 Good morning, good afternoon, and good evening again. Welcome to this third day of the research training on the STG241. We are halfway to the end. So in the last two days, we have seen deep the 241 methodology and its first eight subindicators. Yesterday, we have also seen the Agri program and the 50-by-20 initiative presented by our colleague Flavio. So today, we will finish with the remaining three subindicators of the social dimension. And we will see the two data collection questionnaires related to the 241 and the alternative data source system. Then we will have another colleague presenting how countries report data for the FAUSTAD website. Finally, if we will have time, I if not for sure tomorrow, I will present the results of the first comprehensive patch. OK, so before moving to the social dimension, actually yesterday, Asmanya needs still to show you the Excel on the Agri-by-20 or the supporting practice, because yesterday we saw the theory and now he will present the exercise. So I leave immediately the floor to him. So thank you, Stefania, and good morning, everyone. Please just confirm if you can hear me well. Yes. OK, that's excellent. So immediately we will go to the Agro-Biodiversity Supportive Practices, which is the fourth subindicator in the environmental dimension. And just to refresh your memories, for this particular subindicator, we are proposing two set of criteria. One for countries or agriculture holdings within countries that that is practicing or is in the process of getting organic certification and one for those where organic certification is is not in place as of yet. So if you remember, we have six criteria for for the countries or the agricultural holdings that that have organic certification and five for those who doesn't have organic certification. Now, let me just show you the working of the of the Agro-Biodiversity Supportive Practices Indicator. So like other subindicators that we have discussed until so far, both within the economic as well as the environmental dimension, the mechanics and logic and the process of constructing this particular subindicator is not different from the others. So what we require for this particular subindicator is a set of variables and data items, which will then in turn be used to construct or calculate this particular subindicator. One additional important point that I highlighted yesterday was that for this particular subindicator, we are not taking into account the agricultural land area of the holding, but instead we focus on the entire area of the holding, which is which which may be different from the agricultural land area. So from this perspective, as you can see here, for this particular indicator as a denominator, we are not going to take the total agriculture area of the holding, but instead we will focus on the total area of the holding. And there is a reason for that. The reason being that one of the criteria is designed whereby we see the extent to which the holding has located land area to natural vegetation. And hence, once we take that into account, we must reflect that in the denominator as well for us to have proper estimation of the subindicator. So as I was mentioning, a set of questions are asked of which the first one is, is this holding, in this agriculture holding, are there areas covered by natural or diverse vegetation, include one or a combination of the following? And then basically we pick one of the six options that we have provided here. Now again, let me reiterate that all these concepts as to what we mean by natural pastures or grassland, wildflower strips, stone and wood heaps, et cetera, have been explained in this poor document called Enumerator Manual. So again, for this particular subindicator, as I was mentioning, we are interested in ascertaining because it's built into one of the criteria that we are proposing for this particular subindicator. So we are interested in knowing as to what extent of the agricultural holding is covered by natural or diverse vegetation, the ones which are identified in the previous question. So in this question, the holder or the farmer will tell us about what type of natural vegetation exist. And in this follow-up question, we then ask him as to how much area of the holding is dedicated or covered by these vegetations. Now another criteria that we are proposing, of course, this was thoroughly discussed as I mentioned like any other part of SG-241. And for this particular criteria, we are asking the holder as to whether he or she are using medically important antimicrobial growth promoters for livestock. So we ascertain that, we assess that. And then based on the question, we assess as to whether the holding qualifies this criteria or not. Again, another question that the holding produce crops and livestock that are certified organic or undergoing the organic certification process during the reference period. The answer could very well be yes or no. And then, of course, the information that we have collected within the context of the economic dimension, which is used for land productivity as well as profitability and the resilience indicator, we ask as to what was the total value of crops and its byproducts produced by the holding. Of course, the idea here is to see as to what is the contribution of the different commodities to the farm value of output. So this question remains the same from the first part, as you can see here. Then another question is what is the percentage of agriculture area on which crop rotation or crop pasture rotation involving at least two different crops or pasture of two different plant genesis is practiced? Again, this is the criteria that was proposed to us by the biodiversity experts, both in-house at FAO as well as the countries with whom we were discussing the biodiversity subindicator methodology in detail. So if you remember, when I was setting the context for this particular subindicator, I mentioned that of the 11 subindicator, especially the biodiversity one was thoroughly discussed in 2019 with a group of countries, which was led by Canada and included many others. Like, say, for example, Russia, USA, Argentina, Mexico, and so on. So each and every wording of the criteria that we are proposing for all subindicator, but especially the biodiversity one, is thoroughly discussed and deliberated before it is finalized. Stefania, just one point for us that there are certain rows or cells within an Excel sheet which are not getting displayed properly. Like say, for example, this question, right? OK, yes, thank you. Yeah, so this needs to be amended. Then there is another criteria around which the question is developed, that is, for each animal species, maximum three that are raised on this agriculture holding is the different breeds and the different number of animals that they represent. So for each species of animal, we see as to how many of these are locally adopted breeds. And of course, the definition of the locally adopted is just what do we mean by that. We don't have an internationally recognized consistent definition of locally adopted, though it vary from one country to another. So for the time being, the countries can use whatever definition they are using for locally adopted breeds at the national level. So for each animal species identified, like say, for example, horse, we then ask as to whether it's a local horse, it's a hybrid horse, or is a mix of both. And then we ask about the total number of animals that that particular breed represent. So this is it. So these are all the questions, five or six, of which some are getting repeated from the previous subindicator are required to construct the subindicator on agro-biodiversity support practices. So as I was mentioning earlier, the criterion one as per the methodology that we have finalized is the holding leaves at least 10% of its area for natural or diverse vegetation. So we see from the very first question that is needed for this particular subindication, we see as to how much area of the holding is dedicated for growing natural and diverse vegetation. So we need information on all the categories, not only on the agricultural land area, which stops here, but as well on farm building and farm yards, forest and other wooded land on the holding, aquaculture on the holding, and other areas not elsewhere classified. And from this question, we estimate, we identify, in fact, as to how much area was allocated to natural and diverse vegetation, which is this row. This row. And we from the same table above, we know that the total area of the holding is 11. So we estimate then the percentage of area allocated to natural and diverse vegetation. Then as a cross check, we then basically compare the information provided in question one with the information provided in question two here. Let me just show you. So information comes from this question. Then we validate this information with P17 and V18. And from