Pattern of Grain Production Potential and Development Potential in China–Mongolia–Russia Economic Corridor

Abstract

The China–Mongolia–Russia Economic Corridor (CMREC) region is part of the Silk Road Economic Belt and is critical to world food grain safety, and thus, developing its potential grain production will help counter any global food crisis. In this study, a grain production potential measurement system for the CMREC was designed and developed using spatial data to progressively correct environmental factor functions. The potential yield per unit area and potential area of four grain crops in 48 provinces were scientifically and systematically evaluated, and the grain production potential, development potential, and development potential range were calculated. The results show that the grain production potential and development potential of the corridor are significant. The yield of wheat and maize is mainly distributed in Siberia and the south of the Russian Far East. The development potential of soybean is very large and is mainly concentrated in the Russian Far East and northeast region of Mongolia. However, there is little room for paddy yield improvement, and its potential is mainly concentrated in northeast China. The grain production potential forms a high-value region with a low value from north to south, high value in the middle, extending from northwest to southeast. Grain and cereals in the whole region amounted to 8.45 × 10⁸ t (4.25 × 10⁸ t in Russia, 4.03 × 10⁸ t in China, and 0.12 × 10⁸ t in Mongolia). In terms of grain type, maize has the highest productivity potential with 1.96 × 10⁸ t, followed by wheat at 1.45 × 10⁸ t. The potential for paddy is 0.58 × 10⁴ t, whereas soybean has the lowest potential of 0.40 × 10⁸ t. The results of this study provide evidential support in the form of data for the development of the complementary advantages of agricultural resources, construction of the CMREC, and joint development of a food resource free trade zone. The CMREC will strengthen the development of modern, green, high-yield, high-quality, and efficient grain production zones for soybean and maize, and promote the diversification of grain resource cooperation.

1. Introduction

The China–Mongolia–Russia Economic Corridor (CMREC) region is part of the Silk Road Economic Belt, and as agriculture is a primary industry and a dominant diplomatic resource, it is therefore critical to world food grain safety; thus, developing the CMREC potential grain production will help counter any global food crisis [1]. Correlated increases in population and demand for food over recent decades have caused remarkable changes in cropland areas globally [2,3]. The United Nations forecasts indicate that the global population is likely to grow steadily, from 7.38 billion in 2014 to 11.18 billion in 2100 [4]. At the same time, food consumption per capita is experiencing rapid growth, largely resulting from increases in income per capita [5,6]. This correlated increase in population and consumption creates huge food production pressures [7,8] and will mean that global food demands are certain to increase for at least another 40 years [9,10]. Agricultural exchange and cooperation as well as trade in agricultural products constitute a key area of cooperation in the CMREC. The cooperation in food production of the CMREC plays an important role in global regional food distribution and in promoting global food security. With international trade frictions escalating in recent times, an international CMREC will help explore the development and utilization of international land resources, promote international cooperation and trade, and diversify the sources of world’s grain and oil [11,12]. China, Mongolia, and Russia share the convenience of geographical space, but their respective agricultural products and resources are evidently different and complementary. Before carrying out agricultural cooperation, China–Mongolia–Russia enterprises need to comprehensively analyze the factors affecting agricultural production, such as light, temperature, water, soil, and land, and explore the agricultural advantage of cooperation zones with a good combination of light, heat, water, and soil, as well as low development cost, high food production potential, and wide cooperation prospects. This is a strategic issue that must be clarified to facilitate agricultural cooperation between China, Mongolia, and Russia and to enable these three countries’ enterprises to go global [13], and thus needs urgent relevant scientific and technological support. Therefore, studying the agricultural resource pattern and food production potential of the CMREC region will help in optimizing the agricultural cooperation structure, promoting the construction of the economic corridor itself, and maintaining food security and economic and social stability in the One Belt One Road region.

At present, research on the agricultural development status of the CMREC region mainly focuses on the analysis of the agricultural development status of local areas, international cooperation, and assessment of future potential [14,15]. For example, Yige et al. analyzed the advantages of agricultural cooperation between Heilongjiang and the Russian Far East [14] and reported that the Russian Far East is the main soybean production area of Russia, accounting for 90% of the national soybean output. This provides a scientific basis for China to solve the problem of soybean production cooperation. Nefedova et al. studied the changes in agricultural resources of Russia [16]. Ivanov et al. established a soil quality evaluation model suitable for agricultural development on the basis of the Russian national soil geographic database [17]. Samsonova et al. studied the spatial variability of agrochemical properties of arable soil in Russia by taking Bryansk state as an example [18]. Some scholars have used the trade integration index, trade specialization index, agricultural cooperation potential measure, RCA index, and trade complementarity index to measure agricultural economic and trade cooperation potential of the three countries [19,20,21,22]. Others have studied the characteristics and main issues of agricultural cooperation in the CMREC under the Belt and Road Initiative, the potential of food production in northeast China [23], and the construction of the China–Russia food resource corridor [24]. Scholars have also studied the agricultural development status, agricultural cooperation, and potential of the CMREC from different perspectives [25,26,27]. These research results provide data support for the construction of the CMREC and agricultural cooperation.

In general, the above studies have analyzed the agricultural cooperation and resource development potential of China, Russia, and Mongolia as well as the CMREC from different professional perspectives. Most existing studies have approached the subject from the national level; moreover, they have mainly focused on the analysis of agricultural trade cooperation, the development potential of agricultural product market, and the status quo of agricultural resources in local areas of the CMREC. While there are many qualitative studies in the literature, there is a need to further explore in-depth the different potential areas of administrative units of the CMREC by using spatial data. In this context, based on various attributes and data information of agricultural production potential, such as climate, cultivated land, yield and soil, a calculation model of grain production potential was constructed in this study by using spatial data to revise environmental factor functions step by step, and a calculation system of grain production potential was designed and developed. Based on provincial administrative units, the potential cultivated land area, potential yield per unit area, potential grain production, and development potential and potential range were quantitatively evaluated, and various countermeasures and suggestions were put forward. The research results provide direct scientific and technological support for agricultural investment, the layout and practice of agricultural enterprises going global and other decisions.

2. Materials and Methods

2.1. Study Area

Part of the Belt and Road Initiative, the China–Mongolia–Russia Economic Corridor is a land corridor located between 37–62° N and 28–142° E that covers an area of 9.2 million km² (1.99 million km² in China, 6.45 million km² in Russia, and 760,000 km² in Mongolia) across northeast China and North China along the border areas, Mongolia, Eastern Siberia, and the southern Far East. It includes 4 provinces (autonomous regions) of China, 12 provinces (leagues) of Mongolia, and 32 border regions (republics, prefectures, and cities) of Russia. Most areas of the CMREC are located in the middle temperate zone, with diverse climate types: from east to west, they include the temperate humid climate, sub-humid continental monsoon climate, arid continental climate, and temperate maritime climate. The continental climate gradually increases from the eastern and western regions and ends near the middle. Most areas have long, cold, and dry winters; summers are short and warm; spring and autumn are short seasons with large temperature differences. The terrain is complex and diverse, with the overall characteristics of low terrain in the western and northern regions and high terrain in the eastern and southern regions. The west is dominated by plains, the south by plateaus and mountains, and the eastern coastal areas by plains. From north to south, there are areas of black soil, lime soil, podzolic soil, gley soil, marsh soil, tidal soil, chestnut soil, calcium deposit soil, calcium soil, and so on, as shown in Figure 1.

In 2019, the total grain output in the study area was 1.91 × 10⁸ t, an increase of 57.7% over the previous year. Arable land resources are abundant in the region; the existing cultivated area of the CMREC region was 8096.65 × 10⁴ hm², as shown in

Table 1. Agricultural statistics of China–Mongolia–Russia Economic Corridor region.

