 Good afternoon everybody. Welcome back to this third day of the virtual training on the SDG241. Yes, the first day we have seen some theoretical aspect with the Aspanyar on the SDG241 methodology and the framework and the background. And then yesterday we have started with Jaluigi to see the practical calculation of the subindicators with the start-up tool with your data, your pilot data you have collected in Bangladesh. We have seen the economic dimension with the three subindicators and we have started with the environmental dimension with three out of the five subindicators. So the plan, the agenda for today is the finishing the environmental dimension with the last two subindicators and then moving forward to the last dimension with the social one with the last three subindicators. Then I will present today the FAO data collection questionnaire, which is the tool which FAO collects data from countries regarding of course the SDG indicators 241. So before starting let me just remind you that you are all panelists and you cannot mute yourself alone. So I just ask if you want to take the floor to please raise the hand first and then wait that me or Jaluigi or Aspanyar or Anirur give you the floor. So please do not start talking alone because if not we can have some cross people talking simultaneously and it's not easy to follow the discussion. I have sent half an hour ago more or less, maybe a little bit more, all the final presentations so including the ones that Jaluigi will present today which were the last missing but as a reminder I will send at the end of the fourth day everything all in one package. So I will send you an email with some summary with all the also relevant documents with the links to the page so everything that we have mentioned during the training I will share again after the end of tomorrow so do not worry. As another reminder I will also be sending certificates because some colleagues already asked me so do not worry also about this and I will share also the recording so everything will be shared with you again. Let me now ask Aspanyar if he wants to say something if not I will pass the floor to Jaluigi. So thank you very much Stefania and good day to everyone. Yeah, Stefania summarized the yesterday's proceedings and as to what we are going to cover today. Let me urge you again that if you have any question at whatever slide of the presentation or regarding the status script please do stop us and see clarity because the basic aim behind us having this training is to you know remove all the doubts that you may be having in your mind about the data collection and analysis of respective elements of indicators so please you know feel free to stop us and you know we will appreciate if this training is conducted in a more interactive way so that because the basic goal is for us to engage you and clarify your doubts. So with this I immediately give the floor to Jaluigi for him to proceed. Yeah thank you very much. Can you hear me okay? Good morning. Can you hear me okay? Yes Jaluigi. I like to make it a bit louder you know the voice is not that loud. You want to try again Jaluigi to speak let's see if it's better. Okay so Jaluigi just said that he will disconnect and then we connect again because he had apparently some audio issues so please let's wait a few seconds. Yeah good morning everybody. Perfect Jaluigi we hear you well now. Okay perfect because I was speaking but I wasn't sure whether you could hear me or not so yeah apologize for this. Yeah good morning everybody again and yeah great to be here. I'm gonna start sharing my screen and then we will focus on the last two indicators related to the environmental dimension before moving with the last three indicators related to the social dimension. So what we have analyzed well we analyzed yesterday we're basically the first six indicators three of which are related to the economic dimension and three of which are related to the environmental dimension. We still have to analyze two indicators related to the environmental dimension which basically are the management of pesticide and the use of agrobiodiversity supportive practices. With the first just to anticipate that okay there are I'm trying to be let's say as clear as possible but at the same time in order to have enough time to explain everything including the final dashboard approach and reporting I will try to focus more on the indicators that are more complex and specifically these are the use of agrobiodiversity supportive practices and the food insecurity experience scale. These are going to take a little bit more time than the others indicator which is the reason why I want to focus more on these but of course as Asfandir said before please feel free to stop me anytime you wish. So concerning the management of pesticide the idea behind this sub-indicator is pretty much the same as the analysis of the management of fertilizer but there are few adjustments of course and the idea is to characterize the agricultural the sustainability status of agricultural area associated with a given awarding according to the information that we have of the whether or not the holding uses pesticide and whether or not there is awareness by the farmer of the risk associated with the use of pesticide both related to the environmental let's say the environmental risk and related to the health of the person who uses pesticides. Therefore we will characterize the sustainability status as desirable in cases where the farm uses only moderately or slightly adsorbed pesticides. I have added a link which will direct you to the World Health Organization website and where you have the list of all moderately or slightly adsorbed pesticides. In addition to that we will also we characterize as desirable those who use moderately or slightly adsorbed pesticides but this is important to know whether the agricultural holding adopt a health related measure which must be all three health related measures and the list four of the environmental related measures that I will explain in a second. Concerning the let's say characterization of the agricultural area as acceptable we will use as a criteria for this characterization the information related to the use of moderately or slightly adsorbed pesticides and whether at the same time the agricultural holding take some measure to mitigate both environmental and health related risk which must be at least two from each of the lists that I will show you in a second. And finally unsustainable agricultural area are those where the holding uses highly or extremely adsorbed pesticides and again here you have the list from the World Health Organization report or uses moderately or slightly adsorbed pesticides but without taking any specific measure that can mitigate both environmental and health related risk associating with their use which must be less than than two so basically just to summarize again green in case at least four health related measures three health related measures are adopted and four related environmental measures are adopted if only two from the two lists are adopted the the the agricultural area is going to be acceptable and finally if less than two and if there is also use of highly or extremely adsorbed pesticides then the agricultural area is going to be unsustainable where do we find the information that that we need of course we need information on whether or not the agricultural holding is using a pesticide and the type of pesticide that they are using and here is the information which you can easily trace back in question B10 and B11 which specifically asked about whether pesticide for tropical livestock production are used by agricultural holding and then the type of pesticide that the agricultural holding used in case there is an affirmative answer to question B10 then we need to know about ourness of the environmental and health related risk and if there is ourness whether they take any specific measure now there is of course a screening question asking whether the agricultural holding takes specific measures to protect people from health related risk if yes and if the agricultural holding adopts specific measures to avoid the environmental related risk and of course the list of measures adopted so these are the three measures that are ideally adopted by the holding in case of use of pesticide which is adherence to label direction for pesticide use maintenance and pleasing of protection equipment after use and safety disposal of waste and the and similarly we have a long list of measures that apply to the environment we have a total of 12 measures that have been included in the list i'm not gonna list all of them for reasons of time but you can easily take a look at the later stage so these are the information that we need and of course given the information given that now we are aware about the criteria that should be used for the characterization of the sustainability status of the holding we need to construct our primary variable in the end what we need are six primary variable three of which are instrumental for the calculation of the indicator so the first primary variable is of course about the use of pesticide so we're gonna basically generate a variable which is a dummy and which takes value one if the agricultural use pesticide then we want to know the type of pesticide so we are gonna construct a variable which takes value one if the if the agricultural holding use moderately or slightly at the sport pesticide and zero otherwise where otherwise implies using extremely at the story like illegal pesticides finally we're gonna construct whether the agricultural holding adopt a health related measure which are the three we saw before and therefore we're gonna have the total number of a health related measure in addition to that we need to construct additional 12 dummy variable each dummy variable is gonna take value one if that specific environmental measure has been adopted by the agricultural holding and finally and this is the other key variable which I put in bold here the total number of environmental measure adopted so we have a variable which is a categorical variable going from zero to 12 depending on how many environmental measures are adopted okay and this is how the let's say this is a portion of the data set again and this is how the data set would look like once we have constructed these the variable that I just mentioned for example in case of of the agricultural holding number two you will see that the agricultural labor associated is acceptable and the reason why is basically the adoption of moderately or slightly at the specific side but the number of environmental and health related measure my apologize there is a typo here is only two for each of these two categories of measures and indeed if we go back very quickly to the to the criteria you will see that the farm uses only moderately or slightly