Classification System Of Grasslands Research Articles

Study of Forage Quality of Grasslands on the Southern Margin of the Pannonian Basin

Since grasslands provide many ecosystem services, there are often different opinions on their management (e.g., agronomy, ecology, botany). Multidisciplinary research on this topic is therefore needed. This article focuses on the impact of ecological conditions, functional groups, ecological strategies, floristic composition (through habitat preference of species), major floristic gradients (presented as first two NMDS axes), and the management on forage quality. We estimated the forage quality using indicator values. All of the available vegetation plots in the region on wet and mesic meadows, managed pastures, and tall-herb meadow/pasture fringes on deeper or shallower soils (i.e., grasslands) were collected, organized in a database, and elaborated according to standard procedure. We used a widely accepted grassland classification system that uses floristic composition to define grassland types. Based on an NMDS ordination diagram and according to functional groups, ecological strategies, and habitat preferences (behavior of species) and management, we defined three major groups: mesic meadows (mowed), wet meadows (mowed), and pastures (grazed). We correlated all groups’ functional groups, ecological strategies, habitat preferences (behavior of species), major floristic gradients, management, and forage quality. We found that forage quality mainly depends on moisture conditions and that nutrients and grazing are less important. Within the grasslands under consideration, mesic meadows and mesic pastures have the highest forage quality.

Spatiotemporal distribution of grassland NPP in Gansu province, China from 1982 to 2011 and its impact factors.

The modified Carnegie–Ames–Stanford Approach (CASA) model based on the comprehensive and sequential classification system of grasslands (CSCS, a unique vegetation classification system) was used to determine grassland net primary production (NPP) in Gansu province from 1982 to 2011 and its spatio-temporal variability. The relationship between NPP and climate drivers was analyzed. The results showed that annual NPP of grasslands in Gansu province averaged 139.30 gC m-2 yr -1 during the study period. NPP decreased from southeast to northwest across the province. Grassland NPP showed an increasing trend during the period 1982–2011, and the increase rate over the whole period was 92.91%. The highest NPP appeared in summer with more precipitation and higher cumulative temperature conditions; while the lowest values existed in winter. The largest correlation coefficient was found between the average annual NPP and the average annual precipitation (r = 0.77), followed by annual NPP and solar radiation (r = 0.70) or NDVI (r = 0.69), Annual NPP had no significant correlation with annual cumulative temperature (>0°C) or moisture index (K-value). Thus, precipitation is the major controlling factor on the average annual NPP in Gansu grassland. Solar radiation and NDVI also have important effects on grassland NPP in Gansu. These results may provide basic information for sustainable development and utilization of grassland and for the improvement and protection of the ecological environment as well.

Role of grasslands in pest suppressive landscapes: how green are my pastures?

AbstractGrasslands are valuable non‐crop habitats in the world's agricultural regions, providing more than simply forage for grazing domestic and wild animals. They provide refuge and resources that support high levels of arthropod biodiversity, most importantly for natural enemies that provide vital biological control services to the surrounding cropped landscape. Ideally, farmers could manage and manipulate grasslands to boost their biocontrol services, although to achieve this, knowledge of the ecological function of these habitats is essential. Unfortunately, grasslands are often bundled together with other habitat types, such as scrubland and forest, under the label ‘non‐crop habitats’, and little is known about the contribution that these specific habitats make towards landscape pest suppression. While recent research has been investigating the importance of other non‐crop habitat such as native vegetation remnants, the contribution that grassland habitats may make towards landscape pest suppression remains a significant knowledge gap in biocontrol research. Here, the current understanding of grassland habitats as biological control service providers in the world's mixed farming systems is reviewed. Limited research into whether grassland habitats support natural enemies, and thus contribute biological control services to the adjacent surrounding cropped landscape, has returned inconclusive, even conflicting, results. Potential explanations for this inconsistency are explored, including the lack of studies, inadequate estimates of predator impact and the variety of different grassland habitat types studied, including their diversity in composition and management practices. Conclusions drawn from these studies are discussed, and suggestions for management are recommended, including increasing grassland floristic diversity, limiting intensive management practices and implementing weed control. Future research directions are proposed, along with the need to develop a universal grassland classification system for research, management and conservation purposes, using grassland type, floristic diversity and management practices as key axes for classification.

Spatio-temporal variations in vegetation types based on a climatic grassland classification system during the past 30 years in Inner Mongolia, China

