Irrigation demand for fruit trees under a climate change scenario using artificial intelligence
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, G...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Universidade Federal de Goiás
2024-03-01
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| Series: | Pesquisa Agropecuária Tropical |
| Subjects: | |
| Online Access: | https://revistas.ufg.br/pat/article/view/77917/40915 |
| _version_ | 1797236661991505920 |
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| author | Rafael Battisti Waldemiro Alcântara da Silva Neto Ronaldo Martins da Costa Felipe Puff Dapper Elvis Felipe Elli |
| author_facet | Rafael Battisti Waldemiro Alcântara da Silva Neto Ronaldo Martins da Costa Felipe Puff Dapper Elvis Felipe Elli |
| author_sort | Rafael Battisti |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-24T17:07:25Z |
| format | Article |
| id | doaj.art-84d1b998f7764626a119229e6477fde7 |
| institution | Directory Open Access Journal |
| issn | 1983-4063 |
| language | English |
| last_indexed | 2024-04-24T17:07:25Z |
| publishDate | 2024-03-01 |
| publisher | Universidade Federal de Goiás |
| record_format | Article |
| series | Pesquisa Agropecuária Tropical |
| spelling | doaj.art-84d1b998f7764626a119229e6477fde72024-03-28T21:31:42ZengUniversidade Federal de GoiásPesquisa Agropecuária Tropical1983-40632024-03-0154e7791710.1590/1983-40632024v5477917Irrigation demand for fruit trees under a climate change scenario using artificial intelligenceRafael BattistiWaldemiro Alcântara da Silva NetoRonaldo Martins da CostaFelipe Puff DapperElvis Felipe ElliFruit 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.https://revistas.ufg.br/pat/article/view/77917/40915climate resiliencewater demandmachine learningfuture climate scenarios |
| spellingShingle | Rafael Battisti Waldemiro Alcântara da Silva Neto Ronaldo Martins da Costa Felipe Puff Dapper Elvis Felipe Elli Irrigation demand for fruit trees under a climate change scenario using artificial intelligence Pesquisa Agropecuária Tropical climate resilience water demand machine learning future climate scenarios |
| title | 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_short | 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 |
| topic | climate resilience water demand machine learning future climate scenarios |
| url | https://revistas.ufg.br/pat/article/view/77917/40915 |
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