this, we see as to whether the information provided by the holder is correct. So as you can see here, the first criterion is that the holding leaves at least 10% of the holding area for natural and diverse vegetation. If it is less than 10%, the criteria isn't qualified. If it is greater, if it is equal to or greater than 10%, then the holding is complying with or respecting the first criteria. So in this case, for this particular holding, criterion one is not satisfied. OK. So and we move on. So farm produces agriculture products that are organically certified or its product are undergoing the certification process. OK. We ask the farmer a simple question. And based on his declaration, we see as to whether this holding is practicing organic agriculture. So criterion two is satisfied. Current criterion three is, farm does not use medically important antimicrobialist growth promoters. Now, what do we mean by medically important antimicrobials? Again, this concept has been explained in the support documents. So every term and terminology or concept used, which is new for countries to follow, we try to explain it as comprehensively as possible. So this is, again, a question whereby we ask farmer as to whether he or she is using antimicrobial as growth promoters for livestock. In this case, the holding or the holder said no. So this criterion is satisfied. Then we see criterion four is, at least two of the following contribute to farm production. Temporary crops, pastures, permanent crops, livestock or animal products and aquaculture. And then we see from the information that we have collected in the context of land productivity, profitability, and resilience indicators for the economic dimension. We need the same information. So we need the average or latest prices. At the farm gate level, we need the physical quantities of those perspective commodities produced by the agricultural holding. We estimate the value of production, which is a mere multiplication of prices with the physical quantities in respective unit of measurements. We estimate the total value of production, which remain the same from the previous subindicators. If you remember, we were talking about 1477. Then as we are interested in broad categories of different commodities, we club all the crops together. We club all the livestock together. And based on that, we then contribution from trees, if any, contribution from fish and a culture from, if any, and contribution from others, if any. And then we see the percentages. So check if at least two of the above-contributive farm production of the holding. In this case, yes. I mean, two of the categories do contribute to farm output value. So this criterion is satisfied. Then criterion number five is practice the agricultural holding practice crops or crop pasture rotation involving at least two crops. Our crops are pastures on at least 80% of the farm over a period of three years. OK. So we ask this question to the farmer. This is a bit complex question. And hence, we have provided ample instructions to the numerator for him to first break down the question into its constituent parts. And then in a simple possible way, ask this question to the respondent while explaining as to what is the underlying meaning of the information which is sought in this question. So this question, in other words, is broken down into different parts. And then ask in a stepwise manner to the respondent. And then we see as to what response do we get. If it is greater than 80% then the criterion is satisfied. If it is less than 80% then the criterion is not satisfied. As you can see here, this holding is not satisfying criteria number five. And then criteria number six is livestock includes locally adopted breeds. So first, we need to identify the locally adopted. What do we mean by the locally adopted? So this is a sort of information that we believe the country has access to a priori. So they have this information. And this contextual information needs to be given to the numerator. So all the list of breeds of different species of animals as to whether these are local or foreign or hybrid to the country should be provided to the numerator so that he has an idea. So while he's asking the question, he can probe further the respondent in terms of the information that he or she is given. However, just to emphasize, most of the professional livestock producers they exactly know as to what type of breed they are raising or rearing on their farm or agriculture holding. Even the local small scale subsistence agriculture holder, if they have livestock operations on their holding, even they know. As to whether the livestock they are raising is locally adopted or local breed or whether it's a hybrid breed or whether it's entirely foreign to the country or to his region. Now the actual percentage as to how many of the total livestock is locally adopted, we don't care about that. So of the total livestock, even if one is locally adopted, that will fulfill the criteria number, criteria number six. So the actual percentage for us, for this particular criteria doesn't matter. So now that we have covered the six criteria, we see as to which holding covered what criteria and how many. So as you can see here, holding one meets criteria number two, three, four and six. So out of six, this holding meets four criteria. Likewise holding two meets only one of the six. And we proceed this way. So for green, as I was mentioning yesterday, the agriculture holding should meet at least three of the above criteria. And taking into account this logic, holding one will be considered as desirable because it's qualifying four out of the six and so on. So it's a simple logic that we are using. So as you can see here, holding one is desirable. Holding two is acceptable. Holding two is acceptable because the logic that we are proposing for us to qualify it as yellow is that agriculture holding meets at least one of the above criteria. And hence it's acceptable and so on. And the last step obviously is the same. We start associating the sustainability statuses, yellow, green, and red with the agricultural and area of the holding. We do it by a nationally representative agriculture area to calculate the proportions. So I stop here. So now that we are done with the economic dimension, we have finally entered the third dimension, which we call the social aspects covered within the context of SC241 framework. So as I was mentioning earlier, we covered three subindicates in the environmental dimension. And now we will cover three within the social dimension. So the first one, which is proposed as an indicator within the social dimension is wage rate in agriculture. The theme is decent employment, the reference period last calendar year. And as I was mentioning in the very beginning, when I was showing you the matrix of the 11 subindicator, if you recall, some subindicators were not applicable to all kind of or all types of agriculture farming systems or agricultural holdings. So for this particular subindicator, it's not applicable to farms or holdings that employ only family labor. Now, this theme provide information on the remuneration of unskilled employees working on the holding that belongs to the elementary occupation group as defined by this International Standard Classification of Occupation, IISCO 08. In other words, of course, this IISCO or International Standard Classification of Occupation is developed by international labor organization or ILO. In other words, it informs about the economic risks faced by unskilled workers who are performing simple and routine tasks requiring the use of simple handheld tools and very often considerable physical effort. So here we are focused only on routine labor or unskilled labor who are performing simple tasks. Like say, for example, digging, shoveling, loading, unloading, staking, raking, pitching, spreading manure or fertilizer, watering, weeding, picking fruits and vegetables, feeding animals, cleaning animal quarters, and farm grounds, et cetera. So all the simple routine tasks which require considerable physical effort but can be performed with simple handheld tools. We take into account all those labor classes. Now, the highly mechanized or technical labor is not covered within this particular subindicator. So we see as to what was the average remuneration received or wage rate received by these unskilled workers. And then we benchmark it against the minimum daily national wage rate or minimum agriculture sector wage rate to see and assign sustainability statuses to the agricultural world. Now, one important point that I would like to highlight is that we ask the farmer directly a question as to what was the daily wage rate that he paid to the unskilled labor. And based on his answer, if this information is directly available, well and good, if we don't have this information available, then we have to ask this information indirectly to