Regions	China–Mongolia–Russia Economic Corridor	Chinese Part	Russian Part	Mongolian Part
Cultivated land (104 hm2)	8096.65	3706.87	4359.45	30.19
Grain total output (104 t)	19,068.95	16,885.24	2150.06	33.65
The yield of grain per unit area (kg/hm2)	3016.49	4555.12	493.20	1114.61

Note: Data were collected from the 2019 National Statistical Yearbooks of China, Mongolia, and Russia.

. The cultivated areas of Russia, northeast China and Inner Mongolia, and Mongolia accounted for 53.84%, 45.58%, 0.58% of the whole region, respectively. Heilongjiang Province, Jilin Province, Inner Mongolia Autonomous Region, and Altai Krai were ranked in the top four in arable land area, with areas of 1585.41 × 10⁴ hm², 923.80 × 10⁴ hm², 699.92 × 10⁴ hm², and 655.2 × 10⁴ hm², respectively. The regions with rich cultivated land resources are located toward the south of 55° N, including southern Siberia, the Altai border region, the Omsk Prefecture in Russia, and Northeast and Inner Mongolia in China. However, arable land resources are poor, agricultural land utilization rate is low, the degree of development is very low, and the land area for development is large north of approximately 55° N, as shown in Figure 2.

2.2. Data Resources and Processing

Through comprehensive remote sensing image analysis, field investigation and other means, based on MODIS land use/cover data (modi12q1), the IGBP land use/cover classification system, and China’s Earth System Science Data Sharing Platform land cover classification system, combined with the land use characteristics of China, Russia, and Mongolia, this study analyzed the land use classification system applicable to this area and the land use/cover classification specification of the CMREC. The classification system combines the IGBP classification system and the land cover classification system of China’s Earth System Science Data Sharing Platform to form a transnational and cross-regional land use/cover classification system that includes 18 cover types, such as coniferous forest, broad-leaved forest, mixed forest, grassland, paddy field, dry land, swamp, urban construction land, bare land, glacial snow, industrial and mining land, and bare rock. Based on the Landsat OLI image in 2015 and the field survey data of the comprehensive scientific investigation project of the CMREC, a field survey data set matching the remote sensing image was generated using ArcGIS, with human–computer interaction interpretation accuracy of more than 90%. By comparing the image characteristics of different cultivated land and feature types, combined with the land survey data and high-resolution images of the Ministry of agricultureAgriculture of China, the object-oriented image classification method was used to obtain the distribution of cultivated land resources.

Solar radiation data were sourced from the global high-resolution surface solar radiation data set [28] released by Qinghai–Tibet Plateau Data Center, Chinese Academy of Sciences (http://data.tpdcTpdc.ac.cn, accessed on 22 October 2020), with a spatial resolution of 10 km × 10 km and data unit of W/m², in the format of NetCDF, which is a common data format for networks. The data were read using MATLAB and converted to TIF grid data. Solar radiation data for July, August, and September 2018 were selected to obtain the total solar radiation during the growing period of crops in the study area through clipping, projection transformation, and grid calculation.

Meteorological source data were obtained (in CSV format) from 210 meteorological stations of the United States National Oceanic and Atmospheric Administration (https://www.noaaNoaa.gov/, accessed on 10 November 2020) in and around the CMREC region, including monthly mean temperature and precipitation in 2020 in CSV format. The data from 210 meteorological stations were imported into the same Excel spreadsheet file to calculate the average monthly temperature and precipitation in July, August, and September 2020, that is, the average temperature (°C) and average precipitation (mm) of the growing season (June to August). ArcGIS was used to locate the spatial location of each meteorological station, and the spatial difference of mean temperature and mean precipitation was carried out to obtain the spatial data of mean temperature and mean precipitation in the crop growing season of the study area, and these data were resampled to a grid of 10 km × 10 km.

Soil data were sourced from Food and Agriculture Organization’s (FAO’s) Global Soil Database, which included global grid data in BIL format and a database file. The grid data include grid dot spatial location and soil type database files include the soil type, soil phase, soil physical and chemical properties, and other information regarding each grid dot, with a spatial resolution of 1 km × 1 km. A spatial reference (WGS-84 geographic coordinate system) was defined for the BIL raster data, and the soil raster data were obtained via spatial cropping. Access software was used to convert the database files of global soil database into Excel format, and the spatial data of soil parameters in the study area were obtained by correlation with soil grid data. Arable land data, crop yield, and economic data were sourced from Russia (https://rosstat.gov.ru (accessed on 6 November 2020)), China Rural Statistical Yearbook (https://data.cnki.net (accessed on 18 October 2020)), Russian National Agricultural Census Report [28], Mongolia Statistical Yearbook (http://www.1212.mn (accessed on 20 October 2020)), the World Bank (http://data.worldbank.org (accessed on 6 October 2020)), FAO (http://faostat3.fao.orgFao.Org (accessed on 10 October 2020)), and statistical yearbooks of provincial units (https://data.cnkiCnki.net (accessed on Day November 2020); https://rosstat.govGov.ru (accessed on 6 November 2020); http://www.1212.mn (accessed on 9 November 2020), and so on), as of December 2020.

2.3. Research Methods

2.3.1. System for Measuring Grain Yield Potential Per Unit Area

Based on the four main environmental factors that affect crop growth (light, heat, water, and soil [29,30,31]), a calculation model of grain production potential was constructed by using monthly average agrometeorological, soil, and land data and using spatial data to perform step-by-step correction function. For the design and development of the grain production potential measurement system, we used C# and ArcObjects secondary development component package running on Visual Studio and Windows 10 to create four basic modules: GIS basic function, production potential estimation, and statistical analysis modules. The main function of the GIS basic function module is the establishment and system management of images, meteorological interpolation, environmental factors, and other databases. The main function of the potential calculation module is to calculate food production potential at all levels by calling Excute or point grid data of Imap–AlgebraOp interface according to the expressions in the string. The function of the statistical analysis module is to analyze potential maximum value, mean value, variance, and other statistics based on raster data, and to perform potential classification, zoning statistics, etc. The drawing output module helps generate thematic maps and output, etc.

Through these modules, the system forms a regional resource database of light, heat, water, and soil, which helps in calculating the productivity potential of wheat, paddy, soybean, and maize, and perform statistical analysis and generate thematic maps.

2.3.2. Calculation Model of Grain Yield Potential Per Unit Area

Land production potential is the crop yield potential jointly determined by light, temperature, water, and soil factors combined with the influence factors of crop types, and is the production capacity of grain land per unit area, which is relatively close to the actual high yield level of grain in a region [32,33]. The land production potential model [34] is constructed by adopting the mechanism method model of step-by-step revision, as shown in Formula (1):

Ps = Q × p(Q) × p(T) × p(W) = YQ × f(T) × f(W) = YT × f(W) × Pwf(S) = Pwf(S)

(1)

Here, PS is land production potential, f(S) is the function of soil availability coefficient, fw refers climate productivity potential, Q refers to the total solar radiation in growth period, f(Q) is the photosynthetic efficiency coefficient, YQ refers to the photosynthetic productivity potential, f(T) refers to the temperature efficiency coefficient, YT refers to the light and temperature productivity potential, and f(W) refers to the water availability coefficient.