at the specific side and take some measure which must be at least two from the list and this is reflected in in the agricultural holding number two then we have a number of non-sustainable agricultural area associated with six agricultural holding there are different reasons why these are non-sustainable in the first case is because of the use of highly and extremely hazardous and illegal pesticide and in addition to that none of the measures bought related to the environment and to the health of the person have been adopted in another instance we have that the agricultural holding in reality uses moderately or slightly hazardous pesticide so which is a better condition as compared to the previous but the number of measure adopted is relatively lower and however lower than what we would like to to adopt the number that we would like to adopt and finally the last case is where the agricultural area is sustainable or is desirable because of the use of moderately or slightly hazardous pesticide but you can see here that the number of health related measures is free which is in line with our main criteria whereas the number of environmental measures is four so we must adopt at least four measures for related to the environment in order to be sustainable and free related to the health now i'm going to the yeah Janu is just just one second can can you just go back to the previous slide sure this one you mean yes yes yes this one so there has been a slight revision of the measures that are recommended to agriculture holdings if they are using you know pesticides so these measures have been slightly slightly revised so as per the new the new the latest version of the methodological note as well as the survey questionnaire these have been slightly slightly you know refined let me put it that way i just wanted to highlight this point because you know some of the some of the measures that are shown here are no longer part of the methodological note maybe if i can add something tomorrow we will be sending also the the guidelines for that analysis and you can see in detail exactly this list revised so the the final version of this list it's in that document okay and perhaps if i can add and this is something that i should have said before what i would like to stress is the data collection in the pilot data collection in Bangladesh was conducted before the revision which is the reason why as as Fandia and Stefania just mentioned we might have slightly differences between the scripts so what i can do is to as long as there will be a new data collection a new pilot or a new data collection exercise in a given country we can adapt these scripts according to the revised version of the questionnaire yeah this is just to highlight but i guess that it is it is also important to know that how to construct the variables and of course as i was saying yesterday to really pay attention about the variable to be included in a given indicator based on the latest version of the methodology and survey model yeah i'm going to the to the calculation of this indicator which is indicator number seven which is not this one yeah okay and again here we summarize before starting just as a reminder the criteria we need to calculate we need to implement and secondly we are gonna upload the the section of the survey with data about use of pesticides so you will see that the first thing that i'm gonna do is to as i was explaining before i'm gonna compute a dummy variable a number of dummy variables in reality which takes value one if that specific environmental measure has been adopted so what i do for example if i'm gonna search for this variable this variable you can see here this variable is related to a given environmental measure which is a just planting time for breaking the the past cycle and in case the answer is yes i'm gonna construct a dummy which takes value one otherwise it's gonna be zero so i'm gonna run this loop and finally i'm gonna construct a variable which is basically a categorical variable going from zero to 12 depending on how many environmental related measures have been adopted by the holding the agricultural holding so we no longer need the dummy because what we really interested in is the number of environmental measure adopted which i'm gonna tabulate right now and sorry here we are and you see that like there are approximately 61 percent of total agricultural holding here or 258 in absolute number that do not adopt any measure and then we have four percent adopting one measure and so on so forth of course we still don't know whether among all agricultural holding that do not adopt any measure they use or not pesticide and especially we don't know the type of pesticide which is used that's why we need to cross tab this variable with the variable capturing whether or not the pesticides are used and the type of pesticide i'm gonna do the same now for in order to get the total number of health related measures so what i'm gonna do is to perform is to run a loop and i'm gonna construct accordingly a free dummy variable taking value one if that specific health related measure has been adopted by the holding and zero otherwise and finally as i did before i'm gonna construct a variable which basically is gonna tell me the number of health related measures adopted by the agricultural holding again i'm gonna drop the dummy as i no longer need it what i'm really interested in is the number of health and environmental measures adopted and again just to make sure everything is okay i'm gonna tabulate the number of health related measures adopted you will see that here there is a more or less uniform distribution with 27 percent of total agricultural holding adopting non-health related measures then we have 23.1 percent of total agricultural holding adopted all of the three health related measures now again let me highlight the fact that here the reason why the health related measures have not been adopted is still unknown i mean we don't know whether they use or not pesticide and especially we don't know the type of pesticide which is that's why we need to cross tab this variable on of about the number of environmental measures with the variable informing about the use and type of pesticides used by the holding last step that i need to know is to construct the final sub indicator the final sub indicator to characterize the sustainability status of agricultural area of the holdings so what i'm gonna do in case not pesticide is used i'm gonna characterize the corresponding agricultural area of the holding as sustainable by default so what i'm gonna do is to say generate the seventh sub indicator of our sustainability if the agricultural holding does not use any pesticide which is this variable the one ten thousand in case and you see the code has changed from two to one where one implies that the rest being used of pesticide but the pesticide used are moderately or only slightly adsorbs pesticide let me cross tab this variable just for you to get a better sense of what i'm doing you see this is a variable which basically takes value zero if pesticide are not used value one if only moderately or slightly adsorbs pesticide are used and two if highly or extremely adsorbs or illegal pesticide are used which is the vast majority of agricultural holding apparently they use moderately or slightly adsorbs pesticide but again in case what does illegal pesticides sorry but what does mean of illegal pesticides well i guess okay i'm not i'm not an expert on on pesticide and i don't know if asfandia rostefania want to elaborate more but illegal pesticide i assume this is something that is forbidden by the law so this is something that i guess is purchase on a on the black market that's my assumption this is something that at this point at this point may i you know request salih because salih conducted this pilot can she clarify that what was meant or during the survey what she meant you know explained the truth you know interviewing about this question okay about the use of illegal or highly extremely adsorbs pesticide and this is why it is important to to make reference to the enumerator moral because there we cited the the world health organization report where there is a clear long list of all legal and illegal pesticide including those that are extremely asardus or slightly asardus so so of course i'm not an expert on pesticide and i couldn't say specifically what are the pesticides that are illegal what are the pesticides that are hazard slightly asardus or the pesticides that are moderately asardus so but the reference to the world health organization report should should help in this regard i don't know if i answer properly your question okay okay okay so yeah thank you by the way um so moving ahead moving ahead what i'm gonna do now is to still characterize those agricultural holding using moderately or slightly asardus pesticide has a having a sustainable agricultural area in cases the number of health-related measures is three so it's higher or equal to three but in this case we only have three measures and the number of environmental measures is higher than three you see here this this must be higher than three at least four it implies at least four and again the agricultural area is going to be acceptable in case of use of moderately or slightly asardus pesticide but the number of health-related measures is only two and the number of environmental measures is only two which implies being lower than than what is required for being sustainable or desirable finally i'm gonna categorize agricultural area has a unsustainable in case the agricultural holding uses highly asardus pesticide and in case of use of moderately asardus pesticide but the number of environmental measure and that's related measure is lower than what would be expected in order to be acceptable which is uh which must be lower than three than two i'm sorry for both categories on measures i'm gonna compute the indicator and finally as we did yesterday i'm gonna save my data set here you see for each agricultural area for each agricultural holding we will have the corresponding sustainability status of the agricultural area associated with that holding okay i don't know if you have further question to ask which i'm happy to to answer to the thank you gialluigi let's wait a little bit to see if anyone would like to raise a question take the floor make some comments so yes mrs katun please thank you stephania for giving me the floor uh in bangalore who translated the kurshinian the kurshinia was the 11th was number one option was uh less dangerous and number two option was highly asardus so an illegal was not there the word legal was not used and about the less dangerous and highly dangerous there is a list of i think it was from wall bank so there are very much items of pesticides but in our public interest also we have reported that in our case our