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. 2023 Apr 17:1–27. Online ahead of print. doi: 10.1007/s10668-023-03200-5

Can agricultural digital transformation help farmers increase income? An empirical study based on thousands of farmers in Hubei Province

Xiufan Zhang 1,✉, Decheng Fan 2
PMCID: PMC10107574  PMID: 37362968

Abstract

With the deep integration of the digital economy and agricultural development, agricultural digital transformation promotes agricultural production, and industrial upgrading and broadens sales channels to achieve the strategic goal of rural revitalization in China. To explore whether agricultural digitization can help farmers increase their income and what path can be achieved, this study is based on the theoretical framework of rural revitalization theory and digitization. Through a questionnaire survey of 1500 farmers in Hubei Province, the impact mechanism of agricultural digitization transformation on farmers’ income is empirically studied. The empirical results show that the digital transformation of agriculture can promote the increase in farmers’ income, and promote the increase in farmers’ income by improving production efficiency, broadening sales channels, and promoting the upgrading of agricultural structure. At the same time, both production efficiency and sales channels form a chain double intermediary path with the upgrading of agricultural structure. Under the background of rural revitalization, this study provides theoretical references and guidance for further promoting agricultural digital transformation to increase farmers’ income. The marginal contribution of this study is to construct a theoretical model of agricultural digitization to promote farmers’ income increase, which has important theoretical reference and guiding significance for guiding the development direction of agricultural digitization and promoting farmers’ income increase.

Keywords: Farmers’ income, Digitalization of agriculture, Production efficiency, Sales channels, Agricultural structure

Introduction

The COVID-19 epidemic has had a major impact on the economic order, with agricultural development, rural construction, and farmers’ incomes all being significantly adversely affected by the epidemic. On the whole, the total sales of agricultural products and the average income of farmers show a certain downward trend. At the same time, the employment stability of migrant workers has declined. The income source of farmers is relatively single. Under the background of the impact of agricultural development, the progress of China’s poverty alleviation projects has slowed down, and the shortcomings of rural infrastructure and public services have been exposed. In the process of rural construction, the rapid development of the digital economy brings new growth points for the digital development of agriculture, which may play an important guiding role in increasing farmers’ income. Under the new development concept of vigorously developing and innovating, coordinating, green, open, and sharing the digital economy, the research frontiers, hotspots, and trends of agricultural digital transformation are summarized to strengthen the top-level design and promote the modernization of agricultural digital transformation governance. Digitalization is used to realize the deep integration of digital economy and agriculture, strengthen the support of innovative factors in the digital transformation of agriculture, lead the optimization of agricultural structure with big data as the key production factor, form the fundamental change of agricultural digital production and operation, and provide a reference for the research and decision-making management of agricultural sustainable development. The application of networking, information, and digitization has important strategic significance in the process of agricultural development and rural construction. By the end of 2020, the Internet penetration rate in China’s urban areas was 79.8%, while that in rural areas was 55.9%. However, the proportion of optical fiber and fourth-generation mobile communication (4G) in rural areas has exceeded 98%. Intelligent equipment such as "5G", the Internet of Things, and agricultural special sensors has gradually been widely used in the rural layout. The "Digital Agriculture Rural Development Plan (2019–2025)" points out that with the support of information technology such as big data, agricultural development gradually integrates the characteristics of data, intelligence, and dynamics, and accelerates the process of digital transformation of agriculture. Using big data to achieve precise positioning of poor households. Publicly sharing helps information, and protects the value of people’s livelihood.

In this context, exploring whether agricultural digitization can help farmers to increase income, through which path to achieve, to guide the development direction of agricultural digitization has an important guiding role. At present, scholars’ research on agricultural digitalization and digital rural construction mainly focuses on policy-making (Ehlers et al., 2022), strategic value (Donati & Tukker, 2022; Tokgoz et al., 2020), driving factors (Maria et al., 2021), planning evaluation (Newton et al., 2020), financial support (Abu-Nowar, 2020; Khan & Ali, 2022), high-quality rural development (Liu & Yu, 2018; Rossetto et al., 2019) and poverty alleviation for characteristic industries (Erling & Wei, 2019). The construction of digital rural infrastructure is necessary to realize digital dividends, establish a multi-market subject-sharing income system, and build a digital village compatible with the market, to actively promote the production and transformation of inclusive digital agricultural products (Aleixo et al., 2019). Given the process of agricultural digitization, agricultural digitization can promote the comprehensive application of modern information technology in rural areas. Farmers use the Internet, big data, artificial intelligence, and other means to improve the efficiency of agricultural intensive production, improve the level of production, and explore the application of agricultural digital transformation from a technical perspective (Liao et al., 2022). On the other hand, scholars have focused on the key factors affecting farmers’ income and put forward specific measures to implement farmers’ income from different perspectives. Based on the comprehensive demonstration policy of e-commerce in rural areas, examine whether the development of e-commerce can promote farmers’ income. Technological progress can increase farmers’ income (Caffaro et al., 2020; Wang et al., 2021; Yang et al., 2022). In the process of expanding the scope of agricultural digitization, farmers’ information literacy has been continuously improved, their skills have been gradually improved, and their development concepts have gradually changed, thus forming an endogenous driving force for farmers to increase their income (Pratama et al., 2019; Peng and Huang, 2017).