Photosynthetic potential refers to the yield per unit area determined by solar radiation factors when external environmental conditions such as light, heat, water, soil, crop population structure, growth, and agricultural technical measures are in the most appropriate state [20]. The monthly average total solar radiation value is used as the basis, and the monthly growth period data are used for correction (the growth period of crops selected in this study is June–August). The model [29,30] is shown in Formula (2):

YQ = Q × f(Q) = (c × s × ε × ϕ × (1 − α) × (1 − β) × (1 − ρ) × (1 − γ)(1 − ω)(1 − d) × k × f(L) × ΣQiΣqi)/(q × (1 − η) × (1 − δ))

(2)

Here, YQ is the photosynthetic potential (kg·hm⁻²), c is the unit conversion coefficient, and Σqi is the total monthly solar radiation in the growing season (MJ·m⁻²). The physical significance of the parameters in the formula and the values of the four grains were sourced from relevant literature [30,31].

Photosynthesis–temperature potential productivity refers to the upper limit of irrigated agricultural yield jointly determined by solar radiation and temperature factors under the most suitable conditions of water, soil, and other agricultural technical measures. The photoperiostatic productivity potential model is based on the temperature effective coefficient and revised photosynthetic potential [30,32], as shown in Formula (3):

PT = Pf × P(Ti)

(3)

Here, PT is the photothermal potential (kg/hm²), Pf is the photosynthetic potential, and P(Ti) is the temperature correction coefficient.

Because different crops have different requirements for optimum temperature during their growth period, they can be divided into cold-loving crops and warm-loving crops. The temperature correction coefficient is calculated according to the characteristics of different crops. Temperature influence function of temperature-loving crops, such as maize and paddy, is shown in Formula (4):

$$\mathrm{F}1(\mathrm{T})=\left\{\begin{array}{c}\begin{array}{c}0.027\mathrm{T}-0.162 \left(6 °\mathrm{C}\le \mathrm{T}<21 °\mathrm{C}\right)\\ 0.086\mathrm{T}-1.41 \left(21 °\mathrm{C}\le \mathrm{T}<28 °\mathrm{C}\right)\\ 1 \left(28 °\mathrm{C}\le \mathrm{T}<32 °\mathrm{C}\right)\end{array}\\ -0.0837\mathrm{T}+3.67 \left(32 °\mathrm{C}\le \mathrm{T}<44 °\mathrm{C}\right)\\ 0 \left(\mathrm{T}<6 °\mathrm{C} \mathrm{and} \mathrm{T}\ge 44 °\mathrm{C}\right)\end{array}\right\}$$

(4)

The temperature influence function of cold-loving crops such as wheat and soybean is shown in Formula (5):

$$\mathrm{F}2(\mathrm{T})=\left\{\begin{array}{c}1 (\mathrm{T}>20 °\mathrm{C}) \\ \mathrm{T}/20(0 °\mathrm{C}<\mathrm{T}\le 20 °\mathrm{C})\\ 0 \left(\mathrm{T}\le 0 °\mathrm{C}\right)\end{array}\right\}$$

(5)

The climate potential model was constructed by revising water availability coefficient on the basis of photosynthesis–temperature potential productivity [30,32], as shown in Formula (6):

PW = PTf(W)/(1 − Ir) + PTIr

(6)

Here, PW is climate potential, f(W) is the correction function of water availability coefficient, and Ir is the irrigation coefficient (i.e., Ir = 1 means full irrigation). In this study, the irrigation coefficient was determined as 0.8 by referring to the water conservancy data of Russia and China.

The commonly used FAO model was used to calculate the water effective coefficient, as shown in Formula (7):

f(W) = 1 − Ky(1 − Re/E_tm)

(7)

Here, Ky is the response coefficient of crop yield. The specific value was sourced from The Chinese Agricultural Encyclopedia (Agricultural Meteorology volume) and the standard crop response coefficients of 84 crops as recommended by FAO-56 (

Table 2. Yield response coefficient K_y of major crops in China–Mongolia–Russia Economic Corridor.

Coefficient	Wheat	Paddy	Soybean	Maize
Ky	1.1	1.0	1.3	1.25

).

Re is the effective precipitation during the growing period of crops, and the model was established by using the formula by USDA Soil Conservation Service:

$${\mathrm{R}}_{\mathrm{e}}=\left\{\begin{array}{c}{\mathrm{P}}_{\mathrm{r}}/125\left(125-0.2{\mathrm{P}}_{\mathrm{r}}\right)\left({\mathrm{P}}_{\mathrm{r}}\le 250 \mathrm{mm}\right)\\ 125+0.1{\mathrm{P}}_{\mathrm{r}} ({\mathrm{P}}_{\mathrm{r}}>250 \mathrm{mm}) \end{array}\right\}$$

(8)

In Formula (8), ${\mathrm{P}}_{\mathrm{r}}$ is the actual precipitation.

E_tm is the maximum evapotranspiration during crop growth period, as shown in Formula (9):

E_tm = K1 × ET0

(9)

Here, K1 is the crop coefficient, and its value was sourced from the standard crop coefficient values of 84 crops, as recommended by FAO-56 (

Table 3. Main grain crop coefficient K₁ in China–Mongolia–Russia Economic Corridor.

Coefficient	Maize	Wheat	Paddy	Soybean
K1	0.9	0.85	1.05	0.8

).

ET0 is the standard evapotranspiration of crops. This study referred to the monthly average evapotranspiration data of Russia, China, and Mongolia provided by The Agricultural Encyclopedia of China (Agricultural Meteorology volume), and the value of ET0 was determined according to the calculation results for different climate zones, as shown in

Table 4. Standard evapotranspiration of crops in China–Mongolia–Russia Economic Corridor.

Temperature	10–15 ℃	16–20 ℃	21–25 ℃	26–30 ℃
Standard Evapotranspiration	3	4	5	6

.

The soil availability coefficient data were obtained from the World Soil Database (HWSD) constructed by FAO (Food and Agriculture Organization of the United Nations) and IIASA (International Institute for Applied Systems). The land production potential model was constructed according to the actual situation of the study area:

F(s) = A × C × D × O × G

(10)

In Formula (10), A is the soil effective water content index, C is the suitability index of soil bulk density, D is the soil pH suitability index, O is the organic carbon content index, and G is slope suitability index. Among them:

$$\mathrm{A}=\{\begin{array}{c}0.9, \mathrm{A}\mathrm{W}<0.2\\ 0.9+\left(\mathrm{A}\mathrm{W}-0.1\right)\times 0.2, 0.2\le \mathrm{A}\mathrm{W}\le 0.5\\ 1, 0.6<\mathrm{A}\mathrm{W}\end{array}$$

(11)

Here, AW is available soil water content (mm).

$$\mathrm{C}=\{\begin{array}{c}1, \mathrm{B}\mathrm{D}\le 1.43\\ -0.725\times \mathrm{B}\mathrm{D}+2.04, 1.43<\mathrm{B}\mathrm{D}\le 1.67\\ -6.883\times \mathrm{B}\mathrm{D}+12.321, 1.67<\mathrm{B}\mathrm{D}\le 1.79\\ 0, 1.79\le \mathrm{B}\mathrm{D}\end{array}$$

(12)

Here, BD is soil reference bulk density (kg/dm³).

$$\mathrm{D}=\{\begin{array}{c}0, \mathrm{P}\mathrm{H}\le 2.9\\ -1.31+0.446\times \mathrm{P}\mathrm{H}, 2.9<\mathrm{P}\mathrm{H}\le 5\\ 0.12+0.16\times \mathrm{P}\mathrm{H}, 5<\mathrm{P}\mathrm{H}\le 5.5\\ 1, 5.5<\mathrm{P}\mathrm{H}\le 6.5\\ 2.086-0.1667\times \mathrm{P}\mathrm{H}, 6.5<\mathrm{P}\mathrm{H}\le 8\\ 0.75, 8<\mathrm{P}\mathrm{H}\end{array}$$

(13)

Here, PH is soil potential of hydrogen.

$$\mathrm{O}=\{\begin{array}{c}\mathrm{O}\mathrm{C}\times 0.8, \mathrm{O}\mathrm{C}\le 1\\ 0.8+\left(\mathrm{O}\mathrm{C}-1\right)\times 0.4, 1<\mathrm{P}\mathrm{H}\le 1.5\\ 1, 1.5<\mathrm{O}\mathrm{C}\end{array}$$

(14)

Here, OC is soil organic carbon content (%).

$$\mathrm{G}=\{\begin{array}{c}0.75, \mathrm{C}\mathrm{L}=2\\ 0.85, \mathrm{C}\mathrm{L}=3\\ 1, \mathrm{C}\mathrm{L}=4, \mathrm{C}\mathrm{L}=5\\ 0.95, \mathrm{C}\mathrm{L}=6\end{array}$$

(15)

Here, CL is the drainage grade (where, 1 = very poor, 2 = poor, 3 = poor, 4 = suitable, 5 = good, and 6 = excessive).