farmers um is very much difficult for them to identify which is very much dangerous for which is hazardous so this can be replaced we have also suggestions so now i have uh here that i've already said that this was already replaced maybe the future kurshinia will be better for us thank you uh yeah thank you for your clarification i there was some noise so i didn't get the overall um intervention but yeah thank you very much for your clarification and about the use of pesticides and the typology of pesticides used used and this is uh as you can see i have uh reassured the question concerning how this question is how this information is captured through the survey model now i must admit that data collection in bangalore that she was performed a couple of years ago and therefore i cannot really remember the exact question and now it was asked but and i would ask confirmation from us from there in stefania but i guess that there were no revision um between data collection and the latest version of the questionnaire that's my understanding and that's my therefore there should be i mean the data collected about the use of pesticide and the typology so pesticide should be absolutely in line with the latest version of the survey model um okay unless there are any other input i would suggest we go ahead for reasonable time and we move to the eighth bio diversity supportive to the height sub indicator which speaks about the use of agro bio diversity supportive practices um okay okay this is a one of the this is a super interesting indicator though not easy to uh to uh to calculate because of course it requires a very large number of information and before starting introducing this indicator there were few revision especially if compared to the data collection activities that were done through the pilot survey in bangalore that she had two years ago so the revision were of course done because of expert consultation experts are suggested to slightly revise them that these revisions were were included the data i have about these indicators specifically uh and the calculation of the indicator is related to the previous i mean the previous version uh as compared to the one that i'm showing in this slide however uh therefore before we had a total of seven bio diversity supportive practices that were further reduced to six and there were few revision about bio diversity supportive practices very few i mean so that's why you will notice that the calculation of this indicator is not perfectly in line with the latest version of the question but still uh i mean here what is important to know is how to calculate the indicator and then again pay attention about the information that you need to to include in your specific indicators okay uh moving ahead this indicator wants to measure the proportion of agricultural area according to the level of adoption of biodiversity friendly practices and a total of six bio diversity supportive practices have been identified through expert consultation which are the ones listed in this slide and these are related to at least 10 percent of the ordinary area must be left for natural or diverse vegetation the farm produces agricultural products that are organically certified or the products are undergoing the two certification process the farm does not use medically important anti-microbial as a growth promoter and at least of the two following crops must contribute to the farm production among the crops there are temporary pasture aquaculture livestock or animal products freeze on farm and finally permanent crops and then the farm practices the agricultural holding practices crop or crop pasture rotation which involves at least two crops or crops pasture on 80 percent of the farm cultivated area finally in case of agricultural holding engaging in livestock production at least some livestock must be locally adopted of locally adopted adopted prints okay now there's sustainability criteria that have been thought in order to derive this indicator are at least two out of the five with four countries that do not have organic certification and three out of six four countries with organic certification in which case in case that the agricultural holding meets two out of five or three out of six of the above mentioned biodiversity supportive practice the agricultural area will be sustainable in case only one out of five again for countries with no organic certification or two out of six for countries with organic certification are met by agricultural holding then the sustainability status is going to be acceptable and finally if less than two of the above biodiversity supportive practices are are met by the agricultural holding the agricultural area will be red unsustainable okay where do we find the information we need we need to know concerning the first biodiversity supportive practice practices we need to know if at least 10 percent of the holding area is left for natural or diverse vegetation and these must include of course natural pasture and grassland maintaining wildflowers, ribs, stone and wood heaps and so on so forth the information can be easily found in question b-17 and b-18 where basically it is there is a springing question asking whether the holding area is covered by natural diverse vegetation and which one so answer from i mean among the list of potential answer from one to five you have natural pasture and so on so forth and then if you if the respondent reports that the area is covered by natural or diverse vegetation you're gonna further ask what is the total area covered because what we are interested in is to know that at least 80 percent is covered by natural or diverse vegetation therefore information on b-18 must be combined with the information about total area of the agricultural in order to derive the corresponding share then we want to know whether the farm produces agricultural products that are organically certified or whether the products are undergoing the certification process this is asked in question b-21 and b-22 but which basically which basically ask whether the agricultural holding produce crop or livestock that are certified and if the answer is positive we want to know the registration number and the certifying body what is the institution that has certified organic process of production and finally we want to know whether i mean that's not finally really we want to know whether the farmer use or do not use or does not use medically important antimicrobials as a growth promoter so you will have the question asked in b-19 and you will have yes no or i don't know at least the the fourth biodiversity supportive practice is about which kind of production which kind of crop or livestock or agricultural contribute to the total production of the agricultural holding so we want to make sure that in order to be in order to to to meet the criteria needed for the construction of this indicator we want to know whether the agricultural holding contribute to the total production by temporary crops past your permanent crops tree on farm etc this information again is asked in section two of the survey model where we are going to inquire about all the crops and the corresponding area of the agricultural holding including the farm gate price so this is a an information that is widely used throughout all the indicators that we have for 2.4.1 again with the the fifth bio diversity supportive practice wants to know if at least the agricultural holding practice crops on 80 or past rotation that involves at least two crops on at least 80 percent of the farm area cultivator and the the the question is is very straightforward we want to know what is the percentage of the agricultural area on which crop rotation or crop past rotation that involves at least two crops is practiced and then you report directly the percentage so this is the fifth biodiversity let me sorry let me correct this one this is a typo but this is the fifth biodiversity and this is the sixth this is due to the fact that i change its light and finally the sixth live biodiversity supportive practice wants to know whether livestock includes locally adopted breeds and this is capturing question A4 where we ask for each species of animal maximum we're going to ask for three species that arise on the agricultural holding we need to list the different breeds and the number of animals they represent of course this question must be complemented with country specific information about locally adopted breeds and i recall we had a discussion with the bbs in the past in order to identify what are the locally adopted breeds and in order therefore to derive these six biodiversity supportive practice okay in term of the construction of the this indicator what we are going to do is basically to construct six six biodiversity dummy variable each variable is going to take value one in case that specific biodiversity supportive practice is adopted by the agricultural holding and zero otherwise and finally what we have to do is to count how many of these biodiversity supportive practices have been adopted by the agricultural holding and we will derive the corresponding sustainability status of the agricultural area of the holding based on how many biodiversity supportive practices have been adopted now again i'm gonna back to the to the indicator which i have slightly revised to the extent possible in order to harmonize to harmonize with the latest version of the survey questionnaire but i need to tell you that not all of the biodiversity supportive practices can be constructed so i'm going to only focus on those that are aligned between data collection processing Bangladesh two years ago and the survey the latest survey model so where we have a correspondence between the information collected and the latest version of the survey okay as usual let's first upload the data that we need and the first biodiversity criteria biodiversity practice speak about i'm gonna i'm gonna work now in parallel with the slide because we know that the indicator are not fully aligned so leave at least 10 percent of the holding area for natural or diverse vegetation so this is the first biodiversity what i'm gonna do is to calculate the total agricultural area i convert in hectares as usual and then i'm gonna calculate the total agricultural area which is left to natural or diverse vegetation and again everything must be converted in hectares in order to make the data harmonized the two variable harmonized and finally i'm gonna calculate the percentage of the area which is left to natural or diverse vegetation and finally i'm gonna construct the first dummy variable which is equal to one in case it's the total area which is left to natural or diverse vegetation is greater than one okay there are the second criteria is area the second biodiversity supporting practice is no longer in the latest is no longer in the in the new and latest