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Author: search.filters.author.Camilo SoutoHas files: No

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    Item type:Publication,
    Optimization of Water and Land Allocation in Fruit Orchards over a 20-Year Period
    (MDPI AG, 2024-12-24)
    Luciano Quezada
    ;
    Eduardo Holzapfel
    ;
    Mathias Kuschel-Otárola
    ;
    Mario Lillo-Saavedra
    ;
    RIVERA SALAZAR, DIEGO ANDRÉS  
    <jats:p>This study proposes a nonlinear programming model for the optimization of water and land allocation in a 1000 ha orchard over a 20-year period to maximize farmers’ net profits. Different scenarios were evaluated, including equitable and unrestricted land allocation, and the risks associated with fruit production were considered. Additionally, a sensitivity analysis that focused on the variability of labor and water availability was conducted. The results reveal that with equitable land allocations and no constraints on the cultivated area, cherry emerges as the most profitable crop, although there are large risks associated with its price volatility. The introduction of risk and land allocation constraints highlights the importance of crop diversification in mitigating economic risks. A sensitivity analysis indicated that reductions in water and labor availability significantly affect the optimal cropping pattern of an orchard, suggesting that the efficient and adaptive management of resources is required. The proposed optimal cropping pattern maintains the economic viability of the orchard even with 70% and 24% reductions in water and labor, respectively. This approach underscores the importance of implementing resilient and sustainable agricultural strategies to ensure food security and increase economic stability in the face of changing climatic and labor conditions.</jats:p>
      2Scopus© Citations 6
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    Early Estimation of Tomato Yield by Decision Tree Ensembles
    (2022)
    Mario Lillo-Saavedra
    ;
    Alberto Espinoza-Salgado
    ;
    Angel García-Pedrero
    ;
    Camilo Souto
    ;
    Eduardo Holzapfel
    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%.
      1Scopus© Citations 8  1