Okay, so thank you everybody, Salam Aleykum and good morning here and in F.A.O. in Rome but certainly good afternoon in Bangladesh. You have seen the agenda, the opening session will be done by the two gentlemen from F.A.O. and from DBS. Maybe the F.A.O., the assistant representative of F.A.O., would you like to start with your welcome address? Oh yeah, thank you very much. Good afternoon and good morning colleagues at Rome. My name is Nurkondakar. I have been working as an assistant F.A.O. representative program with the F.A.O. Bangladesh. So nothing to say, just welcome all the training participants and this training is being organized based on the request from the Bangladesh Board of Statistics. And thank you for the government departments for nominating their officers for this training. And I would like to request all the training participants kindly listen the lectures, ask questions to enrich your knowledge. And also I'd like to thank our HQ colleagues for your for acceptance to organize this training program and the resource persons. And thanks to our colleague Dr. Amirul for coordinating with the HQ. I wish a successful training. Thank you very much. That's all from my side. Okay, thank you, Dr. Nur. Now, Mr. Alauddin, as that director of agriculture, if you want to say a few words of welcome, please. Good afternoon and good morning in Rome and assalamualaikum. I have nothing to say just to say hello and welcome like Norway. Thank you all. Thank you for Rome and F.A.O. headquarters and F.A.O. Bangladesh to arrange such a program for BBS and which will help us learning many things about the SDG indicator. And I also want to thanks to your team, Mr. Naaman, Mr. Paridun and others team member for giving us time to deliver your SDG indicator 2.3.1 and 3.2. And also thanks to Mr. Amirul to cooperate with us and for this training. And I also hope that our participants will follow the norms of the training you have signed earlier. How can we ask the question and which way you have signed the norms of the training? I think I hope that our participants will follow the norms and I also hope that this training will be fruitful for us and we will work together in future for this indicator. And thank you again for arranging in this training. Thank you all. Thank you Mr. Alauddin. And now we go to the introduction and objectives of the training. But before we start talking about the objectives, I give the floor to Mr. Paridun, who will be the facilitator for this training all the three days. I think that he will be the one dealing with all the logistical and technical issues. If any participant has a difficulty, please contact him and he will try to help you. So Mr. Paridun, please talk about the rules of the virtual meeting. Sir, may I interrupt? Please, if you show the participant list, those who are not joining, we can communicate with them. But we are not seeing the participant list. I have sent the participant list to Dr. Islam. No, I think he is out now. In the screen in June platform. Okay, Dr. Mr. Paridun, can you show that? Okay, let me. Mr. Alauddin, you are now, okay, I think you are still attendees. Let me. I will prompt you to a panelist. So you will be a panelist now and after some time you will see the list of attendees. When you go to participants and you will push attendees, you should see the list of attendees. Okay, thank you. You're welcome. Can you also send it to Mr. Kamrul Islam? So you should make Mr. Kamrul Islam also as a panelist. Okay. No, no, no, I haven't done it, I think. No, I've seen the panelist. Really? Yeah. So Mr. Kamrul, can you see the list of participants? Mr.  You should see the attendees and yeah, you will identify who is here or who is missing. Can you please also to include Dr. Noor Ahmed Khandogar for the time being. Let him know. Let him be the panelist. Can you say? Our assistant, Noor Ahmed Khandogar. Noor, Dr. Noor. Noor. Dr. Noor is a panelist. Okay, okay. No Faridun, Dr. Noor is among the participants, he's not a panelist at the moment. Okay. Okay. No. Yeah, sometimes it works very slow. Now I don't know what's wrong with this. I'm trying to promote him to panelist but system doesn't allow me to do this. So yeah, shall we continue? So we have two panelists for the moment who can check the list of attendees. We have Dr. Kamrul. Dr. Amirul, yeah, Alauddin. Mr. Kamrul, can you check please? Yes. Sorry, we are not seeing. Sorry, we are not seeing the participant list. Mr. Alauddin, I don't know but yeah, I cannot promote you to panelist. Maybe there is some limitation for panelist. Oh no, now you should, we'll be joining webinar as panelist. Also, also please know where they indicated. I think what he is asking is where can he see the list of the participant? Sorry. Where can he find the list of participants? Okay. So he needs to go to participants and push attendees, which is on the right side. And the attendees, there are now 27. You can see all of that here. Alauddin, you are also a panelist. You should go to participants and push attendees. Thank you. Now we got it. Have you included Dr. Amirul Kondukar, assistant FAA program? He is saying that he had some technical problem adding more panelist. That's why the assistant director, assistant representative was not among the panelist for the moment. Mr. Amirul Kondukar, I don't know but I cannot make him panelist. I don't know why. But we have already three people to monitor this, right? Yes, please. From your side. I think you should go find Amirul. We have Amirul. It's okay. It's okay. Yeah. Okay. If it's okay, can I continue? Can we continue? Dr. Alauddin. Okay. Go ahead, Alauddin. Okay. Thank you. I would like to briefly about some rules of the meeting for today's webinar. And please be attentive. Okay. It's preferably if you use your laptops rather than your mobile phones or tablets. Please make yourself comfortable and please select some silent place. You know, the additional sounds do not disturb you during the webinar. If you have any additional applications opened, please close your applications. But if you see that there is some problems.  And it will save some, let's say, megabits or kilobits of your internet. It is proposed to updates and your zoom before the webinar. Every time you should check the updates and download the new version of the application. Preferably if you have the headset or air phones, please use them. And session from the beginning until the end will be recorded.  Please, every time after your, after your speech, mute your sound. If you have any question, this is a release to attend these. Please push the Q&A part and write your question. Or there is another way, there is another method to ask the question is by pushing the right hand. If you will see that you have right your hand, we will give you a floor so you will be able to provide your question. So it's preferred that you properly write your name. And for the time, the team will ask the questions, the quiz zoom. Please ensure that you reply all the questions. We are planning to do this, this quiz at the end of the webinar on the third day. It will be some evaluation questions. So please reply to this evaluation questions. If you have any questions, as I said, please push the Q&A or raise your hand. And in the chat, please send the message only to me if you need help, if you will need any kind of the help related to zoom. And if you have any technical issues, as I said before, this virtual meeting with virtual training webinar will be recorded. And at the end of the webinar, at the end of the third day, we will provide you, Mr. Naman Keita will provide you all the supporting documentation. This is the PowerPoint presentations and other necessary documents. Thank you very much. And good luck to everyone. Okay, so anybody has any very, very quick and short question regarding how to handle this remote training? If not, we just go to the next part of the presentations. Okay, so I think there is no additional questions. So we go to the next item of the agenda, which is the introduction, the objectives of this training. Before that, I would really like to thank all the people from BBS. I see that initially we are thinking that it would be from BBS, but we saw that also the Minister of Agriculture sent some participants and the Minister of Food also sent some participants, which means that there is a very good interest in this training. So we are very happy with that. And we accept that we will have a very successful presentation, training. And I'm trying to open now my PowerPoint. Okay, I don't know if you can see it. Everybody can see my presentation. Okay. Okay, so this is a very, very short one just to introduce this training and what are the objectives and what we expect at the end of this training. So just few words about the background of this sustainable development goals. They were adopted at the UN summit in September 2015. The title of the summit was Transforming Our World, the 2030 Agenda for Sustainable Development. And during this political high level political meeting, they adopted 17 sustainable development goals. And among those 17, there is a goal number two, which is ending hunger, achieve food security and improve nutrition and promote sustainable agriculture. So that's the goal which is of interest to us for this training. Further down this goal has been broken down into eight targets, including target 2.3. The target 2.3 is to double agricultural productivity and income of small scale food producers. Now, you know that all this process has been started with the political people, but when it comes to monitoring those goals and target, you need to talk to the statisticians. And you need to translate these political statements into actionable statistical objectives and tools and indicators. So that's where this indicator 2.3.1 and 2.3.2 comes in. So after several technical meetings of the interagency group of the UN, it was decided to select these two indicators, 2.3.1 and 2.3.2 in order to monitor the target 2.3. But I think the next presentation by Jacob will provide more detail on the indicators 2.3.1 and 2.3.2. Again, the UN has decided that some of the institutions are custodians of a number of indicators. The number is the custodian of 21 indicators, including indicators 2.3.1 and 2.3.2. And once you have defined the indicators, the issue is to define a detailed methodology that can be accepted by tools, by all, and that can be used to compile the indicator. So after several discussions, meetings, and consultation with member countries, in 2018, indicators 2.3.1 and 2.3.2 have been reclassified as tier 2 indicators, which mean that an internationally agreed methodology is available for the data collection at the required levels of disaggregation, computation, and reporting. So this is a very, very important step because without a clear methodology, you cannot compile the indicator. So that's why the focus of this training will be on the methodology to compile indicator 2.3.1 and 2.3.2. So once the methodology is defined, FAO has been working with the countries for the old 21 indicators that has reached the level of tier 2 to try to build the capacity in the countries in order to collect, monitor, and compile the indicators 2.3.1 and 2.3.2. Because at the end, this should be done by the countries themselves. So the capacity building is a key element in the process of monitoring of these indicators. The issue is that, you know, FAO has a long experience in capacity building, but with the enforcement, COVID pandemic, this resulted in restrictions in international travels so that the traditional way of doing capacity building could not be used anymore. So that's why instead of face-to-face training sessions and thanks to this advanced in technology like Zoom and Ovid platform, it is now possible to do the virtual training, doing it remotely. And so this is a new method with all the challenges that it goes with. Of course, we have seen this morning and also certainly we cannot enjoy seeing each other physically, but at least it's an effective way of providing the training also. So basically the aim of this training is to build capacity in the national statistical offices on the methodology, data collection, tools, and reporting mechanism for SDG 2.3.1 and 2.3.2. Specifically, the objectives or the trainings are four. One is to build capacity of the staff in Bangladesh. And as I said in the beginning, initially we are thinking of BBS only, but now it's expanded to the Minister of Agriculture and the Minister of Food on the methodology, compilation and interpretation of 2.3.1 and 2.3.2. To introduce the tools for data collection, including an overview of possible data sources, the third day we'll talk about that. And to discuss the available microdata useful to compute and report on indicators 2.3.1 and 2.3.2. So this initially we were hoping that we could have a concrete microdata level for these indicators