Assessing Rural Production Space Quality and Influencing Factors in Typical Grain-Producing Areas of Northeastern China

Abstract

Rural production spaces are important sites for agricultural activities, and high-quality rural production space is of great significance for guaranteeing food security and revitalizing rural areas. This study used Songnen Plain, a typical grain-producing area in Northeast China, as the study area and analyzed the spatial and temporal patterns of rural production space quality and its influencing factors from 2005 to 2020 using the rural production space quality assessment model, spatial autocorrelation analysis, and Geodetector. The results showed that: (1) The rural production space quality in the Songnen Plain has undergone a general process of change with 2015 as the node, showing an overall increase followed by a small decrease in some counties. Input–output efficiency exhibited a pattern with a high center and low perimeter, and rural production space quality exhibited a high in the south and low in the north pattern for all the years. (2) The spatial distribution of rural production quality in the Songnen Plain is highly correlated, and H-H and L-L zones had obvious spatial clustering characteristics. There were slight variations in spatial correlations of quality in each year, but the overall spatial quality exhibited a stable pattern of high in the south and low in the north. (3) The purchasing power for means of production, the level of infrastructure, and the level of agricultural mechanization were the main factors affecting the rural production space quality in the Songnen Plain, and the influence of population contraction and urbanization was gradually increasing. The results of the study can provide support for the sustainable development of rural production space and rural revitalization in Northeast China.

1. Introduction

Food security is an important foundation for national security. As the primary site of food production and other agricultural production activities, high-quality rural production space is essential for guaranteeing national food security, maintaining national livelihood, and revitalizing rural areas. China’s 2023 Central Document No. 1 proposes constructing more agricultural infrastructure and strengthening agricultural science and technology and equipment support. It also emphasizes the importance of ensuring stable production and the supply of grains and other important agricultural products. It points out a specific direction for the construction of rural production space. However, the current world economic recovery is fragile, and the challenges of climate change are prominent. In addition, the accelerated urbanization and rural population loss have resulted in the disappearance of villages. The instability, uncertainty, and vulnerability of the countryside have begun to emerge in the face of internal and external shocks [1]. There are real and potential threats to rural production activities. In this context, constructing high-quality rural production space has become increasingly important for food security and rural revitalization.

Rural production space is mainly characterized by agricultural and other industrial activities providing products or services that have corresponding beneficial effects on the natural environment and human socio-economic development [2]. Studying the various links and auxiliary elements of rural production has also become key for evaluating rural production space quality. This is further promoted by the return of rural studies in geography [3], the high priority assigned to food production, and the prevalence of geographic regionalism in post-World War II European countries. Thus, rural production activities are being emphasized in geography studies. Based on agricultural productivism, some scholars have focused on rural agricultural production activities under globalization, technological progress, and the government’s macro-control [4]. Some scholars have also focused on the land resources necessary for rural production activities [5,6], the optimization of agricultural production, and the commercialization of the products [4,7]. The environmental vulnerability of agricultural production activities and the associated ecological and environmental problems [8,9], among other aspects of rural production activities, have also been explored. In addition, there have been more studies on multiple actors in rural production and auxiliary conditions, such as policy guarantees, infrastructure, and mechanization levels to promote rural production [10,11,12,13]. These were to mitigate the problems faced by Chinese rural production in recent years, such as the disappearance of villages, the aging of farmers, de-agriculturalization, the part-time employment of farmers, and the sidelining of agriculture. The current high-cost and high-risk agricultural production situation is worsened by the internal and external threats of limiting resources, environmental constraints, and labor shortage [14]. Some scholars have applied the dynamic changes to rural production in the new period of rural land use transformation and rural production space reconstruction [15,16]. With agricultural production as the basic function integrating the emerging industry and tourism sectors in the countryside [17,18], rural production activities and localized applications have conceptually been expanded and enriched.

