Estimation of Pasture Productivity in Mongolian Grasslands
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Full Paper J. Agric. Meteorol. (農業気象) 66 (1): 31-39, 2010 Estimation of pasture productivity in Mongolian grasslands: field survey and model simulation Tserenpurev BAT-OYUN†, Masato SHINODA, and Mitsuru TSUBO (Arid Land Research Center, Tottori University, Hamasaka, Tottori, 680–0001, Japan) Abstract The Mongolian economy depends critically on products of range-fed livestock. Pasture is the major food source for livestock grazing, and its productivity is strongly affected by climatic variability. Direct measurement of pasture productivity is time-consuming and difficult, especially in remote areas of a large country like Mongolia with sparse spatial distribution of pasture monitoring. Therefore, model- ing is a valuable tool to simulate pasture productivity. In this study, we used a remote sensing-based production efficiency model, that is, the Carnegie Ames Stanford Approach (CASA) model, to estimate pasture productivity in three main vegetation zones of Mongolia; desert steppe, steppe and forest steppe. The present study aimed to explore climatic and grazing effects on grassland productivity in Mongolian grasslands during 2005-2007, using ground-based measurements and simulation model outputs. The ground measurements showed that grazing caused a significant decrease in measured aboveground phytomass and plant height. Simulation results demonstrated that the highest net primary productivity (NPP) of 83.2 gC/m2 and the lowest NPP of 12.6 gC/m2 over the growing season (April-September) occurred at Darkhan (steppe) and Mandalgovi (desert steppe), respectively. Moreover, the comparison of temperature and water stresses on pasture productivity indicated that water stress was stronger down- regulator of NPP, verifying that drought is the major concern of pasture production. Based on the comparison between the measurements and simulation, the ratio of aboveground NPP to belowground NPP in the Mongolian perennial grasslands was estimated as 1:1.5. Key words: Aboveground phytomass, Drought, Grazing, Net primary productivity, Water stress. Ni, 2003; Shinoda et al., 2007; Suzuki et al., 2007; 1. Introduction Zhang et al., 2005). Pastoral animal husbandry plays a key role in the Continuous monitoring and modeling of grasslands' Mongolia's economy, producing 40％ of gross domestic production can provide information on feed availability, product. Production and growth of livestock are greatly efficient management of livestock grazing, and natural dependent on the productivity of natural grasslands, hay preparation for winter; this information is very which cover 80％ of the country (Batima and Dagva- useful for herders and decision-makers. However, due dorj, 2000). Currently, about half the population is to the large area of Mongolia, it is difficult to monitor dependent on livestock production for their livelihood vegetation conditions widely, using field measurements (Johnson et al., 2006). The production of grassland at specific locations, which makes it difficult to gain is regulated by many factors, such as precipitation, a comprehensive understanding of how the grasslands temperature, solar radiation, soil nutrient availability, respond to factors such as climate, grazing, fire and and grassland utilization and management. Water stress management. Modeling is an essential approach to is the most limiting factor for dryland vegetation due to improve our understanding of the complex dynamics of the region's low precipitation and high evapotranspira- ecosystems such as grasslands. In Mongolia, progress tion (Miyazaki et al., 2004; Munkhtsetseg et al., 2007; in estimating and modeling the carbon cycle of grass- land ecosystems has been seriously limited due to the Received; April 13, 2009. scarcity of observational data for the parameterization Accepted; September 9, 2009. and validation of model and the substantial uncertain- †E-mail: [email protected] 31 J. Agric. Meteorol. (農業気象) 66 (1), 2010 ties in net primary productivity (NPP) estimations pasture productivity in Mongolia, using ground-based based on field measurements. measurements and the CASA simulation model based Nakano et al. (2008) measured CO2 fluxes both on satellite data. inside and outside of a site of drought experiment that 2. Materials and Methods was conducted at Bayan Unjuul in Mongolia (Shinoda et al., 2009), using a closed-chamber technique. They 2.1 Study sites demonstrated that the reduction of gross primary Fig. 1 illustrates four study sites; Mandalgovi production per unit aboveground biomass (GPP/AGB) (45.77°N, 106.28°E, desert steppe), Bayan Unjuul was caused by a combination of high vapor pressure (47.04°N, 105.95°E, steppe), Darkhan (49.47°N, deficit and low soil moisture. Bolortsetseg (2006) 105.98°E, steppe), and Bulgan (48.80°N, 103.55°E, analyzed the effects of climate change on AGB forest steppe) located in three vegetation zones of in Mongolia using the Century model (namely, a Mongolia. In general, during the growing season (April- model designed to simulate carbon, nutrient, and water September), precipitation decreases and temperature dynamics for different types of ecosystems including increases from the north to south, resulting in warmer grasslands). The sensitivity analysis indicated that AGB and more arid conditions in the south. The growing was more sensitive to changes in precipitation than season for the grasslands is very short and is limited those in temperature. by low temperature and precipitation. In particular, A number of models have been developed to simulate moisture availability is generally considered the most productivity of different ecosystems; among these important determinant for vegetation growth and the we selected the Carnegie Ames Stanford Approach large seasonal variations in precipitation are clearly (CASA) model, because it does not include complex reflected in plant growth (Gunin et al., 1999). ecophysiological parameters. The NPP component of Average (1995-2007) air temperature at the Institute the CASA model is based on the concept of radiation of Meteorology and Hydrology (IMH) station was use efficiency (RUE). In origin, Monteith (1972) 14.4℃ at Mandalgovi, 14.3℃ at Darkhan, 13.8℃ at explored that primary production was linearly related Bayan Unjuul and 11.8℃ at Bulgan during the growing to the amount of radiation received by plant stand. season. Average precipitation during the growing Later, this approach was theoretically and experimen- season was low at Mandalgovi (124 mm) and Bayan tally strengthened (Monteith, 1977); as a result, it has Unjuul (135 mm) and high at Darkhan (268 mm) and become the most commonly used method of analyzing Bulgan (291 mm). It should be noted that the study and modeling plant growth. It has been found that a years (2005-2007) were among the driest years during remote-sensing-based vegetation index is an indicator 1995-2007 for the study sites except for Darkhan. of absorbed photosynthetically active radiation (APAR) Plant composition varies among the different by green vegetation (Sellers, 1987; Sellers et al., 1992; ecosystems. The Bulgan and Darkhan sites are co- Goward and Huemmrich, 1992). In the CASA model, dominated by Stipa krylovii, Agropyron cristatum, the fraction of incoming photosynthetically active radia- Cleistogenes squarrosa, Leymus chinensis, and Carex tion (PAR) intercepted by green vegetation (FPAR) is spp., while Mandalgovi is co-dominated by Stipa estimated from normalized difference vegetation index (NDVI) data (Potter et al., 1993). Several studies have evaluated the performance of the CASA model for various regions. For instance, the CASA model successfully simulated the spatial and temporal distribution of NPP in northern China using Moderate Resolution Imaging Spectroradiometer (MODIS) data (Yuan et al., 2006), interannual variations of cropland NPP in the United States (Lobell et al., 2002), and maize production across China (Tao et al., 2005). However, there have been very few attempts to apply this kind of model to Mongolia. Given this background, Fig. 1. Vegetation zone map of Mongolia. Study site we aim to investigate climatic and grazing effects on locations are shown as black dots. 32 T. Bat-Oyun et al. : Estimation of pasture productivity in Mongolian grasslands krylovii, Cleistogenes squarrosa, Allium polyrrhizum, hereinafter called height. and Artemisia frigida (Bolortsetseg et al., 2002). Sixteen-day composite MODIS NDVI images with The Bayan Unjuul site is co-dominated by perennial the highest resolution (250 m×250 m) were obtained grasses such as Stipa krylovii, Agropyron cristatum, from the Earth Observation System data gateway and Cleistogenes squarrosa, by forbs such as Artemisia (https://wist.echo.nasa.gov/api/). The products were adamsii and Chenopodium aristatum, and by small corrected atmospherically, considering ozone absorp- shrubs (Caragana spp.) (Shinoda et al., 2009). tion and molecular scattering. 2.2 Data 2.3 Model description Data used for this study were provided by IMH The CASA production-efficiency model was used of Mongolia. The data included climate (temperature, to simulate NPP (gC/m2/month), which downregulates precipitation, and solar radiation), soil moisture, photosynthetic efficiency in response to adverse soil texture, and aboveground phytomass (AGP) temperature and dry soil conditions (Potter et al., 1993; in no grazing plots (fenced exclosures to prevent Field, 1995). The model is based on the RUE model livestock grazing) and grazing plots (unfenced areas) described by Monteith (1972, 1977). NPP is estimated