Optimización de huertos frutales y proyecciones climáticas en cultivos anuales: Un enfoque estratégico para la agricultura del Valle Central de Chile
Loading...
Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
Universidad de Concepción
Abstract
La sostenibilidad de los sistemas agrícolas enfrenta presiones crecientes derivadas del aumento en la demanda alimentaria, la escasez de recursos hídricos y los impactos acelerados del cambio climático. Estos desafíos se manifiestan con especial intensidad en el Valle Central de Chile, donde la agricultura bajo riego constituye una actividad clave. En este contexto, la planificación agrícola predial requiere herramientas que integren de forma sistemática variables técnicas, económicas y climáticas, permitiendo decisiones estratégicas y adaptativas en escenarios de alta incertidumbre. Esta tesis propone el uso de modelos de optimización y simulación como instrumentos para fortalecer la planificación agrícola, aplicados en huertos frutales y cultivos anuales bajo distintas condiciones de manejo y horizontes temporales.
Con el fin de optimizar la planificación frutícola en el largo plazo, se desarrolló un modelo de optimización no lineal para determinar el patrón óptimo en huertos frutales con un horizonte de planificación de 20 años. El modelo considera la distribución anual de agua disponible, la volatilidad de precios, la disponibilidad de mano de obra y otras restricciones operativas, con el objetivo de maximizar las utilidades netas prediales. Los resultados muestran que el modelo permite identificar combinaciones de especies más rentables y adaptadas a las restricciones del predio, concluyendo que la disponibilidad de agua y mano de obra son factores críticos en la planificación de largo plazo.
Posteriormente, el modelo fue aplicado a una empresa agrícola, donde se evalúan tres patrones de establecimiento utilizados en distintos periodos, integrando datos reales sobre superficie plantada, especies cultivadas y escenarios operativos. Además, se analizó cómo distintos niveles de eficiencia en el uso del agua afectan la rentabilidad y sostenibilidad del sistema. El patrón optimizado propuesto aumentó las utilidades netas en un 32.7% respecto al patrón del año 2000. El análisis de sensibilidad mostró que variables como la disponibilidad hídrica y las condiciones de mercado pueden alterar significativamente la rentabilidad, lo que evidencia la necesidad de analizar los patrones de establecimiento bajo distintos escenarios. Estos resultados confirman el potencial del modelo como herramienta de apoyo a la toma de decisiones para mejorar la planificación predial en huertos frutales.
Finalmente, se utilizó el modelo de simulación AquaCrop para evaluar los impactos proyectados del cambio climático (escenario RCP8.5) en, la demanda hídrica y el rendimiento de los cultivos de maíz, remolacha azucarera y trigo en la cuenca del río Itata para el periodo 2035–2065. Los resultados evidenciaron una marcada heterogeneidad espacial y temporal en los requerimientos netos de riego, siendo la remolacha el cultivo más demandante de agua. Las proyecciones indicaron incrementos en el rendimiento y en la productividad del agua de riego para trigo y remolacha, mientras que el maíz mostró una respuesta menos favorable, afectada por la reducción en la duración del ciclo fenológico y su menor sensibilidad fisiológica al incremento de CO2. El adelanto en la fecha de siembra permitió extender el ciclo de los cultivos, mejorar el rendimiento y reducir el requerimiento neto de riego, lo cual demuestra su eficacia como medida de adaptación agroclimática. En conjunto, la tesis entrega herramientas robustas que permiten apoyar decisiones productivas resilientes frente a escenarios de cambio climático y operativa creciente.