from Bangladesh, but we thought that maybe it's better to do the indicator and take some over-country data as examples. And then after that maybe another time to see, to support Bangladesh in using their own microdata because it requires microdata from holding level microdata, not the aggregated data. We need the data at the micro level, the holding level. So we'll see if this kind of data is available in Bangladesh and if not, what are the gaps and how we can work together using the tools that FAO is developing, the aggregates and overs, in order to fill the gap, the data gaps so that the indicators can be calculated. The outputs are too basically, it was 37 but now you need to correct this. I think I counted 41. So we'll have 41 staff trained on the methodology and tools for SDG 2.3.1 and 2.3.2. And hopefully at the end of this training also if you can agree on a short action plan to summarize the actual situation regarding the availability of the data and to identify the gaps and to agree on how we can fill those gaps, data gaps, in order to be able to compute the indicators. So this is basically the purpose of this training and the main points of the agenda, I will not go through all of them, you have seen them in the program. So basically after all this introduction there are two main things, two, three main part of the training. One is to define the small scale food producer. This will be done today. The second one with a practical exercise, also so we'll make a presentation and practical exercise. The second is the methodology and steps for computing the two indicators with practical exercise also. And the third one will be the data sources and cashiness and data items that are needed to compute the two indicators. As I said at the end, we hope that also we'll have here from our colleagues from BBS on what are the surveys going on now, what are the data gaps and how we can work together in order to fill in these data gaps. So more or less this is the objectives of this training and I am sure that we'll have a very successful training and if you have any technical or logistic problems, we are all here. Myself, I am Naman Keita, I am a retiree from FFO, I am acting as a senior consultant. In addition, we have Jacob who is a statistician also at the office of chief statistician in Rome, in the FFO headquarters in Rome. We have Aida Khalil who is also a statistician in the same office, office of the chief statistician. We have two other colleagues who are not present today because they are working on the practical exercises. So we have Audrey Sanu who is also a statistician and we have Max Zhang who is also a statistician. Now to support all this training, we have, of course, you know already, Mr. Faridou Akino who is also from the office of the chief statistician. So this is from our side, what we have and I will stop here so that we save some time and start with the training itself. Thank you very much. And now I think I give, unless there is any urgent question, I give the floor to Jacob. Okay, good morning. Salam aleikum everyone. Okay, can you see my screen? Yes. Good. So my presentation is very short where I'm in a little bit late due to different technical issues. Therefore, I will try to save some time from my side from this presentation. It's going to be very brief and short. It's just an introduction, introducing these two indicators that we're really interested in for this training. 2.31 and 2.32. Maybe before going to that one, I mean, Naman started to not talking about this issue of, you know, custodian agencies. And as you know, we have about 231 indicators to measure these SDGs with the goals of the SDGs and the FAO is responsible or a student for 21 of the SDG indicators, which mainly revolve around six of the goals in hunger, gender equality, clean water, food loss in the west, life below water and forests. So about 21 indicators, which is about 9% of the total indicators, which is, I mean, compared to the MDGs, it is really a large amount of indicators has become a responsibility of FAO compared to the MDGs, which shows that, you know, we showed that, you know, the issue of agriculture and the mandate of FAO in terms of sustainable development is becoming very clear. That's what this shows. So as a custodian agency, what do we do? I mean, as I said, these two under 31 indicators are divided among different agencies to be responsible as a custodian. So the first thing that, as a custodian agency, that we should be doing is develop the methodology, leads the methodology and development and documentation of both the indicators. For these 21 indicators, we all, FAO was able to develop all the methodological developments for all the 21 indicators are either tier one or tier two. Therefore, there is no any indicator under FAO custodian scholarship without, you know, methodological development. Therefore, the minimum, it is tier two and the maximum is, we have started generating data as well. Therefore, tier one. We also, as a custodian agency, are responsible to support the statistical capacity of countries to generate and disseminate data. That's what we are trying to do with 231, 232 today, because there should be a great capacity at the country level to make sure that the data, the indicators are generated. We also try to help countries collect data and ensure their compatibility, quality, consistency, and then finally, as a custodian agency, we contribute to monitoring progress at global level in particular, but also at regional and national level. Having said that, even though we really play a great role in terms of capacity and technical assistance, the driving, I mean, the driving seat or the countries are going to, should be at the driving seat in terms of generating data required for measuring this SDG and indicators. That's why we are trying to to the system. And specifically this issue of 231, 232 is part of a goal too, to end hunger, achieve food security and improve nutrition and promote sustainable agriculture and their goal too. As you know, we have different goals and this indicator of 231, 232 is basically focused on target 2.3, which says, by 27, double agricultural productivity and income of small scale food producers. This is, here is, we need to understand a lot of the technical issues to be understood in this simple statement. First, it is about small scale food producers. Who are they? Who are the small scale food producers? How do we differentiate the small scale food producers? Is this an absolute measurement or a relative measurement? Can we say like, you know, anybody with less than half an hectare or one hectare is a small scale producer. What about, you know, what do you mean by doubling the income? How do you measure this income? What are the critical components? What are the critical components? So, we need to understand this in terms of the big scale food producers that we should really take into consideration to globally, you know, measure this increment in income. Therefore, identification of the small owners. Identification of what components of income as to be considered to have the standard comparability result at the end of the day is very very important. That's what we are going to that no one is left behind. This is when we are able to desegregate this indicator and in different ways, like with respect to gender, indigenous peoples, family farmers, pastoralists and in different desegregation techniques, which is very critical to make sure that no one is left behind. Therefore, indicator 231, 232 are basically related to goal two, target three, and this really requires to make sure that we really know who are the smaller holders and how to really measure their incomes and productivity. How do we measure this productivity? What is the best way of measuring productivity? It is labor, it is land, these are the issues that we really to identify, but there must be some consensus to be built in order to make sure that international comparability among different countries can be committed. Therefore, measuring productivity in terms of what? Measuring income in terms of what type of critical income components if the type of discussions that we are going to have in the next three days. So under this target 2.3 to measure double the income and productivity of the smaller scale holders, there are two indicators derived. One is 231, it talks about the volume of production per level, per level. It aims to measure how much volume can be produced per unit of labor. Therefore, it is to make sure that this thing is doubled by the end of the SDG period by 2030. Therefore, we need the first one measures how much volume can be produced per unit level while the second one is talks about the average income of small scale food producers very disaggregated by different types of disaggregation including sex and indigenous status. Here it refers to the acute and actual net earnings that the food producers obtain from their agricultural activities. As I said, which type of income to be considered is we are going to see later on. So there are two things here. One is a volume of production. The other is the income. As we say, the target talks about double the productivity and income and we are going to measure using two indicators, one dedicated to volume of production, one dedicated to average income. But in order to measure these two things, the best critical thing we need to understand is who are these guys? Who are these small scale farmers? And why are we really interested in these small scale producers? I will come to that point in the next slide. This indicator, 2.31, it doesn't stand just by its own. It's really interlinked with different indicators and goals. In particular, these indicators are related to goal one in poverty, gender equality and good jobs and economic growth and goal 10, which reduce inequality. For instance, by promoting development policies in favor of small scale producers, the local economy will be strengthened. That means we are improving or we are contributing to the end of poverty. Therefore it's related to goal one of the SDG. The same, we are going to disaggregate to make sure that we have this small scale productivity increased and disaggregated by gender. In that case, we are really promoting gender equality. Target 2.3 deals with average level productivity, which is really related to good jobs and economic growth, which is focused of the goal eight. So as I said, this is not standing by its own, but it's really contributes to the achievement of the other important goals. And there is a bit of inter-relationship among different goals of the SDGs. As I said, why are we really interested in small scale producers? This is very, very important. Here, as we speak, more than 500 million small farmers are available worldwide. Most are related in red fat, no technology, no other means of production, but they wait for the annual season of rents, but they provide up to 80% of food consumed in the large part of the developing wallet. Therefore, without considering this large population of the wallet, who are contributing for the large production of food consumed in the wallet, we cannot really be sure that we achieve the SDGs. Significant number of people, mostly relying on rent, but contribute to the large amount of food production. Therefore, these are very important. Therefore, we need to make sure that we have adequate policies for these guys so that they improve their productivity as well as their income, so that we have no one left behind in this endeavor. Investing in small holder women and men is an important way to increase food security because they contribute to food security among the holding level, as well as in the community level, as we speak. Small scale food producers are one of the main workforces, and then we can't just leave this large number of workforces with this type of vulnerability, with less productivity and less income entering before the end of the SDGs. In general, supporting small scale food producers service multiple purposes, in particular, securing food security, sustainable rural livelihood, as well as global food production. Therefore, due to these main important results, this is important to focus on these small scale food producers. That's why we have a specific target, a specific indicator to monitor how they are progressing. This is not a new initiative to be honest, this issue of improving the small scale life livelihoods, but it doesn't move as it has been desired. Why? The first critical problem was absence of a common and clear definition of the target population. What is, who are small scale food producers? There was no any standard definition to be applied globally until you have this 2.3, the definition of small scale holders for computing this 231, 232. Therefore, that was one of the critical limitations. Therefore, different countries use different type of approaches, land size, livestock size, or