China's socialized service industry of agricultural machinery emerged as the times required, which resolved the institutional contradiction between small farm households and large-scale agriculture. However, as cross-operation harvesters are still monitored based on phone calls, the monitoring efficiency and real-time performance are relatively poor. To improve the dynamic monitoring accuracy of harvester working areas, we proposed a working progress monitoring method based on harvester traveling trajectory and header status identification. The position and gap bridge angle information of harvesters was synchronously acquired in real-time. The height of the header above ground was calculated to determine whether the harvester was harvesting on a valid trajectory. Finally, the valid trajectory of the harvester was selected for area calculation. Field experiments showed that the header heights at the working and non-working tracing points collected by the system were discretely distributed at intervals of [0m, 0.5m] and (0.5m, 2m), respectively. The working progress monitoring algorithm proposed in this paper effectively improved the monitoring accuracy of harvester working areas. The mean error between the working areas calculated with identifying the header status and the actual cultivated land area in the five test plots was 0.09 hm², with a mean error rate of 3.10%, 8.59% lower than that without identifying the harvester header status.

Straw return is an important farmland management practice that influences the activity of soil nitrogen. Few studies have examined the distribution of soil nitrogen and its components in wheat–rice cropping fields in subtropical China. This study assesses the influence of different years of straw return on the distribution and variation of total soil nitrogen (TN), light fraction nitrogen (LFN), heavy fraction nitrogen (HFN), particulate nitrogen (PN), and mineral-bound nitrogen (MN). We conducted a field experiment with eight years of straw retention treatments in 2017 (no straw retention, NR; 1 year of straw retention, SR1; 2 years of straw retention, SR2; 3 years of straw retention, SR3; 4 years of straw retention, SR4; 5 years of straw retention, SR5; 6 years of straw retention, SR6; 7 years of straw retention, SR7) and one more treatment in 2018 (8 years of straw retention, SR8) in a rice–wheat cropping system at Yangzhou University Experimental Station in China. The results demonstrated that as the number of years of treatment increases, the content of TN, LFN, HFN, PN, and MN at each soil layer gradually increases. Compared with NR, the highest increase in TN, LFN, HFN, PN, and MN under SR1-SR8 in the 0–20 cm soils was 38.10%, 150.73%, 35.61%, 79.97%, and 27.71%, respectively, but increases in TN, HFN, and MN content gradually slowed after six years of straw return. The contents or variation of TN were extremely significantly correlated (p < 0.01) with that of LFN, HFN, PN, and MN, while LFN had the highest variation. In general, straw return could improve the quality of the 0–20 cm nitrogen pool. LFN was the best indicator of changes to the soil nitrogen pool affected by years of straw return.

In China’s subtropical rice–wheat cropping system, the changes in the soil organic carbon (SOC) pool due to long-term straw return and its connection with crop yield remain unclear. This study aims to provide insights into establishing a sensible straw return system by evaluating the differences in the distribution and variation rates of SOC, light fraction organic carbon (LFOC), heavy fraction organic carbon (HFOC), particulate organic carbon (POC), and mineral-associated organic carbon (MOC) in the 0–20 cm soil layer under different durations of straw return. Additionally, the study analyzes the relationship between the changes in SOC and its fractions and wheat yield. The experiment was conducted in 2019 in a rice–wheat rotation field with ten years of straw return treatments: no straw return (NR) or 1, 2, 3, 4, 5, 6, 7, 8, 9 year(s) of straw return (SR1–9), and an additional treatment in 2020 (10 years of straw return, SR10). The results revealed that with an increase in the duration of straw return, the contents of SOC, LFOC, HFOC, and POC gradually increased, showing the highest increments of 45.88%, 187.22%, 41.55%, 97.89%, and 28.21%, respectively, compared to the NR treatment. However, after eight years of straw return, the compound annual increase in soil organic carbon and its components was lower than in years 1–8, indicating a trend of diminishing increments. The SOC content and its variation were significantly correlated with the content and variation of LFOC, HFOC, POC, and MOC, with the highest sensitivity observed for the variation in LFOC, indicating the strong influence of the duration of straw return. The SOC and its fraction contents showed significant positive correlations with wheat yield, with the highest contribution to wheat yield increase attributed to an increase in LFOC content. In summary, straw return enhances the 0–20 cm deep soil carbon pool, with LFOC being the most sensitive indicator, reflecting the influence of the duration of straw return on soil carbon pools.

The plant protection unmanned aerial vehicle (UAV) scheduling model is of great significance to improve the operation income of UAV plant protection teams and ensure the quality of the operation. The simulated annealing algorithm (SA) is often used in the optimization solution of scheduling models, but the SA algorithm has the disadvantages of easily falling into local optimum and slow convergence speed. In addition, the current research on the UAV scheduling model for plant protection is mainly oriented to static scenarios. In the actual operation process, the UAV plant protection team often faces unexpected situations, such as new orders and changes in transfer path costs. The static model cannot adapt to such emergencies. In order to solve the above problems, this paper proposes to use the Levi distribution method to improve the simulated annealing algorithm, and it proposes a dynamic scheduling model driven by unexpected events, such as new orders and transfer path changes. Order sorting takes into account such factors as the UAV plant protection team’s operating income, order time window, and job urgency, and prioritizes job orders. In the aspect of order allocation and solution, this paper proposes a Levy annealing algorithm (Levy-SA) to solve the scheduling strategy of plant protection UAVs in order to solve the problem that the traditional SA is easy to fall into local optimum and the convergence speed is slow. This paper takes the plant protection operation scenario of “one spray and three defenses” for wheat in Nanjing City, Jiangsu Province, as an example, to test the plant protection UAV scheduling model under the dynamic conditions of new orders and changes in transfer costs. The results show that the plant protection UAV dynamic scheduling model proposed in this paper can meet the needs of plant protection UAV scheduling operations in static and dynamic scenarios. Compared with SA and greedy best first search algorithm (GBFS), the proposed Levy-SA has better performance in static and dynamic programming scenarios. It has more advantages in terms of man-machine adjustment distance and total operation time. This research can provide a scientific basis for the dynamic scheduling and decision analysis of plant protection UAVs, and provide a reference for the development of an agricultural machinery intelligent scheduling system.

The quality of cultivated land is an important basis for guaranteeing grain yield, and improving the quality of cultivated land is an important initiative of the Chinese government to implement its food security strategy. This paper explores the effects of formal technical training and informal online self-directed learning on farmers’ willingness to adopt cultivated land conservation technology using farmer-level survey data. The results show that farmers’ participation in technical training effectively increased their willingness to adopt straw return technologies, but participation in online self-directed learning did not affect farmers’ willingness to adopt, while farmers who participated in both technical training and online self-directed learning had stronger willingness to adopt. These results show that internet-based self-directed learning is a useful supplement to the formal training system. Further analysis revealed that participation in technical training increased farmers’ awareness of the usefulness of the straw-return technology, which in turn increased their willingness to adopt the conservation technology of cropland. This paper clarifies the impact and mechanisms of the two main existing technology learning modes on farmers’ adoption of new technologies in rural China and provides a reference for the promotion of agricultural technology diffusion and the improvement of the quality of cultivated land in China.