Accurate prediction of photovoltaic power is of great significance to the safe operation of power grids. In order to improve the prediction accuracy, a similar day clustering convolutional neural network (CNN)–informer model was proposed to predict the photovoltaic power. Based on correlation analysis, it was determined that global horizontal radiation was the meteorological factor that had the greatest impact on photovoltaic power, and the dataset was divided into four categories according to the correlation between meteorological factors and photovoltaic power fluctuation characteristics; then, a CNN was used to extract the feature information and trends of different subsets, and the features output by CNN were fused and input into the informer model. The informer model was used to establish the temporal feature relationship between historical data, and the final photovoltaic power generation power prediction result was obtained. The experimental results show that the proposed CNN–informer prediction method has high accuracy and stability in photovoltaic power generation prediction and outperforms other deep learning methods.

This study aimed to investigate the impacts of straw and biochar on greenhouse gas (GHG) emissions and grain yield in a double rice cropping system under optimal N fertilizer reduction. Conventional fertilization (CF) was used as the control group, and treatments included optimal fertilization and 15% less nitrogen (OF), together with straw (S) or biochar (B) applied under different fertilization conditions, namely CF + S, CF + B, OF + S, and OF + B. The effects of treatments on soil CH₄ and N₂O emissions were studied, and changes in soil physicochemical properties were analyzed. The results showed that relative to CF, CF + S and OF + S increased the cumulative CH₄ emissions by 11.80% and 2.35%, respectively, while CF + B and OF + B resulted in significant reductions in cumulative CH₄ emissions by 27.80% and 28.46%, respectively. Biochar was effective in reducing N₂O emissions, and OF further increased the potential, with CF + B and OF + B achieving the best N₂O reductions of 30.56% and 32.21%, respectively. Although OF reduced yields by 0.16%, this difference was within reasonable limits; the remaining treatments increased grain yields by 2.55% to 3.47%. CF + B and OF + B reduced the global warming potential (GWP) by 27.93% and 28.63%, respectively, and ultimately reduced the greenhouse gas emission intensity (GHGI) by 30.42% and 30.97%. Both straw and biochar increased the soil organic matter, NH₄⁺-N, and NO₃⁻-N contents, and biochar increased the soil pH, which may be the potential mechanism regulating soil GHG emissions. Overall, OF + B is beneficial for reducing GHG emissions and may be a better agronomic cropping pattern in double season rice growing areas.

The subtropical region of East China is characterized by abundant water and temperature resources conducive to crop cultivation, and large areas of lowland have been widely used for agricultural planting. The objectives of the study were to explore feasible methods of greenhouse gas (GHG) reduction for rice–wheat rotation systems and to explain the mechanism underlying the effect of drainage on GHG reduction. Shallow ditch (SD) and deep ditch (DD) treatments in the wheat season were set up for drainage to control the paddy soil water content, with conventional non-ditching as the control group (CG). CH₄ and N₂O emission fluxes were continuously measured, and related soil physical and chemical properties were also measured in this study. The results showed that CH₄ emissions from paddy soil accounted for most of the global warming potential (GWP) in the rice–wheat rotation system. Drainage led to a significant reduction in cumulative soil CH₄ emissions during the rice and wheat seasons; however, the overall cumulative N₂O flux increased significantly. The total GWP produced by SD and DD in the three years was reduced by 58.21% and 54.87%, respectively. The GHG emission intensity (GHGI) of SD and DD declined by 60.13% and 56.40%, respectively. The CH₄ emission flux was significantly positively correlated with the 5 cm ground temperature but negatively correlated with the soil redox potential (soil Eh). The drainage decreased the soil water and soil organic matter contents and increased soil pH, which were the mechanisms that reduced the CH₄ emissions. The drainage increased the soil nitrogen content, which is the main reason for regulating N₂O. The findings indicate that SD and DD not only ensured a stable increase in production but also effectively reduced GHG emissions, and we recommend SD treatment for agricultural production.