 Good afternoon and welcome again to this second day of the virtual training on the SDG 241. We have seen yesterday with us from jar, the theory behind the SDG indicator 241. We have gone through the three day 11 teams, and we have seen also the expectation of the SDG 241 indicator. So today, as already mentioned, we will have a more practical day today and tomorrow. So we will have exercises and we will see the analysis of your data with this data. So today, our main presenter will be Django Iginico, so I will leave him to introduce himself and let me now give the floor to Aspanyar if he wants to say something like doing a better summary of yesterday's subjects. So Aspanyar, I leave the floor to you. Aspanyar, thank you. Thank you very much and good day to everyone. So now you summarized the yesterday's proceedings very well so we covered the methodological and conceptual aspects of SDG 241 as to how the framework look like what are the dimensions, what are the teams and the sub indicators that are covered, what are the data collection tools as well as the periodicity of data collection and reporting, and as well as the modality of reporting the indicator and we there discuss the dashboard approach as well as the aggregate indicator. We also discussed the process that FAO adopted to develop the methodology of SDG 241 and we highlighted the contributions and collaborations with BBS throughout this process in terms of discussions around the methodology as well as testing the indicator at different stages. So with this brief context, I mean I will immediately give the floor to Gianluigi for him to Aspanyar mentioned introduce himself first, and then walk us through the tool that has been created to analyze Bangladesh pilot tests. So Gianluigi, the floor is yours now. Good afternoon, everybody. I hope you can hear me okay. Yes. Okay, that's great. It's a pleasure to be here of course. My name is Gianluigi Niko. I am an empirical economist by education and I have supported, let's say the technical part of the SDG indicator 2.4.1 with Aspanyar and Stefania. Now, concerning today and the next sessions of this training, we will be focusing a little bit on the, let's say the technical part and the computation of the 11 sub indicators that are attached to the main SDG indicator 2.4.1. In this regard, the way I see the structure of these sessions is to give a brief overview in the next 30 minutes about all the tools that FAO has developed in order to collect, analyze and organize the data, including the reporting and the sampling strategy that is to be implemented before field operations. Whereas in sessions 2.1.3, we will be playing a little bit with the data collected a couple of years ago in Bangladesh using the pilot survey containing approximately 420 agricultural holdings and we will construct the 11 sub indicators. This will be done by giving before an overview of the rationale and the measurement of each of the 11 sub indicators related to SDG 2.4.1. And then we will look at the practical implementation and computation of the variables that are needed in order to get our final sub indicator. In the sessions tomorrow, we will continue to explore the last dimension related to the SDG indicator 2.4.1, which is the social dimension. I guess we won't have time to do it today, but we will have time tomorrow. And then we will present the final dashboard approach according to this traffic light approach that has been explained and explored yesterday by Aspandiar and Stefania. So, I would say I can start sharing my screen. And then depending very much on how Stefania and Aspandiar would like to conduct this session, we can either save some time at the end of the presentation for question and answer, or we can make it more interactive. But we can decide in the course of the presentation, so I will start sharing my screen. And, okay, can you see my screen? Yes. Okay, great. During session one of today, we will look at the process that goes from the sampling strategy to the data analysis. Especially, we will be looking at all the tools that have been developed by FAO in order to make this process successful. In this regard, I envisage three main objectives. The first objective is of course to give an overview about the sampling strategy and the related document that has been developed that specifically applies to SDG indicator 2.4.1. And then we will briefly look at the structure of the survey module that has been developed by FAO in order to collect all the relevant information that we need in order to compute the 11 sub indicator. And then finally, we will look at the template that has been developed for data interactions, right after data being collected, the data must be entered in an Excel spreadsheet in case we go for face-to-face interview, that we need to organize the data and finally we need to analyze the data and report about the sustainability status of agricultural area. Starting from the sampling strategy, I have added some hyperlink to the presentation that you can easily click just to explore more in detail the content of the sampling strategy. In doing it, I would like to remember that what we are going to measure is the proportion of agricultural area which is underproductive and sustainable agriculture. So this is the main goal of the SDG indicator 2.4.1, which means that what we need to sample our agricultural holdings and then we want to measure the sustainability status associated to areas of agricultural holdings that have been sampled during the implementation of the sampling strategy. Therefore, the observational units and the target population are by nature, by definition, agricultural holdings in the country of interest. And when we talk about agricultural holding, of course, we do not only refer to agricultural holding in the household sector, but also to agricultural holding in the non-household sector. Agricultural holding can be defined somehow as economic units of agricultural production. And of course, when we think about agriculture, we, in a broad sense, we make reference to the primary sector, net of mining and query, which is crop livestock, forestry, fishery and aquaculture. In this case, in this specific case, however, and this is important to highlight, we are only considering agricultural holding that engage in the production of crop and livestock and mixed crop and livestock activities. So we are not really considering the agricultural sector related to forestry, fishery and aquaculture. This is out of the scope of the SDG 2.4.1. When we talk about the household sector and the non-household sector, the FAO World Food Program for the Census of Agriculture 2020 has developed a clear definition of the household and non-household sector. Of course, this must be adapted to the country's specificities. But in general terms, the household, the agricultural holding in the household sector can be defined as agricultural holding that are run by household or by family members, by a combination of household members, one or more household members. Whereas the non-household sector, again, in a broad sense, can be defined as agricultural holding that are run by corporation or government institutions. Again, there is a link to the FAO World Program for the Census of Agriculture that you can explore and look more in detail at the definition. Having said, of course, it implies that the sampling unit, what we need to sample are agricultural holdings, whereas the sampling frame must be the complete list of all agricultural holding in a given country. This means that the complete list of agricultural holding can typically be developed to the agricultural census. The agricultural census, I would say, is the key instrument in order to have the list of agricultural holding from which you will be sampling some for your survey, or for the implementation of the survey, the agricultural survey or the survey model developed by FAO. However, there is also perhaps more cost-effective approach, which is based on the integration of sunscreen in question to a population census, in order to arrive at the final list of agricultural holdings in your country. Now, the reason why this can be a cost-effective approach is because, as you know, agricultural population censuses are conducted once every five or 10 years. They are pretty expensive, and therefore, in case UMDs are due to conduct a population census, you may think about adding a sunscreen question in order to have the complete list of all agricultural holding in your country. Having defined the observational units, the target population, the sampling unit, and the sampling frame, the next stage, the next phase is to have a suitable sampling design. For the specific case of the SDG indicator 2.4.1, the two-stage stratified sampling design is typically a suitable sampling design in order to collect the variables that we need for constructing our indicator. In the case of a two-stage stratified sampling design, the primary sampling unit, of course, are enumeration areas, counties, or villages, and the idea is to have a proper stratification of this primary sampling unit. Ideally, by the agroecological zone in the countries, for example, you may have a stratification proportional to the agroecological zone that exhibit the same climatic conditions. Once we have stratified our primary sampling unit, and we have selected randomly our primary sampling unit, of course, we need to select within this primary sampling unit agricultural holdings. And again, even the complexity behind the SDG indicators, it is advisable, strongly advisable, to stratify the secondary sampling unit by, for example, economic typologies of the holding or whether or not the holding irrigate or to not irrigate the land. Economic typology is, again, since we are interested in agricultural holding that carry out crop livestock and mixed crop and livestock activities, a typical certification can be done proportionally to the agricultural holding that engage in any of these type of economic activities. And finally, the reporting units, having also designed our sample, the reporting unit is going to be any representative of the holding. Of course, ideally, for example, in the case of the agricultural holding in the other sector, the ideal reporting unit, which is the person who is going to respond to our questions, is like the head of the agricultural holding, the holder or the co-holder of the agricultural holding. The most informed person typically is the one that can minimize data entry mistakes and to have a suitable reporting of the SDG indicator 2.4.1. The second instrument