The Impact of Internet Use on Production Efficiency of Animal Husbandry: Based on the Evidence of 340 Herdsmen in Inner Mongolia, China

Abstract

Production efficiency is a standard to evaluate the high-quality development of animal husbandry. As an important tool for herdsmen to collect and use information for animal husbandry production and innovation, the internet is not only an inner motivation for improvement of production efficiency but also an important engine for transformation of animal husbandry modernization. It is of practical significance to analyze the impact of internet use on the production efficiency of animal husbandry from the micro-level. This paper, based on the survey data of 340 herdsmen in Inner Mongolia, adopts the Stochastic Frontier Analysis (SFA) model to measure the production efficiency of animal husbandry and selects the Tobit model and moderation effect model to analyze the relationship between internet use and animal husbandry production efficiency under the influence of different capitals. Results show that the production efficiency of animal husbandry is relatively low and internet use has a positive and statistically significant effect on production efficiency; moreover, social capital and financial capital have a negative moderation effect when internet use affects production efficiency. Our findings suggest that the use of internet technology should be paid more attention in order to increase the production efficiency of grass-based animal husbandry in Inner Mongolia.

1. Introduction

The transformation of animal husbandry plays an important role in promoting rural revitalization and accelerating agricultural and rural modernization in China [1]. Improving production efficiency continuously is a good opportunity if Chinese animal husbandry growth wants to shift from “labor intensive quantity-type” to “capital and technology intensive quality-type”. American economist Paul Romer put forward the endogenous growth model of technological progress and indicated that technological progress is the main power for economic development [2]. As a significant driving factor of agricultural technological improvement, agricultural informatization has been valued by the Chinese government because of its outstanding advantages in resource optimization [3]. With the advancement of agricultural production informatization uninterruptedly in China’s counties, intelligent animal husbandry has become an aspect of intelligent agricultural engineering, which has solved the problem of animal husbandry production and management efficiency principally. By using big data, the Internet of Things, artificial intelligence (AI), and other emerging information technologies, the digital monitoring of all input in animal husbandry, including breeding, processing and marketing, can be realized. This benefits allocation efficiency optimization and improvement of production efficiency.

Given the huge advantages of internet use, the internet and Beidou Navigation Satellite System (BDS) are gradually being embedded in the production of grassland animal husbandry and promoted to herdsmen in the Inner Mongolia Autonomous Region. In daily grazing, these technologies can help intelligent devices to achieve real-time remote monitoring of herd location, movement speed, etc. [4]. At the same time, through the establishment of online file management and video surveillance system, herdsmen can obtain information and intelligent services in real-time [5]. The existence of the Internet allows herdsmen to regard mobile phones as a new variety of production tools and live broadcasts as a fresh type of farm work [6]. With pasture as the setting and green ecology as the selling point, high-quality animal husbandry products can be promoted and sold through short videos and live streaming. E-commerce is strengthening the close connection between herdsmen and the market. From the perspective of digital empowerment, “Internet plus Animal Husbandry” has started a new model for the development of grassland animal husbandry in Inner Mongolia. In summary, exploring the specific impact of internet use on herdsmen production efficiency has great significance for promoting the revitalization of pastoral areas, especially the high-quality development of grassland animal husbandry [7].

At present, previous studies in the field of production efficiency of animal husbandry have only focused on the input of land, labor, capital, and other production factors, policy, production scale or mode, regional differences, technological progress, etc. The production efficiency of Chinese herdsmen is generally “small and ineffective” under the current grassland tenure arrangement, [8], and herder technical efficiency mainly comes from the resource equilibration effect rather than the ability effect [9]. However, increasing labor input can improve the total factor productivity (TFP) of mutton sheep [10]. Some countries with strong comparative advantages have a more obvious improvement effect than China, and the heterogeneity of the breeding scale can lead to a threshold effect between labor and productivity [11]. The capital factor, especially feed input, has a positive effect on the livestock scale [12] and a negative role on the production efficiency of animal husbandry [13,14]. The proportion of animal husbandry expenditure and herdsmen’s stocking rate significantly negatively affected the production technical efficiency and cost efficiency in the desert steppe from Inner Mongolia [15]. Besides, The Grassland Ecological Compensation Policy (GECP) in China is also an advantageous element for improving production efficiency [16]. The reason is that GECP can accelerate and improve the turnover rate or offtake rate of livestock and promote the development of benefit-oriented animal husbandry [17]. This effect is more excellent in mutton sheep production and is more obvious in the TFP improvement of professional fattening farms [18,19].

The production scale can also effectively improve output efficiency [20], which is more remarkable in characteristic industries such as dairy cows and sheep [21]. The breeding mode in the park has greater production efficiency than in the family [22]. The production efficiency from high to low is cooperative household ranches, household ranches, cooperative herdsmen and ordinary herdsmen in Xilinguole League in Inner Mongolia [23]. Meanwhile, the grassland ruminant livestock breeding mode may be conducive to production intensification [24], and the multi-household pattern and mixed breeding mode will have a better output and a higher ecological efficiency [25]. However, the effect of livestock scale on production efficiency is not consistent, scale expansion to enhance production efficiency is inconspicuous [26]. Due to the difference in grassland productivity in each pasture and the distance diversity between winter and summer pastures, the production efficiency will be significantly heterogeneous [27]. Long-term temperature leads to decreased net livestock revenue and net revenue per livestock, whereas long-term precipitation has the opposite effect [28]. Herdsmen can adopt climate adaptation strategies such as optimizing water supply conditions to improve their production efficiency [29].

New technology has a positive role to improve production efficiency of animal husbandry [30], especially the breeding technology and the nutritional manipulation technique [31,32], but the weak link between technology research and transfer and the lack of technology promotion services may inhibit the growth rate of production efficiency [33]. If technological progress fails to optimize resource utilization, it will lead to a decline in animal husbandry productivity [34].

In summary, most previous studies to date have tended to focus on the relationship between traditional input and the production efficiency of animal husbandry. In the context of technological innovation, few scholars pay attention that how the changes in channels of information and data elements, especially internet use, affect production efficiency.

In this study, based on the survey data of 340 herdsmen in Inner Mongolia, we employ SFA to measure the production efficiency of animal husbandry and select Tobit estimation methods to estimate the impact of internet use on production efficiency. Meanwhile, the moderation effect model is used to demonstrate the moderation effect of different capital on internet use to improve production efficiency. We attempt to make three significant contributions to the literature. First, we take the herdsmen in Inner Mongolia as the research sample, which is different from the previous studies based on the mixed sample of farmers and herdsmen. Second, under the comprehensive consideration of the complexity of livestock species, we use sheep as a unit to convert the number of livestock and measure production efficiency. It is different from other scholars who only calculate the production efficiency of single livestock, such as mutton sheep, beef cattle or dairy cows, which is more suitable for the actual production of grassland animal husbandry in Inner Mongolia. Third, dividing the capital factor into social capital and financial capital, and synthetically analyzing their moderation effect on the impact of internet use on the production efficiency of herdsmen, is conducive to breaking through the limitation of previous studies that only investigate the direct effect of a single input. This provides a new perspective for existing literature.

The rest of this paper is organized as follows. Section 2 presents the theoretical analysis and research hypotheses. Section 3 introduces the data and methods. Section 4 and Section 5 present results and discussion. Finally, Section 6 draws conclusions. The research flowchart is shown in Figure 1.

2. Theoretical Analysis and Research Hypotheses

In this section, we discuss the direct impact of internet use on animal husbandry production efficiency and the moderation effect of capital factors in this impact.

2.1. Impact of Internet Use on Production Efficiency of Animal Husbandry

Among the driving factors of production efficiency growth, internet technology should not be ignored [35]. Although having a large-scale social network and contacting individuals with diverse socio-economic characteristics is beneficial to obtain rich production information for production subjects, such as science and technology demonstration platforms, primary technology extension institutions, and relatives and friends [36], they also have problems of slow information transmission, poor penetration, high cost and difficult use [37]. Compared with the traditional social network, mobile Internet make communication and information acquisition free from region and time constraints [38], and the operation is fast and convenient, so it has gradually become the main channel of information acquisition. Internet use can affect the production efficiency of animal husbandry by influencing the acquisition of production factors [39], the transformation of information technology to real productivity [40], and the adoption of new sales channels [41]. A simple framework of potential pathways is illustrated in Figure 2.

The first pathway explains that Internet use affects the production efficiency of animal husbandry by influencing the acquisition of production factors and information. Herdsmen can more easily enter the upstream production factor market of livestock breed, feed, grass, veterinary drugs and vaccines and fully understand the trading volume and reputation of resource suppliers’ goods or services by using the Internet [42]. Due to the timely acquisition of favorable information about the production, circulation, marketing and service of means of production, herdsmen can make purchasing decisions that are conducive to improving production efficiency. For example, some means of production of animal husbandry in some offline stores may be expensive and mostly located in the central towns of the banner, but herdsmen can buy high-quality and low-cost production means through e-commerce platforms.

The second pathway shows that Internet use influences production efficiency by affecting the transformation of information technology to real productivity. Through gaining data, information and technical support, Internet users can realize intelligent breeding [43], and directly exchange production experience with other production entities in the online platforms. This can improve the adaptability of production practice and technical information, reduce the trial-and-error cost, and then the transformation of information technology into real productivity will be realized in the middle reaches of the industrial chain [44]. For example, if herdsmen have installed solar high-definition cameras, smart fences for pastures, carried BDS or Global Positioning System (GPS) locators and built automated sheds for livestock, their production may realize intelligent operation. They can not only clearly know the location of livestock in thousands of grasslands by the supporting applications (APPs), but also easily control the drinking water trough, shed and automatic door. When the livestock is ill, herdsmen can obtain rapid diagnosis and treatment services from professional veterinarians on online platforms. If herdsmen have the rent-in or rent-out demand for animal husbandry machinery, they can quote or ask for prices by the machinery rental software to improve the use efficiency of machinery.

The third pathway demonstrates that Internet use affects production efficiency by influencing the adoption of new sales channels. Internet use can broaden the sales network of livestock and related products from the aspects of space, time, cost, safety and personalization, so that herdsmen can cut into the downstream markets of the industrial chain, reduce the intermediary link, and directly connect with the terminal markets [41], thereby reducing production costs. For example, live network broadcasts and short videos from Enke, Darcy and other Internet celebrities in Inner Mongolia on online social platforms, such as WeChat, Tik Tok and Kwai, can effectively shorten distances between livestock products and consumers [45]. For example. Based on the above analysis, the following hypothesis is proposed in this paper:

Hypothesis 1 (H1).

Internet use can increase the production efficiency of animal husbandry. In other words, the production efficiency of Internet users is higher than that of nonusers.

2.2. The Effect of Capital Factor in the Process of Internet Use Affecting Production Efficiency

In the transformation process of animal husbandry, the land, capital, labor, technology and management are indispensable, but capital factor plays a leading role in them [46], including both social capital and financial capital. Internet use can provide opportunities and ways to improve herdsmen production efficiency by influencing the allocation of market resources, the transmission of social messages and the channels of raising financial capital [47]. Although Internet use can enhance the communication efficiency among herdsmen and provide online information services for animal husbandry production, social capital such as interpersonal communication and citizen participation also has the information sharing function of the Internet [36], and the development of information and communication technology has a crowding out effect on traditional offline interaction [48]. Therefore, there may be a substitutional relation between Internet use and social capital.

When social capital is insufficient, the Internet information channel has a partial trade-off effect on herdsmen social capital [49]. They can obtain and transmit useful market information on purchasing and selling, breeding knowledge and skills learning through Internet APPs [50], such as WeChat, Tik Tok, etc. Consequently, the mobile network can provide technical support for herdsmen to improve production efficiency, when they need to broaden the social boundary, reduce information-seeking costs, accelerate the speed of information circulation, and make up for the lack of production factors caused by social capital deficiency. Hereby, we expect that the effect of Internet use on production efficiency is more pronounced among herdsmen with less social capital.

Herdsmen with strong self-saving abilities and sufficient external financing make their production achieve informatization and digitalization more easily because their capital or technology is not constrained [51]. Herdsmen are more likely to build digital pastures by purchasing intelligent grazing equipment or introducing modern information technology. Herdsmen who face a shortage of financial capital, especially difficult and expensive financing, need to use the Internet to expand social networks, improve information asymmetry and reduce credit constraints [52], so as to clear the obstacles of productive technology and capital. For example, financing platforms similar to peer-to-peer lending (P2P lending), crowdfunding, big data finance and others have been launched. Under the background of rural revitalization, various financial institutions have been building new online service channels, which help herdsmen handle loan business by using mobile phones and innovating livestock credit products to improve the availability and convenience of credit [53]. Hereby, we expect that the effect of Internet use on production efficiency is more significant among herdsmen with less financial capital. Accordingly, the following hypothesis is proposed in this paper:

Hypothesis 2 (H2).

Capital factor has a negative moderation effect on Internet use affecting production efficiency. The improvement effect of Internet use is more significant among herdsmen who have weak social capital and strong financial capital constraints.

3. Data and Methods

3.1. Data Source

Inner Mongolia grassland plays a significant role in the Eurasia grassland whose natural grassland area is 13.2 × 10⁸ MU accounting for 22% of the total grassland area and 74% of the total land area (Data source: http://lcj.nmg.gov.cn/lcgk_1/, accessed on 12 October 2019). It is a typical and traditional pastoral area in China where the grassland is mainly used for grazing, and grazing is the main productive activity for the local herdsmen.

Our data were obtained from a survey administered to herdsmen in Inner Mongolia, China, in 2020. Data collection followed a multi-stage sampling procedure. It includes eight banners (a banner is one of China’s administrative divisions. It belongs to the unique county-level administrative region of Inner Mongolia, which is equivalent to the municipal districts, county-level cities and counties), 37 towns, and 115 villages. In the first stage, eight banners were randomly selected according to five types of grassland resources including temperate meadow steppe, temperate typical steppe, temperate desert steppe, temperate steppe desert and temperate desert. Amongst them, at least one banner in each grassland resource type was chosen. These include seven animal husbandry banners of Xinbaerhuyou Banner, Zhalute Banner, Alukeerqin Banner, Zhenglan Banner, Daerhanmaomingan Union Banner, Wushen Banner and Ejina Banner, and a Semi-Farm Semi-Pasturing banner of Keerqinyouyiqian Banner. In the second stage, two or three towns from each selected banner were randomly selected. Next, two or three villages were randomly chosen in each town at least. In the last stage, we randomly interviewed about 10 herdsmen in each selected village. In total, the survey targeted 360 herdsmen and resulted in 340 usable observations after eliminating the samples with serious missing or abnormal information. The regional distribution of the sample is reported in

Table 1. Distribution of sample across grassland resource areas, and banners.

Grassland Resource Area	Banner	Town	Observations
Temperate meadow steppe	Xinbaerhuyou	Arihashate, Keerlun, Baogedewula, Alatanemole, Beier	41
	Keerqinyouyiqian	Manzu, Wulanmaodu	45
	Zhalute	Arikunduleng, Bayaertuhushuo, Gadasu, Gerichaolu, Qiandemen, Taogezha, Wulanhada, Wulijimuren	42
Temperate typical steppe	Alukeerqin	Bayanwendu, Sanhantala, Han	46
	Zhenglan	Sanggendalai, Zhuolangaolei, Shangdu, Baosahaodai	31
Temperate desert steppe	Daerhanmaomingan Union	Bayinaobao, Daerhan, Chaganhada, Mingan	55
Temperate steppe desert	Wushen	Sulide, Galutu	39
Temperate desert	Ejina	Bayantaolai, Ceke, Dalaihubu, Dongfeng, Heishan, Mazongshan, Sanhantaolai, Subonaoer, Wentugaole	41

Source: survey data.

.

Face-to-face interviews were used as our survey method based on a pre-determined questionnaire. The survey consists of information on demographics, household endowments, animal husbandry production and operation activities, financial activities, Internet use status, and socialized service status. Among them, information on demographics mostly included the age, gender, nation, schooling years and political background, etc., of the head of household; household endowments mainly covered the number of labor force, grassland area, savings scale and total income, etc.; animal husbandry production and operation activities referred to livestock scale, the input of forage, total investment in fixed assets, etc.; financial activities contained credit performance and insurance participation; Internet use status consisted of whether livestock monitors and intelligent grazing equipment including BDS/GPS positioning or automatic drinking-water systems that can be connected to the Internet were owned, whether herdsmen have been using Internet channels to sell livestock and related products, the costs of mobile phones and networks, and whether herdsmen have been participating in online technical training, borrowing from Internet financial platforms and expertly using mobile phones and computers about animal husbandry software; socialized service status comprised service subjects, service satisfaction, whether participate in animal husbandry technical training and training contents, etc.

3.2. Variable Selection

3.2.1. Dependent Variables

Production efficiency of animal husbandry is used as the dependent variable in accordance with the objectives of the study, which is a continuous variable between 0 and 1. Taking the input and output in the actual animal husbandry production into consideration, four indicators are selected to measure the production efficiency of animal husbandry by the SFA model, including total sales revenue of livestock and related products [19] and the input of labor, capital [11] and land. Specifically speaking, the total sales revenue of livestock and related products refers to adult livestock, current-year young livestock and by-products (e.g., milk, skin, wool and cashmere) as output indicators. The land input is defined as the grassland area of actual operation. The labor input is the total cost of family labor and hired labor used for animal husbandry production; the capital input refers to the costs of material and services including feed, veterinaries and drugs, water and electricity, mechanical services, and productive fixed assets.

3.2.2. Core Independent Variables

The core independent variable in this study is Internet use, to disclose the contribution of technology in improving production efficiency of animal husbandry. Here, the access or non-access in WIFI/4G/5G is chosen as a proxy variable for Internet use [35]. A 0/1 binary variable is used to represent the Internet using the behavior of herdsmen, where Internet user = 1 and Internet non-user = 0.

3.2.3. Moderating Variable

In this study, capital factors are defined as continuous variables. Among them, “Gift-money expenditure” is selected as the social capital and “Loan scale” is used to represent financial capital.

3.2.4. Control Variables

The types of control variables selected in this paper are as follows: First, age and education level of the head of household is adopted to represent the personal characteristics. The age, education level, and who plays a major role in cognition level, have an impact on the production decision. Second, the size of family labor and total household income are used to represent the family characteristics, which can reflect the household labor status and the size of the household. Third, per capita grassland area, livestock scale, productive assets value and intelligent grazing equipment are used to represent the animal husbandry production status, which can reflect the production capacity and scale. In order to unify different types of livestock quantity units and ensure consistent estimations, we follow the “Basic Grassland Protection Regulations of Inner Mongolia Autonomous Region” Article 42 and standardize livestock scale as “sheep unit”. That is, 1 sheep = 1 sheep unit; 1 cattle = 5 sheep units; 1 horse = 6 sheep units; 1 donkey = 3 sheep units; 1 mule = 3 sheep units and 1 camel = 7 sheep units. Finally, the participation of technical training and professional cooperatives are selected to represent the social network characteristics. These control variables are not unrelated to the production efficiency of animal husbandry.

3.2.5. Descriptive Statistics

Table 2. Variables definitions and descriptive statistics.

Types	Variables	Definition	All Herdsmen		Internet Users		Internet Non-Users
			Mean	S.D. 3	Mean	S.D.	Mean	S.D.
Variables used in the SFA model
Output	Total sales revenue of livestock and related products	Adult livestock, current year young livestock and by-products such as milk, skin, wool and cashmere in 2019 (104 yuan) 1	20.49	33.60	22.38	35.35	7.01	8.30
Input	Labor	Costs of family labors and hired labors in 2019 (104 yuan)	8.88	4.73	9.02	4.86	7.93	3.56
	Capital	Costs of various capital in 2019 (104 yuan)	41.13	53.54	43.57	56.53	23.83	14.22
	Land	Grassland area of actual operation (103 MU) 2	12.23	53.52	11.72	54.18	15.83	49.04
Variables used in the Tobit estimation model and moderation effect model
Core independent variable	Internet use	1 if herdsman has access to WIFI/4G/5G in 2019, 0 otherwise	0.88	0.33	1	0	0	0
Moderation variable	Social capital	Gift-money expenditure in 2019 (104 yuan)	1.43	1.65	1.52	1.69	0.75	0.99
	Financial capital	Loan scale in 2019 (104 yuan)	13.65	12.17	14.52	12.32	7.46	8.94
Control variable	Age	Age of household head (years)	49.99	9.59	49.64	9.39	52.50	10.66
	Education	The schooling years of household head (years)	8.14	2.78	8.27	2.75	7.21	2.88
	Labor size	Number of laborers (persons)	2.19	0.70	2.17	0.69	2.31	0.81
	Income	Total household income in the year before survey (104 yuan)	29.15	27.81	31.49	28.69	12.55	10.44
	Grassland	per capita grassland area (103 MU/person)	3.40	13.08	3.21	13.06	4.73	13.34
	Livestock scale	Number of stocked livestock at the end of the year before survey (103 sheep units)	0.47	0.46	0.49	0.48	0.30	0.29
	Asset	productive assets value in 2019 (104 yuan)	33.03	49.22	35.15	52.10	18.02	10.34
	Intelligent grazing equipment	1 if herdsman has obtained intelligent grazing equipment in 2019, 0 otherwise	0.53	0.50	0.58	0.50	0.19	0.40
	Technical training	1 if herdsman has joined in technical training in 2019, 0 otherwise	0.43	0.50	0.47	0.50	0.14	0.35
	Professional cooperatives	1 if herdsman has participated in professional cooperatives, 0 otherwise	0.34	0.47	0.36	0.48	0.11	0.33

¹ MU = 1/15 hm². ² Yuan is the Chinese currency. ³ S.D. refers to standard deviation.

presents the definitions and descriptive statistics of the variables used in the empirical analysis. It shows that the sampled herdsmen had an average age of 50 years and received 8 years of education on average. The mean labor size was 2.19. Household income was around 291,500 yuan. The per capita grassland area was 3400 MU/person, and the mean livestock scale was 470 sheep units. The average value of productive assets was 330,300 yuan. About 43% and 34% of herdsmen joined technical training and professional cooperatives. Relative to non-users, Internet users tend to be younger, have higher education levels, have less family labor quantity and larger livestock size, are more likely to own intelligent grazing equipment and have more adequate social and financial capital.

Around 88% of sample herdsmen accessed WIFI/4G/5G, reflecting that the backwardness of communication network infrastructure in Inner Mongolia pastoral areas has been changed fundamentally; 53% of the herdsmen have had intelligent grazing equipment, among them, 34.29% installed video surveillance systems and 13.26% introduced intelligent drinking water equipment for livestock; 10.09% and 9.51% had monitoring equipment and drones, respectively, indicating that grassland animal husbandry in Inner Mongolia has been still far from realizing digital transformation and only in the initial stage. In addition, 87.65% of the herdsmen chose to use the Internet to obtain technical information, and their average annual expenditure on mobile phones, networks and other communication is 3627 yuan. About 42.65% of the herdsmen joined in online technical training through the Internet, 6.05% of the herdsmen used the Internet channel to sell livestock products, and 16.14 % of the herdsmen borrowed from the Internet financial platforms.

Regarding the variables used in the SFA model, the information shows that the mean sales revenue of livestock and related products is 204,900 yuan. The expenditure on labor input is about 88,800 yuan, the expenditure on capital input is 411,300 yuan, and the expenditure on land input is 12,230 MU. Comparing the differences in animal husbandry output and input between Internet users and non-users, we find that the average output of Internet users is 223,800 yuan, which is 153,700 yuan higher than that of non-users. Among Internet users, the input of production factors has an obvious tendency of capital substitution, and the capital input of Internet users is 197,400 yuan higher than that of non-users. The mean input of land factors is 4100 MU lower than that of the non-users. There is no significant difference in labor input between Internet users and non-users.

