Measuring the Digitization Level of China’s Grain Industry Chain and Its Spatial–Temporal Evolution

Abstract

Digital transformation is conducive to food security. This paper constructed an index system of the digitization level of the grain industry chain based on the panel data of 30 provinces in China from 2011 to 2022. It adopted the entropy method to measure it and analyzed the law of its spatial–temporal evolution. It was found that the digitization level of China’s grain industry chain has shown a year-on-year growth trend, and, at the same time, there are spatial spillover effects and spatial heterogeneity. Analyzed by Theil’s index, there were evident differences among the four major regions and three major grain functional areas in China, and the differences mainly originated from within the region, and there was a phenomenon of “digital divide”. The digitalization level of China’s grain industry chain has shown a decreasing trend of “East-Middle-West”. In conclusion, this paper proposes practical countermeasures to facilitate the digitization level of the grain industry chain in China, thereby providing developing countries with a reference value for the digital transformation of the grain industry chain. Therefore, we should promote the construction of digital infrastructure, increase the cultivation of digital talents, coordinate regional development, and accelerate the digital transformation of the grain industry chain.

1. Introduction

Food security is a national priority. The world’s food security faces serious challenges due to factors such as the new crown epidemic, global climate change, and regional conflicts [1]. The Food and Agriculture Organization of the United Nations (FAO) predicts that, to feed the world’s 9.1 billion people in 2050, total grain production will have to increase by 70 percent [2]. According to the FAO and the World Bank, digital technologies improve the efficiency and environmental sustainability of grain systems [3], accelerate grain system innovation [4], increase grain production, and contribute to the goal of a ‘zero hunger’ world [5]. Over the past decade, the application of digital technology in agriculture has solved many of the challenges that agriculture and rural areas have long faced [6]. How can China, one of the world’s most populous countries, feed one fifth of the world’s population with only 7 percent of the world’s arable land and scarce water resources? The digital transformation of the grain industry chain may be the solution to this challenge. By 2023, the added value of core industries in China’s digital economy accounted for 10% of GDP. Digitalization has become a new driving force for economic development, and relying on digitalization to promote the development of the grain industry chain is a general trend.

Since 2016, China has taken a series of digitalization initiatives to improve the informatization level of the grain industry chain, accelerating the integration and development of digital technology with all aspects of the grain industry chain. However, the overall level of digital development of China’s grain industry chain is low, and there is a large gap in comparison with developed agricultural countries. The digital level of the ‘production, purchase, storage and marketing’ link in the grain industry chain is not balanced, and there is a large difference in the development of various regions. Since the digital transformation of the grain industry chain is an inevitable requirement for the high-quality development of the food industry, it is necessary to understand the current status of the digital development of China’s grain industry chain; as such, this paper intends to establish a scientific and perfect indicator system to measure and analyze it.

Agriculture is the foundation of grain, and grain is the foundation of the country. The world is facing a great change that has not been seen in a century, with a complex and volatile international situation, frequent natural disasters, and geopolitical conflicts. In order to ensure national food security, protect resources, and ensure sustainable development, the food industry chain should make full use of digital technology, accelerate digital transformation, and enhance its risk-resistant capacity [7]. The development of the digital economy has led to greater access, to the sharing and use of agricultural data, increased productivity in agriculture-related sectors, improved access to agricultural markets, increased total factor productivity in agriculture [8], and has improved the traditional food supply chain [9]. Therefore, the digital transformation of agriculture is a priority policy for countries around the world and will surely trigger a major future grain production change [4] to meet the demand for grain from the growing global population [10]. Ultimately, digital transformation can help improve global food security [11]. The use of digital technologies not only revolutionizes the agriculture and grain industry, but also influences the participants in its value chain and how value is generated while reducing the transaction costs of trade and facilitating the digital transformation of other participants in the chain, with spillover effects [12]. Existing research literature related to the digital transformation of the grain chain focuses on the application of digital technologies in the agricultural industry. Lajoie-O’Malley et al., found that the FAO and the OECD envisioned the use of digital technologies to improve existing grain systems, increase global grain production, and address food shortages when analyzing policy documents and the proceedings of international conferences related to digital agriculture, but there was a controversy as to whether an increase in grain production would be beneficial to improving food security [13]. With the rise of Agriculture 4.0, the digital transformation of agriculture has become a hot topic, and the successful application of the IoT at the scale of agricultural production proves the maturity of the technology [14]. The application of IoT technology to the agricultural chain complements existing agricultural production-based services, and governments are supporting the wide-scale application of IoT technology to change traditional agricultural production methods, increase agricultural productivity, and ultimately meet the growing global demand for grain [15]. The core of the digital transformation of the grain chain is data application, based on upstream and downstream data and thus better decision making and innovation [16], but there are pitfalls in the process of data application, which may weaken the main body of the grain industry chain in adopting a digital transformation program. Rijswijk et al., accelerated the process of digital transformation by developing a framework, SCPS, which connects the three domains, while pointing out that digital transformation is not just about bridging the digital divide, but that it is more fundamental to link the digital transformation of agriculture and rural areas to the way of socio-economic development [10].

Industrial digitization is a systematic and complex process, and academics have not formed a consistent view of the measurement of industrial chain digitization. Throughout the existing research, scholars on the measurement of digitization are mainly divided into the following categories: The first category advocate the use of a single indicator to measure digitization. For example, the proportion of enterprise digital intangible assets to total intangible assets is used to measure the level of enterprise digitization [17]; the number of Taobao villages is used to measure the degree of agricultural digitization [18]; and the level of industrial digitization is used to measure the digital inputs and outputs of the industry [19]. The second category advocate constructing a digitalization indicator system. As digital transformation is known and widely used, many scholars have conducted research on enterprise digital transformation, selected relevant indicators from different perspectives, and constructed a variety of evaluation models [20,21,22]. Chanias and Hess analyzed 36 internationally published models to measure digital maturity; they selected 20 models that were more in line with reality and used them to measure digital maturity [23]. Zeya measured digital intensity and transformation management intensity by distributing questionnaires to small-and-medium-sized enterprises (SMEs) using structural equation modeling [24]. Regarding the measurement of the level of the digital economy, scholars have constructed an index system of digital economic development based on the connotation of the digital economy and its core content to measure Chinese provinces or cities [25,26,27]. The third category involves adopting the text analysis method, which extracts words that can represent digitalization according to the connotation and characteristics of digitalization and the relevant policy documents issued by the state, as well as establishing a digital thesaurus and using Python 3.9 to conduct text analysis on the whole or part of an enterprise’s annual report [28,29,30].

