The health and productivity of rabbits are highly sensitive to the environmental conditions within the rabbit house, particularly to fluctuations and deviations in temperature, relative humidity, and carbon dioxide (CO₂) concentration. However, owing to the thermal inertia and residual evaporation effects inherent in ventilation and cooling systems, environmental changes often exhibit delayed responses, rendering real-time control inadequate. Accurate prediction of key environmental parameters is indispensable for formulating effective environmental control strategies, as it enables consideration of their future dynamics and thereby enhances the rationality of regulation in rabbit farming. Existing prediction models often exhibit unsatisfactory accuracy and weak generalization, which restricts the incorporation of prediction into effective environmental control strategies. To address these limitations, summer indoor and outdoor environmental data were collected from rabbit houses in Nanping, Fujian; Jiyuan, Henan; and Qingyang, Gansu, China—three climatically distinct regions—forming three datasets. Based on these datasets, a multi-parameter time-series prediction model, Patch and Cross-Attention Enhanced Transformer for Rabbit House Prediction (PatchCrossFormer-RHP), is introduced, integrating patching and attention mechanisms. The model partitions the sequences of rabbit house temperature, relative humidity, and CO₂ concentration into patches and incorporates auxiliary parameters, such as indoor air velocity and outdoor temperature and humidity, to enhance feature representation. Furthermore, it applies cross-attention with differentiated encoding to disentangle multi-parameter relationships and improve predictive performance. This study used the Fujian dataset as the primary benchmark. On this dataset, PatchCrossFormer-RHP achieved root mean square error (RMSE) values of 0.290 °C, 1.554%, and 38.837 ppm for rabbit house temperature, humidity, and CO₂ concentration, respectively, with corresponding R² values of 0.963, 0.956, and 0.838, consistently outperforming RNN, GRU, and LSTM. Transfer experiments with single- and multi-source pretraining followed by fine-tuning on Fujian demonstrated that strong cross-regional generalization can be achieved with only limited target-domain data.

This study compared a programmable logic controller (PLC) combining indoor temperature, outdoor temperature, and outdoor relative humidity with conventional temperature-triggered pad cooling and manual fan adjustment in two commercial rabbit houses in Leshan, China, a region characterized by a hot and humid climate. The 33-day summer trial included 108 selected Hyla rabbits aged 39 d per house. The PLC house had a mean air temperature 0.36 °C higher and relative humidity 4.34 percentage points lower than the control house. Final body weight and average daily gain were higher, while feed conversion did not differ significantly. Intra-abdominal temperature near the cooling pads was 0.29 °C lower. Estimated cooling pad recirculation was 81.0% lower and combined fan and pump electricity use was 21.2% higher. A partial-budget analysis at a standardized scale of 3000 retained rabbit production-equivalents estimated a benefit 2955.95 Chinese yuan (CNY) higher for the treatment house than for the control house under the specified prices and scaling assumptions. Each strategy was applied in one house, so these preliminary differences cannot be separated from house effects. These observations support further evaluation in independently replicated houses, with animal responses, resource use, and deployment costs assessed together.