Grain Consumption Forecasting in China for 2030 and 2050: Volume and Varieties

Mingjie GAO, Qiyou LUO,Yang Liu Institute of Agricultural Resources and Regional Planning

Beijing, China gaomingjie@caas.cn, luoqiyou@caas.cn, liuyang@caas.cn

Jian MI China Development Research Foundation

Beijing, China mijian1982@aliyun.com

Abstract—Grain consumption projections are necessary inputs for developments in infrastructure as a means to ensure national food security. In this paper, firstly, we use time series analysis to forecast the key parameters affecting grain consumption in China in 2030 and 2050, and then we employ panel data analysis to estimate the long-run demand functions for feed grain in China across a range of scenarios. Our main findings are as follows. First, we expect that per capita grain consumption (in kilograms) for urban residents increase to 355kg in 2030 and 387kg in 2050, while that of rural residents will at first decrease to 248kg in 2030, and then increase to 262kg in 2050. Second, the gross volume of grain ration and feed grain for urban grain consumption in China will be 349 million tons in 2030 rising to 416 million tons in 2050, while that for rural grain consumption will fall to 113 million tons in 2030 and 77 million tons in 2050. Third, we anticipate that other grain consumptions in China, including food processing, seed, and waste, will increase to 108 million tons in 2030 and 131 million tons in 2050. Fourth, on this basis, total grain consumption in China will be about 571 million tons in 2030 and 624 million tons in 2050. Finally, we forecast the consumption of maize, rice, and wheat in China to be 262, 154, and 114 million tons in 2030, and 318, 156 and 100 million tons in 2050, respectively.

Keywords—grain consumption;, forecasting; grain ration; feed grain; time series

I. INTRODUCTION Over the past three decades, and especially since the start of

the new century, the demand for grain in China has increased continuously alongside China’s rapid economic development and increases in the incomes of both urban and rural households. At the same time, climate change and modifications to the ecological system in China have posed new challenges to national grain security. Consequently, long-term grain consumption forecasts are required to inform the strategic response to these conditions.

In response, domestically and internationally, there has been a significant increase on forecasting studies of Chinese grain consumption. For this purpose, several common forecasting methods have been used, including qualitative prediction, time series models, single equation econometric models, simultaneous equations models, demand system simultaneous equations models, the nutritional requirements and systems engineering methods. In the qualitative approaches, the primary method of research is to estimate per

capita commissariat demand within some prediction period, and then combine this with population forecasts to obtain the forecast value of grain demand (Brown 1995; Cheng and Chen 1998; Mei 1999; Liu 2000; MASSC 2001).

Most early quantitative studies employ time series models, including the growth rate method (Gao 2004), autoregressive moving average (ARMA), and generalized autoregressive conditional heteroscedasticity (GARCH) models (Shao 2009). Usefully, we can use these time series models to estimate separate trends in grain rations, feed grain, industrial grain, seed grain, and the loss of grain through waste (Xiao 2002; Li et al. 2008; Yang 2009). More recently, however, a number of studies have applied simultaneous equations models to the forecasting of grain supply and demand in China (Chen 2004; Lu and Huang 2004; Mei 2008). In contrast, single equation models (Chen and Qin 2007), and the nutrition demand (Luo 2008) and system dynamics (Ma and Niu 2009) methods, are seldom used in this body of research. And most studies forecast demand changes over the next 10 to 20 years, with forecasts for more than 20 years being relatively uncommon. Moreover, two thirds of the studies do not consider differences in grain variety in their forecasts, only five studies identify regional differences in forecasts, and only one in three studies examine the possible differences in forecasts across urban and rural areas. To our best knowledge, no study considers all three of these important possible differences in forecasts together in a single analysis. For this reason alone, it is worthy of further study.

