 sustainable agriculture. The scope is primarily crops and livestock production systems. We have 11 teams and 11 indicators under the framework of SDG 241, which has spread across three dimensions, economic with three indicators, environment with five indicators and social with three indicators respectively. Plus the periodicity of the indicator is three years. And the data collection tool that is proposed by FAO to monitor progress towards, to measure and monitor SDG 241 is farm survey. Now with this very brief recollection from yesterday, we will resume from where we left. I will try to sift through quickly through the slides so that the rest of the sessions are not disturbed because we have a very heavy agenda for today. And there are other colleagues from Canada and from both the headquarters, our survey team, the agris team will join us to present to you their presentations. So without any further delay, let me immediately start with where we left the presentation yesterday. So Stefana, can you please confirm if you can see my presentation? Yes. Perfect. So the six indicator within the framework of SDG 241 is management of fertilizers. In the context of 241, sustainable agriculture implies that the level of chemicals, synthetic chemicals in the soil and water body remains within acceptable thresholds. The dimension is obviously, as I mentioned, environmental. The theme is fertilizer pollution risk. The coverage of this sub indicator is to all farm types, both household and non-household crop livestock and mix, as well as irrigated and non-irrigated holdings. And the reference period for this indicator is last calendar year. Now the sub indicator is constructed using data collected through a set of questions asked to the farmers. These questions are about their use of fertilizers in particular the synthetic and mineral fertilizer and animal manure and slurry. Their awareness about the environmental risks associated with the use of fertilizers and their behavior in terms of plant nutrient management. So I'm not gonna go through these management measures. These have been shortlisted and selected in consultation with both in-house and external experts. You can, these are fairly straightforward and you can see these in the methodological note as well as the presentations that we shared with you yesterday. But broadly speaking, these practices are about how well managed the fertilizer application is at the farm level for fertilization of crops. So the way this indicator is, the threshold for this indicator is designed. Basically the farm are classified as green if they are not using any fertilizer. Again, the approach here is to see the impact of the agriculture on the environment. So we are not looking at the productivity or efficiency of the farm for the use of fertilizer, but rather as to whether fertilizer is used on the farm that will contribute negatively to the environment. So the farm is not using any fertilizer from an environmental perspective is not doing any harm and hence it is classified as green. If the farm uses fertilizers but take at least four specific measure to mitigate the environmental related risks. So we have eight measures which I showed you on the previous slide. Again, I'm referring to that. So out of these eight, if the country or if the particular farm that is currently being interviewed or surveyed, if they follow any of the four of these eight, the farm will be classified as green. Now, if the farm uses fertilizer and at least take at least two measures to mitigate the environmental risk. Again, out of the eight, if the farm is adhering to only two of these selected management measures of fertilizer, it will be classified as yellow or acceptable level of sustainability. And the farm is classified as red if the farm is not taking any specific measure. So out of these eight, if the farm is not taking anyone then the farm obviously will be classified as red because it's not doing anything in terms of fertilizer application to safeguard the environment. Again, the Bangladesh example. So we asked these five questions to the holder of the agriculture holding or of the farm. So the first question asked is as to whether the farm uses fertilizer. And if they say yes, then we ask the follow up question as of the eight measures, of course explaining to them what these measures are about to what extent are they adhering or which practices are they employing on their agricultural land. So in the first case, as you see the farm is only practicing two of the measures which were shown on the previous slide. Hence it is classified as acceptable. The second farm is taking no measures and hence it's not sustainable. And then on 37, as you can see here the farm is using no fertilizer for its agriculture production hence we classified it as green. And likewise, number 39. As you can see the farm is still using fertilizer but they are taking four measures out of the eight. Hence it is classified as green as well. Now to calculate the proportion of agriculture area by sustainability status, of course the step is the same. We aggregate the agriculture area for the farms that have been classified as green. We aggregate the farms that have been classified as yellow and respectively as red. And then we divided by the total agriculture area of the country to arrive at proportions or percentages for this particular subindicator. So if you have any questions, please feel free to ask. If not then I will go to the next slide which is on management of fertilizer or by management of pesticides which is the seventh subindicator in the SDG 241 framework. To contextualize pesticides are important inputs in modern agriculture for both crops and livestock but if not well managed they can cause harm to people health or the environment. The proposed subindicator is based on the information on the use of fertilizer on the farm. The type of fertilizer of pesticide use, sorry excuse me for that. So we are talking about pesticides, the type of pesticide used and the type of measure taken to mitigate the associated risks. Again, the dimension obviously is environment, the theme is pesticide risk, the coverage is all from types and the reference period is last calendar year. Now again on the similar lines for the fertilizer subindicator, the management of pesticide, we are proposing two set of measures. One is health measures as to whether the pesticide has been used has any adverse effects on the human health and what is the agricultural holding doing to mitigate those kinds of risks and environmental measures as to whether the pesticide being used on the holding has any negative impact on the environment and what the agriculture holding on the farm is doing to mitigate those risks. So just to quickly go through the health related measures. So adherence to label directions for the pesticide use including use of protection equipment while applying pesticides. The second one is maintenance and cleansing of protection equipment after use. And the third one is safe disposal of the waste which ones like say, for example, cotton bottles or bags on whether those were disposed of appropriately and likewise for the environmental measures we have seven measures. Now again, these have been discussed thoroughly with experts with both in-house and external with country representatives. And after a thorough discussion with the experts we came up with the proposed measures for health and environment. Now the way this is the threshold for this sub indicator is designed. Again, if the farms are not using any pesticides they are not contributing to any health related or environment related problems. And hence the farm is by default classified as green. Now, if the farm uses only moderately or slightly hazardous pesticides what do we mean by moderately or slightly hazardous pesticides? I mean, these are of course defined by World Health Organization KELOS-2 or KELOS-3 pesticides. I mean, it's already given in the methodological note the reference to the WHO document from which we have taken this definition. So if the farm only use moderately or slightly hazardous pesticides, in this case it adheres to three health related measures and at least four out of the seven environment related measures. So as you can see here if the farm is not using any pesticide by default it is green. If it is using pesticide but the pesticide are moderately or slightly hazardous then in that case the farm should adhere to all three health related measures plus four of the seven environment related measures based on which the farm will be classified as green. If for yellow, if the farm uses only moderately or slightly hazardous pesticides again WHO class two or three and take at least two measures each from health and environment related measures. So if the farm still use moderately and slightly hazardous pesticide take two from the health related measures and two from the environment related measures then it will be classified as yellow. And for red, if the farm use highly or extremely hazardous pesticide which is defined by WHO as KELOS-1A and 1B or illegal pesticides then it will be classified as red or if the farm still uses moderately or slightly hazardous pesticides without taking specific measure to mitigate environment or health related risk associated with their use fewer than two in each category. So if the farm use extremely or highly hazardous pesticides or illegal pesticide then by default whether or not it's taking any measure it will be classified as red. But if it is using moderately and slightly hazardous pesticide then in that case if it is taking less than two measure like say for example one health related measure and one environment related measure or none of these then the farm will be classified as red. Or if the farm is taking all three health related measures and none from the environment still it will be classified as red. So it's a combination of both measures. So based on the Bangladesh data I mean of course we were interested in every single agriculture holding that we interviewed or surveyed all of them use pesticides one way or the other but if the answer to the first question is as to whether you use pesticide is no then by default that farm will be classified as green. Now for holding one they said yes we use pesticide the follow up question was as to whether you use moderately or slightly hazardous pesticide or extremely or highly hazardous pesticides and in that case they said that we use highly or extremely hazardous pesticide as well as illegal pesticide which was very strange. They take two measure from the environment and two measure from the health but they are still classified as non sustainable because the condition the very first condition is not fulfilled which is if you use highly and extremely hazardous pesticide or illegal pesticide by default no matter how many measures you take from the health or environment you are still considered as non sustainable. Holding two yes I use moderately or slightly hazardous pesticide and then take it two measure from environment and two from health so they are considered acceptable and so on. And again the last step I need not to explain it any further we get the area classified as green and yellows and reds divided by the respective representative national agriculture area to calculate the proportions for this indication. The next session. So the eight sub indicator and the last one in the environmental dimension is use of agro biodiversity supportive practices. Let me tell you one thing which is good for you information that this sub indicator was subject of discussion of very intense discussion in fact and refinement throughout 2019 as part of the 2020 comprehensive review of the global indicators framework. These discussion involved a country led working group which was coordinated by Canada and other members included Brazil, USA, Argentina, Chile, France and Russia. I mean this group was really very concerned about the formulation and the criteria that we have selected for this particular sub indicator. And hence they wanted us to review it and basically wanted us to modify it or refine it. So after an air long discussion and consultation which was entirely led by the group of the countries which I just named by the end of 2019 a compromised consensus was reached on the indicator criteria amongst FAO and these countries. So this compromise consensus once reached was tabled again for IAEG SDG review interagency expert group on sustainable development goal which is again, let me refresh your memories. It's composed of 28 countries representing their respective region where the group reapproved and endorsed it in November, 2019. Now from the methodological perspective the sub indicator measures the level of adoption of agro biodiversity supportive practices by the farm at the ecosystem, species and genetic levels for both crops and livestock. One other important point that I would like you