| Journal: | Acta scientiarum. Agronomy |
| Database: | PERIÓDICA |
| System number: | 000460088 |
| ISSN: | 1679-9275 |
| Authors: | Ferreira, Lucas Borges1 Duarte, Anunciene Barbosa2 Cunha, Fernando França da1 Fernandes-Filho, Elpídio Inácio3 |
| Institutions: | 1Universidade Federal de Vicosa, Departamento de Engenharia Agricola, Vicosa, Minas Gerais. Brasil 2Universidade Federal de Vicosa, Departamento de Fitotecnia, Vicosa, Minas Gerais. Brasil 3Universidade Federal de Vicosa, Departamento de Solos, Vicosa, Minas Gerais. Brasil |
| Year: | 2019 |
| Volumen: | 41 |
| Country: | Brasil |
| Language: | Inglés |
| Document type: | Artículo |
| Approach: | Analítico, descriptivo |
| English abstract | Estimation of reference evapotranspiration (ETo) is very relevant for water resource management. The Penman-Monteith (PM) equation was proposed by the Food and Agriculture Organization (FAO) as the standard method for estimation of ETo. However, this method requires various weather data, such as air temperature, wind speed, solar radiation and relative humidity, which are often unavailable. Thus, the objective of this study was to compare the performance of multivariate adaptive regression splines (MARS) and alternative equations, in their original and calibrated forms, to estimate daily ETo with limited weather data. Daily data from 2002 to 2016 from 8 Brazilian weather stations were used. ETo was estimated using empirical equations, PM equation with missing data and MARS. Four data availability scenarios were evaluated as follows: temperature only, temperature and solar radiation, temperature and relative humidity, and temperature and wind speed. The MARS models demonstrated superior performance in all scenarios. The models that used solar radiation showed the best performance, followed by those that used relative humidity and, finally, wind speed. The models based only on air temperature had the worst performance |
| Disciplines: | Geociencias, Ciencias de la computación |
| Keyword: | Ciencias de la atmósfera, Fitotecnia, Inteligencia artificial, Agrometeorología, Evapotranspiración, Splines de regresión adaptativa multivariada (MARS) |
| Keyword: | Atmospheric sciences, Crop husbandry, Artificial intelligence, Agrometeorology, Evapotranspiration, Multivariate adaptive regression splines (MARS) |
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