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1. Introduction

Land degradation is one of the most serious global environmental issues of our time (Dregne, 1998; Reynolds and Stafford Smith, 2002). Land use activities are among the key drivers of land degradation worldwide. These activities shape the land surface and can induce substantial changes to natural phenomena (Steffen et al., 2007). Human activities are at the heart of several environmental challenges. Actually, Humans dominate, transform and modify ecosystems (Zika and Erb, 2009) to their own benefit, yet often at the expense of the global ecological patterns and processes. Sound soil and water conservation measures (Bou Kheir et al., 2006) are needed to mitigate the pervasive and disruptive impact of land degradation on sustainable natural resource management. Moussa et al. (2002) and Souchère et al. (2005) argue that a spatially distributed assessment of erosion risk is mandatory and must be performed before implementing any effective soil conservation measure. Other authors advocate for the use of geoindicators, which are aggregate and efficient proxies of surface processes in the assessment of land degradation (Berger, 1996; Berger, 1997; Dumanski and Pieri, 2000; Gupta, 2002; Hammond et al., 1995; Morton, 2002; Zaz and Romshoo, 2012; Zuquette et al., 2004). Moreover, recent advances in scientific computing, remote sensing, and GIS technologies enable cheap and fast processing of large and complex datasets. This may help alleviate a major practical challenge inherentin implementing erosionmodels (Merritt et al., 2003), as they are data-intensive and time consuming (Vrieling et al., 2006). However, accessing clean data is a pivotal issue in Sub-Saharan African countries that often lack of wellfunctioning data collection systems. Interestingly, Van Rompaey and Govers (2002) show that when data are scare and/or unreliable, simple erosion models provide a more accurate assessment than complex ones. Complex erosion models are often adequate for small scale applications, but loose tractability for large scale implementations, as pointed out by Kirkby et al. (1996), Schoorl et al. (2000), Yair and Raz-Yassif, (2004), among others. Moreover, less data-consuming methods seem more appealing to decision makers (Renschler and Harbor, 2002). According to Bayramin et al. (2003); ICONA (1991, 1997); Zaz and Romshoo (2012), the ICONA model is one of the easiest and flexible qualitative methods for assessing and mapping soil erosion risk. This model has been used by European Union (EU) countries and Mediterranean states (e.g., Turkey, Tunisia, Syria, and Egypt), as documented by Bayramin et al. (2003). In Benin, Atacora Mountain chain is of high ecological and biological value (Adomou, 2005). It has an exclusive vegetation type (the Synsepalum passargei-Broenadia salicina riparian community) and the two Beninese endemics Thunbergia atacorensis and Ipomoea beninensis (Akoègninou and Lisowski, 2004). Soil fertility loss, physical and chemical soil degradation are a few threats identified by many authors (Adegbidi et al., 1999; Mulder, 2000; Tente and Sinsin, 2005). Their harmful effects are potentially increased by steep slope, shallow soil, strong demographic pressures, and transborder transhumance from Sahelian countries such as Burkina Faso and Niger (Meurer, 1994). There is a narrow body of literature addressing global and detailed estimation of land alteration in Atacora Mountain. Tente and Sinsin (2005) investigate at the scope of some hills of Atacora Mountain and find that erosion reduces, on average, 8.6 cm/ ha/year of the soil thickness. The 2008 “National Self-assessment of Capacities to Enhance for the Management of Global Environment” report reveals that Atacora Mountain and its surrounding areas are characterized by slight, average and extreme degradation status (ANCR-GEM et al., 2008). This report is the most recent large scale assessment of land degradation in the study area. Nonetheless,this assessmentis rough and only relies on expert-folk opinions. This study aims at evaluating and determining the erosion risk status of Atacora Mountain and its surrounding areas using GIS and ICONA model.

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