and will be your leading resource person for the three days virtual training. For this training, I'm joined by my colleague, Stefania Bacci, who just got herself introduced. She is the one behind making all the organizational arrangements for this training, and will be playing a key role of facilitator during the course of the next three days. For this training, we are expected to be joined by 171 esteemed officials from 12,000 people of countries belonging to Latin American and Caribbean region. These include Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, Mexico, Paraguay, Peru, Uruguay, and Venezuela. