Spatio-temporal dynamics on the distribution ... - CSIRO Publishing

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The Rangeland Journal, 2013, 35, 409–425 http://dx.doi.org/10.1071/RJ12024

Spatio-temporal dynamics on the distribution, extent, and net primary productivity of potential grassland in response to climate changes in China Huilong Lin A, Xuelu Wang A, Yingjun Zhang B,C, Tiangang Liang A, Qisheng Feng A and Jizhou Ren A A

State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou City, 730020, P. R. China. B Department of Grassland Science, China Agricultural University, Beijing 100193, China. C Corresponding author. Email: [email protected]

Abstract. Net primary productivity (NPP) of grassland is one of the key components in measuring the carrying capacity of livestock. Not only are grassland researchers concerned with the performance of NPP simulation models under current climate conditions, they also need to understand the behaviour of NPP–climate models under projected climatic changes. One of the goals of this study was to evaluate the three NPP–climate models: the Miami Model, the Schuur Model, and the Classification Indices-based Model. Results indicated that the Classification Indices-based Model was the most effective model at estimating large-scale grassland NPP. Both the Integrated Orderly Classification System of Grassland and the Classification Indices-based Model were then applied to analyse the succession of grassland biomes and to measure the change in total NPP (TNPP) of grassland biomes from the recent past (1950–2000) to a future scenario (2001–2050) in a geographic information system environment. Results of the simulations indicate that, under recent-past climatic conditions, the major biomes of China’s grassland are the tundra and alpine steppe, and steppe, and these would be converted into steppe and semi-desert grassland in the future scenario; the potential grassland TNPP in China was projected to be 0.72 PgC under recent-past climatic conditions, and would be 0.83 Pg C under the future climatic scenario. The ‘safe’ carrying capacity of livestock that best integrates a wide range of factors, such as grassland classes, climatic variability, and animal nutrition, is discussed as unresolved. Further research and development is needed to identify the regional trends for the ‘safe’ carrying capacity of livestock to maintain sustainable resource condition and reduce the risk of resource degradation. This important task remains a challenge for all grassland scientists and practitioners. Additional keywords: Classification Indices-based Model, Integrated Orderly Classification System of Grassland (IOCSG), Miami Model, model comparisons, NPP–climate relationships, Schuur Model. Received 29 April 2012, accepted 25 July 2013, published online 3 September 2013

Introduction Grasslands account for 41.7% of land area in China (Ren et al. 2008). Traditionally, China’s grasslands have been economically productive areas and have provided important natural resources for the nation, including meat, milk, wool, and animal pelts (Kang et al. 2007). Today China’s grasslands face increasing pressures. The growing human population of China has led to an increased demand for grassland products, which has caused the carrying capacity of some grassland ecosystems to be exceeded. Over onethird of the total grassland area has experienced some level of degradation (Akiyama and Kawamura 2007), and around 27.3% of this area has experienced desertification (Wang et al. 2008). This has become a widespread concern, as degraded grasslands result in frequent dust storms that affect air quality in the northern Journal compilation Australian Rangeland Society 2013

temperate areas of China (Zheng et al. 2006). In addition, climate change has been identified as having far-reaching implications for the world’s grasslands (Zheng et al. 2006; Harle et al. 2007; Henry et al. 2007; Howden et al. 2008; McKeon et al. 2009; Zhang et al. 2011). The IPCC (2007) states that climate change will present challenges for future grassland use by livestock and other grazers, and for the resulting public policy designed to manage grasslands. Given their vast area and diversity of grassland classes, China’s grasslands play an important role in both regional and global carbon cycling (Ni 2004a). In order to effectively manage China’s grassland ecosystems and maintain their sustainability, a deeper understanding of how these ecosystems will respond to growing pressures is needed. Largewww.publish.csiro.au/journals/trj

