Abstract:

[Purpose/Significance] Digital literacy is essential for driving new quality productive forces in agriculture, yet dominant training approaches tend to reduce it to a set of operational skills. This logic of skill inculcation overlooks structural inequalities in data control and value distribution, thereby exposing farmers to the risk of becoming mere "digital laborers." This study repositions farmers' digital literacy among farmers within the context of new quality productive forces, redefining it as a core capability of new laborers. Its main innovation lies in moving beyond individual skill deficits to examine the relational and institutional conditions that allow digital literacy to become productive agency. The article proposes a progressive model of "technological adaptation and human-machine collaboration value co-creation" and connects it with a rights-based governance framework. This shift is significant because it connects micro-level capacity building with macro-level agricultural production relations, thereby providing theoretical support for preventing the degradation of farmer subjectivity and promoting inclusive growth in smart agriculture. [Method/Process] The study adopts a theory-driven conceptual analysis based on the classical three elements of productive forces - labor object, labor means, and laborer - updated for the digital context. Data were defined as a new type of labor object, intelligent equipment as new labor means, and digital literacy as the core function of new quality laborers. This framework is appropriate because it incorporates digital literacy into the structure of production instead of treating it as an isolated variable of human capital. Building on this framework, the study constructs a three-stage progressive model. Technological adaptation is the ability of farmers to understand and operate digital devices, platforms, and data interfaces in specific farming situations. Human-machine collaboration involves higher-order abilities such as interpreting algorithmic recommendations, monitoring automated systems, making judgments under uncertainty, and adjusting machine behavior according to local ecological knowledge. Value co-creation concerns farmers' participation in data governance, algorithm design, and benefit-sharing arrangements, thereby transforming them from passive data providers into active data asset holders. The model is developed based on reasoning supported by existing empirical evidence from the fields of agricultural digitalization, digital divide studies, and platform economy research. These sources indicate that access to technology alone does not automatically increase productivity. Rather, the transformation of digital literacy into new quality productive forces hinges on whether farmers can exercise agency with regard to data and intelligent systems. The study therefore combines theoretical deduction with institutional analysis to identify barriers at each stage. [Results/Conclusions] The analysis identified three major constraints. First, training provision is often decontextualized and formalistic, creating a shortage of new quality laborers. Many programs are disconnected from real farming scenarios, local data flows, and the intelligent equipment used on farms, so farmers remain unable to engage in effective human–machine collaboration and their local knowledge is marginalized. Second, the disconnect between new labor means and the subject structure results in the alienation of data as a labor object. Intelligent equipment and platforms are usually controlled by technology companies or large agribusinesses, while farmers operate the machinery and generate data that they cannot access or monetize. As a result, data extracted from farmers' labor are used to optimize external supply chains and input marketing, turning farmers into monitored and algorithmically managed workers rather than empowered producers. Third, the absence of value co-creation mechanisms causes distributional imbalance. Without transparent contribution accounting or revenue-sharing rules, the benefits of data-intensive agriculture tend to concentrate among platform operators and equipment suppliers. To address these problems, the study argues for a governance transformation centered on rights reconstruction. It proposes the establishment of data cooperatives to protect farmers' digital subsistence rights by providing them with collective data governance and equitable access to infrastructure; creating algorithmic consultation mechanisms to secure farmers' developmental rights through participation in rule-setting, transparency review, and error correction; and improving data value distribution institutions to realize farmers' income rights through contribution measurement and benefit-sharing contracts. Through these institutional innovations, farmers can be elevated from passive digital laborers to new quality laborers who hold data asset rights. The main limitation of this study is its conceptual and institutional orientation. Empirical validation is still needed in different regions, for farms of various sizes, and in different crop systems and socioeconomic contexts. Future research should examine how heterogeneity in age, gender, education, and land tenure affects the feasibility of rights reconstruction, and compare the effectiveness of different data governance models in promoting inclusive new quality productive forces in agriculture.

Key words: new quality productive forces in agriculture, farmers' digital literacy, data subjectivity, distributive justice of data elements, new quality laborers