the respondent or to the holder. And hence, us proposing this formula. So we ask him about the total annual compensation paid to the laborers, to the unskilled workers. We then ask him as to how many days or hours these unskilled worker work on the holding. And we multiply it by air to convert into days for us to estimate the daily wage rate paid to the unskilled workers. Now, whatever way the information is provided by the respondent is good enough for us. And the only point that we need to make sure is to is the quality of the information provided. So if we are given the daily wage rate paid to the unskilled workers directly, that is fine. That is very nice. If it is not, then we have to go through this more complicated way of seeking information from the respondent. Now, what are the criteria or the thresholds proposed for us to classify the agricultural holding and agricultural land area, green, yellow, and red? So if the wage rate paid to the unskilled labor by the given holding is above the minimum national wage rate or minimum agriculture sector wage rate, whichever is available, then we classify this holding as is green. Now, if the holding, as I mentioned to you earlier, it's not applicable to agriculture holding that is only implying family labor. So in this respect, we would classify the agricultural holding by default green. If they are only using, if they are not hiring any external labor to perform or help on the work on the holding, OK? The holding will be classified as yellow if the wage rate paid to the unskilled labor is equal to the minimum national wage rate or minimum agriculture sector wage rate. And the holding will be classified as red if the wage rate paid to the unskilled labor is below the national minimum wage rate or minimum agriculture sector wage rate. So based on the criteria listed here, we then classify the agricultural holding as agricultural land area. We add up the areas green, yellows or reds. And we divide by the national representative area for us to estimate these percentages. Now, let me go to the Excel sheet. So as I was saying, like all other indicators, we ask two or three questions for this particular sub-indicator as well. Now, the first question is, did this agriculture holding hire any worker for getting out simple and routine tasks? The answer could very well be yes or no, OK? Now, of course, again, I'm repeating myself once more because this is important. So what do we mean by simple and routine task? As I explained to you in the presentation, these have been explained in the enumerator manual to the enumerator. And hence, let me underline this. That the training of the enumerator before going to the field in case of administering SUG-241 survey is instrumental. It's very important, OK? So proper training of the enumerator is the key to the success of administering SUG-241 survey and collecting reliable statistics and data, OK? So the first question is, we ask the holder as to whether they are hiring any worker for carrying out simple and routine tasks. And then we ask them as to how much, on average, the agriculture holding paid in cash or in kind to the worker performing simple and routine tasks, OK? So daily wage rate in local currency units, both in cash and in kind. Or as I was mentioning, if this information is not available, then it can be formulated in a different way in the form of questions, which are obviously not given here for us to estimate the wage rate indirectly from the total annual compensation and total annual hours worked. And of course, for the denominator, we are interested in the total agriculture area of the holding, which I've been showing you as part of each subindicator. So it's nine hectares for this particular holding. So from the first question, we saw that the holding is hiring employees, external laborers, yes. The daily wage in local currency is 359. The minimum wage rate in local currency in that particular country or in that particular region of that particular country, if the wage rate is differing from one region to another in the same country, then we should be using that. It's 265. And then we compare the average daily wage rate with the national minimum wage rate to see as to whether it's equal to above or below that. So in this case, it's above the national minimum wage rate and hence it is desirable. If it is below the national minimum wage rate, then it's unsustainable. And if it is equal to the national minimum wage rate, then it is acceptable. We then associate the sustainability labels or sustainability colors with the agriculture land area of the holding. And then of course, the last step remain the same. So we add up the area of green, yellows, and reds. We divide by national representative area to estimate proportions. So I stop here. No questions for now. Let's wait a few seconds. Apparently, this indicator is quite clear. OK, excellent. Let me just. Hello, everyone. Welcome back. Let's resume the session. We need to see the last two subindicators of the social dimension. So I leave the floor to us, Panjaf. So thank you and welcome back, everyone. So let me immediately go to the presentation. So the second last subindicator within the framework of SDG 241 and the second in the social dimension is the Food Insecurity Experience Scale, or FIES. As many of you may know, FIES is already a tier one SDG indicator 2.1.2 for which FAO is custodian agency, meaning that it has an established methodology and data on it is regularly collected by countries and reported by FAO. Its customized or tailored version in the context of 241 tries to measure the extent to which the household of the holder or the owner of the agriculture holding are food secure despite having some agriculture production, sorry, food insecure despite having some agriculture production. Now, for the sake of time, I will not go into the details of how to estimate the severity of food insecurity using FIES. First, assuming that many of you may know about this indicator and secondly, because as I mentioned, due to the fact that we are pressed for time. However, I will touch upon the basics of its methodology while referring you to the training material on SDG 2.1.2 that is published by FAO in various UN languages. In short, what is FIES? It is a matrix of severity of food insecurity that is measured at the household or the individual level. It is a statistical measurement scale designed to measure unobservable or latent traits, as we call it, and is measured based on people, direct yes or no responses to eight questions regarding their access to adequate food given the resources that they have. The FIES questions refer to the experience of individual respondent, and as I mentioned earlier, or of a respondent household as a whole. Now, these questions focused on self-reported food-related behaviors and experiences associated with increasing difficulties in accessing food due to resource constraints, as I just explained. Now, one other important consideration for this particular subindicator is that it is only applicable to household farms. So it is not applicable to farms that are managed by big corporates or which are publicly listed on the stock exchange or owned by large companies. So in this case, those holdings won't be interviewed based on the eight FIES questions. We are only going to administer these questions to the household farms. Now, here are the eight standard FIES questions that are used to collect data on the food insecurity of the household. Again, I will refrain from going into the detail of explaining each question, a comprehensive explanation on what this question entails, what is the concept, what is the meaning behind these eight questions is given in the PDF file that is attached to this presentation. Just to give you an idea, these eight FIES questions, as I already mentioned, ask the information to establish to what extent the household of the holder of agriculture holding is food insecure. So let me just cover the first question. So the question is during the last 12 months, was there a time when you or any other member in your household were worried that you would not have food to eat because of lack of money, and so on. So the severity of food insecurity that is assessed in these eight questions increases as we go below in these eight questions hierarchically. One other important point is that, as part of the interview, if the holder of the agriculture holding or the owner of the agriculture holding is not present during the interview and instead of him, if it is the manager or another relative of the household of the agriculture, of the holder of the agriculture holding, then in this case, we will also skip the eight FIES questions. These questions are not going to be asked to any other respondent except for the owner or the holder of the agriculture holding. So keep that in mind. And all these instructions have already been given in the survey