An econometric analysis of major Chinese food crops: An empirical study

Rehman, Abdul; Jingdong, Luan Article An econometric analysis of major Chinese food crops: An empirical study Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Rehman, Abdul; Jingdong, Luan (2017) : An econometric analysis of major Chinese food crops: An empirical study, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 5, Iss. 1, pp. 1-23, https://doi.org/10.1080/23322039.2017.1323372 This Version is available at: https://hdl.handle.net/10419/194674 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Page 1 of 23 ECONOMETRICS | RESEARCH ARTICLE An econometric analysis of major Chinese food crops: An empirical study Abdul Rehman and Luan Jingdong Cogent Economics & Finance (2017), 5: 1323372 1980 1985 1990 1995 2000 2005 2010 2015 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000 60000 65000 0000 Tons Years Grain Rice Wheat Corn Beans Tubers Cotton Sugarcane Tobacco Rehman & Jingdong, Cogent Economics & Finance (2017), 5: 1323372 https://doi.org/10.1080/23322039.2017.1323372 ECONOMETRICS | RESEARCH ARTICLE An econometric analysis of major Chinese food crops: An empirical study Abdul Rehman 1 * and Luan Jingdong 1 Abstract:The basic objective of this study was to investigate and explore the relationship between major food crops of China and their relationship with agricultural gross domestic product (GDP) using an econometric analysis. Agriculture is considered an important sector of the Chinese economy as it accounted for about 10% of GDP. The total agricultural land of China covers 36% of the area of the world. In order to highlight the actual performance of the agricultural production and the output of major food crops, this study explored the relationship between agricultural GDP and the major crops output including wheat, cotton, rice, sugarcane, corn, and tubers in China over the period of 35years from 1980 to 2015. The time series data were collected from the China Bureau of Statistics, Ministry of Agriculture China and various publications. Crop data were analyzed using the Ordinary Least Square Method and Augmented Dickey Fuller test and results were interpreted using the Johansen co-integration test. Our study found that output of wheat, cotton, sugarcane, corn, and tubers has positive and significant relation with the agricultural gross domestic product of China, while the output of rice crop has a negative but no significant relation with agricultural GDP of China. The study suggests that the Government of China should start new funding schemes for the development and better production of rice crops. *Corresponding author: Abdul Rehman, College of Economics and Management, Anhui Agricultural University, Hefei, China E-mail: [email protected] Reviewing editor: Xibin Zhang, Monash University, Australia Additional information is available at the end of the article ABOUT THE AUTHOR Abdul Rehman is the PhD research scholar in School of Economics and Management, Anhui Agricultural University Hefei, China. His major interests include Agricultural Economics, Agricultural Development, Applied Economics, Business Economics, Development Economics, Econometrics, Financial Economics, Financial Econometrics, Microeconomics, Macroeconomics, Regional Economics, Sustainable Agriculture, Regional Rural Development, Regional Agricultural Development. Due to his excellent work, he has been awarded with China Outstanding/Excellent International Student Award in 2016. He has good research publications in the world leading journals databases that having indexing of the Web of Science. PUBLIC INTEREST STATEMENT The basic objective of this study was to investigate and explore the relationship between major food crops of China and agricultural GDP using an econometric analysis. In order to highlight the actual performance of the agricultural production and the output of major food crops, this study explored the relationship between agricultural GDP and the major crops output including wheat, cotton, rice, sugarcane, corn, and tubers in China over the period of 35 years from 1980 to 2015. Crop data were analyzed using the Ordinary Least Square (OLS) Method and Augmented Dickey Fuller (ADF) test and results were interpreted using the Johansen co-integration test. Our study found that output of wheat, cotton, sugarcane, corn, and tubers has positive and significant relation with the agricultural gross domestic product (AGDP) of China, while the output of rice crop has a negative but no significant relation with agricultural GDP of China. Received: 14 January 2017 Accepted: 23 April 2017 Published: 02 May 2017 © 2017 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Page 2 of 23 Abdul Rehman Page 3 of 23 Rehman & Jingdong, Cogent Economics & Finance (2017), 5: 1323372 https://doi.org/10.1080/23322039.2017.1323372 Subjects: Sustainable Development; Economics and Development; Economics Keywords: food crops; agricultural GDP; irrigation; crop production; food consumption; OLS 1. Introduction China has the world’s largest population and also is the largest food consumer in the world: it feeds 20% of the population of the world and consumes an average of approximately 5 million tons of food annually. At present, the