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scale analysis and modelling is needed to develop a better grasp of the spatial distribution of grasslands, their productivity, and potential variations in response to climatic changes (Melillo et al. 1993; Scurlock et al. 2002; Del Grosso et al. 2008; Liang et al. 2011, 2012). Grassland classes and their distribution patterns can be shown to correspond with certain climatic types. Thus, the climatic zone can be used to predict grassland classes and their distribution, or the reverse. A grassland classification system, named the Integrated Orderly Classification System of Grassland (IOCSG), uses factors of precipitation, temperature, and humidity to classify grassland diversity (Ren et al. 2008). The theory behind the IOCSG has been developed in the last 60 years since it was first put forward in the 1950s, and it has achieved widespread use in China (Ren et al. 2008; Liang et al. 2011, 2012). Since it classifies grasslands according to climatic factors, IOCSG can be used to predict climate-linked spatial or temporal succession from an original class to a new class as these climatic factors change. Net primary productivity (NPP) can be used as an indicator of the capacity of the grassland ecosystem to accumulate carbon and to support grazing animals. The ability to accurately estimate grassland NPP is critical to the understanding of grassland dynamics (Wang et al. 2007). The precise evaluation of NPP, at a regional or global scale, however, is difficult. Since the direct measurement of grassland NPP is tedious and not practical for large areas, it is appropriate to use computer models, calibrated with existing data, to study the spatial and temporal variations of grassland NPP (Scurlock et al. 2002). In developing countries, mechanistic models cannot be used, because long-term data required for the parameterisation of these models are not available (Zhang et al. 2011), but the required data are available for NPP–climate models, and these models have been shown to yield ‘reasonable estimates’ of global patterns of productivity (Adams et al. 2004; Zaks et al. 2007; Del Grosso et al. 2008; Zhang et al. 2011). Since climate is a major driver of variation in NPP (Melillo et al. 1993), a growing amount of research effort (Lobo and Rebollar 2010) has been focussed on developing NPP–climate relationships (Kira 1945; Thornthwaite 1948; Whittaker 1951; Holdridge 1967; Lieth and Box 1972; Lieth 1972, 1973, 1975; Mather 1974; Uchijima and Seino 1985; Uchijima 1988; Sala et al. 1988; Friedlingstein et al. 1992; Prentice et al. 1992; Zhu 1993; Neilson 1995; Zhou and Zhang 1995, 1996; Lugo et al. 1999; Zhou et al. 2002; Zaks et al. 2007; Del Grosso et al. 2008; Lin 2009; Lin et al. 2012; Lin and Zhang 2013). The Miami Model (Lieth 1972), the Schuur Model (Schuur 2003), and the Classification Indices-based Model (Lin 2009; Lin et al. 2012; Lin and Zhang 2013) are examples of such efforts. A simultaneous comparison of these models, using a consistent classification system, has not been attempted in China. In order to effectively model climate change impacts on grassland distributions and associated NPP, it is important to understand climate dynamics in the recent past (1950–2000), as well as climatic predictions for the future (2001–2050). Hence, in this paper we: (i) compare NPP estimated using the Miami Model (Lieth 1972), Schuur Model (Schuur 2003), and Classification Indices-based Model (Lin 2009; Lin et al. 2012; Lin and Zhang 2013) with NPP derived from measurements at 3767 sites in China, to evaluate the applicability and reliability of the three NPP-climate models; (ii) simulate the spatial distribution patterns