tool that FAO has developed is the survey module. The survey module is, okay, this is a very flexible survey, very shorter, very cost effective, I would say, because it contains basically a peer minimum question that can be administered either as a standalone survey, or can be attached to an agricultural survey in case your country has already envisaged to conduct an agricultural survey, or you can simply integrate in your agricultural survey some of the questions that are within the survey module. So the idea is that if in the agricultural survey, most of the information that you need in order to compute the 11 sub indicators are already available, you can simply integrate the remaining questions in order to have the full list of variables that are going to be used for the calculation of the 11 sub indicators. Again, this was already mentioned yesterday, so I won't be spending much time on these, but we have 11 teams and 11 corresponding sub indicators. Each sub indicator consists of some key questions and the related collected information is then converted into a quantitative information that is used for the construction of the indicator. Finally, FAO, and this is a supporting document that has developed an enumerator manual. Again, here is a link, I just want to show you the enumerator manual where you basically have, for each question, you have a clear explanation of the meaning and the term behind each question, including the reference period, whether it is the last calendar year, the last three year, and so on and so forth. The structure of the survey model, now I'm going to, I'm going back to the survey model before presenting a little bit more in detail, the enumerator manual. The survey model is comprised of the main section that have been structured in a logical order in order to gather the information that we need. Before we have section one and section two where we collect the key information about the sex of the respondent, ease or her role within the agricultural holding, and then in section two we are going to collect the area of the agricultural holding. In section A, B and C, they have been structured in such a logical order in order to collect all the information that we need to construct the indicator that are the indicators that are attached to each dimension of the SDG indicator 2.421. In a few instances, you will need to combine some of the information in section A and section C in order to get an indicator that is, for example, in the social dimension, but this has been done in order to avoid repeating the same question in each section. And as I was saying before, data, the unit of observation is the agricultural holding, even though, and again this is something that I highlight, we are interested in the corresponding agricultural area. So the unit of observation is really the agricultural holding and the order or the co-order or any person who is going to represent the agricultural holding. About the enumerator manual, the enumerator manual provides a detailed description of the survey model and review the standard operating procedures for each question. The main, I would say that the overarching goal of the enumerator manual is to ensure there is a common understanding between respondent on one side and the enumerators and supervisor on the other side about the definition and terms behind each question. So for example, you see here, I'm going to zoom in, you see here that this is apparently a very simple question where we are going to ask about the type of holding, whether it is in the household or in the non-housel sector. The enumerator manual will further illuminate about the meaning of the household and the non-housel sector. That's why there is a box where you can, when you have many references, you can link to look at the proper definition of the household and non-housel sector, which in which specific case must be adapted to the county specific context, but again, you will have a general overview. And then you will have an example of commonly encountered instances where questions and responses might not be easy to administer and record. So there are questions that they can have a different interpretation and this is also the logic behind the cognitive tests that have been conducted. And therefore, I mean, we want to make sure that each question is fully understood by both respondents and the enumerator. And this minimizes, of course, the mistakes during data collection operations. And finally, there are, there is a sort of guidance on how to use a skip question and filter question. For example, if you say no to a given question, you will be asked to skip the next question and go to another question. So this is critically important in order to have a logical process about the data collection through the survey model. Okay, the other tool that FAO has developed is related to, I mean, aims at collecting and entering the data once they have been collected from the field. In this regard, you, we need to imagine a situation where data has been collected in the field through face to face interview. So each information has been feeding in the questionnaire and we need to report in a dedicated, dedicated template developed by FAO where you need to store all the information. Okay, here I added an hyperlink which is directly linked to my desktop, but you can easily access it because I showed the folder today. So this is the typical template for data entry operation. So imagine that I have, I had to hide some of the information which are confidential, but here you imagine that you have a template which is completely empty and you need to enter the information collected through the survey questionnaire. In order to facilitate data entry operation, you have a corresponding code book. So for each question and each variable, you have the specific code and the specific label. For example, question 1041, which asks about the sex of the respondent, the associated code can be one or two, and then you need to know that one stands for male and two stands for female. The same applies to question 1051, which asks about the role of the person within the agricultural holding. So you have different code from one to five, so you have this categorical variable. And for each corresponding label, which can be the holder, the holder, the manager and so on. So for our presentation, there are a few general rules that should be, let's say, followed during data entry operation, which is when to start to start entering data collected, and of course it's better to do it as soon as possible. Because of course we let minimize delays and of course maximize the opportunities for checking, rechecking and validating the data. Confidentiality, it's important that of course the data are confidential, so it's better to avoid the people outside the team can consult the data collected through the survey module. And finally, I want to, let's say they vote the last slide to introduce what is going to be the next session. So the process so far has a little bit explored all the process that goes from the sampling design to data collection, data entry operation and organization, and what is to be done of course is the analysis of data. Since we have organized our data in an appropriate output format, which is ideally an Excel spreadsheet, we need to start to select our variables, the variables that are needed in order to construct the indicators and to transform this variable into primary variable that will be further used to construct the final sub indicator. So the idea about this diagram here is that we want to use the qualitative information collected to the survey, which is collected through asking a specific question. And we want to compute the primary variable. Yeah, but did an example about the format value, the format value of course we need to primary variable which are price and quantities, and we need to multiply price and quantities in order to construct the format with value. And then we will construct an additional variable which is the land area in order to collect to construct the very first sub indicator, which is the format value per actor, which will be further assessed against the sustainability criteria that will be explored in few minutes. So I will say that I cannot stop here since it's 1030, and it was at least 30 minutes for this presentation before moving to the, to the next sessions. And so I don't know, depending on the fund and ask from the area, if you want to answer some questions in case. Okay, thank you very much Luigi was very nice presentation. So for the moment, it seems we don't have any question but let's wait a couple of minutes. In the meantime, let me just remind all the participants that I have promoted everyone as panelist. So, please ensure that you are always muted, and because you are able to unmute yourself alone. So, just raise the hand if you have any question or writing the chat box. If you want to talk or if you, if you, if you want to have any comment or whatever. So it seems we don't have any, any question. So I think we can move with your next session on constructing the sub indicator in the economic dimension if I'm not wrong. I'm sure I'm sure that the questions will will will come to the floor again to you. Okay, thank you very much. I'm going to share my screen again. And, okay. We have a 50 minutes just to, you know, to make my time efficient. We have 15 minutes right for these for this session. I guess so. Yes, yes, it was a question I was. Yes, you have 15 minutes and then we break. Okay, perfect. So I will, I will try to stop like, I don't know a few minutes before in order to answer question in case or if you have questions during the presentation I'm happy to answer. So in the next 15 minutes I will be presenting the construction of the sub indicator in the economic dimension. And as I was anticipating before this free sub indicators have been constructed the using data from the pilot survey conducted in Bangladesh. All of years ago. Okay, we have three main sub indicators related to the economic dimension. The first sub indicator is the format value per actor. So we want to know the value from agricultural production proper livestock. And finally, the very indicator is about risk mitigation mechanism. And the ratio now behind the very first sub indicator is basically a measure of the agricultural area which is associated with farm was out with very perector fulfilled the following criteria. So we're gonna, let's say, assign a sustainability status which is desirable to have the cultural holding was a farm output value per actor is greater than