The purpose of this paper is to explore the mechanism of agricultural digital transformation affecting farmers’ income under the background of double circulation, accelerate the process of agricultural digital transformation, ecological intensification, and sustainability to promote the transformation and upgrading of traditional industries and innovate new ways of economic growth. Digital capability is an important factor to promote the growth of farmers’ income, thus forming the driving force for agricultural development and rural revitalization. There are few studies at the county level. There is a lack of in-depth discussion of big data-driven research from digital elements. Therefore, we take 86 counties and cities in Hubei Province as samples, design scales, and conduct a questionnaire survey to measure the degree of agricultural digital transformation and farmers' income and establish a multi-chain intermediary model to promote agricultural digital transformation. Put forward targeted countermeasures and suggestions to promote farmers’ income increase, and solve the bottleneck problem that restricts the development of agriculture and rural areas.

The novelty of this paper lies in constructing a theoretical model of agricultural digitalization to increase farmers’ income and to deeply study the internal logical relationship between agricultural digitalization transformation, production efficiency, sales channels, agricultural structure, and farmers’ income. We provide theoretical references and guidance for further improving farmers’ poverty alleviation performance under the background of rural revitalization.

The arrangement of this study is as follows: the second part is Mechanism Analysis, the third part is the research design, the fourth part is the empirical process and result discussion, and the last part is the conclusion.

Mechanism analysis

By summarizing the influencing factors of farmers’ income, it can be seen that the ways for farmers to obtain income include poverty alleviation funds, agricultural production, sales income, employment, and entrepreneurial income. The influence of farmers’ income includes internal factors (i.e., production technology and resource endowment) and external factors (agricultural structure, agricultural environment, and policy orientation). With the support of digitization, agricultural digitization improves resource allocation and structural adjustment through the comprehensive effect of influencing factors and completes production through a rational layout, thus forming multiple paths to promote farmers’ income.

Digital information platform achieves precise poverty alleviation

There is information asymmetry in the process of poverty alleviation management (Yaw et al., 2019). Embedding digital management into the process of targeted poverty alleviation, the poverty alleviation digital management system can focus on the goal of targeted poverty alleviation (Chang et al., 2021; Ning et al., 2021). Data support and Management system support help solve the optimization of poverty alleviation fund distribution (Schaafsma et al., 2021). Relevant literature is in Table 1.

Table 1.

Relevant literature on agricultural digitization and farmers’ income increase

Research perspectives Representative author and research results
The digital management of targeted poverty alleviation can improve the dynamic, systematic, and logical nature of financial planning With the help of a cloud platform, digital platform, and data system, the information network of poor households is established to realize the dynamic management of poverty alleviation work, which can match the actual needs of regional development (Martin, 2003; Wang et al., 2020)
The digital management system can use digital information technology to analyze key populations, improve the planning and use efficiency of poverty alleviation funds, and promote the rational allocation of social public resources to ensure the accuracy and efficiency of resource allocation (Liu et al., 2017; Bukchin & Kerret, 2020; Mitchley et al., 2021)
Digital technology is used to monitor the capital investment of supporting projects, effectively improve the efficiency of project monitoring, and make poverty alleviation work more timely and efficient (Ayalew et al, 2022; marshall et al., 2020)
The digital management of targeted poverty alleviation can reduce the uncertainty of farmers’ income With the continuous improvement in digital rural construction, the agricultural product information platform has been gradually constructed, which makes the traceability management information of agricultural product quality and safety more perfect (Wang et al., 2020; Mitchell et al., 2021)
Under the big data construction of the whole industrial chain of agricultural products, farmers strengthen production management according to the information and data in the system, understand the needs in the production process, and improve farmers’ confidence while promoting the upgrading of rural consumption(Zhou et al., 2022)
The construction of rural data centers enables farmers to capture changes in market demand through interregional digital platforms (Liu et al., 2021a, 2021b; Simbizi et al., 2021)
The development of digital industrial parks has become one of the important contents in the process of rural construction. While attracting digital service providers, reduces the risk of farmers’ breeding and reduces the pressure caused by financial uncertainty(Putra et al., 2017; Simbizi et al., 2021)
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The Digital Management of Targeted Poverty Alleviation can play a role as shown in Fig. 1:

Fig. 1.

Fig. 1

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The Digital Management Mechanism of Targeted Poverty Alleviation

Intelligent agriculture promotes the efficient utilization of agricultural resources

Agricultural digitization will lead to the modernization of agriculture and promote agricultural efficiency, forming a digital rural modernization. The construction of new infrastructure such as big data centers is accelerating. Farmers use digital technology, digital information system, and digital resource endowment to improve production efficiency and increase farmers income. Relevant literature is in Table 2.

Table 2.