2.3.3. Estimation of Grain Production Potential and Development Potential

Based on the potential grain yield per unit area and the potential cropland area, this study calculated the potential grain production and development potential:

Qa = yα × Sγ × MCI

(16)

Qb = Qα − (yβ × Sβ × MCI)

(17)

Rb = Qb ÷ (yβ × Sβ × MCI) × 100%

(18)

In Formulas (16)–(18), Qa and Qb refer to food production potential and development potential, respectively (t),), yα potential grain yield per unit area (t/hm²), yβ refers to actual yield per unit area (t/hm²), Sγ refers to the arable land area for grain (hm²), Sβ refers to actual grain production area (hm²), and MCI refers to the grain multiple cropping index. As the average number of grains planted in the same plot in the CMREC region in a year is usually 1, the value of MCI in this study was assumed to be 1. Rb refers to the potential range of grain development.

As the potential cultivated land may not only be planted with a single crop, the potential cultivated land area and arable land area of various food crops were calculated according to the proportion of the existing cultivated area to the cultivated land area. In order to reduce the impact of natural disasters and other accidental factors, the actual grain production data were adopted from the average data of provincial administrative units of China, Mongolia, and Russia for 2015 to 2019.

3. Results

3.1. Potential Cultivated Land Area and Spatial Pattern

The soil quality index function was used to evaluate the soil quality suitable for cultivation in the CMREC region by referring to the soil quality evaluation model and evaluation results of agricultural production land in Russia [2], the arable land quality evaluation of China by the Ministry of Natural Resources, and the relevant data for Mongolia. The productivity of different soils and the quality of the benchmark soil at 100 points were ranked (climate data were not taken into account in the evaluation) (

Table 5. Distribution of Potential Cultivated Land for Grain in China–Mongolia–Russia Economic Corridor.

Region	Soil Productivity Potential Index	Arable Area (104 hm2)	Existing Cultivated Area (104 hm2)	Potential Cultivated Land (104 hm2)	Region	Soil Productivity Potential Index	Arable Area (104 hm2)	Existing Cultivated Area (104 hm2)	Potential Cultivated Land (104 hm2)
Krasnoyarsk Krai	0.19	4445.43	295.98	4149.45	Kemerovo Oblast	0.26	248.30	147.08	101.22
Tyumen Oblast	0.25	3588.00	128.56	3459.44	Udmurt Republic	0.56	235.76	129.45	106.31
Irkutsk Oblast	0.38	2918.02	161.24	2756.78	Ömnögovi Province	0.18	214.99	0.01	214.99
Heilongjiang Province	0.13	2001.12	1585.41	415.71	Oriental Province	0.13	185.40	3.39	182.00
Khabarovsk Krai	0.23	1813.78	7.56	1806.22	Republic of Mordovia	0.66	172.92	100.78	72.14
Inner Mongolia Autonomous Region	0.44	1774.50	923.80	850.70	Jewish Autonomous Oblast	0.43	154.80	8.94	145.86
Amur Oblast	0.44	1600.28	151.42	1448.86	Dornogovi Province	0.13	142.31	0.01	142.31
Sverdlovsk Oblast	0.63	1227.24	130.41	1096.83	Sakhalin Oblast (Sakhalin Island)	0.15	130.65	3.57	127.08
Altai Krai	0.58	980.78	655.20	325.58	Republic of Khakassia	0.18	114.20	64.96	49.24
Perm Krai	0.53	851.18	178.85	672.33	Chuvash Republic	0.62	113.46	74.19	39.27
Jilin province	0.44	840.95	699.92	141.03	Sükhbaatar Province	0.13	106.97	0.55	106.42
Zabaikalsky Krai	0.18	776.70	44.00	732.70	Khentii Province	0.18	104.42	1.85	102.57
Novosibirk Oblast	0.40	712.80	361.35	351.45	Dundgovi Province	0.13	97.10	0.03	97.07
Tver Oblast	0.83	698.03	137.05	560.98	Tuv Province	0.18	96.26	1.09	95.16
Liaoning Province	0.44	641.96	497.74	144.22	Selangor Province	0.13	74.07	20.74	53.34
Kirov Oblast	0.52	628.16	229.61	398.55	Mari El Republic	0.30	69.60	43.55	26.05
Republic of Tatarstan	0.78	530.40	327.81	202.59	Vladimir Oblast	0.20	58.00	52.71	5.29
Omsk Oblast	0.37	516.89	405.31	111.58	Govisümber Province	0.15	7.20	0.01	7.20
Niyhnz Novgorod Oblast	0.62	476.78	189.67	287.11	Ulaanbaatar	0.13	6.12	0.11	6.00
Leningrad Oblast	0.53	447.85	35.97	411.88	Darkhan-Uul Province	2.11	5.90	2.11	3.79
Novgorod Oblast	0.72	398.16	45.00	353.16	Orkhon Province	0.30	1.52	0.30	1.22
Republic of Buryatia	0.11	386.47	69.89	316.58	Mongolian part	0.14	1042.26	30.19	1012.07
Primorsky Krai	0.21	348.39	70.37	278.02	Chinese part	0.36	5258.53	3706.87	1551.66
Moscow Oblast	0.78	345.54	95.55	249.99	Russian part	0.42	25,230.79	4359.58	20,871.21
Tyva Republic	0.18	306.90	13.55	293.35	Whole region	0.31	31,531.58	8096.65	23,434.94

Note: The existing cultivated land area is for 2019 data, calculated according to the statistical yearbooks of Russia, Mongolia, and China. Potential arable land area = arable land area − existing arable land area.

).

The arable land area was obtained according to the number of soil quality fractions suitable for cultivation in provincial administrative units. Arable land accounts for 27.78% of the total area of the whole region. The arable land in Russia accounts for 80.01% of the whole regional arable land. The arable land in China accounts for 16.68% of the whole region, while Mongolia accounts for 3.31% of the whole region.

The potential arable land area is the arable land area minus the existing arable land area; thus, the potential cultivated land area of the CMREC region is 23.43 × 108 hm², of which Russia accounts for 89.06%, while China and Mongolia account for 6.62% and 4.31%, respectively.

