 Yeah, so I don't think so much for the introduction. Maybe I'll redo it a little bit just to get my affiliation right. So my name is Mark Schuett. I'm based in Rwanda with IITA. But my position is with IITA and Wagonea University. It's a joint position. I've been doing that for, say, five years under CRP, human tropics and then RTB. And you're right, I'm also the country director for IITA in Rwanda. And I do a lot of other stuff. But my real passion is in the science of scaling nowadays. And over the last three to four years, me and my colleagues, we have been trying to get our head around the question of scaling and why it is so difficult and why in the CGIR it's giving us sometimes updates in terms of our mandates, in terms of our partnerships, in terms of how we do it, et cetera, incentive systems and all these kinds of things. And I think because we are CGIR, because our roots and our core mandate is in science, we really try to approach it from a scientific point of view and help us to make better decisions on our investments in both research and delivery. Because a lot of the scaling and whether we are successful in scaling less or lower depends on the type of science we invest in and the type of research products that we actually produce. So thank you so much for giving me this platform to share some of those experiences and learnings. And the presentation that I prepared for you actually has three parts. So part one is about the scaling of innovation. So why is it so difficult? You know, why are we struggling with it? And also a little bit about what it is that we are actually trying to scale because that's a very important question to answer before we go into more detail discussion. And the second part of my presentation is about scaling readiness which is basically a system that we have been developing with other CRP roots, tubers and bananas to help us do a better job, to help us in critically thinking about scaling, to help us in deciding which partners to work with and how to develop and implement and monitor scaling strategies that actually work and make a difference. Now in that discussion we will also probably bump into some of the challenges of why this creates tension in CDIR because of the way we are organized because like our business as usual thinking, scaling requires us to take other types of tasks and responsibilities which requires us to do other type of science. And that moves away quite a bit from the technology development science that we have been very strong in over the past years. So scaling also this whole debate so gives quite a big amount of tension in the CDIR because it requires us to do business as unusual as I sometimes refer to. So that's about scaling readiness and then the third part and I think that is where you're probably most interested in is like how can you really use this? So how can it help making the lives of project managers, portfolio managers like most of you, directors and even donors? How can it make their lives easier in terms of making decisions? Well many of you are not managing one project, you are managing 10, 15, 20 projects. And I think that scaling readiness and I'm immediately jumping into what is maybe the biggest difference with scaling scan is the tool that you can use to do portfolio management. So you can look at it like how are the innovations, the early innovations, how are they built in terms of becoming more ready for scaling. I think that is where probably most of you will have an interest in. So those are the three parts and I think I'll jump right into part one. So I quickly looked at the presentations that Larry and Lennard provided yesterday and I actually saw that Lennard borrowed some of the concepts that we have been introducing. So I will not spend too much time on this first part but I guess some of you will find it a bit repetitive. But let me just quickly go through it. So I'll touch a little bit about scaling as a hot topic. What is this about? Why we have not been doing so good? What are some of the reasons of the tool performance? What is scaling the new ways? What kind of directions do we need to think about? How can we use science and evidence to do a better job? Now I think the reason probably why we are organizing this seminar and bringing the big people together is that scaling is a hot topic. And I think even myself, like four years ago when we started working on the science of scaling, we felt somehow that it was in the air but I didn't think that it would take off and become such a big debate in international research and development. And this is not only in agriculture. Last year the two people who are behind the scaling up nutrition they received a wealth of good price, especially for their efforts and developing the partnerships to work on nutrition at a large scale. So it's a topic that is hot in all kinds of sectors. And I feel even like in the health sector and in the nutrition sector they are a bit ahead of science and scaling and their thinking of scaling as compared to the agricultural sector but we are trying to pick up. So that's a good thing. So first of all, what is scaling all about? I think in the past and in many projects we are still working a lot with our end users. So we are spending a lot of money to work with as many farmers as possible and we think that's a good thing. We think that 65,000, working with 65,000 farmers is better than working with five trade or supermarket companies. This is a little bit an old-fashioned way of thinking because for me, scaling is really making optimal use of your available resources and time in your project to have better development impacts and reach more beneficiaries faster and more cost efficient. So if we can change livelihoods of 65,000 people by working with the five trading companies, then that is what we should do. That is where we can create sustainable impacts rather than trying to get all these 65,000 people in a training and then say like, well, we increase their productivity and we change their livelihoods, et cetera. So it's really about understanding the system, whether it's a food system or whether it's an agricultural innovation system and then trying to make decisions on where we are going to invest and how we are going to do that in the most cost efficient way. And I think also that kind of narrative resonates very well with the donors. Nowadays, they want to have values for money and if we can give them evidence of why it is better to work with the five trading companies rather than with the 65,000 farmers, then I think they are very open for that. This is again, it's a cartoon that I made myself but it's a bit about also scaling the old ways. I think we all recognize that numbers are only part of the story. So in a lot of projects, including projects into IIT, we actually pay people to work with our technologies or to adopt our technology. So, you know, the person in the red is obviously the scientist or the person working for an organization that aims to have impact at scale and says, I'll give you $10 if you adopt this new variety. The farmer thinks, I don't like the taste and I don't think there's a market, but okay, let me do it. And then we do that 25,000 times and we put in our annual report that 250,000 farmers adopt our new variety. But that is of course not what scaling is all about. So you cannot say that a project that reaches 250,000 farmers is more successful in scaling than a project that reaches 10,000 or maybe 500 farmers. Like reaching 500 farmers without any project intervention or any direct link to the project is of course much better achievement in terms of scaling as compared to incentivizing 250,000 farmers to adopt your variety. So we need to move away a little bit from this idea that numbers tell the story and that more is better because that's not what it's all about. Now, why is our context so unique? We are like, this is sometimes our people who have the value of death or you can call it whatever you want. But we are on the bridge. So we do research for not so fundamental as universities that they can claim that it's justified to stay on the left side of the bridge. We engage in development but we are not the ones who are actually able to reach an effective scale like Ido also said in his introduction. Those are the government and the private sector. But we are somewhere on this bridge and we maneuver between testing and developing innovations and then making sure that they get in the hands of the right people in a way that is cost efficient and that allows them to actually take them to the market or to the farm. But we can support this as a level of abstraction. We