Pathways Toward Sustainable Dairy Production: Scale Economies and Human Capital Barriers in the Heterogeneous Adoption of Digital Technologies in China

Logical Operator Operator

Search Text

Search Type

add_circle_outline

remove_circle_outline

Article Menu

Font Type:

Arial Georgia Verdana

Font Size:

Aa Aa Aa

Line Spacing:

  

Column Width:

  

Background:

Open Access Article

by
Yuwen Qi

1,
Jiqin Han

2,*,
Xue Chang

3,* and
Chunhua Yang

4

1

School of Economics and Management, Shandong Institute of Petroleum and Chemical Technology, Dongying 257061, China

2

College of Economics and Management, Nanjing Agricultural University, Nanjing 210095, China

3

School of Economics, Nanjing University of Finance and Economics, Nanjing 210023, China

4

Animal Husbandry Development Service Center, Dongying 257061, China

Authors to whom correspondence should be addressed.

Submission received: 17 June 2026 / Revised: 8 August 2026 / Accepted: 9 August 2026 / Published: 11 August 2026

Abstract

Livestock digital technologies are pivotal to advancing sustainable agricultural development. However, their adoption rates in China’s livestock sector remain relatively low. Existing studies have largely overlooked the heterogeneous mechanisms through which herd size relates to the adoption of different types of agricultural digital technologies. Drawing on survey data from scaled dairy farms, this research employs an Instrumental Variable Probit (IV Probit) model to empirically examine the relationship between herd size and the adoption of information-intensive and knowledge-embedded digital technologies, as well as their effects on farm economic sustainability. The results indicate that herd size is positively associated with the adoption of both types of digital technologies. Further analysis reveals that herd expansion is associated with lower material and learning costs and higher sunk costs of farm-specific human capital. Moreover, the results suggest that the adoption of either type of digital technology is positively and significantly correlated with farm economic sustainability. These findings suggest that it is imperative to actively promote moderate-scale farm operations. Additionally, improving agricultural subsidy policies may promote the uptake of digital technologies to advance sustainable dairy farming.

1. Introduction

Digital technologies are increasingly recognized as pivotal instruments in reducing costs, enhancing operational efficiency, and promoting sustainability in agricultural production [1]. Within the livestock sector, the development and adoption of these technologies are essential for ensuring stable supplies of meat, eggs, and dairy products, increasing the income of farming households, and ultimately contributing to rural revitalization. Recognizing this imperative, the Chinese government has released a series of policies to accelerate the advancement of digital technologies in livestock production. For instance, in 2024, the Ministry of Agriculture and Rural Affairs released the Guiding Opinions on Vigorously Developing Smart Agriculture, which explicitly promotes intelligent large-scale breeding. More recently, the “No. 1 central document” for 2025 underscored the need to expand the application of data-driven and artificial intelligence technologies across the agricultural sector. Despite these policy imperatives, the adoption of digital technologies is still constrained within China’s livestock farming, and their application differs sharply by technology type. According to the China Digital Rural Development Report (2022) [2], the informatization rate of agricultural production was a mere 25.4% in 2021, substantially below the target of over 40% set by national guidelines for 2035. At the farm level, evidence from a 2020 survey of large-scale dairy farms in Shandong Province reveals a stark disparity: while 25.8% of farms had adopted digital estrus detection systems, only 14.2% utilized digital manure management systems [3]. Under these conditions, the sluggish diffusion of digital production and management systems has constrained productivity growth in the livestock sector [4]. Accelerating the adoption of digital technologies has become a critical challenge in China’s pursuit of agricultural modernization.

A growing body of literature has examined technology adoption in livestock production from various perspectives. However, systematic analysis of digital technology adoption remains relatively scarce. The first strand of this literature focuses on traditional livestock technologies. For example, studies have found that memory limitations negatively affect the application of disease control technologies [5]. Farm size and technical characteristics also significantly influence the adoption of manure detection technologies [6], while the economic, social, and environmental factors critically influence the adoption of feeding technologies [7]. A second, more recent strand of research has begun to investigate digital technologies in the livestock sector. Several studies have highlighted the importance of herd size in the adoption of such technologies [8], with larger farms more likely to adopt at least one form of precision agriculture technology [9]. Moreover, farmers’ human capital, digital knowledge, gender, perceptions, experience of extreme weather, cooperative membership, and family characteristics have been found to significantly influence farmers’ willingness to adopt digital agricultural technologies [10,11,12,13,14].