Current research on rural production space can be divided into two main categories: (1) targeted research around the spatial structure composition system and elements and (2) the dynamic evolution of rural spatial patterns and related research [19]. Previous studies explored the natural environmental conditions of rural space, which is the basis for rural production activities [20,21]. Among the factors explored were the villagers’ society, which is engaged in and acts as the main body of rural production activities [22,23,24], and the level of agricultural mechanization and infrastructure, which ensures efficient rural production activities [25,26]. Nonetheless, the perturbation of rural production activities by various changes in the external space, such as industrialization, urbanization, and globalization, is also an important research content [27,28]. At present, there are more studies on specific components of internal and external rural production spaces, which have enriched the overall concept and empirical analysis of rural space [15]. However, current research on rural production space mainly involves conceptual analysis, the operational mechanisms of rural production spatial evolution, and quantitative research on the state of regional rural production space [29,30,31]. These are more applicable to the functional evaluation of rural production [32]. Few studies have evaluated the spatial quality and operation logic of rural production spaces from a spatial perspective, thus necessitating a deeper understanding of the operation logic of rural production space and enriching the spatial quality evaluation scale.

Currently, the rural areas in Northeast China are facing continuous population loss and economic decline, and grain production in Northeast China is limited by factors such as unstable and unsustainable growth, and the structure needs to be optimized and quality needs to be improved [33]. This paper aims to clarify the logic and mechanism of the operation of rural production space and explore the obstacles to developing rural production space to achieve sustainable rural development and revitalization in Northeast China. Changes in spatial and temporal patterns of rural production space quality are assessed at the county scale. We analyzed the main factors affecting rural production spatial quality to provide targeted paths for optimization. This research will improve the logic of rural production space and enrich the empirical analysis and research scale. The study also provides theoretical support for food production and regional rural revitalization in Northeast China.

2. Study Area and Data Sources

2.1. Region of Interest

The study was conducted in Songnen Plain, the region with the highest level of agricultural mechanization (up to 95%) in China. The commodity rate of grain in this region is always greater than 60%, making it an important commodity grain base in China. The rural production space in Songnen Plain is relatively mature and can be compared with two other typical agricultural regions: the traditional dry-crop area in the North China Plain and the monsoon paddy farm area in the middle and lower reaches of the Yangtze River. Rural production space has the advantages of high production and a high level of mechanization. However, the region has a single structure of agricultural production, thus weakening its ability to resist disturbances. Moreover, the region faces increasingly serious problems, such as the disappearance of villages and an aging population, which pose potential threats to the optimization and improvement of rural production space quality (Figure 1).

2.2. Date Sources

In this paper, 2005, 2010, 2015 and 2020 were selected as the nodal years, while 34 county-level units in the Songnen Plain were selected as the research objects for the study. The data were obtained from the China County Statistical Yearbook (County and City Volume), Jilin Statistical Yearbook, Heilongjiang Statistical Yearbook, and national economic and social development statistical bulletins of counties (cities and districts) for the relevant years. Some of the missing data were obtained by interpolation based on the data from neighboring years. The data used for the evaluation were obtained by calculating and processing the raw data for each indicator.

3. Conceptual Analysis and Research Methodology

3.1. Elemental Composition and Operational Logic of Rural Production Space

Space is the core concept of geography [34]. Production, living, and ecological spaces are the basic forms of human practice existence [35]. Among these, production space is a spatial territory including aspects such as the primary service object, provision of industrial, agricultural, and service products as the dominant function, and ensuring land use intensification and output efficiency [36]. It operates to guarantee national food security and meet the needs of external markets and internal rural sustainability development. Through the input of production factors such as human, material, and financial resources, the final outputs are products and services that satisfy internal and external demands (Figure 2).

Specifically, external space has two main attributes: supply and demand. It provides capital, technology, and information for rural production, safeguards the production and living needs of rural residents, and supports stable rural production operations. Moreover, the market demand for rural products and the rigid requirements for food security constitute the demand attributes of external space, stimulating rural production factors to invest in production activities. The external space is not located in the traditional rural territory but is inextricably linked to rural production activities. The internal basic space also entails supply and demand. However, supply is mainly based on the natural rural environmental conditions, such as land and climate, and has a fundamental role in supporting rural production functions. Meanwhile, demand mainly stems from the villagers’ survival and development needs, which are at a smaller scale than that of external space. Production is the core function of rural production space, through input and output activities that provide relevant products and services and ensure the stable and cyclical operation of rural production space. As the most direct manifestation of the rural production space function, input–output efficiency is the key to assessing rural production spaces. Therefore, attention should be paid not only to the fundamental roles of internal elements of rural space on rural production function but also to the influence of the external elements of rural space on production activities. The overall level of development of the region should be assessed to evaluate its rural production space quality.

