Achieving sustainable rice production is essential for food security, rural livelihoods, and the resilience of rice-based food systems that supply raw materials to the food processing industry. Improving technical efficiency (TE) at the farm level is a key pathway to reduce resource use and environmental pressures per unit of output while ensuring a stable supply of high-quality rice for downstream processing and value-added products. Drawing on micro-survey data collected in 2021–2022 from 455 rice farmers selected through a multi-stage sampling strategy in Jiangsu Province, China, this study investigates how agricultural productive services (APSs) affect stage-specific technical efficiency along the production process and discusses the implications for sustainable rice production and the rice-based food industry. We apply a stochastic frontier production function to estimate overall and stage-specific TE and examine the effects of different APS combinations for land preparation, sowing, fertilization, pest control, and harvesting. The results show that overall participation in APSs significantly improves rice farmers’ TE. Stage-specific analysis reveals that APSs in land preparation, sowing, and harvesting are associated with higher TE, supporting more sustainable use of machinery and labor, while APSs for fertilization and pesticide application do not consistently improve TE and may reflect potential overuse of chemical inputs. Multi-stage service combinations that include both production and pest-control operations can further enhance TE. These findings suggest that well-designed APSs can contribute to sustainable intensification and low-carbon transformation of rice production, thereby strengthening the sustainability of rice-based food systems. Policy interventions should guide APS providers and farmers toward integrated, precision-oriented, and environmentally friendly service packages that support both farm-level efficiency and the sustainability goals of the broader food industry.

Accurate and efficient screening of nitrogen-efficient rice varieties is crucial for implementing precision agriculture and achieving green and sustainable development. However, traditional screening methods rely on destructive sampling and chemical analysis, which are inefficient and costly, and thus cannot meet the requirements of large-scale breeding applications. Therefore, this study aims to develop a non-invasive, high-throughput screening method for nitrogen efficiency of rice based on unmanned aerial vehicle (UAV) hyperspectral data and machine learning algorithms. Sixty rice varieties were selected as the target, and principal component analysis (PCA) was used to reduce the dimension of seven key agronomic parameters (such as yield, nitrogen utilization rate, etc.). A comprehensive evaluation index for nitrogen utilization efficiency was constructed, and K-means clustering was used to classify the varieties into three categories: nitrogen-efficient, medium-efficient, and low-efficient varieties. On this basis, four machine learning algorithms (decision tree (DT), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN)) were used to establish a variety nitrogen efficiency classification model based on spectral indices. The results showed that the indicators constructed based on PCA and clustering could effectively distinguish different nitrogen-efficient varieties; among the four models compared, the DT model achieved the highest overall performance, with an accuracy of 0.75, precision of 0.80, and F1-score of 0.74. This study confirmed the feasibility of combining UAV hyperspectral data with decision tree models, providing a reliable technical solution for the large-scale, rapid, and non-invasive screening of nitrogen-efficient rice varieties.