total agricultural land covered is 36% of the area of the world (Xiao, Mignolet, Mari, & Benoît, 2014). In broad concept, the agricultural crops and cultivation are important to the environmental evolution and socioeconomic growth of most countries in the world (WB, 2012). On the other hand, the challenges faced by developing countries and the constraints on agricultural development and food safety and security require livelihood security and rural development. This vigorous framework requires innovative facilities and operations for its management systems and agricultural output, if it is to be contributing to socioeconomic and conservational development. From this point of view it can be said that in the next 40 years agricultural output, facilities and operations will have to increase production of agriculture by at least 70% (Kilelu, Klerkx, & Leeuwis, 2013). Agricultural output is described as an essential constituent of social and economic development in some parts of the world (Delmotte, Lopez-Ridaura, Barbier, & Wery, 2013). In particular, the availability of widely spread major agricultural crops may have a significant role for the increasing population (Abebe, Bijman, Pascucci, & Omta, 2013). Similarly, agricultural growth and output of crops is vital for staple food security, increasing or improving income, particularly in agriculture where most of the major sectors of the economy and employment (FAO, 2013). So the question next to be answered is how much agricultural crop production should be expanded to meet the growing demand for future populations (Alexandratos & Bruinsma, 2012). In order to meet these requirements, agricultural strategies and policies must improve the necessary conditions and efficiency of agricultural productivity, make opportunities and ensure sustainability through new agricultural perceptions and standards such as special agricultural crops method innovation and direction (OECD, 2013). China’s water resource per capita is only 2100 cubic meters, about a quarter of the world average (MWR, 1998–2010). The uneven spatial and temporal water resources and distribution in the Yangtze River invites serious water shortage in the North. In addition, increased competition among water sectors as a result of social and economic development has brought about a sharp increase in domestic and industrial water demand, as well as the crop-growing burden on climate and environment. The extended crop concept for water use efficiency, and the looking at water efficiency was initially encouraged by Molden (1997) and the best way to achieve efficient use of water resources to produce better crops (Rodrigues & Pereira, 2009). Agriculture productivity can quantify the amount of water used in the production sector as the output per unit of production in different land areas (Ali & Talukder, 2008). The demand for grain in China is about 600 million tons a year and it has the largest food demand in the world, and its stability of demand should greatly affect the global food market and food security (Zhang, Li, Li, & Xu, 2012). Since the food imports by China are huge, food prices in the world and food markets are steady if the supply of food is stable. Furthermore, in South and Southeast Asia, the grain production of China is also limited due to inefficient water supply. The total water resources of China are abundant, but its sequential patterns do not satisfy the food. The total water supply is enough; source and demand do not match in terms of food production. Approximately 81% of the water resources are found in the South, while 64% of the arable land is in North China (Kang, Khan, & Ma, 2009). 2. Current major food crop scenario in China 2.1. Wheat crop China is the major producer and largest consumer of wheat, following by the United States and India. Generally, the first Green Revolution had seen a stable rise in the production of wheat due to the development that led to better shelter and disease resistance as well as the potential for a high Page 4 of 23 Rehman & Jingdong, Cogent Economics & Finance (2017), 5: 1323372 https://doi.org/10.1080/23322039.2017.1323372 quality, high yield, but also through improved management practices. The wheat production in China also experienced a dramatic increase over more than sixty years since the founding of modern China in 1949, with proper practices of mature theoretical guidance and a barren land policy (FAO, 2013; Yu, 2006). In the 1970s, it was generally agreed that more fertilizers were available for encouraging wheat farming in high yielding areas, with irrigation and especially higher planting densities were used to increase the wheat production (Yu, 1987). However, with improved soil conditions and irrigation, such as soil nutrients, the increase in the production of wheat encountered serious problems, including the excessive use of seed, excessive use of awnings and vulnerable species, leading to high vulnerability to poor shelter, inefficient and light harvesting and small shoots as well as small grain and low crop yields (Yu, 1990). Agriculture is the major sector of the Chinese economy, accounting for approximately 70% of the total water removal (MWR, 2005). To ensure the improvement of crop yields, irrigation plays