3.2. Selection of Indicators

The selection of indicators was based on the logical framework of rural production space, combined with the functional position of a typical grain-producing area of the Songnen Plain. Based on the four major elements of rural production space, namely: (1) the basic conditions to ensure the production process, (2) support of external economic and social factors, (3) inputs to the production function, and (4) output capacity, the evaluation system reflects the rural production space quality. Following the scientific, representative, and accessible nature of the indicators, an evaluation system reflecting the rural production space quality was constructed (

Table 1. Evaluation index system of the rural production space quality in the Songnen Plain.

First-Level Indicator	Second-Level Indicator	Indicator Interpretation
Internal basis (IB)	Cropland area per capita in villages	Reflects scale of self-employment
	Per capita disposable rural income	Reflects the economic level of the rural population
	Per capita rural electricity consumption	Reflects the standard of living of the population
External support (ES)	GDP per capita	Reflects regional economic levels
	Fiscal revenue per capita	Reflects regional financial levels
	Medical care conditions	Number of beds in medical institutions/total population
	Education security capacity	Number of students enrolled in secondary schools/number of teachers in secondary schools
	Number of craft enterprises above scale	Reflects the scale of regional industry
	Gross industrial output value above scale	Reflects the economic volume of regional industry
Factor inputs (FI)	Share of agricultural workers	Reflects human inputs to production activities
	Financial input per unit area	Reflects financial support for production activities
	Fertilizer application per unit area	Reflects fertilizer inputs to production activities
	Total power of agricultural machinery per unit area	Reflects the level of mechanization of production activities
Factor outputs (FO)	Grain production per unit area	Reflects food production capacity
	Food export capacity	Food output/total food production
	Meat output capacity	Reflects meat production capacity
	Fish output capacity	Reflects fish production capacity
	Per capita agricultural, forestry, livestock, and fishery production	Reflects the economic output capacity of the land

).

3.3. Research Methodology

3.3.1. The Model for Assessing Rural Production Space Quality

Considering the mutual influence relationship between internal and external elements of rural production space development, as well as the relationship between inputs and outputs, the input–output ratio (IOR) concept is introduced to construct a model for assessing the quality of the rural production space. The model consists of three parts: internal foundation, external support, and IOR. The specific model is as follows:

$$S_{ij}=f\left\{IB,ES,FI,FO\right\}=IB+ES+\frac{FO}{FI}$$

(1)

Factor inputs (FI) and factor outputs (FO) focus not only on quantity, but also on their efficiency. Therefore, the ratio of the two is obtained and the internal basis (IB) and external support (ES) are added to assess the rural production space quality. Each dimension is weighted and summed by the entropy method [37]:

$$IB,ES,RI,RO=\sum _{j=1}^m{w}_j{z}_{ij}(i=\mathrm{1,2},3\dots n)$$

(2)

where $z_{ij}$ is the data value after the de-measurement process, $w_j$ is the weight of j indicators, m is the number of indicators, and n is the number of research objects.

3.3.2. Spatial Autocorrelation Analysis

Spatial autocorrelation reflects the degree of correlation between a certain geographic phenomenon or an attribute value on a regional unit and the same phenomenon or attribute value on neighboring regional units [38]. Spatial autocorrelation is divided into global and local analysis. In this paper, the global Moran’s I index was used to provide an overall description of the degree of spatial correlation and variability of rural production quality in the Songnen Plain. The formula is [39]:

$$I=\frac{\sum _{i=1}^n\sum _{j=1}^n\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{s^2\sum _{i=1}^n\sum _{j=1}^n{w}_{ij}}$$

(3)

where n is the number of irregular spatial units in the study area, $x_i$ and $x_j$ are the observations of area i and area j, $s^2=\sum _{i=1}^n{\left(x-\overline{x}\right)}^2/n$, and $w_{ij}$ is the spatial adjacency weight matrix of county i and j. When region i is adjacent to j, $w_{ij}$ is 1; otherwise, it is 0.