don't usually go into too much context. But then for development, local conditions and for adoption and use are super important. So we are struggling with that global local discourse. So in the research, usually we work with few stakeholders. And if I say few, you know, we're talking about doing a pilot with 50 farmers or maybe doing an advanced partners pilot with 500 or with 5,000 farmers. But if you talk about impact of scale, you talk about 5 million, you talk about maybe even more. So it's a different ball game. You cannot talk to all of them and ask them, oh, what do you need? How can we help you? This is about trying to reach as many people as possible and you don't really know what they are doing with their technology innovation, et cetera. So we need to let go a little bit of that. So research is very much longer and focused whereas development and demands of scale partners are usually very uprooted and in the moment now. So if there's an outbreak of full army work, for example in Africa, we need to be able to provide solutions now and not say, like, okay, we'll initiate a project and four years from now we'll give you some ideas about what you can do about it. So this is some of the reasons why our complex is so complex and difficult. Well, scaling the outbreak, I'm sure Larry and Larry have also talked about this. I think we all recognize those projects where three months before closure, we still need to work on work package 6 or whatever it's called and which was scaling. Okay, let's develop a policy brief, let's print 60,000 flyers and hand them out to the farmers and then we've done what we call scaling. And I think we all know that those policy briefs, probably the minister, she gets a policy brief every day when they fire up on our desk and by the end of the year she cleans out our office and they end up somewhere where they don't really contribute to having a real impact. And the same with painting flyers or brochures or farmers and all these kinds of things. So that does not work. So also what we often see is one side fits all approaches and combining here a little bit number two and six where the strategies for scaling of innovation are so generic that they don't really do justice to the local conditions in which scaling is to take place. And so if we are talking about countries like Tanzania, like Ethiopia, like Nigeria, conditions, agricultural conditions, social economic conditions, market conditions, partners, they are all so different across those different parts of the country that we cannot really have one scaling strategy to rule them all or to, you know, that makes sense for all those conditions. So I think that's another problem that we have been facing. Research allocation, I think what Larry also mentioned is that scaling requires investment and you need to take a two years from the beginning of a project. So making scaling something that's part of work package six or something that you start doing three months before the project closes usually also comes with the fact that the resources are depleted or finished and also not the right people are involved. Point five is very important. I think often we have very unrealistic ideas about impact. So in many projects or proposals it's like we go from an unproven idea to something that the scientist has an idea about and we think we can reach two million farmers in three years. So I can tell you like if you look at innovation history, if you look at successful scaling trajectory, we are looking at trajectories of 15, 20, 25 years. And that doesn't mean that along those 15, 20, 25 years we should not have scaling in mind. No, it's a continuous process or even from basic research from proof of concept for testing something in a more controlled environment continuously we have in mind like okay, how is this in the end going to function for whom, in which conditions, etc. So it is not a problem that scaling trajectories are a long-term trajectory. It's just something we need to be realistic about. And then we can say that we're going to invest 10 years in research and then we have five years for transitioning from research to development and then we have five years where we actually start doing the scaling together with our private and public partners. But this is very important and I know that this is a big issue and that also the donors they are not always sensitive to this. Now, some of the reasons for why we have not been doing so well is that there is a very strong focus in the CDIR on technology development. So, you know, we are very good at that. I think the majority of our investments and human resources, they have been going into strengthening technological innovation whereas scaling and their actual use in practice is often determined by the environment in which they are supposed to be used. So here we are talking about very simple things, both infrastructure, electricity, the functioning of extension systems, education systems, etc. And we, I think now immediately we're going through a vein where we say like, yeah, but this is beyond what the CDIR does. This is not our responsibility beyond our zone of influence or whatever you want to call it. But it is subtracting the use of our technological innovation where we better start getting our act together and work with the people who actually can make that happen. So it's too easy to say like, this is outside of our mandate where we need to start thinking about like, okay, if there's no roads, if there's no electricity, what can still work? If there's roads, if there's electricity, what can even work better? So these kinds of questions, that is what the scaling partners are waiting for. Now, another thing is I think that we have been not, we have not been doing so well in using evidence-based approaches to identify bottlenecks for scaling. And even if we do, we usually ignore the real bottlenecks. So again, this brings back to the first point. We know often that the problem is that there's no credit facilities. We know often that the infrastructure is poor, etc., etc. But we continue to invest in improving our technological innovation even more, but not working on those enabling conditions. So this also links to point number three. Now, I think point number four is more about the partners that we are working with. And I think we all recognize that. We usually work with the same partners on the broad range of innovation and scaling challenges. So even ourselves here in Rwanda, we always work with Rwanda Agricultural Board. They are the government partner. You have to go through them and for sustainability and for legitimacy reasons. But often we need to be much more critical of what is the scaling bottleneck and who is the partner that can help us to overcome that bottleneck. You can imagine, and I'm going to give a very simple idea, that if a bottleneck for scaling is access to credit, you have to work with the credit facilities, whether it's microfinance or whether it's the bank. If the problem is access to informational knowledge, you need to work with the media or the extension system. And then you will work with maybe the national radio or with large-scale extension or information providers, which can be the government that can also be other organizations such as one-acre funds. They are very big here in Rwanda, etc. So your scaling bottleneck basically determines what partner you are going to work with. And this is a bit of a challenge because usually when we write a proposal for scaling or for innovation, even before we have a clear understanding of what is the bottleneck for scaling, we have already included certain partners in our budget. And then of course in the latest stage it becomes very difficult to tell the credit providers that we actually overlook that the problem is access to information. So we're going to take your budget and give it to the media house. I can see that already that other ways of thinking about scaling and thinking about what partners to work together with can have an impact on how we organize ourselves in terms of project development and implementation. Then last but not least, I think scientists, they are not the best scaling champions. They are very much concerned with their technology and making it better. And that is what drives them. They want to write a good paper. They want to get credit for that. And I think that is reflected in the way we do performance for myself and other colleagues. But that is usually what... Those are very important things for innovation, design and testing, but not so much for delivery. So delivery is very much about understanding the private sector, understanding the