H. Lin et al.

and associated NPP characteristics of China’s potential grassland under recent past and a future climate scenario using the IOCSG approach and NPP–climate models; and (iii) estimate future trends in response to climate change in the first half of the 21th Century by comparing the variation in distribution of NPP with the potential total NPP (TNPP) of grassland between the recent past and the projected future climate scenario. Materials and methods Data acquisition Climate data Two different Chinese climatic datasets were used in this study. The first was the monthly precipitation and mean temperature grid dataset at a spatial resolution of 30 arcseconds (i.e. ~1 km) for Chinese land areas over 50 years from 1950 to 2000, generated using the program package ANUSPLIN Version 4.3 (Centre for Research in Engineering Science, Australian National University, Canberra) (Hutchinson 2004; Hijmans et al. 2005). This dataset was chosen because the method used to create it has been widely applied (New et al. 1999, 2002) and has performed well compared with other multiple interpolation techniques (Jarvis and Stuart 2001; Hijmans et al. 2005). The second dataset was the monthly precipitation and mean temperature prediction (also at a resolution of 30 arcseconds) from 2001 to 2050 under the A2a scenario (see below), which was simulated by Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO) and is available through the website www.worldclim.org/futdown.htm (Evangelista et al. 2011). The A2a scenario takes into consideration the land-use changes (i) high rate of population growth, (ii) slow technological change, and (iii) increased energy use, and describes a highly heterogeneous future world in the light of regionally oriented economies. The gridded baseline climate (recent past) and gridded future (2000–2050) A2a climate scenario were used to estimate changes in mean annual temperature and mean annual precipitation (Fig. 1). Observed NPP database A large reference dataset (n = 3767) of observations of grassland NPP with paired climatic variables was compiled for this study as surveyed by The National Animal Husbandry and Veterinary Service of Ministry of Agriculture of the People’s Republic of China from 2004 to 2005. Total observed NPP dataset from the 3767 sites was the sum of above- and below-ground NPP following standard methods (Long et al. 1989; Ren 1998; Scurlock et al. 2002) and it was based on averages of five duplicated plots at each sampling site. Locations of the 3767 sampling sites were plotted using their associated geographic coordinates, and these sites are shown in Fig. 2. Integrated Orderly Classification System of Grassland approach The IOCSG is based on growing degree-days (GDD) and a moisture index (K value). The GDD is required to drive photosynthetic reactions (Bonan 2002) and denotes the length and thermal properties of the growing season (Cramer and Solomon 1993; Zaks et al. 2007). The GDD is defined as the mean of positive unit-period temperatures with the substitution

Spatiotemporal dynamics on grassland in China


N

(a)

Change in MAT (°C) 0.7–1.9 2–3.9 1000 500 4–5.9

0

1000 km

(b)

N

Change in MAP (mm) –500–0 0–500

1000 500

0

1000 km

Fig. 1. Maps of changes under the future climatic A2a scenario (1950–2000) relative to the average for 1950–2000 for (a) mean annual temperature (MAT) and (b) mean annual precipitation (MAP) in China.

0 200 400

800

1200 Kilometres

Fig. 2. Locations of the observation sites of grassland net primary productivity.

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of zero for all unit-period values below 08C. The calculation formula of GDD was expressed as follows:

1300 1 8

Three NPP–climate models to estimate NPP In this study, three methods were compared using the datasets: (1) the Miami Model (Lieth 1972, 1973, 1975), (2) the Schuur Model (Schuur 2003), and (3) the Classification Indices-based Model (Lin 2009; Lin et al. 2012; Lin and Zhang 2013). Miami Model The first and most well-known model that relates NPP to precipitation and temperature is the Miami Model (Lieth 1972, 1973, 1975). The Miami Model has served as a baseline for three comparisons (Adams et al. 2004; Zaks et al. 2007; Del Grosso et al. 2008). As an empirical model, it links NPP (g dry matter (DM) m–2 year–1) to annual mean temperature (T, 8C), and annual precipitation sum (P, mm), and is constructed as follows: ð3Þ

NPPT ¼ 3000=ð1 þ e1:3150:119T Þ with NPPP ¼ 3000ð1 e0:000664P Þ Schuur Model Schuur (2003) used exponential functions for mean annual temperature (MAT) and MAP to estimate NPP (Mg C ha–1 year–1), which is defined as the minimum value predicted by the two equations. The main difference between the Miami

35

42

1.2

1.5

VII. Tropical

2.0 id

28

m

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VI. Sub-tropical

41

id

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34

F. Pe r-h u

id -a r tra

27

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0.3 Ex

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V. Warm

40

um

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IV. Warm temperate

39 33

id

13

32 26

H

25 19

m

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E.

6

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III. Cool temperate

38

-h u

8000

17 24 31

rid

6200

II. Cold temperate

ub

4

ð2Þ

The values of K are ascribed to six humidity grades as follows: 2.0 in per-humid. The IOCSG recognises 42 classes and allows any vegetative type to be described within the system (Fig. 3) (Ren et al. 2008; Liang et al. 2011, 2012; Lin et al. 2012). To reflect more explicitly the spatial distribution patterns of potential biomes at a large scale, and for ease of comparison and application, the 42 classes of IOCSG were merged into Grassland and Forest categories. The Grassland category consists of seven grassland super-class groups (biomes): tundra and alpine steppe, cold desert, semidesert, steppe, temperate humid grassland, warm desert, and savanna. The Forest category includes temperate forest, subtropical forest, and tropical forest (Table 1).

