Deep Reinforcement Learning for Intelligent Decision-Making in Smallholder Beef Cattle Farming: Model Innovation and Industrial Practice

Xiaodong Zhang^1*Pan He¹Chunrong Guo²

• ¹Inner Mongolia Agricultural University, Hohhot, China
• ²School of Economics and Management, Ningbo University of Technology, Ningbo, China

This study addresses the long-standing challenge of decision-making in smallholder beef cattle farming, where reliance on experience often results in low efficiency and limited resilience to risk. We explore the application of deep reinforcement learning (DRL) to develop an intelligent decision support system tailored for beef cattle production. The research first reviews the current adoption of reinforcement learning in agriculture and identifies key factors influencing farmers' decisions. Specifically, eight macro-level state variables—such as production cost, market price, and animal health—together with five micro-level state variables—including land resources and financial capacity—were defined as model inputs. A decision-making model was constructed based on the Deep Q-Network (DQN), incorporating a composite reward function that accounts for baseline state rewards, seasonal adjustments, stability penalties, risk modifiers, and long-term planning incentives. Model training employed ε-greedy exploration and experience replay mechanisms. Experimental results demonstrate that after 40 training episodes, the model achieved an average reward of 203.85, while the average loss decreased to 0.557, indicating stable convergence of the learned strategy. In a farmer-level case analysis, the dominant decision action accounted for 71.5% of the outcomes. Sensitivity analysis, combined with local interpretability using LIME and global feature importance via SHAP, revealed that risk preference, livestock health, financial status, and production cost were the most critical determinants of decision outcomes, while seasonal variation and labor availability also played significant roles. These findings confirm that DRL can provide precise decision support for farmers, thereby enhancing resource allocation, management priorities, and overall production resilience. The study ultimately offers actionable insights to promote the digital and intelligent transformation of the beef cattle industry.