government, what is on the mind of the minister, what are the discourses that are taking place, et cetera. And then I very opportunistically try to take on to that and see what's available and implementing that actually. So one of the nice things, for example, in IITA is that we, alongside the research for the... sorry, research for development division, we now have partnerships for delivery division. And that division, we have people that have a background in agri-business that come from policy or even from politics. They have no PhDs from higher universities all over the world. No, they know the reality on the ground and they make things happen. And also their incentive systems are aligned with that. So they don't have to write papers, they don't have to write projects, et cetera. But they are really evaluated for delivery and I think that's a good thing. Now, why do we need to do better? I'm stating the obvious here, but I think there's increasing competition in agricultural innovation and scaling domains. So we see actually partners from outside of the CJR they're encroaching on our mandate and where we are in the system. And we need to start getting our act together because otherwise other organizations, like One Acre Fund, I mentioned that before, they will prove more value for money and they will do better. And donors will start investing in those organizations because they have innovation and delivery and they reach scale. So we need to make sure that we defend a little bit our mandate and that we start doing better and create added value. Also donors, they increasingly see themselves as investors. So the charity ages are over. So there are still donors, of course, that approach development from that perspective. But many others, there are a few themselves as investors and they want to have a strong business plan, evidence-based, before they allocate resources. So if Ido or one of the colleagues in the room goes to Gaze or to USAID and they say, like, oh man, we have this pilot project on this new chicken breed or something else, horticrops, and so promising, you need to invest in this with us. Now they will increasingly ask for evidence, like give me evidence, give me an offer I can't refuse and I will fund you. And we also see that the funding, they want to have the funds, they want to put things together, make investments bigger, but then, of course, even more importantly, becomes diverse evidence and the strong business plan. So I will get back to that with skin readiness because I think that evidence is something that can help us to shape those discussions with the donors and also to educate them a little bit about what works and what doesn't work. I think that's also very important. Now some of the donors, like GIZ in the EU, they already started to adopt and integrate principles of readiness in their research allocation and monitoring and evaluation frameworks. So GIZ, I think they have a four-step investment framework where they say, okay, we invest in discovery, we invest in proof of concept, we invest in initial scaling and we invest in method scaling. So that's how they organize their portfolio and you, as a recipient of those funds, you need to give them evidence of how you moved from discovery to proof of concept using the funds that they gave you. Now I think this is a good way because you remember that I talked about scaling as 10 or 15 years trajectory. Moving from discovery to proof of concept is part of scaling. If that step is not made, you will never go into piloting or into scaling. But they want to see evidence that you actually made that step and then they are very interested in becoming a next step. Now I think we have gone way beyond, we use line steps and we have indicators for each of those steps. So we already see actually an interest from the donors to use an approach like scaling readiness to manage their investment portfolio. All right, so scaling the new way, it's a bit the opposite of scaling the old way, but where we start thinking about scaling from project or innovation design where we use evidence-based strategies and we are realistic based on the resources allocated where we work actively with scaling partners and we try to co-invest with them in scaling, retaining the strategies to different contexts where innovations are to be scaled. So this is the opposite of one-side fits all and we do it based on key metrics and defined tools. And I put there that we increase the likelihood that projects reach scale because of course we operate in complex systems where there's all kinds of influences that we don't really have control over, think about weather, think about all these other kinds of things. But I think using this kind of new approach, it really can help us to increase likelihood that we reach scale. Now I saw in Lenin's presentation that he borrowed this slide which is fine, but I just, like similar to Lenin's probably, try to use this to put into your mind that if we are talking about scaling, we are not talking about scaling a single innovation or technology. Scaling is about packages. And packages of innovations are contained usually technological innovations. They can include infrastructure innovations, market innovations, quality innovations, strategy innovation, mindset innovations, et cetera. So I sometimes, you know, when I present this to my colleagues from IIC, I tell them like what would happen to this electronic car that functions perfectly in Europe or in the U.S.? What would happen if you put that car into Lagos and you start driving it up and down to Ribaven? So most of them they stop asking, of course, because after two or three times up and down, highly they say that the car will run out of battery. The second option is the car will be stolen or hijacked. And the third option, they say that well, when it runs out of battery, you will find somebody along the way and they will put some new batteries that you can continue to drive. But of course, I think we get the bigger picture that this technology can function in some context or in some environment, but if you put it in another environment, where there's, for example, no charging stations, where there's no mechanics there or spare parts, it will not work. And that's what we need to keep in mind, that everything needs to be working otherwise the technology or the innovation will not go to scale. So if all of these prerequisites are there, the mechanics are trained, the spare tools are there, the subsidies are there, the battery is good, et cetera, but there's no charging stations. It will not work. So we need to think about innovations as packages. That is very, very important. Too often, we think, we focus too much on the technology. Now, this is another, this is Leibich's parallel and I saw that Bennett also presented that. This is about the law of minimum. And this is just to illustrate, is that if your battery is good, if your mechanics are trained, the spare parts are there, but the charging stations are absent, this will be your bottlenecks for scaling. And you can continue to invest in making the battery better. You can continue to invest in making the supply chain of spare parts better. You can continue to invest in training the mechanics, but if there's no charging stations, all of those investments are a waste of money. So we really need to understand what are the bottlenecks, where is the water leaking out of this barrel, and then develop our scaling strategies around them. And I think, you know, we also recognize this from agriculture where we spend a lot of time at one improving three varieties, et cetera. But as long as the seed systems are not there, and they don't end up in the health of farmers, these innovations, they do not lead to tangible output outcomes and impacts. So this is something we need to be aware of. So in a research project, there's no problem with continuing to invest in improving the variety, I would say. But if you have a project or an investment that is geared towards scaling, then we cannot justify improving a variety where the basic conditions for updates are not there. So that is, again, to put things a little bit into perspective that not all investments have to go into delivery or scaling. You know, we also need to invest in research, but it depends a little bit like what is the source of the funding and what does it tend to do. Now, I talked already a little bit about evidence-based approaches and basically scaling readiness uses this to make better decisions on what it is that we are trying to scale. So what is the package? What are the main bottlenecks for scaling in a specific location? So where is the