Investigating Contribution Factors of Grain Input to Output Transformation for the Inner Mongolia Autonomous Region in China

Abstract

Inner Mongolia Autonomous Region (IMAR) has become the sixth-largest grain-producing province of China, contributing 5.6% of the total national grain output in 2021. Grain production increased by around 821% from 1980 to 2019. The contributions of structure adjustment, sown area, and yield to the increase in grain production was analyzed for IMAR. For yield, the Cobb–Douglas production function was estimated, to analyze the contribution of agricultural inputs (fertilizer, irrigation, seed, pesticide, agriculture film, and machinery operation) and the impact of natural hazards on yield. Results from our study showed that the grain production and yield in IMAR increased by 81.40 × 10⁴ t and 0.11 t·ha⁻¹ annually from 1980 to 2019. The grain-crop sown area increased by 73.65 × 10³ ha annually, and the sown area of maize constitutes 16.82% to 55.30% of the total sown area of the IMAR in 1980–2019. In IMAR, sown area, yield, and structure adjustment can explain 54.06%, 33.31%, and 12.63% of the increase in the total grain production, respectively, from 1980 to 2019. The input per unit area of fertilizer, irrigation, and other inputs (seed, pesticide, agricultural film, and mechanical operation inputs) increased by 362.16%, 259.66%, and 405.55%, respectively, from 1981 to 2019, which contributed 160.44%, 15.06%, and 53.13% for the main cereal grain (maize and wheat) yield, respectively, while the decrease in the comprehensive loss rate from agrometeorological hazards contributed 7.76% to the main cereal grain yield in IMAR. Technologies such as water-saving irrigation, high-efficiency fertilizer application, and agrometeorological-hazards mitigation measures should be adopted in the future to increase production, considering the stable sown area and environmental and resource constraints.

1. Introduction

China’s grain production output has increased substantially and consistently, since the implementation of Chinese economic reforms back in 1979, known in the west as the opening up of China. During the reform period, agricultural production increased significantly, rural industries absorbed a significant proportion of farm labor, poverty decreased dramatically, and the level and quality of food consumption improved sharply. These reforms allowed individual families to lease land from collectives, which ensured that rural households had access to cultivatable land and allowed them to be self-sufficient in food production [1]. This initiative facilitated continuous economic growth despite the limited area of arable land and a significantly large volume of rural labor [2]. Both of these factors were advantageous for cultivating labor-intensive crops, such as fruits and vegetables, and disadvantageous for land-intensive crops, such as grains and seed oils [3]. Being the world’s largest developing economy, achieving food self-sufficiency and maintaining a significant increase in total grain output was an important priority. Despite these limitations, owing to the heavy utilization of traditional input resources, improved crop varieties through scientific research and agricultural practices, the introduction of machineries, production processes improved, and the efficiency of grain production rose drastically, with a slight decline from 1998 to 2003 [4].

Since the economic reforms and continuing on the path to self-sufficiency, increasing grain production remained an important national priority. With continuous developments and efforts, the total grain production of China hit a new record of 682.85 Mt in 2021, which constitutes an increase of more than two-fold compared to 1981 (325.02 Mt). Despite the consistent domestic output, China still remains reliant on global markets for a few major products such as maize, cotton, soybeans, and sugar. Though yield is affected by the regional plantation structure and regional grain production through farmers choices in cultivar varieties, especially among small householders [5], sown area has been a major limitation on the national grain production of China [6]. Interestingly, from 1996 to 2016, the sown area increased in northern China while decreasing in southern China, likely due to rapid industrialization, urbanization, and infrastructure development [7] and due to increases in non-agricultural employment opportunities [8] in southern China, moving the center of grain productivity gradually to the north [9].

Controllable factors, including agricultural inputs (fertilizer, pesticide, agricultural film, machinery, and irrigation), non-controllable meteorological factors [10], and agrometeorological hazards [11,12,13,14] have also had significant influence on yield. Using Cobb–Douglas production function [15], which is used to present the relationships between production, capital, and labor, Yang et al. [10] demonstrated that a 1% increase in fertilizer, irrigation, and precipitation can lead to 0.39%, 0.04%, and 0.21% increases in maize yield, respectively, in the ten main maize-producing provinces of China. However, Yang et al. [10] did not consider the influence of agrometeorological hazards, such as drought, flooding, low temperatures, and hailstorms, in that study. Agrometeorological hazards are responsible for significant loss of crop production in northern [16] and northeastern China [17] and the lower reaches of the Yangtze River [18].

Droughts in China have been one of the major agrometeorological hazards disrupting livelihoods and causing severe socioeconomic losses, particularly in agricultural, industrial, and environmental sectors [19]. Through various efforts in multiple sectors, an important ecological security barrier has been established in northern China. The barrier plan has set a total of 17 indicators, including 11 binding indicators such as the amount of forest land, basic grassland area, wetland area, and ecological restoration area [20]. All of these indicators are closely related to ecological construction and indicate the content of ecological security barriers.