methodology therefore i'm not gonna calculate this one but i move ahead with the with the calculation of those crop tree or tree products livestock or animal product or aquaculture that contribute to the overall agricultural production in this case again the ultimate objective is what is to have a dummy variable taking value one if that specific biodiversity supporting practice is implemented in which case means that any of the above listed crop or livestock or aquaculture production contribute to the total to the total production of the old what i'm gonna do is to specifically investigate about all the crop you can see here all the crop that are basically cultivated harvested by the agricultural holding and the one the same applies to livestock and to fishery and to aquaculture in reality or other on farm products and finally i'm gonna see if at least two of those contribute to the agricultural to the agricultural production of the old in case of affirmative responses we're gonna calculate a dummy which is equal to one otherwise the dummy is gonna be equal to zero so the logic is exactly the same as before so you see i calculate the third i say underscore i put underscore tree biodiversity supportive practice if any of those contribute to the total production or any of those crop livestock or aquaculture production contribute to the total production of the old i'm gonna run everything all together because i'm gonna save a temporary data set and i'm gonna finally merge this one with the other biodiversity supportive practice practices practice that i calculated before so basically if you go here these are our so far three dummy variable each dummy variable takes value zero or one depending on whether the specific biodiversity supportive practice has been adopted i move ahead with the third which is i'm sorry this is the fourth actually which is at least 80 percent of the farm area at least the rotation involved at least two crops because this has changed as you can see here sorry yeah it is practically practice crop or crop passing rotation on at least two crops or crops a pasture for 80 percent of the total farm cultivated area so the question is straight forward and so what we're gonna do is to say in case 80 percent based on the answer given to let me show to to better clarify to this questionnaire i'm sorry i need to find the question here it is you see if question b20 the reported percent of agricultural area on which crop rotation involving at least two crops is greater than 80 percent in that case i'm gonna construct a dummy variable which is equal to one otherwise the corresponding biodiversity supportive practice is equal to zero meaning that this is not implemented i'm gonna construct this biodiversity criteria okay here there is one more biodiversity criteria which is no longer included in the new revision of the methodology and again this could will be only implemented where new data will be collected and and indeed i did no longer considered so i move ahead with the okay where we are right now i got lost meanwhile okay this has been covered this has been covered we need to know whether the farm use medically important antimicrobials as a growth promoter this has been covered and this has not been covered so what we still need to know is this one which i'm gonna highlight and this one which i'm gonna highlight concerning the use of antimicrobials as a growth promoter uh okay let me show you where is it natural vegetation and get in there okay okay let's start let's go with this seventh which in reality is the sixth biodiversity supportive practice which is about livestock with locally adopted the breeds so again there was a consultation i recall about identification of locally adopted brief and the calculation of this variable is basically for each animal species we want to know if there are locally adopted brief and the maximum number of animal species is free so um that are asked in the survey so this is the first animal species this is the second animal species and these are the questions that are asked which again i'm gonna show here because it's important to know where this information has been taken here it is this is the information that we need where we basically collect data about animal species and breeds and the number of animal that they have for each breed and animal species so what we want to know after we have consulting consulted the expert that can inform us about the locally adopted animal species if there is any of them in the that is part of the livestock of the agricultural holding so i'm gonna compute these seventh which in reality is the sixth biodiversity supportive practice this is for the second animal species what i'm what i'm doing here is simply to investigate if within these species there are locally adopted breeds and finally sorry i need to rerun everything because there were some temporary data set um uh i'm finally i need to uh calculate the the total number of biodiversity supportive practice if within for each agricultural holding in order to determine to characterize the sustainability status so what i'm gonna do is to generate a variable which count the overall number of biodiversity supportive practices and i'm gonna tabulate and you will see which which the total number of biodiversity supportive practices goes from zero to six depending on how many have been implemented within the holding and finally i'm gonna construct the indicator now again this indicator uh speaks about the the number of biodiversity supportive practices that have been adopted and depending on that number we're gonna characterize the sustainability status i'm gonna show you the sustainability status of each house depending on how many have been adopted in this case for example in the first agricultural holding the total number is equal to six that's why the sustainability status is desirable so again what i would like you to pay attention is related and i'm going back to the presentation to the fact that the total number of biodiversity supportive practices that are met by the agricultural holding can vary from two to three out of five or six depending on whether or not the country has a organic certification or not so for countries with no organic so certification the agricultural area is desirable if only two out five are met for countries with organic certification the number of biodiversity supportive practices increases from two to three because the total number of potentially biodiversity practices is slightly higher from five to six okay this was the last indicator and now unless you have questions so let's see this was a quite complicated super indicator so maybe let's leave a few seconds to see if someone would like some clarifications or some doubts well i think we can move on so maybe we can we can jaluigi maybe you can go with the first subindicator in the social dimension and then we break because then we will have fears which is quite complicated so we break before fears okay perfect sounds great okay um i'll try to be as concise as possible now we need to look at the rationale and the computation of three main subindicators related to the social dimension these are the wage rate in agriculture the food insecurity experience scale and secure tenure rights to land concerning the wage rate in agriculture the idea is to characterize again the sustainability status of the agricultural era of a given order depending on whether the holding first of all hire a skilled agricultural worker and then if the remuneration paid the salary the wage paid to these skilled agricultural workers is above below or equal to the minimum wage in the country and in the agriculture and of course there is one clarification that has to be done since many agricultural holdings do not hire any skilled agricultural worker then the default is that the agricultural area is sustainable by definition simply because they do not pay any wage we are not able to say whether the wage paid the desire lower or equal to the minimum wage in the country in case however the agricultural holding a higher and skilled labor and the wage paid that one skilled labor is above the minimum national wage rate or minimum agricultural sector wage rate if available so yeah we give a couple of options in that case the sustainability status is gonna be desirable if the wage rate paid to a skilled labor is equal to the minimum wage rate or to the minimum agricultural sector wage rate if available the sustainability status is acceptable in case it is below the wage rate is below then the sustainability status is gonna be unsustainable what do we need in order to construct this indicator of course we need to know whether or not the agricultural holding hires skilled workers in case they say no then their sustainability status is gonna be desirable by default and then we want to know the daily average wage which is paid the both in kind and in cash to unskilled agricultural workers hired by the agricultural holding in order to benchmark what is paid to unskilled agricultural workers and what would be the minimum wage or the minimum agricultural wage rate in a given country we need of course to derive the information on the minimum wage at the country level ideally we want to derive this information we want this information of being provided by the national statistical offices or by the national statistical office of the country of interest but in case this is not available the international organization has data international data repository which also includes the minimum wage which is paid at the country level so we can easily take that that minimum wage in case it's not available from the national statistical office which I don't think is the case but this is an alternative source now typically if we decide to rely on the minimum wage which is provided by the international labour organization and the yeah but the link here the hyperlink here this is typically provided in the US dollar whereas our data are collected in local currency units so we need to convert using the official exchange rate and the official exchange rate can be taken basically and virtually from many external repositories for example just to to name a few we can rely on the world bank world development indicators which which contain the the official exchange rate from 1960 to 2019 perhaps even 2020 we can rely on the international monetary fund there are many international sources that can be used to simply convert into local currency unit the minimum wage that we need to benchmark with our wage rate paid to ask for agricultural work and of course the content of the primary viable are basically are basically two first of all we want to know we need to construct the total daily wage which is simply constructed by adding the daily wage in cash and in kind from this question here this is the question c2 and secondly we want to know whether the