1. Introduction
China has achieved a consecutive grain output growth for the past decade. During 2003–2013, China’s grain output increased from 431 million t to 602 million t with an annual growth rate of over 3%. The continuous growth in grain output indicates an important improvement in capacity of China's grain production, which offers a significant contribution to food security both domestically and internationally [
1,
2,
3].
In fact, the periods of enduring increase in grain production are rather rare in the modern China’s history [
4], as highly volatile trends had prevailed within a longer time span in the past [
5]. Numerous studies have examined factors behind the specific trends in China’s grain production [
6,
7,
8,
9]. However, many of these focused on the aggregate analysis, i.e., the key grain crops were aggregated together, often at the national level, but quite a few have looked at the issue from a disaggregate perspective, i.e., examining the performance of different types of crops across different regions.
As put forward by Zhu et al. [
4] and Yang [
10], there are at least three key reasons for a disaggregate analysis: First, there is a need to meet specific requirements for grain demand, which, indeed, may differ across individual crops. Clearly, not only does the total output of grain matter for China’s food supply, but also types of crops need to meet diverse demand. Second, given that crop yields may differ across both crops and regions, spatial and crop-mix variations might induce changes in the aggregate crop yield even when crop-specific yields remain constant, and, hence, affect the total output. Therefore, analysis of the changes in cropping pattern and their impact on the total grain harvest offers an additional perspective when identifying the sources of growth in China’s grain production. Third, the example of China shows that the growth rates of the overall grain output varied during different time periods, which has obviously been influenced by multiple factors. Comparisons relying on multiple driving forces will not only provide an in-depth understanding of China’s grain output changes in the past, but will also shed light on the potential scope for further developments.
On the other hand, a deeper analysis of driving forces behind China’s grain output change would also be important in regards to sustainability of China’s grain output growth and the effectiveness of the comprehensive agricultural development (CAD) in China. As for the former issue, there have been concerns on potential for further growth and sustainability of China’s grain production [
11,
12]. Since grain production is highly dependent on (depleting) natural resources, especially land and water (both directly and through the use of bio-chemicals), the recent upswing in grain output growth has exerted additional environment pressures in China [
13,
14]. Thus, given severe constraints in land and water endowments, it is important to ascertain whether the changes in China’s grain output are to be sustainable. This can be achieved by analyzing sustainability of the underlying driving forces. Additionally, in terms of the CAD, which is expected to boost crop yields and harvest thereby contributing to the goals of food security [
15], it has been put into practice via modernization of the farming practices, improved irrigation and rural infrastructure (e.g., concrete roads). Besides the CAD, urbanization has played an important role as a limiting factor in shaping the cropping patterns. Wei et al. [
12] argued that urbanization took place to different extent in different regions of China with the coastal regions featuring the highest pace of urbanization. Therefore, the analysis of grain output under interaction of multiple phenomena should identify the sources of changes associated with technical advancement (pure yield change) in order to fully fathom the outcomes of the CAD and similar measures.
This paper, therefore, attempts to look into the issue by considering multiple factors affecting grain output simultaneously and thus identifying intensive and extensive factors behind the changes in China’s grain output. An Index Decomposition Analysis (IDA) model is employed to isolate the dynamics in extensive and intensive factors since China’s rural economic reform in the late 1970s by using crop- and province-specific data. Unlike most aggregate studies focusing just on two sources of changes in grain output, namely dynamics in areas sown and yields, we factorize the changes in crop output into the four terms, viz., area effect, yield effect, crop-mix effect and spatial distribution effect, in this paper. As one can note, the latter three terms comprise the aggregate yield effect. Noteworthy, the inclusion of both spatial and input-mix effects constitutes a novel facet of grain harvest analysis in China. As regards the implementation of IDA, the Logarithmic Mean Divisia Index is applied to operationalize the decomposition. The technique features such desirable properties as perfect decomposition and time reversal (see more details in
Section 4). The results are analyzed in period-wise, crop-wise and region-wise manners.
The rest of the paper is organized as follows:
Section 2 presents data used.
Section 3 examines the general trends in China’s grain production for 1978–2013. Specifically, the patterns of changes in harvest associated with different crops and regions are highlighted.
Section 4 introduces the Index Decomposition Analysis technique.
Section 5 presents the decomposition results and, therefore, identifies the contributions of different factors to the changes in grain output in China in the aggregate, crop and regional dimensions.