Through combing through the literature, it was found that, although the academic community has fully realized the importance of digital transformation to the grain industry chain, existing studies have not measured the level of digitization of China’s grain industry chain. China is a large agricultural country and the largest developing country in the world, and the digital transformation of China’s grain industry chain can provide experience for many developing countries; thus, how to build a scientific and comprehensive indicator system to measure and comprehensively analyze the digital development of China’s grain industry chain is a topic worthy of in-depth exploration and is of practical significance.

The possible marginal contributions of this paper lie in the following aspects. Firstly, this paper measures the digitization level of China’s grain industry chain through five dimensions, namely digital infrastructure, digital technology support, digital capital investment, digital talent teams, and the digitization of grain industry chain links, by selecting indicators from the macroscopic and microscopic levels. Secondly, it analyzes the spatial–temporal evolution of the digitalization level of China’s grain industry chain from the perspectives of temporal evolution, spatial distribution, and regional differences in depth. Thirdly, it analyzes the regional differences in the digitization level of China’s grain industry chain from the perspective of four regions and food functional areas, and it comprehensively analyzes the pattern of the digitization development of China’s grain industry chain so as to put forward targeted policy recommendations to bridge the digital divide and promote the high-quality development of the grain industry.

The rest of this paper is arranged as follows: the second part is the construction of indicators for assessing the digitized level of China’s grain industry chain and conducting analyses of the measurement results; the third part is an in-depth analysis of the digitized level of China’s grain industry chain from the perspectives of time and space in an attempt to find the law of spatial and temporal changes; and the fourth part is the conclusions of this study and countermeasure suggestions, as well as a discussion on the direction of future research.

2. Indicator Construction and Measurement Analysis of the Digitization Level of China’s Grain Industry Chain

2.1. Indicator System of the Digitalization Level of the Grain Industry Chain

2.1.1. Concept Definition

Before constructing the index system of the digitization level of the grain industry chain, it is necessary to define the concepts of a grain industry chain and the digitization of a grain industry chain to provide a clear idea and scientific basis for the subsequent design, measurement, and further analysis of indicators. The industrial chain is a relatively macroscopic concept, belonging to the scope of industrial economics, specifically referring to the industrial form of chain association that is objectively formed between various industrial sectors based on certain economic associations, as well as based on specific logical relationships and spatial and temporal layouts; specifically, the industrial chain covers the whole process from the supply of raw materials to the sale of final products [31]. Concerning the specific concept of an industrial chain, the grain industry chain is a chain-like system with all kinds of “grain products” as the core, covering the orderly operation of all the subjects involved: from the procurement of grain production materials to the sale and consumption of grain.

Digital transformation is a process of integrating digital information technology with internal and external resources to digitally reconstruct organizations, products, businesses, etc., to enhance operational efficiency and improve business performance [32,33,34]. The digitization of the grain industry chain refers to the process of digitization of each subject in the whole grain industry chain, i.e., the digital transformation of the food industry chain, which is essentially the use of digital technology to drive the innovation of the grain industry chain, realize value-addedness, and endow the food industry chain with new kinetic energy for the high-quality development of the food industry chain.

2.1.2. Indicator Construction

According to the connotation of the digitization of a grain industry chain, and in referring to the research results at home and abroad [26,35,36,37], this paper constructs an index system of the digitization level of the grain industry chain with five dimensions, which are digital infrastructure, digital technology support, digital capital investment, digital talent teams, and the digitization of grain industry chain links, of which the first four dimensions serve the whole grain industry chain and the last dimension is related to the ‘production, purchase, storage, plus marketing’ link. The first four dimensions serve the entire grain industry chain, and the last dimension is related to the ‘production, purchase, storage, marketing’ link of the grain industry chain. The specific indicator system is shown in

Table 1. Evaluation system of the digitization level of China’s grain industry chain.

Primary Dimension	Secondary Dimension	Specific Indicator	Category of Indicators
Digital Infrastructure	Traditional digital infrastructure	Internet penetration (%)	Positive
		Number of Internet broadband access ports (10,000)	Positive
		Number of Internet broadband access subscribers (10,000)	Positive
		Number of Internet domain names (10,000)	Positive
		Number of Internet pages (10,000)	Positive
	New digital infrastructure	Number of mobile-phone base stations (10,000)	Positive
		Number of IPV4 addresses (10,000)	Positive
		Mobile phone penetration rate (units/100 population)	Positive
		Length of fiber-optic lines (kilometers)	Positive
Digital Technology Support	Digital industrialization	Revenue from software operations (CNY ten thousand)	Positive
		Total telecoms business (CNYone hundred million)	Positive
	Innovative capacity of food enterprises	Number of patents obtained by food enterprises (number)	Positive
Digitalization Funding	Investment in scientific research in food enterprises	Investment in research and development by food enterprises (CNY one hundred million)	Positive
	Digitalization of financial inclusion	Digitization of inclusive finance in the Peking University Digital Inclusive Finance Index	Positive
	Investments such as the Internet of Things	Fixed investment in transport, storage, and postal services (CNY one hundred million)	Positive
	Investment in food production	Investment in fixed assets in agriculture, forestry, animal husbandry, and fisheries (CNY one hundred million)	Positive
	Investment in the information technology industry	Fixed investment in information transmission, software, and information technology services (CNY one hundred million)	Positive
Digital Workforce	Support for professional and technical personnel in the food sector	Number of persons obtaining national vocational qualification certificates in the food industry (persons)	Positive
		Total number of professional and technical staff in the food industry (persons)	Positive
	Information technology talent support	Employment in urban units of the information transmission, software, and information technology services industry (10,000 persons)	Positive
	Researcher support	Employees in scientific research and technical services (10,000 persons)	Positive
	Undergraduate talent support	Undergraduate enrolment (persons)	Positive
	Logistics staff support	Number of persons employed in the postal sector (persons)	Positive
Digitization of grain chain links	Digital production	Total power of agricultural machinery (10,000 kW)	Positive
		Number of operational agrometeorological observation stations (number)	Positive
		Rural electricity consumption (billion kWh)	Positive
	Digital warehousing	Grain enterprises with intact warehouses (10,000 tons)	Positive
	Enterprise digital supply and marketing	Enterprise e-commerce purchases and e-commerce sales (CNYone hundred million)	Positive
	National Modern Agriculture Demonstration Project	Number of national modern agricultural demonstration zones and industrial parks, and number of national demonstration parks for the integrated development of rural industries and agricultural industrialization countries (number)	Positive
	Digital food industrialization levels	Number of Taobao villages (nos.)	Positive
	Scope of services for information technology applications such as the Internet of Things	Urban delivery routes (kilometers)	Positive
		Rural delivery routes (kilometers)	Positive
		Post office (Branch)	Positive

, and it follows the scientific, available, and typical nature of indicator selection.