Local spatial autocorrelation analysis can reveal the spatial dependence status and the degree of spatial difference between each region within the study area and the neighboring regions. In this paper, the local Moran’s I index was used to analyze the spatial heterogeneity of the rural production spatial quality. Its formula is:

$$I_i=\frac{x_i-\overline{x}}{s^2}\sum _{j=1}^n\left[w_{ij}\left(x_i-\overline{x}\right)\right]$$

(4)

where $I_i$ is positive, indicating that the regional adjacent units belong to a similar value agglomeration; $I_i$ is negative, indicating that the regional adjacent units belong to non-similar value agglomeration, and $I_i$ = 0 means that the values of adjacent elements in the region are randomly distributed.

3.3.3. Geodetector

Geodetector is a statistical method for identifying spatial dissimilarities, as well as the driving forces behind them [40]. In this paper, we analyzed the factors influencing the rural production quality in the Songnen Plain using factor probes and interaction probes.

(1)

Factor detector

Its main model is:

$$q=1-\frac{\sum _{h=1}^L{N}_h{\sigma }_h^2}{N{\sigma }^2}=1-\frac{SSW}{SST}$$

(5)

where $h=1\dots L$ is the stratification of variable Y or factor X, $N_h$ and $N$ are the number of cells in stratum $h$ and the whole region, respectively, and ${\sigma }_h^2$ and ${\sigma }^2$ are the variance of the values of Y in stratum h and in the whole region, respectively. $SSW$ and $SST$ are the sum of the variances within the stratum and the total variance in the whole region, respectively. q has a domain of [0, 1], and a larger value of q indicates that the independent variable X has a stronger explanatory power for attribute Y, and vice versa.

(2)

Interactive detector

The measurement method uses the driving factors X₁ and X₂ as an example. First, the explanatory powers, q(X₁) and q(X₂), of the two independent variables on the dependent variable Y are calculated. Second, the explanatory power, q(X₁∩X₂), of the two independent variables on the dependent variable Y when they interact is calculated. The magnitudes between the three calculations are then compared. From this, a judgment is made as to whether the effect of the two factors interacting on the dependent variable is enhanced or weakened relative to a single factor.

4. Results and Analysis

4.1. Spatial and Temporal Change of Rural Production Quality in the Songnen Plain

The polar coordinates of the corresponding four years were plotted based on the results for the rural production space quality in each county unit of the Songnen Plain (Figure 3). The production space quality greatly varied among counties in the study area and improved from 2005 to 2015. From 2015 to 2020, Shuangcheng, Hulan, and other counties experienced decreased production space quality. Overall, rural production space quality steadily improved over the study period. Quality in Dehui, Qianguo, Anda, and Zhaodong counties was more prominent in all nodal years. Quality in Zhenlai and Dumeng steadily improved and reached a high level during the 15-year period. Although slightly improved, quality in Keshan, Yi’an, and Baiquan lagged behind the other counties and remained lower for a longer time.

Before analyzing the evolution of the rural production spatial quality in the Songnen Plain, this paper first analyzed the spatial and temporal input–output efficiency patterns (Figure 4). The rural production space quality was visualized through ArcGIS, and the input–output efficiency was divided into five levels using the natural breakpoint method. In 2005, the input–output efficiency of rural production space had a spatial distribution of “high in the south and low in the north”. The spatial pattern of “high in the center and low in the surroundings” was obvious in all years, and it gradually stabilized after 2015. The high-value areas were mainly concentrated in the west and south of Daqing City, while the low-value areas were mainly distributed in the south and northeast of the Songnen Plain.