to basically keep in your mind for each sub indicator up until now as a denominator we have always been talking about the agriculture land area of the farm as a denominator for us to calculate the proportions. In case of this sub indicator on biodiversity the scope of this indicator is the entire area of the farm holding as opposed to the agriculture land area. So this is the key differentiating factor amongst this sub indicator and the rest. The reason being that still there are some areas which are dedicated to biodiversity related practices by the farm which are not considered as part of the agricultural land area. And hence for us to estimate the true extent to which the farm is contributing to biodiversity supportive practices we need not only to take into account agriculture area but the area which is used for natural vegetation. So based on whether certified organic agriculture its practice at the country level two set of measures or criteria are proposed. One for countries who are practicing organic agriculture and a set of practices or measures for countries who are practicing traditional agriculture. So the countries with no organic agriculture certification system in place in this case they will be evaluated for this indicator based on the five criteria which are listed here. Of course, I'm not gonna go into the details of the criteria you can read it here. These are as well given in the methodological note and further explaining in the documents. But the only difference between these five measures and the one which are set for countries with organic certification is only number two, okay. So rest of the measures of the five measure remain the same for both set of countries either with or without organic certification. But if the country is practicing organic certification then we added a criteria which is farm produces agriculture products that are again actually certified or its products are undergoing certification process applies only to countries with certification. And there are many other five other criteria which I just explained those remain the same for both set of countries. Now in terms of threshold of course now that we have two set of measures for two set of countries we need to have two set of criteria as well to assign or threshold as well to assign green, yellow and red strategies. So for the countries with organic certification the agriculture holding needs at least three of the six criteria is highlighted as green, yellow if the agriculture holding meets at least one of the above criteria and red the agriculture holding needs none of the above criteria. And the countries with no organic certification system in place, the agriculture holding meets at least two of the five criteria yellow if the agriculture holding meets at least one of the above criteria and red if the agriculture holding meets none of the above criteria. And then of course we estimate the percentages accordingly. Now one point which I would like to clarify is that all these information and these thresholds that I'm showing you now these are framed into or designed into questions which are part of the 241 survey questionnaire or module that we have developed that I will explain to you in the next slide in the next presentation. So with this sub indicator we are done with the five environmental sub indicator. Now we entered the social dimension the first indicator within the social dimension is wage rate in agriculture. But this team provide information on the remuneration of unskilled employees working for the farms that belong to the elementary occupation group as defined by the international standard classification of occupation which we call ISCO 08 code 92 of ILO. In other words, this sub indicator informs about economic risks faced by unskilled workers and how do we define unskilled workers? These are workers who are performing simple and routine tasks in terms of average remuneration received. And this is once we have information on the wages we see by the unskilled labor paid by the agriculture holding. And what we do is we benchmark that against the minimum daily national wage rate or the minimum agriculture sector wage rate if it exists. Now one important point that I would like to highlight is that this sub indicator is not applicable to farm that employ only family labor. So this sub indicator is only applicable to farm that are basically hiring labor to manage its agriculture production. Now in terms of calculating the daily wage rate we do it according to the following formula. It's a total annual compensation paid to the unskilled labor per year. And then total annual hours worked by the laborer we divide it and then multiply by eight to convert into daily wage rate for us to estimate the compensation. Now in most of the instances the farmer would either directly know that how much he's paying on daily basis to the unskilled labor or maybe he won't know as to how much on average he was paying in the beginning of the year toward the mid of the year toward the end of the year or in a high season and low season. From that perspective we ask him, okay fine. An early basis how much did you pay to your unskilled labor? He has a number in his mind. And then we ask him, okay for how many days was this labor engaged and then we can calculate the daily wage rate. Now the criteria for assigning the traffic lights or the green, yellow and red is very simple. If the wage rate, daily wage rate paid to the unskilled labor is above minimum national wage rate or minimum agriculture sector wage rate if available then we classify it as green. Again, as I mentioned to you earlier this sub indicator is not applicable to farm who hire only family labor or who are not hiring any external labor hence by default they will be classified as green. The farm is highlighted as yellow or classified as yellow. If the wage rate paid to the unskilled labor is equal to the minimum national wage rate or the minimum agriculture sector wage rate if available and if the wage rate paid to the unskilled labor is below the minimum national wage rate or the agriculture sector wage rate then it's classified as red. So again, I mean based on the Bangladesh data I mean we then estimated the proportions of agriculture area for this particular sub indicator by green, yellow and red. Now one interesting point that we observed in Bangladesh case was that either the farms were paying above the national wage rate or below the national wage rate. So minimum agriculture sector wage rate was non-existent in Bangladesh and hence that was the benchmark that we used. There were no farm which was paying at par with the national wage rate or equal to that. So it was either below or above. The next indicator is on food insecurity experience scale or FIES, FIES is already a tier one SDG indicator 2.1.2 meaning it has an established methodology and data on it is regularly collected by countries and reported by FAO and hence it is a tier one indicator. Now in context of 241 it is customized or tailored in the context of 241 it tries to measure the extent to which the household of the holder of the farm or the owner of the farm are food secure despite having some agriculture production. I will not go into the details of how to estimate the severity of food insecurity using FIES. First assuming that many of you may know about this sub indicator because it's already an SDG indicator 2.1.2 which I just mentioned and secondly because of the shortage of time. However, I will touch upon the basics of its methodology while referring you to the training material and the e-learning courses on the indicator that was published by the FIES team within FAO. So the reference period for this particular sub indicator is 12 months and the coverage is only household farms. So this is not applicable to non-household sector and the non-household sector by default will be considered as a green on the sub indicator. Now in short FIES is a metric of severity of food insecurity that is measured at the household level. It is a statistical measurement scale designed to measure unobservable or latent trade as we call it and it's measured based on people direct yes and no responses to the eight questions which are given here. Now what these questions help us retrieve or derive is given in this PDF file. We will of course provide you with more resources as well. So I'm not gonna go into details of that. So the FIES question refers to the experience of the individual respondents or of the respondent household as a whole. So the respondent could very well be the will be the head of the household of the holding. The questions focuses on self-reported food-related behaviors and experiences associated with increasing difficulties in assessing food due to resource constraints. So this is the whole idea behind the FIES framework. So once as you go from question number one to number eight the sort of information which is sought in this question the intensity or the severity of the experience of food insecurity increases as we go down these questions. So these are the eight standard FIES question. I will again, different from going through details of explaining each one which I just explained to you. So here are the steps involved in analyzing the data one it's collected through the eight FIES questions. First, we prepare the data for analysis. So the standard levels are added to the eight FIES questions. As a second step, the data is inputted into the model prepared by FAO FIES team for parameters estimations. That is calculation of level of severity of food insecurity associated with each question and each respondent using rush model. Again, rush model, it's one of the models within the item response theory. I will not go into details of how the model works but we can always provide you with the detail information. It's very well documented in a very simple way. In total, two parameters are estimated. One is called item parameter which we also technically called the difficulty parameter. It refers to and are derived from the eight FIES questions and the respondent parameter or the ability parameter that are derived from the number of people who responded to the eight question. The third step is statistical validation where an assessment is made as to whether depending on the quality of the data collected at the country level, the estimated parameters are valid. That is the data is consistent with the theoretical assumptions of the model and then we see as to whether the data can good enough for it to be used for us to measure the FIES parameter. Finally, as the last step, we use the FIES information once collected to assign sustainability statuses to the agriculture holding. Now, I will quickly go through this slide. So we collect information on the eight FIES question. We furnish the data into Excel or SPSS or Stata or any other software where two is for no and one is for yes response. Then as a second step, we add standard labels as required by the model. So instead of the codes that we are using here, this code can vary depending on the survey in which these questions are administered. But then we add the standard labels for each question like worried, healthy, few food skipped. These are the identifiers or the descriptors within the model through which the model codes identify these questions. And then we replace the one and two with yes and no. As I mentioned to you earlier, the methodology underlying the estimation of parameters for prevalence of severity of food insecurity is based on the item response theory. The item response theory measures non-observable or latent traits and rush model is used in case of FIES data, which is one of the several model in item response theory to calculate the severity of food insecurity. So once we input data into the model, it gives us the difficulty parameter, which we also call the item parameters. And likewise, we estimate the respondent parameters which are automatically given to us by the model when the data is plugged in. So the respondent parameter, as I mentioned to you earlier, are called the ability parameters. And then we need the frequency of the people who respondent to each question and the raw scores as to how many responses the household gave. So for us, the three important inputs to estimate the severity of food insecurity is the item parameters that are automatically calculated by the model. The ability parameters, the raw scores and the frequency of the response is... Frequency of the household that respondent to a particular question. All this information, the ability parameters, difficulty parameters are plugged into the simple Excel tool prepared by the FIES team. It's already linked here. So we plugged in the required information at relevant places within this model. Similarly, so first the difficulty parameter or the item parameters are plugged in. Then the ability parameters or the respondent parameters are plugged in. The standard errors