Section 6 further discusses the constraints for different driving forces as well as the sustainability of China’s grain production in the future. The last section summarizes the major findings and their implications on China’s food security.
2. Data Used
The research involves multiple variables to quantify the contributions of different factors of changes in China’s grain output. Specifically, national and provincial data on output, areas sown and yields are obtained from the China Rural Statistics Yearbook for each crop and cover the period of 1978–2013.
Noteworthy, the definition of grain in China is different from the concept proposed by international organizations, i.e., FAO and USDA, which mainly refers to cereals (e.g., rice, wheat and corn). Specifically, Chinese statistics applies term “grain” to a much wider range of crops, including rice, wheat, corn, soybeans, potatoes, millet, sorghum, and other crops [
16,
17,
18]. Thus, to simplify the analysis, we classify the Chinese grain crops into the five groups, namely rice, wheat, maize, soybeans, and “other” grain crops.
In order to deliver more general insights regarding the trends in the grain output, we further group China’s 31 provinces, autonomous regions and municipalities into the seven regions according to the terrain types and cropping patterns prevailing there [
19,
20]: (1) Northeast China (NE) encompasses Heilongjiang, Jilin, Liaoning; (2) North China (NC) includes Beijing, Tianjin, Hebei, Shanxi, Shandong, Inner Mongolia; (3) East China (EC) includes Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi; (4) Middle China (MC) includes Hubei, Hunan, Henan; (5) South China (SC) includes Guangdong, Guangxi, Fujian, Hainan; (6) Southwest China (SW) includes Sichuan, Yunnan, Guizhou, Tibet; (7) Northwest China (NW) includes Shaanxi, Ningxia, Gansu, Qinghai, Xinjiang. (We do not include Hong Kong, Macao and Taiwan here. Hainan and Chongqing appear under Guangdong and Sichuan since the former provinces were established in 1988 and 1997, respectively.)
4. Preliminaries for Index Decomposition Analysis
Index Decomposition Analysis (IDA) allows one to decompose the changes in a certain variable of interest into a number of effects as represented by multiplicatively related factors. Ang [
23] and Xu and Ang [
24] summarized the key preliminaries for IDA. From the methodological viewpoint, there are two main strands of the IDA: techniques based on the Divisia index (type I) and techniques based on the Laspeyres index (type II) [
25]. Originally, IDA has been extensively applied in the field of energy economics [
26,
27,
28,
29].
The underlying idea of the index decomposition analysis is to isolate the effects of different factors affecting a certain variable. Indeed, the factors and the resulting variable are related by a functional relationship, which allows for such decomposition. In this paper, we apply the Logarithmic Mean Divisia Index (LMDI), type I, to decompose the changes in China’s grain output in terms of factors related to both extensive and intensive development.
The general case of the IDA can be presented in lines with Ang [
23]. Say
V is the resulting (aggregate) variable, which can be broken down into
n factors that fully describe the changes in
V throughout the time. Let us denote these factors as
. Assume there is index
for sub-categories of the aggregate variable. For a certain sub-category
, the following relationship describes the influence of
factors upon the aggregate variable:
. Summing over sub-categories, one arrives at the general IDA identity:
where
and
. Furthermore, we look at the changes in the aggregate variable. Therefore, the sub-category value is
for period 0 and
for period
T. The values for sub-categories can be summed up to arrive at the aggregate values
and
.
IDA can be employed to decompose the change in the aggregate variable (i.e., additive decomposition):
where
is the absolute contribution of the
-th factor towards the change in
V.
The techniques for IDA differ in the weighting of factors of decomposition among other issues. The LMDI technique attributes the change in the aggregate variable to the
k-th factor as follows:
where
. This approach can be applied upon an IDA identity, which, indeed, must be adapted to the phenomenon of interest. The definition of the relationships among the aggregate and factor variables, therefore, constitutes a focal element of the IDA.
In a nutshell, the grain output is determined by the two main factors, namely area sown and yield. In case multiple crops and areas (provinces) are covered, the yield factor can be further decomposed into crop-mix, spatial structure and pure yield effects. Therefore, the following groups of factors can be analyzed: intensity effect (pure yield effect), structural effects (crop-mix and spatial distribution), and area effect (total area sown). More specifically, these effects are defined as follows:
A—area sown effect captures the changes in the overall area sown and, thus, the impact of extensive development;
Si—spatial distribution effect captures the changes in shares of areas within different provinces relative to the total area sown and their impact upon changes in the aggregate yield;
Mij—crop-mix effect captures the impact of changes in crop structure within the provinces upon the aggregate yield;
Yij—pure yield effect quantifies the impact of crop-specific yields.