two third is at least two third of the corresponding 19th percentile. So the sustainability status to starts to be acceptable for holding was a format value per actor is between, I mean, is greater than two, one third of the 19 percentile, but is lower than two third of the corresponding 19th percentile. And finally, agriculture or the area associated to a culture or the is unsustainable in case of holding was productivity value is lower than one third of the 19th percentile. So there are a few calculation that has to be done regarding this indicator. This is perhaps one of the most complex but also one of the most interesting indicator that we have in our Mongolian indicators. And the, the first variable that we need to compute there are three main variable, I would say that we need to compute in order to calculate the sustainability status of agricultural area associated with the productivity of agricultural holding. These are the price, the quantity, and finally the agricultural area itself. The farm output value, which is our numerator can be calculated as the summation of the quantity of a given crop related to a given agricultural holding crop or by product or livestock or it's related by product livestock and also on farm commodities of a given agricultural holding which must be multiplied by the corresponding farm gate price. The agricultural area, land area, instead, is calculated by adding up all the area that I have listed here, which are temporary crops outdoors or under raw shelter, under greenhouses and so on so forth. So these are the three main variable that we need to calculate. I'm going to introduce it and then I will show you the practical implementation calculation for this indicator. Well, can the information in the survey concerning price and quantity in the survey modular. There is a very clear section where you can have all the information that you have about the quantity and price of each crop, of each crop by product, of each livestock and of each livestock by product. You see here that you, we have collected information about the quantity producer of a given crop and the corresponding unit of measurement, which must be, well in this case it's not really important, but in theory it should be converted into a standard unit of measurement in case you use no standard unit of measurement. But again, let's forget about it because this is not important. In the end we are multiplying by the farm gate price here and we get the total value of production of each crop, of each crop by product and the same applies to livestock and livestock by product. So this is the first two variables that we need to compute and we can find it in the economic section A, which is the economic dimension of the order. Yeah, this is very well structured, I would say, because it's easy to find the information that we need. Secondly, we need to calculate the agricultural area of the order and this is easily calculated by looking at the information in section two and particularly in question 2.2, where you see that you have the total agricultural area of the order being here it is important to convert the unit of measurement in actors. So in case data about the land area of temporary crops, etc. is collected in either no standard unit of measurement or in a standard unit of measurement, which is not actors, we need to convert this data in actor because in the end what we want is the value of production per actor of land. So these are the two questions that we have and that we need to transform into quantitative variable. Okay, the key indicator before is finally calculated once we have the price, the quantity and the agricultural land area. We need to divide the farm out the price times quantity by the corresponding agricultural land area and we get our key indicator which is the farm out value per actor. There is a very important step that has to be implemented, which is the breakdown by 12 categories of farm. Now we saw before that we are basically collecting data about agricultural order that engage both in the agricultural sector and in the non-housel sector and agricultural hoarding that engage putting crop livestock or mixed agricultural activities of livestock and crop type. Plus we are collecting data about agricultural hoarding that irrigate their land and agricultural hoarding that do not irrigate their land. So you see that these are very much, you know, different from one another. And of course comparing the farm out value per actor of a given category of agricultural hoarding with another category makes little sense because we may end up with some categories of agricultural hoarding very, very having a very low value of production per actor. Perhaps because of their dimension because they are part of the housel sector etc. And other agricultural hoarding with very high value of production per actor. And this is not what we want because we want to have a sustainability status by agricultural hoarding that belong to the same categories of farm. This is the rationale behind the disaggregation that break down by 12 categories of farm. And I will show you in a second how to calculate the relative threshold for each category. Now the idea is to derive the sustainability status based on the threshold that we that we explained before so if the value of production per actor is higher than two thirds of the 19th percentile in that case the land area associated with the agricultural hoarding is going to be green or desirable or in case it is not could be either yellow, which is acceptable or unsustainable, which is red. How to do it. Okay, we need to calculate some threshold and this threshold must be specific to the category of agricultural hoarding so all agricultural hoarding belong to a given category will have a given threshold. In order to calculate the threshold here you see the distribution of the farm output value per actor which is our key indicator that we have constructed before that goes from zero to three million local currency unit. You see that this is a very right skewed though there are very few agricultural hoarding having a value, which is very high, whereas the highest probability to have a given value is around. Yeah, I would say 300,000 local currency. Now, even this distribution we need to know what's what is the value associated to the 19% type, which is this one I have tried to. To highlight it here, which is approximately 864,000 local currency unit. This is our threshold. I mean this is not the threshold. This is the value of the 19% time. And now we can construct the threshold we say that the threshold for the desirable status must be greater or equal to two thirds of this value. In which case, the land area of the agricultural hoarding is going to be desirable. If it is comprised between if it is a greater than one third of a lower than two thirds of the 19% tile, it's yellow otherwise it's going to be red. Now, given this distribution, I'll show you the final output. The final output is basically this one. Okay, you see here you have different categories of farm, you have farm, which engage in agricultural production of crop type in the other sector and then irrigate their land. And then you have mixed agricultural activities in the other sector and that they irrigate their land and so on so forth. The first entail that is stable across the same category, but various across across different types of holding is the first value that we need to calculate and this value correspond to this one. For example, based on agricultural holding that engage in crop production in the other sector and that they irrigate their land. The one third of the 19% tile is approximately 285,000 local currency unit, whereas two thirds of the 19% tile is 570,000 local currency unit approximately. So these are, let's say the lower bound threshold and this is the upper bound threshold. Okay, let's take the example of the first agricultural holding whose farm output value corrector is approximately 387,000 local currency units. This value is exactly in between the up the lower and upper bound. And that's the reason why the agricultural area, which is this one of this farm can be considered as acceptable. In our distribution we will have its value which is around, which is in between the red line and the green line. That's why it's yellow or acceptable. This is slightly acceptable. Another very quick example, let's take agricultural holding whose land area is not sustainable which is this one. You see that the farm output value related to these agricultural holding which is agricultural holding number six is 241,000 local currency unit. The threshold are the same because we are talking about the same farm category. This is over than two thirds of the 19% tile, in which case the sustainability status is red or unsustainable. So that's the rationale behind the indicator. Now, before moving to the next indicator, I'm going to show you, and perhaps, I don't know if we have enough time, perhaps as familiar as Stefania can advise on this, but we can work together in parallel to make it a little bit more interactive. But I shared a folder, you can simply copy, paste your folder in your desktop if you want to work in parallel for the calculation of this indicator. This is the indicator number one, sub indicator number one, paramount value corrector. The only thing that you need to do in this case is to change your computer name. And of course, the pattern, the path where you have saved the folder. So basically, you need to change this one and the computer name. Yeah, this is my computer name, and you simply change it. In any case, I go ahead for, of course, because otherwise I run out of time. So let's look at the practical implementation of this indicator. So again, here I summarized the three sustainability criteria. The first thing that I'm going to do is to upload the data set, which are the data set containing information on the land area, and so on. First, the second step is to calculate the 12 categories of agricultural holding. And in order to do it, we need the information on whether or not the agricultural holding, which is this one, belong to the household sector or the non household sector. And this is the corresponding variable. Okay. And then gonna touch a label. So you will see that I have basically a distribution of agricultural holding by agricultural holding belonging to the other sector, which are more than two thirds and a bit more than 50% belong to the non household sector. Okay, let's go ahead because we need to know the main holding activity, whether they engage in crop