 My name is Joang Amanda, I work with the University of Malawi and also with the straights. So this use case is about the national education management information system in Malawi. This is a national system to support various work practices at community level. So the setup of this service delivery model is such that at the lowest level in the country we have water port community extension workers or the official title is agricultural development officers. So these extension development officers visit households and provide various assistance and education related to agriculture. So what we have then is the national agrarian management information system, which is an effort to consolidate and support practices at community level. So with this system we have different capabilities, some of which are displayed here. So there is an early warning component. So the early warning component is important because agriculture has a lot to do with weather and climate impacts. So there is the component that relates to the weather and climate data. But also the other component to early warning is being able to determine the amount of food that a country is going to produce and also being able to see and determine whether specific households are food secure or the food insecure. So those are the components that make up this component on early warning. So I would just highlight one thing here, like you have this household food situation assessment. So here the extension worker at the lowest level visits households every fortnight looking at the number of people in the household, the type of food they have available, livestock they have available and similarly in the end trying to determine whether the household is going to be food secure for the next fortnight or not. And then based on that different interventions can be organized to support those households. And then there is also the component of the agricultural extension. So this is to support extension services. So there are various areas of support. For example, nutrition or water courts, the tracking of water courts are lead farmers. So there is a lead farmer register. So the lead farmers are model farmers. So you look at the lead farmers, what sort of training they have. You also follow their followership. So who is following them and what are they being trained on. So and then they also, for example, what are called model villages where specific target interventions can be implemented for for studying and then also propagating those to others. And there's a component on resource allocation. So which is at two levels, one at the household level. So we want to see what's happening at households. So like displayed here, this is the household registration form. So with the household register, you get household demographics and also types of enterprise. The household is part of whether they're part of an agricultural cooperative. So because government supports now is being organized around the government. I mean, around these cooperatives. So you want to see whether a household belongs to a cooperative so that they're able to access necessary help. But also one key thing here is that you do check whether the household is getting support from NGOs and also from government projects and what sort of supports these households are getting. So based on that, you can see the areas in which the households are getting support and whether they're benefiting from a multiple intervention. So this will also provide a basis for more equitable allocation of resources. And at the higher level, let's say at district level, then there's the project implementation tracking. So just looking at what projects are implemented, where they're implemented, what sort of activities they are supporting, the budget lines and also the duration for those projects. Then there's a trade and marketing component. So for the trade and marketing component, here we are mainly looking at collection analysis and dissemination of data on commodity prices as well as prices of input. So under this, we have designated government workers that are called enumerators. So these go through markets at the moment they're collecting data on a weekly basis. So they would track different commodities. And so usually they would sample multiple sellers and then get an average price for a commodity and then report on that. And then they also do follow the inputs, realizers and other inputs in the market. So those provide a basis for monitoring the changes in prices and also the possible impacts on livelihoods and agricultural activities. And another key component within the NAMIS platform is we have the components on livestock. Here we have different tools for monitoring livestock dynamics and also for other animals. So essentially you look at things like your births, deaths, transfers in, transfers out and animals storing and livestock storing and for other animals you can also look at vaccination status. So all that comes under this component on animals and livestock. So there is an extensive number of forms that supports that. So one key thing also to note within the system set up, we do have a structured reporting hierarchy for the standard units. So in terms of work and service organization, we have at the lowest level what's called a block. And then blocks are grouped into sections so mainly the eight blocks make a section. And then sections are grouped into extension planning areas and above the extension planning area you have a district and then an agricultural development division and then the national level. So this is the typical administrative structure. But also within the platform we do have embedded all the markets in the country and the weather stations as well as fishing points and reporting units so all that within the platform. So in terms of implementation and daily work you have the extension worker working with a mobile device collecting most of the data and then sending that to a central server from where now the data can be accessed by different stakeholders based on what sort of access they granted. So I think in short I would say that's about the Malang National Advocatory Management Information System which was built on top of DHIS2. And also maybe one note is to extend on the capabilities because for some of the work here it's based on sampling so we can't do that out of the box with DHIS2 so we've built some apps to assist with the sampling and also even for some product households because you're dealing with hundreds of households so you can't have someone manually enrolling those households to the necessary tools for data collection so the app also does handle the bid for making this sort of assignment. And then in some cases we've also built some other apps to extend the platform to do this to best suit this list.