Assuming there are multiple provinces and crops, we define
as a province index and
as a crop index. Therefore, the IDA identify can be established to relate the total grain output to the four aforementioned factors:
where
Q is the total harvest in tons;
A is total area sown in hectares;
Ai is area sown in hectares for the
i-th province;
Aij is area sown in hectares for the
j-th crop in the
i-th province;
Qij is the harvest in tons for the
j-th crop in the
i-th province. Note that
.
Consequently, the change in the total harvest additively decomposes as follows:
Following Equation (3), the respective effects
,
,
, and
can be calculated by employing equations below:
where
.
The presented framework, therefore, allows one to decompose the change in grain output in terms of both extensive and intensive factors. As Chinese provinces are rather diverse in terms of natural and climatic conditions, the structural effect is a rather important factor for changes in the crop output. However, the latter aspect has been neglected in the literature (see, for instance, [
4,
24]). The proposed methodology, therefore, allows for a more detailed analysis of changes in the crop production. The analysis can be applied on different time periods in order to ascertain whether the main drivers of changes in grain output vary with time.
6. Discussion
In summary, the carried out analysis showed that the yield effect has always been the primary factor to simulate grain harvest in China, whereas the area effect has become more important after 2003 and accounted for nearly one-third of the output growth during 2003–2013. Moreover, besides the expansion of area sown under grain and increasing yields, cropping pattern change has also played an important role in ensuring the output growth, while negative impacts of spatial shifts have been observed. In addition, comparison of trends for different effects allows one to gain insights into sustainability and scope of prospective growth in grain output as well as other related issues of, for instance, the effectiveness of CAD and the actual role technical advancement played in promoting China’s grain production.
The area sown effect has been becoming more and more important in China’s grain production. However, given the serious constraints of land resources in the country, further potential for expansion in area sown is quite limited. Although China possesses the world’s third largest arable land area, its huge population renders arable land per capita being just 40% of the world average level [
15,
38]. Meanwhile, along with rapid industrialization and urbanization, the declines in both land quantity and quality have become a serious concern. Speaking of the period associated with the most evident changes in the land quantity,
Figure 8 indicates that the total arable land area in China dropped from 127.1 million ha to 121.7 million ha during 2001–2008, i.e., by 1 million ha per annum. As regards land quality, arable land within high-fertility class currently corresponds to less than one-third of China’s total agricultural land area, while the remaining two-thirds fall within middle- or low-fertility classes [
39,
40]. However, due to the excessive use of chemical fertilizers and pesticides coupled with intensive farming practices, severe land degradation and large scale of land pollution has been observed nation-wide [
41]. Furthermore, as industrialization and urbanization process are likely to accelerate, the decline in agricultural area and land quality may not be reversed in the future, thus the growth based on expansion in area sown will not remain as a primary option for further stimulation of grain production in China [
42].
The effect associated with the spatial distribution of the area sown exerted a negative impact, which indicates that China’s grain production has been shifting from more productive southern regions to the less productive northern regions. Anyway, the effect has diminished and, eventually, had a meagre impact. The latter finding implies that spatial distribution effect may no longer appear as an unfavorable factor for grain production in the future. Moreover, spatial distribution of land and water is highly uneven among Chinese regions, with serious mismatches between land and water availability in certain locations [
43]. For instance, the northern part of China occupies 3/5 of the country’s arable land area, yet only 1/5 of the country’s water resources are available there. In contrast, the southern part of China is endowed with 2/5 of land area and 4/5 of water resources [
44]. Therefore, further shifts of grain production from the South to the North would definitely be restricted by the already tight water situation in the northern part of China.
Though the changes in cropping pattern have been increasingly important, the potential for growth in grain harvest due to the latter effect is likely to diminish because of the following reasons:
First, the recent replacement of soybean by high-yield maize prevailing in China allows satisfying the booming domestic demand for feed grains to a certain extent and would keep high self-sufficiency in staple foods within the country, yet it would decrease self-sufficiency in oil crops, especially soybeans, whose self-sufficiency rate has already been lower than 15% [
45]. Currently, the area sown under soybean constitutes less than 18% of that sown under maize [
4], thus the possibilities for further substitution of soybean by maize should be rather limited.
