Estado actual de los modelos de simulación que estimen rendimiento de cultivos y frutales en función del agua aplicada
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Date
2025
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Publisher
Universidad de Concepción
Abstract
Los modelos de simulación agrícola permiten estimar el rendimiento de cultivos y frutales en función del agua aplicada, constituyéndose en herramientas fundamentales para optimizar la gestión hídrica y anticipar impactos asociados al cambio climático. Su aplicación ha sido ampliamente validada en cultivos anuales como trigo, maíz y papa mediante modelos como AquaCrop, CropSyst, APSIM y DSSAT, mostrando capacidad para representar interacciones suelo-clima-manejo y respuestas al estrés hídrico. En contraste, los frutales presentan un menor grado de modelación debido a su complejidad fisiológica, naturaleza perenne y escasez de datos fenológicos y productivos de largo plazo. Si bien se han desarrollado enfoques estructurales y sistemas híbridos con inteligencia artificial, persisten limitaciones por la baja disponibilidad de información validada y adaptada a condiciones locales. En el caso de Chile, se observa una escasa cantidad de estudios de calibración y validación para ambos tipos de cultivos, lo que restringe la implementación operativa de estos modelos. Fortalecer su desarrollo e integración en contextos productivos reales es clave para avanzar hacia una agricultura resiliente, eficiente y basada en evidencia en un escenario de creciente variabilidad climática.
Crop simulation models estimate yield based on applied water, playing a crucial role in optimizing water use and anticipating climate-related risks. Their application is well established in annual crops such as wheat, maize, and potato, using tools like AquaCrop, CropSyst, APSIM, and DSSAT, which effectively simulate soil-climate-management interactions and water stress responses. In contrast, fruit crops remain underrepresented due to their perennial nature, physiological complexity, and the lack of long-term phenological and yield data. Although structural approaches and hybrid systems combining process-based modeling and machine learning show promising results, limitations persist due to scarce locally adapted and validated information. In Chile, a significant gap remains in calibration and validation studies for both annual crops and fruit trees, limiting practical implementation. Enhancing the development and integration of these tools in real production systems is essential to support a more resilient and efficient agriculture under increasing climate variability.
Crop simulation models estimate yield based on applied water, playing a crucial role in optimizing water use and anticipating climate-related risks. Their application is well established in annual crops such as wheat, maize, and potato, using tools like AquaCrop, CropSyst, APSIM, and DSSAT, which effectively simulate soil-climate-management interactions and water stress responses. In contrast, fruit crops remain underrepresented due to their perennial nature, physiological complexity, and the lack of long-term phenological and yield data. Although structural approaches and hybrid systems combining process-based modeling and machine learning show promising results, limitations persist due to scarce locally adapted and validated information. In Chile, a significant gap remains in calibration and validation studies for both annual crops and fruit trees, limiting practical implementation. Enhancing the development and integration of these tools in real production systems is essential to support a more resilient and efficient agriculture under increasing climate variability.
Description
Tesis presentada para optar al título de Ingeniero Agrónomo
Keywords
Cambio climático, Productividad agrícola, Uso eficiente del agua