The sustainability of agricultural systems is under increasing pressure due to rising food demand, water resource scarcity, and the accelerating impacts of climate change. These challenges are particularly acute in Chile’s Central Valley, where irrigated agriculture plays a key role. In this context, on-farm agricultural planning requires tools that systematically integrate technical, economic, and climatic variables, enabling strategic and adaptive decision-making in scenarios marked by high uncertainty. This thesis proposes the use of optimization and simulation models as instruments to strengthen agricultural planning, applied to fruit orchards and annual crops under different management conditions and time horizons. To optimize long-term fruit production planning, a nonlinear optimization model was developed to determine the optimal crop pattern in fruit orchards over a 20-year planning horizon. The model accounts for the annual distribution of available water, price volatility, labor availability, and other operational constraints, with the objective of maximizing farm net profits. Results show that the model can identify combinations of species that are more profitable and better adapted to site-specific constraints, concluding that water and labor availability are critical factors for long-term planning. Subsequently, the model was applied to an agricultural company, where three crop patterns used in different periods were evaluated using real data on planted area, cultivated species, and operational scenarios. Additionally, the study analyzed how different levels of water use efficiency affect system profitability and sustainability. The optimized crop pattern increased net profits by 32.7% compared to the year 2000 baseline. Sensitivity analysis showed that variables such as water availability and market conditions can significantly alter profitability, underscoring the need to evaluate planting patterns under different scenarios. These results confirm the potential of the model as a decision-support tool to improve on-farm planning in fruit orchards. Finally, the AquaCrop simulation model was used to evaluate the projected impacts of climate change (RCP8.5 scenario) on water demand and yield for maize, sugar beet, and wheat crops in the Itata River basin for the 2035–2065 period. Results revealed marked spatial and temporal heterogeneity in net irrigation requirements, with sugar beet being the most water-demanding crop. Projections indicated increases in yield and irrigation water productivity for wheat and sugar beet, while maize showed a less favorable response, affected by the shortened phenological cycle and its lower physiological sensitivity to elevated CO2. Advancing sowing dates helped extend crop cycles, improve yields, and reduce net irrigation requirements, demonstrating its effectiveness as an agroclimatic adaptation strategy. Altogether, this thesis provides robust tools that support resilient production decisions in the face of climate change and increasing operational challenges.
The sustainability of agricultural systems is under increasing pressure due to rising food demand, water resource scarcity, and the accelerating impacts of climate change. These challenges are particularly acute in Chile’s Central Valley, where irrigated agriculture plays a key role. In this context, on-farm agricultural planning requires tools that systematically integrate technical, economic, and climatic variables, enabling strategic and adaptive decision-making in scenarios marked by high uncertainty. This thesis proposes the use of optimization and simulation models as instruments to strengthen agricultural planning, applied to fruit orchards and annual crops under different management conditions and time horizons. To optimize long-term fruit production planning, a nonlinear optimization model was developed to determine the optimal crop pattern in fruit orchards over a 20-year planning horizon. The model accounts for the annual distribution of available water, price volatility, labor availability, and other operational constraints, with the objective of maximizing farm net profits. Results show that the model can identify combinations of species that are more profitable and better adapted to site-specific constraints, concluding that water and labor availability are critical factors for long-term planning. Subsequently, the model was applied to an agricultural company, where three crop patterns used in different periods were evaluated using real data on planted area, cultivated species, and operational scenarios. Additionally, the study analyzed how different levels of water use efficiency affect system profitability and sustainability. The optimized crop pattern increased net profits by 32.7% compared to the year 2000 baseline. Sensitivity analysis showed that variables such as water availability and market conditions can significantly alter profitability, underscoring the need to evaluate planting patterns under different scenarios. These results confirm the potential of the model as a decision-support tool to improve on-farm planning in fruit orchards. Finally, the AquaCrop simulation model was used to evaluate the projected impacts of climate change (RCP8.5 scenario) on water demand and yield for maize, sugar beet, and wheat crops in the Itata River basin for the 2035–2065 period. Results revealed marked spatial and temporal heterogeneity in net irrigation requirements, with sugar beet being the most water-demanding crop. Projections indicated increases in yield and irrigation water productivity for wheat and sugar beet, while maize showed a less favorable response, affected by the shortened phenological cycle and its lower physiological sensitivity to elevated CO2. Advancing sowing dates helped extend crop cycles, improve yields, and reduce net irrigation requirements, demonstrating its effectiveness as an agroclimatic adaptation strategy. Altogether, this thesis provides robust tools that support resilient production decisions in the face of climate change and increasing operational challenges.
Description
Tesis presentada para optar al grado de Doctor en Recursos Hídricos y Energía para la Agricultura
Keywords
Recursos hídricos, Cambio climático, Planificación agrícola - Chile