Xuegui Zhang

Gross primary productivity (GPP) is a key indicator for assessing carbon uptake capacity and photosynthetic productivity in agricultural ecosystems, playing a crucial role in regional carbon cycle evaluation and sustainable agriculture development. However, traditional mechanistic light use efficiency (LUE) models exhibit variable accuracy under different climatic conditions and crop types. Machine learning models, while demonstrating strong fitting capabilities, heavily depend on the selection of input features and data availability. This study focuses on winter wheat in the Guanzhong region, utilizing continuous field observation data from the 2020–2022 growing seasons to develop five machine learning models: Ridge Regression (Ridge), Random Forest (RF), Support Vector Regression (SVR), Gradient Boosting Regression (GB), and a stacking-based ensemble learning model (LSM). These models were compared with the LUE model under two scenarios, excluding and including solar-induced chlorophyll fluorescence (SIF), to evaluate the contribution of SIF to GPP estimation accuracy. The results indicate significant differences in GPP estimation performance among the machine learning models, with LSM outperforming others in both scenarios. Without SIF, LSM achieved an average R² of 0.87, surpassing individual models (0.72–0.83), demonstrating strong stability and generalization ability. With SIF inclusion, all machine learning models showed marked accuracy improvements, with LSM’s average R² rising to 0.91, highlighting SIF’s critical role in capturing photosynthetic dynamics. Although the LUE model approached machine learning model accuracy in some growth stages, its overall performance was limited by structural constraints. This study demonstrates that ensemble learning methods integrating multi-source observations offer significant advantages for high-precision winter wheat GPP estimation, and that incorporating SIF as a physiological indicator further enhances model robustness and predictive capacity. The findings validate the potential of combining ensemble learning and photosynthetic physiological parameters to improve GPP retrieval accuracy, providing a reliable technical pathway for agricultural ecosystem carbon flux estimation and informing strategies for climate change adaptation.

Maize plays a crucial role in China’s grain production, with a cultivation area reaching 44.22 million hectares and an annual yield of 289 million tons in 2023. However, the challenge remains on how to further increase maize yield and water use efficiency (WUE) without adding to the environmental burden. To systematically evaluate the impact of straw incorporation under varying production conditions on maize yield and WUE, this study collected experimental data from multiple locations across China. A meta-analysis was conducted to compare the effects of straw incorporation versus no incorporation, and the main influencing factors were identified using correlation analysis and a random forest model. The results indicate that straw incorporation significantly enhances both maize yield and WUE, with the most pronounced improvements observed under conditions of an average growing season temperature of 19–23 °C, soil pH of 6.5–7.5, low initial soil organic matter content, and deep plowing for straw incorporation. Additionally, moderate nitrogen application rates and straw incorporation amounts (9000–15,000 kg·ha⁻²) also significantly boost maize yield and WUE. Field management practices and meteorological conditions are identified as the primary factors affecting maize yield and WUE under straw incorporation conditions. Therefore, straw incorporation stands out as an effective agricultural practice for achieving high maize yields and efficient resource utilization. The findings of this study provide valuable insights for global food security and the sustainable development of agriculture.