• Energy Research
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Abstract Smart agriculture aims at generating high harvest yields with an efficient resource management, such as the estimation of crop irrigation. One of the factors on which a productive crop irrigation depends on is evapotranspiration, defined as the water loss process from the soil. This is mainly measured by empirical equations, even though they are conditioned by the specific climatological variables they require. In recent years, data mining techniques are proposed as a powerful alternative to predict evapotranspiration. Among them, ensembles are notable in that they provide accurate estimators in different scenarios. Stacking is an ensemble-building technique aimed at strengthening the prediction capabilities of the system by the combined learning from the original features in the data and synthetic features created from the predictions of multiple models. This research proposes the usage of stacking for evapotranspiration prediction, which has been overlooked in the specialized literature, with the aim of a more sustainable management of water resources. The proposal is compared to other state-of-the-art empirical equations and data mining methods over several real-world climatological datasets of different agricultural areas in Spain. This comparison is performed considering separate datasets with features based on temperature, mass transfer, radiation and, finally, using the main meteorological variables together. The results obtained show that stacking is the best approach in all datasets and each group of features evaluated, running as good alternative to predict evapotranspiration when using data of a different nature and under different conditions.

Exotic annual grasses invasion across northern Great Basin rangelands has promoted a grass-fire cycle that threatens the sagebrush (Artemisia spp.) steppe ecosystem. In this sense, high accumulation rates and persistence of litter from annual species largely increase the amount and continuity of fine fuels. Here, we highlight the potential use and transferability of remote sensing-derived products to estimate litter biomass on sagebrush rangelands in southeastern Oregon, and link fire regime attributes (fire-free period) with litter biomass spatial patterns at the landscape scale. Every June, from 2018 to 2021, we measured litter biomass in 24 field plots (60 m × 60 m). Two remote sensing-derived datasets were used to predict litter biomass measured in the field plots. The first dataset used was the 30-m annual net primary production (NPP) product partitioned into plant functional traits (annual grass, perennial grass, shrub, and tree) from the Rangeland Analysis Platform (RAP). The second dataset included topographic variables (heat load index -HLI- and site exposure index -SEI-) computed from the USGS 30-m National Elevation Dataset. Through a frequentist model averaging approach (FMA), we determined that the NPP of annual and perennial grasses, as well as HLI and SEI, were important predictors of field-measured litter biomass in 2018, with the model featuring a high overall fit (R2 = 0.61). Model transferability based on extrapolating the FMA predictive relationships from 2018 to the following years provided similar overall fits (R2 ≈ 0.5). The fire-free period had a significant effect on the litter biomass accumulation on rangelands within the study site, with greater litter biomass in areas where the fire-free period was <10 years. Our findings suggest that the proposed remote sensing-derived products could be a key instrument to equip rangeland managers with additional information towards fuel management, fire management, and restoration efforts.