Modeling Evaporation Losses and Water Productivity in Cranberry under Flooded Bed Cultivation

Abstract

The efficient management of water resources in flooded bed cultivation systems is critical for improving water productivity and minimizing evaporation losses, particularly in specialty crops such as cranberry. This study presents a modeling framework to analyze evaporation losses and water productivity in cranberry under flooded bed cultivation using data-driven and analytical approaches. The proposed methodology integrates multi-source datasets, including climatic variables (temperature, humidity, solar radiation, and wind speed), water depth levels, soil properties, and crop growth parameters. These data are preprocessed through cleaning, normalization, and feature engineering techniques to ensure consistency and reliability. Advanced modeling techniques, including regression analysis, Random Forest, and Artificial Neural Networks, are employed to quantify evaporation losses and evaluate their relationship with crop yield and water productivity. The system is trained and validated using region-specific datasets to ensure robustness under varying environmental conditions. Experimental results demonstrate that significant evaporation losses occur under prolonged flooding conditions, particularly during high-temperature and high-radiation periods. The developed models accurately estimate evaporation dynamics and identify optimal water management strategies that reduce unnecessary water loss while maintaining adequate moisture for crop growth. Additionally, improved water productivity is achieved through optimized flooding duration and depth control. The findings highlight the effectiveness of integrating artificial intelligence with evaporation modeling for precision water management in cranberry cultivation. The study concludes that intelligent modeling of evaporation losses and water productivity provides a scalable, efficient, and sustainable solution for optimizing water use, enhancing yield performance, and supporting environmentally responsible flooded bed agriculture.