2.2. Data Sources and Explanations

To ensure the continuity and availability of the research data in this paper, 30 provinces in China from 2011 to 2022 were selected as the research sample, and Tibet was temporarily excluded due to its large amount of missing data, while Hong Kong, Macao, and the Taiwan of China are not included. The sample data were obtained from materials such as the Oriental Wealth Choice Financial Terminal, CSMAR Cathay Pacific Database, Wind Database, China Grain and Material Reserve Yearbook, China Statistical Yearbook, China Rural Statistical Yearbook, China Grain Industry Statistics, National Bureau of Statistics, and relevant industry research reports. For some of the missing data, linear interpolation and exponential smoothing were used to fill in the gaps.

2.3. Measurement of the Level of Digitization of the Grain Chain

With comprehensive consideration, this paper chose the entropy value method from the objective assignment method to determine the weights of each indicator. In the first step, there were significant differences between the units and the orders of magnitude of the indicator values since the 33 indicators in the constructed digital evaluation system of the grain industry chain came from different levels. Only a standard dimensionless processing of these different indicators can make them comparable, thus ensuring the accuracy of the final measurement index. The specific methods used are as follows.

Standardized treatment of positive indicators:

$$X_{ij}^{\prime }={\displaystyle \frac{X_{ij}-\min \left\{X_j\right\}}{\max \left\{X_j\right\}-\min \left\{X_j\right\}}}.$$

(1)

Standardized treatment of negative indicators:

$$X_{ij}^{\prime }={\displaystyle \frac{\max \left\{X_j\right\}-X_{ij}}{\max \left\{X_j\right\}-\min \left\{X_j\right\}}},$$

(2)

where $X_{ij}$ is the raw data, $i$ is the region, $j$ is the indicator, $X_{ij}^{\prime }$ is the normalized result, $\min \left\{X_j\right\}$ is the minimum value of the indicator $j$, and $\max \left\{X_j\right\}$ is the maximum value of the indicator $j$.

The second step was to calculate the weight of the sample in the indicator, $P_{ij}={\displaystyle \frac{X_{ij}^{\prime }}{{\displaystyle {\sum }_{i=1}^n{X}_{ij}^{\prime }}}}$, where $n$ is the sample size.

In the third step, the information entropy was calculated for the $j$ indicator, $e_j=-k{\displaystyle {\sum }_{i=1}^n(P_{ij}\times \ln P_{ij})}$, where $k={\displaystyle \frac{1}{\ln n}}$.

In the fourth step, the information entropy redundancy was calculated, $d_j=1-e_j$.

In the fifth step, the weight of each indicator was calculated, $W_j={\displaystyle \frac{d_j}{{\displaystyle {\sum }_{j=1}^m{d}_j}}}$, where $m$ is the number of indicators $j$.

In the sixth step, the score was calculated at $S_i={\displaystyle {\sum }_{j=1}^m{W}_j\times X_{ij}^{\prime }}$.

3. Regional Spatial–Temporal Differences in the Level of Digitization of China’s Grain Industry Chain

To further analyze the spatial–temporal characteristics of the digital development of China’s grain industry chain, descriptive statistical analysis, Theil’s index, the Moran index, and the natural discontinuity point method were used in both the temporal and spatial dimensions; regional heterogeneity was also analyzed. Exploring the root causes of spatial–temporal heterogeneity in the digital development of the grain industry chain provides a factual basis for policy formulation to narrow the gap between the digital development of the grain industry chain between regions and to safeguard China’s food security.

3.1. Results and Analyses of the Digitization Measurement of China’s Grain Industry Chain

Table 2. Results of the digitization measurement of China’s grain industry chain.

District	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	Rank	CAGR (%)
Beijing	0.1138	0.1399	0.1469	0.2045	0.2060	0.2227	0.2791	0.3009	0.3241	0.3352	0.3713	0.4035	5	12.2
Tianjin	0.0247	0.0348	0.0420	0.0472	0.0505	0.0525	0.0543	0.0607	0.0682	0.0760	0.0817	0.0823	27	11.6
Hebei	0.1005	0.1165	0.1363	0.1401	0.1442	0.1546	0.1739	0.1901	0.2195	0.2335	0.2364	0.2544	9	8.8
Shanxi	0.0571	0.0685	0.0796	0.0797	0.0814	0.0865	0.0789	0.0895	0.0943	0.0992	0.0987	0.1044	22	5.6
Inner Mongolia	0.0563	0.0640	0.0728	0.0780	0.0834	0.0903	0.1035	0.1012	0.1123	0.1097	0.1089	0.1107	20	6.3
Liaoning	0.0830	0.1017	0.1129	0.1173	0.1287	0.1363	0.1239	0.1362	0.1361	0.1405	0.1366	0.1408	15	4.9
Jilin	0.0624	0.0704	0.0786	0.0812	0.0855	0.0893	0.1019	0.1042	0.1004	0.1045	0.1002	0.1043	21	4.8
Heilongjiang	0.0816	0.0944	0.1200	0.1162	0.1475	0.1465	0.1439	0.1327	0.1331	0.1388	0.1359	0.1408	14	5.1
Shanghai	0.0564	0.0776	0.0999	0.1212	0.1363	0.1470	0.1564	0.1584	0.1814	0.1904	0.1830	0.2000	13	12.2
Jiangsu	0.1646	0.1898	0.2429	0.2434	0.2860	0.2918	0.3204	0.3333	0.3912	0.4100	0.3869	0.4182	2	8.9
Zhejiang	0.1146	0.1431	0.1542	0.1729	0.2068	0.2488	0.2787	0.3224	0.3624	0.3735	0.3847	0.4175	4	12.5
Anhui	0.1177	0.1196	0.1504	0.1484	0.1713	0.1805	0.1952	0.2278	0.2343	0.2092	0.2426	0.2553	8	7.3
Fujian	0.0828	0.0904	0.0943	0.0961	0.1239	0.1495	0.1935	0.1966	0.2094	0.2004	0.2128	0.2248	12	9.5
Jiangxi	0.0597	0.0667	0.0712	0.0697	0.0866	0.1009	0.1193	0.1287	0.1253	0.1356	0.1416	0.1484	17	8.6
Shandong	0.1545	0.1824	0.2473	0.2473	0.2521	0.2674	0.2939	0.3087	0.3315	0.3569	0.3702	0.3933	3	8.9
Henan	0.1128	0.1451	0.1707	0.1603	0.2034	0.2209	0.2636	0.2751	0.3035	0.3205	0.3297	0.3449	6	10.7
Hubei	0.0927	0.1074	0.1319	0.1254	0.1496	0.1579	0.1737	0.1842	0.2064	0.2066	0.2138	0.2270	11	8.5
Hunan	0.1149	0.1177	0.1471	0.1331	0.1473	0.1488	0.1771	0.1822	0.2123	0.2133	0.2134	0.2197	10	6.1
Guangdong	0.1525	0.1982	0.2675	0.2730	0.3090	0.3406	0.3872	0.4455	0.4985	0.5336	0.5291	0.5647	1	12.6
Guangxi	0.0478	0.0566	0.0679	0.0721	0.0781	0.0875	0.0976	0.1092	0.1281	0.1439	0.1444	0.1532	19	11.2
Hainan	0.0102	0.0147	0.0194	0.0220	0.0274	0.0270	0.0300	0.0319	0.0369	0.0377	0.0405	0.0436	29	14.2
Chongqing	0.0400	0.0478	0.0608	0.0586	0.0644	0.0709	0.0758	0.0870	0.0969	0.1085	0.1112	0.1258	23	11.0
Sichuan	0.1012	0.1133	0.1350	0.1389	0.1646	0.1984	0.2254	0.2627	0.2798	0.2916	0.3006	0.3157	7	10.9
Guizhou	0.0333	0.0374	0.0484	0.0500	0.0544	0.0625	0.0755	0.0980	0.1116	0.1170	0.1218	0.1297	24	13.2
Yunnan	0.0547	0.0611	0.0741	0.0741	0.0758	0.0887	0.1001	0.1151	0.1330	0.1448	0.1489	0.1615	18	10.3
Shaanxi	0.0616	0.0700	0.0835	0.0924	0.0925	0.1004	0.1096	0.1240	0.1384	0.1439	0.1392	0.1419	16	7.9
Gansu	0.0420	0.0465	0.0541	0.0571	0.0551	0.0566	0.0574	0.0641	0.0729	0.0791	0.0793	0.0827	26	6.4
Qinghai	0.0110	0.0144	0.0173	0.0182	0.0232	0.0226	0.0254	0.0279	0.0308	0.0317	0.0335	0.0362	30	11.5
Ningxia	0.0129	0.0153	0.0223	0.0204	0.0261	0.0303	0.0347	0.0384	0.0348	0.0338	0.0365	0.0374	28	10.1
Xinjiang	0.0404	0.0489	0.0586	0.0568	0.0624	0.0641	0.0708	0.0767	0.0836	0.0898	0.0956	0.0952	25	8.1
National Average	0.0752	0.0885	0.1069	0.1105	0.1241	0.1347	0.1507	0.1638	0.1797	0.1870	0.1910	0.2026	-	9.4