are plugged in and the frequency of the people responding to each question is plugged in. This help us estimate the prevalence rate for moderate and severe food insecurity and the prevalence of severe food insecurity. And these are the values that are automatically generated by this Excel tool. And these are the values for indicator 2.1.2. But however, in case of 2.41, we go one step beyond this. So what we do is basically based on the answers of the respondents to each questions, we then start comparing as to whether the probability of severity of food insecurity for that particular household in comparison to the probability estimated by the model for the entire distribution. So we compare the individual household probabilities with the probabilities derived for the entire sample. And based on this, then we have three thresholds. So green are those if the probability of household of the holder of the holding or the probability of the household of the holding. To be moderate to severe food insecure is less than 0.5 and the probability to be severe food insecure is less than 0.5, then we classified it as green. If the probability of the household of the holding to be moderate or severe food insecure is greater than 0.5 and the probability to be severe food insecure is less than 0.5, then we classified it as yellow. And if the probability to be severe food insecure is less than it's greater than 0.5, then we classified it as red. I mean, all this is given here. So let me let me exemplify. So the household one, based on their raw scores, the model estimate their probabilities. So in this case, their probability to be moderately and severely food secure is zero and the probability to be severely food secure is zero as well. So as per the criteria that we have set or the threshold we have set, if moderate to severe food insecure is probability is less than 0.5 and probability to be severe is less than 0.5, then it is classified as green. And hence in this case, it's green. Similarly, in both these cases, the probabilities for this outfall is less than 0.5, it is again green. Let us pick another case. In this case, the probability to be moderately food insecure is greater than 0.5, but the probability to be severely food insecure is less than 0.5 and it's acceptable. And in this case, in case 13, the probability to be both moderately food insecure and probability to be severely food insecure is greater than 0.5, hence it is classified as red or unsustainable. Now you need not to worry about the complications that I just showed you, because it's very simple. All you have to do is to collect information on the eight years question. And once that information is collected and you run the test for statistical validation to see as to whether the data fits the theoretical assumption of the model. Rest is very easy, you just plug in all these numbers into the model that FAO FES team has prepared and it's available freely online and you will get these scores. So don't worry about the complexity that I just showed you, but it's very simple to analyze because just maybe in a couple of minutes you will be able to do this exercise. Let me quickly go to, so based on these probabilities, we assign green, yellow and red statuses to each agricultural holding. And again, the last step remains the same. We aggregate the area green, yellow and red and then divided by the national representative agriculture area generated or produced using the nationally representative sample survey to estimate the proportions. This is again the case of Bangladesh. As you can see, 95% of the holdings that were interviewed out of the 420 in four districts of Bangladesh, 95% of them were classified as green. So the last indicator within the framework of SEG 241 is secure rights to land. The dimensions. Just to inform you, we have already quite a lot of questions. I don't know if you want to finish or maybe you want to. I would like to finish because, yeah, I would like to finish. And then, we will keep on compiling questions and then I answer. So the last sub indicator is the social dimension and within the framework of 241 is secure rights to land tenure. This is a sub indicator allows assessing sustainability in terms of rights over the use of agriculture land areas. Since agriculture land is a key input for agriculture production, having secure rights over land ensures that the agriculture holding have control over this key asset and does not risk losing the land in the short to medium term. Evidence also shows us that farmers tends to be more productive as they are reluctant, tend to be less productive if they have loosely defined rights to their land because they're reluctant to invest if they have limited access to or control of economic resources and services particularly land. This sub indicator is applicable to all kind of farms and the reference period is last calendar year. Now, we ask four questions within the within the SG241 survey questionnaire and based on that, we assess the area agriculture holding as green, yellow and red based on these criteria. So the holding is classified as green if the holding has a formal document which proves the ownership of the holder or the owner with the name of the holder or holding on it. So it will be classified as green from land tenure perspective or if he doesn't have his name of the holder if he doesn't have a document that has the name of the holder on it then in that case if he can sell it or bequeath it any parcel of the holding we still consider that a strong enough right to be classified that agriculture area or farm is green. The farm is highlighted as yellow if it has a formal document even if the name of the holder or the holding is not on it and the holding is classified as red if they say that we don't have a document we don't have our name on the document we don't have any sell or bequeath right so in that case I mean of course they will be classified as unsustainable. So again from Bangladesh example we have a document for all four questions I have a formal document my name is on it I have the right to sell I have the right to bequeath desirable yes I have a formal document no my name is not on it I don't know as to whether I can sell it I don't know as to whether I can bequeath it still be considered as acceptable because he at least have a formal document and number five I don't have a formal document my name you know I have a formal document my name you know if you don't have a formal document we just stop there because your name etc so it is non-sustainable the last step remains the same we calculate the proportions by green yellow and red by aggregating the agriculture area of the respective foldings etc and with this I come to the end of the presentation I've eaten up most of the time for my next session so we will somehow manage it no problem so now that we have covered to the indicator in general and the 11 sub indicators of 241 of course as I mentioned to you earlier the next step for us was to you know come up with the tools and the processes for data collection on SEG 241 so not only data collection in terms of FAAO collecting information from countries on the final values of the sub indicator but the country data collection so in this respect we started working on the indicator methodology and start translating it into a survey questionnaire or survey module and then you know along with that survey model we developed different documents that will facilitate data collection at the country level so the data collection instruments so as I mentioned to you you know yesterday the way the methodology of 241 is designed now it's around you know the only farm survey is the recommended data collection tool to collect information on the 11 sub indicators of SEG 241 as I mentioned to you earlier we have developer standalone farm survey questionnaire model that was shared with you as part of the background material before the training but I will show it to you as part of this presentation as well and likewise you know we started integrating the SEG 241 questions into aggregate survey program and 50 by 2030 initiative now I'm not going to go into details but you know consider point because this will be covered by my colleague Flaglio from from the survey team so on top of this the methodological note of 241 as you may have seen that allows the countries to have the flexibility to use alternative data sources to report on SEG 241 now what are alternative data sources is earth observation, geographical and more remote sensing, administrative records household surveys censuses etc now option one which is on the farm survey is fully developed okay and all the series of documents that will help you collect data process data, analyze data and then you know report on the data to FAO all the series of these documents is end to end finalized and has been shared with you the stream on alternative data sources though the methodology is referring to it you know in one of its section towards the very end is not yet finalized it's still work in progress okay so we are going to have guidelines on how countries can use all these different available data sources or its combination towards the end of next year so we will have practical guidelines prepared on how countries can use the alternative data sources apart from farm survey to report on the indicator so the option one the standalone survey question what is it about it's designed as a module with minimum set of questions now the module is designed in such a way that it can be taken as by the country and administer it as an independent survey so countries can take our module translate it into their respective local language and administer it to collect information on 241 or it can be attached as a module or integrated with existing national farm surveys so you know it's modular so you look at your agriculture survey see where the questions which we need on 241 are missing within your agriculture service and then start plugging in those questions at appropriate places so this is what we did with SDG 241 survey questionnaire and agris and 50 by 2030 initiative questionnaire we took this standalone survey questionnaire module that was developed for 241 and we started plugging in or integrating it with the agris or 50 by 2030 questionnaires the standalone survey questionnaire has been tested in Mexico Bangladesh and Rwanda both from cognitive point of view to refine the design flow comprehension recall and respondent judgment about certain questions assess if the questions are sufficiently and fully understood by a limited number of heterogeneous respondents then data was collected in Bangladesh which I was talking about in my previous presentation to test the sustainability criteria and then play some sensitivity analysis to refine this criteria a bit further then basically assess the time of the survey which we noted in Bangladesh to be 50 minutes on average and revise the respective state of scripts and routines and based on that accordingly the methodological note and support documents now this standalone survey questionnaire has five sections and production area of the holding, economic dimension question in economic dimensions question for the environmental dimension question for the for the social dimension this is how it looks like I mean of course we have shared this with you up front before the training but we will happily share it with you once again this has been translated into three three UN languages of course apart from English it has been translated into Arabic into Spanish and in French and we have plans to translate this into Chinese and Russian as well at least official UN languages but in case of Bangladesh of course it was translated into Bangla in case of Mexico it was translated into Spanish in case of Rwanda it was translated into Swahili so you know due to really you know administer this questionnaire in a proper way if you decide on taking this survey independently administering only for 241 which we won't advise by the way we would always like you to take some questions from here and integrate it into your agriculture survey which are missing on 241 so as I mentioned earlier for this questionnaire we have developed all the support documents so I don't know as to whether you took a look at those documents but we can again share it with you so we have an enumerator enumerator manual we have instruction manual for data into operations and analysis we have guidelines on data analysis to compute the sub indicators we have sampling guidance for 241 and we have a statistical tool kit which provides of code book tabulation plan and modules data scripts to support data analysis so you know all the support documents which are independent document in its own right covers the entire value chain of SD 241 you know data so it starts with the methodological note and from the methodological note if country wishes to collect information on 241 then they