3.2. Differentiation of Potential Yield Per Unit Area of Grain

Based on the potential grain production system, the potential grain yield per unit area in the study area was quantitatively measured. From the perspective of the whole region, the areas with high potential grain yield per unit area are mainly distributed in northeast China and northeast Mongolia. The distribution range of high potential grain yield per unit area is small, while the distribution range of low potential grain yield per unit area is large, as shown in Figure 3. In terms of grain type, wheat and maize have the highest potential value, while soybean has the lowest. The potential range of development of wheat is 2239.31–24,203.32 kg/hm², with an average of 4192.01 kg/hm². The high potential areas are mainly distributed in southern Siberia and southern Far East of Russia, including Primorsky Krai, Amur Oblast, Tyva Republic, Kemerovo Oblast, Omsk Oblast, Jewish Autonomous Oblast, and other regions. The development potential range of maize is 697.38–11,600.70 kg/hm², with an average of 3090.42 kg/hm². The high potential areas are mainly distributed in the Far East of Russia and southern Siberia, including Primorsky Krai, Amur Oblast, Tyva Republic, Omsk Oblast, Kemerovo Oblast, Liaoning Province, and Jewish Autonomous Oblast, mainly because the light and heat conditions required for the growth of maize are favorable in these areas. The development potential range of soybean is 703.374–7795.93 kg/hm², with an average of 1548.3 kg/hm², including Jilin Province, Liaoning Province, Oriental Province, Sükhbaatar Province, Selangor Province, Govisümber Provice, Heilongjiang Province, Primorsky Krai, Amur Oblast, and Tuv Province. The development potential for paddy was in the range 1433.02–14,340.5 kg/hm², with an average of 3767.60 kg/hm²; this was mainly distributed in Primorsky Krai, Amur Oblast, Republic of Tyva, Kemerovo Oblast, Omsk Oblast, and Jewish Autonomous Oblast. In general, the four grain varieties with high potential are all distributed in fertile areas with a good combination of light, heat, and soil at 50° N in the CMREC.

3.3. Grain Production Potential

The average potential yield per unit area, cultivated land area, and actual grain production data for grain, cereal, and four types of crops in this study were used to quantitatively calculate the potential production potential, development potential, and its range in the study area.

In terms of production potential, grain and cereals in the whole region amounted to 8.45 × 10⁸ t (4.25 × 10⁸ t in Russia, 4.02 × 10⁸ t in China, and 0.12 × 10⁸ t in Mongolia). In terms of grain type, maize has the highest productivity potential with 196.38 × 10⁸ t, followed by wheat at 145.45 × 10⁸ t. The potential for paddy is 58.13 × 10⁸ t, whereas soybean has the lowest potential of 39.86 × 10⁸ t. In terms of region, Russia has the largest grain production potential, accounting for 50.6% of the whole region, wherein wheat and soybean have the highest production potential, accounting for 87.45% (127.19 × 10⁸ t) and 66.45% (26.48 × 10⁸ t) of the whole region, respectively. The potential of maize and paddy in China is the largest, accounting for 96.38% (89.27 × 10⁸ t) and 98.8% (57.43 × 10⁸ t) of the whole region (

Table 6. Grain Production Potential and its Distribution in CMREC.

Region	Grain Type		Wheat		Maize		Soybean		Paddy
	Potential Productivity (×104 t)	Rank	Potential Productivity (×104 t)	Rank	Potential Productivity (×104 t)	Rank	Potential Productivity (×104 t)	Rank	Potential Productivity (×104 t)	Rank
Heilongjiang Province	17,823.46	1	85.47	23	6300.75	1	877.12	2	4055.46	1
Inner Mongolia Autonomous Region	13,193.47	2	506.52	9	5680.89	2	282.05	4	207.17	4
Jilin Province	5293.38	3	1.05	44	4168.29	3	76.68	6	914.45	2
Amur Oblast	5206.78	4	550.18	7	104.37	6	1663.84	1	-	-
Khabarovsk Krai	4680.71	5	147.63	19	49.96	11	729.88	3	-	-
Tyumen Oblast	4342.39	6	3011.69	1	0.43	27	1.26	21	-	-
Krasnoyarsk Krai	4298.03	7	2254.08	2	-	-	0.89	23	-	-
Liaoning Province	3978.63	8	2.12	42	2777.12	4	20.61	9	565.23	3
Irkutsk Oblast	3492.55	9	1292.04	3	-	-	0.00	34	-
Altai Krai	2795.61	10	1193.50	4	0.78	26	8.09	13	-
Sverdlovsk Oblast	2288.28	11	467.99	11	0.22	31	0.03	31	-	-
Republic of Tatarstan	1706.84	12	468.92	10	58.70	10	1.21	22	-	-
Novosibirk Oblast	1662.40	13	778.56	5	0.90	25	0.40	27	-	-
Omsk Oblast	1464.44	14	736.89	6	0.00	34	1.45	20	-	-
Zabaikalsky Krai	1301.63	15	515.11	8	0.04	32	1.62	19	-	-
Perm Krai	1064.76	16	149.95	17	-	-	0.01	33	-	-
Niyhnz Novgorod Oblast	952.32	17	187.39	16	13.12	15	0.71	25	-	-
Primorsky Krai	910.83	18	37.28	30	96.64	7	223.85	5	65.87	5
Kirov Oblast	713.14	19	70.96	26	0.34	28	0	34	-	-
Tver Oblast	691.17	20	24.85	38	-	-	0	34	-	-
Leningrad Oblast	648.83	21	30.46	34	-	-	0	34	-	-
Moscow Oblast	625.14	22	93.20	22	2.12	22	11.64	11	-	-
Kemerovo Oblast	615.72	23	207.11	15	-	-	0.71	24	-	-
Udmurt Republic	605.78	24	52.21	27	0.26	30	0.01	33	-	-
Novgorod Oblast	497.26	25	36.60	31	-	-	0	34	-	-
Republic of Mordovia	425.42	26	476.95	11	16.43	14	2.81	14	-	-
Sükhbaatar Province	355.62	27	337.03	12	107.78	5	20.95	8	-	-
Tyva Republic	309.39	28	40.95	28	0.00	34	0	34	-	-
Chuvash Republic	308.60	29	81.77	24	1.88	23	0.16	29	-	-
Tuv Province	277.37	30	266.80	13	82.30	8	27.75	7	-	-
Republic of Buryatia	275.00	31	81.51	25	-	-	-	-	-	-
Sakhalin Oblast (Sakhalin Island)	253.54	32	-	-	-	-	-	-	-	-
Oriental Province	232.32	33	221.85	14	69.50	9	13.81	10	-	-
Selangor Province	147.74	34	127.93	20	49.00	12	8.89	12	-	-
Republic of Khakassia	138.18	35	27.24	35	-	-	0.03	32	-	-
Vladimir Oblast	114.97	36	21.42	39	3.07	21	-	-	-	-
Mari El Republic	88.66	37	31.48	32	0.02	33	-	-	-	-
Khentii Province	85.63	38	148.83	18	3.13	20	2.59	15	-	-
Ömnögovi Province	49.04	39	40.86	29	17.93	13	2.48	16	-	-
Jewish Autonomous Oblast	47.96	40	4.51	40	6.09	18	-	-	3.63	6
Dundgovi Province	31.78	41	30.68	33	9.36	17	1.89	17	-	-
Dornogovi Province	31.71	42	27.17	36	11.19	16	1.65	18	-	-
Darkhan-Uul Province	19.12	43	24.92	37	4.08	19	0.70	26	-	-
Orkhon Province	3.44	44	3.15	41	0.99	24	0.18	28	-	-
Govisümber Province	0.90	45	1.06	43	0.33	29	0.07	30	-	-
Ulaanbaatar	-	-	-	-	-	-	-	-	-	-
Chinese part	40,288.94		595.16		18,927.05		1256.46		5742.32
Proportion of China in the region (%)	47.93		4.09		96.38		31.52		98.80
Mongolian part	1234.69		1230.28		355.60		80.97		-
Proportion of Mongolia in the region (%)	1.47		8.46		1.81		2.03		-
Russian part	42,526.33		12,719.47		355.38		2648.61		69.5
Proportion of Russia in the region (%)	50.60		87.45		1.81		66.45		1.20
Whole region	84,049.95		14,544.91		19,638.02		3986.05		5811.82

). From the perspective of spatial distribution, the cultivated land productivity potential in the study area showed an obvious increasing trend from north to south and from east to west. The regions with higher productivity potential were mainly distributed in northeast China and Inner Mongolia, Siberia and the south Far East of Russia, and Sükhbaatar Province and Tuv Province of Mongolia.