employee where there are employee working on the agricultural hood to simply a dummy variable which takes value one if there are employee on zero otherwise so this is a typical portion of the data set to the right the sustainability status so you will see here that valuable c01 is giving us information about any agricultural employee desired by the agricultural holding you will see that for the first agricultural holding the answer is yes then we want to know the daily wage okay here i converted in us international dollar but just to get a better sense it doesn't matter whether we convert in international dollar or we keep it in local currency unity it is important to have the same unit of measurement both for the daily wage and for the minimum wage then the conversion is done by applying a constant therefore there are no changes to whether we decide to go for international dollar us dollar or local currency unit these are all constant yes please okay the second variable is the daily wage in which case is 12 dollar per employee and the third variable is the minimum wage in on a daily basis which is eight dollar this is the minimum wage which is paid you will see that for the first agricultural holding the daily wage is 12 dollar the minimum is eight which implies that the agricultural worker is paid more than the minimum and that's why the sustainability status is desirable okay let's take agricultural holding number 231 they hire agricultural worker but the minimum wage paid is lower compared to the wage paid is lower than the minimum wage so each worker gets around a seven point five dollar but the minimum wage is eight dollar that's why the sustainability status associated to the agricultural area of holding number 231 is non-sustainable now another case I want to focus is the agricultural holding number 19 where you see that basically the holding does not hire any worker there are no employees working on the agricultural holding which is the reason why by default the sustainability status is desirable okay I'm going very very quickly to the computation of this indicator which is indicator number one and this is a super very easy because we only need three information we need the information whether the agricultural holding hire a skilled worker yes or no and we need the information on the wage paid which is paid in both both in kind and in cash what I'm gonna do is to generate a variable which basically add up the daily wage in cash with the daily wage in kind and then I'm gonna convert it in international dollar just for me to better understand the daily wage paid but you can leave it in logger currency unit finally I'm gonna take the minimum wage this is the minimum daily wage which is measured by taking from the ILO the minimum monthly wage then we divide it by 20 assuming that each month a worker will more or less work 20 working days and then in order to have the same unit of measurement as for the daily wage paid to a skilled agricultural worker I converted in international dollar so at the end of the story what I'm gonna get is the daily wage and the minimum wage the minimum wage of course is a constant because this applies to all workers in the country the daily wage varies depending on how much each worker get in exchange of the labor input given to the agricultural holding I'm gonna compute the sustainability status which is gonna be a green in case no workers are employed in the agricultural holding again it's gonna be green in case the daily wage is higher than the minimum wage paid in the country it's gonna be yellow acceptable in case there is the daily wage overlaps with the minimum wage and finally it's gonna be unsustainable in instances where the daily wage you see here the daily wage is lower than the minimum wage gonna compute the indicator and I'm gonna set my data set here you will see that here we have the sustainability status here we have the household identification the holding identification number whether or not the holding higher any skilled worker and then we have the the daily wage paid this is local currency unit and this is the minimum wage again in local currency unit we can have different Utah measurement for example international dollar if we prefer it in the end again what we are going to do is to divide everything by a constant so there are no issues in terms of the content of the final indicators and and finally we need to save our data set which will appear as all other data set we have developed so far sustainability status related to this indicator and the household identification number okay I'm gonna stop sharing and answer question if any okay thank you thank you John Luigi so this was the the first to submit the data in the social dimension now we we only need to go through other two but before moving to the next one we will take a break if you have any question concerning this one so the wage rate in agriculture please feel free to ask okay otherwise I mean you have the break even to think about questions in case you have so we can you can ask these questions even after the break uh so we will resume in 30 minutes as usual so we come back at three uh 50 we are Bangladesh time okay welcome back I hope you had had some nice rest so let's take again the conversation from what we left we were we have seen all the three sub indicators in the economic dimension we have seen the five sub indicators in the environmental dimension and we have seen the first one in the social dimension so if you have any question please raise your hand now about the these first nine sub indicators especially the last one on the wage rate of agriculture in case you have one if not let me make John Luigi cost again I don't know why it was removed okay so in case we don't have any any question I think John Luigi can take the four again and let's see the fiest one which is another articulated sub indicator so please try to pay attention to this sub indicator so as John Luigi you have the floor okay thank you Stefania um gonna share my screen again and uh yeah we still have a couple of indicator missing one is about food insecurity experience scale and the other one is about the second tenure rights um to land I will start by yeah by explaining the rationale behind the the food insecurity experience scale namely the fias indicator which wants basically to measure the severity of food insecurity which is experienced by a given household uh that is uh related to the holder of the holding and this is sub indicator is basically uh computed according to a standardized approach which is further revised in order to make it aligned to the calculation of the sustainability status of agricultural area now the fias indicator um when let's say contextualized for the SDG 2.4.1 is based on the on three main criteria to characterize the sustainability status in case a household of the holder of the holding they experience mild food insecurity there is a mistake I'm sorry but here we go a mild food insecurity then the sustainability status is going to be desirable in case of moderate food insecurity the sustainability status is acceptable and in case of severe food insecurity the sustainability status uh is uh red or unsustainable to better say um now this indicator requires a little bit of attention because its calculation it's a bit different compared to all other indicators so what we basically have to do is to follow is to follow a three step approach in the first step we need to first of all we need to understand what are the questions that that need to be taken into account for the calculation of this indicator and the information about the food insecurity which is experienced by a given household are basically contained in eight standardized fias question these are questions that applies regardless of whether we are including them in an household survey in the specific survey model developed by FAO for SDG 2.4.1 or in any other type of survey these are standard questions so we need to rely on them and together the information accordingly so these are the eight questions that have been included in this survey model and each question has a reference period which is the last 12 months prior to the date of the interview each question asks about a specific let's say experience of food insecurity for example if you look at question c3 it is asked whether there was a time over the past over the past 12 months when the household was worried that there would not be enough food to eat because of lack of money and then there are additional questions which are around these lines which ask about whether there was a time in which the household has to run out food because of lack of money or if the nutrients of the food were of scarce quality of poor quality and so on so forth all these questions they have a yes no response and in addition of course there is I don't know which is not what we want to get but it's an option as well all these questions have been translated into eight corresponding items so if you look at the matrix in the table at the top of the of this slide you will see that for this question there is a corresponding item for example if you were worried that there would not have been enough food to eat then the corresponding item is going to be worried healthy is related to the second question when the respond when the respondent is asked whether there was a time they were not able the household was not able to eat healthy and nutritious food because of lack of money and so on so forth these items allow these questions allow capturing therefore the eight specific items and each item is further associated with the given degree of severity of food infigurity you will see that if you answer yes to the first question which is about the item which capture whether you were worried to not eat enough food the severity of food insecurity is going to be mild but then it increases the severity increases depending on the question where you answer yes if you skip a lunch meal for an entire day these is gonna this item is gonna have the highest degree of severity of food insecurity you see that these will basically correspond in the second table at the bottom of this slide you will see that having insufficient food quantity is going to be associated with a severe food insecurity status of the household and the corresponding item is World Day now the first thing the first step that we need to implement is to work a little bit on the reshaping of the dataset the way the data are collected basically will according to the way the data are collected we will get a sort of raw data set where we will have you see here that here what we have is simply the question number and then a series of code depending on whether the person said yes or not to that specific number now we need to work a little bit with the label and we need to prepare our dataset in order to analyze the severity of food insecurity