lower stage in the bar? What do we need to do to overcome those bottlenecks? What is our scaling strategy to achieve that and who we need to work with? Is it the finance, the system media, are it lobby groups, et cetera? And what is the best partnership process to work with these partners? And then, of course, eventually, has it led to tangible improvement. So now I want to go into scaling readiness and show you how we are dealing with that. And I think the best way to do that is using a very concrete example from where we applied scaling readiness and how that has helped a project in this case to develop and work on developing their scaling strategy. Now, before explaining scaling readiness, I have a small video that Admin is going to run from your side because I'm afraid that using the share screen is not really going to be helpful here. So Admin, if you can quickly play the animation, then I will continue after that. In 2050, there will be around 10 billion people who need safe and nutritious food. To face this challenge, scientists, farmers, governments, and companies must continually innovate and explore new opportunities. For these innovations to find their way to farmers and companies to invest in them, scaling strategies have to be developed at the onset of research for development projects, be implemented by skilled people, and be realistic based on available time and resources. CGIAR, a global agricultural research partnership, has invested in developing scaling readiness, a step-wise approach that supports research and development organizations in the design, implementation, and monitoring of scaling strategies along nine levels of readiness. Scaling readiness provides a standardized process and scientific tools to assess how ready an innovation is for scaling. This guides teams to identify the bottlenecks that might inhibit the scaling of the innovation, helps prioritize activities and partnerships to overcome these bottlenecks. Scaling readiness supports research, development, and donor organizations in maximizing the impact of their investments and innovations towards a more food and nutrition secure world. So if you have a more brief idea about what's behind... Oh, yes. Do you hear me? Yes, we hear you fine. Okay, okay, great. So it gives a very quick animated impression of what we are trying to do with scaling readiness. But I want to re-emphasize that the tool actually serves three main objectives. So the scaling readiness basically operates for improving scaling performance of individual interventions or projects. So if you have a project, a single project, and that has an objective for scaling, and that will also be the example that I will give after this slide. So it can help you to think about what is the package that we are trying to scale in different locations what are the bottlenecks in different locations, what are the strategies that we can use to overcome the bottlenecks, who are we partnering with, and did it actually lead to tangible improvement. So that's really at the project level. Now, most of you in the room, I guess, you manage people that manage projects and you manage many people that manage projects. So the nice thing and the good thing about scaling readiness and I also think that is where we differ from, for example, the scaling scale is that we do that in a very systematic, structured way. And what is the advantage of that? The advantage of that is that you can aggregate projects into a portfolio. So it can actually give you a dashboard in which people like Ido, people like your DDG, if you are interested in how we are doing as early or how are we doing as impact with scale division, you can look at your 10, 15, 50 projects and see how they are improving in terms of scaling readiness over time. So are the investments that you're really making and that the donors are making, are they actually translating into an increase in the readiness of these innovations to be scale? So that's the second view. Now, the third view is that you can use this information to identify your stars, your star innovation. And that is, of course, very useful if you are developing proposals or if the DDG or the DDG is invited by EFOP and EFOP says, hey, listen, we have 50 million to invest in nutrition in Kenya and in Ethiopia and we would like to work with Hillary, what are your star innovations? Where should we invest in? And if it's about 50 million, that person of EFOP will not just trust the DDG or the DDG by their blue eyes or brown eyes. No, they would want to see some evidence supporting that. So those are three things that scaling readiness can do. I will first, to explain two and three, you first need to understand number one. So how can it help improving the scaling performance of individual projects? And in order to do that, I will take you through a five-step, basically a five-step process of scaling readiness. The first one is also why we call it zero. It's more deciding whether scaling readiness is suitable for purpose. As I said, it's quite a systematic process. It's a bit more laborious as scaling stands, for example, but it generates better evidence, I think. So the first step is really, okay, are we ready, are we willing to invest in using scaling readiness and allocating the appropriate resources and time and people to actually do it in a rigorous way? So that's the first step that is very important to take because otherwise the expectations may not be met. So then we're going to characterize innovations, interventions, the scaling landscape, by ignoring scaling readiness of the innovation package, developing strategies for how to overcome the main bottlenecks, agreeing on the strategy with the key stakeholders and sometimes maybe even with the donor and then implementing and monitoring and evaluating, which we call navigation. So again, I'm going to use an example to talk you through. So step one is really about understanding your innovation but also what the intervention or what the project is trying to achieve and which complex and who are the stakeholders that we are actually working with. So you see on the picture in the photo of me sitting together with two colleagues from IRCA. This was a few years ago, but it's still a good example. And they were working on scaling of Casablanca disease control in southern Tanzania. And initially when I asked the scientist, it's like, okay, so what is it that you are trying to scale? So it's trying to get an idea about their electric vehicle. And they say like, oh, it's very simple. It's about the resistance variety to Casablanca disease. And, you know, we are using demonstration plots to compare the local varieties and the resistant varieties so that the people actually start seeing why they should use the resistant varieties. Okay, so that works for them, their innovation package initially. But then we started asking some more questions. Like, okay, so how do the farmers have access to these varieties? And we said, yeah, we are trying also to improve the government seed system so that it becomes more functional. Okay, and how do you work together with the farmers and with the extension providers and the seed providers? Yeah, we are using an innovation platform approach. Okay, that's interesting. And the next question is like, oh, so what do the farmers do with the Casablanca that they produce? Oh, yeah, yeah, we also have this contract with a local processor that actually guarantees takeoff from the farmers. So, you know, this is a good incentive for them to grow these improved varieties. Okay. And, you know, they were using a quite rigorous approach for the fields that actually had disease. They were uprooting those whole fields. So we were saying like, is this actually something that people do voluntarily? We said, no, no, no, we have a very strong communal action process with the village leader and all these kind of things. So, you know, I'm just, I'm not going to go by all of them, but you understand already that usually the package is more complicated than scientists initially think. So the first step of scaling running is just really to get a good idea of what does your innovation package look like? What are all the conditions that need to be there for your core innovation, which in our example is maybe the resistance right, to be adopted by the farmers in a sustainable way? So in order to do that, we need to understand the project. So what is the timeline of the project? What is the budget of the project? What kind of research and development outcomes does the project promise? What is the context? As I said, southern Tanzania may be different from northern Tanzania. Usually