II. DATA RESOURCES For the purpose of comparability in our analysis, we

convert the important categories of grain consumption in China, including animal products, eggs, milk, and aquatic products, into their feed grain consumption equivalents. We source annual data on these products from the Statistical Yearbook of China, the China Yearbook of Rural Household Survey and the China Price and Urban Households Income and Expenditure Survey Statistical Yearbook over the period 2001–09. To obtain the estimates of feed grain consumption, we multiply livestock production by the corresponding conversion rate. We do not convert beef and mutton production into their feed grain consumption equivalents for the four major pastoral areas of China where grazing is the primary method of livestock production (Tibet, Xinjiang, Qinghai, and Inner Mongolia). We integrate existing research results on the feed

Sponsored by “National Basic Research Program of China” （Project Number: 2010CB951504） Corresponding author: Qiyou LUO, Tel:+86-10-82109623, E-mail: luoqiyou@caas.cn; Yang LIU, Tel: +86-10-82109643, E-mail: liuyang@caas.cn.

conversion rate in China (Liao and Huang, 2004; Luo, 2008) to obtain the feed grain conversion coefficients in TableⅠ.

TABLE I. CONVERSION COEFFICIENTS FOR FEED GRAIN

Pork Beef and Mutton Poultry Eggs Milk Aquatic products 2.8 1.0 2.0 2.0 0.3 0.9

In recent years, both urban and rural residents in China have increasingly eaten outside the home. This is important because the consumption data in the Statistical Yearbook of China only includes home consumption, with the respective actual grain consumption of urban and rural residents being 25% and 15% higher than that suggested. Furthermore, actual per capita meat consumption by urban and rural residents is 50% and 40% higher than indicated in the Statistical Yearbook of China, respectively (Liao and Huang 2004). To address this deficiency in the official data, we use these proportions to calibrate the consumption data for both urban and rural residents. In addition, the statistical data for urban residents are in terms of finished grain, so we convert this to grain at a ratio of 0.75. Figs 1 and 2 plot the per capita amounts of ration and feed grain for urban and rural residents over the period 2001 to 2011.

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Fig 1. Per capita ration and feed grain for urban residents, 2001–2011.

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Fig 2. Per capita ration and feed grain for rural residents, 2001–2011.

As shown in Figs 1 and Fig 2, during the period 2001 to 2011, per capita consumption of feed grain for urban residents has increased from 138 kg to 177 kg, whereas their consumption of grain rations over the same period has remained stable at between 100 kg and 108 kg. In contrast, the per capita consumption of grain rations for rural residents has declined from 238 kg in 2001 to 171 kg in 2011. The reason for this is that in the process of living standards improving in China, grain consumption for both urban and rural residents

has mainly increased through the increased consumption of pork, beef, mutton, eggs, milk, and aquatic products, all of which use feed grain, and indirectly by eating out, but not directly though grain rations as in the past.

III. PARAMETER SETTINGS

A. The grain consumption of processing, seed, and waste The various types of grain consumption in China also

include processing grain, seed grain, and the loss of grain through waste. Fig. 3 depicts the shares of total grain consumption for these various types of consumption over the period from 1978 to 2007.

Fig 3. Grain consumption by type, 1978–2007.

As shown in Fig 3, before 1990, the percentage shares of ration and fodder grains in total grain consumption were relatively stable. However, after 1990, feed grain consumption grew at a much faster rate while that for the grain ration declined. As also shown, between 1978 and 1996, the percentage share of total grain consumption for processing, seed, and waste was relatively stable at between 11% and 12% of total grain consumption. However, after 1996, the percentage share of total grain consumption for processing, seed, and waste increased from 12% in 1996 to 16% in 2007, mainly due to an increase in the processing of grain in China.

By an international comparison, China and Japan are very similar in both diet and the process of economic development. In the past 30 years, the percentage share of processing, seed, and waste grain consumption increased in Japan from 8% to 12%, or about 1.3% every decade. On this basis, we assume that percentage share of processing, seed, and waste grain in China will be about 19% in 2030 and 21% in 2050.

