Markov chains to determine the probability of climate change for planting selection in the city of Caxias do Sul
The Markov stochastic chain model and the analytical hierarchy process (AHP) were used as tools to support decision-making for the best crop-planting choice in the city of Caxias do Sul, Brazil. Temperature and precipitation information were collected from the Meteorological Database for Teaching an...
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Published in | Ciência rural Vol. 52; no. 4 |
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Language | English Portuguese |
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Abstract | The Markov stochastic chain model and the analytical hierarchy process (AHP) were used as tools to support decision-making for the best crop-planting choice in the city of Caxias do Sul, Brazil. Temperature and precipitation information were collected from the Meteorological Database for Teaching and Research of the National Institute of Meteorology of Brazil for the period 1997-2017. The stochastic model was applied to obtain the probability of transition between a range of variations for temperature and precipitation. In the second phase of the study, an algebraic model was developed, making it possible to link the probability of the Markov chain transition matrix to the AHP judgment matrix. In the third phase, the AHP was applied as a tool to determine the most beneficial crop that could be planted for the studied city, considering the evaluated criteria: temperature, precipitation, and soil pH. The alternatives for crop planting were carrots, tomatoes, apples, and grapes. These were chosen because they are the most-planted crops in the city of Caxias do Sul. The ranking of the benefit-force results of applying the model for spring was carrots (0.297), apples (0.259), grapes (0.228), and tomatoes (0.215); for summer: grapes (0.261), tomatoes (0.261), apples (0.238), and carrots (0.230); for autumn: carrots (0.316), grapes (0.243), tomatoes (0.228), and apples (0.213); and for winter: carrots (0.327), tomatoes (0.235), apples (0.222), and grapes (0.216). Thus, it was concluded that farmers would have a better chance of success if they planted carrots during the spring, autumn, and winter, and grapes during the summer.
RESUMO: O Modelo de Cadeia Estocástica de Markov e o Processo de Hierarquia Analítica (AHP) foram utilizados como ferramentas de apoio à tomada de decisão para a melhor escolha de plantio na cidade de Caxias do Sul, Brasil. As informações de temperatura e precipitação foram coletadas de 1997 a 2017 no Banco de Dados Meteorológicos para Ensino e Pesquisa do Instituto Nacional de Meteorologia do Brasil. O modelo estocástico foi aplicado para obtenção da probabilidade de transição entre faixas de variação para temperatura e precipitação. Na segunda fase do estudo, um modelo algébrico foi desenvolvido, possibilitando vincular a probabilidade da matriz de transição de cadeias de Markov na matriz de julgamento do AHP. Na terceira fase, o AHP foi aplicado como ferramenta de apoio à decisão do cultivo mais benéfico para a cidade estudada, considerando os critérios avaliados: temperatura, precipitação e pH do solo. As alternativas escolhidas para fazer o ranking foram: cenoura, tomate, maçã e uva, escolhidas por possuírem a maior quantidade de plantio na cidade de Caxias do Sul. O ranking do resultado de força de benefício da aplicação do modelo para a primavera foi: cenoura (0,297), maçã (0,259), uva (0,228) e tomate (0,215); para o verão: uva (0,271), tomate (0,261), maçã (0,238) e cenoura (0,230); para o outono: cenoura (0,316), uva (0,243), tomate (0,228) e maçã (0,213); para o inverno: cenoura (0,327), tomate (0,235), maçã (0,222) e uva (0,216). Assim, concluiu-se que o agricultor terá mais chances de sucesso se optar por plantar cenoura durante a primavera, outono e inverno, e uva durante o verão. |
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AbstractList | The Markov stochastic chain model and the analytical hierarchy process (AHP) were used as tools to support decision-making for the best crop-planting choice in the city of Caxias do Sul, Brazil. Temperature and precipitation information were collected from the Meteorological Database for Teaching and Research of the National Institute of Meteorology of Brazil for the period 1997-2017. The stochastic model was applied to obtain the probability of transition between a range of variations for temperature and precipitation. In the second phase of the study, an algebraic model was developed, making it possible to link the probability of the Markov chain transition matrix to the AHP judgment matrix. In the third phase, the AHP was applied as a tool to determine the most beneficial crop that could be planted for the studied city, considering the evaluated criteria: temperature, precipitation, and soil pH. The alternatives for crop planting were carrots, tomatoes, apples, and grapes. These were chosen because they are the most-planted crops in the city of Caxias do Sul. The ranking of the benefit-force results