have all the tools and tools for them to do so so now I will go through the respective documents so the enumerator manual has been developed to train the enumerator surveyors and supervisor before their field deployment to administer the questionnaire in this very document we have given the definition of the key terms that the participant has been asking me about as to what does it mean what does that mean I mean it's already captured there so the definition of the key terms concepts and the meaning behind the questions asked you know it also provide guidance on the use of skip questions and filter questions and gives example of commonly encounter instances or issues when questions and responses may not be easy to administer and record respectively so this is a complete guidance tool for the enumerator before they go to the field and they can keep a copy with them and while they are administering the survey they can always refer to this manual for them to ask and probe the question appropriately the second guidance or manual that we have prepared is instruction manual on data into operation so once the information is collected in the field what do you do with that so this document then describe the data into operations all the steps that must be performed in order to organize the collected data into excel spreadsheets or other statistical packages like SPSS or R or STATA or others the procedure to process and analyze data collected and construct the 11 sub indicators according to the dashboard approach you know of course the coding, the cleaning and further processing of the information and you know this instruction manual however assumes that the enumerators and data analysts who are then crunching the numbers are analyzing the data and processing the information are familiar with the survey questionnaire and the methodology of STG-241 if not the enumerators and data analysts are strongly encouraged to carefully read and get familiar with the above mentioned documents that is the methodological note and the survey questionnaire before proceeding with reading this manual and applying the steps that have been outlined here then we have guidelines on data analysis and reporting this is can be used both by data producers you know the national statistical offices or Ministry of Agriculture and the data users are like you know the policy makers this is meant for government data and statistics authorities which I just mentioned in the slide sector civil society research and other organization they generate and or use data and statistics for calculating subindicators of 241 finally steps on calculation of thresholds and finally reporting of the 11 subindicator as a dashboard has already has been provided in this document as well then we have a sampling guidance on STG-241 some participant were asking question about how we have designed the sample or how we are approaching the sampling issues of STG-241 it's already captured in this document this document goes into sampling size, sampling units, frame reporting unit, estimation domains estimator and stratification variables sample allocation and strata and other issues related to sample selection of STG-241 e-learning of course this is an additional tool that we have been insisting you to to take it's a very high level it's not as intense as the virtual training that we are having now but it's a very good starting point for you to familiarize yourself with the indicator it covers the different aspects of the indicator that we already covered as part of this training here are the documents which are available on the STG-241 webpage which we have shared with you and will share with you again so the methodological node, the enumerator manual the sampling guidance, the guidelines for data interpretations the guidelines on computation of the indicators etc now so this was the standalone survey questionnaire and support documents now I'm not going to go into the detail of this because Flavio Bolliger, my colleague from service team will now cover this session in more detail the idea behind you know us integrating the 241 questions into agris survey program and 50 by 2030 initiative was to basically leverage and capitalize on the scale of these two projects of these two big projects in fact that are basically to which FAO partnered along with other multilateral institutions to support 50 lower and lower middle income countries with the survey program by 2030 hence we call it 50 by 2030 now the 241 questions have been integrated fully into the survey instruments of these two initiatives I'm not going to go into the details of this because this will be covered by as I mentioned by Flavio here are some you know, guidance on the agris and 50 by 2030 meant streaming technical note that we have prepared we will provide you with with information on this as well and finally the alternative data sources that I told you is still a work in progress though the methodology refers to it in one of the very last section within the methodological note you will see a similar table like this through discussions with with different experts we were we arrived at you know that we can still use other data collection sources for the respective sub indicator in some cases maybe more than one for a single sub indicator depending on the context of the country and depending on how well how robust the agricultural statistical system of the country is so hence we decided that basically or before that let me take a step back the earlier version of the methodology was exactly based on alternative data sources then we took a decision that based on country recommendations that it's very difficult for us to integrate information from different data sources for the 11 sub indicators because it involves heavy coordination it involves expertise which goes beyond the statistical expertise of the national statistical office which is the custodian of the agriculture SDGs at the national level and hence they said that come up with a simplified option whereby we can use one single data collection instrument to report on 241 and hence we redrafted the methodological note of 241 to focus on only farm survey okay so now we are going back to our earlier position so on top of farm survey which is already matured and prepared we are offering alternative sources of information that can be used and leveraged by the countries to report on the respective indicators now before using the alternative data sources of course conditions are you know some recommendations that we offer before country can use that so the alternative data sources should respect the recommended certification that is a farm type household sector, non-household sector crops, livestock, mix etc and at least the data should be available at the same level of territorial desegregation as farm survey the alternative data sources should capture the same phenomenon as proposed by the farm survey to arrive at least the same picture after a sustainability assessment because this is a huge problem if you go by the practices you will get an entirely different picture of sustainability rather than if you go for impact assessment so this was the problem that we are trying to tackle toward you know between now and the end of this year and early next year before we publish guidelines on the alternative data sources for countries to implement we will of course test alternative data sources with the farm survey results to see to what extent we can triangulate the results of the two options that we will be that will be offered yes and a couple of others compliant with international standards and classification systems and then reference year and periodicity should be homogenous across the sub indicators and now the alternative data sources of course if it is already available so if you are still using farm survey it's good that alternative data sources are there because they can be used to replace farm survey questions especially if the alternative data sources responds to the criteria that we have designed for each sub indicator remember data is easy data can data can be found in different sources no problem the only problem is you know the threshold setting the threshold to assign sustainability statuses or the traffic light green yellow and red based on alternative data sources that's the biggest issue that we will try to tackle in the next few months the farm survey can also complement the alternative data sources can also complement farm survey questions by providing additional contextual information which is helpful to probe the right answers this can be done up front you know by informing the animators or the surveyors about the actual sustainability situation like say for example if an animator is going to a certain area and we know from before from other sources that this area is worse in terms of soil degradation especially water logging and salinization then you know this information should be fed should be given to the animator up front so once he goes to that area and he is interviewing the farmer and he asks him as to whether he has these problems and the farmer say no then the animator can probe further based on the APRI knowledge that he has and it can be also used to cross check the farm survey result to identify any inconsistency and to assure its robustness so exposed evaluation of the farm survey information by triangulating and validating it with the survey results I will stop here because I don't want to go any further of course I mean we will have ample time to discuss this more okay good thank you very much Spandia I think it's now to give the floor to our colleague Flavio Bolliger who is part of the agri-survey team so Flavio you want to share the screen introduce yourself to you everyone it seems that my camera is not working that's okay maybe you will be sharing the screen anyway so yeah yeah but the problem is to share my my screen maybe you can I will okay so let me open it okay okay Flavio just one request for you because soon after your presentation we are going to be joined by our colleague from Canada so we need to be very sharp in terms of 30 minutes that has been allocated to us okay sorry yeah this was the yeah but I was sharing the pdf while let me find okay let me share the pdf sorry okay you have the animation no no no I mean no animation we will just skip some slides I don't know if the pdf is skipping these slides or not in case you tell me okay let me try to you know upload from my site I sent it to you yesterday Stefania yeah I know for sure and just for your information I mean of course I will answer all your questions either today towards the very end or you know early morning tomorrow I mean so don't worry about that bit that you know some of our questions may not be able to you know you are not able to answer so we will do that okay I have it sorry I received 100 emails I was losing okay so pdf powerpoint okay can you see it yes okay good okay so my presentation covers two aspects for the collection you can go to the next slide yes no the first one okay so first I will talk about the agris survey program the projects that were running the agris methodology and how to run could be integrated the agris methodology then I will go to 3x3 the objective of the project how country the country that are onboarding the initiative and the data collection approach and tools used by feedback to propose for feedback okay all the agris survey program I think it's better to give some background about the agris survey program in fact you know FAO has been providing guidance on sensors the last 70 years and but not the same effort in terms of survey annual survey or continual surveys and in fact the in 2008 the crisis of the commodities the community realized that the level of information the quantity of data sound data on agriculture across countries are not so good and a project to start name it a global strategy to improve good statistics a big project for 15 years and the first 5 years is already done and during this first 5 years a big effort in terms of methodology was developed within the global strategy and producing many handbooks for many experts of agricultural statistics and especially one handbook named agris so that is a complete methodology for continual survey for agricultural survey and the last information about more detail information about agricultural surveys in the field was from the 80s so this all this effort update the methodology and this book that Alba just showed the front page of the handbook has a complete information how to implement cost effective survey that goes beyond the traditional data on agriculture and on production covering also aspects of social, economic and environment aspects of the agricultural activity so and after that FAO established this survey team the new branch in the statistical division running this agris survey program so promoting the use of this methodology of this and the idea of the program