module as well as in the numerator and manual. So depending on who is answering the questions, these questions may or may not be asked. So once the eight FIES data on the eight FIES question is collected, the first step is to prepare the data for analysis. So these are the standard four steps that needs to be undertaken for us to then analyze the data collecting using the eight FIES questions. So in this first preparation stage of the data for analysis, we add standard labels to the eight questions. I will explain in the next slide as to what do we mean by that. As a second step, the data is inputted into the model prepared by the FAO FIES team for parameter estimation. That is the calculation of level of severity of food insecurity associated with each question and each respondent using rush model. Now what is rush model? Again, we have published several methodological document. And when I say we, I mean to say the FAO FIES team, you can familiarize yourself with the model as well as the other integral components as to how we go about analyzing this information. But in a nutshell, we estimate two parameters using the rush model, of which the first one is the item parameters. These are also technically known as the difficulty parameters. So the terminology that the model is using for when the estimates are analyzed and produced, these are the model use the terminology called difficulty parameters for item parameters. Now these refers to and are derived directly from the eight FIES questions. The second set of parameters that we estimate are called respondent parameters or ability parameters. These are derived from the number of people who responded to the eight FIES questions. Now, once these parameters are estimated, we then perform statistical validation where an assessment is made as to whether, depending on the quality of the data collected, the estimated parameters are valid. That is the data are consistent with the theoretical assumptions that informs the model. So you need to make sure that the estimates that are produced by the model, both the item parameters and the respondent parameters, qualifies the assumption of the theoretical model that is produced by FEO in collaboration with Cornell University. So once that is done, as the last step, the calculation of sustainability status of the agriculture holding is carried out based on the information analyzed. So put it differently, once the measure of severity of food insecurity condition experienced by each respondent, that is the holder of the agriculture holding based on their answers to the eight FIES questions has been derived, the sustainability status of to the holding that is desirable, acceptable, and non-sustainable as per SG241 methodology is then assigned accordingly. And I will explain that to you as to how. So let's concentrate on the very first step, okay, from the previous slide. Based on the data collected using the eight FIES questions, it is prepared for analysis, where each data item is coded to, is assigned for a no response or zero for a no response and one is assigned for yes response. Depending on the convention you use, you can either use one two or zero one, okay? So of course, once the questions are getting asked within the context of a survey, usually the data entry operators or the analysts assigned codes to each questions, right? So in this case, as you can see here, we have assigned the following codes, C underscore C0300 to each questions. Now the second, so these codes are used and the data is codified. So instead of yes or no, we use one or zero or two or one. And then we add standard labels for the eight FIES questions on which data is collected. Now these standard labels are those which are used by the FAO model for analysis of the FIES data. So instead of like the question codes, we use worried, healthy, few food, skipped, ate less, run out, hungry and hold it. So instead of the codes, we use the standard labels, okay? Now once the data have been properly codified with zero or ones and standard labels are added to the eight FIES questions instead of the codes which are used by the data analysts. The next step implies estimating the parameters associated with the eight FIES question. Now the methodology underlying the estimation of the parameters for prevalence of severity of food insecurity is based on item response theory, okay? Now this item response theory again is well explained in the in the numerator manual as well as as part of the e-learning course of SE 2.1.2. This item response theory or IRT is used to analyze responses to the survey or test questions. Now very briefly, the item response theory is a quantitative measure of non-observable constructions that is latent traits that can be derived from a set of dichotomous or binary variables. As I already mentioned, taking a value one or zero, thereafter Rush model is applied for the analysis of the FIES data. And Rush model is one of the many in the item response theory that is used to analyze unobservable or latent traits. So the item parameters as you know or the difficulty parameters are estimated using the model and arranged by the model from least severe, which is worried to the most severe if someone goes without food for the entire day. So as you know, you can see here, we go from the least severe to the most severe condition. Again, let me clarify. So once you input all the information for the FIES question into the model, these difficulty parameters will be automatically estimated. So you don't have to go through a complex analysis. You only have to run the model and it will itself estimate all these parameters for you. So thereafter, the respondent parameters are estimated from the Rush score. The Rush scores are a number of affirmative responses given to the eight FIES questions. It is an integer number with value between zero and eight. And hence the total number of respondent parameters are in fact, nine. So you can have zero for all the eight questions, which is by the way also a response. And then you can have answer one question or two or three. And hence the reason as to why we will have nine respondent parameters. Now, another important point to remember is that every respondent who answered yes to the same number of questions irrespective of which one they have answered yes to will be assigned the same Rush score, okay? So by this, we mean that the Rush score is an ordinal measure of food insecurity. By that we mean to say that with someone with a Rush score of four or five or six is more food insecure than someone with a Rush score of one, two or three. But we don't know the exact difference in food insecurity severity between these two respondents. So the model will help us estimate the difficulty parameters which are ordered from based on the extent of severity. And it will also help us estimate the Rush scores, the ability parameters, the standard errors, the frequency of the distribution of the people who answered no or yes to a certain question, the expected scores and other statistics. So once the model estimates, once the Rush model help us estimate the Rush scores, the ability parameters, the difficulty parameters, the standard errors and the frequency, this is all the information that we need. So once we have this information, we input all this information into the Excel sheet that has been prepared by the FAO FES team in appropriate places. So these appropriate places are well-labeled and guidelines around these have already been developed. So once you have estimates from the model, you input all the relevant information in the relevant columns in the Excel sheet prepared by the FAO FES team. An example is given here. Okay, so we input the difficulty parameters into appropriate places in the Excel sheet. We input the ability parameters, the standard errors and the frequency or the number of cases who answered yes or no to a certain question into the Excel sheet. And once this parameter have been inputted into the Excel sheet, we will get the following output table, which is already part of that Excel sheet as well. So it's a mere addition of all the data into the Excel sheet once the model process it and the model itself will help us estimate the probability of moderately or severely food insecure associated with each raw score and the probability of severely food insecure associated with each raw score by raw score. As you know, that these are the number of affirmative responses to the eight FES questions. And the model will also help us estimate the prevalence rate of moderate and severe food insecurity and the prevalence of severe food insecurity, which are the values for SCG 2.1.2, okay. Now is for 2.1.2, which is FES indicator, this is the final step, okay. So the model will help us these two values and these are the values for the indicator itself. Now, in case of 2.1, as I mentioned to you earlier, we are using a customized or tailored version of the