As one of the most widespread vegetation types, grasslands play a key role in the terrestrial ecosystem. In China, grasslands are threatened by degradation due to both local human activities and climate change. To analyze the spatiotemporal variation in grassland vegetation under climate change conditions, this study analyzed the climate conditions over the past 30 years in Inner Mongolia, China. The changes in grassland vegetation types over the past 30 years were simulated by the Integrated Orderly Classification System of Grassland (IOCSG), which determined the grassland types by using the >0 °C annual accumulated temperature (AAT0) and moisture index (k-value). To validate the simulated results, the normalized differential vegetation index (NDVI) was used to reflect the real vegetation variations. Over the past 30 years, the AAT0 significantly increased at a rate of 109.8 d °C /decade, and the k-value showed a nonsignificant decrease at the regional scale. The simulated results by the IOCSG showed that the areas of forest and forest steppe and meadow steppe in the past 10 years (2005–2015) decreased by 14.9% and 30.8%, respectively, compared with those in the period from 1986 to 1975. However, the areas of typical steppe and desert and semidesert showed increases of 45.6% and 5.4%, respectively, over the past 10 years compared to those from 1986 to 1975. A typical steppe area of 2.46 × 104 km2 has changed to desert and semidesert, and a meadow steppe area of 10.98 × 104 km2 has changed to typical steppe in the past 30 years. In most regions where the grassland types were predicted to have changed, the annual maximum value of NDVI (amNDVI) showed the same variation trend as the results from the IOCSG. During the study period, the amNDVI significantly decreased in the regions where the vegetation types changed from forest and forest steppe to meadow steppe, while it significantly increased where the vegetation types changed from meadow steppe to forest and forest steppe. However, in some areas, for example, the midland of Ordos, the amNDVI showed the opposite trend with the predicted results, which may have been due to the effects of human activities. Overall, grassland degradation in Inner Mongolia was predicted by the IOCSG, and the results were in accordance with the NDVI variations, which is of great significance for better understanding the grassland conditions under a changing climate.

Estimating net primary production of natural grassland and its spatio-temporal distribution in China

The net primary production (NPP) of grassland largely determines terrestrial carbon (C) sinks, and thus plays an important role in the global C cycle. Comprehensive and sequential classification system of grasslands (CSCS) is a unique vegetation classification system (mainly for grassland) that is dependent on quantitative measurement indices [>0°C annual cumulative temperature (Σθ) and moisture index (K-value)]. Based on the relationship of the quantitative classification of CSCS and grassland NPP, a modified model of Carnegie–Ames–Stanford Approach (CASA) was used to predict the grassland NPP and its temporal and spatial distribution in China from 2004 to 2008. The scatter plot of the estimated NPP and the observed NPP showed that the estimated data can be accepted with correlation coefficient of 0.896 (P<0.05). The average annual NPP of grassland from 2004 to 2008 in China ranged from 443.23 to 554.40gCm−2yr.−1. The NPP also showed spatial–temporal variations. There existed an increasing trend of NPP from the northwest to southeast due to the zonal distribution of vegetation. From the trend of monthly variations, it can be drawn that the NPP accumulation primarily occurred between April and October. The average NPP over seven months from April to October was 482.19gCm−2, or about 88.78% of the annual total. The spatial–temporal trend suggests the importance of water and thermal regimes in determining the grassland NPP (i.e. water and thermal are key limited factors for the grassland production), which is also confirmed by a cluster analysis. The mean annual NPP and the total annual NPP differed significantly among grassland classes corresponding with different Σθ and K-value. The results demonstrate that the grassland NPP and the classes/super-classes in CSCS achieve the optimum coupling.

Preliminary Research on Grassland Fine-classification Based on MODIS

Grassland ecosystem is important for climatic regulation, maintaining the soil and water. Research on the grassland monitoring method could provide effective reference for grassland resource investigation. In this study, we used the vegetation index method for grassland classification. There are several types of climate in China. Therefore, we need to use China's Main Climate Zone Maps and divide the study region into four climate zones. Based on grassland classification system of the first nation-wide grass resource survey in China, we established a new grassland classification system which is only suitable for this research. We used MODIS images as the basic data resources, and use the expert classifier method to perform grassland classification. Based on the 1:1,000,000 Grassland Resource Map of China, we obtained the basic distribution of all the grassland types and selected 20 samples evenly distributed in each type, then used NDVI/EVI product to summarize different spectral features of different grassland types. Finally, we introduced other classification auxiliary data, such as elevation, accumulate temperature (AT), humidity index (HI) and rainfall. China's nation-wide grassland classification map is resulted by merging the grassland in different climate zone. The overall classification accuracy is 60.4%. The result indicated that expert classifier is proper for national wide grassland classification, but the classification accuracy need to be improved.

Evaluation of six methods to predict grassland net primary productivity along an altitudinal gradient in the Alxa Rangeland, Western Inner Mongolia, China

AbstractAccurately estimating grassland net primary productivity (NPP) plays an important role in the study of global carbon budgeting. The six different methods (Miami Model, Schuur Model, Chikugo Model, Beijing Model, Synthetic Model and Classification Indices‐based Model) were compared in terms of their performance in predicting grassland NPP with NPP derived from field‐observed data at eight study sites along an altitudinal gradient in the Helan Mountain range and the surrounding desert in the Alxa Rangeland, Western Inner Mongolia, China. One hundred and twenty plot‐based NPP sets from the eight study sites were used, which were obtained from 2003 to 2005, within areas classified as alpine meadow, cold temperate‐humid montane meadow, cool temperate‐subhumid meadow steppe, cool temperate‐semiarid temperate typical steppe and cool temperate‐arid temperate zonal semi‐desert according to the Integrated Orderly Classification System of Grassland (IOCSG). The relative high model efficiency in predicting grassland NPP using the Classification Indices‐based Model and Chikugo Model indicates that these models outperform others. On the basis of input data requirements and the number of free parameters involved in each model, the Classification Indices‐based Model was found to be the best choice for the given grassland classes. The results presented in this study were not only specific to this region, but more importantly, were specific to the given grassland classes according to the IOCSG approach, which can be scaled up from plots to estimate landscape‐scale effects.

