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Lesson 2: Defining the land degradation neutrality (LDN) baseline, trends and drivers of land degradation


Scaling up land degradation neutrality target setting," a publication prepared by the The Global Mechanism of the UNCCD

Lesson 2: Defining the LDN baseline, trends and drivers of land degradation

Key lesson

All countries were able to establish land degradation neutrality (LDN) baselines, based on a small set of indicators, using national data and/or the global default data provided by the project. Land cover and land cover change, land productivity and soil organic carbon were the three indicators used by the pilot countries to set the land degradation baseline, identify potentially negative trends and formulate corrective measures. Upfront technical assistance and country-tailored advisory services proved relevant for overcoming data analysis challenges and barriers. The close collaboration between the Joint Research Center of the European Union (JRC) as the data provider and the country experts facilitated the interpretation of the data used to set the LDN baselines. Preparatory analysis of the data itself, together with comprehensive support to equip stakeholders to analyze the data, were key aspects in setting LDN baselines and analyzing land degradation trends and drivers.

Key takeaways

Some countries used global data as a main source, while others used national data in combination with – or as an alternative to – global data, according to their needs and capacities. In isolated cases, significant differences between global and national data were found, requiring a closer analysis of compatibility between the data sets and data collection systems. In mountainous regions, small island states and highly fragmented landscapes, countries found solutions to the limitations of spatial resolution of global data sets by introducing medium resolution data from alternative freely available sources.

By using the same three measurable indicators in all pilot countries, taken from the set of six progress indicators agreed by UNCCD, the project succeeded in finding a simple, practical way to consistently and uniformly assess the extent of land degradation and the potential for measures to halt and reverse land degradation.

The participant countries and the JRC both stated that the process of comparing national data sets with international ones was constructive and useful. The accuracy of LDN baselines can be increased through an iterative exchange between national and global data holders, as countries advance in the LDN target setting process.

New open source IT tools such as QGIS – a free open source geographic information system – provided the necessary computing capacity to handle the data analysis. Additionally, the quantitative data sets provided for each indicator facilitated the data analysis process and the articulation of LDN with national climate commitments. This data was extracted from global databases and pre- formatted according to the six Land Cover/Use categories recognized by the Intergovernmental Panel on Climate Change (IPCC; i.e. forest land, cropland, grazing land, wetlands, settlements and other land).

The LDN target setting pilot exercise allowed countries to assess where they stand in terms of not only land degradation but also other land-related resource monitoring, such as soil carbon stocks. The pilot initiative made the case for the multiple cross-sectoral benefits – in particular climate change – of assessing LDN and engaging in the LDN target setting process.

Country case studies

Assessing the LDN baseline by combining global default data with national data:

  • In Costa Rica, global data showed a 0.16 per cent increase in forest cover between 2000 and 2010, while national data suggested a larger increase of 4.7 per cent. Since the country team was confident with the quality of the national data, the preliminary target and the baseline estimations used the national data calculations. These calculations are thus better aligned with previous national assessments and land degradation-related policies. Using national data whenever possible was an effective way to engender country ownership.
  • Belarus faced the challenge of comparing default data on land cover, which was aggregated into six classes based on the IPCC land categories, to 14 additional national categories, in particular the “forest” category. However, the country team acknowledged that the remote sensing data provided by the project is currently the most cost-effective solution for regular monitoring of land cover change. Bhutan used national data whenever possible. As a mountainous country, the medium resolution remote sensing data from global sources was not sufficiently accurate. Global and national data were compared and discussed during stakeholder consultations and it was agreed that national data worked better. However, in the absence of national data for soil organic carbon (SOC), global data sets were used.

Close collaboration between data providers and the country for data processing and analysis:

  • Close contact between the JRC and Ethiopia and Namibia brought added value in the identification and problem solving of data assessments and country-specific data interpretation. Ethiopia's local team utilized the data and advice of the project team (UNCCD and JRC) to deepen the analysis of the productivity trends using statistical and geospatial analysis for different land use types, particularly for grasslands, croplands and wetlands. This exemplifies how country-tailored engagement and collaboration between data providers and country experts can yield more accurate LDN assessment results.
  • A specific example of close collaboration with JRC and Namibia was the interpretation of the land productivity trends in grasslands encroached by shrubs. There were lengthy discussions on this issue. The country's final decision was to consider the detected increase in net primary productivity (NPP) as a negative trend in this particular case. Although shrubs contribute to the restoration of soil carbon stocks and may allow re-opening areas to grazing in the future, they will have to be mechanically removed in order to plant herbaceous species as part of the restoration activities.

Learn more about the other LDN target setting lessons:

Lesson 1Lesson 3Lesson 4Lesson 5

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