RIVERA SALAZAR, DIEGO ANDRÉS
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RIVERA SALAZAR, DIEGO ANDRÉS
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diegorivera@udd.cl
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25822747600

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Publication

A satellite-based ex post analysis of water management in a blueberry orchard

2020 , Eduardo Holzapfel , Mario Lillo-Saavedra , RIVERA SALAZAR, DIEGO ANDRÉS , Viviana Gavilán , Angel García-Pedrero , Consuelo Gonzalo-Martín

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Simulation of Water-Use Efficiency of Crops under Different Irrigation Strategies

2020 , Mathias Kuschel-Otárola , RIVERA SALAZAR, DIEGO ANDRÉS , Eduardo Holzapfel , Niels Schütze , Patricio Neumann , GODOY FAUNDEZ, ALEX ORIEL

Irrigation management is a key factor in attaining optimal yields, as different irrigation strategies lead to different yields even when using the same amount of water or under the same weather conditions. Our research aimed to simulate the water-use efficiency (WUE) of crops considering different irrigation strategies in the Central Valley of Chile. By means of AquaCrop-OS, we simulated expected yields for combinations of crops (maize, sugar beet, wheat), soil (clay loam, loam, silty clay loam, and silty loam), and bulk density. Thus, we tested four watering strategies: rainfed, soil moisture-based irrigation, irrigation with a fixed interval every 1, 3, 5, and 7 days, and an algorithm for optimal irrigation scheduling under water supply constraints (GET-OPTIS). The results showed that an efficient irrigation strategy must account for soil and crop characteristics. Among the tested strategies, GET-OPTIS led to the best performance for crop yield, water use, water-use efficiency, and profit, followed by the soil moisture-based strategy. Thus, soil type has an important influence on the yield and performance of different irrigation strategies, as it provides a significant storage and buffer for plants, making it possible to produce “more crop per drop”. This work can serve as a methodological guide for simulating the water-use efficiency of crops and can be used alongside evidence from the field.

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Estimation of Yield Response Factor for Each Growth Stage under Local Conditions Using AquaCrop-OS

2020 , Mathias Kuschel-Otárola , Niels Schütze , Eduardo Holzapfel , GODOY FAUNDEZ, ALEX ORIEL , Oleksandr Mialyk , RIVERA SALAZAR, DIEGO ANDRÉS

We propose a methodology to estimate the yield response factor (i.e., the slope of the water-yield function) under local conditions for a given crop, weather, sowing date, and management at each growth stage using AquaCrop-OS. The methodology was applied to three crops (maize, sugar beet, and wheat) and four soil types (clay loam, loam, silty clay loam, and silty loam), considering three levels of bulk density: low, medium, and high. Yields are estimated for different weather and management scenarios using a problem-specific algorithm for optimal irrigation scheduling with limited water supply (GET-OPTIS). Our results show a good agreement between benchmarking (mathematical approach) and benchmark (estimated by AquaCrop-OS) using the Normalised Root Mean Square Error (NRMSE), allowing us to estimate reliable yield response factors ( K y ) under local conditions and to dispose of the typical simple mathematical approach, which estimates the yield reduction as a result of water scarcity at each growth stage.

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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 , Consuelo Gonzalo-Martín , Marcelo Somos-Valenzuela , RIVERA SALAZAR, DIEGO ANDRÉS

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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