is to help countries to strengthen national survey and promote this new broad concept of agris and agris is based on a multi-year program so using modern techniques and as I said covering more broadly the aspects of agriculture we can go for the next slide we are running the agris survey program with different projects two main projects are one fund by USAID that brings technical assistance for the countries and financial support to the collection and Cambodia, Senegal and Uganda are covered by the USAID project and another project funded by the the Bukit Foundation covering just technical assistance and we are working with Armenia, Ecuador, Georgia, Kazakhstan Nepal and Uruguay and this project started in 2018 and are going to finish next year but now we are part of therefore and this project was a kind of pilot for a bigger project that's named now 5th by 30 initiative that had started last year and goes to 2030 and supporting even technical assistance and financial support and the onboarding process for the 5th by 30 initiative already started we are the process engagement of Cambodia in this moment the SS activities also provide support to other countries having funds from other sources like the technical cooperation of FAO the TCP could cover statistics in this case we are asked to support countries on that and also other countries that are not eligible for 5th by 30 can ask for support but should cover the cost for technical assistance let's go further ok so the algorithm methodology is a 10 year program 10 year cycle program of battle collection and it's based on our core questioner every year that cover basically crop and livestock production and rotating models that are used in different years this schema in the screen is a standard schema where the model on economy is applied in each year label in the decade and the model on production methods and environment to twice in 10 years and then also the machine equipment and assets so this idea to have rotating models is to minimize the burden for the respondent for different aspects and with that process we can provide the basic statistics every year the traditional statistics and additional information with less frequency when it's not required so in the screen you can have the link for the global strategy for many materials on agris methodology including e-learning courses templates and questionnaires and obviously the handbook that is very complete in terms of methodology on agris so I didn't mention before agris methodology was finalized at handbook in the beginning of 2018 January 2018 was published so before the definition of the indicator 241 the indicator 241 was approved by the UN Commission later so agris methodology as it is in the handbook do not cover all requirements for 241 so that's why with collaboration with colleagues of the team we develop this document on how to incorporate 241 requirements with in the agris methodology and we propose two alternatives the first alternative is incorporate all requirements in the core model it means to add more 32 questions in the core model in the sense this can be adopted for any year so we can decide when to collect data the periods of 241 recommend each three years and another option because we have many requirements for already in the core and other models of agris so another alternative is to incorporate the requirements according the rotating models and a good solution is to incorporate 13 questions the year that going with the economic models and other questions when the year are going with the PMB production methods model so in this case the length of the question each year is not so extent but the indicators some will refers to one year and other will refers to another year consider that sustainability is more stable characteristics of the agriculture so this is an option to minimize the burden and the complexity of the training on the operation of the survey so these are the two options and attached to the document we have this already implemented in the standard questionnaire of agris to implement these two options okay now about 5.13 initiatives so this is a bigger initiative for many institutions and the idea is more or less the same of the agris program the capacity of the countries to implement a survey and provide data statistical data to inform policy and to provide information to build evidence from policies in the country but not only on agriculture but also on rural areas and have a strong commitment with the sg indicators on zero hunger 2-3-1, 2-3-2 and 2-4-1 and also the gender perspective so the one aim behind the 5.13 initiative is really to produce the sg indicator linked to zero hunger so okay we can go ahead there are some countries that the initiative involves three institutions mainly as implemented institutions that is FAO World Bank and EFAT FAO is responsible for data production World Bank on methods development and FAO on the use of data and the methodology of FAO combined achievements or recommendations from agris and also the experience of LSMS ISA from World Bank and there are a number of countries that we are already working with in agris survey program and the LSMS ISI program project that are considered pre-selected countries to be engaged on 5.30 so as I said this year we already have the onboarding of Cambodia and Ethiopia and the idea is in 2021 have six pre-approved countries on board Uganda, Cambodia, Senegal Nigeria, Ethiopia and Georgia plus two new countries in 2021 Burkina Faso Malawi, Ghana, Tanzania Armenia and Nepal plus five new countries in 2013 the Malian Myanmar, Kenya and as approved countries and more six countries the project is a big project and aim to cover 50 low and middle low countries from now to 2030 so you can see that this 3.30 follow more or less the same scheme of agris and have two survey programs one named AgriKuto Survey Program that's very similar to agris but instead to have four rotating models it was defined three rotating models in fact we have one rotating model that covers an economy this name income labor and productivity the production methods environment model, the PME and machine equipment and assets in this case feedback to promote also the coverage of non-household farms and household farms that many countries cover only household farms but the idea is to have a complete coverage of agriKuto activity and similar to agris we have the core every year and a cycle of three years with these models for 2.41 the instruments of 5.30 was developed last year in fact the first version was finalizing in July last year and we are refining up to now so the instruments of 5.30 already incorporate all requirements for 2.41 and okay the second survey program please let's define the next slide namely integrated agriKuto and rural survey program we have one additional rotating model namely income and live standard is mainly based on the LSM ISI household survey collecting data on farm income and live conditions of the household and in this case in this program we have in each three years a known farm household on the rural areas incorporate to the target population so the schema is more or less the same but we have in the each three years a bigger sample and this additional model okay these are the basic tools of 5.30 the core and we name farm income and productivity the ELP model in fact is not exactly a model is really a questionnaire that integrate or add core and ELP questions similar with the PME and machine equipment assets so the material produced for 5.30 is already a questionnaire that allocate the questions in a more suitable way for the rotating model to follow a best flow of the interviewing the PME model the questionnaire core plus PME was selected as the one to receive all requirements of 241 so in the case of the feedback tools in the year that goes with PME all items of 241 are collected all together all the environments and production methods and information so that's the solution that we propose for 5.30 and for this SCG indicator the next slide shows in fact for 5.30 the recommended approach is have post-planting and post-harvest data collection so two visits at least two visits per season or two visits per year data collection so this basic questionnaire are developed with this approach and more one visit a year and also we developed the questionnaire for non household sector that covered the questions that applied to non household sector is a special approach on data collection and have all the compliments in the tools, the overall tools of 5.30 so Agnes has one standard approach in the questionnaire that is one visit year approach that should be adapted according to the interest of the country to do measurements of area or to provide data along the year in the case of 5.30 all these variations are already developed and can be applied directly according to the interest of the country that's all okay so we have seen so far all the theoretical parts and notions with us from the year yesterday and this morning we have seen the data collection tools now let's move to another practical part I will show you how FAO gets the data on the SDG241 from all the countries so indeed the SDG 241 questionnaire we have one single questionnaire that comes in Excel format and it is indeed the key instrument to collect data from countries it covers all the three dimensions and of course some indicators that we have seen yesterday and today it is sent to countries once a year even if we have seen the periodicity for the 241 indicator is three years why? this is because in this way we can monitor the availability of data on an annual basis since it is a brand new indicator identify changes and data points through the years this is considering also that often we do not get many data especially now that we are at the starting phase assess the country needs in terms of capacity development for example the technical assistance and the training on SDG241 exactly how we did for this virtual training and lastly confirm the national focal points contacts which is always a crucial information for us so that we are immediately in contact with the appropriate person what we have done so far so we have tested the questionnaire in 45 countries through a pilot exercise carried out from December 2019 to April 2020 I'm going to show the results of this pilot test probably tomorrow or later today initially the question was only in English as Fandella already mentioned before we have translated everything in July 2020 into three official languages which are Arabic French and Spanish then we have had our first official dispatch on August 10th 2020 this year we have sent the questionnaire to 203 countries including your countries of course the questionnaire has been sent to the SDG241 focal points to the general SDG focal points and to the head of NSO and with copy the FAO local offices and the relevant people this time the deadline for sending the questionnaire back is now set for September 30th this year of course finally the next activity will be to translate all the material into the other remaining official UN languages which are Chinese and Russian here it is shown how the questionnaire is organized it is composed by eight worksheets so we have three introductory sections the cover pages instructions and the definitions we have three data reporting sections so one for each dimension and finally we have two supplementary information sections for metadata and feedback we're going to see all these sections in detail in a minute this is a preview on how the questionnaire is displayed you can see the different sheets here below ok so we see now in detail what these eight sections are about so the first one the cover page simply asks country specific information meaning the national focal point content details as I said are very important information for us for having a smooth communication with the country immediately then there is the page with only instructions on how to complete the questionnaire and it gives also an overview of the questionnaire structure followed by another page that explains the definitions of the key concepts the terms and the international standard used across all the questions the second section is the core of the questionnaire actually where the data are requested so meaning where the countries fill the spaces with their data these include as I said all the three dimensions so the economic dimension with the three sub indicators the environmental with the five and then the social dimension with the last three sub indicators this is how it is displayed so you have on the left all the sub indicators then you have the scale of colors you have the unit of measurement and then here you have the two columns that you need to fill so this is environmental and then the social last section we said it's about supplementary information so metadata part quite intuitive it collects the metadata on country coverage source of data unit of measurement