AI-Based Predictive Agrometeorological Modeling for Climate-Smart Fertilization Optimization to Enhance Yield and Nutrient Use Efficiency in Oil Palm
Kata Kunci:
Agrometeorological, Smart Fertilizer, Enchance Yield, Nutrient Use Efficency, Oil PalmAbstrak
Climate variability poses significant challenges to fertilizer management in oil palm (Elaeis guineensis) production systems, where conventional calendar-based applications often fail to account for dynamic agroclimatic conditions. This study proposes an AI-based predictive agrometeorological modeling framework integrated with a multi-objective fertilization optimization engine to enhance yield and nutrient use efficiency (NUE) under a climate-smart agriculture paradigm. Multi-source datasets comprising agrometeorological variables, soil properties, and historical yield records were processed using nonlinear time-series learning models to forecast short- to medium-term climate and soil moisture dynamics. The predictive outputs were subsequently integrated into a multi-objective optimization module designed to maximize yield and NUE while minimizing nutrient loss risks associated with high rainfall events. Experimental evaluation using a randomized block design demonstrated statistically significant improvements compared to conventional fertilization practices. The AI-based system increased fresh fruit bunch yield by 13.47% (p < 0.001) and improved NUE by 21.98% (p < 0.0001), while reducing fertilizer input by 6–8%. Effect size analysis indicated substantial practical impact (Cohen’s d > 1.8). These findings confirm that integrating predictive agroclimatic intelligence with adaptive fertilization optimization enhances productivity and resource efficiency simultaneously. The proposed framework contributes to advancing decision intelligence in perennial crop systems and provides an operational pathway toward climate-resilient and sustainable oil palm production.Unduhan
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