a vital role (Huang, Rozelle, Huang, & Wang, 2002). The total arable land of China is about 40% and the irrigated land for production of grain is about 75%. The application of chemicals increases the food production and also helps to increase the income of farmers (Jin & Young, 2001). Water shortages will significantly affect the national food production. As the largest wheat producer and consumer in the world, the national wheat production of China may have a significant impact on world food trade (Ahmadi-Esfahani & Jensen, 1994). Due to severe drought, cereal production fell by 20 million tons of grain imports in 1995, accounting for 10% of world total grain exports (FAO, 2005a). The five most stressful factors include water, nitrogen, temperature, phosphorus, and aeration. Crop yields are calculated for each specific crop with the water stress used to adjust the harvest index (Williams, Jones, Kiniry, & Spanel, 1989). Due to the high dependence on irrigation, wheat is preferred because of its importance to China. Chinese wheat (FAO, 2005a) is the second largest crop and uses more than 70% of the total irrigation water in the Northern China Plain (Li, Inanaga, Li, & Eneji, 2005). In 1995–1999, the import of wheat accounted for nearly 40% of the total cereal imports, and the production of wheat accounts for 85% of the acreage in China (FAO, 2005a). The irrigation’s effects on the production of crop are usually quantified through the use of a crop water production function involving crop yields for a large number of water applications (English & Raja, 1996; Yaron & Bresler, 1983). Just a reasonable irrigation can significantly increase the production yield (Gajri, Gill, & Chaudhary, 1997; Huang & Sun, 2006). Hagan, Howard, and Talcoh (1967) in their study emphasize that extreme irrigation interrupts growth, harvesting, and reduces GY. Jin, Zhang, and Sun (1999) state that extreme irrigation results in reduced crop water use efficiency and that effective Water Use Efficiency (WUE) could lead to higher yields. Similarly, Olesen, Mortensen, Jørgensen, and Andersen (2000) show that the impact on irrigated wheat crop is almost entirely due to increased transpiration, and that the water use efficiency and harvest index remained unaffected. There were significant differences between soil water content and irrigation rate under different irrigation conditions, and the yield and water use efficiency were significantly different (Kang et al., 2002). Limited irrigation and soil water stress on crop yield and water use efficiency are dependent on crop growth at a particular stage of growth (Singh, Mishra, & Imtiyaz, 1991). The grain production in China has a dynamic role in the development of the national economy and agricultural policies which assist it is an essential component. Estimations show that the agricultural sector of China supports the essential food supply to maximum nearly 20% of the world population, and it produces 15–17% of the global wheat, rice, and maize (Winters & Yusef, 2007). The main challenges for maintaining and strengthening such outputs to meet a growing demand include maintaining a crop improvement and avoiding land degradation through agricultural technology, changes in food consumption patterns, population growth, competitive demands for agricultural land, and other uses for irrigation (Gale, 2002; Zhao, Luo, Deng, & Yan, 2008). With facing these challenges, it increases the focus on climate change impacts and interactions between climate change, water resources, land use alteration, and socioeconomic development and food security (Gregory, Ingram, & Brklacich, 2005; Rosegrant & Cline, 2003). Water resources are important to agricultural production and have been a major factor in China’s food production pressures, particularly in the northern Page 5 of 23 Rehman & Jingdong, Cogent Economics & Finance (2017), 5: 1323372 https://doi.org/10.1080/23322039.2017.1323372 part of the country (Fischer, Tubiello, van Velthuizen, & Wiberg, 2007; Li, 2006). Irrigation for agriculture in China is the main consumer of water, being more than 70% of its total use. China’s grain production is 75% from irrigated land. Thus water has had the important role of irrigation for food security and poverty alleviation in China, and even in stabilizing the importance of grain imports in the world cereal market (Jin & Young, 2001). Over the past two decades, a middle-season drainage has been used as an alternative water management approach throughout China (FAO, 2004; Shen, Yang, & Pei, 1998). With conventional water management, grain paddies were maintained in a continuously flooded planting season, followed by mid-season drainage regularly or allowed to dry. Mid-season drainage or drying tends to increase the yield, also to increase the soil nitrogen mineralization and to increase the root development in plants (Lu et al., 2000; Wassmann et al., 2000). Field measurements in China indicated that midseason drainage reduces CH4 emissions, while increasing emissions without production (Cai et al., 1999; Zheng et al., 1997). The field of research into interactions between crop production, land use and climate change, and water availability has largely been deserted recently (Betts, 2005). The driving force behind the agriculture response to climate change is its straight biophysical trappings and socioeconomic processes through its arbitration. Enlarged per capita food consumption and populations generate pressure on the land and the water resources to determine its features. Future food security scenarios predict an increased risk of scarcity in the future (Slingo, Challinor, Hoskins, & Wheeler, 2005). Recent studies have used a variety of models and climate scenarios to integrate the impacts of climate change on food production. Some comprehensive assessments have been included in water availability studies and others have identified different socioeconomic development pathways (Fischer, Shah, Tubiello, & van Velhuizen, 2005; Parry, Rosenzweig, Iglesias, Livermore, & Fischer, 2004; Rosenberg, Brown, Izaurralde, & Thomson, 2003; Rosenzweig et al., 2004). Most of the comprehensive evaluations have been made in advanced countries on the improvement of production in the agriculture sector (Holman et al., 2005). Various studies have performed regional simulation using crop models (Hansen & Jones, 2000; Irmak, Jones, & Jagtap, 2005; Moen, Kaiser, & Riha, 1994). However, for a number of developing countries, especially for larger territories, such as China, only a few studies considered detailed regional simulations; there is a lack observational experiments, geographic studies, and statistical data (Matthews & Stephens, 2002). The lack of data is often compounded by studies being limited to selected application or a small number of crop models being used at selected stations (Tao, Hayashi, Zhang, Sakamoto, & Yokozawa, 2008; Yao, Xu, Lin, Yokozawa, & Zhang, 2007). Previously hindered, crop model evaluation at a larger scale and regional stimulations in developing countries is under way (Lin et al., 2005). The planted area for wheat cultivation has improved by 1.5 million hectares during the period 2009 to 2010, but it has been constant since then. With the help of widespread irrigation, high harvests were attained, yielding development of varieties, a good supply and adequate inputs, and strong government financial support. Wheat production in 2014–2015 was recorded as being 126.0 million tons and the quality was higher than for 2013–2014. The wheat crop production from 1980–2015 is shown in Figure 1. 2.2. Rice crop Rice is the main food for more than 65% of the Chinese population, accounting for about 40% of the calorie intake in China. Over the previous three decades, the farming of rice accounted for about 27–29% of the total grain production; nearly 41–45% of the area being used for rice production of the total national grain production. Since 1970, China accounted for nearly a quarter of the rice planting area of the world (State Environmental Protection Administration, 2003). Throughout, the production of rice was challenged by shortages of water, global warming, and other constraints on farmers’ cultivation of this crop (Peng et al., 2004; Tao, Yokozawa, Xu, Hayashi, & Zhang, 2006). Similarly, the susceptibility of production of rice to global warming has become a major worry. Research into several crop models and climate change scenarios has simulated the impact of climate change in Asia (Hayashi & Jung, 2000; Horie, Centeno, Nakagawa, & Matsui, 1997; Kropff et al., 1993; Lin et al., 2005; Matthews, Kropff, Horie, & Bachelet, 1997; Yao et al., 2007). Although the suitability and effectiveness of the techniques presented and their uncertain results are dependent on Page 6 of 23 Rehman & Jingdong, Cogent Economics & Finance (2017), 5: 1323372 https://doi.org/10.1080/23322039.2017.1323372 the context, the probabilistic approach is considered to be valuable for this determination (Dessai & Hulme, 2003). Recently, probabilistic techniques using multi-model integration or Monte Carlo analysis have been used to solve various climatic probabilistic problems such as unexpected precipitation events, seasonal climate predictions, and the identification of dangerous human disturbances (Hagedorn, Doblas-Reyes, & Palmer, 2005; Mastrandrea & Schneider, 2004; Palmer & Räisänen, 2002). The application of multiple model predictions can improve forecasting and greatly benefit users (Cantelaube & Terres, 2005). Land in China has become extremely important; it has attracted a lot of interest for global food security (Mannion, 1995; Matthews et al., 1997; Kaufmann & Seto, 2001). Farming in China has a long history for about 22% of the global population, but only 7% of the world’s arable land. This ratio is so excessive that any change in land use will trigger global environmental changes and affect the world food market. For example, China is the world’s second largest emitter of carbon dioxide. It will surpass the United States in 2015, due to its rapid economic growth and huge dollar reserves and use of inferior grade fuels such as coal (Balzhiser, 1998; Drennen & Erickson, 1998). Rice, maize, and wheat crops account for about 85% of the world cereal exports. In addition, several studies have shown analytical correlations with land use changes in the use of fertilizer for major food crops. According to Cheng, Han, and Taylor (1992), in Chinese agriculture, the entire sector energy usage