The assessment of the rural production space quality showed different spatial and temporal distribution characteristics (Figure 5). Quality for each nodal year exhibited a significant spatial distribution pattern of “high in the south and low in the north”. Specifically, high-quality areas were mainly distributed in a triangular area surrounded by the three cities of Changchun, Harbin, and Daqing, such as Dewei, Anda, and Zhaodong, etc. These counties have relatively well-developed industries and are close to the regional center cities. Their rural production activities obtain more financial, scientific, technological, human resource, infrastructure, and other factor support, and the interaction of materials and information flow is relatively efficient and fast. Low-quality areas were mainly distributed in the northern part of the Songnen Plain. Although there has been a small increase in quality over the past 15 years, counties such as Keshan, Baiquan, and Yi’an have maintained a low quality for a long time. These counties are far from the regional center cities and are on the periphery of economic and technological radiation. This is aggravated by their relatively backward economic level and the increasingly severe population contraction. Their external and internal support for rural productivity development are insufficient, and as a result, the rural production space quality has been slow to improve and lacks strength.

The spatial distribution of the input–output efficiency and rural production space quality do not follow the same trend. The quality in counties with a high input–output efficiency was not necessarily high, whereas the quality in counties with a low input–output efficiency in the south sharply increased after combining the internal base and external support. Compared with the north, rural production space in the southern region could more quickly obtain the external support of large cities, which makes up for the shortcomings of its input–output efficiency. Therefore, the quality eventually reaches a higher level in these areas. However, with the continuous improvement of infrastructure throughout the region, the flow of material, information, and other factors has been gradually reduced by the constraints of geospatial distance; thus, the rural production space quality has developed homogeneously in recent years. As the strategy of revitalizing the rural areas of Northeast China continues to advance, the internal infrastructure gap between villages is gradually narrowing. Moreover, the continuous improvement of infrastructure has made it possible for rural production spaces in all regions to receive relatively fair external support. Thus, it is foreseeable that the process of homogenizing rural production space quality in the Songnen Plain will continue to deepen and that the gap in the quality of rural production space will gradually narrow.

4.2. Spatial Agglomeration Characteristics of Rural Production Space Quality in the Songnen Plain

4.2.1. Global Spatial Autocorrelation Analysis

The results of the global spatial autocorrelation analysis showed that the global Moran’s I indices for rural production space quality in the Songnen Plain were 0.622, 0.698, 0.675, and 0.667 in 2005, 2010, 2015, and 2020, respectively. All values were positive and passed the significance test (Z(I) > 1.96, P(I) < 0.05). This indicated that the spatial distribution of the rural production space quality of the areas is positively correlated, and research units with approximate quality levels tend to be spatially clustered and distributed. In addition, the global Moran’s I index generally increased first and then decreased, indicating that the spatial aggregation of quality gradually increased from 2005 to 2010 over the past 15 years and then declined after 2010.

4.2.2. Local Spatial Autocorrelation Analysis

The results of the local spatial autocorrelation analysis showed (Figure 6) that in 2020, only Zhaozhou exhibited L-H agglomeration, while the rest of the nodal years were negatively correlated with H-L and L-H agglomerations. Except for the non-significant research units, the rest of the units were positively correlated with H-H and L-L agglomerations. This indicated that rural production space quality is characterized by very high- and low-quality clustering. Specifically, H-H agglomerations were mainly located in the southern part of the Songnen Plain, and their distribution range was stable from 2005 to 2010. From 2010 to 2020, the H-H agglomerations began to develop in the north and the west and showed a trend of fragmentation. Over the 15-year period, H-H agglomerations increased from 11.76% to 14.71%, then decreased to 8.82%. The L-L agglomerations of rural production space quality were mainly distributed in the north and northeast, with a tendency to shrink to the north. Over the 15-year period, the L-L agglomerations first decreased from 23.53% to 17.65% and then increased to 20.59%. Quality had an insignificant pattern of agglomeration in most counties of the Songnen Plain. It was mainly distributed in the central and western areas with a wide and stable distribution. There were fewer H-H clusters in each nodal year than L-L clusters and much fewer than the number of non-significant study units. Although the spatial correlations of quality slightly varied to different degrees in each nodal year, an overall stable spatial pattern of high and low existed in the south and north, respectively.