frequency of data collection and so on and finally the last section is the feedback one which is the simple survey with 10 questions that help us understanding if some area of the questionnaire still need improvements now let me show you how to fill so all the file excel properly the first page the cover page is like this one you need to fill this column on the left with the national focal point content details this is please fill this column also if all this information have already been sent to F3O in the past because this will help us understanding if the focal point has been confirmed or if he or she has been changed through the year of course about that these are the focal point content details we have for your countries so please have a look you already have this power point because I sent it yesterday so in case you know that the focal point has changed please inform us and in Afghanistan we still miss the focal point content details for the three data reporting sections so the economic social environmental you have two columns to fill the first columns needs to be filled with the values following all these criteria described here so we ask to fill only one year you should of course report the most recent one that is available in your country the reference is the calendar year from January to December and then if you don't have any data you just report zero in case it is not occurring at all but potentially applicable you just say NA if it is not applicable at all in your country the second column which is not you should insert explanation in case data are reported using national definitions so not the ones that are described in the definition worksheets or if the data are reported using a different reference period not the calendar year from January to December as I just said before and here you also specify the exact year of the data referring to so in case you have filled the first column with the value here you write the year it is referring to the metadata section is composed by a big table with all the 11 submitters listed and then you have columns where you can specify all these kinds of metadata listed here so type of variable availability, unit of measurement and so on and the last one the feedback section there are six questions with the scale response so from strongly agree to strongly disagree and then we have four open questions to say something more in detail I'm going now to open the questionnaire itself just to show you can you see the the questionnaire now yeah okay good so this is the questionnaire we have sent on the 10th of August so this is the cover page you see what we have just described and then you have on the bottom also some extra information the structure of the questionnaire and then you have our contact details just in case you need to contact us then the instruction is quite a short section so you have the general instruction and then again explain the little bit more in detail how the questionnaire is composed the definition section is quite long this is because whatever doubt you have for each term for each definition is really explained here you see it's very long it is also divided by by dimensions and by subindicators so really you find I would say all the doubts you have you can find probably here it's very long we try to be exhaustive then you have the three the core of the questionnaire so the three dimensions even this one is divided by subindicators so you have for the economic dimension and the five subindicators for the environmental sorry and the social dimension so the three sections of course have the same structure then the metadata as I was explaining you have everything divided again by subindicators and then you have all this big table that you are free to fill whatever information you have of course and this is quite long as well because it covers all the 11 subindicators and finally the feedback one which is really for us for improving the questionnaire in the future six simple questions of course even if you have the scale you have a space for specifying any other and you have about this question and then you have four open questions and always an additional space for telling us whatever suggestion or comment you have please let us know if everything is clear or if something is not clear on really the question itself how to feel it or if you have any doubt so as I mentioned before now let's move to this next presentation which is the results of the pilot exercise why we have carried out this pilot exercise what were the objectives the main scope was to collect test data from 45 pilot countries using the SDG 2 for one questionnaire test which we just saw specifically we wanted understand the availability of data although we already knew that availability of data in 2019 would have been low assess the feasibility of data collection understand country readiness in terms of existing national statistical processes and availability of data relevant to sustainable agriculture of course testing the question itself so its structures and clarity and finally evaluate country needs in terms of capacity development and technical support as I mentioned already the pilot test was launching December 19 there has been a coordination and discussion with several countries and finally we prepared a final report with all the results in May 2020 please note that we had some countries that replied after that time so after May 2020 and unfortunately they were not part of the final report and so we don't see them in this presentation how we selected the 45 countries we had four big criteria so some contributed to the SDG 241 methodological development and refinements informal working groups carried out mostly during the 2019 they were Argentina, Brazil, Canada, Chile, France, Russia and United States then other participated in previous national pilot test these were Bangladesh Ecuador, Kenya Kigitz Republic Mexico and Rwanda then we had others that participated in national and regional trainings Fiji, Malaysia Vietnam, Oman Algeria, Egypt Ethiopia, Malawi Cameroon, India Indonesia and Pakistan then we selected some countries that are or will be part of the Agris Survey program or the 50 by 20 initiatives and these are Nepal, Burkina Faso Ivory Coast Ghana, Mali Uganda, Senegal Cambodia, Georgia, Armenia and Kazakhstan and then there were other selected countries so Belgium Germany, Italy, UK, Austria Norway, Sweden Ireland and Trinidad and Tobago not some of the countries that I just mentioned were members of their interagency and Gasper to group on sustainable development goals indicators now let's move to the results of the pilot test so what we have got I would call this slide pilot test in numbers so we had 32 countries that acknowledged the receipt of the questionnaire so 71% we had received 24 questionnaire back partially filled or completely filled which was 50% so a little bit more than a half which was for us a very good result we had 20 field questionnaire either for the survey section or the feedback one and finally not finally but maybe the most important number is the 16% of the countries so seven countries provided actual data and Trinidad stated that they did not have any data so this is really in ancient shape not in the snapshot of the results we got let's see as I said the most important number probably the seven that provided actual data these are the seven countries we have Canada, UK, Indonesia and Norway that provided quite a lot of data using existing data proxies and expert judgment UK also used anecdotal knowledge then we have Burkina and Malawi that provided one subindicators each and Kazakhstan that provided partial data on three subindicators all the respondents they didn't report any data they highlighted the sum data were available or partially available for some subindicators and thus were unable to report on the subindicators or its subset probably in the next years we will get actual data from these countries too this is the situation of the data availability by subindicators we consider the 24 countries which are the ones that sent the questionnaire back so in general we can see that data availability is low or partial for most of the subindicators we would say especially the ones in the environmental and in the social dimension we can easily see that indeed the list reported are the prevalence of soviet conditions they were the ones that are circled management of pesticide wage-raided agriculture and the fierce indicator possibly because these kind of information are not usually collected in agricultural surveys of census and even if the basic data are collected at country level but anyway we know that the 241 methodology requires specific and additional information for the compilation reporting of all the different subindicators instead through this chart we can easily see that the most reported ones were the risk mitigation mechanism and the secure tenure rights to land here is visualized the specific overview of the data availability for your countries for all the 11 subindicators we already talked about Indonesia Kazakhstan stated to have partially available for the three subindicators but they didn't provide any and the same was Nepal so they seems to have quite a lot of subindicators available or anyway partially available or available through proxy but they didn't provide any actual data neither. Realizing the answers given by the countries we managed to understand something maybe more specific about few indicators. Specifically the two subindicators farm output value and the variation in water availability are the ones that further clarity on the methodology was required the ones that have low data availability are the prevalence of the soil degradation in fact we received 18 countries did not have data to calculate it wage rate in agriculture 19 countries did not have data to calculate and for the insecurity experience scale so the FIS indicator even 20 countries out of the 24 responded that they did not have data to calculate it and the use of agro biodiversity supportive practices apparently was the subindicators were information were available but usually only partially available while for the subindicators security and tries to land secure tenure tries to land the data situation is good we imagine that this is due to the reason that the information on land is usually collected using census in fact for these subindicators nine countries have agricultural data from one through proxy let's go to the short survey section now this section is not present anymore in the questioner we dispatched in August we used it only for the pilot phase it included a series of questions that helped us assess the country data collection methods their sources, coverages scope and periodicity and the technical assistance needs here we have considered only 20 countries since not all the 25 that sent the questioner back filled these sections we have asked if the country was already reporting on an indicator on sustainable agriculture and we got six countries that replied that they currently have 30% in particular we had Germany that stated to report organically farmed agricultural land Italy that stated to report percentage of utilized agricultural area under organic farming and Sweden that reported stated to report organic area 100% of the countries have an agricultural census in place and 13 will have it by the next by the next year 16 countries which is the 80% have an agricultural survey in place and 12 of them will have the next round in the next couple of years finally about the coverage of the census and the surveys we had nine countries for the survey and seven for the census that cover both crop livestock, social environmental aspect all together then we have investigated on the technical assistance needed for producing and compiling the indicator so 70% which is 14 countries responded that they needed technical assistance then 57 they need assistance in the short term and 36 in the medium term this is the reason one of the reason why we have organized this virtual training only two countries out of the 20 already received the technical support and none stated that the indicator is not applicable for the country the SDG 241 is very relevant worldwide concerning the feedback section here we can see in one chart all the questions and the answers through the scale from strongly to strongly disagree we can easily see that question one and question five where the countries were more in agreement so we have sent the question to the right person and no important questions categories and commodities were missing in the questionnaire while question three and question six where the country were more in disagreement so not all the definitions were completely clear and the time required to fill the question was quite long finally analyzing the open answers we realized that countries found the SDG 241 methodology challenging in general that they needed more guidance