increased from 1965 to 1988, and growth in the total expenditure was mostly due to usage and manufacture of chemical fertilizers, accounting for 83% of the aggregate energy used in 1988. Ellis and Wang (1997), reveal that a significant increase in agricultural production was due to the extensive use of fertilizers: especially the introduction of new nitrogen and rice varieties which have heavy energy inputs. China presented the household contract concern system in 1978, which associated compensation with output, and began to dismantle the people’s cooperative system, eradicating the association between economic organization and the state power establishments. The decline in cereal production and cereal crop in the region led to increased economic value to the agricultural market from the other categories of crops, particularly vegetables and cash crops, as well as to animal husbandry and fishery production. In addition, the labor force in rural areas began to move to urban areas (Gao, Liu, & Zhuang, 1998; Zhang, Wang, Li, & Cheng, 1989). Farmers wish to raise their income by buying other agricultural resources, particularly fertilizer, to increase the crop yields. In the meantime, the price of cereal crops was so flat that no farmers were ready to grow further cereal crops if they had enough food for their families. Figure 1. Wheat production in China from 1980 to 2015. 1980 1985 1990 1995 2000 2005 2010 2015 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 0000 Tons Years Wheat Production in China Page 7 of 23 Rehman & Jingdong, Cogent Economics & Finance (2017), 5: 1323372 https://doi.org/10.1080/23322039.2017.1323372 Except for Qinghai, a rice crop is cultivated in every province of China. Several varieties of rice were grown in the Japonica variety and the rest with Indica rice varieties. The varieties of Indica rice are cultivated mostly with the Southern and Northern Japonicas crop traits. The varieties of hybrid rice account for about 50% of China’s rice-growing areas (Yuan, 2003). Two to three varieties of rice crops can be planted in the southern provinces of China within a year but in the North only a single rice crop is planted. More than 95% of the rice is produced under flooded irrigation conditions (Maclean, Dawe, Hardy, & Hettel, 2002). An increase in grain production resulted from the growth of new varieties of rice in the 1950s and hybrid rice varieties in the 1970s due to improved crop management practices in irrigation and nitrogen fertilization. The yield prospect of rice increased by about 30% in the semi-dwarf varieties and increased by an extra 15–20% through the use of heterosis (Fang, Zhang, Wang, & Liao, 2004; Yuan, 2003). In 2006, the national average of rice crop per hectare was 6.27 tons per hectare as compared with the world average of 4.11 tons per hectare. However, a stagnation of rice production in China has been observed over the past decade. In 2006, the total production of rice was 9% which is lower than production in 1997, when the production of country was the highest in history (FAOSTAT, 2007). Based on the projected population growth, China is required to produce about 20% more rice to meet its domestic needs by 2030, if the per capita rice consumption remains at its current level (Cai et al., 1999). In the past 20 years, the arable land of China has decreased by 0.25 million ha per year (Zhai, 2000). More importantly, an additional reduction of arable land occurred in fertile soils (Tong, Hall, & Wang, 2003). This reduction in arable land was mostly caused by the construction of new buildings or roads and by marginally arable land for cultivation. In competition for land, rice is frequently at a disadvantageous position with the cultivated area for cash crops. Therefore, much of the forthcoming growth in rice production must arise from superior yields on existing crop lands to avoid conservational degradation, damage to natural bionetworks, and loss of biodiversity (Cassman, 1999). China is recognized for its scarce resources of water, less than a quarter of the world average per capita and alone for its rice production consuming about 50% of the freshwater resources (Li, 2006). Flood irrigation of rice at field level requires two to three times more water than other cereal crops such as maize and wheat (Bouman, Humphreys, Tuong, & Barker, 2007). Freshwater resources scarcity is now threatening the production of rice in China, mainly due to the freshwater resources required in the city and the industrial sector’s from fierce competition for water. Drought stress is considered to be the most important limiting factor for rice production in China (Zhang, 2007). According to available estimations, the consumption of rice crop per capita declined from 78 kg per year in 1995 to 76.5 in 2009. The rice crop production from 1980 to 2015 is shown in Figure 2. Figure 2. Rice production in China from 1980 to 2015. 