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157 results on '"de Oliveira Aparecido, Lucas Eduardo"'

20. Future climate suitability of Hemileia vastatrix in arabica coffee under CMIP6 scenarios.

Author

de Oliveira Aparecido, Lucas Eduardo, Torsoni, Guilherme Botega, Lorençone, João Antonio, Lorençone, Pedro Antonio, de Lima, Rafael Fausto, de Souza Rolim, Glauco, Saqui, Diego, and de Oliveira Junior, Geraldo Gomes

Subjects
*CLIMATE change adaptation, *GEOGRAPHIC information system software, *CLIMATIC zones, *ENVIRONMENTAL risk assessment, *ATMOSPHERIC temperature
Abstract

BACKGROUND: Coffee (Coffea arabica L.) is one of the most important commodities today, with a high economic value worldwide. Coffee leaf rust (Hemileia vastatrix Berk. et Br.) has been showing a high impact on Brazilian coffee trees among the various diseases that attack coffee. The climate has a great influence on the development of diseases, especially when fungi are the causal agents. This study aimed to carry out the zoning of climate favorability for coffee leaf rust in the traditional and main coffee‐producing regions of Brazil. The study was conducted in 13 locations in the states of Paraná, São Paulo, Rio de Janeiro, Espírito Santo, Minas Gerais, Goiás and Bahia. Air temperature and daily precipitation data for the current scenario were collected using the WorldClim version 2.1 platform for the last climatological normal and future climate change data. The ideal climate conditions for coffee leaf rust consist of a mean air temperature ranging from 21 to 25 °C and precipitation >30 mm per month. The Coupled Model Intercomparison Projects scenarios associated with the Intergovernmental Panel on Climate Change reports consisted of the Shared Socio‐Economic Pathways SSP‐1 2.6, SSP‐2 4.5, SSP‐3 7.0 and SSP‐5 8.5, the latter being considered one of the most catastrophic. All steps to carry out the suitability zoning were performed in a tool using the QGIS geographic information system software. RESULTS: Zoning for coffee leaf rust had three classes: favorable, relatively favorable and unfavorable. Currently, the largest coffee‐producing region in Brazil has 49.1% of its analyzed area classified as favorable, 39.2% as relatively favorable and 11.7% as unfavorable. In the current scenario, Patrocínio and Três Pontas are locations with high coffee production in which the favorable class is predominant. The state of Minas Gerais has an annual mean of 55.3% of its entire territory apt for the disease, with the highest occurrence between September and March. CONCLUSIONS: Climate change has a negative impact on the development of coffee leaf rust, mainly in the long term, as in the period of 2081–2100, in which the SSP‐5 8.5 scenario led to a decrease in the favorable and unfavorable areas and an increase in the relatively favorable areas of 9.8%, 18.6% and 71.5% for the Brazilian territory, respectively. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Published
2025
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22. Climate and disease: tackling coffee brown‐eye spot with advanced forecasting models.

Author

de Oliveira Aparecido, Lucas Eduardo, de Lima, Rafael Fausto, Torsoni, Guilherme Botega, Lorençone, João Antonio, Lorençone, Pedro Antonio, and de Souza Rolim, Glauco

Subjects
*ARTIFICIAL neural networks, *COFFEE plantations, *MEDICAL climatology, *MACHINE learning, *STANDARD deviations, *SUPPORT vector machines, *AGRICULTURE
Abstract

Background: Climate influences the interaction between pathogens and their hosts significantly. This is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown‐eye spot, can reduce yields drastically. This study focuses on forecasting coffee brown‐eye spot using various models that incorporate agrometeorological data, allowing for predictions at least 1 week prior to the occurrence of disease. Data were 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 2681 key Brazilian coffee‐producing municipalities. Meteorological data were sourced from NASA's Prediction of Worldwide Energy Resources platform, and the Penman–Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather 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 disease latency to time define input variables. Results: These 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, root mean squared error, RMSE = 10.53), whereas the SVM model performed better under low‐yielding conditions (precision 0.76, RMSE = 12.82). Conclusion: The study's application of agrometeorological variables and ML models successfully predicted the incidence of brown‐eye spot in coffee plantations with a 7 day lead time, illustrating that they were valuable tools for managing this significant agricultural challenge. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Evaluation of the honey and pollen yield of Melipona interrupta bee colonies in the Amazon region

Author

Pires, Adcleia P., Pacheco, Aline, Martorano, Lucieta G., Neto, Jonival M., da Silva Cabral de Moraes, José Reinaldo [UNESP], de Oliveira Aparecido, Lucas Eduardo [UNESP], Federal University of Western Pará (UFOPA), Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA), Universidade Estadual Paulista (Unesp), and The University of Amazônia (UNAMA)

Subjects
Meliponiculture, Community, Bees, Brazil
Abstract

Made available in DSpace on 2020-12-12T01:22:23Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-03-01 The objective of this research was to evaluate the productivity of honey and pollen of Melipona interrupta (Jandaira) colonies during a one-year period. This study was conducted in meliponiculture production colonies located in community of Coroca in the City of Santarem, Para, from October 2016 to December 2017. All collections of meliponiculture material were conducted during the first week of each month. Characteristics such as the total number of food pots, height, diameter, and volume of honey and pollen pots, the mass of the colony, production of pollen and honey, and the productivity of the bee colony as a whole were constantly monitored. Each month, biometric measurements of fifteen colonies of native bees of the specie were conducted and analyzed in the Statistical Analysis System. The greatest honey production by these colonies was strongly correlated with the less-rainy period that occurs in the region, and this production was most pronounced during October through December. Federal University of Western Pará (UFOPA) Brazilian Agricultural Research Agency (Embrapa Amazônia Oriental/NAPT do Médio Amazonas) State University of São Paulo (UNESP) The University of Amazônia (UNAMA) State University of São Paulo (UNESP)

Published
2020

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