Within this context, important grain-producing areas, including Inner Mongolia Autonomous Region (IMAR), have received special attention from policy-makers. IMAR is a land-locked region, located in the transition zone between cropping and nomadic areas, which historically lacked grain-production basics and has been vulnerable to natural hazards [21]. Due to harsh climatic conditions, intensive agriculture was previously very restricted, and the grain production of IMAR was insufficient to meet the local demand; the region therefore relied on grain imports from other provinces in the past. Improved weather information systems, water conservation techniques, and irrigation initiatives (including the use of plastic-film mulching in some drier areas), as well as the use of chemical fertilizers [22], have made it possible to significantly increase the agricultural output of IMAR. Figure 1 shows a rapid increase in grain production since 1981, and now IMAR has become one of the five-largest provinces in China in exporting grain nationally (to other provinces) [23]; it became the sixth-largest grain-production province of China in 2021 [24]. As one of the major grain-producing regions, the autonomous region’s total crop planting area exceeded 8.64 million hectares in the year 2020 [25].

As an important agricultural and livestock production base in northern China [27], the grain output of IMAR has continued to increase over the years, transforming from a province once dependent on grain inputs from other provinces to China’s main grain-output province. Due to its geographical location and arid to semi-arid weather conditions, the agricultural ecological environment of IMAR is fragile [21], and the sustainability of grain production in the future remains uncertain. In addition to these factors, the implementation of a policy mandating zero growth in the use of chemical fertilizers and pesticides [28,29] from 2015 and water-resource restriction [30] result in the cultivated land of IMAR still being limited [31]. In the future, the increase in grain production due to increases in cultivated land area may be limited due to the restricted use of land resources. The increase in output caused by the use of large amount of agricultural resources input may cause environmental pollution and greenhouse gas emissions [32], while the decrease in output caused by disasters may be alleviated by taking disaster prevention and mitigation measures. But information on all these contribution factors is limited in the current literature. The contribution analysis of the availability of arable land, agricultural resource input, and agricultural natural hazards are important in devising local policies to secure the ecological environment and regional food security in the future. For this reason, a clear understanding of the historical influences of different factors on increases in IMAR grain production is essential; therefore, the objectives of this study were to evaluate the influence of factors such as sown area, plantation structure, and yield on the grain-production increase in IMAR from 1980 to 2019. This study also evaluated the influence of agricultural materials inputs, irrigation, and natural hazards on the yield of the main staple grain crops and provides policy recommendations for a sustainable increase in grain production in IMAR.

2. Materials and Methods

2.1. Study Area

Inner Mongolia Autonomous Region (Figure 2) stretches across northern, northeastern, and northwestern China, extending from latitude 37°24′ N to 53°23′ N and longitude 97°12′ E to 126°04′ E, with an area of 1.183 × 10⁸ ha, and an average altitude of 1000 m [33]. Most parts of the region are located in the temperate continental monsoon climate zone [34], spanning from semi-humid, and semi-arid to arid regions [35], with annual temperature ranging from −5 °C to 10 °C, and annual precipitation ranging from 50 mm to 450 mm, decreasing from east to west and south to north [36,37]. The annual evaporation ranges from 500 mm to 2500 mm, which is significantly higher than the precipitation, and annual sunshine duration is between 2600 h to 3400 h [37]. The arable land is mainly distributed in the middle and northeastern IMAR, while other major land types such as forestland, rangeland, and unused land are in the northeastern, middle, and southwestern parts of IMAR [38].

The arable land of IMAR was 6833.17 × 10³ ha in 2020 (contributing 5.9% of the total arable land of China) [24], which was divided into three irrigated agricultural regions (West Liao River Basin, Tumed Plain, and Hetao Plain) and four rainfed agriculture regions (Great Khingan Hill and Plain, Jerim Mountain and Hill, the Yinshan Mountain and Hill, and Erdos Plateau) [39]. The yield of IMAR was 3664.10 × 10⁴ t in 2020 [24], accounting for 5.4% of the national yield, making it one of China’s major grain-production areas.

2.2. Data

The statistics regarding acreage of sown area, production, and yield of maize, wheat, rice, soybean, tuber crops, and miscellaneous grains and beans were obtained from the Inner Mongolia Statistical Yearbook [40]. The crop data in terms of its sown area, covered area, affected area, hazard-damaged area by agrometeorological hazards and efficient irrigation area, and consumer price index (CPI) were obtained from the National Bureau of Statistics (http://www.stats.gov.cn/tjsj/) (accessed on 26 January 2022) and the Ministry of Agriculture and Rural Affairs of the People’s Republic of China (http://zzys.agri.gov.cn) (accessed on 26 January 2022) for the period 1980–2019. Data inputs for fertilizer, seed, pesticide, agricultural film, mechanical operations, and the irrigation of the main cereal crops (maize and wheat) in IMAR for the period 1980–2019 were sourced from the National Agricultural Product Cost-income Data Compilation [41]. The agricultural inputs data was missing for few years that were excluded from the analysis (that included 1980, 1982−1983, 1990−1992, and 1996).

2.3. Methods

2.3.1. Contribution Rate of Grain Production Increase

The main contributing factors of regional grain-production increases were the sown area of grain crops, yield, and plantation structure, and a calculation method was drawn from the study by Liu et al. [6]. The contribution rate of the grain-production increase of different crops was calculated using Equation (1).

(1)${\mathrm{C}}_{\mathrm{i}}=\frac{{\mathrm{Pt}}_{\mathrm{i}}{-\mathrm{Pb}}_{\mathrm{i}}}{{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{k}}\left({\mathrm{Pt}}_{\mathrm{i}}-{\mathrm{Pb}}_{\mathrm{i}}\right)}$

wherein C_i is the contribution rate of the grain i, Pt_i is the yield of the target year of grain i, and Pb_i is the yield of the base year of grain i. The target and base year refer to the end and the start year of the time period.

The change in grain production was calculated using Equation (2), which took into account the influence of change in grain sown area, plantation structure, and yield.

(2)$\Delta \mathrm{G}=\Delta \mathrm{Gs}+\Delta \mathrm{Gy}+\Delta \mathrm{Gt}$

wherein ΔG is the change of grain production (kg); ΔGs, ΔGy, and ΔGt are the contributions of the change of grain sown area, yield, and plantation structure (which means the proportion of regional crop species) to the change of grain production, respectively, also measured in kg.

The contribution of the change in sown area to grain production was calculated using Equation (3). Yt and ΔSn were calculated using Equations (4) and (5), respectively.

(3)$\Delta \mathrm{Gs}=\mathrm{Yt}\times \Delta \mathrm{Sn}$

wherein Yt is the weighted average yield of the target year of the grain for which sown area has expanded (kg·ha⁻¹), and ΔSn is the newly increased sown area, calculated by deducting the increased area due to changes in plantation structure.

(4)$\mathrm{Yt}=\frac{{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{Ya}}_{\mathrm{i}}\times {\Delta \mathrm{Sa}}_{\mathrm{i}}}{{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{n}}{\Delta \mathrm{Sa}}_{\mathrm{i}}}$

wherein Ya_i is the yield of the target year of grain i, for which the sown area has increased (kg·ha⁻¹), and ΔSa_i is the increasing amount of the crop i (ha), for which the sown area has expanded.

(5)$\Delta \mathrm{Sn}=\Delta \mathrm{Sa}-\Delta \mathrm{Sm}$

(6)$\Delta \mathrm{Sa}={\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}{\Delta \mathrm{Sa}}_{\mathrm{i}}$

(7)$\Delta \mathrm{Sm}={\displaystyle \sum }_{\mathrm{j}=1}^{\mathrm{m}}{\Delta \mathrm{Sm}}_{\mathrm{j}}$

wherein ΔSn is the increased sown area (ha) with the deduction of the plantation structure adjustment, ΔSa is the increased area of the grain (ha) for which the sown area was increased and ΔSm is the reduced area of the grain (ha) for which the sown area declined,.

Equation (8) was used to calculate the contribution of the change of the plantation structure to grain production.

(8)$\Delta \mathrm{Gt}={{\displaystyle \sum }}_{\mathrm{j}=1}^{\mathrm{m}}\left(\mathrm{Yt}-{\mathrm{Ym}}_{\mathrm{j}}\right)\times {\Delta \mathrm{Sm}}_{\mathrm{j}}$

wherein Ym_j is the yield of the target year of the grain which the sown area was reduced, in kg·ha⁻¹.

The contribution of the change of yield to the change of grain production was calculated using Equation (9), and the contribution rates of grain yield, sown area, and plantation structure were calculated using Equations (10)–(12), respectively.

(9)$\Delta \mathrm{Gy}=\Delta \mathrm{G}-\Delta \mathrm{Gs}-\Delta \mathrm{G}\mathrm{t}$

(10)$\mathrm{Gy}=\frac{\Delta \mathrm{Gy}}{\Delta \mathrm{G}}$

(11)$\mathrm{Gs}=\frac{\Delta \mathrm{Gs}}{\Delta \mathrm{G}}$

(12)$\mathrm{Gt}=\frac{\Delta \mathrm{Gt}}{\Delta \mathrm{G}}$

wherein ΔGy is the contribution of yield (kg); Gy, Gs and Gt are the contribution rates of grain yield, sown area, and plantation structure, respectively, calculated in %.

2.3.2. Comprehensive Loss Rate of Agrometeorological Hazards

The comprehensive loss rate (CLR) of agrometeorological hazards was calculated based on the method used in the study conducted by IAED [42].

(13)$\mathrm{CLR}=\frac{\mathrm{Sd}\times 90\%+\left(\mathrm{Sa}-\mathrm{Sd}\right)\times 55\%+\left(\mathrm{Sc}-\mathrm{Sa}\right)\times 20\%}{\mathrm{S}}$

wherein CLR is the comprehensive loss rate of agrometeorological hazards (%); S_c, S_a, and S_d, and are hazard-covered, hazard-affected, and hazard-damaged areas and refer to crop failure area by hazards >10%, >30%, and >80% [43], respectively, and S is the sown area of crops. All are measured in ha.

2.3.3. Cobb–Douglas Function

Based on the Cobb–Douglas function (Equation (14)), a multiple linear regression model was used to analyze the influence of fertilizer, irrigation, pesticide, agriculture film, machinery input, and natural hazards on the yield of the main staple grain crops grown in IMAR. Labor input was not included because of the labor redundancy in China [10].