1980 1985 1990 199520002005201 02 01 5 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 24000 0000 Tons Years Corn Production in China Page 8 of 23 Rehman & Jingdong, Cogent Economics & Finance (2017), 5: 1323372 https://doi.org/10.1080/23322039.2017.1323372 2.3. Cotton crop The cotton crop in China is characterized by a limited labor-intensive arable land for cultivation and the availability of a large number of agricultural laborers. In the basin of the Yellow River, intensive production systems were involved in planting, protect i ng, and restrained plant densities of 4.5 plants/m2 with plant pruning being common in China’s largest cotton-growing areas (Dong, Li, Li, Tang, & Zhang, 2005). Plant trimming mainly refers to the manual vegetative removal of sprouts in late June before the flowering, core stem tip, and fruit shoots in the middle or late July peak-fruit period. Several studies have exposed that plant pruning can mitigate over veiling and over somatic growth of cotton under insignificant plant populations (Dong, Li, Li, Tang, & Zhang, 2003). In most cotton-growing countries, cotton planting dates are being studied. Extensive studies have shown that late planting frequently results in a decreased yield and fiber performance, a reduced fruit duration, and delayed maturation relative to normal cult i vation (Bange & Milroy, 2004; Davidonis, Johnson, Landivar, & Fernandez, 2004; Shastry, Sharma, & Mandloi, 2001). Similarly, Munk (2001) reported that the cotton crop partially compensated for the delayed planting by reducing the number of scattered branches and preserving high growth rates in the late season. The later sowing of cotton crops with a higher elongation of fiber resulted, also with a larger fiber extent and low a micronaire (Bauer, May, & Camberato, 1998). Most of these early studies were conducted by planting densities in rows, as well as with a little focus on planting densities and sowing interactions, so it is not clear if increasing plant density would compensate for late sowing in yield and fiber quality. The frequency of cotton premature senescence increased in many cotton-growing countries. This may come from the poor ability of cotton to take up other nutrients and potassium from the surface soil at the end of the season (Brouder & Cassman, 1990), or unbalanced sources and sinks (Wright, 1999). Cotton is an important fiber crop in the world, grown in tropical and subtropical areas in almost in all countries. The world’s most important cotton producers are China, United States, India, Pakistan, and Uzbekistan. Cotton is an important economic source for many developing countries. Nearly 55 million tons of cotton seed were produced on 33.7 million hectares from 1996 to 1998. The average per-unit area yield area was 1.63 tons/ha, fluctuating from less than 0.5 ton/ha in some African countries to 5.1 tons/ha in Israel. China, the largest cotton producer, produced an average of 2.82 tons/ha. Recognizing the negative externalities, the Chinese leadership started a number of steps to control some of the most harmful aspects of pesticide use. Plant breeders of China have successfully improved and host plant’s resistance to insects and disease for thousands of varieties (Stone, 1993). In China, for almost all of the newly released varieties, there are higher levels of resistance in the host plants to disease over the past 20 years. At least in the case of rice, the usage of these species leads to a reduction in the need for pesticides (Widawsky, Rozelle, Jin, & Huang, 1998). Inappropriately, despite this success, more and more problems still exist with crop pests in China. The effectiveness of the old breed has been improved over time to ensure the increased resistance to insect pests in China and improved varieties having natural defences and through chemical pesticides (Crook, 1999). In response to cumulative insecticide resistance in the late 1980s, the Chinese research and investigation system, led by scientists in the United States, initiated the growing of crops that are genetically contrived to be resistant to imperative pests (Huang, Wang, & Zhang, 2001). In the early 1990s, greenhouse testing began. At present, the seed companies in China develop and test different varieties of corn, cotton, rice, and vegetables. The usage of pesticides dramatically increased and has proven to have extensive benefits and expenses beyond the direct influence on cotton crop resources. Current studies on pesticides in China reveal that the use of pesticides has shown that they make an important contribution to the agricultural production of major crops such as rice (Huang et al., 2000). The Official Pest Management of China estimated that pest control methods and the spraying of pesticides save millions tons of food and fiber annually from pest attrition (Ministry of Agriculture, 2000). Page 15 of 23 Rehman & Jingdong, Cogent Economics & Finance (2017), 5: 1323372 https://doi.org/10.1080/23322039.2017.1323372 So, Equation (2) can also be written as; The present study is based on the time series data from 1980 to 2015. First of all, we checked the variable’s stationarity using the Augmented Dickey Fuller (ADF) unit root test. After checking the stationary of the series, to check the long-run relationship between the dependent and independent variables, the Johansen Co-integration test was used. Finally, the Ordinary Least Square (OLS) econometric method was used to examine the impact of wheat cotton, rice, sugarcane, corn, tubers, and area output under these crops and their relationship with the agricultural GDP of China for the period 1980–2015. 