production livestock on mixed activity. And then gonna calculate this variable, taking the information from question a 1000. You will have three code code one stand for crop crop to the code to stand for livestock and three for for mixed activity. And finally, I can have my our 12 categories of agricultural holding. I'm gonna touch a label just to make it more clear. And if I tabulate this variable, which is the last variable I calculated, you will see I don't know if you can see from my screen of a basically you will see that you have the 12 categories of agricultural holding. So the vast majority belong to the household sector, they engage in crop production and they irrigate the land which correspond to one third of total agricultural holding in the country. Yeah, of course, this is a pilot. But again, let's assume that it is representative for the of the country. And then you will have the distribution for the other typologies of agricultural. Okay, I'm going to save this portion of the data set that will be used at a later stage. And what I'm going to do is to calculate the agricultural area of the order. So the agricultural area of the order is basically given by this question, which is the total area of land. Within, I'm sorry, within associated to a given area. Now the problem that we have is that we might have this is not the case. Because everything has been collected in acres, but we might have a different unit of measurement and they must be properly converted. Now we know that the one acre of land of land, I'm sorry, correspond to approximately 0.4 hectare of land. So we need to convert by simply multiplying. Then we might potentially have a land area, which have been collected in a square meter and therefore we need to divide by by the corresponding conversion factor and so on so forth. Okay, this is done. So, so far we have the agricultural area we have the type of holding the agricultural, the categories of farm. And now we need to construct the value added, which is the sum of the quantity times the farm price, the farm gate price. I'm going to upload the data set of interest based on the structure of the survey module and then we're going to compute the value of the value added from from crop production. But so this is the value at the front crop. This is the value out from livestock and this is from other on farm activity and finally I'm gonna calculate the value, the overall value of production. I'm gonna keep only the relevant variable. And I'm gonna save my data set. The final indicator. Okay, the final indicator use information on the categories of farms, the 12 categories of farm, the agricultural value of production and the agricultural area. These are the three information that we need to. I have done some outlier detection because, well, I mean, since we are human it might be that during data collection them some records have not been properly reported in the question or perhaps during data into operation. We can record the record the value which is in reality an outlier. Therefore, outlier detection basically will inform about the value that lie above the distribution of a given variable. And for example, if you are gonna, you see that you have some value which are very high here. Very high value and whereas the vast majority of value, basically they lie around zero. Therefore, what I have, what I have done was to look at the distribution of the agricultural area of which group and then I have detected the outlier and replace the outlier with the medium value. Again, we should be spending like one entire session about these. We can discuss the later stage, but so far I will implement automatically this code, but absolutely I'm happy to respond to your question about outlier detection. Okay, once we have cleaned up our data, we have constructed our farm output value per hectare, which is the value of production divided by the agricultural area, we need to construct. We need to assign sustainability status to each agricultural holding and to the corresponding agricultural area. For example, for agricultural holding that are part of the first farm category, which are crop in the other sector and that they irrigate the land. What I'm gonna do is to look at the value of the 19 percentile, which is this one, and then what I do is to calculate a variable which is equal to the value of the 19 percentile and then I say, your sustainability status is green if the value of production is greater than two third or 0.66 of the 19 percentile and you belong to the very first farm category. It's yellow, I have given the code number two for yellow. If your farm output value is greater than one third of the 19 percentile and at the same time is lower than two third of the 19 percentile and again you belong to the very first farm category. The same applies to the third sustainability status, which is unsustainable or red, in case your farm output value per hectare is lower than one third of the 19 percentile. Now, if I run this part of the code, okay, I have finally calculated the very first sub indicator, I'm gonna keep only the relevant variable, and I'm gonna show you how the dataset looks like. In the dataset here, you will have the agricultural area of the holding for the first holding, which is holding number 026 and corresponding sustainability status, which is desirable. Then you have the other sustainability status that can be acceptable and unsustainable and so on so forth. So that's the rationale behind the first indicator and I will stop here. Okay, and I will go back to my presentation. And then we move to the second sub indicator. I hope I was enough clear while explaining, but again. Before moving to the next sub indicator, let's see if we have any questions on this one. Sorry, Asfandia, you wanted to say something. Exactly, I was highlighting the same point. So, Gianluigi, don't worry about the time we can reshuffle the sessions and make some shorter, but this is the part which is very important. So, let's take some questions, you know, if any. Perfect. Thank you very much. Yeah, that's much appreciated. Okay. The second sub indicator is related to the net farm income. One of the things is that in principle, a very complex indicator, but if you decide to use the information from the survey module, you will make your easy much your life much easier. Because as you know, the net farm income is basically calculated as the difference between the value of production and all the cost that you have. And therefore, okay, let's start by highlighting the criteria and then we will move to the specific formula. The idea behind this sub indicator is to know about the proportion of agricultural areas that are economically viable or profitable. And these economic, these profitability is classified based on three criteria. Therefore, these are the agricultural area of a given holding is desirable in case the profits are above zero over the past three consecutive years prior to the date of the interview or the serve implementation is yellow if the profit have been above zero for at least one of the past three consecutive years. And finally, in case for all the past three consecutive years the profit that were below zero, the sustainability status is read or unsustainable. Now, the, in general, the profit are calculated according to this formula where the net farming come up. This is basically given by the total farm cash receipts, including a diet program prime by payments, I'm sorry, incoming kind total operating expenses, including the cost of label the label cost depreciation and the value of the inventory is the main formula, which is pretty much complex, I would say, therefore you need a lot of the information in order to get it. However, if you decide to rely on the survey model, there is a specific question about the profitability of the agricultural holding, which is this one question 8.7, where a numerator, so we'll ask to the respondent how often the audience was profitable over the last three calendar year. And you see here, the reference here, and then you have a number of options. So they could be unprofitable for all three years, profitable in one of the three years profitable in two out of the three years and so on. And the idea is that based on the answer that will be given by the respondent to the numerator, you can easily assess the sustainability status of the holding under the sub indicator. In which case, so let's imagine that the respondent reported that it was profitable in two out of the three years the sustainability status will be acceptable. Desirable, which is the vast majority of cases, as you see in this table, in case for all three consecutive year the agricultural holding was profitable. Okay, the implementation of these indicator is in principle will be very complex but in practice is a very short and straightforward, I would say. So what we need is of course to upload the portion of the data set with the information about the profitability of the agricultural holding over the past three year, which is this question, holding profitability over the past three calendar years. And then you simply are gonna give a code depending on the respondent on the on the answer given. So if you look at the answer, you will get unprofitable for all three years. Here there is a data entry mistake in reality. So there is an assumption made that zero in reality is one. This is the way I recorded. Unprofitable for all three years, which is 99% of the total distribution of the total holding. Unprofitable in one out of the three years and so on so forth. Depending on the answer given, you can construct your indicator. You will attach the label just to, you know, to have a reference behind each code or a label behind each code. And then you will add the relevant variable and you save the final data set. The final data set will look like this one. And of course, then we will attach, we will merge the corresponding agricultural area here, which is what we are interested in. As we did for the farm output value factor. This will be done during the reporting process when we are gonna. Display the dashboard, the final dashboard. So this indicator was a rather easy to calculate despite the complexity behind the agricultural. Moving forward. Okay, the third sub indicator related to the economic dimension. So first, the access to or use of one of the following risk mitigation mechanism by the agricultural holding so the rational is basically to know whether in case of potential excuse me. Yeah, please. Excuse me, let's just take you know very short break for questions. If there are