Estimation of actual evapotranspiration in barley crop through a generalized linear model

Faramiñán, Adán; Rodríguez, Paula Olivera; Carmona, Facundo; Holzman, Mauro; Rivas, Raúl Eduardo; Mancino, Christian Alberto

doi:10.1016/j.mex.2022.101665

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…It is observed that the RF model is more accurate in ET o estimation with radiation as input. Faramiñan et al [45] proposed daily actual Evapotranspiration (ETa) of barley crops by the development of the linear generalized model, developed from meteorological conditions. López et al [46] proposed ET o estimation with soil moisture conditions using the regression model.…”

Section: Gutierrez Et Al Proposed Optimized Irrigation Water Requirem...mentioning

confidence: 99%

Leaching Fraction (LF) of Irrigation Water for Saline Soils Using Machine Learning

Bashir¹,

Bajwa²,

Iqbal³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Soil salinity is a serious land degradation issue in agriculture. It is a major threat to agriculture productivity. Extra irrigation water is applied to leach down the salts from the root zone of the plants in the form of a Leaching fraction (LF) of irrigation water. For the leaching process to be effective, the LF of irrigation water needs to be adjusted according to the environmental conditions and soil salinity level in the form of Evapotranspiration (ET) rate. The relationship between environmental conditions and ET rate is hard to be defined by a linear relationship and data-driven Machine learning (ML) based decisions are required to determine the calibrated Evapotranspiration (ET c ) rate. ML-assisted ET c is proposed to adjust the LF according to the ET c and soil salinity level. A regression model is proposed to determine the ET c rate according to the prevailing temperature, humidity, and sunshine, which would be used to determine the smart LF according to the ET c and soil salinity level. The proposed model is trained and tested against the Blaney Criddle method of Reference evapotranspiration (ET o ) determination. The validation of the model from the test dataset reveals the accuracy of the ML model in terms of Root mean squared errors (RMSE) are 0.41, Mean absolute errors (MAE) are 0.34, and Mean squared errors (MSE) are 0.28 mm day −1 . The applications of the proposed solution in a real-time environment show that the LF by the proposed solution is more effective in reducing the soil salinity as compared to the traditional process of leaching.

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Section: Gutierrez Et Al Proposed Optimized Irrigation Water Requirem...mentioning

confidence: 99%

Leaching Fraction (LF) of Irrigation Water for Saline Soils Using Machine Learning

Bashir¹,

Bajwa²,

Iqbal³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

Intelligent optimization of Reference Evapotranspiration (ETo) for precision irrigation

Bashir

Khan

et al. 2023

Journal of Computational Science

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Evaluation of a generalized linear model for the actual evapotranspiration using satellite and reanalysis data

Faraminan¹,

Laino²,

Carmona³

et al. 2022

2022 IEEE Biennial Congress of Argentina (ARGENCON)

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Estimation of actual evapotranspiration in barley crop through a generalized linear model

Cited by 3 publications

References 7 publications

Leaching Fraction (LF) of Irrigation Water for Saline Soils Using Machine Learning

Leaching Fraction (LF) of Irrigation Water for Saline Soils Using Machine Learning

Intelligent optimization of Reference Evapotranspiration (ETo) for precision irrigation

Evaluation of a generalized linear model for the actual evapotranspiration using satellite and reanalysis data

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