3.2. OLS method The OLS method’s results will indicate the predictive ability of the model as well as the relative values of the parameters in the short run. To check the long-run relationship between the dependent and independent variables, the Johansen Co-integration test was used. 4. Results and discussion 4.1. Results of unit root test The results of the ADF unit root test are presented in Table 1. The results show that all variables did not attain stationarity at their level form, whilst all variables became stationary after taking the first difference I(1), as indicated by the values of ADF statistics test which are greater than the critical values at the 5% significance level. 4.2. Results of the co-integration test For examining the co-integration based on Johansen, two tests are used such as trace statistics and the maximum eigenvalue. The presence of a co-integration shows that the AGDP, output of wheat, output of cotton, output of rice, output of sugarcane, output of corn, and output of tubers has a longrun equilibrium relationship. The results estimated by the Johansen Co-integration tests are presented in Tables 2 and 3. The values of Trace statistic (328.4223) and the values of Max-Eigen statistic (136.5310), which were greater than their critical values (159.5297) and (52.36261), indicate that there exists a long-term relationship between the dependent and independent variables. This means, the values reject the null hypothesis of no co-integration. In both tests, the Trace statistic and the Max-Eigen statistic reveal 1 co-integrating equation at the 5% level. 4.3. Results of regression To examine the relationship between the output of the major crops and agricultural GDP in China, the OLS method was employed. The results of the regression analysis are reported in Table 4. From the OLS regression result, the high values of R2 are 0.874 or 87.4% and the Adjusted-R2 is 0.843 or 84.3%. This implies that about 84% of total change in AGDP is explained by the independent variables. The computed value of the F-statistic was 27.94631 with a probability value of 0.000000, which shows that the overall goodness of fit of the model is significant. An analysis of the regression results shows that the coefficient of output of wheat crop is highly significant at both the 1 and 5% of significance levels, which means that there is a strong and positive relationship between AGDP and the output of wheat. This implies that a 1% increase in the output of wheat crop showed an increase in AGDP of 5.62%. The results further show that the coefficient of output of tubers is also highly significant at both the 1 and 5% of significance levels, which indicates that it also proves a positive, and strong relationship between the outputs of tuber with AGDP. This suggests that a 1% increase in the output of tubers leads to an increase in AGDP of 5.17%. Similarly, the coefficient of output of cotton crop is also significant and has a positive relationship (3) ln(AGRGDP)=𝛽 0 +𝛽 1 ln(OPCOTTON)+𝛽 2 ln(CROPEDAREA)+𝛽 3 ln(OPCORN) +𝛽4ln(OPRICE)+𝛽5ln(OPSUGARCANE)+𝛽6ln(OPTUBERS) +𝛽 7 ln(OPWHEAT)+𝜇 Page 16 of 23 Rehman & Jingdong, Cogent Economics & Finance (2017), 5: 1323372 https://doi.org/10.1080/23322039.2017.1323372 Table 1. ADF unit root test including (trend and intercept) *Level of significance at 1%. **Level of significance at 5%. Variables At level First difference t-statistic Critical values t-statistic Critical values ln GDP −0.863039 1% −4.284580 −3.684241 ** 1% −4.284580 (0.9478) 5% −3.562882 (0.0387) 5% −3.562882 10% −3.215267 10% −3.215267 ln OPCOTTON −3.621234 ** 1% −4.243644 −6.298653* 1% −4.262735 (0.0423) 5% −3.544284 (0.0001) 5% −3.552973 10% −3.204699 10% −3.209642 ln OPCORN −5.538038 1% −4.243644 −9.910920* 1% −4.252879 (0.0003)* 5% −3.544284 (0.0000) 5% −3.548490 10% −3.204699 10% −3.207094 ln OPRICE −3.021694 1% −4.243644 −5.303921* 1% −4.252879 (0.1409) 5% −3.544284 (0.0007) 5% −3.548490 10% −3.204699 10% −3.207094 ln OPSUGARCANE −4.790393* 1% −4.243644 −9.794996* 1% −4.262735 (0.0025) 5% −3.544284 (0.0002) 5% −3.552973 10% −3.204699 10% −3.209642 ln OPTUBERS −2.273653 1% −4.243644 −6.777595* 1% −4.252879 (0.4365) 5% −3.544284 (0.0000) 5% −3.548490 10% −3.204699 10% −3.207094 ln OPWHEAT −3.125710 1% −4.243644 −4.573942* 1% −4.252879 (0.1163) 5% −3.544284 (0.0045) 5% −3.548490 10% −3.204699 10% −3.207094 ln LANDAREA −1.655467 1% −4.252879 −4.278114* 1% −4.252879 (0.7489) 5% −3.548490 (0.0094) 5% −3.548490 10% −3.207094 10% −3.207094 Table 2. Johansen co-integration test using Trace Statistic Note: The Trace test indicates that the 5 co-integrating equation at the 0.05 level is appropriate. *Denotes rejection of the hypothesis is at the 0.05 level. **Indicates values are accurate. Eigenvalue Trace statistic 5% Critical value Prob** Hypothesized No. of CE(s) 0.981968 328.4223 159.5297 0.0000 None* 0.793591 191.8913 125.6154 0.0000 At most 1* 0.733736 138.2429 95.75366 0.0000 At most 2* 0.683398 93.25184 69.81889 0.0002 At most 3* 0.585857 54.14809 47.85613 0.0114 At most 4* 0.282078 24.17562 29.79707 0.1931 At most 5 0.250454 12.90820 15.49471 0.1182 At most 6 0.087316 3.106436 3.841466 0.0780 At most 7 Page 17 of 23 Rehman & Jingdong, Cogent