(14)${\mathrm{lnY}}_{\mathrm{i}}{=\mathrm{alnF}}_{\mathrm{i}}+\mathrm{b}{\mathrm{lnI}}_{\mathrm{i}}+\mathrm{c}{\mathrm{lnO}}_{\mathrm{i}}+\mathrm{d}{\mathrm{lnCLR}}_{\mathrm{i}}+\mathrm{e}+\mathrm{f}$

wherein i is the year from 1980 to 2019; Y_i is the multiple yield of wheat and maize, which is the weighted average based on sown area (kg·ha⁻¹); F, I, and O were fertilizer, irrigation, and other agricultural inputs (seed, pesticide, agricultural film, and machinery), calculated in RMB·ha⁻¹. The effect of inflation was eliminated using Equation (16). The coefficients of fertilizer, irrigation, and other agricultural inputs have been denoted as a, b, c, and d, respectively, which means a 1% increase in the inputs can lead to a%, b%, c%, and d% increase in yield. In Equation (16), e, and f are the constant and error terms, respectively.

The major staple grain crops of China are maize (Zea mays L.), wheat (Triticum aestivum L.), and rice (Oryza sativa L.) [44]. Rice accounts for only 1.60% of IMAR’s total sown area; therefore, the data for rice was not included in National Agricultural Product Cost-income Data Compilation [41] until 2020. Due to the limitations of data availability, only maize and wheat were included in this study.

Function (15) is used to calculate the Y, F, I, and O.

(15)${\mathrm{Y}}_{\mathrm{i}}=\frac{{\mathrm{Yz}}_{\mathrm{i}}\times {\mathrm{Sz}}_{\mathrm{i}}+{\mathrm{Yl}}_{\mathrm{i}}\times {\mathrm{Sl}}_{\mathrm{i}}}{{\mathrm{Sz}}_{\mathrm{i}}+{\mathrm{Sl}}_{\mathrm{i}}}$

(16)${\mathrm{C}}_{\mathrm{ij}}=\frac{{\mathrm{Cz}}_{\mathrm{ij}}\times {\mathrm{Sz}}_{\mathrm{i}}+{\mathrm{Cl}}_{\mathrm{ij}}\times {\mathrm{Sl}}_{\mathrm{i}}}{{\mathrm{Sz}}_{\mathrm{i}}+{\mathrm{Sl}}_{\mathrm{i}}}$

wherein i is the year from 1980 to 2019. Y_i is the multiple yield of wheat and maize, in kg·ha⁻¹. Yz_i and Yl_i are the yield of maize and wheat, respectively. Sz_i and Sl_i are the sown areas of maize and wheat, respectively. Cij is fertilizer, irrigation, and other agricultural inputs, eliminating the effect of inflation, in RMB·ha⁻¹, and j is the different input of fertilizer, irrigation, and other agricultural inputs. Cz_ij and Cl_ij are the yield of maize and wheat, respectively.

Equation (16) is used to eliminate the effect of inflation [10].

(17)${\mathrm{C}}_{\mathrm{ij}}=\frac{{\mathrm{D}}_{\mathrm{ij}}}{{\mathrm{CPI}}_{\mathrm{i}}}\times {\mathrm{CPI}}_{1981}$

wherein i is the year from 1980 to 2019. CPI_i is the consumer price index. D_ij are fertilizer, irrigation, and other agricultural inputs, in RMB·ha⁻¹, respectively.

2.4. Statistical Analysis

The statistical analysis was carried out using SPSS Statistics (Version:25, Creator: IBM company). The consistency of the two continuous variables were assessed using Pearson correlation analysis, while least significant differences (LSD, p < 0.05) analysis was used to detect the differences. Cobb–Douglas function was formed with the ordinary least squares method.

3. Results

3.1. Change in Grain Production and Contributing Factors

3.1.1. National and Regional Comparison of Change in Grain Crop Sown Area and Planting Structure

In China, the sown area for grain crops decreased at the rate of 742.66 × 10³ ha·yr⁻¹ from 1980 to 2003. There was a sharp decline from 1998 to 2003 (decreased by 2396.17 × 10³ ha·yr⁻¹). From 2004 to 2016, the sown area increased by 1355.70 × 10³ ha·a⁻¹, and then there was a slight decrease from 2017 to 2019 (decreased by 641.82 × 10³ ha·yr⁻¹), as shown in Figure 3. In comparison, the grain sown area of IMAR showed the same trend as the whole of China, increasing from 3882 × 10³ ha to 6828 × 10³ ha from 1980 to 2019 (an average increase of 73.65 × 10³ ha·yr⁻¹), and constituted 3.14–5.88% of the total sown area in China.