B. Population prediction and assumption In the past 40 years, China’s annual population growth rate

has decreased from about 2.7% in 1971 to 0.51% in 2009. A number of different population predictions believe that China will reach a peak population in 2030 (we therefore assume the population growth rate will be zero in 2030), and that the change in the rate of population growth will be relatively stable from 2010 to 2050. Under this scenario, China will have a population of 1444 million in 2030 and 1377 million in 2050. However, as shown in Fig. 4, there are significant differences in the trends for the urban and rural populations in China over the period 1949–2009.

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Fig 4. Urban and rural populations in China, 1949–2009.

As shown in Fig. 4, since 1949, the urban population has continuously grown, reaching 6.20 billion in 2009, while the rural population reached a maximum of 8.60 billion in 1995, falling to 7.10 billion in 2009. From 1978 to 2000, the rate of urbanization fluctuated between 1% and 1.5% every year. Since 2000, the urbanization rate has on average increased by 1% annually. For the purpose of our analysis, we assume that the rate of urbanization will increase by 1% annually between 2010 and 2030 and by 0.5% from 2030 to 2050.

Using these assumptions, the level of urbanization in China will be 67% in 2030 and 77% in 2050. Combined with the population forecast, this suggests China’s urban and rural populations will be 9.70 billion and 4.80 billion in 2030 and 10.60 billion and 3.10 billion in 2050, respectively. Given that over the past five years, the proportion of the regional populations in the various urban and rural areas has not changed dramatically, we assume that in 2030 and 2050 each region’s urban and rural populations will have the same proportions as in 2010.

C. Income and price assumption From 2001 to 2011, there has been a high correlation

between the consumer price index (CPI) and grain price index (FPI) in China. During this period, the CPI averaged 4.0% annually, and the FPI average was similar. Accordingly, we assume that from 2010 to 2050, China’s CPI and FPI will both average 4%, but with high and low price scenarios of 4.5% and 3.5%, respectively. From 2001 to 2011, the average annual growth rate of urban and rural resident income in China was 12.28% and 11.50%, respectively. Given that China has maintained rapid economic growth for more than 30 years and will now enter a period of more stable growth for the next 40 years, we consider three different scenarios for incomes and prices in 2030 and 2050, as shown in TableⅡ.

TABLE II. INCOME AND PRICE ASSUMPTIONS FOR 2030 AND 2050

Scenario Year Incomes

Prices Urban Rural

Scenario I 2010–2030 7.0% 7.0% 4.5% 2030–2050 5.0% 6.0% 4.5%

Scenario II 2010–2030 8.0% 8.0% 4.0% 2030–2050 6.0% 7.0% 4.0%

Scenario III 2010–2030 10.0% 10.0% 3.5% 2030–2050 7.0% 8.0% 3.5%

IV. ECONOMETRIC MODEL AND PARAMETERS ESTIMATION

A. Model specification We use panel data with fixed effects estimation method to

control the regional differences. The determinants of per capita grain consumption consist principally of income and prices, along with some individual observable and unobservable factors that do not change over time, including gender, ethnicity, grain preferences, etc. Our initial econometric model is as follows.

it it it i it ,(1)y Income Price Othersβ γ δ ε=∂+ + + + As shown, the demand for grain by individual i at time t is

decided by income and price in period t and a number of other individual factors that do not change significantly over time. Given it is difficult to control or observe these individual characteristics; we average equation (1) over time to obtain:

_ _ __ __ _

i i iii y ,(2)Income Price Othersβ γ δ ε= ∂+ + + + As shown, the average grain consumption of individual i at

different times remain subject to income, prices, and these other factors. By taking the difference between (1) and (2), we eliminate these other individual factors:

~ ~ ~ ~ ~

i i ii y , (3)Incom e Priceβ γ ε= ∂ + + + As shown, the per capita grain consumption function now

depends only on the differences between income and prices over time. Using equation (3), we can then obtain the same coefficient values as (1) and (2) after controlling for these other factors.

