 Okay, welcome back. Very warm welcome. Once again to our distinguished guests and participants at SEI's third science conference. The first session today will focus on water governments, tools, vulnerability, and conflict management. I think it's fair to say that historically when water was more abundant, water management was viewed very much as a technical issue that was essentially the province of civil engineering and closely related disciplines. But as water resources have become more constrained and scarce both in relative and absolute terms, it's evident that technical interventions, no matter how well designed, have to be increasingly sensitive to the social, environmental, and policy context in which they are enacted. And this context can encompass statutory law, informal law and local custom, different policy regimes, a wide range of stakeholder involvement. And these interests, it's fair to say, are not often easily reconciled. And as water becomes increasingly scarce, water allocation decisions become more frequently contested. And in these environments, the value of properly conceived and implemented decision support, of course, continues to grow. This session emphasizes the development and use of decision support tools in a widening range of contexts in which these tools are used to support decisions. And SEI has developed, I think, a well-deserved reputation for being on the cutting edge of developing and applying innovative DSS tools that perhaps weep and leap are the best known, but there are many others and we'll hear about some today. And what the session essentially will focus on is some of the expanding range of applications of DSS in supporting water governance and decision-making in a range of, a very different range of contexts. We'll hear five presentations, again, each focusing on a somewhat different context. And hopefully together they bring some of these issues into clearer focus and give a good account of the work that SEI is doing in this area. We're trying to adhere to a strict timeline. Speakers will have essentially nine minutes, and we have an enforcer here, who will not let you get away with going over. You'll hear a speaker, you'll hear a bell at seven minutes, which will inform you you have two minutes remaining, and at nine minutes the next speaker will take the floor. So thank you very much. We will ask that all questions essentially be held until a Q&A session after all five presentations. I think that way we keep our program on track here. Our first presentation is by Dr. Jenny Barron from SEI York, and she holds a PhD in Natural Resources Management. She's an internationally recognized expert in the area of agricultural water management in rain-fed smallholder systems, and serves as co-leader for the SEI Research Theme Managing Environmental Systems. Dr. Barron has authored over 25 peer review publications and book chapters on topics relating to food security, agricultural sustainability, water management, and smallholder agriculture, and ecosystem services. Dr. Barron will present on TAGME, an interdisciplinary decision support system for agricultural water management outscaling for the Volta and Limpopo River basins. For those who don't know, Volta is in West Africa and Limpopo is in South Africa, both big, important interstate basins. Dr. Barron, can you please take the podium here? I wouldn't know that. So technical. Thank you very much, Charles. And this tool is something that SEI has been working with together with partners in the Volta and Limpopo basins for the last three years. And one of the ways that we try to push how decision support tools are developed and what they contain in terms of the evidence embedded in the decision support tool to help, hopefully, to support decision making in investment and agriculture development in these particular basins. We call it TAGME, Targeting Agriculture Water Management Interventions. And this is part of the challenge program, Water and Food, in the Volta and Limpopo basins. And together with colleagues, Eric Campanedict, Joanne Morris, Anna-Marie Cadabruun, Douglas Vang and Amanda Fentz, who has worked on this project for the last three years. And I'll try to stick to the tight schedule. So how do we develop these decision support tools that they can actually be used in a meaningful way that we as a research community is coming from their search community help and provide the evidence in a meaningful way for decision support. And I would just like to say that this is one way. The TAGME is now in a ready-to-use concept stage where we think that we have been pushing the boundaries in this sense and trying to include the best possible knowledge about agriculture water management outscaling into a comprehensive tool which everybody can access on the internet, including both biophysical and socio-economical factors we know are important for agriculture technology uptake. We integrate multiple sources of expertise of knowledge around the agriculture water management technology context. So it's not just the researcher's knowledge that is embedded in this tool, but it's actually the policy makers, the farmers, the development agents like NGOs or public sector extension service knowledge that is taken into account into the decision support system. And we think also we have to be very explicit about what we don't know in terms of prediction of outscaling technologies. So this tool is explicit about expressing that as a measure of strength of prediction. I nearly pressed on the microphone. And why do we do this? Don't we know what works and what don't works? Yes we do, but still we have a persistent yield gap, we have a persistent income gap in rural communities in sub-Saharan Africa and in particular in the Volta basins and the Lepopa basins this was identified as a key issues that the challenge program needed to address in several ways where the decision support tool was one part. Of course there is lots of evidence that we know what technologies can make a difference in terms of closing those yield gaps in sustainable ways and providing income and nutrition for local as well as national countries. And then we know that the policy is actually conducive. The national policies in these countries are now having targets on how to eradicate poverty, how to address food insecurity and how to address rural development issues at the countryside. But just because the policies are in place and agreed on paper it doesn't mean that it always happened and in that stream of trying to operationalize the policies that is where we think that this decision support tool can be of assistance. So previous to the project started this was identified by policy makers and researchers alike in consultations that there is an issue here that we know that the technologies work but where can we take them and where can we facilitate their uptake on the ground in a sort of low hanging fruit kind of context which of course every investor would like to see. Leased money and most impact. So the innovative approach we took here was a process of consultation to make sure that we embed the different communities of expertise and knowledge. So it's not as a researcher we can go and do with the review and find out lots of the important things in our nice scientific papers but we also have the practitioners views and the farmers themselves and how do we make sure that this knowledge is also part of the decision support because at the end of the day it's not intended for research use but it's intended for an investment and policy action use. So we designed it as a continuous process of engagement with the stakeholders that we think part can provide the information but also who might be potential end users of this tool at the end of the day. In the different countries where we worked South Africa Zimbabwe in particular and in Burkina Faso and Ghana. Critical questions what technologies to address and which ones can we say have had a success in the past and in fact that was really tricky because although we could together make a huge list of different technologies from the very rain-fed interventions with fertilizer, mulching and ripping and whatever to the full-scale irrigation of vegetables and fruits with high value they were not so easy to define what is successful interventions in the past. Could you show us the example where technology outscaling had happened and spontaneously continued to outscale after an end of an intervention project intervention. The third thing that we think that would then to merge all this knowledge in different communities of expertise. We used the Bayesian approach to incorporate both the biophysical factors. So here we have a technology that is defined by the success is defined by up to more than 20 different factors and indicators and they are both biophysical in terms of soils, rainfall, slope for example which are very common biophysical indicators and decision support of technologies but there is also a lot of social and economic, labour, health status of the local community, leadership and food security. And now I rush on and this is a website and it's freely available, you can go in and test it for three technologies per the two bases matching from rain-fed soil and water conservation measures to full-scale irrigation and small reservoirs for all the countries. And I would like to say that our tool shows that even when incorporating the social and economic factors there is a huge potential to increase the agriculture water management to close yield gaps in these contexts. And the data on the social human factors which we know are super critical for successful outscaling they are essentially lacking at the sub-national level that we need to have it on. There is a high agreement between the different factors across technologies, basins and countries. So even if a technology is soil and water conservation or a small-scale irrigation the factors that influence the level of success is still very similar in the irrespective of country. And the importance, even though we can define this context for successful outscaling with a certain level of prediction power, the process of intervention is of course probably even more significant than the context we can describe with this tool. So when once you decide that this is the area, the geographic space where we think we want to make investments for this technology we have to think very carefully about the process of interaction and how that actually takes place because that can make and break the successful outscaling at the end of the day. So this was a three-year initiative and I think that the approach is generic. We have already had other researchers approaching us asking can you develop this modelling tool for our shallow groundwater irrigation scheme so we have developed new models on this kind of concept. And we have had requests from funders and development agents for possibilities to develop further in other spaces. Thank you.