The plantation structure in IMAR was also observed to have changed considerably during the study period. Miscellaneous grains and bean (47.24%) and wheat (24.65%) represented the majority of the grain sown area in IMAR in 1980 but thereafter declined to 12.64% and 7.88%, respectively (Figure 4). Maize became the dominant grain crop in IMAR, and its sown area increased from 653.00 × 10³ ha in 1980 (16.82% of the total crop grain sown area) to 3776.00 × 10³ ha in 2019 (55.30% of the total crop grain sown area). The sown area of soybean also increased from 4.40% to 17.43% from 1980 to 2019. In contrast, the sown area of wheat decreased from 957.00 × 10³ ha to 538.00 × 10³ ha, accounting for 7.88% of the total crop sown area in 2019. In IMAR, tuber crop and rice both had a small planting area, 8.79% and 1.62% on average from 1980 to 2019, respectively.

The average sown area of maize significantly increased every 10 years from 571.50 × 10³ ha in 1980–1989 to 3522.10 × 10³ ha in 2010–2019 (Table 1). The average sown area of tuber crop and soybean increased significantly by 108.43% and 280.32%, respectively, from 1980–1989 to 2010–2019. The average sown area of rice increased significantly from 24.90 × 10³ ha in 1980–1989 to 92.80 × 10³ ha in 1990–1999, and then there was a moderate increase of 108.50 × 10³ ha in 2010–2019. In the case of wheat, despite two period of significant increases, from 1980–1989 to 1990–1999 (increased by 19.92%), and from 2000–2009 to 2010–2019 (increased by 27.45%), the average sown area of wheat decreased from 934.80 × 10³ ha to 617.00 × 10³ ha. The average sown area of miscellaneous grains and beans dropped from 1697.10 × 10³ ha (1980–1989) to 773.90 × 10³ ha (2010–2019).

3.1.2. Grain Production and Yield

China’s total grain production grew from 1980 to 1997 (increased by 964.53 × 10⁴ t·yr⁻¹), with a decline from 1998 to 2003 (decreased by 1360.00 × 10⁴ t·yr⁻¹) before increasing again from 2004 to 2019 (increased by 1214.84 × 10⁴ t·yr⁻¹) (Figure 5). The total grain production of IMAR showed the same trend as the whole of China, increasing from 396.50 × 10⁴ t to 3652.60 × 10⁴ t from 1980 to 2019 (increasing by 81.40 × 10⁴ t·yr⁻¹).

From 1980 to 2019, yield observed an increasing trend in IMAR and also in China overall. During this period, the every-year yield of IMAR was 6.47–62.65% lower than the domestic average (Figure 6), but the growth rate of yield from 1980 to 2019 in IMAR was higher than in China.

3.1.3. Agrometeorological Hazards

The disaster-covered area, disaster-affected area, and disaster-damaged area first increased and then decreased during the 1981 to 2019 period both nationally and in IMAR (Figure 7). The disaster-covered area, disaster-affected area, and disaster-damaged area was observed to be minimal during the 2000–2009 period compared to other time periods in the study duration and were significantly lower than the year with maximum disaster impact (23.95–44.26% and 48.00–53.53% lower for IMAR and the whole country, respectively). The disaster situation in China from 2010 to 2019 was significantly lower than that in the previous three decades, and the disaster situation of Inner Mongolia in 2000–2009 was significantly higher than in the other three decades.

3.2. Contribution Rate of Different Factors Affecting the Grain Production of IMAR

The contribution rates of all factors affecting grain production have been provided in Table 2. During the 1980 to 2019 period, expansion in sown area had the highest contribution (54.06%) in improving IMAR’s grain production, followed by yield and plant structure adjustment, both of which contributed 33.31% and 12.63%, respectively (Table 2). Initially, during the 1980–1989 period, increases in yield were the dominant factor for improving grain production (91.51%), while the contribution of sown area was −11.88%, due to the declining trend in sown area in 1980–1989. However, from 1990 to 1999, expansion in sown area became the dominant contributor to improving grain production (73.00–77.37%). Sown area remained a major contributor to increases in yield during the 2000–2009 period. From 2010 to 2019, the increases in sown area (46.92%) and yield (47.35%) were both the main contributors to improving grain production in IMAR. In 2010–2019, the newly increased sown area, by deducting the increased area due to changes in plantation structure, decreased by 8.74% and 18.71% compared with 1990–1999 and 2000–2009, respectively; thus, the contribution of sown area was decreased.

Improvement in grain production was mostly due to increases in maize cultivation (79.33%) in IMAR from 1980 to 2019 period (Table 3). Increases in soybean, miscellaneous grains and bean, and rice contributed 6.56%, 3.63%, and 4.06%, respectively, to the increase in IMAR grain production from 1980 to 2019. Wheat contribution declined from 37.24% to 0.64% from 1980–1989 to 2010–2019, with an overall contribution of 3.07% for the period of 1980–2019.