any and then. So, you know the floor is open now to the participants if they have any questions regarding the two sub indicators that have been covered, you know during the session. So, if there are any questions please feel free to ask. If there is any question the participants can unmute themselves and ask the question. If any of you have any comment. Please feel free to intervene. Okay, please. Amirul you have the floor. Yeah, I was following this, you know, slides. Very interesting using stata and then have you shared this is you know stata documents of the participants, and the data for elected for 4.1 so that they can try on the road. Do you get my point. Yeah, absolutely sorry I was muted. Yeah, we basically shared this morning, the folder containing all the status creeps, plus the data that were collected by BBS Bangladesh. I mean, as I was saying before, the material should be in there. And if you envisage to collect these data for this indicator in the future, you only need to adapt a little bit. The data scripts, but in theory and of course I mean, we're happy to collaborate together, but of course, I mean there are the scripts and the presentation should help in calculating the indicator. There are a few adjustments that must be done. I mean, yeah, my suggestion would be to not apply the data script automatically but to also take a look at each variable to make sure everything is done properly. But the basis is in there. And this has been shared by Stefani I guess. Yeah, I see one email with the attachment, but I didn't really go into that one but you know I will request the participants know after the presentation if they try at home, if there's any problem you know arises, we can you know discuss the And I would just like to add to this Amirul and Gianluigi that this status script and routines are available on SG241 webpage as well. Plus, you know, this has been shared with BBS already as part of the pilot exercise a couple of years back so basically once we develop these scripts, these were duly shared with the BBS colleagues for them to have it and then basically use it for the next round of data collection. Yes, I mean it has been shared this morning as well so yeah it's a very good point if the participants want to practice with some changes of course, then you know they can. And if they find any difficulties or issues they can always get back to us. Again, you know, after this training when the real data will be available with them, when they will start using those data to this indicators at the point if some problem arises, we can also add in some discussion with you so that and everything is clarifying and stays. Is it okay. For sure, for sure no problem. Okay, in that case we can proceed and then participants will they know take all the documents with them and try to follow the instructions given by Niko. Okay, carry on then. Thank you. Okay, thank you very much. Okay, we don't have any question anyway participants you can, of course, as the question anytime in the chat box and then we will ask the question at the end of the next subindicator so you have the floor again. Okay, thank you, Stefano. Yeah. So the next subindicator is about risk mitigation mechanism and the idea behind this indicator is to know whether. You want to share the screen again. Sorry. No problem. Okay. And the idea behind this indicator is basically to measure the proportion of agricultural area of a given wording that can be exposed to extreme weather condition climatic condition or external shocks and whether they have mitigation mechanism to cope with with these with these shocks. There are three main risk mitigation mechanism that have been identified, which I, which is the first, the first one is access to or availed credit access to or availed insurance and on farm diversification which implies that the value of production of a single agricultural commodity is no greater than two third of the total value of production of the opening. So if you're going to like to diversify your production, you will likely face less, you can better cope with external shocks. If at least two of these mechanisms have been adopted by the agricultural hoarding then the sustainability status is desirable. If at least one has been adopted or implemented, then the sustainability status is yellow. And finally, if there is no access to the above mitigation mechanism, the sustainability status is red or unsustainable. Okay. I'm going to show you where to find the information. So we need to know about whether the agricultural access to or availed credit or insurance. And this can be easily find here in question 888. Did these holding of access to or avail any of the following mechanism for protection against external shock. And here you have the list of options. And finally, we need to know about on farmer diversification. So this is the third variable that we need to construct. This is the third variable. Again, we need the information on section two about the agricultural. The value of production from, sorry, that's a question section a, not section to section a about the value of production of the agricultural holding. Plus, we need specific information from the value of production of each crop to know whether livestock or whether there has been a diversification in agricultural activities. And here is one that each agricultural commodity is the value of each agricultural commodity is not greater higher than 66% of total, total value of agricultural production. And here is where you have also the information on other on farm products and the corresponding name. Why the first two variable, which is access to or availed credit or insurance are pretty straightforward so they can be easily computed from the information. Based on the information from the survey, this is the third variable needs to be constructed by combining the value of production of each agricultural commodity. In a given farm. And the total value of production from the farm. So we want to know what's the share of which agricultural commodity over total value of production. And therefore agricultural holding that diversify their own farm production. Of course, those was with a share of a single agricultural commodity which is lower than 66% of the total value of production. Now here is an example which is based on on the data from the pilot survey. You see here you have the total value of production of a given agricultural holding and you have the corresponding share from all the agricultural commodity. Of course, for reason of space, I had to include only few agricultural commodities. So I, I had the crop one and crop five, but in reality, you can have you and also livestock one, but in reality you will have much more agricultural commodities that have been the value of much more agricultural commodities that have been collected through the survey. Therefore, okay, this is an example based on, for example, holding number three and four, you see that the value from the first crop commodities is only 3.2% of total agricultural production. Therefore, the value from the first livestock is 80.8%. So there is not really diversification of agricultural activities that seem to be concentrated around the given agricultural commodity, which is livestock number one. And the same applies to the fourth agricultural holding. So that's the rationale behind the risk mitigation mechanism sub indicator. Now I move to the status script, which is the one for sub indicator number three. I'm going to show you the technical implementation. Again, I'm going to upload the data set that we need, which are basically the data set from section A. I'm gonna compute the value of production from crop activities and the final value of production from the agricultural holding, the overall value of production. This is a sort of loop where I calculate for each crop. So this is crop number one, crop number two, etc. This is the by product crop number four, three, etc. Let me zoom in to make it more visible. Here you have livestock number one, two, etc. What I do is to generate a new variable, which corresponded to the share of each crop over total value of production. And then I'm gonna, let's say highlight this variable by constructing a dummy, which takes very one if the share of any of these commodities is greater than 66%. This is what I basically do using this loop here. And as you can see, if I run this portion of the data set, I will get, okay, you see here is the loop. I have 99 agricultural holding of 25% whose value of production is basically concentrated around a single agricultural commodity, which is the dummy variable that I constructed here. Then what I do is to generate three variables, one for each risk mitigation mechanism and the same, okay, generate a dummy which is equal to one if the agricultural holding can access credit, generate another dummy equal to one if the agricultural holding can access insurance. And finally, if the agricultural holding do not do diversify, I'm sorry, if the agricultural holding diversifies is on production. So each commodity is not greater than 66%. And then I generate a variable which is equal to the total number of risk mitigation mechanism. This is the key variable that we have, which I'm gonna show you, which is here. You see these are the number of risk mitigation mechanism for each agricultural holding that you have. In this case, this agricultural holding has two risk mitigation mechanism. And if I go back to my slide, and I see that adding at least two means that the sustainability status of the agricultural area is green or desirable. In this case, there is no risk mitigation mechanism, therefore, is red. In this case, only one, therefore, is yellow. I generate my sub indicator, and I attach a label. And finally, as I did before, I'm gonna keep only the portion of the data set that I need for sub indicator number three. If I look at the distribution, I can see that almost 60% of total agricultural holding adopted at least the two risk mitigation mechanism. Therefore, the sustainability status is desirable. Whereas we have more than 80% of total agricultural holding whose sustainability status is red is unsustainable because they do not adopt any risk mitigation mechanism. Stefania, should we take a break or do we want to? Yes, so let's wait a couple of seconds to see what these events have. Okay, so welcome back. I hope you have had a nice break. You have refreshed your minds. Now, so we have seen with Jaluigi the first dimension, so the economic dimension with the trees of indicators. And now we move to the next dimension, the