income. Therefore, comparability was not really impossible. Therefore, lack of standardized definition of identification of these small scale holders hinders how to properly measure the productivity and income of small holders. So without this definition, if you don't have such standard definition, there is no systematic information about small scale food producers because you can't collect data. Based on what type of definition, I will go to collect data about these small scale holders. Therefore, no standard definition, it hinders unavailability of data. So if you don't have timely data, timely data and continuous the system of data generation based on standard definition, there is no way that you can develop effective, be able to design effectively a policies that should target this population. Therefore, even though there were some attempts in different very dismantled way, but there was no any standard things due to the three main the points that I raised. Absence of standard definition that lacks an ability of data and then therefore there is no any policy development process in this regard. Now, this issue of target 2.3 extraction plan is to fill this gap and come up with a proper approach so that this can be really measured properly. Therefore, the action plan of this target 2.3 focus in identifying small scale food producer which in the next minutes I will be presenting how to really identify the small scale producers, how to collect to make sure that data is collected based on this to address to generate data for these indicators and then through this service filter data gaps. Then once we have standard definitions, once we are able to have to fill the data gaps, then the next step would be to compute to measure the development on this small scale producers. Therefore, this training will be focusing on two issues. Now defining the small scale for our holders and how to manage that to generate data and compute the SDG to 3.1 indicator. Then the policy part will be a part of this training as you can expect. So this is what we will be covering in this 2.3 two days of the training. So when we try to report on target 2.3 it requires a regular monitoring of these two indicators 2.3, 1.2, 3.2. Therefore, we have developed methodologies that can entail into three steps. One is, as I said repeatedly, how do we identify the small scale for producers? That's one component of the training. The second would be how to compute indicator 2.3.1 which is output per labor unit. And the third one would be how to compute the income component which is 2.3.2. Therefore, we focus, we spend a reasonable amount of time for the first part which is very, very critical because we need to identify who are these guys, who are these small scale holders. Therefore, the next session will be describing that one. Once we identify those things, then how do we compute the productivity? What are the components that we should be considering in terms of computing the productivity? How do you compute this productivity per labor? And what are the components that we really should be considering in terms of computing the income as well? Which variables are critical in this methodological definitions that we developed? Therefore, these are the three things that we should be first focusing. Finally, before I close, the critical problem is having the data. This requires farm level data. Farm level data is required to make sure that we generate this indicator. This can be obtained in a regular service. In many countries, this is the challenge because regular annual agricultural service are not a usual practice in most parts of the country. To fill these gaps, FOS is proposing the agricultural integrated survey or agris program. And the purpose of this agris is to reach the 10-year gap that normally exists between agricultural services and the agris survey collects data every year for core module, which includes current agricultural production and its value. And then we can have a rotating module which collects about the economy, which collects about the labor, which are really important to compute this indicator.  And then, as Naman was saying, we should be hearing from you how your system is working and then how we can really come up with an action plan to make sure that this type of data can be regularly generated so that Bangladesh can compute SDG 231232 in a regular basis. So we need some type of system which can generate the data so that the data is required to compute 231232 so that we can generate data on 231232 in a regular basis. I think this is what I have as an introduction and the technical issue of discussing how to identify smaller scale producers how to compute 231232 is going to follow. Thank you very much. We have a question. Mr. Yakob, I see Mr. Mehenaz has a question. Mehenaz, please unmute your microphone and ask your question, please. Your microphone is muted. Mehenaz. Mehenaz, just click on the small icon for the microphone. Maybe it was a mistake. Okay, so if there is no... Anyway, the questions, we left some wrap up at the end and if the question, we skip any question now, we have time to raise them. So for the sake of time, maybe Aida, if you are ready, you can go with your presentation. Thank you. So salamu alaykum everyone. Good afternoon and thanks to all the participants for joining us today and to Naman and Yakob for the nice introduction. So I'm Aida Khalil, a statistician from the Office of Dishistatistician and I will present on one of the components of the methodology for indicator 231 and 232 precisely on the definition of small scale food producer. So I will start by sharing my screen. Okay, can you all see my screen? Yeah, yes. Perfect. So as I was saying, this presentation will only focus on the identification of small scale food producers, both in theory and in practice. In this session, we will see the theory behind this methodology and after the break, we will do a practical exercise to put this theory in practice. So this is the structure of the presentation. First I will recap target 2.3 and indicators that have been selected to monitor this target. Then I will give you a brief overview of all the frequently adopted criteria to define small orders that we found in the literature. When we were developing the methodology to define small scale food producer for SDG monitoring, you will see that all the approaches that are available in the literature can be either absolute or relative and we will discuss the difference between these two types of approaches. And then after giving all the, let's say all the background, I will introduce the methodology that has been adopted by DFO explaining all the concepts and definition and discussing the data items that are needed to identify small orders. So during my presentation, you will have various ways to ask for questions, either in the chat or in the Q&A section. Otherwise you can raise your hand and we will let you present your questions at the end of my presentation. Okay, so as Jakob presented earlier, the target 2.3 aims at doubling by 2030 the agricultural productivity and incomes of small scale food producer, in particular women, indigenous people, family farmers, pastoralist and fissures, including through secure and equal access to land, other productive resources and inputs, knowledge, financial services, markets and opportunities for value addition and non-farm employment. So the critical aspect here is to understand who the small scale food producer are. So the income of whom we should double and the productivity of whom we should double. Indicators, indicator 2.3.1 and indicator 2.3.2. Indicator 2.3.1 is about the volume of production of small scale food producer per labor unit. And indicator 2.3.2 is about the income of small scale food producers. Okay, so both these indicators are now in tier two. So meaning that there is an international agreed methodology for their computation and their reporting rate unfortunately is below the 50% of countries. So the reporting rate of this indicator is still very low. When these two indicators were endorsed, they were both in tier three. So there was no international agreed methodology that could be used to compute them. And the main reason why methodology was missing was the lack of an international standard and international clear definition of the target population, meaning the small scale food producer. So the methodology that the FAO has proposed covers three areas, basically the identification of the target population, the small scale food producer, the computation of indicator 2.3.1, so the productivity, and the computation of indicator 2.3.2, so the income. Now we will focus only on the identification of the target population. So here I would like to give you a brief overview let's say of the history of the endorsement of this definition. So the FAO started working on this definition in 2016 and beginning of 2017. And then it was submitted by the Interagency and Expert Group on SDGs in May 2017. In August 2017, this definition was endorsed by the share of the EIG SDG. And after endorsement, the FAO wanted to consult member countries on the proposed definition. So during the fall 2017, a FAO called for a global consultation thanks to which we received feedbacks from 58 national and regional institutions. So these refinements that were included in the definition and let's say that the 70 EIG SDG meeting, the methodology for the two indicators was considered acceptable, but some refinements were still needed. First of all, we needed to understand if including or not including no professional farm from the target population. And then like the definition needed to be adapted to countries with quite homogeneous farm scale where large size farmer may end up being considered small. So following an in-depth discussion and additional test between May and July 2018, it was agreed that the small scale food producer would be identified by using the approach proposed by the FAO that we will see in the rest of the presentation and then excluding OB farms from the competition. So OB farms should not be included in the definition of smolders and applying a maximum cap to exclude farmers that have revenues above 25,000 euro that have been converted in PUS dollars as 35 and 387 international dollars. So this is more or less all and this methodology was approved on the 6th of September 2018. So from that day, this is the methodology that we will see during this training is the official methodology that has been agreed by the EIG STG. So before entering into the details of the approach proposed by the FAO, I will provide a brief overview of all the alternative approaches that were found in the literature to define a small scale food producer. So indeed, before proposing a definition, the FAO performed an extensive literature review of all the approaches that were available both in the scientific journal but also the definition that were used by national statistical offices or international organization producers. Let's say that it was a challenge to reach a consensus on a definition so on who the small scale food producer are and this is because they have been defined in various ways depending on the context so on the use of the definition that needed to be done and on the specific countries. So in the, let's say generally speaking small scale food producer can include farmers, pastoralists, artisans, fishers and forest dependent communities. So some of the presumed characteristics of this population are in terms of type of production units. Small scale food producer are normally imagine as producer that cultivates small volumes on or use small plot of lands or use literal or new technology and rely mainly on family labor. On the other side for what concerns the economic situation it is assumed that small scale food producer very often belong to the informal economy are vulnerable in the supply chains with relatively low revenues and have often, let's say beside the on-farm activities they have also other off-farm activities in terms of income generation. So as in the literature based on different criteria based on they can be group on let's say four groups criteria based on the amount of factors of production for example the amount of operated land the amount of labor the amount of production then there are criteria based on the share of family workers in the old things the amount of family labor that is used in the olden criteria based on concepts referring to the connection between the olden and the market so the amount of consumption or the market orientation of the olden and criteria based on the economic size of the olden so the revenues generated by the olden so by reviewing all these definitions of the land size so the first type of criteria criteria based on the amount of production and particularly on the amount of land use was the multinational definition of small-scale food and the number of or the number