3.3. Influencing Factors of Main Staple Yield of IMAR

The Cobb–Douglas function was used to analyze the influence of fertilizer, irrigation, pesticide, agriculture film, and machinery input on the yield of the main staple grain crops. The established mathematical relationship has been stated to be as follows:

lnY_i = 0.443**lnF_i + 0.058lnI_i + 0.131**lnO_i − 0.129**lnCLR_i − 1.559 (R² = 0.90, p < 0.01)(18)

The results revealed that a 1% increase in the input of fertilizer, irrigation, and other inputs resulted in 0.443% (p < 0.01), 0.058%, and 0.131% (p < 0.01) increases in the yield, respectively, and a 1% increase in the comprehensive loss rate of meteorological hazards resulted in a 0.129% (p < 0.05) of loss in yield. The cash input of fertilizer, irrigation, and other inputs (seed, pesticide, agricultural film, and mechanical operation inputs) were 362.16%, 259.66%, and 405.55% higher in 2019 than that of 1981, respectively (Table 4), while the comprehensive loss rate of agrometeorological hazards was 60.30% in 2019 as compared to 1981. The input of fertilizer, other inputs, and irrigation contributed 160.44%, 53.13%, and 15.06%, to the main staple yield (maize and wheat), respectively, and the decrease in the comprehensive loss rate of agrometeorological hazards contributed 7.78% to the yield.

4. Discussion

4.1. Effect of Sown Area on the Change in Grain Production

Sown area in China has been identified to be the main contributor in improving the total grain production [6]. This study has also identified sown area as an important contributor to improving the total grain production in IMAR, which has been continuously on the rise since 1980. Similarly, yield in IMAR has also been observed to improve since 1980 and had significant contributions to improving total grain production in IMAR.

It is important to note that sown area is also strongly influenced by socio-economic factors, as well as climatic and environmental factors. For example, regional economic development, population growth, policy, and governance decisions may incentivize increases or decreases in agricultural land use or substitute land use, with industrial or residential uses taking up cultivated land [45]. Social and agricultural policy considerations therefore have significant implications on land management and changes in the acreage of sown area. The Chinese government has always prioritized agricultural development and aims to maintain 1.22 hundred million ha of crop land by 2030 [46]. After the launch of Grain for Green Program in IMAR, 3004.89 × 10³ ha farmland was converted to woodland and grassland from 2000 to 2018 [47]. The sown area in IMAR has continued to increase, but it is projected to stabilize in the future due to the restrictions currently in place on the use of space [48]. Even though the total grain production and crop yields in China are increasing [49], the grain production in China may be constrained by the scarcity of resources, soil degradation, and climate change [50]. Therefore, stable sown area and scientific and technological progress are needed to further improve potential yield capacity in IMAR in the future.

4.2. Effect of Yield and Plantation Structure on the Change of Grain Production

The increasing yield and an appropriate plantation structure can help to improve grain production while maintaining a stable sown area. To improve the overall agricultural production, grain receipts can significantly motivate small householders to be willing to plant crops [51]. The yield of maize and rice in IMAR was significantly higher than that of wheat, soybean, and tuber crop (Figure 8), despite the fact that the sown area of rice is smaller (1.6 % of the total sown area from 1980 to 2019) and is only located in the Hetao irrigation district. Maize is an important grain-forage crop and energy crop [52], with strong demand and a high purchase price. Therefore, small householders are more motivated to plant maize than other crops. Despite the enforcement of the “Sickle curvature area maize plantation reduction policy” to reduce the maize-planting area of sub-suitable and unsuitable growing areas including IMAR from 2015 [53], the proportion of maize in grain production of IMAR still increased (Figure 3). In reality, the “sickle ben” areas of IMAR are typically favorable for dry farming and the integration of farming with animal husbandry, in a fragile ecological environment.

Small farmholders prefer to plant maize due to its high production and profit in IMAR, but it is not completely in line with the regional agricultural development plans and policies to ensure food security and protect the environment. From the aspect of food security, the farming–pastoral ecotone of northern China (FPENC) is an area with scarce water resources, is sensitive to climate change, and faces desertification and serious natural hazards due to drought and aridification trends [53]. Due to the climate sensitivity and availability of agricultural climate resources, maize planting on large tracts of land is not suitable for the regional combination of farming and animal husbandry and to upgrade agricultural industry in the region [54]. This issue requires special measures such as increasing the silage corn plantation area, switching high water-consumption crops such as maize for low water-consumption crops (flax and forage rape) [53] in the Hetao plain irrigated agricultural area. Furthermore, in IMAR, production and sown area should be expanded for tuber crops [55] and miscellaneous grains and beans [56] using varieties that are drought and low-temperature-resistant, as well as suitable for arid regions at high latitudes.

From the aspect of environment protection, Inner Mongolia is an important ecological security barrier in northern China and produces one-third of the country’s green organic food, being the country’s agricultural and livestock production base [27]. The input, yield, and risk were both high for the maize-production system in northwest China, and the average greenhouse gas emission, soil acidification potential, and water eutrophication were 4188 kg CO₂-eq·hm⁻², 155.3 kg SO₂-eq·hm⁻², and 52.6 kg PO₄-eq·hm⁻² [57]. The single-crop structure and the continued cultivation of maize caused environmental problems such as soil hardening and desertification [58]. Maize–soybean belt compound planting can be adopted to gain high production with less nitrogen fertilizer amendment [59]. It is important to design and manage landscape, forest, farmland, lake, grass, and sand in a systematic manner that requires long-term efforts. In order to achieve the national goal of food self-sufficiency, it is important to save ecological capital, specifically water resources. Northern China’s grassland ecosystems, especially water-resource conservation regions for the Yellow, Yangzi, Lancang, and Luanhe rivers, are major ecological security barriers. Due to the overexploitation of ecological functions, the grassland ecosystems of Northern China have seen extensive decline in functions and services over the last several decades. Strategic decisions and plans are needed for regional development to achieve national food security and ecological conservation goals and the agricultural and livestock production base of China [27].

