Materials and methods

2.2.1 Background of study area

Located in the southern part of the Mongolian Plateau (37o01‘–03 o 02‘ N and 95 o 02‘–123 o 37‘E), IMAR covers 11.8 million km2, and it is the third largest province in China. The region is characterized by an arid to semi-arid continental climate (Yu et al. 2003) with strong climatic gradients and supports varied land-use practices (Figure 2.1). Annual precipitation ranges from 100mm to 500m and decreases from north-east to south-west. The annual mean, minimum and maximum temperatures in the temperate grasslands are 1.68 o C, -18.3 o C and 18.7 o C, respectively (Yu et al. 2003). Hulun Buir, in the northeast of IMAR, is a transitional zone where the meadow steppes meet the Greater Hingaan Mountains. The meadow steppes are the most productive type of grasslands (Yu et al. 2003) and develop in areas with moist fertile soils that are rich in organic matter (Kang et al. 2007). The north central area of IMAR, Xilin Gol, borders the semi-desert and is dominated by typical steppe (Ji et al. 2009). Typical steppe land is drought-tolerant with multiple vegetation species. The south-western area of IMAR, Ordos, is dominated by semi-desert steppe and is the most arid ecosystem with the least biomass (Yu et al. 2003). Typical steppe and meadow steppe are the predominant grassland ecosystems and are commonly used for grazing and animal production (Kang et al. 2007). The local populace depends mainly on husbandry and the grassland ecosystems to supply almost all of the forage needed for their livestock and to support the livelihood of the region‘s herders (Zhen et al. 2010a). IMAR is mainly a self-sufficient region, but certain foods need to be purchased, such as rice, flour and fruit. It is also an energy rich region, especially rich in coal. In June 2007, the proven reserves of coal were estimated to be 685.3 billion tons, ranking first of all Chinese provinces (Liu et al. 2012).

The socio-economic situation from north-east to south-west in the study area varies greatly. The north-east (Hulun Buir), a traditional pastoral area, has become the largest milk and meat producer in China. Half of the north central region (Xilin Gol) was a

25 traditional pastoral area and half was a farming area. The southwest (Ordos) leads in economic development due to the rapid development of mining. Many inhabitants have moved from the countryside to the cities, caused by restoration policies and more job opportunities and income sources, and the attraction of a modern lifestyle for young people. The general trend in livestock husbandry and crop farming activities is moving away from individual participation to larger-scale operations and population engaged in husbandry and farming has decreased greatly over the past 15 years.

Figure 2.1 Location map of study sizes in the Inner Mongolia Autonomous Region.

2.2.2 Grassland restoration policy implemented in IMAR

To reverse the increasing tendency of grassland degradation, a series of policies and counter-measures have been put forward and enforced to alleviate the anthropogenic stress at national and local levels in the past decade; among which the most important one implemented in heavily degraded areas is called ‗Fencing grassland, forbidding grazing and moving user‘. The policy was brought out around 1998 and broadly extended after several years‘ experience. The policy included five measures during its implementation, namely (i) seasonal grazing; (ii) rotational grazing; (iii) grazing prohibition; (iv) user moving (also called herder emigration); and (v) livestock-rearing control.

Seasonal grazing means pastures could only be grazed throughout the period of grass growth from April to November. In the winter period, herders feed livestock on

26

conserved forage indoors. These policy measures were broadly implemented across grasslands in IMAR, especially in the slightly degraded grassland, such as Hulun Buir, that could be restored by management intervention.

Rotational grazing was implemented in slightly and moderately degraded grassland during the summer grazing period to control grazing intensity. The grassland was fenced and divided into paddocks and then used in rotation.

Grazing prohibition was mainly carried out in severely degraded grassland, such as in the Xilin Gol and Ordos areas. Grazing was forbidden and the objective was to encourage grassland recovery.

User moving (migration) was imposed on severely degraded grassland (e.g. Ordos area), with the aim of improving the living conditions of local residents through migrating to a more favourable area and running more profitable enterprises.

Livestock-rearing control refers to the farming area in the farming-pastoral zone, where limited numbers of livestock could be grazed by the administrative authority, and the objective of the policy was to lower the impact of grazing. Xilin Gol is the typical area influenced by this measure. The number of livestock is limited according to the carrying capacity of local grassland, and nomadism is prohibited and replaced by rearing indoors.