Relevant literature on intelligent agriculture to improve the resource utilization efficiency

Research perspectives Representative author and research results
Agricultural digitization can improve the allocation efficiency of production factors Data have become a new type of production factor, adjusting the factor structure, thereby improving the efficiency of agricultural factor allocation. The digital transformation of agriculture can strengthen infrastructure and improve service efficiency (Klein et al., 2020; Ucum et al., 2018)
The reconstruction of digital elements forms a ‘structural dividend’ to improve agricultural production efficiency. Data elements penetrate deeply into the industrial chain of production supply, fine manufacturing, processing, and marketing, forming high-end factor agglomeration, improving rural output efficiency, and enhancing the competitiveness of agricultural products (Guo et al., 2013; Zhou, 2017; Yang & Sun, 2020)
Digital technology reduces agricultural production costs Agricultural digital transformation realizes the upgrading of production technology by systematically collecting the elements and product data information of agricultural products (Groher et al., 2020; Mclennon et al., 2021)
According to social exchange theory, digital technology improves productivity, services, and production safety, and develops sustainable agricultural practices. (Salambier et al., 2020; Harkness et al., 2021; Kumar et al., 2021; Barati et al., 2021). Improve the judgment and monitoring of land assessment, precision agriculture, and agricultural supply chain management through data analysis, and establish sustainable agricultural advantages (Chelladurai et al., 2020; Aleixo et al., 2019)
According to the theory of production efficiency, new digital technology promotes the upgrading of agricultural product quality and efficiency and forms a modern development model of specialized production. Digital technology reduces the material cost of agricultural development (Lund et al., 2017). Economic growth, low-carbon emissions, and industrial investment are necessary conditions for strengthening energy efficiency policies (Tang et al., 2022). Policymakers and governments should take measures to solve these problems. Their results verify the financial resource curse hypothesis. There is a one-way causal relationship between resource volatility to economic performance (Xie et al., 2022). Digital rural construction forms the endogenous power of farmers’ income growth
Digital rural construction forms the endogenous power of farmers’ income growth According to the new growth theory, the spillover effects of technological externalities and human capital can endogenously promote technological progress (Sarr et al., 2017; Pratama et al., 2019). Digital technology gives full play to the spillover effects of land, space, and resources, stimulates endogenous development momentum, and forms a virtuous cycle cumulative effect (García et al., 2016)
The digital transformation of agriculture can help farmers gain experience in automated production, reduce labor costs and enhance the driving force of rural endogenous development. Agricultural production has fully obtained the endogenous development ability of external market profits and achieved sustainable growth (Bukchin &Kerret, 2020; Kang et al., 2021)
Resource-saving circular agriculture promotes the reciprocating multilayer and efficient flow of various agricultural resources in the agricultural system, forms a digital rural modernization complex led by digital technology and industrial digitization, and improves the energy conversion rate and resource utilization rate (Alvarado et al., 2021). The impact of economic complexity on the ecological footprint in Latin America is asymmetric (Powell et al., 2016; Santos & Ferreira, 2019). Using digital technology, digital information system, and digital resource endowment to improve production efficiency, realize agricultural efficiency, shape the core sustainable high-yield model, and change the traditional intensive production mode of the one-sided pursuit of high yield (Zakari et al., 2021; Lv & Zhou, 2014)
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Broaden the sales market to achieve sustainable development

Embedded in the sales market, digital technology can broaden sales channels, provide a large number of employment opportunities, and create a high-end product sales market, forming a digital cooperative sales model (Huang et al., 2017). Relevant literature is in Table 3.

Table 3.

Relevant literature on intelligent agriculture to broaden the sales market

Research perspectives Representative author and research results
E-commerce develops agricultural sales channels The development of the rural e-commerce industry further positions characteristic agricultural products standardizes the management mode and promotes the improvement in agricultural products logistics facilities (Chelladurai, 2020). The development model of e-commerce is endogenous, inclusive, and characteristic (Zhou and Dan, 2014; Jiang et al., 2022). Farmers in poor areas should seize the opportunity for e-commerce development
The e-commerce industry has achieved a leap from decentralization to intensive production of traditional agricultural products (Zühre & Öz, 2020; Garcia-Blanco et al., 2017)
Digital diversified agricultural cooperation Agricultural digitalization promotes the development of the agricultural industry chain, drives the development of rural construction, related industries, and the sales market, and makes the sales of high-end agricultural products possible (Zhang & Pan, 2021; Wang et al., 2020)
Agricultural digitization can expand the development potential of modern agriculture, promote the development of the modern agricultural service industry, promote the integration of urban and rural areas, and expand the channels for increasing farmers' income (Erling et al., 2019)
Using organizational digitization to form diversified agricultural cooperation The introduction of digital platforms and information applications in rural cooperatives will form diversified cooperation between agricultural organizations, thereby bringing positive growth in farmers’ income (Luz et al., 2019; Aksoy & Öz, 2020)
Improving the rural cooperative economy can better protect the interests of farmers, and bring macrosocial benefits (Groher et al., 2020). Using digitization to form cooperation and interaction between organizations, government, companies, and farmers, achieve a good cycle of agricultural production, thereby increasing the enthusiasm of farmers to produce, while increasing the output of agricultural products to achieve economic benefits (Guo et al., 2013). Digital technology broadens sales channels, provides a large number of employment opportunities, and creates a high-end product sales market, forming a digital cooperative sales model (Khan & Ali, 2022; Ozdogan et al., 2017)
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Digitization sales market operation is shown in Fig. 2.

Fig. 2.