NPP ¼ minðNPPT ; NPPP Þ

3 10

A.

K ¼ MAP=0:1GDDðunitlessÞ

3700

5

2000 I. Frigid

2300

5300

1600

36

2 9 16 30 37

i-a

where Ti is the daily mean temperature. Values of GDD were ascribed to seven thermal zones as follows: 80008C in the tropical zone. A moisture index (K value) is the ratio of mean annual precipitation (MAP) to GDD, which provides an index of biological humidity conditions:

GDD

i¼1

1200

D. S

ð1Þ

em

maxð0; Ti Þðdegree-daysÞ

rid

365 X

800

C. S

GDD ¼

MAP (mm) 400

0

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K = MAP/0.1 × GDD

Fig. 3. Index chart for determining class in the Integrated Orderly Classification System of Grassland (IOCSG). The division of classes is based on a set identified along the axes of growing degree-days (GDD), mean annual precipitation (MAP), and K value. The class name is made up of three components of thermal gradient, humidity gradient, and dominant vegetation. IA 1, Frigid–extra-arid frigid desert, alpine desert; IIA 2, cold temperate–extra-arid montane desert; IIIA 3, cool temperate–extra-arid temperate zonal desert; IVA 4, warm temperate–extra-arid warm temperate zonal desert; VA 5, warm–extra-arid subtropical desert; VIA 6, subtropical–extra-arid subtropical desert; VIIA 7, tropical–extra-arid tropical desert; IB 8, frigid–arid frigid zonal semi-desert, alpine semi-desert; IIB 9, cold temperate–arid montane semi-desert; IIIB 10, cool temperate–arid temperate zonal semi-desert; IVB 11, warm temperate–arid warm temperate zonal semi-desert; VB 12, warm–arid warm subtropical semi-desert; VIB 13, subtropical arid subtropical desert brush; VIIB 14, tropical arid tropical desert brush; IC 15, frigid–semi-arid dry tundra, alpine steppe; IIC 16, cold temperate–semi-arid montane steppe; IIIC 17, cool temperate–semi-arid temperate typical steppe; IVC 18, warm temperate–semi-arid warm temperate typical steppe; VC 19, warm–semi-arid subtropical grasses–fruticous steppe; VIC 20, subtropical-semi-arid subtropical brush steppe; VIIC 21, tropical–semi-arid savanna; ID 22, frigid–subhumid moist tundra, alpine meadow steppe; IID 23, cold temperate subhumid montane meadow steppe; IIID 24, cool temperate–subhumid meadow steppe; IVD 25, warm temperate–subhumid forest steppe; VD 26, warm–subhumid deciduous broad-leaved forest; VID 27, subtropical–subhumid sclerophyllous forest; VIID 28, tropical–subhumid tropical xerophytic forest; IE 29, frigid–humid tundra, alpine meadow; IIE 30, cold temperate–humid montane meadow; IIIE 31, cool temperate–humid forest steppe, deciduous broad-leaved forest; IVE 32, warm temperate–humid deciduous broad-leaved forest; VE 33, warm–humid evergreen-deciduous broad-leaved forest; VIE 34, subtropical–humid evergreen broad-leaved forest; VIIE 35, tropical–humid seasonal rain forest; IF 36, frigid per-humid rain tundra, alpine meadow; IIF 37, cold temperate per-humid taiga forest; IIIF 38, cool temperate per-humid mixed coniferous broad-leaved forest; IVF 39, warm temperate per-humid deciduous broad-leaved forest; VF 40, warm–per-humid deciduousevergreen broad-leaved forest; VIF 41, subtropical per-humid evergreen broad-leaved forest; VIIF 42, tropical–per-humid rain forest.