of each household in order to do so a key step is to recode all questions and we're gonna use all code that correspond to two for no answer to a given question and the code that corresponds to one for yes response this implies adding a sort of standard labels for the eight question collected and basically what we do is to transform this dataset the raw dataset into a more refined dataset having both the item you will see that you have the item corresponding to the first question and the corresponding question for each agricultural holding household of the older of the old to better say but for simplicity let's say agricultural holding for example the first agricultural holding responded yes to the first question which is related to the item worried and so on so forth this is the way the dataset must be reshaped before estimating the severity of food insecurity Nico may I just you know ask a question at this point absolutely yes I know you know due to this back we have conducted one training on SDC 1.1 and 1.2 there we also discussed the pious and in the pious question there were four options one was yes one was no one was don't know and another was don't want to reply something like that and in this module do you consider the type of response or only two okay no of course as you can see here we all we also have the four options these are fully harmonized according to the standard yes and how do we manage that? yeah but of course I mean if it is I don't know or I don't want to answer these are not going to be captured in the estimates of the probability of paying food insecure but ideally what we aim well in theory I don't know and I don't want to answer should only correspond to a very small share of the overall sample otherwise there is I understand that I understand that but in the Excel sheet you are transforming the data to the pious sheet so there is a yes or no and then if for some question there is don't know or then should it be missing dot or something like that in the in the yeah it's gonna be missing it's gonna be missing because you don't know if you don't know if you don't want to answer it implies that the the information is missing you cannot invent the information that's why you only go for the 1 and 2 because a missing value implies that you don't really know the answer uh and unless you have more information and that's why okay these uh let's say these goes back to what we discussed yesterday there is always the sooner you start data into operation the better you have let's say you can more timely adjust for uh no responses for potential data entry mistake but just too long story short to answer your question if you don't know then the data the value should be missing you you cannot say I don't know means that simply I don't know therefore I cannot say yes I was worried or no I was not worried simply that I don't know uh so yeah uh long story short uh option three and four are gonna be missing in our dataset but for the specific case of Bangladesh for the pilot in Bangladesh I didn't find any missing value any I don't know or I don't want to answer in our variables so I mean there are no issues for for the pilot survey in Bangladesh okay moving on the second step once we have our data dataset recodified according to the standard label and according to the eight items we need to estimate the severity of food insecurity which is associated with each household of the older of the old being um this is done by running a probabilistic model now the probabilistic model basically estimate some coefficient which are the beta i parameters and the theta h parameter which are gonna be used in a standard template which I'm gonna show in a second to estimate the probability to be either moderate food insecure or severe food insecure the idea behind this model is that all that's being equal more severe items or more severe experiences will be reported by fewer respondents and just to give you an example about this we can imagine that if we ask about being worried to not have enough money to eat let's say in the in the United States of America we will get very few people within our large sample which will say yes I'm worried about not having enough money for buying for purchasing enough food this implied that in the United States that item is going to have a very high severity of food insecurity but if we if we ask the very same question to let's say a rural area of a given country let's say country a whatever country it is in a rural area perhaps given the economic condition etc many respondents will say yes I'm worried that perhaps in the coming days or in the last 12 days 12 months I didn't have enough money to purchase enough food but this is a more widespread situation among many more respondents that reside in the rural area of country a now there's a variety of food insecurity in the rural area related to that specific item which is being worried it's going to be much lower compared to the USA this is a more widespread situation and people will know how to get food even if they don't have enough money but in the USA where perhaps the economic condition are a little bit better compared to rural area having a few respondents that are worried implies that the severity of food insecurity is much higher compared to the rural area and that's the logic behind this probabilistic model which is the RASC model now when we run the model we basically estimate two parameters I would say overall the first parameters are going to be estimated and associated to each item so that we can categorize items from let's say the less severe to the most severe if you look at this table here this one you will see that all the items that are associated with the eighth, fifth question have a corresponding parameter in this case being worried as is the least severe parameter whereas the most severe is to skip a meal for an entire day so one day as the most severe food insecurity experience and in this and you will see that the increasing severity depend on where the item is and the associated parameter so I would say that run out of food they're hungry and skipping food for an entire day are the most severe parameter whereas being worried or do not eat nutritious food are the least severe parameters related to the item the second parameter that we need to estimate is related to the raw score now the raw score simply is the number of affirmative responses given by a person related to the eighth fias question therefore the raw score is a categorical variable going from zero to eight so we have potentially nine values from zero to eight and the idea behind the raw score is that if a respondent the raw score given respondent is four this would imply that that respondent is more food insecure than another respondent with a raw score of two however the raw score tells nothing about the difference in the food security severity between the two respondents we only know that the first respondent who has a raw score of four is more food insecure but the distance between the first and the second respondent with the latter having a raw score of two in term of the food security severity we still don't know and in order to know we need to estimate the parameter associated to the item now once we have done it and this is let's say the key of this indicator the fias team within f at fio has developed this template the template basically is used to fill in the estimated parameter and to derive the probability to be moderate or severe food insecure so what we are going to do once we have estimated the rust model and the four d parameters is to simply copy paste the associated parameter in the fias template and i'm going to show you the way this is done now if i if i okay if i answer if i go to the fias template which is okay here it is okay imagine that you have a template which is completely empty so you know you don't have it so this is completely empty but we have estimated them okay let me try i don't know if you can see if i work like these okay we have estimated the item parameters these item parameters for each item are gonna be copy paste here so what i do is to copy paste the parameters therefore for example being worried there's a parameter of minus 3.44 and i'm gonna report this here and the same applies to all other parameters we still have to add the the coefficient associated to the rust core you see here that we have nine value overall from zero to eight and for this nine value we will have the what is called in technical term the ability parameters what we need to do is to copy paste the ability parameter in this table here you see like if you have a raw score of zero the ability parameter is minus five if you have a raw score of one is minus 3.6 and indeed this is reflected from the estimation of the rust model and so on so forth and you go ahead we also have to copy paste the standard error and finally which is basically you know the error the average error that you do while running a regression analysis and finally we need to report the number of cases the number of cases say simply the number of respondents that report a given raw score in this case we have 292 agricultural holding or household of the holder of the holding having a raw score of zero 57 percent they have a raw score of one and so on so forth 34 and they have a raw score of two and if you look at the and if you look at the sorry at the final number you will get let me zoom in we will get the 420 cases which are exactly the number of sand polled agricultural holding now once we have done it we will automatically get in this table this is perhaps the most important table i'm gonna highlight in yellow we are gonna get both the percentage of individual adding a given raw score so we know that 16 almost 70 percent of the overall sample as a raw score of zero 13.6 percent as a raw score of one and so on so forth but what we get and this is the most important is that our two probabilities the probability to be moderate to food insecure given the corresponding raw score and the probability to be severe food insecure given the corresponding raw score if you look at this probability you will notice that the higher the raw score the higher the probability to be moderate or severe food insecure and the same applies to the probability to experience a very high a very high degree of food insecurity situation the way we have i mean the technical person behind the sdg indicator has decided have decided to use this information to characterize the sustainability status of the of the agricultural holding is pretty straightforward and it's effective i would say because we are going to use this probability and depending on the probability and on the raw score reported by each agricultural holding we're gonna say that the sustainability status of the area associated with the agricultural holding is desirable which correspond to a mild food insecurity status