In southern Burkina Faso, a small reservoir, about 300 by 1,300 meters, supplies eight communities with water for livestock, domestic uses and irrigation of vegetable gardens during the dry season. But the reservoir is filling with sand. Since constructed in 2002, a three-meter thick layer of sediment has been deposited, which both obstructs the supply of water and continues to reduce the reservoir’s storage capacity.

Several possible solutions are being considered, including dredging the stream inlet, constructing check dams, which can filter out sediments, or instating buffer zones around the reservoir. But each solution comes with its own potential benefits, costs and risks, so how can reservoir managers select the best option, the one that leads to the best possible outcomes?

When the Ladwenda reservoir is full, it provides a host of benefits.

Denis Lanzanova/University of Bonn.

This question is increasingly relevant during an era when agricultural development is becoming more and more complex. Too often, agricultural challenges are approached as singular issues that can be addressed in isolation, stalling progress and sometimes causing adverse effects. Strategies for incorporating multiple objectives in supporting development decisions, while acknowledging risks and uncertainties, are urgently needed.   

Scientists from the CGIAR Research Program on Water, Land and Ecosystems (WLE) are working to promote a new, integrated approach to decision making for sustainable intensification, which acknowledges underlying complexities and uncertainties. The goal is to arm decision makers with tools and methodologies that can support improved decision making for development outcomes.

Laying the foundation for good decision making

One issue that challenges good decision making is lack of data, or lack of access to data, on which to base choices. This problem is especially apparent when it comes to management of river basins that span several countries, in which case decision makers must rely on national, fragmented sources information.  

The Nile River basin, for example, is shared by eleven countries. Here, comprehensively measuring basin-wide flows of water has historically been difficult. That’s why WLE scientists have been testing the concept of water accounting+ across the basin, seeking to share and validate public domain remote sensing data and outputs from global hydrological models in an easily understandable and standardized way.

A water distribution canal in the Nile River basin.

Loney, Prue/IWMI.

Water accounting+ quantifies how much water is in a system, where, when and in what quality it is available, how much is demanded and consumed in time and place, and how well it is currently managed with respect to meeting those demands.

The accounts are aimed at a wide range of users across the water sector. They allow donors to identify the impact of their investments; water managers to define and track targets; water planners to assess the impact of drought, climate and land use change; basin authorities to get a better understanding of what is happening in their basins; and government agencies to measure baselines and identify progress towards national level targets.

“By having more complete information and providing up-to-date, transparent and politically independent data in a consistent and coherent format, we hope to help governments and river basin authorities to make better and more informed decisions. This is the ultimate objective of the open access water accounting+ platform,” says Lisa-Maria Rebelo of the International Water Management Institute.

Embracing complexity essential to achieve development outcomes

Another issue is complexity. Agricultural systems are very complex and therefore predicting the outcome of changes to the system – such as instating a buffer zone around the reservoir mentioned above – is difficult. To address this challenge, WLE scientists are exploring the field of Decision Analysis, which offers a holistic, systems-level approach.

Simply put, the decision analysis approach suggests to focus research on a particular decision, use the current state of knowledge to forecast decision impacts, draw on the knowledge of experts and stakeholders, take into account uncertainty, consider everything that matters, and use indications of uncertainty to identify information needs.

For example, in the case of the Burkinabe reservoir, scientists conducted a series of stakeholder consultations during which participants considered a wide range of risk factors – everything from costs of solutions to potential corruption – and detailed the degree of uncertainty for each factor. This data will be compiled in a model that projects the range of plausible losses and benefits for each decision and for each stakeholder. Ultimately, the model offers decision makers a clearer view of likely outcomes.

“The significant difference here is that we try to include everything into the analysis, even what we can’t quantify,” explains Denis Lanzanova of the Center for Development Research (ZEF) at the University of Bonn. “Expressing the degree of uncertainty enables us to distinguish between important and less important unknowns, giving direction for where to gather more information.”

In the past, WLE scientists used a similar approach to assess a Kenyan water-transfer project designed to supply the county capital of Wajir with drinking water. The analysis predicted a significant risk of a negative investment return in the project.

Current pressures on the planet imply a need for sustainable intensification of farming practices, and the transformation needs to happen fast. But making such fundamental, global changes hinges on smart decision making. WLE scientists are providing options that allow flexibility to deal with issues at different scales, whether that be to communities in Burkina Faso, at basin level in Wajir County in Kenya or at the transboundary level in the Nile River basin.

Acknowledgments

The solutions mentioned in this article are being developed by the following projects:

• Targeting agricultural innovation and ecosystem service management in the northern Volta basin led by Bioversity International.
• DAI: Water Metrics and Indicators for shaping policies and practices led by the International Water Management Institute.
• DAI 1: Decision Analysis and Risk Assessment - Stochastic Impact Evaluation led by the World Agroforestry Centre in collaboration with international partners.