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Present agricultural practices confront issues such as mismatches between tractors and implements, imprecise machinery allocation, and excessive machinery investment. Optimization of agricultural machinery systems was a potent remedy for these concerns. To address inaccuracies in calculating objective functions and the incompleteness of constraints in existing models for agricultural machinery system optimization, a comprehensive mixed integer nonlinear programming (MINP) model for agricultural machinery system optimization was established. The model introduced timeliness loss costs for multiple key operations across various crops into the objective function, and constraints were enhanced by including operation sequence constraints and boundary constraints on initiation and completion dates of those key operations. Taking corn and soybeans as examples, timeliness loss functions of sowing and harvesting operations were derived through experiments. Solving the MINP model by Lingo (V.14.0) software, improvements in total power, workload per unit power, and total operational costs were shown when comparing the optimized machinery system through the MINP model against current systems. When the model omitted considerations for timeliness loss functions and operation sequence constraints, issues arose including an increase in total operational costs and an inversion of operation sequence. The model’s application in devising machinery allocation plans for production units of various operational scales revealed a gradual decrease in total power and costs per unit area with expanding scale, approaching stability when scale exceeded 1600 hm2. This study enriches theory and methodology for optimizing agricultural machinery systems, provides theoretical and technological underpinnings for rational machinery acquisition, and promotes the high-quality progression of comprehensive agricultural mechanization.

With the continuous development of agricultural product e-commerce platforms and the rapid growth of trading volume, conducting in-depth mining and analysis of online review data to improve consumer satisfaction is of considerable importance. This paper uses JingDong’s self-run online reviews of rice agricultural products as the research object and analyzes the logistics factors that affect consumer satisfaction through text mining technology. A multi-objective model for logistics center distribution path optimization under a soft time window was constructed. The model used the results of online review analysis, namely, packaging integrity, delivery timeliness, and logistics cost, as the goals, and the model used ant colony algorithm (ACO) and genetic algorithm (GA) to solve the optimal distribution solution to minimize the penalty cost and transportation cost. Through examples to solve the optimal distribution vehicle number and shipping routes, in addition, a comparison of the two types of algorithm performance of the model under different node number indicated that the number of nodes affects algorithm performance. With a node number below 50, the ant ACO has high precision and a better distribution path. With a node number above 50, GA has more comprehensive performance. The average efficiency of the GA is 12.28% higher than that of ACO.

In the northeast plains of China, the intensive utilization of agricultural soils has been a persistent issue, and finding ways to utilize soil resources efficiently and sustainably through a scientifically-driven management system has become a crucial challenge for agricultural production. Conservation tillage is a crucial technology for sustainable agriculture. Currently, plow and rotary tillage are the dominant methods used in Mollisols, but there is limited information on the effects of different conservation tillage practices in this region. The objective of this study was to investigate the short-term impact of tillage and sowing methods on soil physical properties and corn plant growth and to examine the relationship between soil physical properties and plant characteristics during various stages of growth. This study consisted of four tillage and sowing methods: plow tillage and precision seeder sowing (PTS), rotary tillage and precision seeder sowing (RTS), no-tillage and no-tillage seeder sowing (NTS), and no-tillage and precise sowing in stubble field (STS) (all four treatments involved total straw return). The results indicated that the soil penetration resistance (SPR) in the 10–40 cm soil layer under the PTS treatment was significantly lower (by 11.9% to 18%) compared to the other treatments (p < 0.05). On average, the soil moisture content in the NTS treatment was 2.7% and 1.4% higher than that of the PTS and RTS treatments. Additionally, soil temperature was 5.6% to 8.6% lower under the STS treatment compared to the other treatments during late corn growth. The RTS treatment also significantly reduced the bulk density of surface soil. High SPR impeded early crop growth but did not impact mid-crop development, while low soil temperature was one of the main factors affecting late corn growth and development as temperatures decreased. Based on the comparisons, we found that the short-term implementation of conservation tillage did not result in a significant decrease in corn yield. We believe that the short-term implementation of NTS tillage sowing practices in Mollisol regions is a feasible option.