From the perspective of development potential, the total potential of grain in the whole region is 6.25 × 10⁸ t, and that of the Russian region is 3.85 × 10⁸ t, accounting for 61.61% of the region. Here, the potential for wheat is 1.23 × 10⁸ t, accounting for 87.12% of the region, and the potential for soybean is 0.27 × 10⁸ t, accounting for 83.73% of the region. In China, the total potential of grain is 4.03 × 10⁸ t, accounting for 36.45% of the region. The grain potential for Mongolia is 0.12 × 10⁸ t, accounting for 1.95% of the region. In terms of grain type, wheat has the greatest development potential, at 1.42 × 10⁸ t, followed by maize at 1.4 × 10⁸ t and soybean at 0.31 × 10⁸ t, with paddy having the lowest potential of 0.19 × 10⁸ t (

Table 7. Grain Development Potential of China–Mongolia–Russia Economic Corridor.

Region	Grain Type		Wheat		Maize		Soybean		Paddy
	Potential Productivity (104 t)	Rank	Potential Productivity (104 t)	Rank	Potential Productivity (104 t)	Rank	Potential Productivity (104 t)	Rank	Potential Productivity (104 t)	Rank
Heilongjiang Province	10,714.10	1	85.47	23	4710.62	1	267.58	3	1354.48	1
Inner Mongolia Autonomous Region	9147.66	2	506.48	9	4662.25	2	110.22	6	102.27	4
Amur Oblast	4895.67	3	547.97	7	104.28	6	1563.60	1	-	-
Khabarovsk Krai	4668.51	4	147.62	19	49.96	11	726.59	2	-	-
Tyumen Oblast	4253.36	5	2983.34	1	0.43	27	1.25	21	-	-
Krasnoyarsk Krai	4136.28	6	2214.91	2	-	-	0.84	23	-	-
Irkutsk Oblast	3438.12	7	1284.34	3	-	-	0	0	-	-
Altai Krai	2174.48	8	1115.25	4	0.78	26	4.62	14	-	-
Sverdlovsk Oblast	2164.71	9	464.51	10	0.22	31	0.03	32	-	-
Liaoning Province	1588.29	10	2.12	42	1794.49	4	12.07	11	-	-
Novosibirk Oblast	1392.84	11	739.54	5	0.90	25	0.35	28	-	-
Jilin Province	1334.78	12	1.05	44	2171.11	3	44.91	7	146.85	3
Zabaikalsky Krai	1290.83	13	513.07	8	0.04	32	1.61	20	-	-
Omsk Oblast	1102.74	14	639.61	6	-	-	1.20	22	-	-
Perm Krai	985.64	15	149.07	17	-	-	0.01	35	65.87	5
Primorsky Krai	859.21	16	37.20	30	96.16	7	202.19	4	-	-
Niyhnz Novgorod Oblast	825.20	17	177.18	16	13.10	15	0.61	26	214.25	2
Republic of Tatarstan	803.89	18	430.32	11	58.10	10	0.76	24	-	-
Kirov Oblast	664.83	19	70.38	26	0.34	28	-	-	-	-
Tver Oblast	632.77	20	24.75	37	-	-	-	-	-	-
Leningrad Oblast	627.16	21	30.46	34	-	-	-	-	-	-
Moscow Oblast	537.91	22	91.30	22	2.12	22	10.98	12	-	-
Kemerovo Oblast	508.15	23	196.15	15	-	-	0.60	27	-	-
Novgorod Oblast	475.99	24	36.51	31	-	-	-	-	-	-
Udmurt Republic	417.04	25	51.35	27	0.26	30	0.01	34	-	-
Sükhbaatar Province	355.55	26	336.75	12	107.73	5	20.95	9	-	-
Republic of Mordovia	332.85	27	113.62	21	16.29	14	2.04	17	-	-
Tyva Republic	308.67	28	40.94	28	-	-	-	-	-	-
Republic of Buryatia	272.40	29	81.22	24	-	-	-	-	-	-
Tuv Province	271.35	30	266.51	13	72.00	8	27.75	8	-	-
Sakhalin Oblast (Sakhalin Island)	253.54	31	-	-	-	-	-	-	-	-
Oriental Province	231.69	32	221.84	14	69.48	9	13.81	10	-	-
Chuvash Republic	225.97	33	74.57	25	1.88	23	0.16	30	-	-
Selangor Province	140.54	34	127.40	20	48.70	12	8.89	13	-	-
Republic of Khakassia	125.45	35	26.52	36	-	-	0.03	33	-	-
Khentii Province	85.22	36	148.67	18	3.13	20	2.59	15	-	-
Vladimir Oblast	69.30	37	20.38	39	3.04	21	-	-	3.63	6
Ömnögovi Province	49.04	38	40.86	29	17.93	13	2.48	16	-	-
Jewish Autonomous Oblast	47.87	39	4.50	40	6.09	18	136.88	5	-	-
Dundgovi Province	31.78	40	30.68	33	9.36	17	1.89	18	-	-
Dornogovi Province	31.71	41	27.17	35	11.19	16	1.65	19	-	-
Mari El Republic	23.42	42	31.48	32	0.02	33	-	-	-	-
Darkhan-Uul Province	18.08	43	24.38	38	4.07	19	0.70	25	-	-
Govisümber Province	0.90	44	1.06	43	0.33	29	0.07	31	-	-
Orkhon Province	0.32	45	2.90	41	0.94	24	0.18	29	-	-
Ulaanbaatar	-	-	-	-	-	-	-	-	-	-
Chinese part	22,784.83		595.11		13,338.47		434.78		1817.84
Proportion of China in the region (%)	36.45		4.20		95.02		13.71		96.32
Mongolian part	1216.17		1228.23		344.87		80.97		-
Proportion of Mongolia in the region (%)	1.95		8.67		2.46		2.55		-
Russian part	38,514.81		12,338.05		354.02		2654.37		69.50
Proportion of Russia in the region (%)	61.61		87.12		2.52		83.73		3.68
Whole region	62,515.82		14,161.39		14,037.35		3170.12		1887.35

). Mongolia has the lowest development potential for all kinds of food, except wheat.

The grain development potential of the CMREC region is generally large. Mongolia has the largest potential range of grain development, with highest potential range for paddy, wheat, maize, soybean. The potential range of Russian paddy development is the highest, while that of wheat, maize, and soybean is the second highest. The potential development range of paddy in China ranks second, while that of the other three food crops trails the region. In terms of grain type, the potential growth rate of wheat is 269.04%, with 370.68% in Mongolia and 252.54% in Russia. The development potential range of soybean is 219.71%, with the highest of 16.40% in Mongolia. Similarly, the potential growth rate of maize is 214.20%, with 284.9% in Mongolia and 114.87% in China. Finally, the potential range of paddy development is 112.86%, with 191.20% in Russia (

Table 8. Potential range of grain development of China–Mongolia–Russia Economic Corridor (%).

Region	Grain Type	Wheat	Soybean	Maize	Paddy
Russia	207.72	252.54	295.55	242.82	191.20
Mongolia	332.70	370.68	316.40	284.90	--
China	118.05	183.92	47.20	114.87	34.51
Whole Region	219.49	269.04	219.71	214.20	112.86

).

