Yu Zhan

Zhejiang Green Giant Biotechnology Co., Ltd., Jinhua, 321071, Zhejiang, China
Author    Correspondence author
Computational Molecular Biology, 2026, Vol. 16, No. 5   
Received: 31 Jul., 2026    Accepted: 08 Sep., 2026    Published: 20 Sep., 2026

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Small-scale farms play an important role in promoting sustainable agricultural development, rural revitalization, and ecological conservation in Zhejiang Province. However, the assessment of sustainability performance in these farms is often limited by fragmented production data and insufficient quantitative evaluation approaches. This study proposes a data-driven framework for evaluating sustainable agricultural practices in Zhejiang small-scale farms by integrating multi-source agricultural data, sustainability indicators, and analytical models. The framework incorporates ecological, economic, and social dimensions, including resource-use efficiency, soil health, production performance, environmental benefits, and farm management efficiency. Advanced approaches such as machine learning, spatial analysis, and multi-objective optimization are discussed to identify key drivers affecting sustainable outcomes and support precision decision-making. A case study of a typical small-scale farm in Zhejiang Province is presented to evaluate the effectiveness of sustainable practices, including precision irrigation, optimized fertilization, and digital management strategies. The results provide insights into how data technologies can enhance resource efficiency, reduce environmental impacts, and improve the resilience of small-scale farming systems. This study highlights the potential of integrating digital agriculture and sustainability assessment frameworks to accelerate the green transformation of small-scale farms.

Small-scale farms; Sustainable agriculture; Data-driven evaluation; Precision agriculture; Zhejiang Province