However, these studies have largely treated digital technologies as a homogeneous category, and little empirical attention has been paid to the divergent associations between herd size and the uptake of heterogeneous digital technologies within the dairy sector. This research gap warrants attention, given the high capital requirements and substantial learning costs associated with digital technology adoption [15]. To address this, we draw on a theoretical framework that classifies digital technologies into two distinct types according to their value-delivery mode and required operator skill level: information-intensive and knowledge-embedded digital technologies [16].

Information-intensive digital technologies are defined as those that employ controllers, onboard sensors, and actuators to capture production data, convert it into digital information, and provide timely information to support decision-making. However, their effective use requires operators to invest time and resources in acquiring additional specialized skills [17]. For example, digital estrus detection systems rely on wearable sensors to record daily step count data, generating monitoring information that supports decision-making in the dairy sector. The core value of such technologies lies in mitigating information asymmetry in traditional agricultural production. However, this value cannot be realized if operators lack the ability to interpret the provided information. In contrast, knowledge-embedded technologies are defined as those that integrate advanced intelligent digital systems and deliver value that is inherently embedded in the technology itself, without requiring additional operator skills [18]. For instance, digital manure management systems enable automated continuous manure removal in dairy farms. The core value of such technologies lies in addressing inefficiency of manual agricultural management.

This conceptual distinction raises several interrelated questions with both theoretical and policy-related significance: Do these two categories exhibit heterogeneous adoption patterns? Is herd size associated with the heterogeneous adoption of digital technologies? And which policy instruments are most effective in encouraging digital technology application in dairy production? This study aims to provide rigorous answers to these questions by analyzing survey data from scaled dairy farms in Shandong Province. Our empirical investigation focuses on the relationship between herd size and the uptake of both technology types, as well as the mechanisms underpinning this relationship. Our findings reveal three main results. First, herd size is positively associated with the adoption of heterogeneous agricultural digital technologies. Second, larger herd size exhibits lower material and learning costs for both technology types, but with higher sunk costs of farm-specific human capital for information-intensive technologies. Third, compared with non-adoption, the joint adoption of digital technologies on larger farms is associated with higher dairy productivity and farm profitability.

This research makes three marginal contributions to the existing literature. First, by explicitly distinguishing between information-intensive and knowledge-embedded digital technologies [19], we offer nuanced empirical evidence on the scale-technology adoption nexus within China’s dairy sector, a context that remains empirically underexplored. Although the general relationship between farm size and technology adoption has been extensively studied [20,21]. Our disaggregated approach reveals whether this relationship holds uniformly across different types of digital technologies. Second, we propose a novel analytical framework that links herd size to technology adoption through three mediating channels: material costs, learning costs, and sunk costs of farm-specific human capital. While prior work has examined the role of material costs [22,23], the mediating roles of learning costs and the sunk costs of human capital (recognized as critical barriers to adoption [24,25]) have been largely overlooked. Our research directly addresses this gap. Third, we extend the analysis to examine the economic implications, exploring linkages between the interaction of herd size and digital technology adoption, dairy productivity, and farm profitability. While numerous studies have linked farm size, technology, and productivity indicators such as technical efficiency [26] and total factor productivity [27], the specific links between herd size, heterogeneous technology adoption, and yields and profits remain unclear. Farm profit is a central goal of agricultural production, yet data on farm profit are difficult to collect. Drawing on a comprehensive survey, this study contributes to the literature by shedding light on these previously underexplored effects.

2. Technical Background and Theoretical Analysis

2.1. Background of Digital Livestock Technologies

Digital technologies have significantly enhanced both resource allocation efficiency and productivity in livestock production systems [28]. Digital technologies applied in dairy production include digital estrus detection systems and digital manure management systems. We select digital estrus detection systems as representative of information-intensive digital technologies, given that reproductive performance constitutes a core determinant of lactation output. Timely and accurate estrus detection is essential for successful insemination, conception, and calving, which is a prerequisite for milk production. Delayed detection reduces conception rates and prolongs the non-pregnant period, thereby increasing feeding and labor costs and disrupting milk supply. Digital manure management systems are chosen as representative of knowledge-embedded digital technologies, as manure management is essential to dairy operations. Manure decomposition generates ammonia (NH 3) and hydrogen sulfide (H 2 S). Elevated NH 3 levels reduce cow immunity and productivity, while H 2 S impairs reproductive performance.