4.3. Factors Influencing Rural Production Space Quality in the Songnen Plain

4.3.1. Selection of Impact Factor Indicators

As an open spatial structure, rural production space is sensitive to internal and external socio-economic changes. In particular, the current demographic contraction and economic recession in Northeast China might have a substantial impact on the sustainable development of the region’s rural production space. Considering the economic and social factors that may affect quality in the Songnen Plain, eight indicators were selected from four aspects: social impact, economic impact, external impact, and internal impact (

Table 2. Selection and explanation of factors influencing the rural production space quality in the Songnen Plain.

Source	Indicator	Indicator Interpretation
Demographic change	Degree of population contraction X1	Number of population decreases in the last five years
	Degree of population aging X2	Percentage of population over 65 years of age
	Level of education of the population X3	Average years of schooling
Economic level	Regional economic development dynamics X4	GDP index
	Purchasing power for means of production X5	Rural per capita savings deposit balance
External influences	Urbanization level X6	Share of urban population
Internal influences	Infrastructure level X7	Transportation network density
	Agricultural mechanization level X8	Total power of agricultural machinery/cultivated area

). The influencing factors were analyzed. The main problems facing the sustainable development of rural production space were clarified, and targeted optimization strategies were proposed.

4.3.2. Impact Factors Identification and Analysis

The Geodetector results showed (

Table 3. Detection results of the factors affecting the rural production space quality in the Songnen Plain from 2010 to 2020.

Indicator	q-Value		Place
	2010	2020	2010	2020
X1	0.037	0.168	8	4
X2	0.139	0.117	5	6
X3	0.161	0.042	4	7
X4	0.091	0.009	6	8
X5	0.459	0.536	2	1
X6	0.074	0.163	7	5
X7	0.537	0.340	1	2
X8	0.406	0.245	3	3

) that the q-values of the three factors of purchasing power for means of production, infrastructure level, and agricultural mechanization level were all greater than 0.2 in different years. The influence of purchasing power for means of production and infrastructure level alternated, while that of agricultural mechanization level was relatively stable. All three factors significantly impacted the regional rural production space quality. Meanwhile, the effect of population contraction on quality significantly increased from the last score position to the fourth in 2010. This was closely related to the increasing population contraction in Northeast China. The influence of regional economic development on the quality of rural production space was insignificant.

The analysis results of the factors influencing the rural production space subsystem showed (

Table 4. Detection results of the factors affecting the subsystems’ quality of rural production space in the Songnen Plain from 2010 to 2020.

Indicator	Internal and External Support				Input–Output Efficiency
	2010 q-Value	Place	2020 q-Value	Place	2010 q-Value	Place	2020 q-Value	Place
X1	0.080	6	0.104	6	0.033	7	0.048	7
X2	0.215	4	0.245	4	0.140	2	0.234	1
X3	0.090	5	0.095	7	0.124	3	0.065	6
X4	0.051	7	0.060	8	0.237	1	0.082	5
X5	0.440	2	0.483	1	0.019	8	0.043	8
X6	0.349	3	0.254	3	0.037	5	0.118	4
X7	0.046	8	0.207	5	0.034	6	0.227	2
X8	0.538	1	0.422	2	0.079	4	0.172	3

) that the agricultural mechanization level, purchasing power for means of production, and urbanization level were the top three influencing factors in different years. They were also the main factors affecting the ability of internal and external space to support rural production activities. Factors affecting the input–output efficiency greatly varied across the years. In 2010, the most influential factors were the degree of population aging, regional economic development dynamics, and the level of education of the population. In 2020, the influence of the two latter factors significantly declined, and the infrastructure level and agricultural mechanization level were the main factors affecting the input–output efficiency of regional rural production space. There were large differences in the factors influencing the subsystems of rural production space. However, the agricultural mechanization level, purchasing power for means of production, and infrastructure level remained the main factors influencing the quality of the subsystems of rural production space.