2.2.3 Research design and data collection

A survey of 209 households was conducted from June to July 2010. Three typical areas were selected in IMAR on a transect from north-east to south-west (Figure 2.1). Criteria for selection of the areas included (1) representativeness of grassland types, which included meadow steppe (Hulun Buir), typical steppe (Xilin Gol) and semi- desert steppe (Ordos); (2) grassland restoration policies having been implemented; and (3) representing the typical production activities of each of the areas. In Hulun Buir, the principal activity is traditional animal husbandry, with livestock rearing as the main land use with 89% of the population involved in livestock rearing, and arable land accounts for only 0.9% of the total land area (Hulun Buir Statistics Bureau 2012). In Xilin Gol, 39% of the population lives on the steppe and arable farming and animal husbandry are predominant. Arable land accounts for only 2.2% of the total area but produces food for ~43% of the total population (Xilin Gol Statistics Bureau 2012). In Ordos, there is a combination of arable farming, animal husbandry with a range of grassland types, and mining with 15% and 18% of the surveyed populations relying on animal husbandry and arable farming, respectively, and over 31% of the population working in the mining industry and related services (e.g. transportation) (Ordos Statistics Bureau 2011a; 2011b).

Using a stratified random sampling method (Weber and Tiwari 1992), 10 villages were selected as the survey units, three in Hulun Buir, two in Xilin Gol, and five in Ordos to trace the basic consumption patterns of households. In each of the villages, we selected households randomly for interviews to obtain answers for our questionnaire. Over 65% of total households of each village was investigated as appropriate sample sizes based on the suggestion of Tabachnick and Fidell (2007) that a sample should be over 50% when the total households of the survey unit group are lower than 100. Because the survey was carried out using face-to-face interviewing of the respondents or having the respondents complete the questionnaires under the research group members‘ guidance, a high response rate of 90.5% was obtained.

27 Prior to the formal surveys, test surveys were conducted by using individual interviews and family group discussions with herders and other key informants, and the information collected in the test surveys guided the development of the formal questionnaire. The formal survey contained questions designed to obtain information regarding: (a) background information of households (information on available household characteristics, cultivation activities and other economic activities); (b) the consumption of food (agricultural crops and meat), fuel and water during the year prior (2010); and (c) the consumption of food (agricultural crops and meat), fuel and water around the year 1995 before the implementation of the grassland restoration policy (as recalled by the respondents). The respondents reported the variety and quantity for each category.

Quantities eaten included food from the respondent‘s own production and food purchased at markets. Out-of-home meals were not taken into consideration, as this survey was conducted in rural and underdeveloped areas where the occasional out of- home meal happens infrequently, perhaps one or two times per year on special occasions. Additionally, quantifying the amounts for out-of-home meals is very difficult due to the uncertainties in the amounts of materials used for a dish.

The same method was applied to estimate annual water and fuel consumption per capita. The survey collected the total cost or kilograms of water and fuel (e.g. bio-fuel, coal, electricity and gas) bought or gathered in a year for cooking, heating and other domestic uses, as estimated by respondents of each household. For each household visited, we asked the head of each household or a family member who was familiar with the household to answer the questions. The survey revealed that households could accurately recall their consumptions in the year before the survey and the main consumption patterns in 1995. We primarily used closed-ended questions, but added open-ended questions where there was an opportunity to expand on the topics during the interview.

2.2.4 Data analyses

The statistics software SPSS, Version 17.0 (SPSS Inc., Chicago, IL, USA) was used for data analysis. Specifically, the results of this chapter applied SPSS functions of frequency analysis and descriptive analysis, including mean values and percentages, for resource consumption and perceptions; used one-way ANOVA to examine significance levels between the three areas; and used ‗independent-sample t-tests‘ to identify significant differences of consumption between 1995 and 2010. Cluster analysis is the task of grouping a set of objects in such a way that objects in the same group (clusters) are more similar to each other than to those in other groups. Such analysis is a main task in exploratory data mining and a common technique for statistical data analysis that is used in many fields. In our research, the K-means clustering method was adopted to classify food consumption patterns in 1995 and 2010. For food, water and fuel consumption, We took the weight per capita as an approximation of unit for the sake of simplicity, which increases the comparability and recognition of trends in consumption.