B. Parameter estimation After specifying the per capita income and consumer price

indexes (in logs) as independent variables, we use a panel data method to separately estimate the demand functions for the average grain ration and feed grain. We then estimate the total per capita consumption of grain based on the proportion of dining out by households. TableⅢ provides the estimated coefficients for both urban and rural residents in China.

TABLE III. ESTIMATED GRAIN RATION AND FEED GRAIN CONSUMPTION FUNCTIONS

Urban residents Rural residents Grain ration Feed grain Grain ration Feed grain

Log (income) 0.00 0.11** –0.25** 0.21** Log (CPI) –0.14 –0.52** 0.17 –0.42* Log (FPI) 0.71 0.21 –0.19 –0.19 Constant 1.94 5.07** 7.43** 5.13** Note：1. ** and * indicate significance at the 1% and 5% level, respectively.

2. After BP test, the model conforms to the fixed effects model. As shown in Table Ⅲ , for urban residents, the ration

consumption does not significantly change with the levels of income and grain prices. Put differently, the income and substitution effects are not obvious, as with the standard of living for urban residents in China now at a relatively high

level, grain consumption has become a low-grade product, stabilizing at around 100 kg per person.

As shown by the estimates for urban residents in TableⅢ, for each 10% increase in per capita income, feed grain consumption will increase by 11%, while for every 10% increase in the CPI, feed grain consumption will decrease by 52%. The estimated coefficient for FPI is not statistically significant. Accordingly, for urban residents in China, the income effect of feed grain consumption is positive (but inelastic), while the substitution effect is not significant. Overall, this suggest that for urban residents, poultry, eggs, milk, and other products have now become normal goods and are no longer considered a luxury.

As also shown in TableⅢ, a 10% increase in per capita income of rural residents reduces grain consumption by 25%, currently about 110 kg per person (Chen and Qin 2007). The impact of overall prices and grain prices on grain consumption for rural residents is not significant. As for urban residents, for rural residents the income effect in grain consumption is negative while the substitution effect is not significant. This suggests that after many years of economic development, China’s rural residents now have easy access to grain and the grain ration is quickly becoming a low-grade product.

Further, we can see that for a 10% increase in per capita income of rural residents, feed grain consumption increases by 21%, while the same increase in the CPI decreases feed grain consumption by 42%. Once again, the FPI has an insignificant effect on feed grain consumption. The income and substitution effects for feed grain consumption are positive and inelastic. This shows that poultry product consumption by rural residents is now a normal good, with the differences in grain consumption between urban and rural areas becoming increasingly narrow.

C. Forecast accuracy analysis To verify the model, we compare the actual grain needs of

the urban and rural population with the forecast values from 2001 to 2011. Fig. 5 depicts the results.

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city actual feed city forecast feedrural actual ration rural forecast rationrural actual feed rural forecast feed

Fig 5. Forecast accuracy analysis.

As shown in Fig. 5, from 2001 to 2011, the actual and predicted values of per capita feed grain increase for both urban and rural individuals. The actual and predicted values of rural ration also move together while decreasing. Overall, the predicted values of the model are very close to the actual data. For urban per capita feed grain in 2001–11, the average difference between the projections and the actual values is 5%: 5% for rural per capita feed grain, and 1% for rural per capita ration. This suggests that the credibility of our models is very high.

V. SCENARIO SIMULATION AND PREDICTION RESULTS

A. Grain consumption prediction for China in 2030 and 2050

Combining per capita grain consumption functions with price and income assumptions in 2030 and 2050, we estimate the per capita grain consumption for the different scenarios discussed earlier. In the basic growth rate assumption (Scenario II), grain consumption per capita for urban residents will increase to 355kg in 2030 and 387kg in 2050, while that for rural residents will at first decrease to 248kg in 2030, and then increase to 262kg by 2050.Combining per capita grain consumption with population assumptions and the differences between regions and across urban and rural areas, we obtain grain consumption in China by region for the different growth scenarios in 2030 and 2050. Table Ⅳ details the results.