of the household of the order of the holding i'm sorry for if the probability of being food insecure and sorry moderate food insecure is less than 0.5 and the probability to be severe food insecure is less than 0.5 in case the probability to be moderated food insecure is greater than 0.5 but the probability to be severe food insecure is less than 0.5 then the sustainability status is going to be acceptable finally the probability of the household of the order of the holding to be severe food insecure is greater than 0.5 most likely corresponding to a very high raw score then the sustainability status is read which correspond to a severe food insecurity situation now i'm going to show you the way this is practically constructed this indicator using okay this is indicator number 10 and i'm going step by step so the first thing that i do i hope you can see my screen but i'm gonna zoom in the first thing that i'm gonna do is to okay again to upload the information that i need in which case i'm only gonna keep the eight fierce question that are needed in order to uh cause to to estimate the parameters and what i'm gonna do is to record now we said before that if the answer is yes to a given question then the code is two and one if the answer is zero and i'm gonna do this record that the dataset will look like this one and this is still a raw dataset and now i'm gonna add the standard label to our dataset what i'm gonna do is to rename each variable raw variable with the item name and i'm gonna add the label to the corresponding uh answers given to each label so the dataset has been reshaped or has been recodified a little bit and you will see that you have the agricultural holding a unique identification number and the number of affirmative responses and negative responses uh for each food insecurity item and these are eight food insecurity items moving ahead what i need to do is to generate the raw score the raw score is simply the sum of the affirmative answer to the eight fierce question in this case you see that there are four affirmative answer for the first agricultural holding to worried healthy few food and finally eight less uh so if we if we sum up we get four and so on so forth the second there's no affirmative answer now i need to run the rush desk this probabilistic model to get our estimated the parameters okay this is this is gonna take some time but basically what we get uh meanwhile uh this is running what we get our code is uh this table here with all the coefficient associated to each item and this table here with the ability parameters associated to each raw score okay this uh and if you look at this graph you see that the number of affirmative response to the item a world day which implies i felt worried to skip a meal for a world day is very low this implies that this is the most severe food insecurity experience of a given house you have very few uh respondents who uh said yes to this item and a much larger number of respondents who said yes to being worried now if i'm gonna reduce a little bit perhaps i got to put it yeah this is worried the first bar chart that you see is worried the latest the the last is skipped a meal for an entire day okay once we have run our model oh there is i need to sorry i need to close it okay we have our parameter that for the for ease of reference you have copy paste in the uh status script you see these are all the parameters estimated related to each item and then i also have the raw score so the ability parameter associated associated to each raw score what i have to do now is to take this parameter and to copy in my uh in the fiesta template exactly here we need to copy paste this one here and this one here including this thunder error and the number of cases in order to get the probability once we have our probability from the rask model what we are going to do is to generate a variable which two variables actually one variable where which captures the probability for each raw score to get let's say a moderate food insecurity experience food insecurity status and another variable which capture for each raw score the probability to have uh a severe food insecurity status in this case this probability have been taken from this table here simply so what i did was to copy and paste the estimated probability in two variables for example if i if i have a raw score of zero my probability to be moderate food insecure is zero and the probability of being of experiencing a severe food insecurity status is zero however if i have a raw score of seven the probability to experience a moderate food insecurity status is almost one and the probability to estimate a severe food insecurity status is greater than 0.5 so once i compute i calculate these two variables i can simply derive the sustainability status of each house by doing by constructing a final variable which is equal to one meaning desirable one stands for desirable if the probability to have a moderate food insecurity status is lower than 0.5 and at the same time the probability to have a severe food insecurity status is less than 0.5 if the probability to have a moderate food insecurity status is greater than 0.5 but the probability to have a severe food insecurity status is less than 0.5 then my sustainability status is acceptable which correspond to moderate food insecurity finally if the probability to experience a severe food insecurity status is greater than 0.5 the sustainability status is not sustainable if i calculate this variable and i'm gonna look more closely to my data set you will see that in this case okay let's keep it short but in this case the first agricultural holding has a raw score of four corresponding to a probability to experience a moderate food insecurity status which is 91 percent given the item whereas the probability to be to experience a severe food insecurity status is a bit more than zero percent in this case this is acceptable because the estimated probability is greater than 0.5 for moderate food insecurity but lower than 0.5 for severe food insecurity in this case if i take this household number three this agricultural holding number three i will have a probability to be moderate food insecure which is 0.1 percent and the probability to be severe food insecurity which is 0 which correspond to a desirable sustainability status of the agricultural holding last case you see here the he has a raw score of eight therefore the probability to have a severe and moderate food insecurity status is is one a hundred percent that's why the sustainability status is red or not sustainable again i'm gonna save my data set which i'm gonna use in a second and i move to the final indicator the final indicator which is about secure tenure rights to land now this indicator basically capture the proportion of agricultural area according to the sustainability status which is defined as desirable if the agricultural i mean the holder of the agricultural holding has a formal document with the name of the order or of the holding on the on the document or has the right to to sell or has the right to be equipped any of the person of the order is the sustainability status is acceptable if the agricultural holding or the order has a formal document even if the name of the order is not on it in case of no positive responses to any of the four question then the sustainability status is red not sustainable okay we need sorry let me we need a few information from the survey module the first one is the information about having or not a formal document and whether there is the name of the order or of the holding on the document then we need to see whether the holder has the right to sell or to be equipped any person of the order these are the four information that we need and therefore we need to construct four dummy variables the first dummy variable is equal to one if the order has a formal document zero otherwise the second is equal to one if there is the name of the order on the legally recognized document and two more variable which are equal to one if the order or the holding has the right to sell or to be equipped any parcel of the land okay I'm going to show you the calculation of this final indicator which is very straightforward uh if we decide to use the survey module basically what I'm going to do is to first generate a variable to know whether or not the holding has uh or the order has a formal document so if uh if it has it and this is captured by question c 11 0 81 uh the value is uh one and zero otherwise then I'm going to construct let's say a specular variable in case there is no they have the document but there is not the name of the order or of the holding on that document so the first variable capture holding having a legally recognized document with the name of the order or the holding the second dummy variable instead capture those having a formal document but not the name on that document then I'm going to construct whether the holder has the right to sell and whether the holder has the right to be equipped any parcel of the land I'm going to construct this variable and finally and I'm going to generate this final indicator you see I added a short description and they say the sustainability status is green in cases there is a formal document with the name of the holder on it the sustainability status is still green if they have right the right to sell the any parcel of the land or if they have the right to be equipped any parcel of the land finally the sustainability status is acceptable in case the agricultural holding has a formal document but there is no name on it in all other cases no positive responses then the sustainability status is going to be red or unsustainable so this is the typical data set where you see here the first agricultural holding is desirable because actually this holding does not have any formal document but he has with the name on it but the holding has a formal document with no name on it but he has the right to be equipped any parcel of the land acceptable which is holding number two because the holding has no right to sell or be equipped but he has a formal document even if there is no name on the legally recognized document and yeah okay let's take this one which is red or not sustainable because as you can see there is no formal document neither with the name nor with no name on it the holding has not right to sell any parcel and the holding has not right to be equipped any parcel of the land so I'm gonna save again the last data set and I'm gonna close everything and Stefania I don't know there would be the last few slides to show about reporting on SDG if I can but I don't want to. No of course of course it seems we don't have any question on these last two subindicators. Yeah the very last let's say part of this exercise is about constructing the final data set and about the modality of reporting on SDG 2.4.1. Now what we