Model and Schuur Model is that the Schuur precipitation equation shows NPP peaking at 2200 mm and then gradually declining as precipitation increases up to 8000 mm. The model equations are:



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Table 1. Relationships between super-classes (biomes) and classes according to the IOCSG approach Class name and code refer to Fig. 3 Name of super-class group (biome)

Corresponding class code

Tundra and alpine steppe Cold desert Semi-desert Steppe Temperate humid grassland Warm desert Savanna Forest, including temperate forest, subtropical forest, and tropical forest

IA 1,IB 8,IC 15,ID 22,IE 29,IF 36 IIA 2, IIIA 3, IVA 4 IIB 9, IIIB 10, IVB 11, VB 12 16IIC,17IIIC,18IVC,19VC,25IVD IID 23, IIID 24, IIE30 VA 5, VIA 6, VIIA 7 VIB 13, VIIB 14, Vic. 20, VIIC 21 VD 26, VID 27, VIID 28, IIIE 31, IVE 32, VE 33,VIE 34, VIIE 35,IIF 37, IIIF 38, IVF 39,V F40, VIF 41, VIIF 42

NPP ¼ minðNPPMAP ; NPPMAT Þ

ð4Þ

NPPMAT ¼ 17:6243=ð1 þ eð1:34960:071513MATÞ Þ with NPPMAP ¼ 0:005212MAP1:12363 =e0:000459532MAP Classification Indices-based Model The Classification Indices-based Model, dubbed the Holdridge life-zone system (Holdridge 1947) and IOCSG (Ren et al. 2008), was originally built using eco-physiological features and a regional evapo-transpiration model with the elimination of the common variable RDI (radioactive dryness index) by the chain rule. It results in a value for NPP (Mg DM ha–1 year–1) as a function of GDD and the moisture index (K value). Its ecological base is the IOCSG (Ren et al. 2008; Lin 2009; Liang et al. 2011, 2012; Lin et al. 2012; Lin and Zhang 2013). The method of integrating the classification indices of IOCSG to estimate the NPP is of the form: pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 2 3 NPP ¼ 0:1GDD L2 ðKÞ e 13:55þ3:17K 0:16K þ0:0032K ðK6 þ LðKÞK3 þ L2 ðKÞÞ= ðK6 þ L2 ðKÞÞ ðK5 þ LðKÞK2 Þ ð5Þ where: L(K) = 0.58802K3 + 0.50698K2 0.0257081K + 0.0005163874 Model evaluation To evaluate the predictive capability of the three models, three performance indicators were used. These were: (i) the mean bias error (MBE); (ii) the coefficient of variation of the root mean square error (RMSE); and (iii) forecast efficiency, E (Nash and Sutcliffe 1970). The mean bias error (MBE) is calculated from: MBE ¼

n 1X ðyi ^ yi Þ n i¼1

ð6Þ

where yi and ^y are observed and predicted NPP values of the ith site, respectively. The coefficient of variation of the RMSE is defined as the RMSE normalised to the mean of the observed values: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n X ð7Þ ðyi ^ yi Þ2 =n RMSE ð% valueÞ ¼ 100= y: i¼1

The symbols are as defined in Eqn 6; y is the average of measured NPP values. The prediction is considered excellent

if the RMSE 30% (Lin et al. 2008). The RMSE error (%) is the relative difference between the simulated and observed data, while the Nash–Sutcliffe model efficiency statistic (E) (Nash and Sutcliffe 1970) is regarded as a measure of the overall fit between observed and predicted values. The E value is the primary criterion of this paper. The model efficiency is defined by: X n n X ðyi ^ yi Þ2 ðyi yi Þ2 E¼1 ð8Þ i¼1

