Our Research

Introduction

Our research work emphasises on understanding land degradation and how it can be prevented, reversed and its significance better communicated and recognized. Also our research entails coming up with well-targeted information systems that reduce critical uncertainties in key investment decisions facing stakeholders on improving land health and livelihoods. Basically, our research focuses on two themes.i.e. Land Health Surveillance and Decision Analysis and Risk Assessment.

Our Research Projects

Our research projects aims at:

Developing effective land health surveillance methods 

The organization’s research advances in land health surveillance are based on principles adapted from public health surveillance, where accurate measuring and monitoring of changes and improvements in the health of populations is closely integrated with statistical methods to form a basis for policy development, priority setting and management. The initiative aims at understanding how land health surveillance systems deploy modern science and technology to strengthen evidence-based decision-making on land and agroforestry management to help better understand hazardous and protective factors affecting land health risk.

Assessing land health risks and targeting agroforestry interventions to reduce and reverse land degradation

Through application of the land health surveillance methods, the project aims to determine the main environmental and behavioral risk factors associated with land and soil degradation syndromes in the tropics, and how they are distributed in relation to different settings and factors such as ecoregions and poverty levels. It also seeks to explore the types of agroforestry interventions which can help reduce or reverse key risk factors associated with land degradation and what the cost efficiencies of alternative preventative and rehabilitation interventions under different circumstances.

Check more in our projects pages:

Our Ongoing Projects

Our Completed Projects

Research Questions

  • How can land health surveillance systems deploy modern science and technology to strengthen evidence-informed decision-making on land and agroforestry management?
  • Which remote sensing and spatial metrics and indicators are most informative for measuring and monitoring productivity, scarcity and use of land resources and indicating scope for improvement at different scales? 
  • What land and water metrics can be used as a basis for reward schemes for environmental services? Can we measure soil carbon cost effectively? 
  • How can land and water surveillance be put into routine operation and incorporated into decision making processes, from local participatory land use planning to national, regional and international policy processes?
  • What are the main environmental and behavioural risk factors associated with land degradation syndromes in the tropics and how are they distributed in relation to different settings and factors such as ecoregions and poverty levels? 
  • What are the few key risk factors that are common to several land degradation syndromes? 
  • What types of agroforestry interventions can help reduce or reverse key risk factors associated with land degradation and what are the cost efficiencies of alternative preventative and rehabilitation interventions under different circumstances?
  • What measures or systems can be developed to ensure better or informed decision making that reduce critical uncertainties facing stakeholders on improving land health and livelihoods? 
  • How can development research be well coordinated to ensure alignment with the needs of decision-makers or avoid the misalignment between research and decisions? 
  • In which manner can priorities be identified to ensure that scarce resources for research are expended to close critical knowledge gaps rather than addressing issues that appear obvious and have little information value? 
  • How should uncertainty be considered and accounted for in research decision making? 
  • How can relevant risks be identified and mitigated in projecting decision outcomes?
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