we work in projects that cut across different countries. So we need to understand those two contexts in which scaling needs to happen. And also we need to understand like who are the players in that system? So who could be the potential partners? Who are the influential partners that we may need to work with if we want to change things, such as this system, et cetera? And what are other things that they are already working on? From an innovation systems approach, we know that, for example, if IIT and EURI have the same project working on similar things in the same country, so we may actually be complementary to each other or we may be competing with each other. So EURI is promoting organic agriculture and IIT is promoting highly intensified non-organic agriculture. And this may have an impact on the adoption of the innovations that we are both trying to scale. So it's very important that we have a good understanding of the context and the networks in which we are intervening. This is the first step of scaling revenue. Now the second step is that where we actually make the diagnosis. So all of these, you can say components or innovations that the team has come up with in the previous slide, we are assessing them or diagnosing them for their innovation readiness and for their current use. So what is very important is that you keep in mind the barrels here. So in the barrel, when we were talking about the electric vehicle, we said that, you know, all of the innovations for all of the components may score high or they may be very ready for scaling but the ones that score lowest, they actually keep this whole innovation package from going to scale. Now we use two times a nine level axis and on the Y-axis you see innovation readiness. Now I'm not going to go in a lot of detail but from zero to nine, we're talking about level one to three is more idea development. So this is a scientist who has a good idea. You know, they maybe have done some literature review, they've maybe done some paper on it or whatever but it's not at all going into real life testing. So it's more like maybe if you go to step two or three, you're going to a laboratory and you try to test it in very controlled conditions. If you go to step four or five, maybe you do an on-station trial where everything is still very controlled but you at least are already outside of the laboratory and once you move up to level six, seven, eight, you know, you start letting go more of your innovation. So first you test in uncontrolled conditions but you still monitor and evaluate and then maybe you hand over to a private company or to a government and they actually start doing it without any direct project support or with any direct influence from ILRI or ILK or whatever. So this is the Y axis. So we have very clearly defined indicators and metric systems for that and what is also very important to keep in mind is that we do not ask the project members or the scientists to score this. So in that it's also very different from Stoneman's scan that is more a self-assessment by a project team. We go for independent experts to assess the readiness of this innovation in terms of their development because what you get if you do a self-assessment is that people of course always overestimate their, the readiness of their innovation to go to scale. Okay, so this is the Y axis. Now the X axis is about current use. Now again, low use means that it is only used by the project. So you can imagine, think again about these 250,000 farmers that I was talking about. If we all pay those farmers to use our innovation, they are part of the project. They are in the budget, they are project partners, all of them. So low and high has nothing to do with numbers. It has everything to do with who is using this innovation already. So if two or three people are using our innovation but they have never heard of theory, they have never heard of our project, that's of course actually much higher than when 25,000 or 250,000 farmers are using our innovation but they are all incentivized by the project. So and of course like this says something about the readiness to scale because if people that have never heard about the project or about our organization are already using it, it means that it's beneficial for them. So in a nutshell, all of the components of the innovation practice are scored along these two axis and that will give you a graph like this. Now in this particular example, it's clear that the team was right that they have a different variety and also the demo plots that they were using. They are quite ready for scaling and they are actually already being used by quite a number of people. So in this example, the real bottlenecks are the functional government key system and the collective communal action. These are really the lowest states in the barrel so if we don't address them, we can continue to improve contract farming and sensitization and the credit schemes and all these kind of things but it will not result in a higher adoption. So the government key system is the main bottleneck. Now there are scaling readiness proposes seven strategic options for dealing with these bottlenecks. So the first option is can we replace going through the government key system by another innovation that has higher readiness for use. So this is like another example can be if you don't have access to the electricity grid, can we put a generator that serves the same purpose but it replaces, it performs the same function of that innovation that is called the electricity grid but it's much easier for us to control and we simply can buy it, next month we can have one and I think we all understand that extending a government electricity grid may be much more difficult than ever. So the first option we consider is substitution. If that is not possible then we start thinking about outsourcing. So are there other organizations or external experts that can improve the scaling readiness in a more cost efficient, faster way than our project? And you know I think it was very good at that and he once taught me that if somebody else can do it faster, easier, more cost efficient than please outsource. I think we should use that kind of thinking more in our institutional context. So if it's about a media campaign, if it's about a radio campaign, if it's about working with the high level politicians, let's work with the media houses, let's work with the lobby groups, let's not make a scientist a lobbyist, let's not make a scientist a media specialist. Outsource is possible. If outsourcing is not possible we can decide to invest money, time, human resources in developing the innovation. So we can actually say substitution was not possible, outsourcing was not possible. So we need to maybe rechannel some of the investments from the project in developing the government seed system. That's the choice. Now this is still, you know, probably still quite in the realm of what we are comfortable with. So if all of these three options are not possible we should start thinking about moving the project to a different location where the conditions are more enabling. That the government seed system is not there in southern Tanzania but it's quite well developed in northern Tanzania where they are facing the same problem of the cassava diseases then maybe we can relocate our project so that the conditions are actually more enabling. I think we should really seriously consider that. The same as you can think about if there is all of a sudden a flood in the area where you were trying to work on a specific project and it's all devastated and you're trying to do, can we do the same thing or achieve the same objective in maybe a different location? Reorientation is the next option. So if we cannot relocate then maybe, you know, the focus of the project that the company that I gave should be on developing the government seed system rather than scaling the resistant variety for better life use in outcome very impact. So if we, if we as a projector with our donor decide that we are going to invest all of our money not so much in the adoption of the resistant variety but in developing the government seed system and the donor is okay with that I think that is a serious strategy that we should consider. If that is not an option we can try to postpone. So can we postpone the project so that we actually re-initiate it or start it at a later point in time when maybe the system, the government seed system or any kind of other enabling condition is more developed. And in the worst case postponing is also not an option. We should stop. It's also something that many of us like we are not so used to but