3.3. Methods Selection

3.3.1. SFA Model

Aigner et al. (1977) and Meeusen et al. (1977) proposed a stochastic frontier production function model [54,55]. It is also known as Stochastic Frontier Analysis (SFA), which mainly believes that the production frontier itself is in a state of random change in different production units [56] and can distinguish various controllable and uncontrollable factors that lead to production inefficiency effectively [57]. Because this method has become the current mainstream model of measuring production efficiency, we choose it to estimate the production frontier of herdsmen. It can be shown as follows:

$$Y_i=f\left(x_i,\beta \right)exp\left(v_i\right)exp\left(-u_i\right)\left(i=\mathrm{1,2},\dots ,N\right)$$

(1)

where $Y_i(i=\mathrm{1,2},\dots ,n)$ represents the real output of herdsmen; $x_i$ represents the input elements of production factors; $\beta$ is the regression coefficient; $f(·)$ represents the frontier production function, which is the optimal output; $v_i$ represents the random error term, which is an independent, identically distributed normal random variable with a mean of zero and a constant variance with the shape of $v~N(0,{\delta }_v^2)$; $u_i$ represents the inefficiencies of production, which is generally defined as a gap between the actual output and the theoretical output frontier of herdsmen and assumed to be a nonnegative random variable subject to truncated normal distribution (cut off the part less than zero) with the shape of ${u~N^{+}(0,\delta }_u^2)$.

3.3.2. Functional Form of Production Function

SFA models are mostly based on the Cobb–Douglas (C-D) production function and translog production function. The economic meaning of the C-D production function is simpler and clearer, which can avoid the multicollinearity problem caused by more explanatory variables. Although the translog production function can relax the assumptions of technical neutrality and fixed output elasticity, it is difficult to calculate the elasticity of factors directly and is prone to multicollinearity problems. Based on this, this paper selected the C-D production function and built an SFA model with the actual situations of grass-based livestock husbandry production and operation, which can be shown as follows:

$${lnY}_i={{\beta }_0+{\beta }_1{lnT+\beta }_2{lnK+\beta }_{3}lnL+v}_i-u_i$$

(2)

where $Y_i$ represents the total sales revenue of i-th herdsman’s livestock and related products; T, K and L represent the input value of i-th herdsman’s land, capital and labor factors. Afterward, establishing a production efficiency evaluation model of animal husbandry can be shown as follows:

$${PE}_i=\frac{E\left(Y_i\right|u_i,x_i)}{E\left(Y_i\right|u_i=0,x_i)}$$

(3)

where ${PE}_i$ represents production efficiency of animal husbandry; $E\left(Y_i\right|u_i,x_i)$ represents herdsmen’s real output; $E\left(Y_i\right|=0,x_i)$ represents herdsmen’s potential best output. The production efficiency score for each herder is defined as the ratio of real output to the corresponding potential best output. If the score tends to 1, it means that the production is in full efficiency; on the contrary, the score tends to 0, which means that t the production is non-efficient.

3.3.3. Tobit Estimation Model

The production efficiency score is usually between 0 and 1, and there is no observation value outside the interval, which belongs to the truncated data. In the regression of truncated data, the Tobit estimation model is used by more scholars and becomes the main method to study the factors affecting efficiency [58,59]. Thus, we selected the Tobit estimation model to identify the impact of Internet use and other control variables on production efficiency, which can be shown as follows:

$${Proeff}_i={\beta }_0+{\beta }_1{Internet}_i+\sum _{i=2}^n{\beta }_i{x}_i+{\epsilon }_i$$

(4)

where ${Proeff}_i$ represents the production efficiency of i-th herdsman, which is measured by the SFA model; ${Internet}_i$ represents Internet use of i-th herdsman; $x_i$ represents control variables; ${\beta }_0$ represents constant term; ${\beta }_1$ represents the regression coefficient of the kernel variable; ${\beta }_i(i=\mathrm{2,3},\dots ,n)$ represents the regression coefficients of control variables; ${\epsilon }_i$ represents stochastic perturbation term.

3.3.4. Moderation Effect Model

Referring to the test steps of Fang and Wen (2022) on the moderation effect [60], we choose the moderation effect model to verify the mechanism of capital factors in the process of Internet use affecting production efficiency, which can be shown as follows:

$${Proeff}_i={\delta }_0{+\delta }_1{Internet}_i+\sum _{i=2}^3{\delta }_{i}C+\sum _{i=4}^5{\delta }_{i}C·{Internet}_i+\sum _{i=6}^n{\delta }_i{x}_i+{\lambda }_i$$

(5)

where $C$ represents capital factors as a moderator variable, which includes social capital and financial capital; $C·{Internet}_i$ represents the interaction between Internet use and social capital and the interaction between Internet use and financial capital, which explains the moderation effect of social capital and financial capital on “Internet use-production efficiency”; $x_i$ represents control variables; ${\delta }_i\left(i=\mathrm{1,2},\dots ,n\right)$ represent the regression coefficients; ${\lambda }_i$ represents stochastic perturbation term. Due to the introduction of interaction in the model may lead to multicollinearity problems, the explanatory variable is centralized in the moderation effect analysis.

4. Results

4.1. Estimates of SFA Model

This paper uses Stata 17.0 to output the result of the SFA model.

Table 3. The estimation results of SFA model.

Variables	Coefficient	Standard Error
Ln(Labor)	0.765 ***	0.148
Ln(Capital)	0.356 ***	0.055
Ln(Land)	0.079 **	0.037
δu	0.616	0.102
δv	0.829	0.601
λ	0.743	0.150
γ =δ2u/(δ2u + δ2v)	0.3557
Log likelihood	−488.88
Wald${\chi }^2$ test	128.75
Observations	340

** and *** represents 5% and 1% significance, respectively. The output from Stata 17.0 includes estimates of the standard deviations of the two error components are sigma_v and sigma_u, which are labeled δ_u and δ_v, respectively. Stata 17.0 also reports two useful parameterizations. The estimate of the total error variance is sigma2 (δ²_s = δ²_u + δ²_v). The estimate of the ratio of the standard deviation of the inefficiency component to the standard deviation of the idiosyncratic component is lambda (λ = δ_u/δ_v). γ is the proportion of δ²_u in δ²_s.

presents the results, which are estimated by the C-D production function. Our findings generally highlight the rationality of using the SFA model to measure the production efficiency of herdsmen. γ is 0.3557, indicating that 35.57% of the error in the SFA model comes from the non-efficiency term and 64.43% comes from the random error term.