Fig. 2

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Digitization sales market operation

Build a circular agricultural industry chain

Agricultural digital transformation has the characteristics of high permeability, increasing marginal efficiency, self-expansion, and external economy (Martens & Zscheischler, 2022). The market demand structure for agricultural products has dynamic changes. The production and planting structure of agricultural products changes with the demand, agricultural production, and market demand tend to coordinate, to realize the effective use of agricultural resources, sustainable development of agriculture, and promote farmers’ income. Relevant literature is in Table 4.

Table 4.

Relevant literature on intelligent agriculture to build a circular industry chain

Research perspectives Representative author and research results
Digital information improves agricultural products and service levels With the reduction of agricultural information cost, the development of agricultural digitization has promoted Internet operation, platform-based economic operation, and data sharing, and the demand market for agricultural products will be further expanded (López-Morales et al., 2020)
Digital technology is constantly improving the production technology and processing methods of traditional agricultural products. In the key link of digital agricultural products management, based on agricultural Internet and infrastructure, the construction of digital agricultural products network, the establishment of new product investment and trade relations, and the provision of new services have significantly increased the sales of traditional agricultural products while improving the industrial level and promoting farmers’ income.(Prasad et al., 2022; Basso & Antle, 2020)
Agricultural digitalization produces a multi-level consumer market Digital rural construction can create a logistics system that integrates urban and rural areas, making the production and sales of agricultural products more diversified. Agricultural digitization and intelligence drive the employment of surrounding farmers and increase their income (Song et al., 2015; Micheli & Muctor, 2021)
Digital villages introduce high-level services and products such as tourism, health care, and intelligent electronic products to improve the quality of life in rural areas. The integration of cross-regional development has improved the quality and efficiency of resource supply and formed a multi-level rural consumer market(Wan et al., 2021; Wang et al., 2022)
Digitalization promotes the transformation of the whole industrial chain of agriculture Agricultural digital transformation has the characteristics of high permeability, increasing marginal efficiency, self-expansion, and external economy. Digital technology opens up the agricultural industry value chain and builds a vertical integration of the agricultural industry development model. Digital agriculture uses modern scientific and technological achievements and modern management methods to obtain higher economic, ecological, and social benefits (Lu et al., 2016; Zhang et al., 2022)
The use of digital technologies has a significant impact on the environment, encouraging economic growth and environmental sustainability (Klerkx et al., 2019; Dagar et al., 2021). There is a trilemma of energy balance, clean energy transformation, and economic expansion in the process of environmentally sustainable development. The digital transformation of agriculture can gradually link the contradiction between ecological engineering, coordinated development, and environment, resource utilization and protection, form a virtuous cycle of ecology and economy, and realize the effective utilization and sustainable development of agricultural resources (McLennon et al., 2021; Du et al., 2016). Clean energy transformation promotes economic growth and environmental sustainability (Khan et al., 2022a, 2022b)
Digital agriculture coordinates production and market demand. The market demand structure for agricultural products has dynamic changes. The development of digital agriculture gradually changes the mode of production, solves the environmental problems brought by agricultural intensification and scale, and improves the efficiency of agricultural management (Newton et al., 2020; Liu et al., 2020). Under the concept of sustainable development of ecological civilization construction, a new agricultural intensification model is constructed
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Research design

Sample selection

In the “14th Five-Year Plan”, Hubei Province has built a platform economy, promoted digital transformation and industrial integration, and focused on regional public brands of agricultural products, aiming to move from a major agricultural province to a strong agricultural province. Jingmen city promotes the agricultural industry value chain and cultivates new momentum for rural revitalization and development. Hubei Province has integrated digital pilot work in poverty alleviation, upgraded the digital management system for poverty alleviation, and developed a supervision module for low-income farmers. In July 2020, the Wuhan Municipal Government issued the “Implementation Plan for Wuhan’s Breakthrough Development of Digital Economy”, which promotes the digital transformation of agriculture as one of the project contents. Therefore, we take Wuhan City, Huangshi City, Shiyan City, Yichang City, Xiangyang City, Ezhou City, Jingmen City, Xiaogan City, Jingzhou City, Huanggang City, Xiantao City, and Qianjiang City in Hubei Province as the research object, and collects data using a questionnaire survey. The capital of Hubei Province is Wuhan, which is located in the middle reaches of the Yangtze River and north of Dongting Lake. The total land area of Hubei Province is 185.9 thousand square kilometers. It has 12 prefecture-level cities, 1 autonomous prefecture, 39 municipal districts, 24 county-level cities, 37 counties, 2 autonomous counties, and 1 forest area. The map of Hubei Province is shown in Fig. 3.

Fig. 3.

Fig. 3

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A flowchart of the experiment

We use stratified sampling from county to village, and from village to household. In the research process, in March 2022, a total of 1558 farmers’ questionnaires were distributed, with 1426 valid samples, and the sample efficiency was 91.53%. The descriptive statistics of the samples formed by the characteristics, measurement indicators, and distribution of the specific survey samples are shown in Table 5.

Table 5.

