Estimation of soil moisture through multiple linear regressions in Llano Brenes, Costa Rica
DOI:
https://doi.org/10.15517/am.v33i2.47872Keywords:
soil water content, Coffea, tropical zoneAbstract
Introduction. Soil moisture is a very important variable in the water supply for agriculture and it is its main resource. However, its field measurement usually has limitations, so its prediction is necessary for various agricultural planning and research activities. Objective. To predict daily soil moisture at the crop scale from meteorological information through multiple linear regression models. Materials and methods. The study was carried out in Llano Brenes, Alajuela, Costa Rica. Time domain reflectometry (TDR) sensors were installed and soil moisture information was recorded every twenty minutes from November 2018 to December 2019. The soil was classified at the taxonomic level as Lithic Ustorthents, in a farm with coffee cultivation in production. Undisturbed soil samples were taken for TDR calibration and a temporal stability analysis was performed. The first model (RLM1) was a multiple linear regression with meteorological variables, in the second model (RLM2) in addition to the meteorological variables, the precipitation was separated into sub-periods which were introduced as dummy variables, while the third model (PCA) consisted of a main component analysis and a linear regression model. Results. The RLM2 (R2 = 0.838) and PCA (R2 = 0.823) models performed better than the RLM1 model (R2 = 0.540). However, the RLM2 model was considered more useful due to its simplicity and the fact that it presented the best goodness-of-fit indicators. Conclusion. The linear regression models with meteorological variables allowed estimating soil moisture, because it tends to follow seasonal patterns and variations in precipitation, as observed in the RLM2 with the separation of sub-periods.
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