The interaction detection results showed (Figure 7) that any two factors exhibited a stronger influence than a single factor. The combined effect increased the explanatory power of the spatial differentiation of the regional rural production space quality. The interaction type was dominated by a two-factor enhancement, supplemented by a nonlinear enhancement. Specifically, the interaction effect of the level of agricultural mechanization combined with purchasing power for the means of production and infrastructure level reached the maximum in 2010. The interaction between any of the three influencing factors significantly increased the effect. By 2020, the interaction effects of the purchasing power for means of production, agricultural mechanization level, and infrastructure level were still very strong, while the interaction effects of the urbanization level with the purchasing power for means of production and infrastructure level sharply increased. This indicated that urbanization strongly influenced these two factors.

4.3.3. Impact Mechanism Analysis and Optimization Direction

The purchasing power for means of production had a central influence on the rural production space quality in the Songnen Plain. At present, about 90% of the basic units of agricultural production activities worldwide are family farms. Larger-scale individual farming is conducive to the initiative and responsibility of producers, thus improving agricultural production efficiency. At the same time, the large-scale individual business model also places high demands on the ability of farmers to purchase and input equipment, fertilizers, and other production materials. Considering the large scale of individual farming operations in the Songnen Plain compared to other regions of the country, the purchasing power for means of production has the most direct impact on the rural production space quality. Promoting stable growth in income and the economic level of the local population also improves the rural production space quality.

The level of regional infrastructure, reflected in the density of the transportation road network, is the main factor affecting the rural production space quality in the Songnen Plain. It plays an important role in connecting the production, distribution, exchange, and consumption stages of rural production activities and guarantees the flow of factors within the rural production space. Road transportation access and coverage affect key aspects of rural production, such as the cross-regional movement of modern agricultural machinery and the outward transportation of grain and other agricultural products. They also significantly impact the final quality of the rural production space. The level of agricultural mechanization is directly related to the efficiency of rural agricultural production activities and is a key link in the development of modern agriculture. For villages in Northeast China, where food production is the main agricultural activity, developing advanced mechanization is more important for enhanced rural production space quality.

Notably, the influence of population contraction on the quality of the region’s rural production space is increasing. Population contraction is the main development issue facing the northeast region of China at present. For rural production, especially agricultural production, rural population loss, which is a normal phenomenon in the transition stage of a large agricultural country, does not necessarily cause negative impacts. Instead, moderate rural population contraction is conducive to integrating capital, technology, and advanced management systems into rural production space. Therefore, the gradual increase in the impact of population contraction on the quality of rural production space in the Songnen Plain reflects the excessive contraction of the regional population and the mismatch between the development of rural production space and the process of population contraction.

Based on the analysis results of the influencing factors, this study proposes the following optimization directions to provide a reference for the sustainable development of rural production space in Northeastern China’s typical grain-producing areas:

(1) Increasing the targeting of financial support and subsidies by gradually establishing a special subsidy system. This would promote subsidies in favor of large-scale growers, agricultural cooperatives, and other new business entities, and encourage large-scale rural production and management. It would improve the minimum purchase price policy for relevant food crops to raise farmers’ income expectations and production incentives.

(2) Strengthening rural financial support by providing loans, credit guarantees, and other financial services to enhance the ability of new business entities to purchase means of production and inputs. This could also be achieved by encouraging rural financial institutions to innovate and develop rural financial products and services to provide more financial support for rural economic development.

(3) Optimizing infrastructure, such as the road transport network and rural transport system, and solving the “last kilometer” problem between urban and rural areas, counties, and villages. Accelerating logistics and transportation networks between urban and rural areas would also ensure the smooth flow of residents’ travel, agricultural machinery operations, and product flows. At the same time, improving internal road, water, and electricity networks in the countryside could ensure that the main body of production in the countryside meets rural life demands.

(4) Strengthening urban–rural links to expand channels for exchanging material and information flows between low-level areas in the north and large cities. Additionally, maximizing the positive effects of urbanization on the rural production space quality could promote advanced production technologies and business models, thus expanding marketing channels for rural products, promoting the optimization and adjustment of rural production structures, and integrating the use of land resources.

