Assessment methods & techniques in grazing land

Authors: Conceptión Alados, Erea Paz, Frederico Filliat, Maite Gartzia, Constantinos Kosmas, Ahmed El Aich

Editor's note 10Sep12: Source D711-8.2. To be reviewed in the context of the articles. Eleni - the text of this category introduction cannot be edited from the front end. You need to copy and paste into a word document, make the corrections using track changes and email it to Jane.

Methods and techniques used to assess land degradation in grazing lands are related with the main regional syndromes inherent to grazing land, which are associated to the loss of primary production evaluated through soil erosion or through changes in vegetation. One of the most important contemporary techniques to assess land degradation at regional scales is the use of satellite images (Chuvieco 2007). Satellite images permit monitoring and assessment of vegetation production, vegetation change, and soil loss at multiple spatial and temporal scales, determining the land degradation dynamics and the main factors that influence it. Landsat Tematic Mapper 5 image (TM) provides a valuable source of information available for all world regions since the 1980s. The main drivers of land degradation can be analyzed through different models such as generalized mixed models and spatial distribution models once the main indicators have been identified. Those models, relating field observations to environmental predictor variables based on statistically or theoretically derived methods can be used for assessing the main factors driving land degradation (Zuur et al. 2009).

Self-organized spatial patterns allow a more efficient use of water and nutrients in comparisons with random distributed vegetation (Reynolds et al. 2011). We can use vegetation spatial distribution as early indicator of critical transition to irreversible degradation (Rietkerk et al. 2004). Theoretical (Rietkerk et al. 2002; Lefever et al. 2009; Wainwright et al. 2011) and empirical studies (Alados et al. 2003; Barbier et al. 2006) have demonstrated the importance of spatial patterns as predictor of the state vulnerability of an arid ecosystem. An increase of autocorrelation of system components or the species of the pristine vegetation is expected in well preserved areas, while as degradation increases randomization of system components occurs (Alados et al. 2003).

In order to assess the influence of population, market, technology and policy on grazing land dynamics, the social network analysis has been suggested (Bodin and Crona 2009). The study of these networks results in valuable information when applying management strategies, especially in sparsely populated or dispersed areas.

NB: Each of the Articles below corresponds to one of the Task 2.2 deliverables.

2014-11-28 10:49:18