shows the results of the digitization level of China’s grain industry chain, as measured by the entropy method, from 2011 to 2022. According to the results of this province, the digitalization level of each province over 12 years was averaged and sorted, and the average annual growth rate of each province was calculated. Firstly, from the national mean value, there was an obvious heterogeneity to the digitization level of China’s grain industry chain in terms of time and space; this developed from 0.0752 in 2011 to 0.2026 in 2022, with an average annual growth rate of 9.42%, which indicates that the digitization level of China’s grain industry chain in the past 12 years has significantly improved, and the overall trend of development has been good. Secondly, from the viewpoint of each province, Guangdong, Jiangsu, Shandong, Zhejiang, and Beijing rank in the top five, and Qinghai, Hainan, Ningxia, Tianjin, and Gansu rank at the bottom, with large differences between regions. In terms of the digitization level of the food industry chain in 2022, the highest level was in Guangdong Province at 0.5647 and the lowest level was 0.0362 in Qinghai Province, with the former being 15.6 times higher than the latter, which is a large difference between different provinces. Again, from the point of view of the average annual growth rate of each province, Hainan Province and Guizhou Province were found to be at the forefront, although the level of digitization of the food industry chain in these two provinces was not high, its development momentum was more vigorous, and there is a tendency to catch up. The average annual growth rate of the digitalization of the grain industry chain exceeded 10% in 14 provinces, which is a good development trend, but the inter-provincial gap is still relatively evident. Finally, there is a regional imbalance in the digital development of the grain industry chain, with the eastern region leading the digital development of the grain industry chain and the overall digitization level being higher, followed by the central region, the northeastern region ranking in the middle, and the western region being at the bottom of the overall development. In short, regarding the digital level of the grain industry chain in different areas, there are large differences between different regions; however, there is still a great development potential, so narrowing the gap between the four major regions of the east, central, west, and northeast and bridging the ‘digital divide’ is a pressing task.

3.2. Temporal Evolution of the Digitalization Level of China’s Grain Industry Chain

This paper divides China’s grain industry chain digitization level from 2011 to 2022 into four time periods, and it uses the arcgis10.8 natural discontinuity grading method to divide the digitization level of China’s grain industry chain into five levels, as shown in Figure 1. From the perspective of these four time periods, the overall development was found to be relatively stable, and the digitization level of the grain industry chain showed a trend of continuous improvement. The higher level of digitization of the grain industry chain was in the eastern coastal region, and, over time, the eastern coastal region has driven the development of the surrounding areas. Although the digitization level of the grain industry chain in the northeastern region is developing, it has not been able to surpass the eastern coastal region. Several provinces in the west have been in the region of weak digitization levels of the grain industry chain, and the overall development is slow. From Figure 1, we can see that the digitization level of China’s grain industry chain has changed in these four time periods, and there are spatial spillover effects and agglomeration effects, but obvious spatial heterogeneity is still present.

3.2.1. Four Major Economic Regions

The development depth of the grain industry chain depends on the natural resources of each region, and its digital transformation depends on the economic development level, digital infrastructure, and relevant digitalization policies of each region. As shown in Figure 2 and the time evolution of the digitization of the grain industry chain in the four major economic regions, it can be seen that the eastern region, the central region, and the western region have shown a growth trend, while the northeastern region first rose slightly and then fell back into smaller steps. In terms of the average value, the eastern region and the central region exceeded the national average, while the northeastern region and the western region were lower than the national average. From the regional increase in the level of digitization of the grain industry chain, the eastern region increased the most, followed by the central region and then the western region, which was followed by the smallest increase in the northeastern region. The northeastern region is the ballast of China’s food security as half of the country’s increased grain production is from the northeast, so it is necessary to pay attention to the development of the grain industry chain digitization of the northeastern region, and there is a need to make full use of the development of digital technology to further enhance the “Northeast grain silo” so that “China’s rice bowl” can serve a more secure role. From the perspective of the development gap, the digital development of the grain industry chain in the four major economic regions has not shown any convergence, but rather, over time, the gap between the regions has become wider and wider, with the eastern region leading the way and growing rapidly, and the gap with the central, western, and northern regions widening, thus further accentuating the problem of the “digital divide”.

