Morchella mushroom is a nutritionally rich and rare edible fungus. The traditional cultivation model, which relies on expanding the cultivation area to meet market demand, is no longer sufficient to address the rapidly growing market demand. Enhancing the yield and quality of Morchella without increasing the cultivation area is an intractable challenge in the development of the Morchella mushroom industry. Against this backdrop, this study investigates the effects of different amounts of wood ash (WA) application on the yield and quality of Morchella, and conducts an in-depth analysis in conjunction with soil physicochemical properties and microbial communities. The results indicate that the application of WA improves both the yield and quality of Morchella, with the highest yield increase observed in the WA2 treatment (4000 kg/hm²), which showed a 118.36% increase compared to the control group (CK). The application of WA also modified the physicochemical properties of the soil, significantly improving the integrated fertility index of the soil (IFI, p < 0.05). The soil microbial community structure was altered by the addition of WA. Redundancy analysis (RDA) revealed that pH and total potassium (TK) were the main environmental factors influencing the bacterial community, while pH, TK, and total nitrogen (TN) were the main factors influencing the fungal community structure. In addition, bacterial community diversity tended to increase with higher WA application rates, whereas fungal community diversity generally showed a decreasing trend. Furthermore, the relative abundance of beneficial microbial communities, such as Acidobacteriota, which promote the growth of Morchella, increased with higher WA application, while the relative abundance of detrimental microbial communities, such as Xanthomonadaceae, decreased. Partial least squares path model (PLS-PM) analysis of external factors affecting Morchella yield and quality indicated that WA application can alter soil physicochemical properties and soil microbial communities, thereby improving Morchella yield and quality. Among these factors, soil fertility was identified as the most important determinant of Morchella yield and quality.

The temperature difference between day and night in a solar greenhouse is large. Heat in a greenhouse is typically in excess during the day while the temperature is low and the humidity is high at night. This study designs and tests an active heat storage and release air-source heat-pump system with a thermally insulated water tank as the energy storage body. By comparing air temperature and humidity in a test greenhouse with a control greenhouse in typical weather conditions, the power consumption and performance of the system are evaluated. The results show that compared with the control greenhouse, the average daytime temperature of the test greenhouse is lowered by about 3 °C during the operation of the system in typical weather conditions. At night, the average temperature is increased by about 4 °C, and the relative humidity is decreased by about 20%. When optimized, the maximum coefficient of performance (COP) of the system can reach 4.32 in heat storage mode. The nighttime heat release from the energy storage tank accounts for 26.9% to 51.2% of the nighttime energy consumption, and the energy utilization efficiency is 59.6% to 497.0%. This study provides a new way to control environmental parameters in solar greenhouses.

The high energy consumption CEA building brings challenges to the management of the energy system. An accurate energy consumption prediction model is necessary. Although there are various prediction methods, the prediction method for the particularity of CEA buildings is still a gap. This study proposes some derived features based on the CEA scenarios to improve the accuracy of the model. The study mainly extracts the time series and logical features from the agricultural calendar, the botanical physiological state, building characteristics, and production management. The time series and logical features have the highest increase of 2.8% and 3.6%, respectively. In addition, four automatic feature construction methods are also used to achieve varying degrees of influence from −9% to 8%. Therefore, the multiple feature extraction and feature construction methods proposed in this paper can effectively improve the model performance.