of life sockets. So all these criteria need to be quantitative there is a need to set thresholds that allow to separate small-scale food producers from other producers these thresholds can be set either in absolute terms or relative terms so let's see what is the difference before continuing I want just to make sure that my connection is working well and you can hear me so if someone would confirm that you are hearing me you are a good idea okay thank you Jakob so as I was saying all these criteria need to be quantitative so for example if we look at the amount of land we need to find the variables or the amount of land that can be quantified and the threshold should be established to separate small-scale food producers from other producers so these thresholds can be set in absolute or relative terms so an absolute threshold is a threshold that is fixed and it's the same for all farmers of all countries let's say so a threshold that is the same for all countries regardless the for example the agricultural logical zone the socio-economic condition of the countries of absolute threshold if you consider for example the land size approach is to for example coal small all the food producers that operate less than a given number of hectares of land so for example two hectares of land a relative approach instead is a relative threshold that changed depending on the reference system so on the context so within the monitoring framework the threshold would correspond to a specific percentile of the selected criterion variable in each country so for example all the production unit falling below the 25th percentile of the land distribution can be considered as part of the group so absolute or relative approach this is the main distinction so the pros of an absolute approach is that it can enhance comparability across country but at the same time it disregards difference among national context so what is small in one country could be big in another country let's consider for example like Australia that operates or countries like Brazil that operate big lands so all the farmers have relatively very big lands so if we would have an absolute threshold we may end up with zero small scale food producer in that specific country although you may have for example producers that are relatively small compared to the countries the producers in the countries on the other hand the relative approaches are let's say don't allow perfect comparability but allow identifying in each country producers who are relatively disadvantaged in terms of this criteria so this type of approaches reflect more the country specific differences so for example in a country like Brazil where all the food producers operate big amounts of land you would still have small scale food producers because there will be a producer that operate an amount of land that is small if compared with the let's say for example the average size of land in the country so let's see now the approach that has been used by the FAO first of all we focus on the criteria that have been used so the FAO adopted a methodology based on the combination of two criteria the physical size of the farm and the economic size of the farm we will see later how this criteria are combined together but for now let's just focus on the concept so to define the physical size of the farm we look at the size of the land and the size of the earth so the number of animals in the farm to see the economic size of the farm and the amount of revenues generated by the farm so using this criteria the FAO adopted a relative approach so to identify producers who are relatively disadvantaged in terms of access to land availability of livestock and economic revenues with an homogeneous criteria so the three criteria are combined adopting a relative approach and this was done because in a context like the SDG monitoring framework where many like all different countries need to be put let's say on a comparable scale using an absolute approach would disregard national differences and perhaps in the end turns also in less comparability because as I was saying what is big in one country it may be small in another and here I put a little example to explain a paradox that we could incur in when using for example an absolute threshold such as I don't know five vectors of land to define small owners imagine like Australia where everyone has big parcel of land you would have a very very very small percentage of farmers operating less than five hectares perhaps only of the farms and then you would have countries like the Netherlands where there are a lot of farmers operating small parcel but using very productive agricultural systems such as I don't know greenhouses or these let's say enhanced technologies where very small parcel can be very productive so you would say classify a small small scale and vulnerable the 80% of farmers while perhaps they are not actually so if you decided to use this relative approach by combining we said two criteria so the physical size of the farm which is expressed by the land size so all if we look at the land size all the producers that fall in the bottom 40% of the cumulative distribution of land size in actors are classified as small with respect to the land we will see later out with the meant this definition so producer falling in the bottom 40% of the cumulative distribution of the land size then with respect to livestock you have the producer falling in the bottom 40% of the cumulative distribution of total livestock heads then with respect to the livestock type define the physical size of the farm for this small scale food producer definition and then we have the economic size of the farm where we classify as small all the farmers that fall in the bottom 40% of the distribution of total revenues measured in PPP so in the PPP of 35,387 US dollars so this was a cup that was suggested by by Canada or yeah but by some developed countries that we're saying okay a farm can be small but should like should not exceed revenues of this size because with revenues greater of this size is considered a farm as small so in order to be classified as small-scale food producer a food producer needs to satisfy condition 1a 1b to plus the cup so all together so to conclude we have this situation so we have the producer in the bottom 40% of the cumulative distribution of total revenues and the intersection of this group give us the small-scale food producers so all conditions need to be satisfied okay so let's see important concepts and definitions so we said no that we use an absolute sorry a relative criteria and then we have the producer a relative approach and three criteria the physical size of the farm that looks at the land size and the earth size and the economic size of the farm that look at the total revenue so here we have to understand okay how do we compute the land size what type of land do we consider in this calculation when we look at the earth size we have to understand how do we compute the total number of like the life's accounts in an internationally comparable way and then for the economic size we have to understand which are the components of revenues that should be considered so for what concern the land size the definition of small-scale food producer use the terms of the concept of operated land the operated land includes only plots that are actually used for agricultural activities so these include when you will compute the land size you will have to consider all the land that is cultivated with permanent crops then all the land that is cultivated with temporary crops and the fallow land so the land that is left uncropped and not dedicated to grazing so the land that is let's say it's left there to rest and use for next years and it exclude all the land rented out so the ownership of the land is not considered in this section you will include also all the plots that are used for these purposes but are rented from someone else and we exclude the land that is rented out so that is used for other farms the forest land and the land that has been abandoned prior to the reference period so these are the three components that should be included in the computation for what concern the number of livestock we needed like to use something comparable at international level so instead of looking just at the number of cattle plus I don't know the number of buffalo sheep goats and so on we refer to let's say an international scale of conversion factor that is called tropical livestock units so for each type of livestock you have a conversion factor of course this conversion factor depends on the on the region you are in so for example for cattle you will have a conversion factor of 0.5 for buffalo 0.5 sheep 0.1 and this unit of measures standardize different types of types in a single measurement through a conversion factor that is valid for specific livestock types in specific areas of the world so basically what you should do is when you have the you look at the livestock stock in agricultural owned in spire livestock category this should be converted in tropical livestock unit before determining the livestock size for the definition of small owners so then the total revenue this is the last component as you know like it wouldn't make sense to consider the economic size in terms of volume of production per se because it doesn't make much sense to some for example apple the production of apple with the production of maize and carrots and so on so looking at revenues allow us standardizing the production volume for different products so normally when you compute revenues you have the volume of production multiplied by a cost and selling cost and selling price so in the small older definition depending of course on the data that are available from your survey in theory you should include revenues from crop livestock fisheries, aquaculture and forestry very often we work with countries where only revenues from crop and livestock are available but of course if other components are available they should be included so revenues are computed with this formula so where you have the VEKT stands for the volume of reduction for product E of producer K during year T and then PAKT stands for the cost and selling price received by the small scale food producer K for the agricultural product T during the same year T so basically what you have to do is for an even producer you have to find let's say the volume of production by product in again here multiplying it by the cost and selling price and some the value of production for each product to get the total revenue of the farm so the revenues should not be expressing your local currency unit but should be converted in purchasing power policy US dollars so that we have a comparable measure of these revenues like we put this revenues in an international comparable way the conversion factor to produce PPP US dollar revenues can be found in the link that I've put in these slides you see there's you will be able to select your country and find the PPP for a given given here so normally you have the revenues expressing your local currency unit it's enough to multiply it by the conversion factor that you will find at this link and you will get the value of revenues that you should use for the small older definition okay so now we can see together which are the main components of revenues so very often you will find yourself with the value of production directly to be multiple sorry the quantity produced minus ways to be multiplied by the price but otherwise like the revenues components are the crop sold so the quantity of crop that has been sold the quantity of crop that has been used for on consumption the quantity of crop use for feed so that they've been used to feed livestock crop save the seeds the crop stored for next seasons the crop used for by-products so for example flower or straw the crop given as a gift the crop used for paying input so paying labor rent and other input and the crop exchange in sharecropping agreements similarly there are the components of the livestock revenues such as the livestock sold live so the animal that you sell live then the livestock given as gift the livestock products such as the meat the livestock products such as dung sold livestock products used for on consumption livestock products and by-products used as pay for inputs labor and or as loan repayment but in general to pay for any input used in agricultural production and then livestock by-product that is self-use so for example and this is also considered under as a cost for crop production similarly the fishery revenues and the forestry revenues so if you have data on fishery the main components are the capture fresh fish that is sold the capture processed fish that is sold so fresh fish is just the sales of fish while the processed fish are for example dry fish or processed fish a capture fresh fish used for on consumption capture processed fish used