Accurate forecasting of aboveground biomass (AGB) is essential for sustainable pasture management. Existing approaches, such as traditional machine learning (ML) and biophysical models, have limitations: ML models fail to effectively capture temporal dependencies and underlying biophysical processes, whereas biophysical models are difficult to scale due to their requirement for extensive calibration. Although hybrid frameworks that integrate biophysical model outputs into ML models show potential, they still cannot effectively exploit temporal information. To address these issues, we propose a novel framework that combines a multi-view Gate-Controlled LSTM (GC-LSTM) with the biophysical model Modvege for short-term AGB forecasting. Our GC-LSTM employs dual branches to model both climate data and Modvege-derived biophysical variables, adaptively balancing their contributions through a gating mechanism. We evaluate GC-LSTM against nine baselines, including Modvege and eight ML and deep learning models that incorporate biophysical variables using three independent yearly datasets (2021, 2022, and 2023) of 56 Australian paddocks. Results demonstrate that GC-LSTM consistently achieves higher accuracy, highlighting its robustness and practical utility for AGB prediction.