3.4. Analysis of the Grain Yield Differentiation Pattern

Based on the statistical analysis results of the measurement system, taking the potential development range of grain and cereal as an example, according to the natural breakpoint method, the potential development range of grain can be divided into five potential areas: lower potential area, lower potential area, general potential area, high potential area and higher potential area, as shown in Figure 3.

3.4.1. Overall Analysis of Potential Spatial Differentiation

In terms of provincial administrative unit, grain development potential areas are mainly distributed in the following areas: Inner Mongolia Autonomous Region, Heilongjiang Province et al. in China; Darkhan-Uul Province, Selangor Province, Sükhbaatar Province, eastern Oriental Province et al. in Mongolia; Amur Oblast, Khabarovsk Krai, Irkutsk Oblast, Tyumen Oblast et al. in Russia. In terms of grain type, the distribution of wheat potential areas is the same as that of grain areas. The regions with great potential for soybean development are mainly Far East and Siberia in Russian and in China’s northeast, especially including Khabarovsk Krai, Tyumen Oblast, Zabaikalsky Krai, Sverdlovsk Oblast, and Amur Oblast. The regions with great potential for maize development are mainly distributed in eastern Mongolia, the Russian Far East, and Siberia, including Dundgovi Province, Ömnögovi Province, Sükhbaatar Province, Oriental Province of Mongolia, and Khabarovsk Krai of Russia. The regions with great potential for paddy development are distributed in the Russian Far East, Inner Mongolia Autonomous Region, and northeast of China, mainly the following areas: Jewish Autonomous Oblast and Primorsky Krai of the Russian Far East, the Inner Mongolia Autonomous Region, and Liaoning and Jilin Provinces of northeast China.

3.4.2. The Higher Potential Area

In general, the higher potential areas are mainly distributed near 50° N and south of accumulated temperature ≥2200 °C, with good thermal conditions but poor water guarantee. The climate is relatively dry, with precipitation of approximately 500 mm and great inter-annual variation. The soil is dominated by black soil with high fertility and developed irrigation system. The combination of light, heat, water, and land conditions in this area is good, which is suitable for the growth of a variety of crops. Meanwhile, it is also the region with the largest distribution of high-yield fields in Russia and China (approximately 3.4 million hm²), which creates conditions for stable and high crop yields. This region is the main production area of wheat, rice, soybean, and corn, and the unused cultivated land area in this region is 2.4 million hm².

3.4.3. The Developing Potential Area

The developing potential areas are mainly located in the peripheral regions of the superior potential areas near the 45° N, including the following areas: Irkutsk Oblast, Zabaikalsky Krai, Amur Oblast of Russia; Sükhbaatar Province, Oriental Province and eastern Khentii Province of Mongolia; and northeast China. Most of the region has a good hydrothermal soil combination. The heat condition is very good with accumulated temperatures between 1800 °C and 2200 °C. The soil is fertile, but there is not enough water; the annual runoff is very small, between 50 mm and 150 mm. Most regions have less precipitation (300–500 mm) with evapotranspiration in the range of 700–900 mm. Soil productivity potential is high, and this region is a concentrated wheat and corn production area [34]. The high-yield field covers an area of 5.6 million hm², with the largest high-yield area distribution in Russia. It is suitable for developing irrigated agriculture with a variety of mesothermic and thermophilic crops. At present, the unused cultivated land area in this area is 490,000 hm², which has great potential for agricultural development.

3.4.4. The General Potential Areas

The general potential areas are mostly located south of the Siberian Federal District and south of the Far Eastern Federal District on both sides of 55° N, and mainly include the following areas: Moscow Oblast, Leningrad Oblast, Tver Oblast, Republic of Buryatia, the central area of Zabaikalsky Krai, and the central area of Amur Oblast in Russia; Darkhan-Uul Province, Dundgovi Province, and west of Khentii Province in Mongolia; other regions. The terrain is flat and the plain area is large. The heat condition is insufficient in most areas, with accumulated temperatures between 1800 °C and 2000 °C. The region belongs to the cold zone and the crop growing period is short. Water condition is adequate, with abundant annual runoff (150–400 mm). The annual precipitation is 500–700 mm, the evaporation is weak (400–600 mm), and the annual precipitation is higher than the annual evaporation. The soil is podzolic soil with high acidity and low fertility. The combination of light, heat, and water is uncoordinated. The water condition is good, but the amount of light and heat are insufficient; the land condition is poor, and the productivity of agricultural resources is low. However, the regional range is large and the regional differences in the combination of natural conditions of the area are evident. Most of the soil potential in this area is between 0.45 and 0.6; approximately 7.5 million hm² area constitutes wasteland that is suitable for agriculture, and 52 × 10⁴ hm² is high-yield field that has a certain potential for agricultural development.

3.4.5. Low Potential Area and Lower Potential Area

Low potential areas and lower potential areas are located north of 55° N in Russia (the Siberia Federal District, the north and south areas of Far East Federal District, Primorsky Krai, Khabarovsk Krai, northern Zabaikalsky Krai, and northern Amur Oblast) and south of 45° N in Mongolia and Inner Mongolia in China (Ömnögovi Province and southwest of Dornogovi Province in Mongolia; the west of Inner Mongolia Autonomous Region in China). In this area, the combination of light, heat, and water is not coordinated, the soil fertility is low, and the soil acidity is high. In most areas, the soil potential is below 0.4, making it is difficult to carry out agricultural development and designating it as the area with the lowest land potential [35].

4. Discussion and Conclusions

4.1. The CMREC Region Is Rich in Cultivated Land Resources and Has Great Potential for Cultivated Land Resource Development

Based on the above discussion, it can be deduced that the CMREC region is rich in cultivated land resources, which is mainly concentrated near the Moscow River and Volga River Basins in the European part of Russia, as well as the Obi River Basin, Yenisei River Basin, Amur River Basin, and Baikal Lake Basin. Russia’s cultivated land area accounts for approximately 60% of the whole economic corridor. However, Mongolia’s arable land area is very small, and its exploitable potential is limited. From the perspective of potential area, it can be said that there is a large area of arable land in the CMREC region. The grade 2 arable land with suitability index above 0.4 covers 13,058.17 × 10⁴ hm², accounting for 41.33% of the total arable land area. The suitability index of 58.67% of the cultivated land is thus below 0.4. The potential arable land area is 23,499.6 × 10⁴ hm², of which 89.06% is in Russia. It is also possible that this region will become a hot spot area for cropland increases rather than losses in the future [36].

4.2. Grain Production Potential Is Large and Spatial Differentiation Is Evident in the CMREC, and Plays an Important Role in Global Regional Food Distribution

Different area grain productivity system develops imbalanced in the CMREC. In terms of potential yield per unit area, the spatial variation of potential yield per unit area is evident in different regions. The average potential yield per unit area within the study area is 7718 kg/hm², and the potential difference (the difference between production potential and actual yield) is 4701 kg/hm². The potential grain yield per unit area in China is 8060 kg/hm², with a potential difference of 7194 kg/hm², showing an increasing trend in the east and reducing trend in the west, with high in the north and low in the south. The potential grain yield per unit area in Russia is 8039 kg/hm², with a potential difference of 7194 kg/hm². Siberia and the Far East show an increasing trend in the west and reducing trend in the east. For Mongolia, the potential grain yield per unit area is 7333 kg/hm², with a potential difference of 6713 kg/hm², showing an increasing trend in the north and low yield in the south. Therefore, the regional resource development potential is huge. Evidently, Russia’s regional food production potential and development potential range are larger.