Because the input variables used in the SFA model are shown in logarithmic form, the variable coefficients can represent the output elasticity of factors. The output elasticity of labor, capital and land input are 0.765, 0.356 and 0.079, respectively, which are positive at the 1% and 5% significance level. The output elasticity of labor input is the largest. It shows that animal husbandry production in Inner Mongolia still needs more labor, and the part-time jobs of herdsmen or non-pastoral transfer may have a greater constraint on animal husbandry production. The age of herdsmen heads engaged in animal husbandry production in the pastoral area is nearly 50 years old, indicating that it is an inevitable choice to accelerate the mechanization of animal husbandry in the future. The output elasticity of capital input is also higher. Various production factors that were originally separated into real productivity can be transformed by capital through optimal combination. Therefore, broadening the capital channels of herdsmen is conducive to the transformation of animal husbandry production from an extensive to an intensive development paradigm. The output elasticity of land input is the least, indicating that the improvement of livestock production efficiency cannot rely solely on the expansion of grassland area. Under the constraints of a strong ecological security barrier in northern China, the key role of technology factor should be considered to improve the quality and efficiency of animal husbandry.

4.2. The Distribution of Herdsmen’s Production Efficiency

Table 4. The distribution of herdsmen’s production efficiency.

Types	Production Efficiency			Groups of Production Efficiency	Number
	All Herdsmen	Internet Users	Internet Non-Users		Internet Users	Internet Non-Users
Mean	0.6193	0.6430	0.4513	Low (≤0.3)	2	14
Standard Deviation	0.1432	0.1120	0.2137	Medium-low (0.3~0.5)	38	10
Minimum	0.0221	0.2437	0.0221	Medium-high (00.5~0.7)	154	12
Maximum	0.8458	0.8458	0.7600	High (>0.7)	104	6
				Total	298	42

presents the distribution of herdsmen’s production efficiency. The mean production efficiency of herdsmen is 0.6193. The average production efficiency of Internet users is 0.6430, which is 0.1917 higher than that of non-users, indicating that there is a large room for improvement in the production efficiency of animal husbandry in Inner Mongolia at this stage. As far as the distribution of efficiency value is concerned, 38 sampled Internet users’ production efficiency is between 0.3 and 0.5, and 154 households are between 0.5 and 0.7. Only 104 households are above 0.7. Among the Internet non-users, the production efficiency of 14 households is 0.3 and below, 10 households are between 0.3 and 0.5, 12 households are between 0.5 and 0.7, and only 6 households are above 0.7. On the whole, 81.17% of the herdsmen’s production efficiency exceeded 0.5, and Internet users accounted for 75.88%, showing a left-tailed distribution.

Figure 3a,b demonstrates the further analysis of the relationship between herdsmen’s Internet use and production efficiency through the kernel density and probability cumulative distribution function, respectively. Figure 3a shows that the production efficiency kernel density curve of Internet users is distributed between 0.2 and 0.9, the peak value is about 0.75, the center is right, the left tail is elongated, and the distribution is concentrated. The production efficiency kernel density curve of Internet non-users is distributed between 0 and 0.8, the fluctuation is gentle, the peak value is insignificant, the center is left, and the values are generally small and scattered. Therefore, there are differences in production efficiency due to different choices of Internet use. The production efficiency of Internet users is significantly higher than that of non-users, and a large gap exists between the two. The trend of improving the production efficiency of Internet users is more obvious. Likewise, Figure 3b shows that the cumulative probability gap between Internet users and non-users is gradually increased when the production efficiency value is less than 0.4. This indicates that the production efficiency of Internet non-users will be significantly improved if they use the Internet. The cumulative probability gap between Internet users and non-users is significantly reduced when the production efficiency value is larger than 0.4, which means the proportion of Internet users at a medium-high level of production efficiency is much higher than that of non-users.

4.3. Estimates of Tobit Regression Model

We identify the influencing factors of production efficiency based on the Tobit regression model (

Table 5. The estimate results of Tobit regression model.

Variables	Model 1		Model 2
	Coefficient	Standard Error	Coefficient	Standard Error
Internet use	-	-	0.132 ***	0.018
Age	0.001 *	0.001	0.001 **	0.001
Education	0.007 ***	0.002	0.006 ***	0.002
Labor size	−0.023 **	0.009	−0.020 **	0.008
Income	0.002 ***	0.000	0.001 ***	0.000
Grassland	−0.002 ***	0.000	−0.001 ***	0.000
Livestock scale	0.169 ***	0.034	0.167 ***	0.032
Livestock scale 2	−0.065 ***	0.015	−0.061 ***	0.014
Asset	−0.001 ***	0.000	−0.001 ***	0.000
Intelligent grazing equipment	0.041 ***	0.014	0.026 **	0.013
Technical training	0.047 ***	0.014	0.038 ***	0.013
Professional cooperatives	0.028 *	0.014	0.024 *	0.013
Constant	0.435 ***	0.046	0.330 ***	0.045
Log likelihood	267.49		292.71
LR${\chi }^2$ test	177.36		227.81
Observations	340		340

*, ** and *** represents 10%, 5% and 1% significance, respectively. In order to explore the specific impact of livestock scale on the production efficiency, the square term of livestock scale (Livestock scale ²) is added to the Tobit regression model.

) outputting by Stata 17.0. The result of Model 1 provides the impact of control variables on the production efficiency of herdsmen. The result of Model 2 shows the effect after adding the variable of Internet use. Both models pass the significance test of ${LR\chi }^2$, which means better equation fitting effect integrally. The results highlight that Internet use has a significant positive impact on the production efficiency of herdsmen at the 1 % level. Hypothesis H1 is verified that using Internet technology can significantly improve the herdsmen production efficiency of animal husbandry.

