Agrometeorological modeling for coffee productivity forecast in the south region of Minas Gerais state
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Coffee Science (Online) |
Texto Completo: | https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1049 |
Resumo: | Knowledge of effective crop forecasting techniques is of great importance for the coffee market, enabling better planning and making more sustainable this activity. This study aimed to adapt a predictive model of coffee yield, based on water availability, to the cities of Lavras and Varginha, in southern Minas Gerais, Brazil. The models were generated from multiple linear regression of productivity loss (Ye/Yp) as a function of the previous year productivity (Ya/Yp) and water deficit in the different phenological phases, represented by relative evapotranspiration (ETR/ETP)i. During the parameterization, the water deficit response coefficients (Kyi) and the previous year production coefficient (Ky0) were obtained. By the backward selection methodology, were obtained models that presented only significant coefficients. In this process, in general, the models were highly sensitive to the rainy season (November to April), and variables related to important periods such as flowering were not significant. It was concluded that the models have good potential for coffee crop forecasting. In these, previous year’s yield should be considered and the phenological sequence with best performance was Sep./Oct, Nov./Dec., Jan./Feb., Sep. /Apr. |
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Agrometeorological modeling for coffee productivity forecast in the south region of Minas Gerais stateModelagem agrometeorológica para a previsão de produtividade de cafeeiros na região sul do estado de Minas GeraisCoffeewater deficitbackward selectionCafeeirodéficit hídricoseleção backwardKnowledge of effective crop forecasting techniques is of great importance for the coffee market, enabling better planning and making more sustainable this activity. This study aimed to adapt a predictive model of coffee yield, based on water availability, to the cities of Lavras and Varginha, in southern Minas Gerais, Brazil. The models were generated from multiple linear regression of productivity loss (Ye/Yp) as a function of the previous year productivity (Ya/Yp) and water deficit in the different phenological phases, represented by relative evapotranspiration (ETR/ETP)i. During the parameterization, the water deficit response coefficients (Kyi) and the previous year production coefficient (Ky0) were obtained. By the backward selection methodology, were obtained models that presented only significant coefficients. In this process, in general, the models were highly sensitive to the rainy season (November to April), and variables related to important periods such as flowering were not significant. It was concluded that the models have good potential for coffee crop forecasting. In these, previous year’s yield should be considered and the phenological sequence with best performance was Sep./Oct, Nov./Dec., Jan./Feb., Sep. /Apr.O conhecimento de técnicas eficazes de previsão de safra é de grande importância para o mercado cafeeiro, possibilitando melhor planejamento e tornando a atividade mais sustentável. Objetivou-se, neste trabalho, adaptar um modelo de previsão da produtividade do cafeeiro, baseado na disponibilidade hídrica, para as cidades de Lavras e Varginha, no sul de Minas Gerais. Os modelos foram gerados a partir da regressão linear múltipla da quebra de produtividade (Ye/Yp), em função da produtividade do ano anterior (Ya/Yp) e do déficit hídrico em diferentes fases fenológicas (ETR/ETP)i. Durante as parametrizações, foram obtidos os coeficientes de resposta ao déficit hídrico (Kyi) e o coeficiente relativo à produção do ano anterior (Ky0). Através da seleção backward, foram obtidos modelos que apresentassem apenas coeficientes significativos. Nesse processo, os modelos apresentaram grande sensibilidade ao período mais chuvoso (novembro a abril) e variáveis referentes a períodos importantes, como o de florescimento, não apresentaram significância. Foi concluído que os modelos apresentam bom potencial para a previsão de safras de cafeeiro. Nestes, a produtividade do ano anterior deve ser considerada e a sequência fenológica que apresentou melhor desempenho foi Set./Out, Nov./Dez., Jan./Fev., Mar./Abr.Editora UFLA2016-05-13info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://coffeescience.ufla.br/index.php/Coffeescience/article/view/1049Coffee Science - ISSN 1984-3909; Vol. 11 No. 2 (2016); 211 - 220Coffee Science; Vol. 11 Núm. 2 (2016); 211 - 220Coffee Science; v. 11 n. 2 (2016); 211 - 2201984-3909reponame:Coffee Science (Online)instname:Universidade Federal de Lavras (UFLA)instacron:UFLAporhttps://coffeescience.ufla.br/index.php/Coffeescience/article/view/1049/pdf_1049Copyright (c) 2016 Coffee Science - ISSN 1984-3909https://creativecommons.org/info:eu-repo/semantics/openAccessVictorino, Euler CiprianiCarvalho, Luiz Gonsaga deFerreira, Daniel Furtado2016-05-13T03:36:21Zoai:coffeescience.ufla.br:article/1049Revistahttps://coffeescience.ufla.br/index.php/CoffeesciencePUBhttps://coffeescience.ufla.br/index.php/Coffeescience/oaicoffeescience@dag.ufla.br||coffeescience@dag.ufla.br|| alvaro-cozadi@hotmail.com1984-39091809-6875opendoar:2016-05-13T03:36:21Coffee Science (Online) - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Agrometeorological modeling for coffee productivity forecast in the south region of Minas Gerais state Modelagem agrometeorológica para a previsão de produtividade de cafeeiros na região sul do estado de Minas Gerais |
title |
Agrometeorological modeling for coffee productivity forecast in the south region of Minas Gerais state |
spellingShingle |
Agrometeorological modeling for coffee productivity forecast in the south region of Minas Gerais state Victorino, Euler Cipriani Coffee water deficit backward selection Cafeeiro déficit hídrico seleção backward |
title_short |
Agrometeorological modeling for coffee productivity forecast in the south region of Minas Gerais state |
title_full |
Agrometeorological modeling for coffee productivity forecast in the south region of Minas Gerais state |
title_fullStr |
Agrometeorological modeling for coffee productivity forecast in the south region of Minas Gerais state |
title_full_unstemmed |
Agrometeorological modeling for coffee productivity forecast in the south region of Minas Gerais state |
title_sort |
Agrometeorological modeling for coffee productivity forecast in the south region of Minas Gerais state |
author |
Victorino, Euler Cipriani |
author_facet |
Victorino, Euler Cipriani Carvalho, Luiz Gonsaga de Ferreira, Daniel Furtado |
author_role |
author |
author2 |
Carvalho, Luiz Gonsaga de Ferreira, Daniel Furtado |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Victorino, Euler Cipriani Carvalho, Luiz Gonsaga de Ferreira, Daniel Furtado |
dc.subject.por.fl_str_mv |
Coffee water deficit backward selection Cafeeiro déficit hídrico seleção backward |
topic |
Coffee water deficit backward selection Cafeeiro déficit hídrico seleção backward |
description |
Knowledge of effective crop forecasting techniques is of great importance for the coffee market, enabling better planning and making more sustainable this activity. This study aimed to adapt a predictive model of coffee yield, based on water availability, to the cities of Lavras and Varginha, in southern Minas Gerais, Brazil. The models were generated from multiple linear regression of productivity loss (Ye/Yp) as a function of the previous year productivity (Ya/Yp) and water deficit in the different phenological phases, represented by relative evapotranspiration (ETR/ETP)i. During the parameterization, the water deficit response coefficients (Kyi) and the previous year production coefficient (Ky0) were obtained. By the backward selection methodology, were obtained models that presented only significant coefficients. In this process, in general, the models were highly sensitive to the rainy season (November to April), and variables related to important periods such as flowering were not significant. It was concluded that the models have good potential for coffee crop forecasting. In these, previous year’s yield should be considered and the phenological sequence with best performance was Sep./Oct, Nov./Dec., Jan./Feb., Sep. /Apr. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-05-13 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1049 |
url |
https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1049 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1049/pdf_1049 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 Coffee Science - ISSN 1984-3909 https://creativecommons.org/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 Coffee Science - ISSN 1984-3909 https://creativecommons.org/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora UFLA |
publisher.none.fl_str_mv |
Editora UFLA |
dc.source.none.fl_str_mv |
Coffee Science - ISSN 1984-3909; Vol. 11 No. 2 (2016); 211 - 220 Coffee Science; Vol. 11 Núm. 2 (2016); 211 - 220 Coffee Science; v. 11 n. 2 (2016); 211 - 220 1984-3909 reponame:Coffee Science (Online) instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Coffee Science (Online) |
collection |
Coffee Science (Online) |
repository.name.fl_str_mv |
Coffee Science (Online) - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
coffeescience@dag.ufla.br||coffeescience@dag.ufla.br|| alvaro-cozadi@hotmail.com |
_version_ |
1789440322035515392 |