FES. So we go one step beyond the FES process. And this one step beyond is remember that we have to assign the agriculture holdings. And by virtue of that agriculture land area that the holding manages, owns or operates sustainability statuses, right? The green, yellow and red. So all the information that is required for us to indirectly assign the status as to whether this holding is green, yellow or red based on the holder responses to the FES questions have been elaborated here. So the holding will be classified as green or what we call mild food insecurity. If the probability of the household of the holder of the agriculture farm to be moderate to severe food insecure is less than 0.5 and the probability to be severely food insecure is also less than 0.5, okay? And how do we do that? So we will go here again. So based on the, we will see here the probabilities, okay? So every question or every raw score has an associated probability with it. So depending on how many question the household of that particular agriculture holding has given affirmative or yes responses to will be assigned a raw score and we will have that information in the database. So let's say, for example, if holding one has given affirmative response to only one question, then in this case, we will check the probabilities associated with, with the household with the data within the table as to what is the associated probability with one affirmative response. And as we can see here, both are less than 0.5, okay? Moderate and severe and as well as severe and hence we will classify that particular agriculture holding as a screen. If the probability of the household of the holder of the agriculture holding to be moderately and severely food insecure is greater than 0.5 and the probability to be severely food insecure is less than 0.5, then we will classify it as yellow. And if the probability of a household of agriculture holding to be severely food insecure is greater than 0.5, then we will assign this agriculture holding and its agricultural land area, red or unsustainable status. Now there is one another important point that I would like to highlight. Here, you know, the yellow acceptable is the convention or the terminology that we are using in order to be consistent with within the context of SC241. Now this level of food insecurity which we call moderate food insecurity or acceptable level of food insecurity is by no means endorsed by FAO to be acceptable, okay? So this is just for the sake of consistency. We are using, you know, this terminology. So we didn't want to introduce another term which will then confuse the both the data analysts as well as the policymakers. Now, as I was mentioning earlier each agriculture holding has a raw score. And with each raw score there is a probability estimated the probability to be moderately and severely food insecure and probability to be severely food insecure estimated by the model. So we then see as to how many questions this holding has replied to. What was its raw score? And then we associate the probabilities from the model to that agriculture holding. And as you can see here, the first condition was for the holding to be green, the probability of both these columns to be moderate and severely food insecure and to be severely food insecure should be less than 0.5. And hence this holding is classified as desirable. In this case, holding number four, we said that the probability to be moderately and severely food insecure if it is greater than 0.5, which is this case but less than 0.5 in case of severely food insecure it will be assigned yellow status. And if the probability to be severely food insecure is greater than 0.5, then it will be considered as red or non-sustainable. So once we associate these statuses to the agriculture holding and agriculture land area that those holdings operate we then add up the areas greens, yellows and reds we divide by the nationally representative agriculture area to estimate the proportions. So now let me go to the Excel sheet. So as I was saying, we ask a set of questions in fact, in total eight questions to the holder of the agriculture holding or the owner of the agriculture holding on behalf of this household. And then we collect information based on yes or no responses and those yes or no responses are then inputted into the model to estimate cross-course ability parameters and difficulty parameters which are then transferred into the FAO Excel sheet that help us calculate the probabilities based on which we then assign the holding sustainability status. So these are the eight questions the standard eight FES questions. Okay, so I'm not gonna go through these. Remember just one thing that the as we go down in these questions the level of severity of food insecurity that we are trying to assess increases, okay? So we start with being worried about availability of food to be hungry for the entire day. So hungry for entire or whole day is in fact a more severe food insecurity situation than to be worried about having food. So we ask these eight questions and we get responses. One another point that I would like to highlight I mean, of course this is captured very well in the e-learning course is if the person has said yes to the question, let's say for example, if he has said yes to question number C5 then it's natural for him to say yes to question number C4 as well, okay? So if there are any inconsistencies in the data which is very well explained in the and the methodological note as well as the guidelines and the e-learning material developed for 2.1.2 if there are any consistency in terms of data that has been collected, then those instances should be removed from the data set to make sure that we have consistent and reliable and accurate information collected and analyzed. So depending on the responses to the eight questions as you can see here, so we have four yeses, okay? Okay, for this particular agriculture holding or the holder of the agriculture holding, so he provided four yeses of the eight questions. We then add standard labels, so instead of the codes that we are using in the data entry phase or stage of processing, we then replace it with the standard labels that are used by the model. We then codify the yes or no responses into one or zero, one being yes, zero being no. So up until now, it's fairly straightforward. There is nothing complicated. We are just doing some tweaks and adjustment to the information for it to be ready to be inputted into the model. As I mentioned to you earlier, the number of positive answers or affirmative responses is called the raw score for that particular respondent. In this case, because the respondent gave us four positive answers, four yeses, we assigned it a raw score of four and we do the same exercise for other agriculture holdings. Some may have provided six yeses, other two, some three, some one and so on. So we do this for the entire distribution or the entire sample that we have selected for us to administer our agriculture survey. We input all that information into the model, so for all household. So if there are any weights assigned, we should mention that otherwise we will say not available. If there are any, so these are household weights. In fact, these are individual weights. These are rural and urban weights, if any. These are regional weights, if any. In our case, we are using none. It's not available. So then as I was mentioning to you, we have developed this shiny app, the PS app, which is supported by this manual, right? Which provides stepwise guidance on how to use this app and how to analyze the data. So we take this information for all eight questions and using the item response theory, where the least parameter obviously is worried, which I was explaining. And the most severe is the whole day if someone goes hungry without food for an entire day. So we input this information to the model and the model help us estimate the difficulty parameters, the standard errors, the infits and the outfits, the standard errors, infit, the number of cumulative cases missed or valid numbers. And then we calculate the difficulty parameters as well as the raw score, the ability parameters or the respondent parameters and the standard errors. Okay, so the model will help us estimate all these. And then once the model help us estimate, again, the raw scores, the ability parameters, the respondent, the ability parameters, the difficulty parameters, the standard errors, et cetera. We will input this into the template Excel model, again created by the FAO CS team, which is available here. And we plug in the information at appropriate places. So once we do that, the model will help us estimate the probability associated with each raw score. And then based on the raw score, on the household or the individual holding raw scores, we then start assigning these probabilities. And then we see as to whether it's greater than 0.5 in