Descriptive statistics (N = 1426)

Sample characteristics Measurement indicators N Proportion (%)
Sex Male 732 51.33
Female 694 48.67
Nature of visited users Filing Households (Poverty Alleviation Households) 1071
Filing households (poor households) 355 24.89
Education level Primary school and below 972
Junior high school 386 27.07
High school and technical secondary school 39
College and above 29 2.03
Per capita disposable income (years) Under 760USD 37 2.59
760-3041USD 568
3041-6082USD 665 46.63
Over 6082USD 156
Net transfer income (year) Under 436USD 284 19.92
436-872USD 621
872-1307USD 432 30.29
Over 1307USD 89
Net Wage Income (Year) Under 726USD 46 3.23
726-1452USD 583
1452-2905USD 562 39.41
Over 2905USD 235
Net operating income (year) Under 436USD 162 11.36
436-872USD 294
872-1307USD 632 44.32
Over 1307USD 338
Net property income (years) Under 145USD 35 2.45
145-363USD 398
363-580USD 744 52.17
Over 580USD 249
Credit situation Loaned 675 47.34
Not loaned 751 52.66
Repaid 466 69.04
Unpaid 209 30.96
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From the perspective of the education level of poor households, the proportion of samples with junior high school education and below is more than 90%, and the education level needs to be further improved. In the sample, 605 poor households have an annual per capita income of 6000 yuan, accounting for 42.4%. With the vigorous development of digital rural policy and rural revitalization policy, the poverty alleviation rate will be further improved, to better complete the targeted poverty alleviation work in China.

Variable measurement and reliability analysis

Variable measurement

In the research, the scale with high recognition and wide application in the related fields of agricultural digital transformation research, precision poverty alleviation research, rural e-commerce development research, and farmers’ income is used for reference, and multiple scales are integrated to sort out, and the research objectives are moderately adjusted to obtain variables.

  1. Agricultural digital transformation

According to the relevant definitions and typical characteristics of agricultural digitization, drawing on the scale established by scholars, the measurement indicators of variables are set from the aspects of digital rural network construction, digital rural infrastructure construction, data center construction, data service platform construction, industrial parks, and service providers.

  • (2)

    Production efficiency

In the context of the digital transformation of agriculture, based on mature scales, the variables are measured from the perspectives of agricultural production methods, optimal allocation of resources and factors, input–output efficiency and production mechanization levels, and farming and irrigation methods.

  • (3)

    Sales channels

For sales channels, based on mature scale sales channels, from the village service station construction, farmers' innovation and entrepreneurship services, rural Taobao service center, and village-level postal or express outlets to measure the level of development of sales channels.

  • (4)

    Agricultural structure upgrading

For the measurement of agricultural structure upgrading variables, draw lessons from the mature scale established and the measurement method of the variables set by the agricultural structure, select the types of land management, the output of high value-added products, the proportion of grain planting, the development of circular agriculture and the big data construction of the whole industrial chain of agricultural products to measure the level of agricultural structure upgrading.

  • (5)

    Farmers’ income

The questionnaire survey of farmers’ income, including per capita disposable income, transfer income, wage income, operating income, and total property income. Taking the logarithm of total property income as the variable of farmers’ income.

  • (6)

    Control variables

Considering that whether the respondents are out of poverty will have a certain impact on their production efficiency, sales channels, and income, the credit situation of poor households also reflects their entrepreneurial situation to some extent. Therefore, whether farmers belong to poverty alleviation users and credit conditions are used as control variables. If the user is poor, it is recorded as 1, if the user is out of poverty, it is recorded as 0; if the user has a loan situation, it is recorded as 1, and if there is no loan situation, it is recorded as 0. At the same time, according to the existing research, gender and education level are also used as control variables. In the gender variable, the gender is set to 1 for men and 0 for women; the education level is set to 1 for primary school and below, 2 for junior high school, 3 for high school and secondary school, and 4 for college and above.

Reliability analysis

Integrate the scale of each variable to test the reliability and validity of the scale. The results showed that Cronbach’s α coefficients of agricultural digital transformation degree, production efficiency, sales channel, and agricultural structure upgrading were 0.935, 0.878, 0.889, and 0.914, respectively. According to the reliability standard of previous scholars’ tests (Cronbach, 1951), Cronbach’s α values were all greater than 0.7, so the scale had good reliability.

The results of the exploratory factor validity test showed that the KMO values of agricultural digital transformation degree, production efficiency, sales channel, and agricultural structure upgrading were 0.904, 0.803, 0.746, and 0.842, respectively. According to the reliability standard of previous scholars’ tests (Glen, 2016; Hill, 2011), the KMO values were all greater than 0.7, and the above four variables passed the test. The total variance interpretation rates were 79.310%, 68.530%, 83.556%, and 86.985%, respectively. The factor loading coefficient is the rotation component matrix obtained by principal component analysis using SPSS22.0, where the coefficient is the factor load coefficient and the correlation coefficient (degree) between the response variable and the common factor. After using the variance maximization orthogonal rotation matrix, the factor load of each measurement index of each variable on its latent variable reaches the standard of 0.5, and the scale has good discriminant validity. The relevant results of the measurement indicators of the scale are shown in Table 6.

Table 6.