Theil’s index (Theil, 1967) is based on the understanding of “entropy”, which is a special form of the generalized entropy index and is an important tool for analyzing the differences in regional income levels [38]. Currently, Theil’s index is widely used to analyze the differences between a sample and the main sources [39,40,41], so this paper introduced Theil’s index to analyze the differences in the development of the digital level of China’s grain industry chain and its contribution rate. The specific formula used is as follows.

$$T={\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n(\frac{y_i}{\overline{y}}\times \ln \frac{y_i}{\overline{y}})},$$

(3)

$$T_p={\displaystyle \frac{1}{n_p}}{\displaystyle \sum _{i=1}^{n_p}(\frac{y_{pi}}{{\overline{y}}_p}\times \ln \frac{y_{pi}}{{\overline{y}}_p})},$$

(4)

$$T=T_w+T_b={\displaystyle \sum _{p=1}^4(\frac{n_p}{n}\times \frac{\overline{y_p}}{\overline{y}}\times T_p)+{\displaystyle \sum _{p=1}^4(\frac{n_p}{n}\times \frac{\overline{y_p}}{\overline{y}}\times \ln \frac{\overline{y_p}}{\overline{y}})}}.$$

(5)

In Equation (3), $T$ represents the overall difference in the digitization level of China’s grain industry chain, the size of which is between [0, 1], and the larger Theil’s index is, the larger the overall difference in the digitization level of the grain industry chain is, and the smaller the opposite is. In addition, $i$ denotes province; $y_i$ represents the digitized level of the grain industry chain in $i$ province; $\overline{y}$ represents the average value of the digitized level of the grain industry chain in the whole country; and $n$ represents the number of provinces. In Equation (4), $T_p$ is the overall difference of the digitized level of the grain chain in the region $p$; $n_p$ is the number of provinces in the region $p$; $y_{pi}$ is the digitized level of the grain chain in the region $p$ in the province $i$; and ${\overline{y}}_p$ is the mean value of the digitized level of the grain chain in the region $p$.

In Equation (5), the overall difference in the digitization level of the grain industry chain is further decomposed into the intra-regional difference $T_w$ and inter-regional difference $T_b$. In addition, ${\displaystyle \frac{T_w}{T}}$ and ${\displaystyle \frac{T_b}{T}}$ are defined as the contribution rates of intra-regional and inter-regional differences to the overall difference, and $\left({\displaystyle \frac{y_p}{y}}\right)\times \left({\displaystyle \frac{T_p}{T}}\right)$ is the contribution rate of each region to the overall difference within the region, where $y_p$ represents the sum of the digitized level of the grain industry in each province within the region $p$, and $y$ represents the sum of the digitized level of the grain industry chain in the whole country.

Based on Theil’s index, the contribution rate of the digitization level of the grain industry chain in the four major regions of China from 2011 to 2022 can be calculated, and the specific results are shown in

Table 3. Theil’s index and its contribution to the digitization level of China’s grain industry chain in four major regions during 2011–2022.

Year	Theil’s Index							Theil’s Index Contribution
	Nationwide	East	Central	West	Northeastern	Regional	Intra-Regional	East	Central	West	Northeastern	Regional	Intra-Regional
2011	0.1646	0.1681	0.0401	0.1351	0.0080	0.1132	0.0514	0.4408	0.0599	0.1821	0.0049	0.6877	0.3123
2012	0.1637	0.1561	0.0381	0.1251	0.0118	0.1071	0.0566	0.4267	0.0549	0.1657	0.0072	0.6545	0.3456
2013	0.1745	0.1754	0.0468	0.1143	0.0159	0.1166	0.0579	0.4546	0.0628	0.1418	0.0088	0.6680	0.3320
2014	0.1744	0.1587	0.0421	0.1211	0.0134	0.1116	0.0629	0.4301	0.0522	0.1500	0.0073	0.6396	0.3604
2015	0.1763	0.1526	0.0507	0.1216	0.0243	0.1107	0.0656	0.4051	0.0649	0.1444	0.0134	0.6278	0.3722
2016	0.1776	0.1526	0.0477	0.1408	0.0212	0.1147	0.0629	0.4043	0.0594	0.1711	0.0110	0.6458	0.3542
2017	0.1876	0.1542	0.0625	0.1449	0.0097	0.1199	0.0676	0.3942	0.0742	0.1667	0.0042	0.6394	0.3606
2018	0.1887	0.1617	0.0585	0.1519	0.0069	0.1249	0.0638	0.4096	0.0686	0.1809	0.0028	0.6619	0.3381
2019	0.1957	0.1610	0.0655	0.1514	0.0089	0.1275	0.0681	0.4003	0.0730	0.1754	0.0031	0.6518	0.3482
2020	0.1961	0.1637	0.0627	0.1519	0.0087	0.1290	0.0670	0.4089	0.0675	0.1787	0.0030	0.6581	0.3419
2021	0.1943	0.1575	0.0651	0.1497	0.0097	0.1261	0.0683	0.3955	0.0725	0.1775	0.0032	0.6488	0.3512
2022	0.1996	0.1597	0.0644	0.1511	0.0093	0.1278	0.0718	0.3952	0.0690	0.1731	0.0029	0.6403	0.3597

. From the national level, Theil’s index of the digitization level of the grain industry chain ranged from 0.1646 in 2011 to 0.1996 in 2022, indicating that the difference in the development of the digitization level of the grain industry chain is on a widening trend. From 2011 to 2022, Theil’s index in the region was higher than that in the inter-regional Theil’s index, indicating that the intra-regional differences were higher than the inter-regional differences. Sub-regionally, except for the eastern region, the development difference in the digitization level of the grain industry chain narrowed slightly. The central, western, and northeastern regions have tended toward becoming larger in the digitization level of the grain industry chain in terms of Theil’s index, and the development difference between the provinces within the region has expanded. From the viewpoint of the contribution rate of Theil’s index in the four regions, the contribution rate within the region was greater than 60% from 2011 to 2022, indicating that the differences in the digitization level of China’s grain industry chain mainly come from within the region, which may be related to the creation of a digital city within the province, or that the grain industry may be concentrated in some cities within the province, which produces an imbalance in the development of the province. Regarding the regional decomposition of Theil’s index, the contribution of the eastern region was the largest, followed by the west, while the central and northeastern regions had a small contribution, and the regional development was unbalanced.