(5) Developing agricultural mechanization as a key direction for the future optimization of rural production space. This could be achieved by increasing subsidies for the purchase of agricultural machinery, strengthening the construction of agricultural machinery service systems, promoting the model of agricultural machinery cooperatives, and strengthening research and development and innovation in agricultural machinery. It could aid in realizing high-quality and high-efficiency development of rural production space in the northeast region and other parts of China.

5. Discussion

Most of the previous studies on rural production assessment were limited to the interior of the rural territory [2,41,42]. However, as the urban–rural integration continues to deepen, the role of factors outside the rural area in supporting production activities is becoming more important [43]. This makes it necessary to consider and incorporate the key aspect of external support in assessing the spatial quality of rural production, so that the assessment results can more accurately reflect the true level of the rural production space quality. Therefore, this study was based on practical problems facing rural production space in Northeast China as it enters a period of rapid transformation and development. This study expanded the concept of rural production space by summarizing previous research. The influence of external space was considered and incorporated into the evaluation system, and we analyzed the logic of rural production space with regard to the core production function. Based on this logic, a quality assessment model of the rural production space was constructed, and a typical plain agricultural area was selected for assessing the rural production space quality. The study fills gaps in the theoretical and empirical research on rural production space in rural geography. Due to the increasingly severe demographic contraction and economic recession in Northeast China [44], this paper focused on the impact of demographic changes and economic fluctuations on rural production space. The results confirmed that population contraction exhibits a progressively stronger impact on the quality of rural production space and that rural production space has stronger stability in the face of economic fluctuations. These analyses provide a reference for future studies on rural production in the context of contraction, as well as a clear direction for the optimization of regional rural production space.

Weighing the availability of data and the accuracy of the evaluation results, the research scale focused on counties. Due to the rural production structure in the northeast region of China, which is dominated by food production, the quality level of other rural secondary and tertiary production activities, such as rural tourism, was not taken into account while selecting the indicators during the study. Thus, the results of this study may have limited applicability to regions with more developed secondary and tertiary industries, such as rural processing and tourism. In addition, Geodetector can only reveal the correlation of geographic phenomena and cannot infer the causal relationship. Thus, this paper combined certain subjective judgments when analyzing the influencing factors, necessitating further verification of the accuracy of the analysis results of influencing factors. Future research on rural production space should expand data acquisition to include the operation of micro-rural production space at the township and even village levels. There is also a need to deeply analyze the interactions between various subsystems and internal and external factors and refine research for different types of rural production space to provide more accurate and scientific or theoretical support for the sustainable development of rural production space in different regions.

6. Conclusions

This study constructed a model for the rural production space quality in the Songnen Plain, and used spatial autocorrelation and a Geodetector model to analyze the spatial agglomeration characteristics and the influencing factors. The following conclusions were obtained:

(1) The spatial and temporal evolution of the rural production space quality in the Songnen Plain from 2005 to 2020 was clearly characterized. Overall, the pattern exhibited a dynamic process of change that increased year by year. The input–output efficiency of rural production space showed a spatial and temporal distribution higher in the center than at the edges. Conversely, the quality of the rural production space showed a spatial distribution pattern higher in the south than in the north.

(2) The distribution pattern of the quality of the rural production space was characterized by clear spatial agglomeration, with H-H agglomerations mainly in the southern area. L-L clusters were mainly distributed in the north and northeast, with a tendency to shrink to the north. Non-significant units accounted for the largest proportion in all nodal years, indicating that rural production space is overall still at an intermediate stage of development.

(3) There were differences in the influencing factors between the rural production space and its subsystems. However, overall, the purchasing power for the means of production, level of infrastructure, and level of agricultural mechanization were the main factors affecting the rural production space quality. The effects of population contraction and the level of urbanization were gradually increasing.

(4) Strengthening rural financial and monetary support, optimizing rural transportation and other infrastructure, and enhancing regional urban–rural linkages are important directions for optimizing the rural production space quality in the Songnen Plain. The continuous vigorous development of agricultural mechanization plays a fundamental role in the improvement of rural production space in the northeast region and other parts of China.