Indicators of measurement

Variables Variable explanation and explanation factor loading KMO Cronbach’s α
Degree in Agricultural Digital Transformation Is a data center built locally? Yes = 1, No = 0 0.872 0.904 0.935
Is there a local agricultural information service platform? Yes = 1, No = 0 0.913
Is the construction of agricultural Internet of things monitoring base? Yes = 1, No = 0 0.884
Can agricultural intelligent production management system be used locally? Yes = 1, No = 0 0.856
Is there a local professional digital service provider? Yes = 1, No = 0 0.828
Is there a digital industrial park? Yes = 1, No = 0 0.795
Does the local area achieve full coverage of broadband communication networks, mobile Internet, digital television networks, etc.? Yes = 1, No = 0 0.823
Production efficiency Has modern agricultural technology transformed your agricultural production? Yes = 1, No = 0 0.767 0.803 0.878
Do you achieve optimal allocation of resources and factors for agricultural production? Yes = 1, No = 0 0.778
Is the input–output efficiency of your production improved? Yes = 1, No = 0 0.837
Are the level of production mechanization, tillage, and irrigation methods improved? Yes = 1, No = 0 0.751
Sales channels Is there a local agricultural sales channel? Yes = 1, No = 0 0.911 0.746 0.889

Do you agree that rural live streaming can gain higher attention and a larger market?

Very agree = 5, agree = 4, generally = 3, disagree = 2, very disagree = 1

 0.921
Is there a village service station or a rural Taobao service center? Yes = 1, No = 0 0.826
Is there a farmer innovation and entrepreneurship service center? Yes = 1, No = 0 0.854
Is there a village postal or courier outlet? Yes = 1, No = 0 0.775
Agricultural structure upgrading Has your plot changed the type of operation in the past 3 years? Yes = 1, No = 0 0.847 0.842 0.914
Does your plot operate high value-added products in the past 3 years? Yes = 1, No = 0 0.788
Has the proportion of grain farming in your plot decreased in the last 3 years? Yes = 1, No = 0 0.901
Are circular agriculture, creative agriculture, and ecological agriculture developed locally? Yes = 1, No = 0 0.814
Whether the local implementation of agricultural products industry chain big data construction? Yes = 1, No = 0 0.823
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Research methods

Establishment of multiple mediation model

Referring to the hierarchical regression test method of mediating effect proposed by Wen Zhonglin and other scholars, the mediating variables in the process of agricultural digital transformation affecting farmers’ income are tested. First, verify the influence of the independent variable agricultural digital transformation degree on the dependent variable farmers’ income; secondly, verify that the degree of agricultural digital transformation of the independent variable has a significant impact on the production efficiency and sales channel development level of the intermediary variable, and in the relationship between the degree of agricultural digital transformation and farmers’ income, after bringing the intermediary variable production efficiency and sales channel into the relationship between the degree of agricultural digital transformation and farmers’ income, verify whether the intermediary variable has a significant impact on the farmers’ income of the dependent variable. The third step is to further analyze whether a full mediating effect or partial mediating effect exists.

Bootstrap test of the chain mediating effect

The Bootstrap method is established to test the mediating effect, make up for the possible defects of the hierarchical regression method in the process of testing accuracy, and strengthen the reliability of the test results.

Results

Research problem

Based on the theoretical framework of rural revitalization theory and digital transformation, our research reveals and tests the influence mechanism of agricultural digital transformation on farmers’ income increase, and promotes the research of agricultural digital operation management. Based on the theories of resource acquisition, marketing channels, and industrial structure, this paper explores the impact mechanism of agricultural digital transformation on increasing farmer income through ‘development-oriented’ and ‘relief-oriented' poverty alleviation.

Solutions

Common method bias test

The common method bias test results of the ‘Harman single factor method’ show that the principal component analysis of the sample does not only precipitate a factor with an eigenvalue greater than 1, and the interpretation rate of the first factor is 45.305%, lower than the standard of 50%. The research model (digital countryside, production efficiency, sales channels, agricultural structure, and farmers’ income) fitted well (χ2/df = 3.063, GFI = 0.936, AGFI = 0.919, CFI = 0.825, RSMEA = 0.067). Therefore, there is no common method bias.

Correlation analysis between variables

It can be seen from the correlation coefficient between variables that the degree of agricultural digital transformation has a significant positive relationship with production efficiency (r = 0.327, p < 0.05), sales channels (r = 0.415, p < 0.01), and agricultural structure upgrading (r = 0.318, p < 0.05). There is significant reciprocity between the degree of agricultural digital transformation and farmers’ income (r = 0.345, p < 0.05), production efficiency and agricultural structure (r = 0.562, p < 0.01), sales channels, and agricultural structure (r = 0.532, p < 0.01). The descriptive statistic and correlation coefficient of the variables is shown in Table 7.

Table 7.

Variable mean, standard deviation, and correlation coefficient

Variables Mean Standard deviation 1 2 3 4 5 6 7 8 9
1 Gender – —– 1
2 Nature of the users interviewed — — 0.046 1
3 Education level 1.532 0.350 0.033 0.301* 1
4 Credit situation 0.473 0.108 0.013 0.031 0.012 1
5 Farmers’ income 6509.46 1113.62 0.016 0.349** 0.423** 0.337** 1
6 Digitalization of agriculture 0.635 0.171 0.022 0.373** 0.018 0.326** 0.345** 1
7 Production efficiency 0.567 0.086 0.025 0.016 0.311* 0.308* 0.372*** 0.327** 1
8 Sales channels 0.511 0.106 0.042 0.021 0.126 0.302* 0.377*** 0.415*** 0.227 1
9 Agricultural structure 0.682 0.211 0.021 0.042 0.053 0.038 0.398*** 0.318** 0.562*** 0.532*** 1
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N = 1426, *** means p < 0.001, ** means p < 0.01, * means p < 0.05, the same below

Test of the main effect and the mediating effect

The main effect test results of the impact of agricultural digital transformation on farmers’ income are in Table 8. In Eq. (4) of Table 8, the degree of agricultural digital transformation significantly positively affects farmers' income (r = 0.453, p < 0.05).