Second, the main drivers behind the changes in crop-mix, as manifested by substitution of maize and rice for soybean and the other grain crops during 2003–2013, are the differences in revenues and profits associated with the latter two groups of crops. These differences are partly due to the price support policies.
Table 3 presents yield, price, and profit data for maize normalized by respective data for soybean. Given that maize and soybean are direct substitutes in many areas across China in terms of planting season, the higher net profit for maize motivates famers to expand the production of the latter crop and contract the cropping of soybean. A rapid expansion in area sown under maize has resulted in maize output nearly doubling during 2003–2013 (from 115.8 Mt to 218.5 Mt). However, on the other hand, the growth in maize output has already outpaced that of its demand and led to increased surplus production and stocking in China [
46,
47]. Thus, having faced this serious situation, the central government has already announced intentions to slowdown stockpiling by reducing purchase price for maize [
48]. It can be expected that the relative profit for maize will consequently decrease, as well as the previously positive effects of the crop-mix change.
Third, the changes in international food markets along with interaction between domestic and international food prices will also affect the relative prices of crops under consideration, viz. maize and soybean, and, thus, will impact Chinese farmers’ revenue, as well as their decisions regarding crop structure. Indeed, this will add uncertainty to the future crop-mix adjustments in China’s grain sector. Thus, in the long run, the room for cropping pattern change in the same direction, as it has been observed in the recent years, will be rather limited.
Methodologically, our findings show the aggregate analysis may overstate the impact and importance of technical progress in promoting grain yield in China, if changes in crop-mix and spatial distribution are not accounted for. It is generally accepted that CAD has positively contributed to China’s grain yields and can explain 50% to 60% of increase in the grain output in recent years (cf. [
49,
50]). However, based on our estimates, though almost 65% of China’s grain output growth during 2003–2013 can be attributed to aggregate yield improvement (i.e., the effects of spatial distribution, crop-mix, and yield), the pure yield effect, which should capture the immediate effects of the implementation of CAD, was just 47%. This implies that the effects of the CAD might be overestimated in Chinese agricultural sector.
Given the difficulties associated with relying on land expansion and structural adjustment effects in stimulating grain production, increasing the yields of grain crop varieties through biotechnology might be an unavoidable choice in China in the future. In fact, if compared with the situation abroad, China’s grain yields are still lagging behind. As shown in
Table 4, China features lower yields of the main grain crops if contrasted to the major producers and/or the countries with the highest yields in the world. Such gaps imply that there is still quite some potential to increase the yields of different grain crops in China.
Looking at results regarding rice production, one can note the area effect has become more and more important, while the importance of yield effect has declined, indicating a rather subdued improvement in rice yield. Though rice also has been enjoying a positive crop-mix effect, especially in recent years, the shift of production from the South to the North has somehow offset the positive effects. Thus, more attention should be paid to R&D thereby fueling technical progress and spillover of technology in rice production.
As regards wheat, the pure yield effect remained as the major effect behind the output change throughout years 1978–2013. Wheat production was rather stable in terms of structural effects. Accordingly, extensive development and pure yield gains remain the main sources for harvest growth.
Extensive development, i.e., the area effect, along with the yield effect, has contributed significantly to growth in maize output. Noticeably, intensive development seems to have become more important as the effects of crop-mix change and spatial distribution change exceeded those of factors associated with extensive development. All in all, maize has become a more preferred grain crop in the past decade as suggested by the crop-mix effect. However, since the favorable relative revenue and net profit of producing maize might no longer be in effect, it can be expected that maize will lose such a favorable position, and the production of maize will shrink in China in the near future.
For soybeans, the area effect and pure yield effect have always been important in terms of harvest variation. Though the area effect, yield effect and spatial effect were all positive in the past decades, they have been offset by unfavorable cropping pattern change. This indicates that soybeans are losing their popularity in less fertile regions.