3.2.2. Grain Functional Areas

According to Figure 3, the digitization level of the grain industry chain in the three major food functional areas has steadily increased, and the digitization level of the main grain-producing areas has been higher than the national average and is steadily increasing; however, it was overtaken by the main grain marketing areas in 2016, which may be due to the main grain marketing areas being generally more economically developed. The digitization development has been rapid, and the main grain producing areas have been mainly responsible for guaranteeing the supply of grain, and the digitization construction has been mainly concentrated in the grain production link, which is associated with a long period of investment. The main grain-producing areas are mainly responsible for guaranteeing grain supply, and the digital construction has been mainly concentrated in grain production. The digital construction project related to it is a long cycle and large investment, and farmers in China are unable to bear the cost alone, so the digital construction of the grain production link mainly relies on the government’s input. The digitization level of the main grain producing area and the main grain marketing area are higher than the national average, while the digitization level of the grain production and marketing balance area is lower than the national average. The growth rate has been lower than that of the main grain producing area and the main marketing area. From the point of view of the development gap of the digitization level of the grain industry chain, the digitization development gap of the three major grain functional areas is further expanding. The intelligent transformation of grain production should be accelerated to promote the digital transformation of the grain industry chain, promote the intelligent transformation of grain processing, guide the digital transformation of grain circulation, further promote digital technology to serve the development of the grain industry chain, and promote the digital upgrading of the whole grain industry chain to guarantee national food security at a higher level.

According to

Table 4. Theil’s index and its contribution to the digitization level of China’s grain chain in the three major food functional zones during 2011–2022.

Year	Theil’s Index						Theil’s Index Contribution
	Nationwide	Major Grain-Producing Areas	Major Grain Purchasing Areas	Grain Production and Marketing Balance Areas	Regional	Intra-Regional	Major Grain-Producing Areas	Major Grain Purchasing Areas	Grain Production and Marketing Balance Areas	Regional	Intra-Regional
2011	0.1646	0.0507	0.2124	0.0980	0.0989	0.0658	0.1777	0.3171	0.1057	0.6006	0.3994
2012	0.1637	0.0533	0.2026	0.0928	0.0996	0.0641	0.1827	0.3259	0.0996	0.6082	0.3918
2013	0.1745	0.0710	0.2158	0.0819	0.1102	0.0644	0.2305	0.3178	0.0829	0.6312	0.3688
2014	0.1744	0.0689	0.2058	0.0889	0.1111	0.0634	0.2143	0.3334	0.0891	0.6368	0.3632
2015	0.1763	0.0669	0.1953	0.0698	0.1039	0.0724	0.2090	0.3152	0.0653	0.5894	0.4106
2016	0.1776	0.0636	0.2033	0.0739	0.1064	0.0712	0.1935	0.3364	0.0690	0.5989	0.4011
2017	0.1876	0.0667	0.2098	0.0712	0.1111	0.0765	0.1900	0.3412	0.0609	0.5921	0.4079
2018	0.1887	0.0716	0.2251	0.0746	0.1195	0.0692	0.1983	0.3681	0.0668	0.6332	0.3668
2019	0.1957	0.0847	0.2236	0.0866	0.1283	0.0673	0.2236	0.3563	0.0759	0.6558	0.3442
2020	0.1961	0.0895	0.2278	0.0925	0.1331	0.0629	0.2337	0.3619	0.0834	0.6790	0.3210
2021	0.1943	0.0894	0.2213	0.0868	0.1304	0.0639	0.2342	0.3583	0.0787	0.6712	0.3288
2022	0.1996	0.0940	0.2248	0.0892	0.1348	0.0648	0.2381	0.3588	0.0785	0.6754	0.3246

, the Theil’s index of the main grain production area showed a slight increase, the balance of production and marketing area showed a decreasing trend, and there was an overall maintenance of stability. The inter-regional Theil’s index maintained stability, and the intra-regional difference further expanded, thus indicating that the difference in the digital development level of the grain industry chain was mainly intra-regional. Regarding the further decomposition of Theil’s index, the contribution rate of intra-regional differences reached more than 60%, and the relevant departments should pay attention to it and focus on intra-regional synergistic development. Regarding the contribution rate of Thiel’s index, the main marketing area ranked first, followed by the main production area, and the balance of production and marketing area contributed less. The tasks and focuses of digitization and construction of the three major functional areas of grain were different, thus showing obvious differences between the regions. The geography, soil, climate, and other natural conditions of the main grain-producing areas are suitable for planting grain crops, ensuring self-sufficiency while also exporting commercial grains, so the focus of the digital construction is on the application of digital technology to the construction of high-standard farmland, thus empowering grain production. The main sales area is relatively developed economically, but there are many people and little land, and the gap between grain production and demand is large. The focus of the digitalization construction is on the grain circulation area to guarantee grain consumption, and the balance of production and marketing area has a limited contribution to the national grain production but it can basically maintain self-sufficiency, and the construction of digitization should be a comprehensive construction, which not only promotes the construction of intelligent farmland in grain production, but also guarantees the digitalization construction of grain circulation area to ensure grain production and self-sufficiency while striving to export commercial grain.

3.3. Spatial Distribution of the Digitization Level of China’s Grain Industry Chain

Since the element of data had strong mobility, there was a spillover effect due to the digital transformation [30,42,43]. When the digital transformation of the grain industry chain in a certain region has made some progress and the competitiveness of the grain industry has been enhanced, it sends a “positive signal” to the neighboring regions and promotes the acceleration of the digital transformation of the grain industry chain in the neighboring regions, so there may be a spatial correlation. Academics generally use the Moran index for verification, and the global Moran index is mainly used to describe the average degree of correlation between all spatial units in the whole region and the neighboring regions [44,45]. The calculation formula used is as follows:

$$I={\displaystyle \frac{n{\displaystyle {\sum }_{i=1}^n{\displaystyle {\sum }_{j=1}^n{w}_{ij}(y_i-\overline{y})(y_j-\overline{y})}}}{({\displaystyle {\sum }_{i=1}^n{\displaystyle {\sum }_{j=1}^n{w}_{ij}}}){\displaystyle {\sum }_{i=1}^n{(y_i-\overline{y})}^2}}}.$$

(6)

If the global Moran index is significant, it can be assumed that there is a spatial correlation over the region. However, we needed to conduct a further analysis to find the spatial aggregation phenomenon, which required the involvement of the local Moran index to help illustrate [46,47]. The calculation formula used is as follows:

$$I_i={\displaystyle \frac{y_i-\overline{y}}{\frac{1}{n}{\displaystyle \sum {(y_i-\overline{y})}^2}}}{\displaystyle \sum _{j\ne i}^n{w}_{ij}(y_i-\overline{y})}.$$

(7)

In Equations (6) and (7), $n$ is the total number of regions; $y_i$ is the index of the digitization level of the grain industry in the region $i$; $\overline{y}$ is the average value of the digitization level of the grain industry chain in the whole country; and $w_{ij}$ is the spatial weight matrix based on the relationship of provincial proximity. The value of the global Moran index was [−1, 1]: when the Moran index is greater than 0, it means that the digitization level of the grain industry chain in all regions had a positive spatial correlation, and the larger the value is, the stronger the correlation is; when the Moran index is less than 0, it means that the digitization level of grain industry chain in all regions is negatively correlated, and the smaller the value is, the larger the spatial difference is; when the Moran index is 0, it means that the regions are randomly distributed, and there is no spatial correlation.