4.3. Change of Yield and Influencing Factors

Agriculture inputs (fertilizer, pesticide, agriculture films, machinery inputs, and irrigation) [10] and the adverse impact of natural hazards [12] impact yield. Production functions such as stochastic plateau functions, quadratic response, Mitscherlich–Baule, and deterministic methods can be used to fit crop yield to agricultural inputs, for example, nutrients for crops in a specific geographic region, and help to estimate the optimal inputs to ensure food security [14]. Most studies have focused on the effect of typical drivers, such as nitrogen [60,61] and potassium [62], on yield from an agronomy point-of-view. The yield fit with the improper response function may affect the accuracy [63]. In this study, a Cobb–Douglas production function [15] was chosen to synthetically analyze the multiple relationships between yield, agricultural inputs, and natural hazards. Historically, fertilizers have not only had significant effect on the yield in IMAR (Figure 9) but have also led to environmental degradation, such as soil acidification [64] and greenhouse gas emissions [32]. The use of pesticides [65] and agricultural machinery development [66] has boosted grain production in China, with evident positive impacts [67]. Agricultural films have improved crop water-use efficiency and improved yield within a short span of time, but, after 36 years of continuous use, plastic film residue might cause crop failure, because residual films can affect the root development of crops and the transport of water and fertilizer, leading to crop failure [68].

IMAR is located in an arid region, and agriculture is completely dependent on irrigation. Water use for agriculture accounts for 64.88% to 76.36% of the total water supply of IMAR [37]. The efficient irrigation area of IMAR has increased from 1103.5 × 10³ ha in 1980s to 3199.12 × 10³ ha in 2020 (Figure 10), which accounts for 46.82% of the total crop sown area of IMAR in 2020s. Although water-saving irrigated land accounts for 70.3% of the total efficient irrigation land, but the irrigation water-use efficiency was 52.1%, which is much lower than the average rate of China [37]. The irrigated agricultural region of the Hetao plain is heavily reliant on water from the Yellow River and is restricted by the irrigation quotas [69]. Drought, being the major agrometeorological disaster [70], has impacted 68.17% of the total area of IMAR in 2014. The implementation of water-saving irrigation techniques to improve water-use efficiency [71] can help overcome restrictions on the use of water resources and the impacts of agrometeorological hazards on potential grain production in the future.

5. Sources of Uncertainties

Due to the location of IMAR, which is situated in a transition zone between cropping and nomadic areas, there were uncertainties in this study. The data for grain production, yield, covered land area, affected area, and hazard-damaged area by agrometeorological hazards and the inputs of IMAR and China were both regionally averaged data; the local data in fact differs by location. Statistical data was only included for the covered area, affected area, and hazard-damaged area by the agrometeorological hazards of the total crop acreage. Due to the lack of data for wheat and maize, this study has taken into account the crop sown area as homogeneous and the comprehensive loss rate of agrometeorological hazards of the crop acreage as the same as with main staple grains, which could lead to inconsistencies. Furthermore, the study area covered both scattered irrigated agricultural area and dry farm land area and considered different varieties of crops that were planted in different areas across IMAR, which may have caused inconsistency in the provincial data. Uncertainties can also exist in the choice of appropriate production functions (Dhakal and Lange, 2021); the Cobb–Douglas function may impose an arbitrary level for substitution between inputs.

6. Conclusions

Inner Mongolia Autonomous Region has been one of China’s largest grain-producing provinces. This study analyzed the contribution factors of grain production in IMAR, and results have shown that structure adjustment, sown area, and yield can explain 12.63%, 54.06%, and 33.31%, of the increase in the total grain production, respectively, in IMAR from 1980 to 2019. A Cobb–Douglas production function was used to analyze the contribution of these factors to yield, which showed that the input of fertilizer, irrigation, and other agricultural inputs (seed, pesticide, agricultural film, and mechanical operation inputs) contributed 160.44%, 15.06%, and 53.13% for the main cereal grains (maize and wheat), respectively, while the decrease in the comprehensive loss rate of agrometeorological hazards contributed 7.76% of the yield of IMAR from 1980 to 2019. This study also concludes that, considering the increases in sown area are physically limited, new technologies are needed to increase yield and store grains, create innovation in fertilizers and pesticides with an improved utilization rate, employ water-saving irrigation, and take measures to minimize the impact of agrometeorological hazards.

Author Contributions

Study design, B.L. and Y.L.; Literature research, Y.Z.; Writing, Y.Z., B.L., Y.L., S.Z.S., C.C, J.H., S.L., F.Y. and X.Z.; Data collection, Y.Z., J.H., S.L., F.Y. and X.Z.; Data analysis, Y.Z., B.L., Y.L., S.Z.S. and C.C.; Figures, Y.Z. and C.C. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. Grain export to other provinces from Inner Mongolia Autonomous Region from 1980 to 2019. Source: National Bureau of Statistics (http://www.stats.gov.cn/tjsj/) (accessed on 26 January 2022). Note: Grain demand per capita of China is 400 kg with a balanced diet achieved [26].