Economics & Finance (2017), 5: 1323372 https://doi.org/10.1080/23322039.2017.1323372 with agricultural (AGDP). This means that a 1% increase in the output of cotton crops leads to an increase in AGDP with 2.63%. Results further show that the output of sugarcane and corn also has positive relationship with AGDP of 0.48 and 0.49%, but statistically both are insignificant. However, the output of the rice crop shows that there is a negative relationship with agricultural GDP and the result is insignificant with a coefficient of −9.34%. The output of land area shows a positive relationship with agricultural (AGDP) and it is significant with a coefficient of 1.13%. Furthermore, the results show that there is a negative relationship between the output of rice crop and agricultural GDP. This result was not expected. The reason for the negative relation was probably due to climatic conditions and the ups and downs of prices to support it. 5. Conclusion and recommendations Agriculture is an important sector of the Chinese economy and it contributes nearly 10% to the GDP. This study examined the relationship between agricultural GDP and the output of major food crops namely wheat, cotton, rice, sugarcane, corn, and tubers in China over the period 1980–2015. The time series data used for this study was collated from the China Bureau of Statistics, the Ministry of Agriculture (MOA) of China, and various other publications. Crop data was analyzed using the OLS Method and the ADF test and the results were interpreted using the Johansen co-integration test. Table 3. Johansen co-integration test using Max-Eigen statistic Note: The Max-eigenvalue test indicates that the 5 co-integrating equation at the 0.05 level is appropriate. *Denotes rejection of the hypothesis is at the 0.05 level. **Indicates values are accurate. Eigenvalue Max-Eigen statistic 5% Critical value Prob** Hypothesized No. of CE(s) 0.981968 136.5310 52.36261 0.0000 None* 0.793591 53.64843 46.23142 0.0068 At most 1* 0.733736 44.99105 40.07757 0.0129 At most 2* 0.683398 39.10375 33.87687 0.0109 At most 3* 0.585857 29.97247 27.58434 0.0242 At most 4* 0.282078 11.26742 21.13162 0.6207 At most 5 0.250454 9.801768 14.26460 0.2253 At most 6 0.087316 3.106436 3.841466 0.0780 At most 7 Table 4. Regression analysis Dependent variable: ln (AGDP) Method: least squares Sample: 1980–2015 Included observations: 36 Explanatory variable Coefficient Std. error t-statistic Prob. C−37.50079 53.81423 −0.696856 0.4916 ln (OPWHEAT) 5.625429 2.462039 2.284866 0.0301 ln (OPRICE) −9.346193 5.726903 −1.631980 0.1139 ln (OPCOTTON) 2.673763 0.735022 3.637665 0.0011 ln (OPSUGARCANE) 0.489134 0.653759 0.748186 0.4606 ln (OPCORN) 0.490809 0.280599 1.749148 0.0912 ln (LANDAREA) 1.136971 3.697611 0.307488 0.7607 ln (OPTUBERS) 5.179403 1.380525 3.751765 0.0008 R 2 0.874790 Adjusted R 2 0.843487 F-statistic 27.94631 (F-statistic) 0.000000 Durbin–Watson stat 0.942394 Page 18 of 23 Rehman & Jingdong, Cogent Economics & Finance (2017), 5: 1323372 https://doi.org/10.1080/23322039.2017.1323372 Our study found that the output of wheat, cotton, sugarcane, corn, and tubers has positive and significant relation with the AGDP of China, while the output of the rice crop has a negative and no significant relation with agricultural GDP of China. This result was not expected, and the reason for the negative relation is probably due to climate conditions and the ups and downs of a support price. Hence, the study recommends and suggests that the Government of China should focus on and start new funding policies for the better growth, development and production of rice crops. In the next decade, China should adapt new policies to increase the yield production and become the major factor in grain production. The output of these major crops of grain, wheat, cotton, rice, sugarcane, corn, tubers, and even livestock products should witness a steady progress. Acknowledgements The authors are grateful to the School of Economics & Management, Anhui Agricultural University Hefei, China for its financial and moral support. Moreover, the authors are also indebted to the reviewers for their positive suggestions that helped to improve the content of this study. Funding The authors received no direct funding for this research. Author details Abdul Rehman 1 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0001-7809-5124 Luan Jingdong 1 E-mail: [email protected] 1 College of Economics and Management, Anhui Agricultural University, Hefei, China. Citation information Cite this article as: An econometric analysis of major Chinese food crops: An empirical study, Abdul Rehman & Luan Jingdong, Cogent Economics & Finance(2017), 5: 1323372. Cover image The image has taken from the China Bureau of Statistics (Annual Reports). Source: Author. References Abebe, G. K., Bijman, J., Pascucci, S., & Omta, O. (2013). Adoption of improved potato varieties in Ethiopia: The role of agricultural knowledge and innovation system and smallholder farmers’ quality assessment. Agricultural Systems, 122, 22–32. doi:10.1016/j.agsy.2013.07.008 Abu-Awwad, A. M. (1999). Effects of sand column, furrow and supplemental irrigation on agricultural production in an arid environment. 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