 So, my name is Captain Pfeiffer and I work as a special analyst for ILRAE. As a special analyst, you do a lot of maps. For example, livestock distribution maps or poor livestock keeper maps. And then one day these people come into my office and say, let's integrate gender. And I look at them and I think, really? I was quite upset that they never really understood that what's the gender component of a livestock distribution. And I felt really misunderstood. But then I thought, let's take up this challenge. Let's challenge these people and show them, guys. I thought about it, maybe we can do some gender maps. So I went back to my office, put some thoughts in and decided, let's just do it. So, I arrived there and thought, how are we going to go about this? Crazy gender maps. And I started looking at many data, we had many frameworks. And I started to focus really on the concept of value chains. I used an economic model, which then I expanded with some gender concepts. And when this was nicely set, from there I defined which variable we're going to use and how we're going to go about that. So the data we ended up using was an OECD data, which looked at which policies are in place in every country and whether customary law was more important than a national policy. So, just if a woman might by law get access to them but men were never allowed in a country, that would be accounted for. Then we also used the so-called DHS data, that's the demographic health surveys from USAID. This is a women interview, that's a lot of women have been interviewed and there we have information on whether she owns land or doesn't. If her husband is actually allowed to beat her up. So there's quite a lot of information in this DHS that is individual data. We combined all the data and put it in a factor analysis. Factor analysis is just a method that reduces this big amount of data in something that you can handle. And we ended up for Africa, for example, with what we call six factors which you can also see as six different gender contexts. We could map those then and have an African map. It's an African map with gaps because we don't have data for all the countries to show these different contexts. So for example, we looked at labour in the household. A woman has not so much time to be involved in the value chains if she has a lot of children on a big family to take care of. So we saw that for example in Senegal, this is very, very important whereas this is much less important in East Africa. So it means if you come with a technology that's very time intensive it might not have so much success in Senegal whereas it might have more success in East Africa. Now we have these maps and we want to use them and how we use them is what we refer to as targeting. That means we try to understand the context where something has worked and if something has worked somewhere it might not work in the other place just because the context is different. Until today we looked at contexts mainly as biophysical like rainfall, soil quality. These maps now we have actually a new world opening to us because we can also look at what is the context, the gender context where a woman is moving in and therefore take this into account when we define what works where.