Table 8.

Mediation level regression results

Variables Production efficiency Sales channels Agricultural structure Farmers’ income
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Gender 0.030 0.015 0.013 0.013 0.013 0.021 0.013 0.013 0.014 0.013
The nature of the users interviewed 0.072 0.049* 0.047 0.048 0.045 0.043 0.041 0.057 0.042 0.039
Education level 0.019 0.038 0.043 0.045 0.042 0.024 0.015 0.014 0.031 0.012
Credit situation 0.093* 0.072* 0.032 0.037 0.041 0.029 0.047 0.063 0.069 0.042
Digitalization of agriculture 0.325*** 0.244* 0.175*** 0.168** 0.126*** 0.453** 0.351*** 0.341*** 0.327*** 0.268***
Production efficiency 0.293*** 0.461** 0.368***
Sales channels 0.375*** 0.348*** 0.301***
Agricultural structure 0.369*** 0.314***
R2 0.183 0.276 0.260 0.268 0.269 0.432 0.540 0.651 0.604 0.674
△R2 0.182 0.198 0.253 0.253 0.198 0.128 0.135 0.149 0.369 0.419
F 25.46*** 40.69*** 37.73*** 39.37*** 40.69*** 56.62*** 77.49*** 105.78*** 92.18*** 107.09***
Open in a new tab

It can be seen that the degree of agricultural digital transformation can positively affect farmers' income (r = 0.321, p < 0.01), production efficiency (r = 0.325, p < 0.001) and sales channels (r = 0.244, p < 0.01). Production efficiency can have a significant impact on the upgrading of agricultural structure (r = 0.193, p < 0.01).

After adding the intermediary variables production efficiency, sales channels, and agricultural structure upgrading, production efficiency (r = 0.368, p < 0.01), sales channels (r = 0.301, p < 0.01) and agricultural structure upgrading (r = 0.351, p < 0.01) have a significant positive impact on the dependent variable farmers' income, and the impact of agricultural digital transformation on farmers’ income has decreased from 0.351 (p < 0.01) to 0.268 (p < 0.01).

Bootstrap mediating effect test

Further, test the mediating effect of production efficiency and sales channel development level as shown in Table 9.

Table 9.

Bootstrap test results of mediating effect

Mediation path Total effect Direct effect Indirect effect (Boot)
Effect P LLCI ULCI Effect P LLCI ULCI Effect P BLLCI BULCI
Production efficiency 0.417 0.000 0.410 0.502 0.242 0.000 0.169 0.265 0.175 0.000 0.145 0.225
Sales channels 0.426 0.000 0.355 0.458 0.257 0.000 0.195 0.275 0.169 0.000 0.150 0.245
Agricultural structure 0.590 0.000 0.465 0.684 0.306 0.000 0.255 0.326 0.284 0.000 0.252 0.316
Open in a new tab

N = 1426, sampling 5000 times, 95% confidence interval, same below

At the 95% confidence interval level, the Bootstrap test intervals of the mediating effect are (0.410, 0.502), (0.355, 0.458), and (0.465, 0.684), respectively. Therefore, agricultural digital transformation can have a significant positive impact on farmers’ income by improving production efficiency, broadening sales channels, and promoting the upgrading of agricultural structure. Further, the Bootstrap test is carried out on the chain mediating effect of production efficiency and sales channel, and agricultural structure. The results of 5000 repeated samplings of valid samples are shown in Table 10.

Table 10.

Bootstrap test results of chain mediating effect of agricultural digital transformation level

Effect β P LLCI
BLLC1		 ULC1
BULC1
lnd1: Agricultural digital transformation level-production efficiency-farmers’ income 0.142 0.021 0.105 0.175
Ind2: Agricultural digital transformation level-sales channel-farmers’ income 0.137 0.018 0.098 0.162
Ind3: Agricultural digital transformation level-agricultural structure upgrading-farmers’ income 0.168 0.005 0.145 0.187
Ind4: Agricultural digital transformation level-production efficiency-agricultural structure upgrading-farmers’ income 0.094 0.012 0.075 0.136
Ind5: Agricultural digital transformation level-sales channel-agricultural structure upgrading-farmers’ income 0.085 0.016 0.057 0.103
Total mediating effect 0.757 0.000 0.595 0.805
Open in a new tab

The results show that at the 95% confidence interval level, the β value of the path lnd1 is 0.142, and the Bootstrap test interval is (0.105, 0.175), excluding 0, that is, there is an intermediary path that agricultural digital transformation can increase farmers' income by increasing productivity. There are also two other intermediary paths Ind2 and Ind3.

Mediation path importance difference test

We further test the importance of the five intermediary paths to analyze whether there are obvious differences among these paths. The test results are shown in Table 11.

Table 11.