have to do is basically now that we have all the subindicator calculated we need to construct a couple of final data sets at the national level. The first data set that we construct basically reported for each subindicator the value in actor of the agricultural area under a given status so the data set will look like. Can you increase the size of the slide? Yeah sure I'm sorry. Thank you thank you. Yeah I'm sorry so basically as you can see here we have the sustainability status and then we have for under each sustainability status and the under each subindicator these are 11 subindicator linked to the three dimension we have the corresponding area which is desirable acceptable and non-sustainable in actors this is expressed in actors I want to very much I want to highlight that these are in actors and indeed if you look at the total area this should of course these are unweighted statistics because we don't have the weights but this area will correspond to the total area agricultural area in the countries and we know for each subindicator the value that the total value of the area which is desirable acceptable and non-sustainable in reality the SDG indicator 2.4.1 talk about the proportion of agricultural area so what we have to do simply is to calculate the share of the total agricultural area which is desirable acceptable and non-sustainable for the subindicator that we have in the case of the farm output value per actor we will immediately notice that of the overall total agricultural area in the country nearly 11 percent is desirable with regard to the first subindicator 18 percent is acceptable 17 percent is non-sustainable now this data set in reality is really mirrors the logic behind the SDG indicator 2.4.1 what we have done here is simply to add up for each subindicator the total agricultural area of those agricultural holding having a desirable sustainability status the total agricultural area of agricultural holding having an acceptable sustainability status and finally the total agricultural area of all holdings in the country having a non-sustainable status so this is really the final indicator I'm going to show you how to construct it and finally we'll go to reporting on SDG 2.4.1 before closing this session okay here we are you may recall that before we constructed 11 dataset which we are now going to merge all together so you will notice that we still have a data set which is at the agricultural holding level so the reporting unit is still the agricultural holding for each agricultural holding we have the total the corresponding agricultural area which is this variable here and then for each indicator subindicator we have the sustainability status now what we have to do now is to know for each subindicator what is the proportion that is sustainable desirable acceptable and non-sustainable so what I'm gonna do is to simply generate 11 variables which correspond to the total agricultural area of the holding and I'm gonna merge these 11 variables in such a way that I can then calculate the sum under each indicator by sustainability status I'm gonna show you how to do it this is very much a standardized script so you can apply it if all the other indicators subindicator have been properly calculated you can simply run it and of course better to double check but you can simply run it okay if you look now at this indicator perhaps I want to go back before what I have done it let me show you for example for one indicator just for one indicator I have you see here okay let's forget about the the other 10 indicator and let's focus on the first subindicator now we see here that we have for this agricultural holding the actors the corresponding actors of the agricultural area and the sustainability status what I'm gonna do now is to simply sum at the national level all the all the actors of agricultural area which are acceptable desirable and non-sustainable so if I submit I will get that 55 actors are desirable because I have add up all the agricultural areas of all agricultural holding with undesirable status 93 actors are acceptable and 360 actors are non-sustainable and I'm gonna repeat this exercise for each subindicator in order to get something which is basically this one this one is for each subindicator the actors that are under a given sustainability status and finally what I need to do of course is to also calculate the share I need to calculate the share the share can be simply calculated by constructing a variable which is equal to the total area in the country and then by dividing I need to go back again and then by dividing the total so imagine here that we are going to construct the total which is going to be equal for each subindicator and then we're going to divide the agricultural area which is desirable for the first subindicator by the total agricultural area in the country a better way to see it is perhaps to an excel file because the excel file will give us let's say a more flexible way to calculate this indicator and I'm gonna show you the excel file which is this one this is the excel file that I have derived so let me zoom in here we have the dimension here we have the most important let's say breakdown which is the sustainability status here we have the dimension for each dimension we have the corresponding subindicator for each subindicator we have the corresponding area which is not sustainable acceptable and desirable and if you see at the total area which is at the national level this is a constant across all indicator or subindicator the last thing that we have to do is to know what's the share for this subindicator which is sustainable acceptable and desirable in order to do it we simply divide what we divide is the total area which is not sustainable by the total area in the country and we get for this first subindicator 70 percent which is not sustainable 18 percent which is sustainable and finally 11 percent which is desirable if we sum if we sum the three shares we will get 100 percent for each indicator we're gonna get 100 percent so if we sum 70 plus 18 plus 11 we will not gonna get 100 percent but this is irrelevant okay now about the reporting so another way to see is according to the dashboard approach so the dashboard approach will look like this one and you will immediately get a general overview of the subindicator and the corresponding agricultural area which are more critical for which policy action should be ideally be implemented and so on so forth but in reality what we want to know is that the larger proportion of the agricultural area which is unsustainable in order to do this let me okay in order to do this we have this formula where SDG underscore 2.4.1 index d stands for the proportion of agricultural land area that has achieved the desirable level and then we have the s i index is d and then which is the proportion of some indicator and that is classified as desirable and finally the minimum that goes from 1 to 11 where 1 to 11 stands for the 11 subindicator which refer to the minimal level of the desirable level of the desirable status at the national level across all 11 subindicator and indeed for the case of Bangladesh and using the data from the pilot survey carried out in Bangladesh it is pretty straightforward to see that the subindicator with the highest level of unsustainability is farm output with with almost 71 percent of the total agricultural area that is classified as being unsustainable. Gianluigi can you go back to the previous slide? This one yeah this one so actually there is a slight you know error in the formula that you have shown here so the final proportion of agriculture area under productive and sustainable agriculture would be sd underscore 2.4.1 a plus d so the area acceptable plus desirable and then you would say minimum n1 minus up to 11 sd a plus a plus dn yeah you're right I'm sorry I'm gonna correct it immediately and I'm also gonna tell you that you can easily find the right formula under the sdg 2.4.1 web page here which is this one let me show you for the sake of clarity here it is and thank you Asfandar for yeah for correcting and here it is yes these are the two formulas that are used to so so if the country wants to track only the green area then they will use the first formula which is sdg 2.4.1 d which is you know the best possible scenario whereby the country is doing great in terms of sustainability in agriculture and everything is green so they may want to use the first formula if they just want to track the progress as to whether their agriculture sustainable in general terms then you know the second formula which is d plus a okay that one should be used or on the flip side they can use the the third one which is sdg 2.4.1 u which is 1 minus sdg 2.4.1 d plus a or it is on the flip side maximum unsustainable across the 11 subindicators the maximum bread reported across the 11 subindicators yeah yeah thank you very much for clarifying it it was very late yesterday so it's i'm sorry again okay um from my side i hope i was able to give you an overview of the overall process of the sdg 2.4.1 and i don't know how stephanie and dustfinger wants to want to proceed yes so um i don't see any any question for this uh i imagine giallugio you will not come tomorrow right you will not join us no i'll be joining absolutely okay happy to join absolutely okay but anyway just for the information of our colleagues so the practical aspect that giallugio shown in these two days have finished so tomorrow we will we will be i will be presenting my presentation because today is already late it was planned for today but we will move to tomorrow morning which is the feo data collection questionnaire and then we will leave the floor to to you so in case uh you have any questions for giallugio so he said that we'll join also tomorrow so you can also ask tomorrow or in case uh now you still have a couple of minutes so uh no i don't see i don't see any raised hands um so i think uh it's one sharp here so it's um four or five no it's five five p.m. in your country right it's five yes it's five you know can we just you know you know uh discuss it again because in yesterday we have discussed that you know on friday that is tomorrow we will start the session half an hour no exactly i was i was just i'll go into to say this so tomorrow we will start at 2 30 p.m your time to allow the pray time of course so we will join we will start half an hour later let's see then during the course of the day tomorrow if maybe we need to make a shorter break or i mean it depends on the discussion so we will see tomorrow in the course of the training uh i think we can we can close officially and really thank you to giallugio for all these two days for having presented this uh calculation on this data and so see you tomorrow at 2 30 p.m Bangladesh time for the last day bye bye yeah thank you very much thank you very much okay thank you