both these cases. It's greater than 0.5, but less than 0.5 in case of yellow or if it is greater than 0.5 in case of severe for us to assign the green, yellow and red statuses. Okay, so I've already explained this. So the probability of the household or the holder of agriculture holding to be moderate plus severe food insecure is less than 0.5. And the probability to be severely food insecure is also less than 0.5. Then the holding will be classified as green. Otherwise, if the probability is greater than 0.5 for moderately severely food insecure, but the probability for severely food insecure is less than 0.5, then it's yellow and so on. So once these sustainability statuses or levels are assigned to agriculture holding based on their probabilities and raw scores, we then associate the same statuses to the agriculture area of the holding. Okay, like, you know, for all other sub indicators. And then we add up the areas classified as greens, yellows and reds and divided by the national representative area to estimate the proportion of agriculture area for this particular sub indicator by sustainability status. So I stop here. Okay, so participants, we have now our colleague Nathan, which will present the next presentation, which is a snapshot of the FAO start. So it's the big FAO database. So Nathan is a statistician in the environmental statistics team who has worked also on Prosa. He has worked in the areas of food security statistics and food balance sheets in the division. And he has also worked for two years as a statistician at OECD. So I give the floor to Nathan. Good morning, participants. Just to do a sound check to verify that you can hear me, Stefania. Yes. Okay, great. So maybe I can just turn my video on to say hello. And then I'll start the presentation. So yeah, my name is Nathan Warner. I'm a statistician as Stefania mentioned in the environmental statistics team. And I'm happy to be here today to have some time to talk about the reporting processes that we have here at FAO with also a focus on our FAUSTAT dissemination platform. So I can go ahead and share the screen. Can you also verify that you can see the screen, Stefania? We see our messages, so yeah, go on PowerPoint. Okay, yes. So I'll be talking about the country reporting to for statistics here at FAO. Also with a focus on the reporting of the countries that are participating in this workshop. So as background, we have knowledge generation that are based on food and agriculture statistics which is a pillar of FAO activities. And it's expressly mentioned in article one of our constitution. We have internationally known statistical products which include FAOSTAT, which I would also like to show you the actual website and dissemination platform. After the presentation, just make sure that you can know how to navigate it. FISHSTAT, which is a platform which has statistics on aquaculture and fisheries and aquastat, which focuses on water use in agriculture and irrigation. So the focal points that we have for our collection processes for our questionnaire based domains in FAOSTAT, we generally send our questionnaires out to focal points that are from usually national statistical offices, ministries of agriculture and other relevant agencies. Some of you are in attendance today and may be familiar with some of the questionnaires that we will focus on later in the presentation. So within this framework, member countries report regularly to FAO in general on an annual basis for national statistics on crop and livestock production environment. And for example, land use, fertilizer use and pesticides use, as well as social economic issues that are relevant to the themes of 2.4.1. So when we go and look at the actual reporting for the questionnaires, we will also link which of the themes for 2.41, the questionnaires are directly linked to. So these data collection processes are well-established here at FAO, as a food and agriculture organization. We send them annually to the focal points. We then process the questionnaires, have contacts with the countries for data discrepancies, do quality assurance, quality control on the data and disseminate the data later in the year. The data dissemination is also in general accompanied by an analytical brief for the domain in which global regional and country highlights are made in the analytical brief. The FAO stat domain itself is a pre-available data platform. It's provided in official UN languages and covers over 245 countries and territories with some differences in coverage among the different domains. Data are available over long time series, often since 1961, for example, for the land use domain. Some of the different domains that are covered are production, trade, food balances, which I'll also go a little bit into more detail when we look at the questionnaires. But the food balances also have an important indicator which is the dietary energy supply, which is one of the important inputs to the prevalence of undernourishment indicator, which is also part of the SDG reporting. Food security, prices, inputs, population, investment, macro statistics, acro-environmental indicators, emissions. So for the emissions domains in FAO stat, we have them categorized in terms of agriculture, land use, and forestry, and then research and development indicators and emergency response. So the dissemination tools that we have are we have the web page, as well as we have the analytical briefs that I mentioned earlier to accompany the dissemination and working papers, which in general highlights some of the methodological improvements for the different domains, as well as statistical yearbooks in which a big picture of the statistics for trends over time are highlighted and discussed. So I wanted to highlight the thematic coverage and how it's broken down in terms of some of the teams here in the statistics division at FAO. So as Dipanya mentioned, we are part of the environmental statistics team for which we have questionnaires for land use, pesticides, and fertilizers, as well as disseminating some acro-environmental domains that are not questionnaire based. We have the social and economic statistics team, which is focusing on prices and government expenditure and the crops, livestock, and food statistics team, which focuses on agricultural production, trade, and food balances. So for the domains that come from environmental statistics, as well as social and economic statistics team and production, we have two joint annual dispatch processes in which the questionnaires are dispatched and we collect data from the focal points within the country. On the other hand, for trade, the raw data is taken from the UNSD for food and agricultural products, excluding fish. And the food balances are a combination of using the trade and production data with an allocation to different utilizations of the products, including losses and industrial utilization, seed, and feed. So this is an example of the cover page for the land use, but this is the crop and livestock production and utilization questionnaire in which you can see the contact details for the National Reporting Office and contact name. I'll also open up at the end of the presentation the land use questionnaires just so that you can have an idea for those that people aren't familiar with it, how it is structured and how it is filled in. So now I'd like to go and focus on the actual reporting for the different questionnaires. So as mentioned, the questionnaires dispatched to focal points within the country and we have reporting rates for the different questionnaires. So here's part of, for example, the questionnaire for pesticide use and the structure in terms of how it is structured and the way that it is filled in. So for the land use questionnaire, for the countries that are participating in this workshop, we would like to thank those countries that have already reported to respond into the questionnaire for 2020. We will have another dispatch that will be coming up in later this year with a deadline for responding of October of 2021. So Australia, we would like to thank Australia, Bhutan and China as some of the countries that have reported not only for 2020 but also for other years for our land use questionnaire which is linked not only to theme five for water and irrigation and theme eight for biodiversity and organic agriculture but also as you are well aware to the actual denominator of the 2.41 indicator for agricultural land. Other countries that we would like to thank for reporting to the questionnaire include Japan, Malaysia, Maldives and Mongolia, New Zealand, Palau, who responded in 2018, the Philippines, the Republic of Korea, Samoa for 2019 and Thailand both for 2018 and 2020. So our fertilizer's questionnaire on the other hand which is linked more directly to theme six for fertilizer risk. We have differences in terms of reporting for the fertilizer use questionnaire. Some of the