on especially the conversation into actors in aggregating disaggregating results and maybe in clear restructions and definitions we already saw that the lack of data availability and lack of time series at country level was quite high moreover countries indicated two challenges to indicators the net farm income and the use of agro or biodiversity supportive practices finally some suggestion which indeed we took immediately in consideration where to organize trainings and evaluate the possibility of using alternative data sources which is indeed exactly what we did so in this last slide we have highlighted some conclusions and next steps although the low response rate and the low availability of data there is a very high level of interest from countries to implement the SDG 241 and we definitely understood that there is a need for capacity development assistance looking at the next steps underlined in the report as already mentioned before we have translated the SDG 241 material in Arabic, Spanish and French we have completed the first discussion on the 10th of August in progress we are still in the phase of collecting data making analysis, gap feelings quality assurance, quality control processes we have just started with our training so you know you are the first group we are also investigating already on the use of alternative data sources that's it for the pilot phase results do we have any question okay I don't see any question Asfandeya do you have any news on the Canada colleagues I think he's online okay great good morning can you hear me yes we can hear you very well thank you very much for to be this early morning to present to our esteemed audience so would you kindly introduce yourself more formally and then the floor is yours so thank you very much I'm a senior analyst with the statistics Canada being for over 25 years with the division in different section looking at different farm survey, census of agriculture and financial so different a lot of background with different agricultural statistics and farm survey and census and also some research as well so I'm a bit more familiar with anything related to farm practices as well because we add some surveys following census year since 2001 which it's called now it's called a farm management survey if I speak too fast let me know if I raise your hand if I speak too under I saw there's a button as well if I need so I guess Stefania you're going to move the screen and I just need to tell you when to move to the next one okay sure I will share my screen then wait I need to open your presentation so Canada okay now I share my screen okay can you see yes so I just need to tell you when to go to the next one is it oh it works yes you tell me and I move okay you can go to the next one so how much how many minutes have this morning you should have 30 minutes 30 minutes okay so I think I have enough time for also for questions okay it's not working wait I don't know why click next one my email before was struggling at the beginning okay okay now it's working as as just just I think that the presentation this morning is it starts to review our approach that we took to provide an answer to the FAO questionnaire to the indicator 2.41 we did it in collaboration with our colleague from as I said I'm with the agriculture division of statistics Canada we are two separate department and we have a department of national federal department of agriculture so I have colleagues in the department of agriculture who specialize more into anything relate to agriculture or environmental educators so it was really a collaborative efforts and and we we try to to respect as much as possible to align with the FAO methodology like like many of you were at first some were pretty straight we thought we were pretty straightforward other we we came with some measure that we thought they're going they are good proxy response that but there's like anything else there's always data limitation and I think tomorrow I mean this morning I'm going to try to see to explain the approach we took just to give you some idea because it's not always easy to when you receive the questionnaire and to complete it with a timely fashion and actually just to mention that we may have a new version now too because we kept some work kept working on it as well on some sub indicator where we see maybe we can do a better job okay next slide please sorry it's not working again maybe page down or okay okay so yes so I won't read all of these because for those that are maybe go to the next one you can go to the next slide okay so I will come back to like a slide that's very similar to the one I just skipped that's why one of the challenge that we had it's some time the reporting sustainability metrics especially we everything because you know we we transfer we transform everything into actor or we using so information and we also mainly realize to be with other data source including as I said the census of agriculture we're very fortunate in Canada we have it's mandatory to have a census of agriculture every 10 years and one every between those two census there's one that could be depends of government it could be it could be cancelled but so far since 1956 we had one every five years and also we are data rich with so we use also remote sense since in land cover data and we had a lot of my friends colleagues at the Department of Agriculture also specialized into into these metrics that they use either using census data and then a lot of geometric geometric and maps. Canada is a really large country very diversified into agro-climatic condition we have roughly arts organized it's five eco zone but these which are based on they're based on different soil type different terms of weather condition weather like also the watersheds they have different way to divide these eco zone which are actually subdividing to 12 eco regions which are subdividing so to say that when you try to aggregate it at the national level it's it also provide quite a challenge as well because each eco region could basically produce a different a different educator especially the environmental educator so when also one challenge actually that we even also at the farm level when you try to extrapolate it could be only one area of the farm that has for example problems with soil erosion and the rest is or so it's always how do you really do the extrapolation to to raise it up to the national to prevent the sub-prevention what we call province and then after that it's national level so next slide please so in terms of scope it's pretty like the land used primarily to grow crops and raise livestock there's no issue like the what we use as what we call the denominator there for most of educators is not is because we have a good census and also we have every year we have surveys so it's not a problem in terms of economics as well we're able to provide answer to all the sub-educators will go through of them quickly or just then tell me if I need environmental I said I put a question mark because in social we we we use like you see a question mark because we use proxies for for ensuring some of them other most I would say most of them for especially for environment for environmental sub-indicators but for social we we use some of them we had to use proxy a farm level with already mentioned the aggregation problems or issue or I would say challenge because nothing is is impossible to but one challenge that we face is like especially it's for I hear that for the last almost eight years or we really want to reduce response burden so every effort to reduce survey even the census itself like we're working maybe in 2026 we'll have more and more use of administrative data or model to estimate some some of the the data point that we used to collect through a national census and just by the way our national census just quickly explain is sent actually now it's it's mostly electronic because we had a good like 2016 we had a good response rate 60 more than 65% what I see is it interesting to know that implement remote sensing from cross-estimation we had some the question from what is main case of response burden in Canada that sorry main response burden is we calculated in the number of hours of spend the more surveys because farmers get surveys from us national agency they can get also from sometimes they receive it from questionnaire from it could be for for example a fertilizer or service provider so they're very so this stated to and for example we when we and now we try to get to get easy way for them to answer questions so even if they sometimes they have the extreme case where especially if it's during seeding season in the spring for example our census of agriculture it's around me the 10 but many farmers are sowing or seeding their soil and they're on their tractor it's not and they're working really long they so we really try to reduce the number of questionnaires to them and we I think we for the other question had for the estimation of crop we had we were able with the model and remote sensing and some climatic information to actually reduce one survey that was September field crop survey where we're able to estimate the the harvested area and the production for for I forgot the name of its major crops and we had maybe 12 I forgot exactly and we were able to cancel actually the survey because we had we reproduced that survey over the over the we had one in July and we have the number November one that's confirmed the number so but that development of I have a colleague who could actually actually made other presentation to FAO and we have some presentation available so it's something we can make it available but it's a I would say it's it took a few years to to when I say a few like in terms of development maybe more than 10 years but let's say intensive 10 years before we transition to cancel the survey and eventually now we we start to come back to question this July there were part of the questionnaire that was assess through that method and so we're reducing the number of survey this way so I hope we can answer but we can come back to question at the end too just close that through that okay yeah response burden I mentioned already large country team with some indicators we were yes we're able to have at least and as I said we couldn't probably even as of today we can probably send more some indicator to to answer the FAO FAO questionnaire the periodicity for us okay every 3 years very unlikely we'll be able to do every 5 years and reporting the dashboard that was something that the concept of was for us it was easy to easier to answer whether it was acceptable or forgot the other one but so next slide please as I said earlier so rich we're rich in terms of data source census agriculture for 5 years and also we have an annual census of farm taxation taxation data so where we have data and those are actually we can actually every year we link together the the census of the taxation data with the farm we had the census so of course on the taxation we don't ask for most of the taxation data we don't have the commodity or for example what type of crop they had they don't report that information to the taxation agency but we can do it for those that participate to farm program and with the department of agriculture and the so we had also there's alternative source of data that give us that information so basically when we had the output value that was pretty straight forward to use the measure and it was desirable in terms of profitability was it was a bit more difficult because if you want to follow strictly the methodology it's like especially at the farm level it was we don't measure the for example value inventory change income and kind depreciation at the farm level so for this would be much easier pardon me would be much easier to use aggregates that we produce for a national account pardon me and for the risk mitigation most program are open to and have people farm producer have access to credit and insurance even now we have government insurance and also some private insurance so we just make an assumption or it's not it's not a problem next slide please so now the from the dimension and environmental dimension we're not as rich as I said we have quite many question to farm management on the census of agriculture we had the base question about the land use and the area we have a survey that's called that I said every five years that follow usually follow the census of agriculture since 2001 and that survey evolved over time as well because I think we keep improving with it's not easy but we keep improving with that survey it's an agreement with the department of agriculture and we collect the information for them it's what we are based on the cost and we also have scientist research and I really encourage you to go to the report that the hyperlink I provide here from my colleague of agriculture and agriculture in Canada but it tells you a bit of the