Molecular Pathogens, 2025, Vol.16, No.3, 100-110 http://microbescipublisher.com/index.php/mp 108 7 Recommendations for Promotion and Application and Future Research Directions 7.1 Optimization recommendations for regional rotation patterns In order to bring the effect of sorghum rotation in alleviating pests and diseases to a wider range, it is necessary to optimize the rotation pattern according to the ecological conditions and production needs of different regions. First, the dominant pests and diseases in the region should be identified and targeted rotation plans should be formulated. For example, in North China, soil-borne diseases and underground pests are prominent, and multi-crop rotation of sorghum with non-host crops such as legumes and crucifers can be adopted to improve disease suppression (Sun et al., 2024); in the humid and hot southern regions, sorghum anthracnose is prevalent, and the planting period of non-sorghum crops should be extended, and disease-resistant varieties should be used (Little et al., 2023). Second, considering water and fertilizer resources and soil fertility, the combination of rotation and land conservation should be coordinated. Sorghum has a large amount of straw, and organic matter can be increased by returning straw to the field in the rotation system, while legume rotation can fix nitrogen and conserve the land (Li et al., 2024). Local governments should adapt to local conditions and combine sorghum rotation with green manure planting and fallow rotation to achieve simultaneous cultivation and land conservation. Third, pay attention to the economic value of crops. The promotion of crop rotation must take into account farmers' benefits. A combination of crops that complement each other in the market can be selected. For example, sorghum brewing has a high demand, and it can be rotated with edible beans to increase land output and diversify income sources. In addition, regional crop rotation demonstration bases should be established to promote successful experiences. Through field observation and technical training, farmers can truly understand the effects of crop rotation in reducing pesticide use and increasing yield (Bowles et al., 2020). At the government level, incentive policies such as crop rotation subsidies can be introduced to guide the transition from single planting to crop rotation. Taking China as an example, a sorghum-soybean-wheat rotation pilot can be carried out in the Northeast Corn Belt, a sorghum-peanut/cotton rotation can be carried out in the Huanghuaihai area, and a sorghum-leguminous-millet composite rotation can be promoted in the Northwest Arid Area, etc., and a rotation layout optimization map can be formulated according to the climate and soil characteristics of each region. In the long run, we should improve the supporting agronomic measures, such as returning straw to the field, conservation tillage such as no-tillage or minimum tillage, and cooperate with the rotation system to further improve soil health and stress resistance (Al-Shammary et al., 2024). At the same time, we should strengthen the dynamic optimization of rotation technology: with climate change and the emergence of new varieties, timely adjust the types of rotation crops and the length of the cycle. For example, in drought-prone areas, drought-resistant crops such as sorghum can be introduced to increase the diversity of rotation and improve the climate resilience of the entire system (O’Donoghue et al., 2024). 7.2 Research prospects of combining digital agriculture with precision policy As agriculture enters the digital age, integrating information technology with the rotation system is an important development direction in the future. On the one hand, remote sensing and big data can be used to monitor the occurrence of pests and diseases and soil conditions in farmland as a basis for dynamically adjusting the rotation plan. For example, through drone hyperspectral imaging, the spatial distribution of a disease in sorghum fields can be discovered in time, and the pathogen hotspot area can be inferred based on this, so that non-host crops can be arranged in this area in a targeted manner in the next crop rotation to accurately reduce the source of pathogens. For another example, real-time acquisition of soil moisture and fertility data based on soil sensor networks can help determine the choice of rotation crops (such as rotating leguminous nitrogen-fixing crops when soil nitrogen is low) and achieve intelligent decision-making (Sun et al., 2024). On the other hand, artificial intelligence models can be used to optimize crop rotation plans. Through machine learning algorithms, historical meteorological, soil and pest and disease data are integrated to predict the pest and disease risks and expected yields under different rotation combinations, and recommend the best rotation strategy
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