environmental one, which has five indicators. For sure we will not finish today, the environmental dimension, but it's not a problem. We will continue tomorrow actually was already in the plan. So, Jaluigi, don't worry about timing as as Pandia said, we can for sure also adjust the next two days. So I leave again the floor to you and you have the floor. Thank you. Can you hear me okay? Yes. Okay, perfect. So again, I'm going to show my screen now. We're now going to focus on the first three subindicator related to the environmental dimension. So again, I mean, the process will be the same. We will first explore a little bit the way they have to be computed. And then we will look at some practical exercise. And using state. Let's see if we can finish this dimension by today, I don't think so, because there is a relatively long list of sub indicator to be calculated. There are five sub indicator in total. These are the prevalence of soil degradation, variation in water availability, management of fertilizer, management of pesticide and use of agro biodiversity supportive practice. For sure, I will try to finish at least the first three sub indicators and then we will see if we can anticipate the remaining two sub indicators so that we will have more time for discussion tomorrow. Yeah, even because I guess that strong focus will be on the day the final dashboard approach to see how all these indicator will be combined together and we will assess the sustainability status of agricultural holdings of land areas associated to agricultural Okay, the first sub indicators. Basically captures the proportion of agricultural areas. According to the sustainability status on the basis of for soil health threats that the agricultural holding may experience over the past three years prior to the date of the interview. There are basically four threats that have been identified for the construction of this indicator. These are erosion reduction in soil fertility water longing, water logging and salinization. And then there might be any other specific threats that can be reported by the respondent to the enumerator, which need to be, let's say, specifically assessed. The idea behind, you know, the assessment of the sustainability status of agricultural area is to look at whether the combined area affected by any or any combination of these threats is negligible. And when we say negligible, we means that less than 10% of the total agricultural area of the fund has been affected by any or more of these soil health threats. In case the percentage of the agricultural area affected by these threats is comprised between 10 and 50% of the total agricultural area of the farm, then the sustainability status will be acceptable. And finally, if the portion of the agricultural area affected is above 50%, the sustainability status of the agricultural area of the holding is going to be red or unsustainable. Now, perhaps something that I forget to mention is that of course, if the agricultural area has not been affected by any threats, the sustainability status will be by default desirable or green. Okay, what information do we need and where we can find the information to construct the sub indicators. Basically, we need three main variables to be constructed. The first variable to be constructed is to know whether or not the holding was affected by any of the soil degradation threats. And, and of course, in case there is an affirmative response, then we want to know what's the proportion of the cultural area that has been affected by these threats. So you will see here, and I'm going to zoom in again, that in question 2.2 section two question number two, you will have the typical question concerning the total agricultural area of the holding, which is our numerator. And then the agricultural area that was affected by potentially affected in reality by these threats. Therefore, there is a sort of logical order, you will first ask whether the area of the agricultural area of the holding was affected by the threats that we listed before. So in erosion reduction in soil fertility, water loading and sanitization, and finally, none of the above, which implies that by the effort that the sustainability status is desirable. In case the respondent reported that one or more threats affected the agricultural area, then we will further ask what is the total area that has been affected. Again, in theory, in principle, the unit of measurement should be the same, both for the total agricultural area and for the agricultural area affected. But in case the respondent will report two different unit of measurement that we need to standardize that in order to calculate the share of the agricultural area affected. The decision process basically applies to apply a conversion factor to convert everything in actors. So these are the key information that we need. And the sustainability status will be calculated accordingly. For example, the example basically illuminate about the calculation of the share of the agricultural area affected, which is given by the total area of the actors affected by any threats and the total agricultural area of the holding. And this is reported that you see in the third column of this table, you will see that this is the percentage affected, which must be multiplied by 100 in order to get the share. So, for example, in the case of the first agricultural holding, the total agricultural area is 0.9 actors, what has been affected by one of the threats that we saw before is approximately 0.4 actors. Therefore, the corresponding share is 44.8%. This implies that the sustainability status associated with the agricultural area of the first holding is going to be acceptable. Because if we go back to the sustainability criteria we said before that in order to be acceptable is between 10% and 50% of the total agricultural area. Okay, I'm going to focus on a few agricultural holding that reported a proportional area affected by one of those threats, which is higher than 50%. Here, from holding number four to holding number eight, that the percentage goes from a minimum of 57.9% to a maximum of 75.2%. All these holdings, agricultural holdings are unsustainable, and the reason is because the proportion of the affected is higher than 50%, which is the sustainability criteria for, or to better say the unsustainability criteria for associated to these agricultural holding. This is the rationale behind the computation of the indicator, and now as usual, I'm going to move to the calculation of this indicator. So this is indicator number four, as you can see here. So indicator number four need a few information which are related to the total area of the holding, plus a few information that have been captured using section B, which is the economic section of the agricultural model. And we need to merge these two portions of the raw data set together in order to calculate our indicator, our sub-indicator. So again, the first thing to be done is to convert the total agricultural area into a standard unit of measurement, in which case is hectare. So what I'm going to do is simply to apply a conversion factor in order to have everything in hectare, and then I need to also to know the land area that has been affected. And again, in order to harmonize this process, everything must be converted in hectares, which is our standard unit of measurement. So once I have computed the two primary variables, which are basically the total agricultural area and the agricultural area affected, I can compute, calculate the share of the agricultural area that has been affected by one or any combination of the threats that we saw before. So I'm going to compute the share. Here is the share affected. These are the summary statistics you see on average 50% of the total agricultural area based on data from pilot survey have been affected by any of the threats. But we want to know specifically which are the agricultural holding affected. So what I'm going to do is to say, okay, the sustainability status is desirable, whose code is one, in case, and I'm going to show you the population of this variable. The agricultural holding was not affected by any threats that we listed. So 43% of the total agricultural holding reported that none of the threats affected the agricultural area. So by default they are desirable. But the sustainability status is still desirable. In cases where the area affected that we calculated before here is lower than 10%. And we have a 50 hate, you can see here, there are 50 hate agricultural holding who have been affected by any of the threats that we listed before, but the proportion of their affected is lower than 10%. So they are still sustainable. In case the area affected is between 10% is at least 10% or lower than 50%, sustainability status is acceptable. And finally, we will have a sustainability status, which is unsustainable. In case the area, the share, the proportion of their affected by any of those threats is higher than 50%. So I'm going to compute this last two. I'm going to codify this last two sustainability status. I'm going to touch a label again, and I'm going to save this portion of the data set which is related to the fourth sub indicator. This is now associated to the environmental dimension. I'm going to save my data set in a specific folder, and you will see again that we have a very simple, but immediate data set where for each household, for each holding, we know the corresponding sustainability status of the associated area related to this sub indicator, of course. Needless to say. Okay, I'm going to close everything now, and I'm going to move to the next sustainability, to the next sub indicator. Okay, let's see if we have questions for this one. Yeah, sure. Yeah, it seems not so you can move on. Okay. Okay, the fifth sub indicator is related to variation in water availability. These sub indicator again measure the share of the proportion of the agricultural area that is sustained according to the sustainability status, which is defined according to three main, let's say, variables. These variables are whether the agricultural holding use water to irrigate their crops. And this must be on at least 10% of the agricultural area. If the agricultural holding experienced any reduction in water availability over time. And if there is an inefficient functioning of organization that are in charge of water allocation. So we want to know whether in case there is a reduction in water availability. The only reason we want to know if there is at least some organization that can basically that as an efficient functioning