countries that in general respond to one of the other questionnaires can be good responders for all of our questionnaires. We realize from our perspective and that this can be due to one of the challenges for many reasons. It could be that we are not actually contacting the right person within the country to provide this data. And what I will point to at the end of the questionnaire is a mechanism in which countries can provide feedback whether or not we are actually contacting the right person within the country to provide this data. And we also recognize that even at the level of national level statistics, these data can be difficult to collect, even at the national level for some of the more disaggregated categories of our questionnaires, countries themselves have trouble sometimes in collecting or organizing this data to be able to be sent back to us. For the fertilizers questionnaire, we would like to thank those countries that have also reported for this questionnaire, including Australia for 2018, Bhutan for the most recent two years and China also for the most recent two years, as well as Japan who has a good response rate for all three of the last years, highlighting Malaysia and Maldives that have provided data in 2020, Mongolia in 2018, New Zealand for 2019 and 2020, as well as the Philippines, the Republic of Korea and Samoa for 2020, as well as Thailand for 2018. So on the other hand for the pesticides questionnaire, which is most closely linked to theme seven for the 2.41 indicator for pesticides risk, we would like to thank those countries that have responded to the pesticides questionnaire including Bhutan for the most recent two years, Japan for 2018 and 19, Malaysia for 2019 and 20, Maldives for 2018 and 20, and Mongolia for 2018, as well as the Philippines for the most recent two years, the Republic of Korea and Samoa for all three of the last reporting years and Thailand for 2018 and 2020. As part of this separate data collection process that is done with a joint dispatch for the production questionnaire, which is linked most closely to theme one for productivity and also three, or sorry, yes, theme one for productivity and theme three for resilience, we would like to thank those countries also here that have been reporting through our production questionnaire which include Australia and to highlight China and BG as well as Japan, Malaysia and Mongolia New Zealand, Philippines, Republic of Korea, Singapore and Thailand that had been reporting for 2020 and earlier years. The prices questionnaire which is most closely linked to theme one for productivity and theme three for resilience, we would also like to thank those countries that have reported for this questionnaire. Again, the countries that are participating in this workshop can see in this column, the institution that is contacted for each of these questionnaires. For example, for Australia, the Australian Bureau of Statistics. So for the prices questionnaire, we would like to thank Australia and Renee and Darcy Lam that have reported for all three of the last reporting years, Bhutan for 2018, China, Cook Islands and Fiji for 2020 and Fiji also for earlier reporting years as well as Iran, Japan, Malaysia, Maldives, Mongolia, New Zealand, Philippines, Republic of Korea, Singapore and Thailand with Samoa also providing data in 2019. So to conclude and before I, maybe I can open up also now what I would, a couple of the things that I would like to highlight before jumping to the conclusions and then I can also do the conclusions. So one thing that I would like to just kind of display for the participants is in general the way that the questionnaires are structured. So we highlighted the cover page in the presentation as well as for the prices that's used questionnaire, some of the different data to have is where the data is actually collected. I would like to just point to more specifically for the land use questionnaire, the different data tabs that are used for the data collection. So in general, the questionnaires for environmental statistics are structured with a T minus two. So dissemination date minus two years with the preceding three years as well, data collection for the columns in which we have the different, in this case, land use items that are broken down for data collection. So in addition to agriculture land, which is the important denominator for SDG 2.4.1, we do collect more disaggregated information on land use specifically for cropland, for example, with a further disaggregation in terms of cropland, in terms of arable land, which is land under temporary crops, land under temporary meadows and pastures and land with temporary fallow and land under permanent crops. So just as an example for cropland, the different sub items that we collect for the item for cropland or land use. One thing that I wanted to highlight for in the questionnaire is this feedback tab in which countries, focal points that are contacted have this space where they can provide feedback in terms of if we are actually contacting the right person to collect this information from the country. So this is one of the mechanisms that countries can use to let us know if we are contacting the right person to collect this information and if not, who that person would be. And this information is quite important to us to be able to strengthen our data reporting from countries and make sure that we are contacting the right person. So now I think I can jump to the conclusions before I can also go back and just highlight a little bit of how that is actually structured. So jump all the way to conclusions. So we think it's important that you keep in mind as you prepare to report on 2.4.1, the existing reporting processes that are already well-established here at FAO for national level statistics. There's a wealth and a wide range of information that's already available on FAOSAT regarding these existing data processes. And expert knowledge on topics that are relevant to 2.4.1. Some of these national statistics, which I'm sure our Bob and Stefania have gone into more detail and can be used for initial proxy reporting for countries as they gear up and they strengthen the data reporting for 2.4.1. And we think it's important to leverage on the existing expertise or the reporting processes to FAO, which can serve as an excellent basis to strengthen data reporting for also 2.4.1 and also to plan improvements in the future on national surveys and census processes that may be in conjunction with some of the information that you are gearing up to collect for 2.4.1, this information can be improved in terms of data collection. With the important difference being always that we're collecting data here at, in general, the national level as reports as opposed to the farm level for 2.4.1. So, what I would like to lastly do is open up FAOSAT so that we can just have a look at how the website and the data dissemination platform is structured. So, we have the different relevant themes that are with each of the domains for dissemination under each of the themes for FAOSAT and important. So, I mentioned some of the different agro-environmental indicators that are not part of the actual questionnaire-based domains. So, the questionnaire-based domains that we highlighted for environment, for example, were fertilizers, pesticides use and land use, but we have a wide range of other agro-environmental indicators for which we have especially the emissions domains focusing on emissions that come from agriculture as well as a newly added soil nutrient budget domain which focuses on either excess or deficiencies in terms of nutrient load or crop production in countries. So, that's kind of probably at the larger level of the FAOSAT dissemination platform is structured. Then more in general for more information on the specific domain, what you can do is you can click on the actual domain. We have data available in bulk downloads as well as country, item, element, and year selections. And I'd like to point also to the related documents for each of the domains which have some information on the methodologies, the update history, and the country notes. So, for country, if you're interested on specific information for your country, you can also find information in the country, the country notes. So, that I think is it for the presentation one thing that I would like also and that I will include in the chat will be a recent document that was published by the Sixth Division, which is the progress towards sustainable agriculture, which I will provide a link to in the chat in which we have many national level indicators with themes that overlap quite a lot with the themes for 2.4.1 in which we look at trends over time for the specific indicators. And we also present a different traffic light approach in which we're focusing on progress over time for different food systems, typologies for progress towards sustainable