for on consumption then the traded fresh fish that is sold and the traded processed fish that is sold and then for what concerns the forestry revenue we ask we have all the forestry products that are sold the forestry products for on consumption for a city product store forestry product used for paying labor rent for paying inputs and given out in sharecropping agreement. So after this training, we will share this presentation so we will have a complete reference of all the components that should be considered for the revenues. So with this, my presentation on this model, their definition is finished. And I leave space for questions or I can also go back to specific part of the presentation if something is not clear. Thank you very much. So I don't know if Naaman wants to or Jacob. Yeah. I think it would be good to give the floor for any questions. Yes. Who wants to break the ice? I think there was a lot of conceptual things which has been bothered by idea. It might be difficult to capture all in this 15 minutes. Hello, I think I was off. Okay, now I leave it for you Naaman. No, no, it's okay. I was just saying that we are a little bit behind the schedule, but we can take maybe five, 10 minutes for questions because this is a very, very important part of the training. So anybody who wants to ask questions to either from the participants. Just one thing Faridun, I cannot activate my video. I don't know why it says that you have. Okay. Yeah, good. Okay, Dr. Amirul and all the participants, any questions, clarifications you have? Not at this moment because definitions are very clear, but when we will go to the exercise. So how to capture from the main survey question, that would be a very learning point actually. So proceed for the time being now. And when any question is there, participants will raise their hands. And we see one raised hand, you can add this. Mr. Akhter. Yes, Dr. Hassan Khan from VBS Bangladesh. So I want to know how can you calculate the white factor for cow, buffalo, sheep, goat. How can you calculate the white factor in the by region to South Asia? Either you got the questions. Yes. Yes, I have a question. How can you calculate the white factor for cow, buffalo, goat, sheep, sheep. Just we mentioned Adiba. Yeah. We mentioned Ida.  How can you find out the white factor for those. By region. Okay. So I'm not sure I understood, but I will share my screen on the, on this slide that you are mentioning. And we can see together. Can I clarify the point we raised? Yes. I think it's clear. This slide, yeah. That is region is 0.5. Ida, I just want to know. How can you calculate the white factor? The question is like, you know, I think the question is clear. How can you develop this conversion factor?  So how can you say like for South Asia, it is 0.5. It is for the foreign and so forth. I think you can answer that one. It is outcome of different research activities. And it's a scientific research output and it's an international standard table. Yes. Which has been developed through different researches. Therefore, it is not existing like, you know, it is related to competing 231232. This has been there through the research output. And for any comparison in terms of, you know, for example, how much livestock do you have in Ethiopia and India and Bangladesh? You know, the numbers are different. If you want to make an international comparison, you have to bring them into one unit, which is called Tropical Livestock Unit. It is an output of a research activity. Of Ethiopia, actually. Yeah, basically in Ethiopia, we have a department that only works on livestock related issues. Also livestock statistics. And they have developed this international scale that is used not only for this indicator is used for any many other purposes. And basically, the objective is, for example, that a cow in the US is not the same as a cow in Africa or in South Asia, like, for example, the meat that you can get from a cow in different agricultural zone, the quantity of meat is different. And the size is different. So there's this conversion scale should bring everything to the size of this type of animals, like cattle in the US. So all refers, you see the North America, for example, is one. Yeah, so everything is compared to a cattle in the US, basically. Also other examples, for example, Bangladesh has two cows. It is comparable to North America one. Yes. Yes, exactly. And that depends on the size of cattle in Bangladesh. I think there is a research behind this, the biological considerations of different species of animals. There could be a link sent to. Yes. More detail on what is behind these numbers, how they come about. So there is a whole body of research. Yeah, we can share that perhaps after this session. Yeah. Is that. Okay, any other question. Okay. Okay, I see someone else. Who is asking the question. You are muted. He do Islam. I do this love. My questions. Yes, please. In Bangladesh, one cattle is big and one cattle is small to cattle that then it is a tool, but then it is a one, then we count it is one. One is big and one is a small. Mm hmm. Then how, how do we calculate. Is it one. I think you should use the conversion factor in any case, because that's what's what are the other countries do like you, you find the conversion factor for. Yes, for all sides. I would say so. Yes. Okay. What do you think. No, no, I think that's a very relevant question. But I think those are really kind of proxies in order to be able to overcome the idea of not adding apples and oranges so if you have a cow you have a chicken you have a pig. The main consideration is how to add the pigs and the cows and the chicken. Now you are right because if you say a cow, you may have different ages of the cow different. Yeah, yeah, the big cow a small cow, etc. That's another aspect of the problem. But this is tackling the higher problem of adding the species together. But you are right. I think there should be. I don't know the answer of that but I think this is also another consideration if you consider cow, a small cow very small cow and the big cow. So the value may not be the same. You're right. You know, maybe. Yeah. How do we calculate? How do we, how do we find out the, yeah, the smallest scale food producers? How do we find out is there any thresholds? Yes. Yeah, yeah. I will show you. So now, I will, I will go back to my slide just one thing to add on the TL use conversion factor. What I think those conversion factor represent an average size of, for example, a cattle in South Asia. That's why you can multiply that by, I don't know, both big cows and small cows, because that represent the average size of cuttles in, in farms in South Asia. So, so in the end that should be fine. I will share my presentation again. Okay. Okay, so I think I will better answer your question with the with the practical exercise, but just to clarify so. So this is a relative approach, meaning that the threshold is not like five actors or two actors or I don't know $1,000 of product is a relative threshold that will be different in Bangladesh with and I don't know Pakistan or another country. It will be a relative threshold that it depends on on the data that you have. And the threshold will be computed for land, the TL use so the livestock and the revenues, and you will determine the threshold by identifying I will show you with the example, but you will identify the land size of the farm that divides the bottom 40% of the cumulative distribution of land with the remaining one. I will show you like an example, it's very simple you have to sort by the the the holding by their land size. Then you accumulate the land size, you identify the maximum and the 40% of this maximum accumulated value. Looking at this 40%, you will identify the farm that the one farm that divides the small farms from the big one. But I think it's it will be much clearer with a practical example. Okay, okay. Okay. Thank you. We have a question in Q&A. Yes. Okay. I have already read the question. If in Bangladesh, Australian freezer is brought up then the TLU, which TLU should be used. I mean the SPC from Australia, but it's brought to Bangladesh so what would be used. Okay, so this is a good question I think this this issue wasn't tackled before. So more than an answer like we can have a discussion on this. So far I think for any, for example, for any cow in Bangladesh, we would have used one conversion factor. It's true that you have local breeds and like foreign breeds. So, Naman, what do you think maybe in this case? I think it needs more, we take the question. Yeah, I myself have this. I have some breeds from Holland in my park, and I have some local breeds and they are completely different. So the question is really very relevant. I think we have also some specialists in the room among the participants. What do you think from the Minister of Agriculture? I don't know if life is talking about that. Can I add something, Naman? Yes. You know, just to know the individual level there may be big differences. But as the whole country level as I was telling, the average would be exactly 0.5 to compare with other countries, especially Latin America. So I think, you know, the individual level, this kind of breed, we may find, OK, these are differences. How come, how come we consider 0.5 only? But as at the national level, when you help with the indicator, this will average out as a statistician I can do. But anyway, every five years or every three years, this, you know, conversion factor should be revised, actually. So that when collectively there are so many and I have been cultivated in Bangladesh. So this, you know, conversion factor will go up close to the, you know, Latin America level. That is my understanding, too. Yes. I think that's a good answer. And as Aida also was pointing out, all these numbers are averages, you know, so and you said it rightly because it's not applying to one individual farmer because it's average out through the whole country. And it's average out through all the species. And so maybe this mitigates a little bit, reduce a little bit the impact of one specific type of species. Yeah, that's what I can say. I don't know. Aida or Yakov, what do you want to add? I think for me, it is the same, like, you know, maybe not only in this type of thing, but in most of the statistics, you know, there are exceptions, you know, therefore those, you know, those exceptions may not be able to be treated in such a national level survey. And therefore two or three individuals may have some something from outside and then, you know, I mean bringing those type of things in this type of national level analysis it would be difficult. Yes, it is good if you can be as accurate as possible, but at the same time we need to face the reality and we are dealing with the statistics. I think in general, this is the conversion factor that's been available irrespective of, you know, where the breeds coming from. As long as it's a cattle in Bangladesh, we use this conversion factor for the time being, I think. Okay. Anyway, those are very, very interesting questions. Any other question before we break? Okay. There is no more question I think it is. Actually, yes, yes, actually, when you, if I calculate computing our two, three, one or two, three, two, then we understand everything. What about our conversion factor? Certainly, and as I was saying, the next session will be about practical exercises. You know, all these presentations, when it comes to the practical exercises, you can put the finger on all the specific things that come into the play. So, if there is no more question, I propose we break normally, we are supposed to start the, hello? No, now I'm going to continue. Yeah, so. For how long? For how long we should take the break? Yeah, exactly. We are a little bit behind the schedule, because it's... 14 minutes maybe? 10 minutes? Okay, let's take 10 minutes. So, now what time is it there? 3.46. So, we'll resume at 3.56. 