functioning to reduce these reduction in water availability. The sustainability status are defined according, which is, we will have a sustainability status. Desirable in case the water availability remains stable over the years. So for those farmers that irrigate their crop on more than 10% of the agricultural area. The default is that the sustainability status is desirable if the irrigation process, let's say, or course on less than 10% of the total agricultural. Just to better explain, the idea is that in case farm are irrigated their land on more than 10% of the agricultural later, there are no reduction in water availability, which has remained stable over years. Therefore, we can classify that specific holding as sustainable. The same applies to farm that irrigate or less than 10% of the total agricultural area. So the sustainability status is acceptable in case agricultural holding uses water to irrigate their crops. Again, on at least 10% of the cultural area of the farm. We are not fully aware on whether there has been a reduction in water availability or water availability has remained stable over the year. Or they know there is a reduction in water availability. And even if there is this reduction, there is an organization that effectively allocates water among users. There are a number of basis, which basically experiences reduction in water availability on more than 10% of the agricultural land. There is no organization that effectively allocate water. So the agricultural, the sustainability status of the agricultural holding is going to be read or unsustainable. What information do we need in order to derive this sustainability status. Again, looking at the information collected to the survey modular. We want to know whether or not the agricultural holding irrigated slam by default if there is no irrigation then the sustainability status is green. We want to know what's the percentage of area of the agricultural holding that experience that was used for irrigating crops. The answer and the information collected through the survey. So basically, the respondent reports that they that he or she irrigates their agricultural land. And also the area in a tarse or whatever unit of measurement that is irrigated. And finally, we want to know. It's not really finally because there is one more variable. We want to know whether water remains stable over here. And again, the information can be can be collected using the question before, which she would specifically ask whether the holder is observing a reduction in water availability from many from many sources which are located in the lake, the canal, the river, etc. And finally, if in the area of interest, there are organizations that effectively allocate water among us. This is all the information that we need in order to construct our sub indicator and to derive the system. And this information, of course, need to be combined together. Now, we need a total. So these are the raw variable that we need. And then we need to construct the six primary variables to derive the system. I'm going to list each of these variable. The first variable is whether or not the water was used for for crop irrigation. This variable is basically a dummy variable takes value one in case the agricultural holding uses water for crop irrigation and zero otherwise. We want to know the total area irrigated. And this variable is a continuous variable expressed in actors, which will give basically the volume of area that is irrigated for a given agricultural holding. Now, I'm going to construct an additional dummy variable, which takes some value one if the agricultural holding experienced a reduction in water availability, or if the order observed any reduction in water availability to better say an additional dummy variable, which basically will inform us on whether there is an organization dealing with water allocation in the area where the holding is located. Of course, we need again the agricultural area of the holding, the total agricultural area of the holding, because we want to know the share that is irrigated. And in order to know the share that is irrigated, the percentage of total area irrigated, we simply need that basically to divide the total agricultural area irrigated, which is our second primary variable by the total agricultural area, which is our fifth primary variable in order to get the percentage of area irrigated. Now, here I have reported an example, which is based on real data from the pilot, where you have the sustainability status for three agricultural holding. One sustainability status is desirable. This apply to agricultural holding number one. You see here that the percentage of area that is irrigated is basically 90% of total agricultural area of the holding. However, variable B03, which is going to inform us on whether or not there was an irrigation process within the agricultural holding. And then we have an additional variable, which is B04, which tell us whether the water is progressively going down if it is stable and so on so forth. And then we have a final variable, and this row variable is variable B05, which tells us whether there are organizations and how these organizations are working. In the first case, we have defined the sustainability status of agricultural holding number one as desirable, because even if the total agricultural area irrigated is a bit less than 90% of the total agricultural area, the water is always available in sufficient quantity. So in this case, if we go back to our sustainability criteria, we say that the water availability remains stable over the years, for those farming irrigating more than 10% of the agricultural area. And indeed, looking at the information associated to this agricultural holding, despite irrigating 90% of the total agricultural area, the water is available in sufficient quantity. There is no reduction in water availability. The second agricultural holding instead, it has a sustainability status which is acceptable. That's because the first of all, the share of the agricultural area which is irrigated is higher than 10%, is 71%. In addition to that, that agricultural holding is reporting that the water level is progressively going down, so there is a reduction in water availability. However, despite this, there are organizations that are located around the area where the agricultural holding is, that are working well and are allocating and distributing water among users in an efficient way. Again, just to clarify these sustainability criteria, I go back to the criteria for making the agricultural sustainability status acceptable, which is, uses water to irrigate at least 10% of agricultural area of the farm. In the case of the second agricultural holding, we have approximately 71% that is irrigated. That's not what does not know whether the water availability remains stable, or there is a reduction, which is our specific case. There is a reduction of water availability, but there is an organization that allocates water efficiently among users. And these are exactly the, let's say, the information corresponding to the second agricultural holding. Last case is related to the agricultural holding number 36, which is not sustainable. The other slide is that the share of agricultural area irrigated is approximately 74%, so much higher than 10%. Water is progressively going down, and there are no organizations that are basically allocating water among users. Again, very quickly going back in all other cases, in all other cases, it implies that we need to exclude the criteria that apply to sustainability status, which is desirable or acceptable. Okay, this was the second indicator, and now I'm going to show you the calculation process for the second indicator related to the environmental dimension. In this case, again, this is relatively short, but of course it depends on how the information is being collected, whether using the survey module or any other information collected through other survey. So I'm going to upload the relevant data set that we need, and again the process is to convert the total agricultural area, which is our one of our primary variable. Then we're going to calculate the land area that is irrigated, and finally we are going to calculate the share, the proportion of the area of the farm, which I'm sorry, which is experiencing a reduction in water, which is irrigated, I'm sorry. Now, the final indicator that we calculate, which is this one, I'm going to zoom in, this has a sustainable status, a desirable status, in case, and here we need to explore all the variable that we use, which are the primary variable that I showed you before, whether water was used or not. In this case, if the respondents said that water was not used, because either they do not need irrigation or there is not water available, in this case, by default, since the area is not irrigated, we can assign the green sustainability status to this agricultural area. Then we focus on those agricultural holding that irrigate more than 10% of the total agricultural area. And you see, I'm sorry, at least at most 10% of total agricultural area, water for irrigation is at most 10% of the total agricultural area, which is. If the area irrigated is at most 10%, again, the sustainability status is going to be green. And finally, we're going to assign a sustainability status, which is green, to all agricultural holding who self reported that they do not experience any reduction in water availability. And this information is captured by this variable here. No, water is always available in sufficient quantity. So again, I'm going to construct this subindicator, subindicator number five. Now we move to the agricultural holding whose sustainability status is accepted. In order to be acceptable, the area irrigated must be greater than 10% as we saw before. And at the same time, they do not know whether there is a reduction in water availability, you may recall looking at the information here. They use water to irrigate crops on at least 10% of the agricultural area, but they do not know whether the water remains stable over the year. So this is the first, the first two criteria in order to be acceptable. If you compute this value, you will see that there are no agricultural holding, there are no changes made to the final subindicator. Another case is instances where the water availability is reducing. So there is a reduction in water availability, but there are also organization that are effectively allocating water among user. And this is the key value of what we need here. The question asked is whether