There is a big gap in grain output in different regions. The potential yield per unit area of grain is higher in the following areas: Siberia and the south of the Far East in Russia; Inner Mongolia, Heilongjiang, and Liaoning in China; northeast and central regions in Mongolia. The potential yield per unit area of all kinds of grain crops (except buckwheat) is higher in areas with good irrigation conditions. In terms of grain type, wheat has the highest potential, followed by maize and soybean, with paddy having the lowest potential, which is concentrated in northeast China and the Russian Far East. Due to regional differences in light, temperature, and water conditions, most areas of the CMREC region are not suitable for paddy growth.

The spatial distribution of grain production potential is low in the north and south, and high in the middle, forming a high-value area extending in the direction of northwest to southeast. The advantageous potential area and developing potential area are concentrated in this region, while the general potential area and the inferior potential area are mainly distributed in the north and south regions. The advantageous potential area is located near both sides of 50° N, that is, in the middle of the study area; it extends in a northwest-to-southeast direction. The development potential area is mainly located near 45° N and 50° N, and it is distributed in the periphery of the advantageous potential area. Most of the general potential area lies on either side of 55° N. The inferior potential areas are located in Russia to the north of 55° N and Mongolia and Inner Mongolia to the south of 45° N. The optimal agricultural cooperation regions of China, Mongolia, and Russia are distributed in the advantageous potential area and developing potential area, while the regions with greater cooperation potential are distributed in the general potential area.

4.3. The CMREC Region Is Highly Complementary in Grain Production Potential and Has Huge Market Potential for International Cooperation, Promoting Sustainable Global Food Development

The three countries in the CMREC have strong complementary advantages in grain production potential and huge market potential for international cooperation.

The agricultural resources of the CMREC region are highly complementary. The heat resources show a latitudinal zonality decreasing from south to north, with moisture decreasing from east to west. The most suitable soil for cultivation is distributed in the southern Siberian plain, northeast China plain, and north and northeast Mongolia. The regions with good combination of light, heat, and water resources are located south of 50° N, south of Siberia, south of Far East, northeast plains of China, and border areas of China, Mongolia, and Russia; evidently, these regions are suitable for crop production.

From the perspective of grain supply and demand, the agricultural resources and agricultural production advantages of the CMREC are highly complementary, but the structural contradiction between supply and demand is prominent as well. From 2003 to 2020, China’s comprehensive grain production capacity achieved a historic “17 Consecutive Years of Abundant Crops” [35]. The basic balance between grain supply and demand has coexisted with prominent structural contradictions: grain output has maintained a stable growth and grain consumption has increased year on year, and grain supply will still be in short supply in general [34]. Grain self-sufficiency rate represented by maize, paddy, and wheat is relatively high. Imports are mainly used for variety and structural adjustment, but the gap between supply and demand of maize and soybean is gradually expanding [35], which still needs to be covered through trade. From 2010 to 2019, China’s grain output grew at an average annual rate of 2%, and its grain consumption grew at an annual rate of 2.7%, with the average annual growth rate of grain consumption 0.7% points higher than that of grain output. In 2020, China’s total grain output was 670 million tons, and its total grain import was 1.40 × 10⁸ t.

Russia is one of the five largest grain producing countries in the world, and its overall grain production capacity ranks it fifth [36]. According to the Russian Federation Food Complex 2035 Development Strategy (2019), grain production in the Russian Federation currently fully guarantees domestic demand and creates huge export potential [37]. In 2019, the total output of cereals and legumes was 1.21 × 10⁸ t and domestic grain demand was 7.7 × 10⁴ t; grain exports reached 0. 55 × 10⁸ t. Grain is one of Russia’s main exports. The basic goal of the strategy is to increase the annual grain output and export volume to 1.4 × 10⁸ t and 0.56 × 10⁸ t, respectively, by 2035. The ideal goal is to increase the above two indicators to 1.50 × 10⁸ t and 0.64 × 10⁸ t by 2035. Grain yield per unit area is expected to increase to 3.53 t/hm² by 2035, and 1.67 × 10⁸ t of storage will need to be ensured by the end of 2035 [36]. In 2018, Russia went from being a net importer of grain to one of its largest exporters, with wheat as the world’s largest export and grain as the world’s second largest export. Russia’s grain export to China has been increasing year by year since 2012 [2], making Russia the largest source of imported wheat for China. In 2019, data from the World Bank showed that the export of Russian agricultural products to China increased by 27%, and the export of Russian agricultural products to China accounted for 12.5% of the total export of Russian agricultural products. Russia’s export of grain and other diversified agricultural products to China has already achieved a formidable scale with its own advantages [38]. Therefore, according to the data, theoretically, Russia has the conditions and ability to produce grain on a large scale and achieve a secure and stable food supply [37].

Mongolia is a country based on animal husbandry, with its main crops as wheat and potatoes. Since the three-year reclamation plan of Mongolia was launched, the grain output has been stabilized at 40 × 10⁴ t; self-sufficiency has been achieved in grain crops and main agricultural products; in addition, grain, nuts, and other agricultural products can even be exported now. In 2020, Mongolian grain output was 43.22 × 10⁴ t and grain occupancy per capita was 131.3 km. The wheat yield was 41.11 × 10⁴ t. Mongolia imports 15.62 × 10⁴ t of grain from Russia, and the localization rate of grain reaches 96.7%. Only 46.5% of vegetables and some fruits are imported [39]. Mongolia imports flour and rice mainly from China and Russia. In terms of grain production and demand of the CMREC region, China is the world’s largest importer of barley, paddy, and soybean, as well as Russia’s largest wheat importer.

4.4. The CMREC Region Should Intensify Existing Production, and Mitigate the Immediate Crisis, Which Plays an Important Role in Promoting Global Food Security

The war in Ukraine and trade sanctions are triggering a high level of volatility. The Ukraine–Russia war has directly impacted global wheat prices, trade sanctions is disrupting wheat markets on Russia, raising wheat futures at a near-linear rate to their highest levels since 2012. In March 2022, the wheat price alone rose by 19.7%, the wheat price alone rose by 19.7%. Some of the most food-insecure countries are highly reliant on wheat imports from Russia and Ukraine, which is posing social and economic concerns for food security. Current dependence on wheat imports from Russia and Ukraine imperils food security in lower-income and middle-income countries in North Africa and the Middle East, the Mediterranean, sub-Saharan Africa, South Asia and throughout Southeast Asia. According to the Food Outlook report released by FAO in June 2021, due to the global spread of COVID-19, international capital speculation, and global liquidity, world corn and soybean prices are likely to continue their upward trend in the long run and further exacerbate inflation expectations. This will have a great impact on global independent and controllable grain supply.

With this in view, Different organizations around the world should focus on grain crops with great potential, strengthen international cooperation in grain production such as soybean and corn, and focus on wheat and corn with great potential for production and development to easing the global supply crunch. In order to avoid monopoly and unilateral trade in the global food system, the CMREC Region shoud providesregion should provide humanitarian assistance to Africa, South Asia and other food-shortage areas in the world, and promtepromote the sustainable development of global food. Production of the CMREC Regionregion should be stimulated to meet demand, intensifying existing production where there is capacity, and production can be increased in traditional high-productivity wheat regions. Where wheat yields are generally high, direct economic incentives to expand wheat production can contribute to access to grain ensured. This must mitigate near-term food security crises through increased production and demand-side interventions supported by appropriate policy incentives (such as price guarantees and subsidized agricultural inputs). Demand-side interventions that conserve grain stocks for human consumption and shift to lower-cost flour blends can also improve food insecurity in the short term.