4 to 3.56. Okay. Dear participants, please stay connected. So, you know, you will be in connection. And just to know, stay alert, you know, we'll start exactly on 3.55, okay? 3.56. Yeah, thank you. And in the meantime, I think either we'll send some data for the practical exercise. Please check your emails and either we'll be sending some material. Okay, see you in 10 minutes. I would call not to log out. Please stay in Zoom, like for 10 minutes. We will mute our microphones. Yeah, after 10 minutes, you will just start. Yeah, and I think you can stop recording for the time being. Sure, of course, I will stop recording. Okay, good. Let's start the next and last session for today. Very, very important. As Aida was saying, there are two main aspects with this indicator. One is to identify the small holder. And you see that it can be quite a bit complex, how to identify. And the next that we will see tomorrow is how to compute the indicators themselves. So today, as you have seen from the questions raised, the first step is really how to identify the indicators. Now after the presentation, Aida is going to go through the practical exercises so that we all understand how this process is put in practice. Aida, you have the floor. Yes, thank you. During the break, I've sent two Excel files. One is called File for Practical Demonstration. That's the file that I will use to show you the exercise that we have prepared. And then there's one for end zone exercise. So that file will be for you in case you want to replicate the exercise that I will present. So I will use a mix of PowerPoint and Excel. And then, again, if you have questions, you have the chat, the Q&A, or you can raise your hand and we will address questions at the end of the presentation. Okay. So I will start by sharing the presentation. Okay. For the practical computation, so the practical identification of a small order, we have three main steps. The data preparation, the computation of thresholds to identify small scale food producers. So this I hope will answer to one of the questions that was raised before the break. And then how to, the third step is the combination of the sessions that we identified in step two, to identify small scale food producers. So this is the main concept, implement the steps. Of course the data that I will use are simplified data. So, certainly the survey data that you will use will be much more complicated to handle than those of this fictional example, because for this exercise I've used a fictional data, where we have all the variables that we need already at the household level or the farm level. But as you know, you may have for example crop related information that are collected by plot, or like, for example, item related information that may be collected by parcel, by other subgroups. So you will have to work at the file to bring all the information at the household level. So let's see step one, data preparation. So the data preparation is basically go from plot level data or other level data to household level data and compute the overall farm operated land. So basically you have to compute the operated land by summing the land cultivate with permanent crops, land cultivated with temporary crops and fallow land. You will have to include all the plots rented in that are used for other purposes to define this total, sorry, use for agricultural purposes to identify the operated land and exclude all the plots that are rented out. In this case you cannot compute the operated land, which normally depends on whether the fallow information on fallow land is available or not, you can use the cultivated land as a proxy. Okay, so these are, this is the part for the land size. For the earth size we have seen that by livestock category, we have to first take the livestock stock at holding level by livestock category. We have to take the livestock category converting TLU and determine the total number of TLUs. Then we have to compute the revenues from all agricultural activities that ideally are crop, livestock, fishery and forestry. Of course, this will depend on the type of data that you have. So if you have only crop and livestock, you will use only crop and livestock. You will need to have some all the revenues components to get the total revenues. And finally this point is not included in the slide. You have to convert in PPP US donors. So, I cannot hear you. I will now go to Excel to see in practice so I will share the Excel file. Okay. So, basically, this is the first step. So for the land, for the operated land. Increase the size of the stream. Okay, sorry. Is it better? Yeah, now it's okay. So you see here I have taken really it's a fictional data set. We consider like accounting with 50 oldings. And for these oldings, so the PU1, PU2 is the production unit number. And then for each olding we have the cultivated land, the land with permanent crops, temporary crops and fallow land. So the cultivated land is simply given by the sum. We do this, this operation. Of course, before we have to exclude all the land that is rented out and the land that is rented in. Then for livestock data. Okay, here I've taken the conversion factors for South Asia. We have 0.5 for cattle, for buffalo, then for sheep and goats, we have 0.1 for pig, we have 0.2, then and so on. So let's suppose that in the survey we have information on the number of cattle, number of buffalo and all these livestock categories. So for each livestock category and by olding, for example, like this olding of 5 cattle, I have to multiply the 5 cattle by the TNU conversion factor for cattle. So and this is repeated for all oldings and for all livestock category, of course, changing the conversion factor, depending on the type of livestock that is considered. Then at the end, I can sum to determine the total number of livestock in stock and the total number of TLUs by summing each TNU component. This is quite straightforward operations. Then, okay, here again, here I've skipped all the steps that go from plot level data to also level data. So that, I mean, we would have done that with data from, if we would have had data from Bangladesh. So here we have to use a fictional example, but in normal cases, you will have some pre-processing operation before that. But this is to say that you have to bring your data to a structure that is similar to the one that is presented here. Then for revenues, we have seen this F to determine the crop revenues by considering all these components that are listed here. When you have the revenues from crops, revenues from livestock, fishery and forestry, you sum all these components to determine the total revenues. And then you multiply these total revenues by the PPP conversion factor, which can be found from the link that I included in the slides presented before. So you can easily find these conversion factors for Bangladesh. So this is just a fictional example. You don't have necessary to use this also because the conversion factor change every year. So you will, for example, if you have a survey for 2016, you have to look for the conversion factor for Bangladesh in 2016, going at the website that is reported in the slide. So once we have the operated land, the total TLU, these three columns that I've shown into a consolidated data set. So we have the production unit number, the operated land in hectares. So this is important. The operated land should be expressed in hectares, not in acres, for example. TLU is in number and the total revenue seen PPP USD. So now this is what concerns the data preparation. I will start on you. Okay. So are there any questions on now on step one? Oops. Otherwise, I will continue with with step two. There's one question. Yes, one question. I have a question. Okay. Okay, I didn't understand the type of fellow land that you have. Yes, we have two types of two types of fellow land. One is currently and there is permanent. We call it both types of data. Okay. And what is the, sorry, the purpose of permanent fellow land in our country. So what's the difference between the permanent fellow and abandoned land? Permanent fellow and current fellow, current friend, these fellow land is cultivated next year, but this year he did not cultivate. Permanent fellow land farmer did not cultivate for the long time. They seated as a permanent fellow land. Maybe three, four, five years. Let me, let me further clarify this one. Say, for example, you know, there are two types, one is current fellow. Current fellow means to know farmers sometimes don't want to use this year, but next year they have planned to use that piece of land. It is agricultural land. Anyone can cultivate there, but for their own purpose or any kind of decision, more than five years of time, these are left uncultivated. So VBS college, this kind of information. So according to your indicator and your slide, what will be your solution? Should we combine these two and consider in the equation or they will leave the permanent fellow from the equation. So in my opinion, it should be included the permanent fellow because sooner or later, like Naman and Jacob can correct me if I'm wrong, but sooner or later this land would be used for agricultural production. So here we have, we want to understand the land, the possibility that the farmer has in terms of agricultural production. So how much of land he could use, he could operate for his agricultural production. And this is, I think, why also the fellow land is included because that's a land that can potentially be used for agricultural production and it's not abandoned. One thing is, if it's an abandoned land, but fellow land, it means that it's left there but will be probably used. Yeah, you're right. You know, as the soil fertility is there anytime in the future, then we can reuse so we can consider in the equation. Yeah, I think for the identification of the small folder, probably someone who has a, because the land belongs to him and he can operate it any moment. So it's part of his asset in terms of land. Now the difficulty may come maybe tomorrow when we calculate the indicator because the denominator and the numerator should be consistent, meaning that the production should be divided by the land cultivated. So, if the land is not producing any, any product, maybe we should consider the land that are productive, let's say, but for the identification I agree with either that probably we should consider that as an asset, it's an asset for the holder. Sorry, Naman, for tomorrow will we consider the land or the labor at the denominator? Okay, okay, I was still in there. Yes, may I say something? Yes. So, from your learning course and to this presentation I understand what Mr. Hassan said there are two types of fellow land in our country we consider in our country. One is temporary and one is permanent that means it is fellow for more than five years, but you are considering the volume of production. So, maybe in that case you are considering the only temporary fellow land not for my fellow land. Before that about my intervention, I was, because tomorrow we are not talking about the land, we are talking about the labor. So, I, yeah, so. So, we can review this matter. Can we consider the permanent fellow or only temporary fellow? I was saying that for the identification of the small order, maybe it's better to consider permanent fellow. Aida, can you show us that table, which one is included, which one is excluded? The fact is that my table only talks about fellow land, but we can further review this also. So, we can come back on this question. I don't know tomorrow. We could further review the. This is the idea. This is like land, land, land. This one. Not dedicated to grazing, so not for a pasture. Yeah, the fellow land. There is an answer. If you go to the right column, land abandoned prior to the reference period. So, if it is abandoned or not used in prior to the reference period, it is out from the definition. But is it very common in Bangladesh to have permanent fellow? Sometimes, you know, say for example, their owner lives in the city and he or she doesn't have the intention to use that piece of land for agriculture. Yes, yes. And this fellow is not dedicated for grazing, right? Grasing, you know, not to know formally, informally. If it's not dedicated to grazing, it is part of the kind of computation. And it says fellow land, land left uncropped, it is uncropped. It doesn't say that it's uncropped for one or two years. It is uncropped, but it is not dedicated for grazing. So as long as it is left follow, but it's not dedicated for grazing, then this is an asset as Naaman was saying it should be part of the computation. And then land abandoned, you know, prior to the reference period, it is completely out. So for abandoned, it's clear it is out. Then for fellow land, so as long as it's not dedicated for grazing, it should be part of the calculation. And the grazing component was excluded because in many countries, still for international comparability, because in many countries like the pasture land is very often like communal land. So land used by multiple producers at the same time. So it's difficult to find, let's say the, they find the ownership of that land or like who is actually using that land. So that's the reason why grazing land was was excluded. Is this clear, right? Respective of whether it's temporary or permanent, as long as it is not dedicated for grazing land, it is part of the calculation. As long as it's not. I think it is it also but in a has just indicated a 501 grazing land also included in the agricultural land. So there is a difference between 231 and a five day one. No, there are two, two aspect one is the identification of the small order. And then when we come to the to the computation of the indicator service another thing, but here we are trying to see what are the assets. So how do we classify a holder as a small or