As of 2017, SGP has awarded over 3,800 small grants to land degradation projects in over 120 countries, many of which are in regions with extreme levels of poverty and food insecurity across Africa and Latin America. Africa, in particular, is experiencing the highest population growth of the developing world, while being exposed and vulnerable to the rising impact from climate change.

Sustainable Development Goal (SDG) target 15.3 commits countries to strive towards land degradation neutrality (LDN) by 2030. LDN requires reductions in land quality to be balanced by efforts to restore or rehabilitate degraded areas. However, decisions need to be made as to where to invest given limited budgets and the impossibility of targeting all degraded land. Any prioritisation process is likely to be controversial and needs to be underpinned by transparent, justifiable, repeatable decision processes.

As one of the few remaining countries with a robust, nomadic pastoral culture supported by extensive natural rangelands, Mongolia is well positioned to offer sustainable, rangeland-based goods and services to its citizens and to global consumers who place a premium on sustainable products. The primary challenge to sustainable livestock production in Mongolia is that rangeland health, the set of environmental conditions that sustain the productivity and biodiversity of rangelands is in decline in many areas.

Históricamente América Central es una región con importantes niveles de inequidad en la distribución y uso de los recursos naturales. Estos niveles de inequidades, partes de procesos económico socios y políticos (concentración de las cadenas de valores, agricultura de escala industrial apoyada por políticas publicas hacia los mercados de exportación), han generado algunas problemáticas relacionadas a la gobernanza de los recursos y su tenencia, tanto para la población como para los ecosistemas que sustentan en gran medida el crecimiento económico de los países de la región.

Históricamente América Central es una región con importantes niveles de inequidad en la distribución y uso de los recursos naturales. Estos niveles de inequidades, partes de procesos económico socios y políticos (concentración de las cadenas de valores, agricultura de escala industrial apoyada por políticas publicas hacia los mercados de exportación), han generado algunas problemáticas relacionadas a la gobernanza de los recursos y su tenencia, tanto para la población como para los ecosistemas que sustentan en gran medida el crecimiento económico de los países de la región.

Background: The Himalayan country of Bhutan is typically an agrarian country with about 57% of the people depending on agriculture. However, farming has been constrained by the mountainous topography and rapid changes in environmental variabilities. With climate change, agricultural production and food security is likely to face one of the biggest challenges of the twenty-first century.

Drylands cover over 40% of the earth's surface and support over 2 billion people, globally (Millennium Ecosystem Assessment, 2005). In East Africa alone, over 250 million people depend on drylands for their livelihoods (De Leeuw et al., 2014) and in Kenya, 70% of the total land area is classified as arid- and semi-arid (Batjes, 2004). Over the last several decades, an increasing and more sedentary human population has resulted in more pressure on these lands, and an expansion of agricultural production into marginal dryland areas that were traditionally rangelands.

Stipagrostis pungens is a perennial grass species which belongs to the Poaceae family. It is usually found with several erect culms, grows up to 1.5 m in height, and forms substantial tufts. It is a tall stiff glabrous grass with pungent leaves, and a C4 grass with sclerophyllous, spine-tipped, inrolled leaves, with sunken stomata. The root system extends laterally for a radius of 20 m or more. The roots are covered throughout their length by a sandy sheath, which is penetrated by the root hairs that occur throughout the entire length of the sheath.

Forests and other lands are essential for achieving climate and development ambitions. If appropriately leveraged, natural climate solutions can contribute upwards of 37 percent of cost-effective CO Indigenous Peoples and local communities are key to achieving such outcomes. 2 mitigation by 2030,1 and evidence shows
This report presents the most comprehensive assessment to date of carbon storage in documented community lands worldwide.
 

The conceptual framework for Land Degradation Neutrality (LDN) highlights that land degradation in developing countries impacts men and women differently, mainly due to unequal access to land, water, credit, extension services and technology. It further asserts that gender inequality plays a significant role in land-degradation-related poverty hence the need to address persistent gender inequalities that fuel women’s poverty in LDN interventions. This paper presents recommendations for moving towards a twin-agenda: gender equality and land degradation neutrality.

El presente estudio sobre las causas directas y subyacentes de deforestación y degradación de los bosques en Guinea Ecuatorial se ha realizado en el marco del proceso de desarrollo de la Estrategia Nacional REDD+ (EN-REDD+) y del Plan Nacional de Inversión REDD (PNI-REDD+). La EN-REDD+ y el PNI-REDD+ aspiran a guiar y apoyar los esfuerzos de todas las partes implicadas en la implementación de REDD+ , que tiene como objetivo reducir las emisiones, aumentar el secuestro de carbono en los bosques y mejorar la gestión y conservación de las reservas forestales de carbono.

Soil erosion is a natural process causing serious land degradation problems. In Tunisia, soil
erosion represents a serious environmental problem. The Rmel watershed located in the
Governorate of Zaghouan in north-eastern Tunisia and covering an area of 679 square
kilometers, suffers from this problem. It was thus selected to estimate annual soil loss using the
Revised Universal Soil Loss Equation (RUSLE), and geographic information system (GIS).
RUSLE model’s parameters (R, K, LS, C, and P) were derived from digital elevation model