they are technologies capable of optimizing the use of nutrients by plants, by reducing losses through leaching and volatilization (Dominghetti et al, 2016). These fertilizers can contribute to the reduction of the brown eye spot incidence, since the disease is favored by nutritional imbalance (Chaves et al, 2018;Silva et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Brown eye spot incidence during the vegetative stage of coffee grown in soil under sustainable management

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Pozza

Luz

et al. 2022

Pesq. agropec. bras.

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The objective of this work was to evaluate the influence of different soil coverings, fertilizer types, and soil conditioners on the incidence and severity of brown eye spot (Cercospora coffeicola) in coffee (Coffea arabica) during the vegetative stage. The experiment was carried out in a randomized complete block design, in a 3×2×5 factorial arrangement (soil coverings × fertilizer types × soil conditioners) in subdivided plots, with three replicates. Comparisons were made among: three soil covering (plastic film, Urochloa decumbens, and no covering) in the plots; two fertililizer types (conventional and controlled release) in the sub-plots; and four soil conditioners (coffee husk, agricultural gypsum, water retention polymer, and organic compound), as well as the control, in the sub-sub-plots. The area under the progress curve for disease incidence and severity showed the lowest mean without soil covering, whereas that for number of leaves was greater with the use of the controlled-release fertilizer. The plastic film provided a greater soil moisture and a lower soil resistance penetration. The chemical composition of the organic compound reduced coffee plants growth. Soil covering with plastic film or U. decumbens favors the high incidence of brown eye spot; however, it provides a greater soil moisture and, therefore, improves the growth of coffee plants in the vegetative stage.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Brown eye spot incidence during the vegetative stage of coffee grown in soil under sustainable management

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Pozza

Luz

et al. 2022

Pesq. agropec. bras.

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“…27,28 Coffee leaf rust and Cercospora leaf spot are caused by fungi that directly attack the leaves, causing intense defoliation. [29][30][31] Coffee leaf rust is favored by air temperatures ranging from 20 to 25 °C, 32,33 while Cercospora leaf spot requires higher air temperatures (above 30 °C) for spore germination. 34,35 Degree-day methodology is used to relate the influence of air temperature to pest development in the crops.…”

Section: Introductionmentioning

confidence: 99%

Can nonlinear agrometeorological models estimate coffee foliation?

Aparecido

Lorençone

et al. 2021

J Sci Food Agric

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BACKGROUND The loss of coffee leaves caused by the attack of pests and diseases significantly reduces its production and bean quality. Thus this study aimed to estimate foliation for regions with the highest production of arabica coffee in Brazil using nonlinear models as a function of climate. A 25‐year historical series (1995–2019) of Coffea arabica foliation (%) data was obtained by the Procafé Foundation in cultivations with no phytosanitary treatment. The climate data were obtained on a daily scale by NASA/POWER platform with a temporal resolution of 33 years (1987–2019) and a spatial resolution of approximately 106 km, thus allowing the calculation of the reference evapotranspiration (PET). Foliation estimation models were adjusted through regression analysis using four‐parameter sigmoidal logistic models. The analysis of the foliation trend of coffee plantations was carried out from degrees‐day for 70 locations. RESULTS The general model calibrated to estimate the arabica coffee foliation was accurate (mean absolute percentage error = 2.19%) and precise (R2adj = 0.99) and can be used to assist decision‐making by coffee growers. The model had a sigmoidal trend of reduction, with parameters ymax = 97.63%, ymin = 9%, Xo = 3517.41 DD, and p = 6.27%, showing that foliation could reach 0.009% if the necessary phytosanitary controls are not carried out. CONCLUSION Locations with high air temperatures over the year had low arabica coffee foliation, as shown by the correlation of −0.94. Therefore, coffee foliation can be estimated using degree days with accuracy and precision through the air temperature. This represents great convenience because crop foliation can be obtained using only a thermometer. © 2021 Society of Chemical Industry.

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Climate and disease: tackling coffee brown‐eye spot with advanced forecasting models

de Oliveira Aparecido,

de Lima,

Torsoni

et al. 2024

J Sci Food Agric

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BACKGROUNDClimate significantly influences the interaction between pathogens and their hosts, and this is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown eye spot, can drastically reduce yields. Our study focuses on forecasting coffee brown eye spot using various models that incorporate agrometeorological data, allowing for predictions at least one week prior to disease occurrence. Data was gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais State. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown eye spot, identifying one with potential for advanced decision‐making. The top‐performing models were then employed in the next stage to forecast and spatially project the severity of brown eye spot across 2,681 key Brazilian coffee‐producing municipalities. Meteorological data were sourced from NASA's POWER platform, and the Penman‐Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather (1955) water balance calculation. Six ML models — K‐Nearest Neighbors (KNN), Artificial Neural Network Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forests (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Regression (GradBOOSTING) — were employed, considering a 7‐day disease latency to define input variables.RESULTSThese models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high‐yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low‐yielding scenarios. The incidence of brown eye spot varied noticeably between high and low‐yield conditions, with significant regional differences observed. The accuracy of predicting brown eye spot severity in coffee plantations depended on the biennial production cycle. High‐yielding trees showed superior results with the XGBoost model (R2 = 0.77, RMSE = 10.53), while under low‐yielding conditions, the Support Vector Machine (SVM) model performed better (precision 0.76, RMSE = 12.82).CONCLUSIONThe study's application of agrometeorological variables and ML models successfully predicted the incidence of brown eye spot in coffee plantations with a seven‐day lead time, illustrating a valuable tool for managing this significant agricultural challenge.This article is protected by copyright. All rights reserved.

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Spatio-temporal aspects of brown eye spot and nutrients in irrigated coffee

Cited by 7 publications

References 26 publications

Brown eye spot incidence during the vegetative stage of coffee grown in soil under sustainable management

Brown eye spot incidence during the vegetative stage of coffee grown in soil under sustainable management

Can nonlinear agrometeorological models estimate coffee foliation?

Climate and disease: tackling coffee brown‐eye spot with advanced forecasting models

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