Mottaleb Khondoker Abdul, Kruseman Gideon, Frija Aymen, Sonder Kai, Lopez-Ridaura Santiago
Department of Agricultural Economics and Agribusiness, University of Arkansas, Fayetteville, AR, United States.
Sustainable Agrifood Systems, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
Front Nutr. 2023 Jan 26;9:1077443. doi: 10.3389/fnut.2022.1077443. eCollection 2022.
The combined populations of China and India were 2.78 billion in 2020, representing 36% of the world population (7.75 billion). Wheat is the second most important staple grain in both China and India. In 2019, the aggregate wheat consumption in China was 96.4 million ton and in India it was 82.5 million ton, together it was more than 35% of the world's wheat that year. In China, in 2050, the projected population will be 1294-1515 million, and in India, it is projected to be 14.89-1793 million, under the low and high-fertility rate assumptions. A question arises as to, what will be aggregate demand for wheat in China and India in 2030 and 2050?
Applying the Vector Error Correction model estimation process in the time series econometric estimation setting, this study projected the per capita and annual aggregate wheat consumptions of China and India during 2019-2050. In the process, this study relies on agricultural data sourced from the Food and Agriculture Organization of the United States (FAO) database (FAOSTAT), as well as the World Bank's World Development Indicators (WDI) data catalog. The presence of unit root in the data series are tested by applying the augmented Dickey-Fuller test; Philips-Perron unit root test; Kwiatkowski-Phillips-Schmidt-Shin test, and Zivot-Andrews Unit Root test allowing for a single break in intercept and/or trend. The test statistics suggest that a natural log transformation and with the first difference of the variables provides stationarity of the data series for both China and India. The Zivot-Andrews Unit Root test, however, suggested that there is a structural break in urban population share and GDP per capita. To tackle the issue, we have included a year dummy and two multiplicative dummies in our model. Furthermore, the Johansen cointegration test suggests that at least one variable in both data series were cointegrated. These tests enable us to apply Vector Error Correction (VEC) model estimation procedure. In estimation the model, the appropriate number of lags of the variables is confirmed by applying the "varsoc" command in Stata 17 software interface. The estimated yearly per capita wheat consumption in 2030 and 2050 from the VEC model, are multiplied by the projected population in 2030 and 2050 to calculate the projected aggregate wheat demand in China and India in 2030 and 2050. After projecting the yearly per capita wheat consumption (KG), we multiply with the projected population to get the expected consumption demand.
This study found that the yearly per capita wheat consumption of China will increase from 65.8 kg in 2019 to 76 kg in 2030, and 95 kg in 2050. In India, the yearly per capita wheat consumption will increase to 74 kg in 2030 and 94 kg in 2050 from 60.4 kg in 2019. Considering the projected population growth rates under low-fertility assumptions, aggregate wheat consumption of China will increase by more than 13% in 2030 and by 28% in 2050. Under the high-fertility rate assumption, however the aggregate wheat consumption of China will increase by 18% in 2030 and nearly 50% in 2050. In the case of India, under both low and high-fertility rate assumptions, aggregate wheat demand in India will increase by 32-38% in 2030 and by 70-104% in 2050 compared to 2019 level of consumption.
Our results underline the importance of wheat in both countries, which are the world's top wheat producers and consumers, and suggest the importance of research and development investments to maintain sufficient national wheat grain production levels to meet China and India's domestic demand. This is critical both to ensure the food security of this large segment of the world populace, which also includes 23% of the total population of the world who live on less than US $1.90/day, as well as to avoid potential grain market destabilization and price hikes that arise in the event of large import demands.
2020年,中国和印度的总人口为27.8亿,占世界人口(77.5亿)的36%。小麦是中国和印度第二重要的主粮。2019年,中国小麦总消费量为9640万吨,印度为8250万吨,两者合计占当年世界小麦产量的35%以上。在中国,到2050年,在低生育率和高生育率假设下,预计人口将分别为12.94亿至15.15亿,印度预计为14.89亿至17.93亿。那么,2030年和2050年中国和印度对小麦的总需求将是多少呢?
本研究在时间序列计量经济学估计框架下,应用向量误差修正模型估计方法,预测了2019 - 2050年中国和印度的人均及年度小麦总消费量。在此过程中,本研究依赖于来自美国粮食及农业组织(粮农组织)数据库(FAOSTAT)的农业数据以及世界银行的世界发展指标(WDI)数据目录。通过应用增强迪基 - 富勒检验、菲利普斯 - 佩伦单位根检验、奎特科夫斯基 - 菲利普斯 - 施密特 - 申检验以及允许截距和/或趋势存在单个断点的齐沃特 - 安德鲁斯单位根检验,对数据序列中的单位根进行检验。检验统计结果表明,对变量进行自然对数变换并取一阶差分后,中国和印度的数据序列均具有平稳性。然而,齐沃特 - 安德鲁斯单位根检验表明,城市人口份额和人均国内生产总值存在结构断点。为解决该问题,我们在模型中纳入了年度虚拟变量和两个乘法虚拟变量。此外,约翰森协整检验表明两个数据序列中至少有一个变量是协整的。这些检验使我们能够应用向量误差修正(VEC)模型估计程序。在估计模型时,通过在Stata 17软件界面中应用“varsoc”命令确定变量的适当滞后阶数。根据VEC模型估计的2030年和2050年人均小麦消费量,乘以2030年和2050年的预计人口数量,以计算2030年和2050年中国和印度的预计小麦总需求。在预测出年度人均小麦消费量(千克)后,乘以预计人口数量得到预期消费需求。
本研究发现,中国的人均年小麦消费量将从2019年的65.8千克增加到2030年的76千克和2050年的95千克。在印度,人均年小麦消费量将从2019年的60.4千克增加到2030年的74千克和2050年的94千克。考虑到低生育率假设下的预计人口增长率,中国的小麦总消费量在2030年将增加超过13%,在2050年将增加28%。然而,在高生育率假设下,中国的小麦总消费量在2030年将增加18%,在2050年将增加近50%。就印度而言,在低生育率和高生育率假设下,与2019年的消费水平相比,印度的小麦总需求在2030年将增加32% - 38%,在2050年将增加70% - 104%。
我们的结果凸显了小麦在这两个世界主要小麦生产国和消费国的重要性,并表明研发投资对于维持足够的国内小麦产量以满足中国和印度国内需求的重要性。这对于确保世界上这一大部分人口的粮食安全至关重要,这部分人口还包括世界上23%每天生活费不足1.90美元的人口,同时也有助于避免因大量进口需求导致的潜在粮食市场不稳定和价格上涨。