First of all, in a global analysis, through the global Moran index test, the p value is less than 0.1, which could reject the original hypothesis, and there are sufficient reasons to think that the Moran index is significant. The Moran’s index calculated is shown in Figure 4. From 2011 to 2022, the overall Moran index of the digitalization level of China’s grain industry chain was significantly positive, and there was a positive spillover effect on the digitalization level of the grain industry chain between regions. From the value of the global Moran index, the value had a fluctuating state in these 12 years, with a slight decrease, and the average value was 0.145. Additionally, the overall positive correlation was not strong, which may be because the digitalization transformation of China’s grain industry chain is mainly dependent on policy inputs.

The coordinate system was divided into four regions by measuring the local Moran index and plotting a scatter plot of the Moran index. The first quadrant is the high–high (HH) region, the second quadrant is the low–high (LH) region, the third quadrant is the low–low (LL) region, and the fourth quadrant is the high–low (HL) region, with the specific meanings being as follows: the digitization level of the grain industry chain in the HH region was relatively high and also high in the surrounding region; the LH region, that is, the digitization level of the grain industry chain, was relatively low but high in the surrounding region; in the LL region, the digitization level of the grain industry chain was relatively low and the surrounding region was also low; and the HL region was relatively high but the surrounding region was low. According to the local Moran index scatter plot, the distribution of provinces in the four quadrants from 2011 to 2022 was organized, as shown in

Table 5. The spatial distribution of the localized Moran index during 2011–2022.

Year	HH (Facilitation Zone)	LH (Transition Zone)	LL (Low-Level Zone)	HL (Radiation Area)
2011	Jiangsu, Zhejiang, Shandong, Anhui, Fujian, Henan, Hebei, Hubei	Hainan, Shanghai, Tianjin, Jiangxi, Guangxi, Chongqing, Shanxi	Guizhou, Jilin, Shaanxi, Inner Mongolia, Yunnan, Qinghai, Gansu, Ningxia, Xinjiang	Liaoning, Heilongjiang, Hunan, Beijing, Guangdong, Sichuan
2012	Jiangsu, Shandong, Zhejiang, Henan, Hebei, Anhui, Fujian, Hubei	Hainan, Shanghai, Tianjin, Jiangxi, Guangxi, Shanxi	Chongqing, Jilin, Guizhou, Shaanxi, Inner Mongolia, Yunnan, Qinghai, Ningxia, Gansu, Xinjiang	Liaoning, Hunan, Beijing, Heilongjiang, Sichuan, Guangdong
2013	Jiangsu, Shandong, Zhejiang, Henan, Hebei, Hubei,	Shanghai, Hainan, Jiangxi, Fujian, Tianjin, Guangxi, Shanxi	Chongqing, Jilin, Guizhou, Shaanxi, Inner Mongolia, Yunnan, Qinghai, Ningxia, Gansu, Xinjiang	Hunan, Liaoning, Beijing, Guangdong, Heilongjiang, Sichuan
2014	Jiangsu, Shandong, Anhui, Shanghai, Hebei, Henan, Zhejiang	Hainan, Tianjin, Jiangxi, Fujian, Guangxi, Shanxi	Chongqing, Jilin, Guizhou, Shaanxi, Inner Mongolia, Yunnan, Qinghai, Ningxia, Gansu, Xinjiang	Hubei, Hunan, Liaoning, Heilongjiang, Beijing, Guangdong, Sichuan
2015	Shanghai, Anhui, Shandong, Jiangsu, Zhejiang, Henan, Hebei, Fujian	Hainan, Tianjin, Jiangxi, Guangxi, Shanxi	Chongqing, Jilin, Guizhou, Shaanxi, Inner Mongolia, Yunnan, Qinghai, Ningxia, Gansu, Xinjiang	Hunan, Liaoning, Beijing, Guangdong, Heilongjiang, Sichuan
2016	Shanghai, Fujian, Anhui, Shandong, Jiangsu, Zhejiang, Henan, Hebei	Hainan, Tianjin, Jiangxi, Guangxi, Shanxi	Chongqing, Jilin, Guizhou, Shaanxi, Inner Mongolia, Yunnan, Qinghai, Ningxia, Gansu, Xinjiang	Hunan, Hubei, Liaoning, Heilongjiang, Beijing, Guangdong, Sichuan
2017	Shanghai, Fujian, Anhui, Shandong, Jiangsu, Zhejiang, Henan, Hebei	Hainan, Tianjin, Jiangxi, Guangxi, Shanxi	Chongqing, Jilin, Guizhou, Shaanxi, Inner Mongolia, Yunnan, Qinghai, Ningxia, Gansu, Xinjiang, Liaoning, Heilongjiang	Hunan, Hubei, Beijing, Guangdong, Beijing, Sichuan
2018	Fujian, Anhui, Shandong, Jiangsu, Zhejiang, Henan, Hebei	Hainan, Shanghai, Tianjin, Jiangxi, Guangxi	Chongqing, Jilin, Guizhou, Shaanxi, Inner Mongolia, Yunnan, Qinghai, Ningxia, Gansu, Xinjiang, Liaoning, Heilongjiang, Shanxi	Hunan, Hubei, Beijing, Guangdong, Sichuan
2019	Fujian, Anhui, Shandong, Jiangsu, Zhejiang, Henan, Hebei, Hunan, Shanghai	Hainan, Tianjin, Jiangxi, Guangxi, Shanxi	Chongqing, Jilin, Guizhou, Shaanxi, Inner Mongolia, Yunnan, Qinghai, Ningxia, Gansu, Xinjiang, Liaoning, Heilongjiang	Hunan, Hubei, Beijing, Guangdong, Sichuan
2020	Shanghai, Fujian, Anhui, Shandong, Jiangsu, Zhejiang, Henan, Hebei, Hunan	Hainan, Tianjin, Jiangxi, Guangxi, Shanxi	Chongqing, Jilin, Guizhou, Shaanxi, Inner Mongolia, Yunnan, Qinghai, Ningxia, Gansu, Xinjiang, Liaoning, Heilongjiang	Hubei, Beijing, Guangdong, Sichuan
2021	Fujian, Anhui, Shandong, Jiangsu, Zhejiang, Henan, Hebei, Hunan	Hainan, Shanghai, Tianjin, Jiangxi, Guangxi	Chongqing, Jilin, Guizhou, Shaanxi, Inner Mongolia, Yunnan, Qinghai, Ningxia, Gansu, Xinjiang, Liaoning, Heilongjiang, Shanxi	Hubei, Beijing, Guangdong, Sichuan
2022	Fujian, Anhui, Shandong, Jiangsu, Zhejiang, Henan, Hunan, Hebei	Shanghai, Hainan, Tianjin, Jiangxi, Guangxi	Chongqing, Jilin, Guizhou, Shaanxi, Inner Mongolia, Yunnan, Qinghai, Ningxia, Gansu, Xinjiang, Liaoning, Heilongjiang, Shanxi	Hubei, Beijing, Guangdong, Sichuan