why should we waste investment if we know we cannot solve the scaling bottleneck. So, you know, we have many options but if all of these strategic options do not work out then we should be honest to ourselves and we should make sure that we, you know defend our credibility and legitimacy as a center or as a system until like, well, you know, to the donor this is, we cannot achieve this here and now. It's not possible because of these reasons and I'm very sure that if you provide a good argumentation and you show them why all of the options will not work that this is something we can discuss about rather than pushing forward trying to achieve something that you don't achieve and maybe it will result in having a bad name with the donor. So that's why I was saying these options can also help us to educate maybe sometimes the donors what they want is not realistic or what we thought we could achieve is not realistic and it's not our fault, it's just because of the conditions. Now, I want to go back to my example and in this case they chose to replace the government chief system or going through the government chief system by working with or trying to deal with seed notification more locally. So they said like, we are not going to change the government chief system in the time that we still have left in the project so if we want to still achieve our objectives we need to think about something else and apparently in Southern Tanzania the local seed multiplication systems are quite well developed so they decided to go for that option. You can imagine that if you make such a decision that your partnerships will change so you need to get new partners on board and maybe you actually have to exchange to the government that we are going to invest part of the project money to work with the local seed multipliers. You remember from step one we were characterizing the stakeholders and the stakeholders networks. We did we used to develop social networks and social networks may give us a lot of information of who is working on what where so that is also how scale and readiness can help us to make more informed decisions about who are the partners that we should work with. So again this is an example of how we use science to make informed decisions about who are the partners that have the highest likelihood of helping us to achieve our objectives rather than always working with the same partner in the same location for all kinds of different purposes. Now... Sorry Marc, I'm just interjecting I just see you have a few more slides and we're into the hour. So can you please time yourself for another five to ten minutes maximum of speaking? We may need to rush through some of the slides a little quicker. Yeah, so of course those kind of decisions to stop working with the government on developing their seed system and to start engaging a local NGO so you can imagine that there can be quite some political consequences of that. It may not be so easy to tell the government sorry we are going to stop working with you or we're going to work in a different way and we decided to work with its local seed multiplier so this is something we need to agree on with the broader stakeholders and also with the donor probably and sometimes we may find it unfeasible so the best option which is to work with this local seed multiplier instead of with the government the political point of view it may be unfeasible because it will harm how the government perceives theory or we are working with them in many other projects and it would not be good for our relationships so sometimes we have to go for the second best option and that is why there is an iteration between step three and four because the best possible option may not be always the most desirable one or the most feasible one from a political point of view. Now the last slide is about monitoring and evaluation so if you look on the left-hand side you see the original situation on the right-hand side you see the news situation this strategy is that the actions to work with these local seed multipliers they actually result in a higher scaling readiness of this package and you see that the functional government seed system bottleneck is now gone, the new bottleneck is collective communal action and if you still have time and resources in your project you say okay now this is the next thing that we want to work on or that we are going to develop a strategy on. So this is about this is what scaling readiness is all about so it's a step-wide approach to assessing the readiness of innovation packages identifying the key bottlenecks developing strategies implementing them and seeing if they have the desired effect. Now it's always a pity if you put it at the end what makes it more interesting like what can you review with it? Now let me skip the project management part because you remember in the beginning I said this is useful for three kinds of objectives project management, portfolio management and research mobilization so I think for project management you get the picture. So in a project identify bottlenecks develop scaling strategies identify departments to work with that and see if you have if it resulted in a good insight. Now for portfolio managers and that is the most of you in the room this kind of approach becomes of course very interesting if you can aggregate scaling readiness of this kind of project and if you can help that to make investment. Now first of all this is one that is useful for at the institutional level so we have different kinds of investments that are coming into the CGR so some are more research oriented some are more development oriented some are more commercial commercially oriented. You can imagine of course that that we can use scaling readiness to decide where the research funds should go so what are the promising new ideas that the scientists and the laboratories are developing that they have already tested on station and that now are ready for testing in more uncontrolled environments. You know you can use scaling readiness to identify your star innovation so which are the ones that are growing fast that have a very high potential and that is where your Windows 1, Windows 2 investments should go because the donors they may not be aware of them or not be willing to invest in them so the strategic funds should go into pushing those innovations up in terms of their innovation readiness. The development funds are of course a different story. There you want to invest in the innovations that already have a very high level of readiness in terms of that they have been proven under controlled and uncontrolled environments and the investments is more about making sure that they are used outside of projects by the private and by the public systems so that can help you to decide where your delivery funds should go. So some of the innovations at least in IITA they are already taken up by private sector. For example Atlas safe is a very good example. There you know we don't have to invest all that much because it's already in the commercialization sphere or in the business development sphere. So scaling readiness can help you very easily to identify those innovations that deserve research funding, development funding or commercialization funding and also to say like well you know we have invested for five or ten years into improving this product but it doesn't seem that you know it's improving in terms of innovation of scaling readiness. So maybe we should stop investments in that as early because you know it doesn't seem to make to start moving in terms of the levels. Now I'm not sure if I have a lot of time for this but this is an idea of how it can help you as a portfolio manager. This is a dashboard and I actually have it live so I think I can very quickly show you that if you go to your office in the morning you can have a look at and see all of your projects in one graph that has the same actions of innovation readiness and innovation use along the mind steps and you can see where your projects are at the morning of innovation readiness and use. Now if of course it becomes more interesting if you can see change over time and that is something that we also do because scaling readiness is an exclusive process so you can see over the last three years this project or this innovation moves from level three to level five or level six or something like that. Now of course like in Ilri you have people that are more research and more delivery oriented so you can apply different kinds of filters of course for where these projects are focusing on the research projects of course they are more in this left below corner because they are not