different approach you took for to some of the measure so next slide please so in the questionnaire you have the applicable for soil degradation so most of it like there was four that we were able to we had some measure in the report I just mentioned earlier so so this was this was easier to answer next slide and basically because of all the work they've done but when I say easier it was because they already have a report that took a few years to produce and it was based on land covers land cover area it was based also as they have different sites on the field where they did some field testing with intensive use of soil maps and so on so but they came to like these I won't repeat everything that's there but so 90% of it agriculture land is low to very low risk of soil erosion and no increase no change or increase in soil matter actually depending of practice we for example they were over the last 30 years there was especially in the prairie provinces where we produce most cereals and you see the practice of no tillage gun was very grew really fast and so it helps to protect soil organic matter so that's an but now we kind of plateau with that practice because it cannot be applied more in more area 92% land susceptible to salinization it's very it's a very small part of Canada that Canada crop land that's susceptible to that it's salinization so again if you try to I'm sure if you try to go with using farm practices sometime a measure that's applied doesn't necessarily say that it's applied to the old farm so you have more chance to measure that these soil degradation using using research on the field and observation as well and the other on the soil degradation we had no data next slide please okay so so that was what we need to reach the desirable level so you can go to the next slide so basically for us it was the we farm irrigate less than 10% of their agriculture area we didn't use micro data or farm level data there but actually we my colleague when he calculated the estimate the the aggregate data that was used for need irrigation and this is a practice that measure the actual irrigated area is measure on the census so so it's a combination of the two actually get maybe a better precision if we had gone to every farm on the census of agriculture but again it's I think we would have achieved at the end the same in terms of answering that question we would have achieved the same result as desirable so next slide please just so acceptable as was to for fertilizer management it's to use all these at least two measures so so those are many are actually already used except maybe I would say to some to some of X then it's all all of you so could you explain sorry question here could you explain the term earth observation okay sorry I will you can move to the next slide I will earth observation is oh yeah okay earth observation is remote sensing data so we use remote sensing for many many decades now when I start in agriculture division they already had a program in place and convert that first for many years it was used for the crop condition over to monitor crop condition over the season in the in the central province so I will give you an example maybe for the way I use sorry can you give example that you use as proxy for environmental and social okay when I actually this might be a good example of a proxy because here we say like for example fertilizer fertilizer management we say acceptable like for example the first point 72% of crop operation applied commercial fertilizer at recommended rate so we know that the answer to that the farm management survey yes we we the answer yes to that question but it doesn't mean that they did that to all their crops you can assume they did it for all their crop but for some of them they may just have especially now with many of them are practicing and have machine to have precision agriculture where it measure exactly area of the field that need more fertilizer than another and so on so but overall we said okay we in that case we we use the percentage of of a field crop operation or that method same thing for the manure and composting and so on so does it when I go to social dimension I will ask for example does it answer that was a question from okay if you need more example I can go through okay next so those pesticide management again from the farm management survey um so yes same idea here like we use a proxy because we didn't really need we didn't really measure the area where they applied for example the protocol or the use organic nutrient and so on or we didn't measure for example the we could probably use the the census of agriculture for example number three because we have we have these these actors for each of this components but and okay so the question is what did the percentage number in the proxy farm survey it just move to the next slide so so in that one for example we know that those who answer the survey 98% said yes we rotate crops so we don't know the exact we can actually in that one we could have got a step further and say okay from the survey and we could have extrapolate these crop operation when you link it with what the report and the census of agriculture for the field crop then we could have derive the the actual acreage or acres companion crops 8% but we don't have that which which crop they use as a combined together so we didn't measure that in the census of agriculture this one get a bit more difficult so we just know that 8% do it 1% use biopesticide so so again when they say use biopesticide is it for all the operation or is it just all the crop or is just some of the crop so that's you know it's as you dig further into you almost need to have a field you know and like be an interviewer with the farm operator and to try to to measure this and having a really large country and like us be very expensive to conduct the survey by the way the the method of collecting maybe I skipped too fast on that was self-reporting so the farm farm operator answer this question the census of agriculture or the farm agriculture farm management survey through through electronic questionnaire and the point as well like that I tried to say okay and I just give like its answer and directly when you try to extrapolate the number of farms the number of operator to the whole farm are we like in some time it's it possible if we had other measure like we have the census of agriculture but then are we getting really close to the truth and so not necessarily so and as I said earlier so it's difficult to have one national measure because we have different eco region so eco zone so one or two eco region could be really acceptable so you have to kind of waited to the to the depending of the area of these regions next slide please I guess that's the one that that could be especially with organic certification criteria it's but the bios biodiversity sportive practice for example first point here we could we don't have everything that like we have on the census we have we have the the pasture grass oops I lost sorry sorry I lost my screen which I just shut down some of the I lost the zoomer am I okay can I help you that's I will just find my presentation and because I lost your screen so okay the zoom screen somehow I'm still on so let me if I cannot find you have another way to continue just where I was can you remind me I just so you lost the zoom interface yeah so don't you find it at the bottom I don't know which computer you have I don't oh okay okay got thank you so thank you biodiversity yes so yeah for example yes we have we could measure at least 10% because we had for example the pasture grassland and so on on the census of agriculture organic certified this is this is we just know we just ask on the census of agriculture what if they're certified or not and what's their certification agency but we don't know what's the area for what kind of livestock there's production they're certified so that's something we should we want to improve with administrative data I won't go to the growth promoters because we don't have information on that temporary crop we have these information so basically those were mainly answer from the the could be answer from the census of agriculture when it comes to and we had this on the crop rotation it's not always because we we could ask we just ask them do you have crop rotation or not but and based on the mixture of crop we can estimate okay what kind of and the region where they are what kind of patterns crop rotation pattern they use but it's not it's not straightforward next next slide please so basically we just this one we the only answer we have is what was the challenge was this one we as I said we could continue to improve but do you use biopesticide for specific crop as I said it's just one percent of the if you go back that's a question that had so just go back so the criteria was okay I don't find maybe it was before anyway biopesticide I think we had field it was pest management so it was really the crop operation so we didn't ask okay what crop then what you know when you start to it becomes really quite complex to to ask this question and I talked earlier about response burden so we try to minimize the number of minutes or the number it could take hours sometime to take to answer all these the survey so imagine if you have a field survey and you ask all these questions and also you try to measure so you can be there for more than a day so okay you can move forward to one slide okay now the social dimension so here we have in Canada there's no there's minimum wage for sure when a specific to agriculture sector no and the thing is so we assess that it's desirable and wage rate paid to unskill labor we had survey and we again we can cross we can link the survey like labor force survey with agriculture or census of agriculture or some taxation data so it's possible but it's require much more work that we could do for to end the time we had to do the answer the questionnaires so you can go to the next one so as I said there's no minimum wage or there's one but it's really equivalent it's at the provincial level it's not national and it's there's different law and and sometime also we were not we had we struggle a bit with that question because to link the employees to the specific actors so unless because employees can be using especially in the it's one hour time we should be at the end of the training okay I'm almost done here so I consider I think I have two more slides okay more work using existing minutes okay so yes so option we will require more work next slide thanks for the reminder so mindful then security so yes we I think it's we desirable there you can go to the next slide so and then it's again we could confirm this with the census of population and census of agriculture in the labor so but it's again difficult to link employees with actors okay next slide sorry to take too long time security rights was not an issue because it's every pretty much every farmers as formal documents for about the rights for the land so and so it's not really a question so next slide and the last one so yeah but might be some instead of personal property exception especially for some small community like communities like indigenous and religious community there's no personal rights next slide so yes it's a way it's a great challenge to measure land sustainability or 2.4 4.1 provides a framework to start measure it and it's actually we've been tried to partner giving feedback to FAO and and that for that investigator we always consider the objective that discussion we had to it has to be comparable across country so sometime we cannot be perfect but at least we we we tried to follow as close as possible methodology if not then we have reasonable proxy measure that we're confident for different sub-inscuitor that's the time that's it that was his last slide so if there's I try to answer some on the chat some answer as I went through it so I wish to everyone a successful workshop thank you very much okay okay bye bye okay so we are at the end of the day we have covered quite all the presentation actually we miss only the pole exercises two pole exercises but maybe we can do this tomorrow what do you think? yeah that's perfectly fine let's just you know do it tomorrow as we have reached you know the time limit for this yeah exactly even because we started half an hour before so we want to leave the participants sharp today we are only four minutes late oh six so we are six minutes late I think we are quite satisfied thank you again so see you tomorrow we see we keep the correct time this time I mentioned right yes so we we see each other tomorrow as communicated to you earlier as per the meeting request so we are not going to start earlier but as per the time communicated in the in the meeting okay perfect thank you again see you tomorrow for the last day bye-bye everyone thank you very much everyone take care bye-bye bye-bye Mr. Cadavaranto