Arable land requirements based on food consumption patterns: Case study in rural Guyuan District, Western China

In comparison with all data for rural China, deficiencies of animal protein and fat intake were identified using the method of Gerbens-Leenes et al. ([Gerbens-Leenes, P.W., Nonhebel, S., Iven, W.P.M.F., 2002. A method to determine land requirements relating to food consumption patterns. Agriculture, Ecosystems and Environment, 90: 47-45]) in examining food consumption patterns and arable land requirements of Guyuan District, a remote rural area of western China. Population growth and rapid economic development have increasingly been reducing the land available for primary production, creating potentially serious risks for China's food security. Land required to produce food is determined by population size, consumption patterns, land resource endowment - or “agro-ecological” conditions and the level of farm intensification. Per capita land requirements in Guyuan District were identified to meet basic consumption needs, and to evaluate nutritional conditions related to current consumption patterns. Data used for this analysis were obtained from surveys of household food consumption. Food consumption involved only meeting basic requirements for sustenance, with grains, potatoes, vegetables, fruits, and plant oils being the most commonly consumed foods. Per capita intake of calories totaled 11.1 MJ·day− 1, matching the recommended level for China to meet basic health requirements. Daily protein intake was 66.8 g·person− 1·day− 1, being below the recommended standard of 77 g·person− 1·day− 1. Of this total, protein from animal meat accounted for only 7.5% of total protein. Fat intake totaled only 47.4 g·person− 1·day− 1, being far below the standard of 70 g·person− 1·day− 1. Yet, farmers must sell their limited livestock to earn enough income to meet their daily consumption needs. This expenditure accounted for nearly 33% of mean annual household income, so only 28% of domestic animal products were consumed locally. Benchmark data is provided to assist with improving living standards of rural people.