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The implementation of the United Nations Convention to Combat Desertification (UNCCD) needs agreed, scientifically sound and practical methodologies for monitoring and assessing the state and trend of land degradation as well as for monitoring the performance of management programmes. The lack of sufficient and integrated monitoring and assessment (M&A) has in the past been identified as a major constraint for combating desertification. Implementing efficient M&A programmes, however, requires careful analysis of the information needs of the different stakeholders, a clear scientific concept of the processes and drivers of land degradation and an analysis of the theoretical and practical possibilities for adequate M&A. This paper briefly analyses the information needs of diverse stakeholders, reviews existing M&A systems, and highlights key aspects for a scientifically sound approach to monitoring and assessment. Analysis of existing approaches shows that in spite of their relevance, standardised procedures for their implementation at operational scales are lacking. This is partly attributable to the lack of agreed and clear definitions, related difficulties in defining and hence in measuring the attributes chosen to represent land degradation and desertification and the varying degrees of paucity of field data. There is also the urgent need to better integrate bio-physical and socioeconomic aspects of desertification through a suitably robust scientific framework that links the drivers, processes and symptoms of desertification. Such a framework will allow for the identification of key variables to be monitored and will provide a basis for an improved forecasting and assessment of vulnerability, thereby providing highly important information for policy-and decision-making.

Accurate ground-based estimation of the carbon stored in terrestrial ecosystems is critical to quantifying the global carbon budget. Allometric models provide cost-effective methods for biomass prediction. But do such models vary with ecoregion or plant functional type? We compiled 15 054 measurements of individual tree or shrub biomass from across Australia to examine the generality of allometric models for above-ground biomass prediction. This provided a robust case study because Australia includes ecoregions ranging from arid shrublands to tropical rainforests, and has a rich history of biomass research, particularly in planted forests. Regardless of ecoregion, for five broad categories of plant functional type (shrubs; multistemmed trees; trees of the genus Eucalyptus and closely related genera; other trees of high wood density; and other trees of low wood density), relationships between biomass and stem diameter were generic. Simple power-law models explained 84-95% of the variation in biomass, with little improvement in model performance when other plant variables (height, bole wood density), or site characteristics (climate, age, management) were included. Predictions of stand-based biomass from allometric models of varying levels of generalization (species-specific, plant functional type) were validated using whole-plot harvest data from 17 contrasting stands (range: 9-356 Mg ha(-1) ). Losses in efficiency of prediction were <1% if generalized models were used in place of species-specific models. Furthermore, application of generalized multispecies models did not introduce significant bias in biomass prediction in 92% of the 53 species tested. Further, overall efficiency of stand-level biomass prediction was 99%, with a mean absolute prediction error of only 13%. Hence, for cost-effective prediction of biomass across a wide range of stands, we recommend use of generic allometric models based on plant functional types. Development of new species-specific models is only warranted when gains in accuracy of stand-based predictions are relatively high (e.g. high-value monocultures).

For land degradation monitoring and assessment (M&A) to be accurate and for sustainable land management (SLM) to be effective, it is necessary to incorporate multiple knowledges using a variety of methods and scales, and this must include the (potentially conflicting) perspectives of those who use the land. This paper presents a hybrid methodological framework that builds on approaches developed by UN Food & Agriculture Organisation's land degradation Assessment in Drylands (LADA), the World Conservation Approaches and Technologies (WOCAT) programme and the Dryland Development Paradigm (DDP), and is being applied internationally through the EU-funded DESIRE project. The framework suggests that M&A should determine the progress of SLM towards meeting sustainability goals, with results continually and iteratively enhancing SLM decisions. The framework is divided into four generic themes: (i) establishing land degradation and SLM context and sustainability goals; (ii) identifying, evaluating and selecting SLM strategies; (iii) selecting land degradation and SLM indicators and (iv) applying SLM options and monitoring land degradation and progress towards sustainability goals. This approach incorporates multiple knowledge sources and types (including land manager perspectives) from local to national and international scales. In doing so, it aims to provide outputs for policy-makers and land managers that have the potential to enhance the sustainability of land management in drylands, from the field scale to the region, and to national and international levels. The paper draws on operational experience from across the DESIRE project to break the four themes into a series of methodological steps, and provides examples of the range of tools and methods that can be used to operationalise each of these steps.

The Global Drylands Observing System proposed in this issue should reduce the huge uncertainty about the extent of desertification and the rate at which it is changing, and provide valuable information to scientists, planners and policy-makers. However, it needs careful design if information outputs are to be scientifically credible and salient to the needs of people living in dry areas. Its design would benefit from a robust, integrated scientific framework like the Dryland Development Paradigm to guide/inform the development of an integrated global monitoring and assessment programme (both directly and indirectly via the use of modelling). Various types of dryland system models (e.g. environmental, socioeconomic, land-use cover change, and agent-based) could provide insights into how to combine the plethora of monitoring information gathered on key socioeconomic and biophysical indicators to develop integrated assessment models. This paper shows how insights from models can help in selecting and integrating indicators, interpreting synthetic trends, incorporating cross-scalar processes, representing spatio-temporal variation, and evaluating uncertainty. Planners could use this integrated global monitoring and assessment programme to help implement effective policies to address the global problem of desertification.