agriculture. So, I'll put that in the chat as well as what I'll put in the chat is the contact that we have for our questionnaire based domains for environment in which this is one of the emails that we use for countries to provide feedback, not only for actually reporting the questionnaires, but also for example, suggestions for focal points for the right person to contact for the questionnaire. So, I think I can hand it back to Sipanya. Sure. So, just confirm if you can hear me well because I'm not entirely sure. Yes, yes, I hear you perfectly. Oh, okay, okay. So, let me share my presentation with you once again. So colleagues, this is in fact the last subindicator within the framework of SG241, the third one in the social dimension. It's named Secure Tenure Rights to Land. Of course, the dimension is social, the theme is land tenure, the coverage is all from types. And the reference period for this particular subindicator is last calendar year. So, this subindicator allows assessing sustainability in terms of rights over the use of agriculture land areas. The reason being, agriculture land is a key input for agriculture production. Having secure rights over land ensures that agriculture holdings have control over a key asset and does not risk losing the land in the short to medium term. Empirical evidence globally shows that farmers tends to be less productive as they are reluctant to invest if they have limited access to and control of economic resources as well as services, particularly agriculture land. Sorry. So, how do we frame this indicator? It's very straightforward. Again, a set of questions which are asked to the farmer or the holder of the agriculture holding, asking about his security of tenure rights. So, we in fact ask a total of four questions. These four questions have been adopted from another SDG indicator that FOS custodian agency for 5.8 million. 5.8.1, which is on land rights as well. So, those questions are tailored or modified for and these questions are asked and based on the data received, we then assign sustainability statuses as per the criteria listed here. So, the holdings will be classified as green if it has access to a formal document with the name of the holder and the holding on it. Or if it doesn't have the formal document, then in this case, the holding or the holder should have the right to sell or bequeath any parcel or plot of the holding, okay? So, this is the condition for us to assign green status to the holding and agricultural land area. Holdings are classified as yellow if they have a formal document, okay? An official document, even if the name of the holder or the holding is not on it. And this is usually the case in many developing countries whereby the land is named after the ancestors, the grandfathers or the fathers, but it's still considered as a valid document even if the name of the direct descendant or the owner is not on that particular document. The holding will be classified as red if there is no positive response to the criteria listed above. If the holding doesn't have a formal document or if he doesn't have the right to sell or bequeath any parcel of the holding, then in this case, the holding will be classified as red. So, based on the data collected through these questions in Bangladesh back in 2018-19, here is some brief extract of the analysis. So, holding one formal document, they said, yes, we do have it. Is your name on it? Yes, I have my name on it. Do you have the right to sell or bequeath? Yes, yes. So, hence this holding is classified green in terms of secure rights land. Holding two, they have a formal document, but the name of the holder is not on that document even though it's considered as a valid piece of evidence that this person is the legal custodian or in charge of that particular piece of land. So, hence we classified it as acceptable. And then non-sustainable is the instance whereby you don't have a formal document, you don't have the right to sell or bequeath the land. And hence, this kind of arrangement is considered as non-sustainable from secure tenure rights perspective. The last step is the same, like for other sub-indicators, we add up the areas classified as green, yellow and red. And again, reflect it as a ratio of the total agricultural land area and calculate these proportions. So, let me go to the Excel sheet. Okay, so here are the questions that are asked within the agriculture survey module. Usually these kind of questions exist in the census, but if these are not there, then we should make sure that these questions are integrated in either census or the agriculture survey for you to collect information. So, the first question is about having a formal document. The second question, if the answer is yes, and the second question is, is the name of the holder or any member of the holding is listed as an owner or use right holder or use right holder on any of the legally recognized documents. So, this is the second question. The third question is, do you have the rights to sell any parcel of the holding? And the fourth question is, do you have the rights to bequeat any parcel of the holding? And based on these responses, we then analyze the data using the logic given here. So, holding one has a formal document, the name is on it, right to sell, right to bequeat. And so on. So, this holding is desirable and holding two doesn't have a formal document with no name, so hence it's unsustainable. Holding three has only access to a formal document. The name of the person is not on it. So, even that is considered as some kind of security of land tenure rights, hence it is acceptable. And the last step, again, is the same. We assign sustainability statuses to the agricultural land areas. We add it up by statuses. We divide by nationally representative area to estimate the proportions. So, with this we came to an end of the framework of SDG 241, which comprises of 11 sub indicators. By now you may have sufficient understanding as to what SDG 241 framework is about. You may have noticed one thing, which is that all the information for all the sub indicators, the methodology is designed in such a way that farm surveys can be used or agriculture surveys can be used to collect information on all the sub indicators. Now, there is this implicit limitation of the indicator that was, and I've been discussing those as part of each sub indicator that for some of these, the gold standard data collection vehicle or data collection instrument is not agriculture survey. It is some other data source like say for example, for land tenure related information, census are better equipped. For FIS related information, though now we have refined the methodology to cover agriculture household as well, but household surveys are better suited to collect that type of information for some of the sub indicator within the environmental dimension. Soil sampling and monitoring systems and remote sensing may be better suited to collect that kind of information. But why haven't we considered those? We did have and currently we are considering it. We will discuss it tomorrow as to how we are working on making possible the use of alternative data sources apart from agriculture surveys to report on SG241. So that's something that we will discuss in detail tomorrow. So I stop here. There is one sheet remaining which I will show to you tomorrow on the conversion of the units and as well as the dashboard. The dashboard wants all the information on the 11 sub indicator is collected. How do we derive dashboard and how do we then derive the aggregate SG241? So we will briefly touch upon that tomorrow before us entering into the data collection and reporting discussions. Oh, perfect. Thank you very much, Aspanya. Do we have any question on this sub indicator on the last one? Apparently it was clear. Anyway, of course, if you have any question tomorrow we can take again this sub indicator quickly and answer your question in case you have. I think we have reached the end of this third day, 8.33. So again, another day we finished sharp. So thank you everyone. And we have, as Aspanya said, we have finished the framework. And tomorrow we will have other discussion the questionnaires, alternative data sources, the results of the dispatch. Let's see, we have quite a lot of the presentation skills to give. We see how the time allows if we need to skip maybe some or not. Anyway, tomorrow will be a very important day for us because we will have an open discussion with you and we will try to see country experience also from Indonesia. And we will be happy to listen to your concerns, to your plans, how to collect the data and report the SDG for one. So be ready for a big discussion because we really look forward to it. Thank you again. And have a nice day, have a rest of your days or your evenings in some countries quite late already. So thank you again and see you tomorrow. Thank you. Bye-bye. See you tomorrow.