. The development of the digitization level of China’s grain industry chain was spatially correlated, with a clustering trend in the digitization development between regions and a certain cohort effect in the digitization development between provinces. In the past 12 years, the digitalization level of the grain industry chain in most provinces in China has not changed across quadrants, and only a few provinces have changed and moved to neighboring regions, with the eastern coastal area generally in the HH region and the western region in the LL region. The five regions of Hainan, Tianjin, Jiangxi, Guangxi, and Shanxi have been in the LH region for a long time, and the three regions of Beijing, Sichuan, and Guangdong have been in the HL region for a long time, indicating that there is obvious spatial heterogeneity in the level of digitization of the grain industry chain. The largest number of provinces being in the LL region indicates that the level of digitization of the grain industry chain in some of the provinces in China needs to be improved.

4. Conclusions and Policy Implications

4.1. Conclusions

A people with enough food feels secure, and a country with strong agriculture keeps stable. With the development and further popularization of digital technology, how to integrate digital technology into the traditional grain industry chain and how to stimulate the new growth momentum of the grain industry chain will be a new issue for the development of the world’s grain industry in the future. Based on the panel data of 30 provinces from 2011 to 2022, this paper conducted an in-depth analysis of the digital level of China’s grain industry chain and drew the following conclusions. First, China’s grain industry chain digitization level is rising year by year and the eastern coastal region has a higher digitization level. In the three major functional areas of grain, the digitization level runs from high to low in order of the main grain producing areas, the main grain marketing areas, and the balance of grain production and marketing areas. However, the level of grain industry chain digitization in the regional development of the ‘East-Central-West’ has been decreasing in turn. Secondly, the developmental difference in the digitalization level of the grain industry chain has shown an expanding trend, and the digitalization developmental difference mainly originates from within the region, so we must be vigilant about the digital divide problem. Thirdly, the digitization level of China’s grain industry chain is spatially significantly positively correlated, and there are regional agglomeration phenomena and spatial spillover effects. In short, the overall level of digitization of China’s grain industry chain is low and uneven, and how the digital transformation of the grain industry chain should be efficiently promoted is an urgent matter. Through measuring and analyzing the digital level of China’s grain industry chain, this paper found that the digital transformation of China’s grain industry chain has problems, and this has certain reference significance for developing countries.

4.2. Policy Implications

Based on the findings of this paper, the following policy recommendations are proposed to further accelerate the digital transformation of the grain industry chain. Firstly, digital infrastructure needs to be vigorously promoted. The digital transformation of each link of the grain industry chain “production, purchase, storage and marketing” cannot be separated from the development of digital infrastructure, and the construction of digital infrastructure has a large investment and a long recovery cycle, so it needs strong financial support. Given the phenomenon of uneven regional development, each region should, according to its conditions, identify the shortcomings, as well as overcome the differences in terrain, resources, the level of economic development, and other factors. The construction of digital infrastructure in the region should be strengthened, the regional development gap should be narrowed, and a basic guarantee for the digital transformation of the grain industry chain in the region should be provided.

Secondly, the ability of digital technology to efficiently connect businesses should be fully utilized to facilitate the development of grain supply chain finance. The digital transformation of the grain industry chain cannot be separated from a large amount of financial support, and the relevant departments should strengthen the policy guarantee and institutional mechanism innovation from the grain supply chain as a whole. Digital technology should be used to open up the upstream and downstream information channels of the grain supply chain to solve the financing difficulties of the “pain point”, to simplify the grain supply chain of the credit procedures, to help the grain industry full-chain digital transformation, and to promote the rapid development of the grain industry. Digital transformation boosts the rapid and healthy development of the grain industry.

Thirdly, the construction of digital talent teams should be strengthened to accelerate the digital transformation of the grain industry chain. To improve the training system for digital talents in the grain industry in colleges and universities, we can encourage colleges and universities with grain characteristics to open relevant specialties and innovate professional training programs to deliver professional digital talents for the grain industry. The channels for the introduction of talents in the grain industry should be broadened, talent introduction policies should be innovated upon, the flow of human resources should be promoted, and the level of digitization of the grain industry chain should be comprehensively improved. The formation of grain industry chain digitization-related talent assessment standards should be accelerated, talent level echelons should be built, and vocational skills training should be strengthened.

Fourthly, the digital transformation cohort effect should be utilized to coordinate regional coordinated development. The grain industry chain is not fragmented, and digital transformation should be upstream and downstream and go hand in hand. Full play should be given to the regional and industrial cohort effect, cooperation and exchange should be strengthened, and the digital transformation of the grain industry chain in neighboring regions should be promoted. Developing countries should actively learn from neighboring developed countries to adapt to local conditions and find a suitable road for the digital transformation of the grain industry to ensure national food security.

4.3. Research Limitations

In this paper, the digitization level of the grain industry chain was measured for 30 provinces in China. The difference in the digitization level between cities was evident, and a future study could be carried out using Chinese cities as samples. Although the digital transformation of China’s grain industry chain can provide some experience for developing countries, this paper did not conduct a comparison between countries, such as comparing the digitization level of the grain industry chain with developed agricultural countries such as the United States, Canada, Australia, Germany, etc. Future research should focus on a comparison between countries to provide more experience for developing countries to carry out their digital transformations of grain industry chains.