of applying the model for spring was carrots (0.297), apples (0.259), grapes (0.228), and tomatoes (0.215); for summer: grapes (0.261), tomatoes (0.261), apples (0.238), and carrots (0.230); for autumn: carrots (0.316), grapes (0.243), tomatoes (0.228), and apples (0.213); and for winter: carrots (0.327), tomatoes (0.235), apples (0.222), and grapes (0.216). Thus, it was concluded that farmers would have a better chance of success if they planted carrots during the spring, autumn, and winter, and grapes during the summer. The Markov stochastic chain model and the analytical hierarchy process (AHP) were used as tools to support decision-making for the best crop-planting choice in the city of Caxias do Sul, Brazil. Temperature and precipitation information were collected from the Meteorological Database for Teaching and Research of the National Institute of Meteorology of Brazil for the period 1997-2017. The stochastic model was applied to obtain the probability of transition between a range of variations for temperature and precipitation. In the second phase of the study, an algebraic model was developed, making it possible to link the probability of the Markov chain transition matrix to the AHP judgment matrix. In the third phase, the AHP was applied as a tool to determine the most beneficial crop that could be planted for the studied city, considering the evaluated criteria: temperature, precipitation, and soil pH. The alternatives for crop planting were carrots, tomatoes, apples, and grapes. These were chosen because they are the most-planted crops in the city of Caxias do Sul. The ranking of the benefit-force results of applying the model for spring was carrots (0.297), apples (0.259), grapes (0.228), and tomatoes (0.215); for summer: grapes (0.261), tomatoes (0.261), apples (0.238), and carrots (0.230); for autumn: carrots (0.316), grapes (0.243), tomatoes (0.228), and apples (0.213); and for winter: carrots (0.327), tomatoes (0.235), apples (0.222), and grapes (0.216). Thus, it was concluded that farmers would have a better chance of success if they planted carrots during the spring, autumn, and winter, and grapes during the summer. RESUMO: O Modelo de Cadeia Estocástica de Markov e o Processo de Hierarquia Analítica (AHP) foram utilizados como ferramentas de apoio à tomada de decisão para a melhor escolha de plantio na cidade de Caxias do Sul, Brasil. As informações de temperatura e precipitação foram coletadas de 1997 a 2017 no Banco de Dados Meteorológicos para Ensino e Pesquisa do Instituto Nacional de Meteorologia do Brasil. O modelo estocástico foi aplicado para obtenção da probabilidade de transição entre faixas de variação para temperatura e precipitação. Na segunda fase do estudo, um modelo algébrico foi desenvolvido, possibilitando vincular a probabilidade da matriz de transição de cadeias de Markov na matriz de julgamento do AHP. Na terceira fase, o AHP foi aplicado como ferramenta de apoio à decisão do cultivo mais benéfico para a cidade estudada, considerando os critérios avaliados: temperatura, precipitação e pH do solo. As alternativas escolhidas para fazer o ranking foram: cenoura, tomate, maçã e uva, escolhidas por possuírem a maior quantidade de plantio na cidade de Caxias do Sul. O ranking do resultado de força de benefício da aplicação do modelo para a primavera foi: cenoura (0,297), maçã (0,259), uva (0,228) e tomate (0,215); para o verão: uva (0,271), tomate (0,261), maçã (0,238) e cenoura (0,230); para o outono: cenoura (0,316), uva (0,243), tomate (0,228) e maçã (0,213); para o inverno: cenoura (0,327), tomate (0,235), maçã (0,222) e uva (0,216). Assim, concluiu-se que o agricultor terá mais chances de sucesso se optar por plantar cenoura durante a primavera, outono e inverno, e uva durante o verão. |
Author | Corso, Leandro Vargas, Vanessa Bertholdo Vallejos, Rolando Vargas |
AuthorAffiliation | Universidade de Caxias do Sul Universidade Federal de Goiás |
AuthorAffiliation_xml | – name: Universidade de Caxias do Sul – name: Universidade Federal de Goiás |
Author_xml | – sequence: 1 givenname: Vanessa Bertholdo orcidid: 0000-0002-0247-1186 surname: Vargas fullname: Vargas, Vanessa Bertholdo organization: Universidade de Caxias do Sul (UCS), Brazil – sequence: 2 givenname: Leandro orcidid: 0000-0001-9962-9578 surname: Corso fullname: Corso, Leandro organization: Universidade de Caxias do Sul (UCS), Brazil – sequence: 3 givenname: Rolando Vargas orcidid: 0000-0001-9795-8909 surname: Vallejos fullname: Vallejos, Rolando Vargas organization: Universidade Federal de Goiânia (UFG), Brazil |
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Keywords | cultivos crops processo decisório precipitação precipitation Hierarchical analytical process pH do solo soil pH operational research decision-making process temperature processo analítico hierárquico temperatura pesquisa operacional |
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Title | Markov chains to determine the probability of climate change for planting selection in the city of Caxias do Sul |
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