The results of the Path importance test

Path importance test LLCI ULCI Conclusion
lndl and lnd2 0.010 0.065 A Significant gap in importance between lndl and lnd2
lndl and lnd3 − 0.053 0.115 No significant difference in importance between lndl and lnd3
lndl and lnd4 − 0.037 0.053 No significant difference in importance between lndl and lnd4
lndl and lnd5 -0.063 0.062 No significant difference in importance between lndl and lnd5
lnd2 and lnd3 0.017 0.139 A Significant gap in importance between lnd2 and lnd3
lnd2 and lnd4 0.048 0.096 A Significant gap in importance between lnd2 and lnd4
lnd2 and lnd5 − 0.032 0.028 No significant difference in importance between lnd2 and lnd5
lnd3 and lnd4 − 0.062 0.024 No significant difference in importance between lnd3 and lnd4
lnd3 and lnd5 − 0.087 0.016 No significant difference in importance between lnd3 and lnd5
lnd4 and lnd5 − 0.069 0.021 No significant difference in importance between lnd4 and lnd5
Open in a new tab

The test results show that the path of Ind2 is different from other paths, while the importance of other intermediary paths is not significantly different.

Theoretical contribution

The empirical results show that with the deepening of the process of agricultural digitization, the use of digital technology has realized the accurate identification of poor households, and the rational planning of funds through accurate assistance has enabled poor households to obtain loans and poverty alleviation funds reasonably and increase farmers’ income. Our results complement the results of Chandra and Collis, (2021). They discussed how digital technologies can benefit small-scale producers, smallholder farmers, and small livestock operators. Increasing the use of digital technologies can address the challenges of global food production and improve the livelihoods of millions of peasant households. We further point out the important application of digital technology in poverty alleviation fund planning, to increase farmers’ income more directly and concretely.

The results of the intermediary model show that production efficiency, sales channels, and agricultural structure are three parallel intermediary paths in the relationship between agricultural digital transformation and farmers’ income. With the continuous improvement in the level of agricultural digital transformation, the Internet of Things system can improve farmers’ production efficiency and improve the quality of agricultural products, thereby increasing farmers’ income. The results further confirm the findings of Klerkx (2019), who proposed the concept of digital agriculture practices and analyzed their impact on agricultural productivity through case studies of Turkish digital agriculture companies Doktar Inc. and Tarla.io. With the construction of village service stations, the development level of the e-commerce industry in rural areas of China has been greatly improved, expanding sales channels and increasing farmers’ income. Therefore, we can further carry out e-commerce training in rural areas, give play to the leading role of e-commerce demonstration villages, and improve the enthusiasm of farmers to participate in e-commerce operations.

The theoretical contribution lies in extending the chain of advantageous industries, effectively promoting the transformation and upgrading of agricultural production, and playing an important role in increasing farmers’ income. Our results further extend the results of Fountas et al. (2020). In the development of digital agriculture, the progress of key technologies will have a significant impact on the agricultural structure. Artificial intelligence technology solves the challenges of agricultural production in terms of productivity and sustainability, transforming agriculture from traditional farm practices to highly automated and data-intensive industries. At the same time, we propose two intermediary paths: agricultural digital transformation level-channel-farmers’ income and agricultural digital transformation level-agricultural structure-farmers’ income. The e-commerce platform based on digital technology can help local farmers build characteristic agricultural product brands. Improve the added value of agricultural products, promote the upgrading of agricultural structure, and increase farmers’ income. The improvement in production efficiency reduces costs, deeply explores the multiple functions of agriculture, and promotes industrial integration to increase farmers’ income. This further explains the role of agricultural digital transformation in increasing farmers’ income and deepens the research mechanism in related fields.

Conclusion

This paper discusses the influence mechanism and action path of agricultural digital transformation on farmers’ income increase in Hubei Province, China. This is an extension of the discussion of sustainable development theory related to rural livelihoods and farmers’ income increase, taking into account the urgent need for the upgrading of agricultural structure and the digital development of agriculture. Using the microdata of the questionnaire survey, we analyze the role of agricultural digitalization in the process of increasing farmers’ income. The study found that the digital transformation of agriculture can increase farmers ' income. Our findings are of great value to the sustainable development of agriculture, rural areas, and farmers. Research shows that agricultural digital transformation can promote farmers’ income, and increase farmers’ income by improving production efficiency, broadening sales channels, and promoting the upgrading of agricultural structure. At the same time, improving production efficiency and broadening sales channels are important antecedents of agricultural structure upgrading.

Our research can guide the digital transformation of agriculture, such as transforming agricultural production methods through modern agricultural technology, optimizing the allocation of resources and factors in agricultural production, improving the input–output efficiency of production, and opening sales channels to help farmers increase their income. Therefore, this study proposes a sustainable development model of agricultural digital transformation to form a way to increase farmers’ income. By exploring the causal mechanism of digital transformation and farmers’ income increase, this study proposes to improve farmer’ income increase ability from the source by promoting agricultural digital transformation, to release the great potential of agricultural digital transformation. An important contribution of this study is to clarify the impact mechanism of agricultural digital transformation on farmers’ income increase. Future research can further conduct more detailed investigation and research based on heterogeneous agricultural production methods and farmer types, and enrich empirical research results.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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