The region-wise results correspond to those in the other studies [
13] in that we have identified similar patterns of crop output change, yet we take a different perspective on decomposition thereof. In general, NE region has seen serious improvements both due to climatic conditions and policy measures taken there. At the other end of spectrum, the coastal region has lost its productive potential due to urbanization and policies promoting abandoning of crop farming. The results indicate that SW region showed moderate growth in the grain output, which was mainly driven by the yield effect. This echoes Wei et al. [
13], who argue that “a lack of management techniques” is evident in the area. Indeed, managerial decisions could further impact the spatial distribution and crop-mix and thus contribute to the growth in the aggregate yields.
7. Conclusions and Policy Implications
This study examined the patterns of China’s grain output growth since its rural economic reform in 1978 from a disaggregate perspective. The LMDI technique was employed to quantify the extensive and intensive factors behind the output growth for different grain crops in period-, crop-, and region-wise manners. The results showed that some factors (e.g., spatial distribution) can have little influence at the aggregate level, yet region- or crop-level decomposition rendered somewhat different conclusions. Additionally, accounting for different effects also allows one to gain reasonable insights into sustainability of and scope for prospective growth in grain production as well into impacts of such schemes as the CAD and agricultural technical advancement in China. Therefore, the proposed approach might be useful for revealing the underlying trends in crop harvest which would be neglected otherwise.
Our findings indicate that yield gains have been the major source of growth in grain harvest. The extensive development, as represented by the area sown effect, has also been an important factor since year 2003, when the direction of change in the area sown was reversed due to shifts in the support policies. The most recent period of 2003–2013 is also distinct with the highest impact of the crop-mix effect, which indicates the need for crop-specific analysis in order to address the recent developments in China’s crop farming and ensure the implementation of food security objectives.
Looking into the future, the constraints imposed by limited land and water endowments and climate change, among other unfavorable factors, are to emerge. The pressures associated with further growth in grain harvest in China will be even more severe and induce serious ecological costs related to groundwater overuse and nutrient leakage. So far, China’s endeavor in ensuring absolute self-sufficiency in staple food has not secured the country’s food security needs and, what is more, has caused excessive economic, social, and environmental burden [
51]. For instance, almost half of the remarkable increase in China’s grain production following 1980 can be attributed to an increasing use of chemical inputs [
52]. As shown by Zhang [
53] and Luan et al. [
54], possessing only 9% of the world’s arable land resources, China is responsible for 35% of world’s total fertilizer and pesticide consumption due to excessively high rates of application. Overuse of chemical fertilizers and pesticides coupled with intensive farming practices have already contributed to severe degradation of land and large scale of pollution [
55]. Thus, to ensure food security and to promote sustainable agricultural development in China, more attention is needed to the following three aspects:
First, given the serious scarcity of arable land and water resources, the primal priority should be to thoughtfully protect and maintain the existing land and water resources. On the one hand, it is important to prevent the loss of the most productive farmland during the ongoing urbanization process. On the other hand, China should pay more attention to improving the quality of cultivated land by encouraging rehabilitation of arable lands that are suffering from ecological degradation and defining strategic measures for improvements of the underperforming cropland. According to some estimates, partial improvements of middle- and low-yield farmlands would increase China’s grain production by some 20% [
56]. Therefore, improving land productivity through strengthening the agricultural infrastructure and, particularly, irrigation systems, especially in the middle- and low-yield areas, should become important goals of China’s agricultural policies in the foreseeable future.
Second, as the structural adjustments will not remain the main impetus for growth in grain output in China, technological progress aimed at improving the specific grain yields will become more important in this sense. As discussed previously, significant gaps among the yields of major grain crops in China and other countries reflects that there is still great potential for improvements in China’s grain yields. Therefore, more efforts should be made to intensify the R&D aimed at developing improved varieties corresponding to domestic resource and environmental endowments; i.e., more varieties suitable for the middle- and low-yield areas are needed. In addition, extension and advisory services are highly important in promoting and applying high yield varieties beyond the R&D activities. Finally, besides increase in land productivity, development of water-saving technologies and water price regulation also remain topical issues. These adjustments would allow mitigating hazards resulting from climate change and urbanization.
Third, from an economic perspective, a market-oriented approach is required to achieve food security goals. Indeed, rational exploitation of the international markets would ease China’s great environmental and ecological pressures. In recent years, China’s total net agricultural imports equaled to substituting 66.7 million ha of agricultural land and 250 billion m
3 water [
51,
57], thus effectively alleviating domestic shortage of land and water resources. Therefore, rational utilization of the international food markets can effectively supplement the domestic grain supply.