non small. Consider is assets is land and it's. The focus here is only how to define this, the small holder. And also, yeah, or you mentioned indicator five a one. I think that's referred to the agricultural land that we are referring to operated land, which is defined through these components that we are seeing here but your agricultural land may include other components. Yes. So grazing. It is a, if it's not extensive grading, maybe concept that case, and so many of the things can be included in that case. Okay, there is another question. I don't know the chat. It says how you get the groups P one P you took you see a three etc. I think you are trying to explain but you can repeat it again. Okay, okay. So I stopped sharing this and I shared the fight. Okay, so here P, P you one P you two P you three are just fictional this is the key the identifier of the old thing but it could be anything could be like 123 is just like the the identified the key of the of the farm. So, sometimes you have data that are presented already at farm level. So you already have this structure. Some other times you may have information such as for example the components of the operated land that are reported that plot level. So you will first have to combine values reported that plot level to get the total values for the, for the old thing, and then you will get these so these steps of these preparatory steps are not covered by by this exercise because let's say real world data but tomorrow, there will be an example for the computation of the indicator but still my colleague one of my colleagues will show how to go from plot level data or partial level data to also level data so this will be perhaps clear. Can I add something. Of course, yes. Okay, as you can see, you know this this part is to identify small holders and the data, the first thing have to be at the holding at the holder. You need to we need to have to identify who are the small holders. If your data is as a plot level level for an instance, you know, it is a survey maybe it's a plot level fit parts of the label it depends you know how you collected your survey data. The first thing you need to bring is bring this data one in whatsoever level that you have up to the holding therefore you have to calculate the operator land per holding. Okay, you have to add it. You have to calculate the operating land per holding. And once you have the operating land per holding you have to attach the number of livestock per holding. And then you have to calculate the revenue per holding. When you say the revenue per holding, it is a production times the price, you know, that's how you are going to to commit therefore how much was produced in that in that holding and you multiply them by the price. For example, if the holding is producing different crops will have different prices. Therefore, we have to multiply the production of different crops by the respective prices of these crops and aggregate them together to get into the holding level. That is the PSU level thing. Therefore, it's depending on how you strike your survey data is there. Then there is one step that that that we haven't shown in this process to bring them into this level that I just shown, which means you have to aggregate at the holding level because once you have the holidays, we have to run that cumulative distribution to make sure that which are below the 14% cumulative below the 40% completely. That's what where she's heading. Therefore, that's how we will be trying to come up with this P1, P3, depending on how your micro data is structured when you do your service. Thanks. Thank you, Jacob. So, now I will go. Okay. I have another question for revenue purpose. Yes, we can calculate the revenue for the international conversational sector as a dollar. Can you use the conversion factor in our survey view or time here? So, your question is if you can use the conversion factor in your survey. Yes, yes. Not to say the calculating here. Just to follow your point, you conduct the survey, then we notify our international conversion currency will be considered. Yes, is it right? No, I will show you. No, just let me clarify here. Mr. Hasan, when you survey and you calculate first hand, you will calculate the revenue using one of the currency. Once everything is done, the column is finalized, then you convert using the international conversion rate for one of this, particularly on the sensors that we use.  Okay. I think that's a good answer. Thank you. Thank you. Okay, so I would go back to the president. Someone else has a question. I just want to know that you have shown as an example, this is not a real part of that, I think. So have you tried the AJ's data of Bangladesh for this exercise? Sorry, I'm not sure. No, maybe let me interfere. I think Namanu is trying to- Hi, I got your question. I'm sorry. No, I got the idea. You got it? Yeah. So Namanu was explaining earlier. I mean, when we are preparing for this training, we wish to use the actual Bangladesh data to show all of these computations. Unfortunately, we are not able to get real Bangladesh data. That's why we are using, you know, this is this data as an example. But I mean, the bottom line is it was good if you can have, you know, the Bangladesh data, but the issue is it's more or less the same. As I said, we wish to have the Bangladesh data to be honest. And Namanu was exchanging emails with Amirul several times in order to get that data, but it was not possible. Yeah, in the communication, our counterpart from VBS also there and they reported that currently they don't have the data available. So Naman and her NS team use this proxy data. But anyway, if you understand this one, VBS can replicate this one. Exactly. I think it's not a big problem because the idea is to understand the process. So whatever data is available, you can replicate the same process. The big difficulty is probably what we discussed tomorrow is how to get to get this nice table with all summarize, you know, before that there is a lot of work to be done on the real data coming from the cash on us. The aggregations and putting them in this form. It is in this format that the process of calculating the thresholds, etc. That's what I think I will show now. And tomorrow probably we'll have a more more discussion on how you really go from the plus or whatever level and aggregate everything at the holding level. You need everything at the holding level. We have one reason I die. And also on the third day we will see your. Sorry. There's one question. Okay. Okay. Yeah. Please go ahead with your question. Mr. Mehdi unmute and I know boy with the question. Mr. Mehdi, Mehdi has done so hard. You raise hand and you can, you can talk to us. Just unmute yourself before that one. Oh, he has, you know, it was my mistake. Okay, please go ahead. Hello. Okay, so I will. Hello, I have a question. Hello. Hello, I have a question. Yes. You. We know that the threshold level landed operating land dependent on operating land and livestock, etc. And also have revenue status. My question is here that only we convert. Operated and livestock into revenue. Revenue for measuring threshold. Is it this household it's smaller scale or bigger scale food producer. Okay, so let's see if I understood you have three variables now you have the. Yeah, yeah, yeah. Yeah, land size, livestock size, operated plenty and use and revenue. You, you determine the three variables. And then you have to define a threshold, identify a threshold for each of these variables, each one.  So you consider a small only the farmers that satisfy the three thresholds, all the three, and all the rest are known small. I think it will be computed by small and non small. And you divide this group with the next step that I will illustrate so identifying three thresholds one. Think about this one one very please. One variable is known as more. Another is a small. Okay, this part. Why are we calculated or fall it smaller scale or. Yeah, a bigger scale. So in this known as small, and then you have the rest that is more in this case you will have a known small so a big farm, you have to satisfy the. But this will be shown in the presentation. Yeah, exactly. I will go through the presentation and probably you will, it will be more clear with the practical. You will see. Okay, thank you. Okay. And then yeah if you have questions you go a bit quick on the steps now and then we can see everything in Excel. So we, we said that we saw how to prepare data now. Now, the step one.  So, for the land threshold. These are the steps we will see it in practice in Excel things with respect to the total operated land. So we will order the old things from the smallest in terms of where it is led to the biggest. Then you humiliate the land so you compute the humiliated distribution of the operated land meaning that you take the first element then the second, some to the first and so on. Then and identify the corresponded land I will show you how this work, and then the corresponding holding area is the threshold that will allow you to identify small older so divide small orders for non small orders with respect to the operated land. The same is done for the information you rank holdings by the Tlu from the, the only with the smallest number of Tlu to the only with the biggest number of to accumulate the distribution. And you take the 40% of this humiliated distribution and you will check what is the number of Tlu that correspond to this 40%. And this will give the threshold to identify small scale food producers with respect to livestock. The same approach is adopted for revenues. I go a bit quick here because I will show this in Excel and it will be much, much clearer. And then once you have these three threshold you go to step three so you combine the three threshold to identify small scale food producer that will be the food producer that satisfy the three threshold at the same time. So this answer to the question we just received so if a farmer satisfy only two threshold but not one. And not we classify that small scale food producer. So let's see I will stop sharing the power point, and I will share the Excel file, and we see now how to continue so here I have consolidated the data to identify small older. Now I have to identify the threshold for operated land, the threshold for Tlu and the threshold for total revenues. So let's go to the first one the land size. So, here I have first I have sorted my farms, you see that you don't have any more P1, P2, P3 before, but the farm are sorted by the land size so the operated land size in actors. And this for example in Excel is done very simply by highlighting these columns, then going to data you sort sort. And then you say okay you have column B column C you say sort by you select this and now I will not by which you want to sort your the largest and this will give you the land sorted. Then you compute the accumulated distribution so the first element will simply be sorry. The first element will be equal to the first element. Then you have the second one that is equal to the second plus the first so the one that is above. Then you have the second plus the sum of the first two which is here and and this for for all. So this is the accumulated distribution. The last element of the cumulative distribution is the maximum value of the cumulative distribution. So I took this value, this one. Okay, and I brought it here. Okay, then I want to identify the 40% of the community distribution so I take this, this value. H3. So the maximum and I multiply it by 0.4. Okay, so and I get 101. So I go here, and I check okay where I have a value that is smaller than 101. If I go here for example, I don't have any more accumulated value that is smaller than 101. So this is the last possible one. Okay. So this means that the land size that corresponds to the 40% of the accumulated land size is 5.3. So this 5.3 is my thresholds in terms of land so basically here what I've done first of all is with any value is smaller than the 40% put one otherwise put zero. Okay, and I have applied this to all cells. Okay. And I will take a threshold of operated land, the operated land of the old thing the last holding for which I have one. Okay, so that the accumulated land size is smaller than the 40%. So this is my threshold. Okay. I do the same for the livestock so what do I do I first sort the old things with respect to the number of TLUs. So these are all my old things you see again I don't have P1 P2 P3 but all things are sorted by the number of TLU. I compute the accumulated distribution so I take the first element, then the second element plus the accumulated value above. Then the third element plus the accumulated value above and so on. I take here I take the maximum accumulated value you see 182.3 is this is this maximum value I compute the 40% of this maximum value. Okay, and taking as a reference this 40% I look okay is this accumulated