Browsing by Author "Lillo-Saavedra, Mario"

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    A satellite-based ex post analysis of water management in a blueberry orchard
    (2020) Holzapfel, Eduardo; Lillo-Saavedra, Mario; Rivera, Diego; Gavilán, Viviana; García-Pedrero, Angel; Gonzalo-Martín, Consuelo
    In the scenario of current water scarcity caused by climate change and increasing water demand for food production, farmers must adapt their water management practices by shifting from supply-driven water management to demand-driven water management, considering trade-offs among quality, quantity and costs. Thus, agricultural practices must take full advantage of technology, research and development and adapt to local requirements. Nowadays, remote sensing is a useful tool for estimating crop water demand (evapotranspiration) as well as mapping their spatial and temporal variability. In this work, we present a new methodology that allows the user to audit (ex post) the irrigation strategies of a blueberry field in central Chile using a decision support system for irrigation decision called AquaSat® as the main tool. This tool combines satellite information with field data and provides spatially distributed information on crop water use for managing irrigation at a farm scale. The main contribution of this work is to detail a new approach for irrigation management through the comparison of volume of applied water, against evapotranspiration and potential demand. This procedure allows the user to audit current irrigation management and to determine the impacts on productivity. From our results, we can conclude that the applied water levels used at the farm during both seasons throughout of the irrigation sector were insufficient to reach the potential blueberries yield.
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    Early Estimation of Tomato Yield by Decision Tree Ensembles
    (2022) Lillo-Saavedra, Mario; Espinoza-Salgado, Alberto; García-Pedrero, Angel; Souto, Camilo; Holzapfel, Eduardo; Gonzalo-Martín, Consuelo; Somos-Valenzuela, Marcelo; Rivera Salazar, Diego
    Crop yield forecasting allows farmers to make decisions in advance to improve farm management and logistics during and after harvest. In this sense, crop yield potential maps are an asset for farmers making decisions about farm management and planning. Although scientific efforts have been made to determine crop yields from in situ information and through remote sensing, most studies are limited to evaluating data from a single date just before harvest. This has a direct negative impact on the quality and predictability of these estimates, especially for logistics. This study proposes a methodology for the early prediction of tomato yield using decision tree ensembles, vegetation spectral indices, and shape factors from images captured by multispectral sensors on board an unmanned aerial vehicle (UAV) during different phenological stages of crop development. With the predictive model developed and based on the collection of training characteristics for 6 weeks before harvest, the tomato yield was estimated for a 0.4 ha plot, obtaining an error rate of 9.28%.
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    Ex Post Analysis of Water Supply Demand in an Agricultural Basin by Multi-Source Data Integration
    (2021) Lillo-Saavedra, Mario; Gavilán, Viviana; García-Pedrero, Angel; Gonzalo-Martín, Consuelo; Hoz, Felipe de la; Somos-Valenzuela, Marcelo; Rivera, Diego
    In this work, we present a new methodology integrating data from multiple sources, such as observations from the Landsat-8 (L8) and Sentinel-2 (S2) satellites, with information gathered in field campaigns and information derived from different public databases, in order to characterize the water demand of crops (potential and estimated) in a spatially and temporally distributed manner. This methodology is applied to a case study corresponding to the basin of the Longaví River, located in south-central Chile. Potential and estimated demands, aggregated at different spatio-temporal scales, are compared to the streamflow of the Longaví River, as well as extractions from the groundwater system. The results obtained allow us to conclude that the availability of spatio-temporal information on the water availability and demand pairing allows us to close the water gap—i.e., the difference between supply and demand—allowing for better management of water resources in a watershed

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