An assessment of progress 2002 : the IUCN programme

In Mongolia’s rural administrative system, an aimag is a province, a soum is its district, and a bagh is the smallest unit within a soum, covering households and herder communities. Across 13 soums in Uvurkhangai, Dornogovi, Sukhbaatar and Tuv aimags, open grazing, weak coordination and limited land-use information contributed to pasture degradation. The Sustainable Land Management for Combating Desertification Project formed herder groups and co-management committees, introducing participatory planning, GIS mapping, rotational grazing, field monitoring and locally managed funds. Herders submitted pasture-use proposals at bagh meetings and helped implement annual soum plans. Seasonal grazing restrictions covered over 900,000 ha, while three plots in each target soum compared grazed and ungrazed vegetation. It established 109 herder groups, linked community knowledge with local government decisions, and created a replicable model for restoring and sustainably managing dry rangelands while supporting pastoral livelihoods.

The Arabian oryx became extinct in the wild in 1972 after decades of overhunting and habitat destruction. Its recovery required a long-term, multidimensional approach rather than a one-off, single release programme. Key measures included conservation-breeding; legal protection and enforcement; effective management of protected sites; phased reintroductions; and post-release monitoring. In Saudi Arabia reintroductions began in 1989 starting with Imam Saud Bin Abdul Aziz Reserve (Mahazat as Sayd PA) followed by Uruq Bani Marid in 1995 and several other Reserves later on. Recent estimates indicate that around 3,064 Arabian oryx live under wild and lightly managed conditions across 11 sites forming a coordinated network of populations across its historical range in Saudi Arabia, with the population showing an increasing trend (NCW 2026).This recovery demonstrates that extinction in the wild can be reversed when conservation-breeding, translocation and coordination amongst partners are integrated and sustained over the long-term.

The ENSURE Project combined Knowledge, Attitudes and Practices (KAP) assessments with household livelihood surveys to strengthen evidence-based SLM and landscape restoration in four Mongolian aimags. A 2019 baseline and 2022 follow-up assessed environmental knowledge, conservation practices, training participation, income and livelihood conditions; the 2022 livelihood assessment covered 376 households. Results refined awareness activities, capacity development, livelihood support and natural-resource management. By 2022, the combined knowledge score had risen from 3.09% to 7.0%, conservation practices by 12.8 percentage points, and training participation 2.7-fold. The approach complements biophysical land-degradation-neutrality monitoring by explaining social and livelihood factors that influence land-use decisions. It shows how repeated socio-economic evidence can support adaptive management, although surveys do not themselves measure ecological restoration.

Lesotho is a small, landlocked mountain kingdom entirely surrounded by South Africa. More than 80% of its land lies above 1,800 metres, making it the ‘Water Tower’ of Southern Africa and a vital source of freshwater for the region. Despite this, Lesotho’s water resources are under threat from soil erosion, overgrazing, and climate change. ReNOKA (“We are a river”) is Lesotho’s national programme and citizen movement for addressing these challenges and sustainably managing the country’s water and land resources. It supports local communities in restoring degraded catchments through a pioneering climate finance mechanism. The approach empowers community councils to implement their own nature-based solutions, including stone bunds, wetland fencing, and tree planting. These actions reduce erosion, improve water retention, and restored vegetation across degraded landscapes. 

Embedded in national systems, ReNOKA connects policy, people, and finance — securing Lesotho’s water and land resources for future generations.

In 2011, Snake Island (Kalumpang Island)  was declared a National Coastal and Marine Center for Research (NCMCR) through DENR Administrative Order (DAO) No. 2011-12 issued on November 3, 2011. The policy established the island as a model site for sustainable coastal and marine management and prohibited tourism and fishing activities. It also directed DENR bureaus and field offices to formulate the island’s operational plan and research agenda.

An initial resource inventory in 2016 recorded a mean hard coral cover (HCC) of 46.25%. However, a massive coral bleaching event later reduced HCC to 3.69%. In response, interagency biannual assessments were instituted to document recovery trends and identify disturbance indicators. The assessments integrated benthic surveys, fish visual censuses and seagrass and mangrove assessments in post-bleaching recovery surveys. With limited pressure from tourism and fishing, coral recovery was observed, with a nearly five-fold increase in HCC (16.89%) 10 years after the bleaching event.

The Elsentasarkhai living laboratory is a permanent platform for testing dryland restoration solutions before wider investment and replication. Established in 2011 on 45 hectares at Mongolia’s dry steppe-desert steppe transition, it combines field research, multi-year monitoring, demonstration, training and knowledge exchange. Trials address drought, strong winds, shifting sand and limited water through 1 x 1 m straw checkerboards, revegetation, windbreak forest belts and cultivation of 21 rare and endangered native tree and shrub species. A 10-hectare moving-sand trial stabilized sand for three to four years when barriers were combined with revegetation. Managed by the Institute of Geography and Geoecology, the station has continued beyond the original UNDP-supported project, hosted 40+ research activities, including five international collaborations, and provided learning for researchers, students, herders and officials. Its lesson is simple: test locally, monitor over time, learn openly and scale only what works.