The results of other control variables show that household heads’ age, education level, herdsmen’s household income, livestock scale, intelligent grazing equipment, uptake of technical training, and participation of professional cooperatives all have a positive effect significantly; labor size, per capita grassland area and productive assets value have a significant negative effect. Production efficiency not only is affected by Internet use but also by the characteristics of herdsmen’s individual, family, production and social capital. The higher the household’s age, the richer the breeding experience, and the breeding proficiency is conducive to improving their production efficiency. The education level of herdsmen is higher, and the ability to accept and master new production knowledge and adopt new breeding technology is stronger. With the increase in household income, herdsmen will have the economic foundation to improve the mode of production, which make the transformation of animal husbandry production to modernization smoothly. The livestock scale affects the production efficiency of herdsmen positively, but its square has a negative impact, indicating that the animal husbandry production in Inner Mongolia has already had a scale economy effect and the number of livestock needs to be maintained at a moderate scale. With the construction of digital pastoral areas, more than half of the herdsmen have installed primary intelligent grazing equipment to save labor effectively. The labor size, per capita grassland area and the value of the productive assets have a significant negative effect on the production efficiency of animal husbandry. The reason may be that only relying on increasing the input of labor, grassland and machinery has a limited impact on the production efficiency improvement if herdsmen do not introduce modern breeding technology and management methods. Receiving professional knowledge, skills training and joining cooperatives can lead to the improvement of livestock management level and production efficiency. The specific reason is that the information exchange within the cooperative is conducive to understanding the technical knowledge and market information and reducing the loss of information asymmetry to the herdsmen. In addition, peer effects in cooperatives, especially experience sharing, can help to improve the ability of herdsmen to solve production and management problems.

4.4. Robustness Test

The robustness test using the group regression method reaffirms the above judgment. We divide the samples into low-level and high-level based on the average household income. At the same time, the samples are distinguished as eastern and central according to the region.

Table 6. The estimated results of group regression method with different income levels or regions.

Variables	Income		Region
	Low Level (<mean 29.15)	High Level (≥mean 29.15)	East	Central
Internet use	0.129 *** (0.020)	0.114 ** (0.047)	0.144 *** (0.025)	0.151 *** (0.031)
Control variables	Yes	Yes	Yes	Yes
Constant	0.338 *** (0.057)	0.479 *** (0.077)	0.334 *** (0.055)	0.371 *** (0.009)
Log likelihood	182.94	129.49	178.92	83.48
$\mathrm{LR}{\chi }^2$ test	126.16	62.14	133.23	66.00
Observations	228	112	205	94

** and *** represents 5% and 1% significance, respectively. 41 households in Ejina Banner lead to a small number of samples in the western region, so the robustness test only includes the eastern and central Inner Mongolia.

presents the estimated results of group regression with different incomes or regions. It shows that the direction and significance of Internet use affecting production efficiency are consistent with the Tobit estimation model, which proves the robustness of the results. Moreover, the difference in income levels and regions leads to heterogeneities in the process of Internet use promoting production efficiency. Concretely speaking, Internet use has a greater impact on the production efficiency of herdsmen in low-income and central regions.

4.5. Estimates of Moderation Effect Model

Table 7. The moderation effect test of capital factor.

Variables	Production Efficiency of Animal Husbandry
	Coefficient	Standard Error
Internet use	0.251 ***	0.023
Social capital	0.009 **	0.004
Financial capital	0.001 ***	0.001
Internet use × Social capital	−0.049 ***	0.016
Internet use × Financial capital	−0.010 ***	0.002
Control variables	Yes
Constant	0.228 ***	0.044
Log likelihood	320.02
$\mathrm{LR}{\chi }^2$ test	282.42
Observations	340

** and *** represents 5% and 1% significance, respectively.

presents the estimates of the moderation effect model. The results show that under the 5% and 1% significance level, the influence coefficients of social capital and financial capital are positive, the regression coefficient of the interaction between Internet use and social capital is −0.049, and the regression coefficient of the interaction between Internet use and financial capital is −0.010. The findings suggest that both social capital and financial capital can negatively regulate the effect of Internet use on the production efficiency of herdsmen. In other words, Internet use has a more important role in improving production efficiency among herdsmen with weaker social capital and financial capital. With the expansion of the social capital scale and the mitigation of financial capital constraints, the effect of Internet use on production efficiency is gradually weakened, so hypothesis H2 is verified. We emphasize that Internet use, social capital and financial capital have a significant positive impact on the production efficiency of herdsmen, and the influence coefficient of Internet use is larger than that of the two types of capital. This result indicates that Internet use can replace social capital and financial capital in the process of improving the production efficiency of herdsmen.

5. Discussion

Production efficiency growth of animal husbandry is mainly composed of the increase in factor input and total factor productivity (TFP). In the improvement of TFP, the technological revolution is the main driving force to promote the substantive development of China’s agriculture [61]. Induced Technological Innovation Theory holds that the relative price change of factors caused by the change of resource scarcity has an induced effect on technological revolution, microscopic production subjects will consciously replace expensive and scarce factors with cheap and abundant factors, and introduce advanced technology to save scarce factors [62]. Under the new round of scientific and technological revolution and industrial transformation, the new production method represented by the Internet is gradually replacing the traditional production method [63]. Mobile Internet, big data and other technologies are accelerating producers to substitute capital and technology for labor and land. Technological progress embedded in capital goods such as machinery has a positive impact on agricultural TFP [64]. On the one hand, data and information elements are fundamentally changing the mode of agricultural production through modern information and communication technology, thus livestock production efficiency is improved significantly. On the other hand, existing research has found that Internet use can optimize the allocation of production factors, expand access to information, help herdsmen adopt advanced technologies, and make them more likely to use online models such as websites and communities to trade livestock products. Therefore, Internet users are more likely to break through the constraints of time and space than non-users. With the advantages of data, information and technology, they can obtain market prices and production technologies anytime and anywhere, optimize the allocation of production factors, learn advanced production knowledge and broaden the sales channels of livestock products, so as to make higher marginal revenue. However, the authenticity and reliability of the information collected by the network are difficult to judge [65], so herdsmen may face higher risks such as false information and Internet fraud, which is desiderated to urgent attention.

