Based on real county-level surface ozone and PM2.5 data, an econometric model was constructed to comprehensively consider its impacts on rice yield in China. At the same time, other economic, climate, and regional variables were included in the econometric model to accurately measure the interaction between surface ozone and PM2.5 on rice yield in China. The results showed that an increase in ozone concentration in different periods and an increase in PM2.5 in the growth period would reduce the rice yield. The interaction coefficient between ozone and PM2.5 suggests that PM2.5 partially mitigated the amount of ozone absorbed by rice plants, which in turn had a positive effect on rice yield. Therefore, while controlling PM2.5 and reducing near-surface ozone concentration, it is necessary to comprehensively consider its impact on rice yield.

This study uses multidimensional indicators of macroeconomics, supply and demand, cost, and market microstructure to construct an intelligent framework integrated with optimized Exponentially Weighted Moving Average (EWMA) denoising for price forecasting and black early warning for egg futures in China from 2014 to 2023. Black early warning serves as a non-parametric early warning method that identifies abnormal price increases and falls based on historical fluctuation thresholds. As the first livestock future contract listed in China, accurate egg price forecasting is crucial for risk prevention and market control and regulation. First, LASSO regression was used to screen the core driving factors of egg futures prices. Nine key indicators were identified and input into the hybrid Temporal Convolutional Network–Gated Recurrent Unit (TCN-GRU) prediction model. To address the high-frequency noise in the original price series, two-dimensional optimization was performed on traditional EWMA denoising to achieve more adaptive noise filtering. By applying the black early warning method, the obtained future egg price fluctuations were more consistent with the actual situation. In addition, empirical analysis of multi-horizon forecasting and early warning for t + 1, t + 5, and t + 10 was carried out to further verify the model’s prediction accuracy. The results show that compared with the single TCN model, the single GRU model, and the TCN-GRU model without denoising, the TCN-GRU model integrated with optimized EWMA denoising achieves better prediction performance on the test set. In terms of the early warning matching rate, it reaches 83.33% for the t + 1 horizon, and the prediction accuracy for the t + 5 and t + 10 horizons decreases regularly but remains stable above 60%. In contrast, the highest early warning matching rate of the model without denoising is only 22.22% across all horizons, which has no practical early warning value. The early warning signals generated by the optimized EWMA denoising-based TCN-GRU model can effectively identify abnormal sharp rises and falls in egg futures prices, providing effective support for hedging and risk management for market participants. The study’s limitations are discussed, as well as future research directions. The findings provide a basis for decision making for agricultural producers and future investors and support the development of China’s agricultural product market.

Land use transitions increasingly hinge on how governance arrangements shape resource use efficiency and knowledge diffusion in agricultural systems. Collective irrigation, as a key institutional form of water governance, has been widely promoted to improve irrigation performance, yet its potential linkages with peer effects in agricultural production remain insufficiently understood. Using plot-level panel data from Jiangsu Province, China, for 2015 and 2019, this study examines how peer effects and collective irrigation affect the technical efficiency of rice production. A time-varying stochastic frontier production function is employed to estimate technical efficiency, while a linear-in-means model is used to capture peer effects and compare their effects across different irrigation types. The results indicate that peer effects play a significant role in improving technical efficiency in rice-growing areas. These effects are more pronounced under collective irrigation arrangements. Moreover, peer effects under collective irrigation are stronger in villages without public technology extension services, among farmers with lower grain production capacity or without off-farm employment, and on smaller plots. Mechanism analysis indicates that peer effects under collective irrigation operate partly through input decisions, especially pesticide use. Overall, the findings highlight the importance of considering both peer interactions and irrigation institutions when improving agricultural production efficiency.