Predictive Modeling in Restoration
There has been increasing awareness of the need for enhanced use of evidence in the assessment of land degradation status and the factors or drivers that led to degradation. What has been less focused on, is the need for rigorous data and evidence to inform land restoration inteventions. Research suggests that when ecosystems are degraded, it can be challenging to restore them as their degraded state can be highly resilient to change (Suding, Gross, and Houseman (2004)).
Despite the recognition of the need for stronger ecidence to guide land restoration, large gaps remain in terms of data and evidence. This is partly due to the complexity of the processes leading to land degradation and recovery, including interactions between biophysical and non-biophysical, including socia, processes. Also, there is a need for continues investment into the collection of data and evidence, particularly across the tropics.
This does not mean that it is impossible to assess and monitor land degradation and recovery over time. In fact, large strides have been made towards more rigorous and scalable solutions for such assessments. The Land Degradation Surveillance Framework (LDSF), for example, has been implemented across more than 40 countries in the tropics to fill these gaps, and to develop systems and tools to bring evidence to decision-makers. It is also well published and has a proven track record having been applied across a diverse portfolio of projects (T.-G. Vågen et al. (2013); T.-G. Vågen and Winowiecki (2013); Winowiecki, Vågen, and Huising (2016); Massawe et al. (2017); Bargués-Tobella et al. (2024)).