value, minor than this 40% if yes I put one, if no I put zero, and I look for the last farmer with one here. This is the threshold in terms of TLU. Okay, it's not the accumulated value, but it's the size of operated land that corresponds to that specific accumulated value of land. Okay. The number of small holders with respect to livestock will be given by all these so all those that are small with respect to the threshold identified. Then I go to revenues and I, sorry. I go to revenues and I do exactly the same so first I run with respect to revenue, I compute accumulated distribution, identify the maximum value of the accumulated distribution and then the 40% of it. I identify the last farm that has accumulated distribution of revenues smaller than this 40%. And the corresponding value, like not accumulated value but the corresponding value of revenues is my threshold for the revenues. Okay, so I have now three threshold, one for the land size, which is 5.3. And one for livestock that is four and one for the revenues that is $3600 more or less. Okay. So here I summarize my threshold. I put again I took this consolidated data here, and I put them here. And if function I ask, okay, if for example, if the operated land is minor or equal to 5.3 or the TLU is minor or equal to four, or the revenues is minor or equal to this put one, otherwise put zero. And doing this for all the cents, I identify those that satisfy all the three conditions and this will be my small holders. Right, is it or or end, please? Sorry. Is it or or end? End, end, end, end. Sorry. It's a if and so this is the way you asked to accept to check all the three condition and see that they check if they are all satisfied. So it's end. Thank you. Yes, can I ask a question? Of course. You have assigned one and zero to all the three variables. Can we can we use those three variables to online the final, you know, small scale run? Of course, it's the same. If all of three, that will be, you know, yeah, of course, of course, yes. Otherwise, you know, using this, creating this column won't help. We'll do sorry. You have already created one and zero everywhere. Yeah. With one condition if all of the one, then you can regard it as a smaller. Yeah, you can try, but I think it's not the same because this one condition is based on the accumulated distribution you see, and it only serves to identify 5.3. Okay. But yes, I think you, you can, you can because all these land are smaller than 5.3. Yeah. Yeah. So yes, you can. You are right. Both can work. Yes, both can work. So you can also, yes, combine this one and you see which old thing have all these one. Of course, just one one consideration, the ranking of all things will be different like here you have the old thing with respect the ranking with respect to the operated land. So the, the one like the ED is different than here you have a ranking with respect to TLU and here you have a ranking with respect to revenues. And this is why it was easier to look at the new consolidated data set with no ranking. Let's say, and also like Excel is just a tool that I use for this exercise you don't need necessary to use Excel you can use any any software of your choice. Okay. It's okay I think you may post it now. So I think I'm, I finished the presentation so now it's, it's open for questions if there are any. Okay. I do also foresee that they try to the participant try. We just go to the questions. So, we have two possibilities either they try now to replicate. So I sent two files before the break one is this one that we have seen. One is a similar file but with empty some empty columns to check whether you you are able to reproduce the steps that I've implemented. So we can either try. You can either try now to reproduce this exercise or perhaps these are like for tomorrow. If it's too late now man it's up to you. So what I'm, what I'm doing left. It's 14 years left. I'm what do you think I think you know let us consider these as the homework of the participants. This is a simple Excel, let them try and tomorrow first few minutes we'll discuss you know where someone find any problem or not something like that. Okay, that's a good idea so let's take let's say, I don't know how many minutes 30 minutes, but it's too much. You know, 20 will be fine because problem will be similar actually. So we give the participants few minutes to try to reproduce this what I've shown. And then we come with questions. No, I think I think I mean only suggesting this exercise as a homework for that so that they can do the exercise and tomorrow morning before we start the new session, we just reviewed that I think that's what you're saying. Yeah, yeah, yeah, I just mentioned that one. Yeah, as a homework you wanted this thing to be as a homework. Okay. Okay, so the rest of the time you go for the questions. Yes. Because you have seen a few few time left now, it'll be for time left. Okay, so let's go for questions. In the meantime, you can try to exercise on your computer in parallel and then you can identify the difficulties and the questions so that we ask the questions, but we leave it for you to try today and report to tomorrow morning. So any any questions on your side. I see something here, could you please. You have, you have sent this calculation in Excel file, but would you please, please send us do file with the data. Is it possible.  Stater, do file Mama status clips. Yeah. We can calculate it in Excel file. Okay, but we need to do file for for last five years would be better for a start of. So I need to do file as for an example. If you send us do file. Okay, so we have, yeah, so we don't have a do file like specific to a data set, we have a data template. So I do file that is a generic state of the file that you will have to adapt to your specific data so that we can do we can share data template. Yeah, I think you know, Mr. With some command that you can you can you know customize to your specific survey. You can, you can give us any data, any data in the context of 2.3.1 any data and with the do fight. When we follow this, we can calculate in our context we will manage it. Can you please send the data in Excel. And you remember the routine that Piero's team has shared which is maybe that can be shared also because this is a public note. What do you think. I was saying that there is a state of the plate that we can share but that's not, let's say, specific of what for one that is it is just that in place that then they will need to. You can use dummy data for an example. Then we can follow it and we will apply it in our context in our data we can follow it. Because when the data set is large, then it will handle in Excel file it's complete. We'll do it in Compia. Yeah. Okay, yeah, we will prepare something. Yes. I think let's do this now man. This is a specific question this training has been organized. It's a general training type of thing okay so for some country use do start some other doesn't do, and it's it was not like you know one size fits all for it comes to be like starter for instance, but this can might not be applied to some other countries. Here in the training we are giving the conceptual in that they know that how to compute these things without being specific to the software. But since you are really interested in having this thing in starter, we need to work on that one and you know, send you the new files as a separate thing. And all already a standard generic routine within the with a bureau that they are sharing with countries. I'm, I'm right. Yes, this is one and start a either has a routine. I can just share this this is the public FAO routine that they are sharing so any country can read it and put it in any format they want. They are in whatever it is, but it's at least show the process and the protocol to follow to identify the small older. Yeah, public product of FAO. No. Yes, I think we. Yeah. Anyway, tomorrow also we will talk about this, but you are right. This was just a demonstration this was just for excellent for 1015 2030 holders, but we know that in real surveys you have thousands and thousands of holders so you don't have the tool to to use for this work. So you will need to work out the specific package in order to do all the work that is needed in order to complete. The FAO in parallel I think Jacob can elaborate more is also working on the even better package is not yet completely adopted but that will facilitate this calculation of the indicators. So, certainly in some time countries will have a package that will facilitate a lot the computation of these indicators, but in the meantime the focus is here to show the process. So you calculate the threshold how you identify the small orders, etc. And then you can translate this into a specific language of computer language that you are familiar with. Yes, as you know we have already exercised the Excel one, and we have three days you know training, but suddenly we cannot incorporate this data here. But as the as proposed by Mr. David Islam, if you have any data set that is ended by no stata. So, along with the data and before that you share that will give the guide for the for those who want to use that actually. You can share later on, but for the time being as you have planned on Excel, so you can proceed with Excel so that so far I understand you know all the methodology how the how to calculate how to know condition this you know variables, these are very much straightforward we can understand easily. So, if you understand this logic, anyone who knows this space is on a stata or in an hour or something, they can easily find the code, but if you provide the code that will be ready made actually to just jump into the data actually that will help. Otherwise, you know, they should have to try and error some some codes play around that you can play prepare and we have time you can share after this training as well if you. Okay. Okay. Okay, so are we at the end of the time. Nearly nearly nearly to the. We can just you know save save quality day actually if you want to. Okay. So there is no more questions for the time being so the data will be shared with you by either. And then tomorrow morning, if some some people has tried to to reproduce the process. If they are willing to to present some result or to ask for some clarifications, we can allow some few time to do that for tomorrow. Yeah, and they know. Yeah, yeah. And before we close, you know, I request either to just know whatever you share with the participants, please also include my email so that I can also have those you know, because I don't have any you know any data or anything you shared. Okay, so. And for, you know, for the month, you know information, lately we have one and one and a nomination from Minister of Food. I have just forwarded that link to the person, but I will share that email to you so that you can include in your list. I have shared the latest list that I had before we start the meeting. And if someone can check that list to make sure that. Yeah, I will check I will check and do you need to know the definition of all the participants or you need the email address. The email is also we need all the information. Okay, okay. As well. In the file which I sent you, you have a list of participants with the email address. Okay, because you know. I have a few participants that Islam sent yesterday. I had the email and the telephone numbers but I did not have the names and the title. I deliberately didn't share with you because it will create confusion. There is a, there is no more questions I think it's time now to conclude the session. This has been a very, very interesting session because they at least with. That's what we really wanted to have an interactive session not just to come and do lectures and things like that, but this is an interaction between the experts in Bangladesh and the experts from FAO, so that we don't have the answer to all the questions and as we progress, we also learn from you. And we will try to refine and we take note of the questions. I hope that the rest of the two days will do the, we have the same type of sessions and we have taken note very carefully for the very relevant questions that have been asked. And I think that the presentation of hi that was also well received. So we stop here and see you tomorrow morning. I will request the participants please write this Excel Excel file on your own if you have any problem. We'll share tomorrow so this is learning, learning by doing so, as we are on zoom platform we are not in person so it will help if you just also spend some time personal time. And again, I will request our FAO colleagues to address all of the questions you know whatever question is raised, and it will clarify the whole conclusion around the indicators. So for today it was a good session. I expect that this will continue for the further two days.  Hope to see you tomorrow. Okay, thank you. Thank you. Bye bye.