 They will arrive from there. OK, the use of it. Just give it a press, and it will appear when you go forward. And backwards. Thank you. Thank you. OK, thank you. Morning, everyone. So my name is Rodrigo. I'm a senior lecturer and a researcher in the National University of Agriculture in Dine. And this work that I have a pleasure to present to you was in a frame of a PhD work of the first offer at the study, which is already published. So it's entitled, Carpene Genetic Structure and Ecology Coordination Model in Casing Grown Uts in West Africa. The outline of the presentation is as follows. First, we will present the context. Then we follow the data and methods used. We will follow with the main finding and some take-home messages regarding the context. Alphan crops are really important. Resources in smallholder farmer food systems, especially in sub-Saharan Africa, because they are seen as staple crop with high nutritional and economic value and largely grown in marginal area. Unfortunately, they are neglected in the mainstream research agenda. And data regarding those species are scarce about their potential to withstand current and future climate variation. However, defining how and where both species will adequately respond to environmental variation is a central topic, especially regarding plant science research. So this study, which was implemented in 2021 and published in 2022, uses Casing Grown Uts scientifically named as macrotiloma geocarpom. As a case study, to undermine the response of these crops to future climate using genetic information coupled with a critical niche modeling approach. So this is what we call macrotiloma geocarpom, Casing Grown Uts. And across West Africa, we have six landways, six of each. And the landways is domesticated, locally adapted, often traditionally recognized variety of species. And we have this spread across climatic zone in West Africa. So this is the area that we cover for the study. It's come from Bukina Faso to Benin, Togo, and Ga. On feed, a total of 3,361 accession of Casing Grown Uts were collected from both countries. And wood data were used. Leaves, neon leaves of the species of the landways were collected and used for DNA extraction and other lab manipulation. And the program structure was used to assign both individual collected on the ground to different genetic cluster. And different genetic analysis were performed, such as molecular analysis for viruses, farewell, F-test, and et cetera. Other analysis were performed on it. As regards the ecological niche modeling path, during accession collection, occurrence data of landways were also collected on the ground, also gathered from the labs, and added to online databases. In addition to that, we also collect environmental data, especially climate and soil data. Climate data were mainly bio-climatic data gathered from AFICLIM websites. And for soil data, we got them from ISRIC websites. So both data were processed, and the model were run using the maximum entropy methods. To test for equivalency identity of a niche between population and also the species and the population, we used the Schöner-D, Schöner-Indus, and the Simularity-D-Indus. Of these were implemented using NTBus with R software. In addition, we also implemented the MOP analysis, which were used to describe the changes in the distribution pattern between the calibration area and the validation area, especially between the present and the future condition. What did we get as main funding? It's come out that, sorry, this was the study area that we covered, and I can show you that we have the population spread across all this area. So getting back to the results, we found out that despite the low level of diversity that we found within the Kessing Grand Dots, two main populations were discovered. For each, the population one and the population two. So here on this graph, you have two populations from the green and blue, and all those land races were spread across both populations. So on this, you can have a vertical line which represents individual within population, and those with more than one color share genetic information with other populations. So we can see a random distribution of land races within population of the species, and this was shown in this map. You can have a population one in, oh, sorry. You can have population one in blue colors, population two in green colors, and the species data that we collected on the species from the beef on red colors. And here are the five zones that you have. We have a tropical rainforest, we have a southern guinea zone, we have a northern guinea zone, we have a southern Sudanian zone, and the northern Sudanian zone. So data were collected across all this area and model also run across the area. Regarding the critical niche modeling, both climate data and soil layer were important in running the model. So you may have for each population of, and the species, different combination of climate and soil layers. As shown on these graphs. This is for the species overall. This is for the population one, and this one applies for the population two. And data there have a contribution of each of the variable to the model. Regarding the identity or similarity of niche among the species and each of the population, we notice that niche between the species and the genetic population were not identical at all, but they were similar. On each of these graph, you can find the environmental space for the species, environmental space for one of the population, then the difference between environmental space of the species and the population, and here the correlation cycle coming from the principal component analysis describing radiographic distribution of the environmental layers. And these graphs also apply for other combination that's the species and population one, and the population one, and population two. When we take into account the spatial distribution of the suitable area for the cultivation of the species and each of its population, we found globally that we have large area with suitable condition for the cultivation of the species and each of the genetic population. So here you have for the continuous map for the species, for the present day, for the future one, and for the future two, and here you have a binary map for the species, sorry, for the present day, for the future one, and future two. And the common maps show the same trends for the population one, and also for the population two of the species. So we have similar finding for them. When we consider the map analysis, the result indicated some dissimilarity from extrapolated condition in the two features scenario. That's mean when we consider mapping the spatial distribution of species in the present with the future, we have some area in red which represents dissimilarity, complete dissimilarity in the map area of the spatial distribution and blue area which looks like a similar area where the species is found in the present and also could be found in the future as suitable area for cultivation. So this map is really useful for strategies, strategy towards a valorization of such species, especially in sub-Saharan Africa. And I can tell you that this is one of the most expensive kind of ground that we have in sub-Saharan Africa. It can cost from one euro in availability season to four or five euro, sometimes six euro when it is not available. So this is really important for decision-making regarding these species, especially expansion is cultivated on area. So what can we have as take home message? When we use our approach, we identify two genetic populations and also cultivable area for the gen plus and our results also enhance the available gen plus and better direct breeding priority for the future of such species. And our study highlight the importance of incorporating genetic data in the ecological niche modeling to obtain a finer information for future distribution and also explore the implication for agricultural adaptation with a particular focus on identify priority action, especially regarding often crops conservation and breeding in sub-Saharan Africa. And our overall trend shown by the result indicates an increase in the climate suitability for the species cultivation in West Africa. So next to this research, what we have to do next is to set experimental, feed experimental on the ground to see how well the area that we identify suitable to harvest species will really respond with time. So thank you very much for your attention. Thank you.