there are organization dealing with water allocation and all agricultural holding saying yes and they are working well. In this case, despite a reduction in water availability, we're going to consider them. We're going to define the sustainability status as accepted. In this case, as we saw before, the sustainability status is going to be read or unsustainable. So again, I'm going to rerun the entire script. I'm going to touch the label to the fifth sub indicator, and I'm going to save these additional portion of the data set. If you take a closer look at the data, the structure is exactly the same as for the other sub indicator where you have the agricultural holding unique code and corresponding sustainability status associated with sub indicator number five. Okay, so I move to, I go. Yeah, and I'll move to the sixth sub indicator related to the environmental dimension and the sixth sub indicator of basically capturing the cultural area according to their sustainability status. This is defined based on how fertilizer are managed by the agricultural holding itself. Okay, the information that we need in order to compute this primary variable is whether the agricultural holding uses or not the fertilizer. The information can be easily collected through question number B6. This is agricultural holding use any synthetic mineral fertilizer. No, in case the fertilizer are used the number of specific measure adopted to mitigate, of course, the environmental risk that are associated with the use of fertilizer. You see that the two information that we need. Okay, so the logical order is that, first, we're gonna collect information on whether or not the fertilizer are used. So in case fertilizer are used, we want to know whether the agricultural holding took a specific measure to mitigate the environmental risk associated with the use of a synthetic fertilizer. I say yes. In this case, we are gonna ask to the respondent what are the specific environmental measure adopted by the agricultural holding in order to, you know, to reduce the risk associated with the use of synthetic mineral fertilizer. There are a number of measures that can be adopted. I'm not gonna list all of them, but just whether they use, they follow protocol, as per extension services, whether they use organic source of nutrients, and so on so forth. These are all the measures that have been considered to reduce the environmental risk associated with fertilizer. Okay, these are the variables that we need to compute. This is the content of the primary variable. The content of the primary variable. These are basically all dummy variables. The first one, which is the key primary variable is whether or not the fertilizer was used. In case the agricultural holding does not use any fertilizer, then the sustainability status is desirable by by default. The additional dummy variable that we need to compute are basically eight dummy variables, taking value one if the specific measure was adopted by the agricultural holding. So just to give you an example, if the agricultural holding follows protocol as per extension services, then we're gonna compute a dummy variable which is equal to one and zero otherwise. And the same applies to all other dummy variables related to measure adopted to to reduce the environmental risk of fertilizer. Finally, we will compute the only categorical variable which goes from zero to eight and which basically captures the total number of measure taken by the old thing in order to mitigate the environmental risk, which is this one. You see, this is the, this variable is basically the summation of all the measure adopted by the agricultural holding. Okay, here is an example that's that is being extracted by the data set for agricultural holding. All of them uses use a fertilizer in their for their agriculture production related activities, but the number of measure adopted in order to reduce the risk associated with fertilizer. The number of measure adopted varies depending on the agricultural holding. In the case of the first agricultural holding, the sustainability status is acceptable, because the number of measures adopted is two. The number of measure adopted is at least two, but lower than four, the sustainability status is acceptable. If the number of measure adopted is lower than two, so either zero or one. If the number of measures adopted using uses fertilizer, then the sustainability status is read. And this is the case of the second agricultural holding. You see here that the cultural holding uses fertilizer, but no measures have been adopted to mitigate the environmental risk of fertilizer. Would be one the sustainability status would still be read because in order to be acceptable at least two measures must be adopted. Finally, and this is the case of agricultural holding number 13 and 14. The sustainability status here is desirable because despite using fertilizer, both agricultural holdings. They adopt, at least for environmental for a measure to reduce the environmental risk of fertilizer. So the real difference in order to define a given sustainability status given the large utilization of fertilizer is really given by the measures that have been adopted by the holding in order to reduce the environmental risk of fertilizer. Okay, I'm now gonna show you the calculation of these six indicator. Here I summarized for ease of reference, the number of the sustainability criteria. The green does not use fertilizer, but or uses fertilizer but takes a specific measure to mitigate the environmental which must be at least four yellow if the holding uses fertilizer at least and but takes at least two measures to to mitigate environmental and read in case that the number of measure adopted by the holding is lower than two. Okay, I'm gonna, I'm gonna upload the portion of the data set. Okay, these are all the measure that have been collected. From the survey. For example, just to give you an example, if you search the first measure is follow protocol as per extension services. So, which is basically that we go back here, which is basically what you will find in. With the B9 follow protocol as per extension services, we simply converted this information into a dummy variable, which takes value one, if the measure has been adopted and zero otherwise you will see from the distribution that 160 agricultural holding follow protocol as per extension services or really helpful to recommendation. And the same applied to the remaining measures measures that are listed in the survey. What I'm gonna do is to simply generate a loop and to calculate dummy which is equal to zero if the, the specific measure was not adopted and one otherwise. And then gonna generate the total number of agricultural of measures adopted. To calculate this variable, you see that there are approximately 197 agricultural holding corresponding to 46% of total agricultural holding sampled that did not adopt any measure, even if we don't really know whether they simply did not adopt measure because they were not used. And then we have like 13 and 12% of agricultural holding that adopted at most two measure and so on so forth only less than 1% of total agricultural holding goal three agricultural holding adopted the C6 measures to mitigate environmental risk of fertilizer. Okay, I'm going to calculate this indicator as per the criteria that are listed here. So, I say the sustainability status is is going to be green. If the fertilizer was not used. This is the variable that capture whether or not the fertilizer was used and there are 21 agricultural holding you see here that did not use any fertilizer. If the fertilizer was used, which is this variable here variable D06. But the number of measures is greater than four, the sustainability status is still green or desirable. If the fertilizer were used but the number of agricultural measure is greater than two but lower than four than the sustainability status is going to be acceptable. And finally, if the number of agricultural measure adopted by the ordering is lower than two, and the ordering uses fertilizer, then the sustainability status is going to be read. I'm going to rerun the script. You see this is very much straightforward. I'm going to keep my portion of the data set, and I'm going to save it again. I know this is redundant, but again, here we will have for each agricultural holding. We will have the corresponding sustainability status given adoption or not of measure to mitigate environmental risk of fertilizer for holdings that use fertilizer, of course. Okay, this was in reality. The last indicator that I wanted to present today. Now my question is whether we want to keep going. I think we still have 15 minutes. I don't know if I want to spend a few minutes for further recommendation or if you want to move ahead with the other sub indicators. So, yes. Let's see first if we have some questions. And then we leave the first one. So we have time to close, let's say. We will take the conversation from here, of course, tomorrow, in case anyone else would like to ask something else to Joe Luigi to a span yard. Let's, if you all agree, let's switch on our video so I can take a proper picture because yesterday you know we have had some issues. I asked all of you to switch on the video. I mean, the ones that of course would like to be visible in the in the future. Oh, it says that you know I cannot open this video because probably. Most of this. Probably you stopped all of us to open the video really. Yes. Let me, let me see. Why this time I know like yesterday we can you know think of this position. We are way way way. A low price to start okay. Now you should. Okay, great. Okay. You know some sometimes have some have some these hidden features. They switch on or off by themselves. Okay, so we still have a few people with no video let's wait a few seconds is they want if not that will take the picture like this. It's very nice to see all of you. Okay, so let me take the picture now. 123. Okay. Yes. Okay. So, thanks again for having participated to this third day. No, second day, sorry, second day of the virtual training so we still have two days, and have a nice day and see you tomorrow same time so 2pm Bangladesh time. Before you close, you know, before you close, you know, do you remember I requested one thing on for Friday, you know, there's, you know, Friday Fridays or week. So if you delay up an hour. Yes, that was, yeah, so on Friday we will start half an hour later but tomorrow we start still. Okay, perfect. Yeah, no problem. Okay. Bye bye. Bye bye. Thank you very much. Thank you. Bye bye.