As digital technologies, digital estrus detection systems and digital manure management systems share several common characteristics. On the one hand, both technologies exhibit a degree of indivisibility. Digital estrus detection systems require the joint usage of signal receivers and computing systems, while digital manure management systems are usually developed as fixed facilities and typically cannot be separated into independent components. On the other hand, both are marked by substantial capital requirements and uncertain returns, with effective use depending on learning processes and information sharing.

At the same time, however, the two technologies differ in functional terms. Digital estrus detection systems are decision-support tools designed to identify reproductive status based on monitoring outputs from sensing technologies. They replace human observation experience with data-driven analysis to support production decisions. In conventional farm management, estrus identification has depended on manual observation, with farmers drawing on their experience to judge outward behavioral and physiological signs, including mounting behavior, rumination, body temperature, and feed intake. Under large-scale production conditions, however, the detection rate achieved through such observation typically falls below 50% [29]. This constraint depresses reproductive efficiency, which in turn reduces total milk production. In operational terms, digital estrus detection technologies depend on wearable sensors affixed to animals, most commonly on the leg or neck, to capture daily step count data via a signal receiver. Those data are then sent to computer software for statistical analysis, generating the monitoring information system for an analysis report. By displacing manual, experience-based assessment with automated, data-driven monitoring, such systems improve estrus detection accuracy markedly, thereby contributing to higher milk yield.

In contrast, digital manure management systems are directly implemented in the production process by replacing conventional equipment, such as loaders, and improving production efficiency. Large-scale and intensive farming have increasingly led to a decoupling of crop cultivation from livestock raising, resulting in the generation of substantial volumes of dairy manure. Under the conventional system of farming, manure removal involves manual labor or use of loaders, which slows the process and often leads to severe environmental pollution [30]. Powered by electricity, digital manure management systems automate continuous manure removal, effectively overcoming the inefficiencies of traditional methods and ensuring sanitary farm conditions [31].

2.2. Theoretical Analysis and Research Hypotheses

Digital technologies share several common characteristics, including indivisibility, high capital intensity, and uncertain returns [32]. First, according to economies of scale, an expansion in production size reduces material costs. Given the indivisibility of both types of digital technologies, their unit acquisition costs decline as herd size increases, thereby promoting adoption. Second, knowledge acquisition also exhibits size economies, implying that larger herd size reduces average learning costs [33]. Since the returns to both types of digital technologies are uncertain, effective learning and information exchange are required prior to adoption. As herd size expands, the per-unit learning cost decreases, which in turn facilitates the adoption of both types of digital technologies.

In addition, the two types of digital technologies exhibit distinct characteristics. Information-intensive digital technologies provide knowledge that substitutes for experiential know-how and supports production decision-making, whereas knowledge-embedded digital technologies derive value from the technologies themselves and substitute for other capital inputs to improve productivity [34]. Drawing on asset specificity theory, production experience constitutes a form of farm-specific human capital that is accumulated through on-farm learning and tailored to its production environment. Unlike general human capital, which is readily transferable across farms, this specific human capital offers limited value outside the current operation. When a farmer considers adopting an information-intensive technology that disrupts herd management and decision-making routines, the accumulated production experience may become a sunk cost. Accordingly, more extensive production experiences raise the sunk costs of adopting information-intensive digital technologies. By contrast, because the value of knowledge-embedded digital technologies is embodied in the technologies themselves, their adoption does not increase the sunk cost of farm-specific human capital. Moreover, as farms expand in scale, the degree of internal specialization tends to increase, which may further raise sunk costs [35]. Within the context of dairy production, farm expansion is likely to inhibit the adoption of information-intensive digital technologies by raising the sunk costs associated with production experience as a form of farm-specific human capital, while having no such effect on the adoption of knowledge-embedded digital technologies.

Based on the above analysis, the following hypotheses are proposed:

Hypothesis****1.

An increase in herd size promotes the adoption of both information-intensive and knowledge-embedded dig