Irrigation demand for fruit trees under a climate change scenario using artificial intelligence
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Pesquisa Agropecuária Tropical (Online) |
Texto Completo: | https://revistas.ufg.br/pat/article/view/77917 |
Resumo: | Fruit growing, especially in family farming, has a significant income potential in small areas, but climate change is a major challenge. This study aimed to quantify the irrigation requirements for citrus, papaya, mango and passion fruit, in the Vão do Paranã region, Goiás state, Brazil. The climate data encompassed the observed periods from 1961 to 2020 and future scenarios from 2021 to 2100. The irrigation demand was obtained from the daily water balance, while the reference evapotranspiration (ETo) was estimated using the Penman-Monteith method and then compared with an artificial intelligence tool. The future scenarios indicated a higher increase for air temperature and a lower increase for rainfall. The ETo levels raised from 1,528 mm year-1, in 1991-2020, to 1,614-1,656 mm year-1, in 2021-2050. The artificial intelligence performance was limited in the ETo estimation, with a mean absolute error of 0.71 mm day-1 and an “r” value of 0.59, when considering the air temperature as the input variable. For the 2021-2050 period, when compared with 1991-2020, there was an increase in irrigation demand, in which, under the extreme scenario, the citrus demand reached 690 mm year-1 (+11 %); papaya (+10 %) and passion fruit (+5 %) surpassed 800 mm year-1; and mango reached 491 mm year-1 (+14 %). An increase in demand for irrigation was observed, with management alternatives in association with strategies for maximum cultivation area based on water supply being recommended. KEYWORDS: Climate resilience, water demand, machine learning, future climate scenarios. |
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Irrigation demand for fruit trees under a climate change scenario using artificial intelligenceDemanda de irrigação para frutíferas sob cenário de mudanças climáticas utilizando-se inteligência artificialFruit growing, especially in family farming, has a significant income potential in small areas, but climate change is a major challenge. This study aimed to quantify the irrigation requirements for citrus, papaya, mango and passion fruit, in the Vão do Paranã region, Goiás state, Brazil. The climate data encompassed the observed periods from 1961 to 2020 and future scenarios from 2021 to 2100. The irrigation demand was obtained from the daily water balance, while the reference evapotranspiration (ETo) was estimated using the Penman-Monteith method and then compared with an artificial intelligence tool. The future scenarios indicated a higher increase for air temperature and a lower increase for rainfall. The ETo levels raised from 1,528 mm year-1, in 1991-2020, to 1,614-1,656 mm year-1, in 2021-2050. The artificial intelligence performance was limited in the ETo estimation, with a mean absolute error of 0.71 mm day-1 and an “r” value of 0.59, when considering the air temperature as the input variable. For the 2021-2050 period, when compared with 1991-2020, there was an increase in irrigation demand, in which, under the extreme scenario, the citrus demand reached 690 mm year-1 (+11 %); papaya (+10 %) and passion fruit (+5 %) surpassed 800 mm year-1; and mango reached 491 mm year-1 (+14 %). An increase in demand for irrigation was observed, with management alternatives in association with strategies for maximum cultivation area based on water supply being recommended. KEYWORDS: Climate resilience, water demand, machine learning, future climate scenarios.A fruticultura, especialmente na agricultura familiar, possui grande potencial de renda em pequenas áreas, mas as mudanças climáticas são um grande desafio. Objetivou-se quantificar a demanda de irrigação para citros, mamão, manga e maracujá, na região do Vão do Paranã, Goiás. Os dados climáticos compreenderam os períodos observados de 1961 a 2020 e cenários futuros de 2021 a 2100. A demanda por irrigação foi obtida com base no balanço hídrico diário, enquanto a evapotranspiração de referência (ETo) foi estimada pelo método de Penman-Monteith e então comparada com uma ferramenta de inteligência artificial. Os cenários futuros indicaram aumento da temperatura do ar, em maior intensidade, e de chuvas, em menor intensidade. A ETo passou de 1.528 mm ano-1, em 1991-2020, para 1.614-1.656 mm ano-1, em 2021-2050. O desempenho da inteligência artificial na estimativa da ETo foi limitado, com erro médio absoluto de 0,71 mm dia-1 e r de 0,59, quando considerada a temperatura do ar como dado de entrada. Para o período de 2021-2050, em relação a 1991-2020, houve aumento na demanda por irrigação, em que, no cenário extremo, os citros atingiram 690 mm ano-1 (+11 %); mamão (+10 %) e maracujá (+5 %) ultrapassaram 800 mm ano-1; e a manga chegou a 491 mm ano-1 (+14 %). Observou-se aumento na demanda por irrigação, sendo recomendadas alternativas de manejo associadas a estratégias de área máxima de cultivo com base na oferta de água. PALAVRAS-CHAVE: Resiliência climática, demanda hídrica, aprendizado de máquina, cenários climáticos futuros.Escola de Agronomia - Universidade Federal de Goiás2024-03-28info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufg.br/pat/article/view/77917Pesquisa Agropecuária Tropical [Agricultural Research in the Tropics]; v. 54 (2024); e77917Pesquisa Agropecuária Tropical (Agricultural Research in the Tropics); v. 54 (2024); e77917Pesquisa Agropecuária Tropical; v. 54 (2024); e779171983-4063reponame:Pesquisa Agropecuária Tropical (Online)instname:Universidade Federal de Goiás (UFG)instacron:UFGenghttps://revistas.ufg.br/pat/article/view/77917/40915Copyright (c) 2024 Pesquisa Agropecuária Tropicalinfo:eu-repo/semantics/openAccessBattisti, RafaelSilva Neto, Waldemiro Alcântara daCosta, Ronaldo Martins daDapper, Felipe PuffElli, Elvis Felipe2024-04-09T18:31:56Zoai:ojs.revistas.ufg.br:article/77917Revistahttps://revistas.ufg.br/patPUBhttps://revistas.ufg.br/pat/oaiaseleguini.pat@gmail.com||mgoes@agro.ufg.br1983-40631517-6398opendoar:2024-05-21T19:56:40.919893Pesquisa Agropecuária Tropical (Online) - Universidade Federal de Goiás (UFG)true |
dc.title.none.fl_str_mv |
Irrigation demand for fruit trees under a climate change scenario using artificial intelligence Demanda de irrigação para frutíferas sob cenário de mudanças climáticas utilizando-se inteligência artificial |
title |
Irrigation demand for fruit trees under a climate change scenario using artificial intelligence |
spellingShingle |
Irrigation demand for fruit trees under a climate change scenario using artificial intelligence Battisti, Rafael |
title_short |
Irrigation demand for fruit trees under a climate change scenario using artificial intelligence |
title_full |
Irrigation demand for fruit trees under a climate change scenario using artificial intelligence |
title_fullStr |
Irrigation demand for fruit trees under a climate change scenario using artificial intelligence |
title_full_unstemmed |
Irrigation demand for fruit trees under a climate change scenario using artificial intelligence |
title_sort |
Irrigation demand for fruit trees under a climate change scenario using artificial intelligence |
author |
Battisti, Rafael |
author_facet |
Battisti, Rafael Silva Neto, Waldemiro Alcântara da Costa, Ronaldo Martins da Dapper, Felipe Puff Elli, Elvis Felipe |
author_role |
author |
author2 |
Silva Neto, Waldemiro Alcântara da Costa, Ronaldo Martins da Dapper, Felipe Puff Elli, Elvis Felipe |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Battisti, Rafael Silva Neto, Waldemiro Alcântara da Costa, Ronaldo Martins da Dapper, Felipe Puff Elli, Elvis Felipe |
description |
Fruit growing, especially in family farming, has a significant income potential in small areas, but climate change is a major challenge. This study aimed to quantify the irrigation requirements for citrus, papaya, mango and passion fruit, in the Vão do Paranã region, Goiás state, Brazil. The climate data encompassed the observed periods from 1961 to 2020 and future scenarios from 2021 to 2100. The irrigation demand was obtained from the daily water balance, while the reference evapotranspiration (ETo) was estimated using the Penman-Monteith method and then compared with an artificial intelligence tool. The future scenarios indicated a higher increase for air temperature and a lower increase for rainfall. The ETo levels raised from 1,528 mm year-1, in 1991-2020, to 1,614-1,656 mm year-1, in 2021-2050. The artificial intelligence performance was limited in the ETo estimation, with a mean absolute error of 0.71 mm day-1 and an “r” value of 0.59, when considering the air temperature as the input variable. For the 2021-2050 period, when compared with 1991-2020, there was an increase in irrigation demand, in which, under the extreme scenario, the citrus demand reached 690 mm year-1 (+11 %); papaya (+10 %) and passion fruit (+5 %) surpassed 800 mm year-1; and mango reached 491 mm year-1 (+14 %). An increase in demand for irrigation was observed, with management alternatives in association with strategies for maximum cultivation area based on water supply being recommended. KEYWORDS: Climate resilience, water demand, machine learning, future climate scenarios. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-03-28 |
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://revistas.ufg.br/pat/article/view/77917 |
url |
https://revistas.ufg.br/pat/article/view/77917 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://revistas.ufg.br/pat/article/view/77917/40915 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2024 Pesquisa Agropecuária Tropical info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2024 Pesquisa Agropecuária Tropical |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Escola de Agronomia - Universidade Federal de Goiás |
publisher.none.fl_str_mv |
Escola de Agronomia - Universidade Federal de Goiás |
dc.source.none.fl_str_mv |
Pesquisa Agropecuária Tropical [Agricultural Research in the Tropics]; v. 54 (2024); e77917 Pesquisa Agropecuária Tropical (Agricultural Research in the Tropics); v. 54 (2024); e77917 Pesquisa Agropecuária Tropical; v. 54 (2024); e77917 1983-4063 reponame:Pesquisa Agropecuária Tropical (Online) instname:Universidade Federal de Goiás (UFG) instacron:UFG |
instname_str |
Universidade Federal de Goiás (UFG) |
instacron_str |
UFG |
institution |
UFG |
reponame_str |
Pesquisa Agropecuária Tropical (Online) |
collection |
Pesquisa Agropecuária Tropical (Online) |
repository.name.fl_str_mv |
Pesquisa Agropecuária Tropical (Online) - Universidade Federal de Goiás (UFG) |
repository.mail.fl_str_mv |
aseleguini.pat@gmail.com||mgoes@agro.ufg.br |
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1799874821662703616 |