TABLE IV. GRAIN CONSUMPTION FOR URBAN AND RURAL RESIDENTS, 2030 AND 2050 (UNIT: KG, MILLION TONS)

Scenario Year Area Per capita ration and feed grain

Total ration and feed grain

Process, seed and loss of grain

Total grain consumption

Scenario I 2030

Urban 349.7 344.08 107.02 563.26

Rural 246.8 112.15

2050 Urban 375.5 403.8

127.26 606 Rural 255.2 74.94

Scenario II 2030

Urban 355.0 349.35 108.41 570.57

Rural 248.2 112.81

2050 Urban 386.6 415.74

130.96 623.62 Rural 261.8 76.92

Scenario III 2030

Urban 365.3 359.52 111.16 585.03

Rural 251.5 114.35

2050 Urban 402.6 432.98

136.01 647.67 Rural 267.8 78.69

As shown in Table Ⅳ , in the basic growth rate assumption (Scenario II), urban grain consumption in China will be 349 million tons in 2030 and 416 million tons in 2050. Rural grain consumption will fall to 113 million tons in 2030 and 77 million tons in 2050. In addition, grain consumption in processing, seed, and waste will increase to 108 million tons in 2030 and 131 million tons in 2050, with total grain consumption across China being about 571 million tons in 2030 and 624 million tons in 2050.

B. The consumption prediction of maize, rice and wheat

The consumption trends in the different grain varieties also indicate some remarkable differences. Fig.6 details the percentage consumption of corn, wheat, and rice in total grain consumption.

Fig 6. Percentage consumption of corn, wheat, and rice in total grain consumption, 1961–2007

As shown in Fig6, from 1961 to 2007, corn consumption increased in total grain consumption. The ongoing process of urbanization in China will continue, and the demand for corn, which is necessary for feeding livestock, will continue to display stable growth. We assume that from 2010 to 2030 the percentage share of corn consumption in total grain consumption will increase annually by 0.5%, growing to an annual rate of 0.25% from 2030 to 2050. Thus, by 2030, corn will account for some 46% of grain consumption and 51% by 2050.

As also shown in Fig6, from 1961 to 2007, the share of rice consumption in total grain consumption slowly but steadily declined. We assume that from 2010 to 2050, rice consumption as a percentage of total grain consumption will decrease by 1% every 10 years. Thus, rice consumption will account for 27% of total grain consumption in 2030 and 25% in 2050. Finally, from 1961 to 2007, wheat consumption as a percentage share of total grain consumption at first increased and then decreased. We assume that from 2010 to 2050 the percentage share of wheat consumption in total grain consumption will decrease by 2% every 10 years. Thus, the percentage shares of wheat consumption will be 20% in 2030 and 16% in 2050. Based on the above assumptions, we forecast that maize, rice, and wheat consumption in China will be 262, 154 and 114 million tons in 2030, and 318, 156 and 100 million tons in 2050, respectively.

VI. CONCLUSION The main results of our analysis are as follows. First, we

find that the per capita grain consumption of urban residents will increase to 355 kg in 2030 and 387 kg in 2050, whereas that of rural residents will decrease to 248 kg in 2030 and then increase to 262 kg in 2050. Second, total urban grain consumption will be 349 million tons in 2030 and 416 million tons in 2050. Total rural grain consumption will fall to 113 million tons in 2030 and 77 billion tons in 2050. Third, grain consumption for processing, seed, and waste will increase to 108 million tons in 2030 and 131 million tons in 2050. We expect total grain consumption in the whole of China be about 571 million tons in 2030 and 624 million tons in 2050. Finally, maize, rice, and wheat consumption in China will be 262, 154 and 114 million tons in 2030, and 318, 156 and 100 million tons in 2050, respectively.

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