With decreasing aquifer levels, increasing groundwater pollution, inequitable access, and generally poor management outcomes, better groundwater governance has been put forward as a recipe to address these challenges worldwide. Existing recommendations focus on improved legal frameworks, monitoring and control of access and abstraction through permits or formal rights. In addition, decentralized water management, enforcement of regulations, and supply-side technological solutions are seen as cornerstone components of good groundwater governance systems.

Climate- and human-induced land degradation endangers the future survival of our planet. A new focus on achieving Land Degradation Neutrality (LDN) seeks to spark and grow transformative efforts to avoid, reduce and reverse land degradation through gender- and socially-equitable means.

This document provides a clear monitoring framework for the implementation of best practices across selected Woreda Land Administration Model Offices (WLAMOs)..This resource was published in the frame of the Land Investment for Transformation (LIFT) Programme. For more information;please check: https://landportal.org/community/projects/land-investment-transformation...

This manual illustrates a monitoring scheme for monitoring large-scale agricultural investments. It aims at providing orientation for establishing a monitoring scheme and implementing monitoring activities in the framework of LSAI with the collaboration of the authorities responsible for leasing out land to agricultural investors and supervising related activities. While focused on Ethiopia, it will be a useful resource for other countries too.

A 16 minute documentary film developed by ESAFF Uganda detailing how small-scale farming communities in northern Uganda are using LSLBI tools to raise the awareness;especially of women;to engage with local leaders;produce resource maps;including scoping;negotiating;monitoring and implementation. The training also used drama. The aim is to empower farmers who engage with investors.

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.

To address food insecurity, the Government of Ethiopia embarked on a rapid transformation in the agriculture sector to increase production, productivity, market access and create employment opportunities by promoting large scale agricultural investment projects. However, progress towards achieving set objectives has been rather limited so far due to various challenges. One of the major challenges is that information on commercial farming is outdated and unreliable for decision making.

This report was produced in the frame of the Project titled “Sustainability and Operationalization of Established Regional Agricultural Research Centers in Five Arab Countries”, funded by the Arab Fund for Economic & Social Development and implemented by ICARDA.

Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information. However, detection of short-term processes and land use patterns of high spatial-temporal variability is a challenging task. We present a novel framework using multi-temporal TerraSAR-X data and machine learning techniques, namely Discriminative Markov Random Fields with spatio-temporal priors, and Import Vector Machines, in order to advance the mapping of land cover characterized by short-term changes.