 Good day all. On behalf of the Food and Agriculture Organization, FAO, I would like to thank you for this opportunity to participate in this share fair. Climate action is a global priority and geospatial tools and applications play a critical role in targeting, monitoring and evaluating climate change mitigation and adaptation investments. As highlighted by the COP26 goals we currently rise to the challenge of the climate crisis by working together. FAO is proud to be present and concretely contribute to meeting these challenges. FAO's mandate includes supporting and assisting countries in the transformation towards more efficient and inclusive resilient sustainable agri-food systems for better production, better nutrition, better environments and a better life. Our big force, leaving no one behind. The FAO hand-in-hand initiative aims to accelerate agricultural transformation and sustainable rural development, to eradicate poverty and to end hunger and all forms of malnutrition. A territory approach has been adopted, identifying the biggest opportunities and designing interventions and investments that will raise the incomes and reduce the inequalities and the vulnerabilities of rural populations who constitute the vast majority of the world's poor experience. In the agricultural development and the latest advances in agroinformatics, we have developed the hand-in-hand geospatial platform. Here I'm preparing an example of how the platform has been used to identify mobile storage locations. The platform federates and integrates data on crops, livestock, water, climate, fisheries, forestry, trade, social economic factors and it's sourced from across the organization and our partners. The platform is accessible through a web browser and there's no need to install any additional software. It leverages open source software and cloud technologies. So let's take a look at the platform in a bit more detail. So I will remove these layers and let's have a look in the catalog. Let's start with crops and vegetation and take a look at the agricultural stress index. So we'll start out with this decadal data so it's near your time, that's every 10 days and the resolution is approximately one kilometer. So I'm going to add that to the map and let me zoom out a little bit so you have a better view of that. And as I said this supports time so we're able to either pick a particular time so let's jump right the way to today and let's see what's the latest one. So the latest one we have is for the end of October and we can also step through time so I can go back to the previous time period or I can jump to a specific time on the timeline by clicking here. The other thing we can do is to click on the map itself and we'll get some information about that specific area that I just clicked on. Okay so staying with time we can also play it as a video so if I play here the map will start to update so I hope you can see that changing as the time updates. So these are the days going by so let me change to a bit different part of the world for you. So you'll see that the map here is updating so we can see an idea of the evolution in this particular dataset. So I'll pause that there and let's continue it at all. Let's remove the climate stress index and go back to the catalogue and let's choose some climate data so let's take some historical precipitation. We'll start out with the monthly precipitation from the climate hazard group chirps and we'll put that onto the map. Let's make it a little bit more obvious. Okay let's zoom in a little bit perhaps and I hope we can come down here to eastern Africa. So you can see again that we can we can step through time in a similar way as we could with the agricultural stress index but what I'm going to do now is to add some long-term historical averages. So this is a long-term monthly historical average I'm going to split the screen into two. I will put the long-term average on the right hand side of the map and I will put the the current year monthly rainfall on the left hand side. So here we're looking at this September so let's do the same over here we'll change this also to September and in that way we can get a very quick a very quick way of seeing how the current rainfall is in comparison with the the long-term average. So let me make the long-term average a little bit bolder. Okay so you'll see that actually it's been a bit wetter here in the region where I'm moving back and forth than the historical long-term average. So let's remove the long-term average and let's add let's add some future information so I'm going to add the future forecast. So let's not link this with time so they don't move at the same time and again put it only on the on the right hand side and so we are looking at the November forecast on the right hand side and it's scheduled to be well forecast to be quite a bit drier and on the left hand side we have October so you can see that that the forecast here is for it to be rather drier than typical so this is in fact an anomaly so what we probably should do is to remove that and to go back to our historical and add a three monthly anomaly instead of instead of the actual so this is showing the difference in the expected rainfall so over here you'll see in the in the Hall of Africa it's it's drier than anticipated and its forecast to also be drier than expected okay let's remove that and let's go back to the