i¼1

The symbols are defined in the same way as in Eqn 7. IOCSG approach and NPP–climate model operation The IOCSG potential super-class group (biome) maps and its NPP were created and processed by ArcGIS software (ESRI Inc., Redlands, CA, USA) under the baseline climate (1950–2000) and future climatic A2a scenario (2000–2050) on average level. In the IOCSG biome (super-class group) classification map, lake water, permanent snow, and ice were excluded by using the Moderate Resolution Imaging Spectroradiometer-International Geosphere Biosphere Program (MODIS-IGBP) land-cover classification dataset in year 2001, found at http://earthdata.nasa.gov. The MODIS-IGBP land-cover classification dataset was used to calculate the area of potential grassland biome. The simulated NPP was an ideal potential value and land-use practices were not taken into account. The recent past map was chosen as a baseline, and used to compare with projected future climate to estimate trends in changes of class or grassland biome distribution, and grassland NPP distribution, in response to climate change. For the purpose of comparison with model simulations, observed or projected NPP was expressed in g carbon (C) m–2 year–1, where 1 g C is equivalent to 2.2 g oven-dry organic matter (Whittaker and Likens 1975; Ni 2003, 2004b). The total NPP has a unit of Tg C (1 Tg = 1012 g) for grassland biome or Pg C (1 Pg = 1015 g) for China. Results Comparison between NPP observations and projections Figure 4 plots projected grassland NPP v. observed NPP values. The bias that each model introduces is reflected by the relative


position of the 1 : 1 line. Data points above the 1 : 1 line are overestimates, whereas those under the 1 : 1 line are under-estimates. The results indicate that the Schuur Model under-estimated grassland NPP. The Miami Model over-estimated the large observations, while the Classification Indices-based Model overestimated as many data points as it under-estimated. Considering the slopes and intercepts of the regression of projected NPP by the Miami Model and Classification Indices-based Model v. observed NPP, the regression of the Miami Model has a slope similar to that of the Classification Indices-based Model (0.79 and 0.72, respectively), but the intercept of the regression line of the Classification Indices-based Model (64.19) is much closer to zero than is that of the Miami Model (113.40). The intercept differences suggested that the Miami Model over-estimated the high values of observed NPP, whereas the Classification Indices-based Model slightly under-estimated the high values. Comparison of the projected with the observed grassland NPP indicated that the RMSE value using the Classification Indices-based Model was 30 years) (McKeon et al. 2009). Predictions of NPP give a good estimate of the annual production of the grasslands, and therefore NPP can be used to balance the food demands of different kinds of livestock, allowing a calculation of the maximum carrying capacity of livestock that can be supported on the grasslands. This maximum carrying capacity of the grasslands should be used to develop appropriate policies of grazing control to preserve sustainable production on the grasslands (Zhang et al. 2011). The implication of climate change projections for ‘safe’ livestock carrying capacity remains an open question. Global warming causes changes in temperature and precipitation patterns, resulting in fluctuations in the NPP of grasslands. Several researchers have predicted that increases in temperature will cause a shift in the ratio of C4 to C3 grasses, with C4 increasing (Ren et al. 2008; Craine et al. 2010). This may decrease carrying capacity, since C4 grasses are generally considered to be of lower nutritive value to grazing animals than C3 species (Ehleringer et al. 2002). Moreover, increasing temperature and declining precipitation are also considered to decrease dietary crude protein, protein availability, and digestible organic matter


(Craine et al. 2010). In situations where climates become warmer and drier, grazing livestock are likely to encounter a protein-limited diet. The future declines in forage quality would also produce greater methane production from ruminant livestock (Craine et al. 2010). Thus, it is important to assess climate change projections in terms of their implications for future ‘safe’ livestock carrying capacity and the risk of resource degradation (McKeon et al. 2009). However, the ‘safe’ livestock carrying capacity of how best to integrate a wide range of factors, such as grassland classes, climatic variability, and animal nutrition, is unresolved. So further research and development is needed to identify the regional trends for the ‘safe’ livestock carrying capacity to maintain sustainable resource condition and reduce the risk of resource degradation. This important task remains as a challenge for all grassland scientists and practitioners. Acknowledgements The authors thank Kevin Kelsey and David Pfahler for their assistance in editing the English in the initial manuscript. Special thanks go to Dr John Milne who kindly helped the authors to amend the revised manuscript to an acceptable level of English. The research was funded by the National Natural Science Foundation of China (No. 31172250 and 30972135), the National Department Public Benefit Research Foundation (No. 200903060) and the project of the Humanities and Social Sciences Planning Fund of the Chinese Ministry of Education (No. 10YJAZH047). This paper is dedicated to Wanquan Lin, the father of Huilong Lin, who left this earth on 4 July 2013. He was a source of constant encouragement, wise counsel, and a remarkable example of hard work, integrity and a loving father.

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