Journal of Global Agriculture and Ecology

Modern agriculture faces mounting pressures from climate variability, resource scarcity, and the need to sustainably feed a growing population. Predictive analytics supports anticipatory decision-makings in yield forecasting, pest and disease modeling, irrigation scheduling, and resource optimization, yet traditional discriminative models often struggle with data scarcity, class imbalance, and the inability to simulate novel conditions. Generative artificial intelligence (GenAI) including diffusion models, GANs, VAEs, and large vision-language models addresses these limitations by synthesizing realistic data, generating diverse scenarios, and integrating multimodal inputs to produce robust, probabilistic predictions especially in data-constrained smallholder systems. This review examines recent applications of GenAI in agricultural predictive analytics, covering crop yield estimation, pest and disease forecasting, climate impact simulation, smart irrigation, and soil/resource optimization. Real-world implementations demonstrate improved forecast accuracy and generalization through synthetic augmentation, while a dedicated future vision outlines scalable deployment. Key challenges include hallucinations, computational demands, biases, limited explainability, privacy risks, and unequal access, raising ethical concerns around over-reliance and the digital divide. Future progress depends on hybrid generative-discriminative systems, lightweight edge models, farmer-centered validation, and transparent governance. Responsibly deployed, GenAI can amplify human judgment and local knowledge, advancing anticipatory, resilient, and sustainable agriculture for food security and climate adaptation.