being used by so many people yet they are still under development. If you go more into your scaling investments you will see they are more in the left upper corner because they are already very ready and they are supposed to receive investments to increase the revenues. So this is a system that can help you to filter. You can also think about what kind of areas are we talking about you can use Central Africa. These are the projects that operate in Central Africa and their innovation and scaling readiness. You can go for countries etc. So you can use this to apply all sorts of filters to see where your projects are where your innovations are in terms of the readiness. This is actually a mock-up that we use with dummy data but this is what scaling readiness can do. Now the last slide is on resource mobilization which is the third objective of what this can serve. If you have a comprehensive database of theory innovation and information about their scaling readiness then of course it becomes very easy that if you are invited to develop a proposal for USAID or for GSS or for ETA and we know already in which kinds of conditions to which kinds of life use these innovations are contributing that we can use that kind of information to convince the donor that they should work with us. So for each of the innovations in our system we have information on the outcomes so what are they contributing to? Is this particularly good for nutrition income or for better productivity or whatever? We know what kind of sectors they are being used in so is it only agriculture or is it race management or is it agriculture and climate change? We have information about location about the partners we work with etc. You can imagine that that is of course very powerful tool if the DDG delivers a talk when you submit a proposal. So also for that I just want to quickly show how this works. So we have another system that says like well we want to develop a project on let's say climate change so we have all the SDGs and all the SDG indicators in the system and of course we are most interesting in the agriculture sector then we already see that there's basically three innovations that pop up that have proven like scaling readiness in a specific size so let's go for why is this not popping down let's go for Central Africa again we see that there's only one etc. So we can go for country Rwanda that's only one innovation actually that we have information about that hasn't made the proven contribution to climate action which is one of the SDGs and we can use this to sell it to the donor. This probably needs another year of research investment and then we can start focusing on delivery with specific partners. So if we take this we have information on what is the core innovation who are the partners that we are working with and who are the projects that are currently investing in developing this innovation in Rwanda. So we see the systematic approach behind scaling readiness named to be used eventually as an information system that can help people like yourself to make better decisions about where you need to invest in how to have more evidence based resource mobilization strategies etc. So I think that is the main the core of my story and I would like to thank you for your attention. Thank you very much Marc. Let us open the floor for questions. We'll start again with people in the room in Nairobi and then go to the online participants so the floor is open. Hi, thanks Marc. I'd like to talk about I was very much interested with the mutual hand option I think you have nine or four options in case the innovation is not ready for scaling and I was just wondering how many actually what are the most most used options based on you have like exit, obviously you have stops, you have relocate how many different options have been used so far from your experience given the fact that obviously as you said you said if we work with partners we often use work in other projects in other countries so many of these options are vertically all valid but in practice it may be very difficult to actually follow because of non-project, non-innovation related reasons. Thank you very much. Yeah, so that's a very good question like what we usually see is that it's very difficult indeed to change partnerships when your project has already been improved and when you have engaged a specific partner of which you thought were useful from the beginning onwards they are there, they have a budget etc and so you know people are quite creative sometimes what we see is that like somebody like yourself or you know they will redirect some of the early funds rather than taking the budgets from the partners to work with those kinds of organizations I think the main point I'm trying to make and this links back to how products and other ways of thinking about scaling it will mainly help us in the future I think to make other more open decisions about which partners we should include when we are developing projects so I think I always advise people like try to postpone partner commitments as long as possible so try to postpone it until after you have a good idea of the bottleneck and who do you need to work with to overcome that kind of bottleneck so what we try to do sometimes in projects is that we have flexible scaling or innovation funds in the project so we have like a hundred thousand flexible funds that are to be allocated based on the bottleneck because usually we don't really have a good idea at the beginning of the project in the current situation so for new projects, scaling projects I always try to convince the project leaders and the people who are developing the project to have as much flexibility as possible in terms of budget allocations but maybe we all know that fund owners are more flexible with that than others so that's my answer Hi Marc, this is me off All three presenters have started off with explaining just how important scaling is for the survival of the CG but that numbers are not a good indicator for the success of scaling but it is so important it is probably good to have a common understanding of how to measure or demonstrate the success of scaling and if the numbers are not or the numbers of the doctors are not the right indicator Okay, that's also a very good question I would go more for for example call for process tracing so where we can see that for example private or public partners have incorporated innovations or specific approaches as part of their business strategy so just to give you an example we are working with one acre sons here in Rwanda and they are applying this one size fits all approach towards delivery of their innovations and we have been working with them over the past two years to see if we can go from one size fits all to let's say two or three size fits all approaches and now they are reconsidering at a high level management whether this can be an approach that they are going to mainstream or in other countries so I think these kinds of outcomes where we see here or adopt inclusion of innovation in delivery packages with our delivery partners I think those are actually much better indicated and that also links a little bit to what I was saying that if we start seeing adoption or use of innovation without any project support that for me is a much stronger indicator rather than saying like well 25,000 people this or that so it's probably in a combination of who is using it and how many of those people are using it but if there's like five people using it without any link to the original innovation team or project then that is good but it is of course better if 25,000 people without any link to the project are using it because I think those are the main indicators so we first look at who and then how many of those people Does that make sense? Yeah, thanks a lot. Okay, let us see if there are any participants online if anyone would like to come in with a question please go ahead. Yes, please go ahead Amos and then Helen. Yeah, thanks a lot I've been able to participate today I tried yesterday and successfully I've liked this presentation very much I've got one question on whether Matthew have examples of skill readiness but low uptake and the role of research at that point, I mean given the tools that you've been developing do you have some of those examples I'm thinking of, for example, you make it still not that much so what's the role of research at that point given the what either refer to as international ones you refer to as bridging the gap at that point between research and development and if the role of government versus the role of the private sector also bearing in mind that in government