Social capital has the function of information sharing and exchanging. In rural China, social capital is a structural factor connecting individuals and village society, providing key resource support for producers [66]. Internet use and social capital may have an alternative or complementary relationship [50] when they affect livestock productivity. Internet use can promote herdsmen’s information acquisition and social participation by breaking information barriers and maintaining social network relationships at a lower cost [67]. Nevertheless, according to the diminishing marginal effect principle, the technical level and market position of herdsmen with sufficient social capital are better than those with less social capital, so the similar information acquisition function makes Internet use more effective in improving the production efficiency of the latter.

The development of digital inclusive finance can improve agricultural TFP, but paying attention to credit allocation behavior is necessary. Only when credit funds are used as intermediate inputs in agricultural production can efficiency be improved [68]. The production efficiency of herdsmen who have less financial capital constraints and invest funds into modern animal husbandry production and management [69] through the channels of data, information and technology can be optimized. Digital inclusive finance, formed by the combination of modern information and communication technology and financial instruments, can roundly bring herdsmen with insufficient financial capital into client groups [70]. Obtaining online financial service information reduces the entry threshold of the financial market for herdsmen, makes financial institutions more conveniently and quickly meet the herdsmen’s needs, and further improves the flow speed and allocation efficiency of herdsmen’s capital. Therefore, the impact of Internet use on production efficiency may have a marginal diminishing effect due to the adequacy of financial capital, that is, herdsmen with insufficient financial capital use the Internet to improve efficiency more significantly.

Furthermore, heterogeneities exist in the process of Internet use affecting production efficiency due to different herdsmen characteristics. Internet use has a greater effect on the improvement of herdsmen’s production efficiency and adjustment of livestock production mode in low-income [71] and central regions in Inner Mongolia [72]. There is no doubt that high-income herdsmen have stronger financial strength and the eastern region in Inner Mongolia has higher livestock production capacity. They introduced Internet technology and assisted livestock production earlier and were more skilled. Due to the law of diminishing marginal utility, the space of the Internet improving production efficiency is gradually reduced. Because of the constraints of income, education level, new technology adaptability and information value recognition [4,73,74], the level of herdsmen’s production intelligence and technology adoption in low-income and the central region in Inner Mongolia is low, so the improvement effect of introducing Internet technology on production efficiency will be more obvious.

6. Conclusions

Based on survey data of 340 herdsmen from Inner Mongolia in China, this paper adopts SFA to measure the production efficiency of animal husbandry, estimates the effect of Internet use on production efficiency using Tobit methods, and explores the heterogeneity problem caused by differences in income and region. The moderation effect of different capital on Internet use to improve production efficiency is also analyzed by using the moderation effect model. The findings of this paper can be seen as follows:

(1)

The inputs of labor, capital and land have a significant impact on the output of animal husbandry, and the input-output elasticity is 0.765, 0.356 and 0.079, respectively. Specifically, the mean value of herdsmen’s production efficiency in Inner Mongolia is about 0.6, which is generally at a low level, and there is a large improvement room. The production efficiency of 81.17% of herdsmen exceeded 0.5 with a left-tailed distribution, and the efficiency value of Internet users is 0.1917 higher than that of nonusers.

(2)

Herdsmen who use the Internet exhibit a significant increase in production efficiency of animal husbandry in Inner Mongolia, which is consistent with theoretical analysis and Hypothesis 1. This finding remains robust even after employing the group regression method. In other control variables, herdsmen’s age, education level, household income, livestock scale, intelligent grazing equipment, uptake of technical training, and the participation of professional cooperatives all have significant positive effects; labor size, per capita grassland area and productive assets value have a significant negative role.

(3)

The findings of the moderation effect model suggested that social capital and financial capital all have negative moderation effects in the process of Internet use affecting the production efficiency of herdsmen, which is consistent with theoretical analysis and Hypothesis 2. The result means that the significant improvement of Internet use on production efficiency will be strengthened with the reduction in social capital and financial capital.

Overall, our study offers the following suggestions to improve herdsmen’s production efficiency: (1) The government needs to pay more attention to Internet use and promote the digital transformation of animal husbandry production. This strategy can be implemented by strengthening technical research, focusing on the perception and recognition technology of the breeding environment and animal physiological signs, breaking through the key technology of intelligent animal husbandry equipment, and improving the integrated application and demonstration of technical products. Using big data, the Internet of Things and other technologies to coordinate data including the resources of factor, livestock and market resources, herdsmen and new business entities is also the key to building data sharing platforms. (2) We should improve the herdsmen’s ability to use professional APPs in animal husbandry production. The government departments can guide herdsmen to use various APPs (such as livestock breeding, trading of livestock products, forecasts of meteorological disasters, remote learning, online medical treatment, etc.) through publicity and training in order to further application of “Internet plus Animal Husbandry”. Besides, authoritative professional APPs, such as “Learning Power”, can be developed according to the herdsmen’s needs, the APP operation, management and promotion can be well done, and the illegal content can also be accurately identified and efficiently processed so as to reduce the potential risks faced by herdsmen. (3) A social network with balanced strengths and weaknesses can be structured to expand the social capital of herdsmen. On the basis of the original strong blood ties and geo-relations, the government can enhance the horizontal connection among herdsmen by building standardized cooperatives and using Internet technology to organize online technical training, which will integrate them into the industrial chain, thereby extending the weak relationship based on social division and providing the impetus to improve production efficiency. (4) Financial institutions can accurately supply Internet financial products and services by adapting to the in-depth development tendency of digital inclusive finance and the differentiated demands of herdsmen.

Lastly, this study acknowledges the following limitations, which need to be further discussed in the future. First, this paper specifically focuses on the production efficiency of animal husbandry in Inner Mongolia, China. However, animal husbandry production in Xinjiang, Qinghai and Tibet in China is neglected. Thus, a certain representativeness insufficiency is due to the lack of a broader discussion and comparison of production efficiency of animal husbandry differences in different regions. Additionally, limited by data availability, this study uses cross-section data for empirical analysis. It is not easy to know the dynamic change in Internet use improving the herdsmen’s production efficiency. Therefore, when future studies examine the impact of Internet technology on livestock production efficiency, more regions should be considered and the limitations of data need to be broken through. Third, this paper only used 340 herdsman samples. When we explore the influence of Internet use on the production efficiency of animal husbandry, more samples would be needed to ensure the significance of the results.