Figure 2. Location of Inner Mongolia Autonomous Region.

Figure 3. Grain crop sown area of different crops of Inner Mongolia Autonomous Region, in comparison with China, for the period of 1980 to 2019.

Figure 4. Grain plantation structure of Inner Mongolia Autonomous Region from 1980 to 2019.

Figure 5. National and regional comparison of the grain production of different crops from 1980 to 2019.

Figure 6. Yield in Inner Mongolia Autonomous Region from 1980 to 2019, and a comparison with the national average.

Figure 7. Disaster-covered, disaster-affected, and disaster-damaged area in the whole country and Inner Mongolia Autonomous Region from 1980 to 2019. Note: (a,b) are disaster-covered, disaster-affected, and disaster-damaged area in the whole country and Inner Mongolia Autonomous Region, respectively. Different letters between different time periods represent significant differences at p < 0.05.

Figure 7. Disaster-covered, disaster-affected, and disaster-damaged area in the whole country and Inner Mongolia Autonomous Region from 1980 to 2019. Note: (a,b) are disaster-covered, disaster-affected, and disaster-damaged area in the whole country and Inner Mongolia Autonomous Region, respectively. Different letters between different time periods represent significant differences at p < 0.05.

Figure 8. Yield of different crop varieties of Inner Mongolia Autonomous Region from 1980 to 2019. Note: Different letters between different grain species represent significant differences at p < 0.05.

Figure 9. Yield and agriculture fertilizer input of Inner Mongolia Autonomous Region from 1980 to 2019.

Figure 10. Effective irrigated area of Inner Mongolia Autonomous Region from 1980 to 2019.

Table 1

Sown area of different crop varieties of Inner Mongolia Autonomous Region from 1980 to 2019.

Time Period	Average Sown Area of Different Crop Varieties (103 ha)
	Wheat	Maize	Rice	Soy Bean	Tuber Crop	Miscellaneous Grains and Beans
1980–1989	934.80 ± 11.76 a	571.50 ± 30.06 a	24.90 ± 3.76 b	240.30 ± 15.95 b	240.80 ± 3.61 a	1697.10 ± 75.46 a
1990–1999	1121.00 ± 35.19 b	1039.00 ± 96.92 b	92.80 ± 6.23 a	551.10 ± 56.97 b	356.90 ± 39.55 b	1154.00 ± 28.69 b
2000–2009	484.10 ± 25.89 c	1840.40 ± 127.32 c	92.60 ± 4.61 a	775.50 ± 34.57 a	586.70 ± 13.44 c	888.50 ± 39.28 c
2010–2019	617.00 ± 12.68 d	3522.10 ± 134.19 d	108.50 ± 8.68 a	913.90 ± 45.39 a	501.90 ± 41.27 d	773.90 ± 28.82 c
1980–2019	789.23 ± 41.90	1743.25 ± 186.80	79.70 ± 5.95	620.20 ± 45.31	421.58 ± 25.57	1128.38 ± 61.36

Note: Different letters between treatments represent significant differences at p < 0.05.

Table 2

Contributing factors and contribution rates of increases in grain production during selected periods in Inner Mongolia Autonomous Region.

Time Period	Contributing Factors
	Structure Adjustment	Sown Area	Yield
1980–1989	20.37%	−11.88%	91.51%
1990–1999	16.02%	77.37%	6.61%
2000–2009	6.87%	73.00%	20.13%
2010–2019	5.73%	46.92%	47.35%
1980–2019	12.63%	54.06%	33.31%

Table 3

Contribution of the grain-production increases in different varieties of crops to the grain production of Inner Mongolia Autonomous Region.

Time Period	Contribution to Grain Production Increase of IMAR (%) *
	Wheat	Maize	Rice	Soybean	Tuber Crop	Miscellaneous Grains and Bean
1980–1989	37.24%	51.85%	5.37%	8.71%	4.44%	−7.60%
1990–1999	2.50%	83.05%	8.28%	7.64%	10.85%	−12.32%
2000–2009	0.74%	96.85%	−0.74%	4.36%	−3.18%	1.96%
2010–2019	0.64%	82.44%	5.26%	5.85%	−3.70%	9.50%
1980–2019	3.07%	79.33%	4.06%	6.56%	3.35%	3.63%

Note: * the baseline year of each increase was the starting year of each period.

Table 4

Driving factors for the yield of the main staple grain of Inner Mongolia Autonomous Region from 1980 to 2019.

Items	Driving Factors
	Fertilizer(RMB ha−1)	Irrigation(RMB ha−1)	Other Inputs (RMB ha−1)	Comprehensive Loss Rate of Agrometeorological Hazards (%)
Initial value (1981)	72.43	30.25	97.61	15.51
End value (2019)	334.75	108.80	493.47	6.16
Change rate from 1981 to 2019 (%)	362.16	259.66	405.55	−60.30
Contribution to yield (%)	160.44	15.06	53.13	7.78

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