there's often a lot of changes the political economy of many governments is like that the people you're dealing with in one office changes within, say, two or three years you've got to deal with different people and a number of times the policies that you may peg your research questions on may shift from time to time depending on, you know, the people you are dealing with so the weight of focus in terms of what who do you then work with to enhance scaling of a particular technology if you can comment a little bit on those I like that question a lot so I think in the beginning I also said that this kind of other types of thinking and making bottlenecks for scaling would be obvious it can somehow redirect a little bit how we think about research in CGR I think for those examples that you mentioned if the technology or innovation is ready or if it's already at level eight we should consider whether we still want to invest large amounts of money in pushing it up to level nine or whether it becomes time to invest those same funds in doing other types of research and focus on understanding some of the things that you just mentioned so like understanding why are the governments not interested in incorporating afla state in their input subsidy programs understanding why the big companies are not why are the fertilizer companies or why don't they want to include that as part of their package is it too expensive, are they not aware is it politically sensitive is it not feasible for them because you know the market is too small so I think we really there we still do science but it's a different kind of science it's more like behavior customer behavior, user behavior market research understanding like the reasons for a politician to start promoting afla state openly I can tell you here from Rwanda when we start talking about afla state there's very mixed feelings everybody knows it's a very big problem here in Rwanda but once you start preaching about afla state and that is very important you also have to acknowledge that there is a big problem with afla section in the main practice you actually may scare people and people will not buy names anymore so I'm just saying that there's all kinds of reasons for why the scaling partners may or may not be interested in doing this in their strategy and I think we should invest much more in understanding those kinds of processes and reasons if our objective is to scale innovation so I hope I've asked your question it's not about not doing research it's about doing different kind of research there's a quick follow up on the same question here in the room so please go ahead now and then Helen the final point was what I would see a difficulty it's not research but it's a different kind of research so I fully understand that these locally specific constraints are really important but as a CG centre we are always pressed that our comparative advantage is the cross country, the larger scale of you so how would you think CG centres will be able to invest of a lot of efforts into these really specific issues which are only really relevant for that situation yeah I think there's nothing wrong with doing context specific research on that what I've learned from working with scaling partners is that they have research questions but they will never if you ask them what are your research questions they will say oh we don't do research but if you really engage with them and you try to understand what's on their mind whether it's like how the minister is deceased by the people in the country whether or not including a technology or innovation in a business strategy is feasible from a cost perspective yes or no how big is the market governments and private sector they do have research questions it's not a coincidence that so many big companies they have they invest a large amount of money in doing that kind of market research etc. so I think it's really about engaging and trying to understand what are their questions and then asking ourselves the question can we address them or do we need to outsource this I won't see like theory doing a big market study you know their competency terms that are better faster and more experienced in doing that kind of stuff but if it's about for example trying to reduce the cost of research from like two dollars per hector to one and a half dollars per by maybe reducing active ingredients or stuff like that I think that is where an organization like IITA or Together with Summit and others that is where we could still test efficacy and see if this like trade-off was making something a little bit cheaper and maybe a fraction more less efficient whether this is acceptable you know from a user perspective so I think there's it's not again you know we need to then think about like what are the questions that we should address at the CG centers aligned with our expertise our mandates on people are expecting from us in terms of public goods and what are the things that we should maybe put on the plate of others that are faster, cheaper and more efficient so that would be my follow-up answer Thank you Helen please go ahead Thank you Marat the presentation was really interesting I just wanted to come back to this issue as sort of indicators I work in the live.clp and when we're discussing the common indicators at the CGIR level and you know this kind of went for the simplest ones the number of people who are participating in research or projects which you know with the CG rather than the number of people reached or the number of people adopting outside of projects which is obviously much more useful to measure so I'm wondering for you know what kind of discussions are happening at the system level about what happens after the CRPs and what kind of indicators we should be using to show the impact of the CGIR work because it means a lot more investment of resources in trying to study adoption and impact outside of the work that we're directly doing Very good question, I have a slide but it was hidden other uses of scaling readiness and one of them was in monitoring evaluation of course we want to be able to demonstrate that because of investments in the CGIR the overall portfolio of innovations improved in terms of their scaling readiness so that is another discussion that we can have because you can aggregate at the OV level or at the vision level but if the whole CGIR would be reporting against scaling readiness indicators then of course you can also aggregate that at the systems level and I think honestly if you look at the mail system that they are using at the moment and the indicators you cannot really attribute those numbers to impact or to really the fact that whether or not we are doing a good job last year I think in their annual reports they were talking about that 400 innovations are ready for uptake I don't really I think that a lot of it is based on self-reporting and I don't think that that is a good idea but you start asking some critical questions I think you can easily punch some holes in that so our perspective is really that we should go for independent assessment by experts who know the context and know the innovation so I'm not going to ask a livestock expert to assess the innovation readiness of an innovation platform I'm not going to ask a social scientist to assess the innovation readiness of a new chicken breed so you know we really need to become a bit more serious I think about what kind of evidence do we use to underlie these kinds of statements that innovations are ready for uptake it's about the car it can be ready for uptake in Europe or in Tanzania but not at all in Nigeria so I don't see how these kinds of monitoring the evaluation tools and making these kinds of statements they don't make sense for me as an innovation and training scientist because scaling is way too complex to generalize in that sense so we need to try to think more locally about the packages, about the constraints what are we doing to overcome them and then you can say like in this district because it's close to the market this innovation may work but in the next district which is maybe a bit further there's no road, there's no trader that's interested in going there to source feedstock the innovation is not ready for the market at all it's not ready for scaling at all that's my feeling Thank you very much I think we'll have to stop it here for today's session Mark, really thanks a lot for spending the time and for sharing your experience on scaling readiness with us and we'll look forward to continue the interaction with you Thank you very much Thanks so much for the platform and for the interesting feedback and questions