Spatio-temporal dynamics on the distribution, extent, and net primary productivity of potential grassland in response to climate changes in China

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.

Comprehensive and sequential classification system of grasslands in Gansu province based on remote sensing

Comprehensive and sequential classification system of grasslands in Gansu province based on remote sensing

Modelling global-scale potential grassland changes in spatio-temporal patterns to global climate change

Grassland is one of the most widespread vegetation types worldwide and plays a significant role in global carbon cycling. Understanding the sensitivity of grassland to climate change and the effect of climate changes on the grassland ecosystems is a key issue in global carbon cycling. One of the goals of this study was to evaluate the three net primary productivity (NPP)–climate models, i.e. 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. In this research, changes in the spatial pattern of global potential grassland from recent past (1950–2000) to future (2001–2050) A2a scenario were analysed with the integrated orderly classification system of grassland (IOCSG) approach in a Geographic Information System (GIS) environment. NPP was evaluated with the classification indices-based model. Results indicate that under recent past climatic conditions, the main parts of global grassland are the savanna and tundra and alpine grassland and will be converted into the savanna, steppe and semi-desert grassland in A2a scenario. As a whole, areas of grassland will increase by 31.76 million hectares. The classification indices-based model estimated a 12.40% increase of total NPP in grassland from recent past to A2a scenario. It will impose a new issue for future grassland researches to support sustainable development and to provide action relevant knowledge to meet the challenge of climate change.

The ecological status of grasslands on lowland farmlands in western Ireland and implications for grassland classification and nature value assessment

The identification and protection of High Nature Value (HNV) farmland is an objective of the European Rural Development policy which has yet to be met by Member States. Remote sensing and models based on farm statistics are commonly used to identify HNV farmland. Use of datasets such as Corine Landcover Classes is widespread but it has been acknowledged that such datasets can significantly overlook fine-scale biodiversity features. In countries where farmland is predominantly grass-based, there is an added difficulty in distinguishing between grassland types without undertaking field-scale survey work. This study was conducted to improve our knowledge of grassland biodiversity on lowland farms with a view to helping assess their potential conservation value in a High Nature Value farmland context. We analysed the grassland species composition of 603 fields on 32 lowland farms and investigated their relationship to management, ecological and spatial descriptors. Non-metric Multidimensional Scaling (NMS) and Multi-Response Permutation Procedure (MRPP) analyses of the grasslands and their ecology on these farms revealed a continuum between semi-natural and improved agricultural grasslands, including an intermediate Semi-Improved Grassland type. This gradation from improved to semi-natural grassland highlights the biodiversity variation that occurs on farms that are frequently considered to be of low nature value. Statistical analyses showed that management practices, and especially soil fertility, were most strongly associated with grassland type. The detailed description of the grasslands that occur on these lowland farms has the potential to provide a better assessment of the overall nature value of a farm, potentially aiding the identification of Type 2 High Nature Value farmland. Before this can be achieved, however, there is a need to amend the grassland classification system used in Ireland in order that intermediate semi-natural grassland assemblages can be identified at the field level. Field surveys are necessary for this level of detail.

A grassland classification system and its application in China

China possesses vast grassland resources that include alpine meadow, tundra, steppe and desert. It is, therefore, desirable to establish a grassland classification system that involves the formative factors contributing to this diversity. This paper reports a grassland classification system called the Integrated Orderly Classification System of Grassland (IOCSG), which was formulated through grouping or clustering units with similar properties. The IOCSG involves a hierarchy of three classification levels. At the first level, grasslands are grouped into classes according to an index of moisture and temperature. At the second level, grasslands are differentiated as subclasses by the edaphic conditions. At the third level, grassland types within a subclass are distinguished by vegetation types. Under the IOCSG, seven thermal zones and six humidity zones have been identified and used to differentiate grassland classes. The IOCSG recognises 42 grassland Classes, of which 41 are present in China.

Grassland classification and evaluation of grazing capacity in Naqu Prefecture, Tibet Autonomous Region, China

Grassland animal husbandry is an important component of the regional economy in Naqu Prefecture of Tibet. A grassland survey was initiated to specify biological and socio‐economic characteristics of animal husbandry. The information from the survey was used to classify grassland resources and to evaluate grazing capacity. Based on the China Grassland Classification System, the grasslands in Naqu Prefecture were classified into 4 classes, 10 groups, and 32 types. The assessment of grazing capacity indicated that the grasslands in the eastern counties of the temperate wet zone and some counties in the central and western parts are severely overgrazed, which has resulted in serious grassland deterioration. Clarifying the ownership of the grazing lands should be the top priority for the government if a policy of sustainable development of grassland animal husbandry in Naqu Prefecture is to be adopted.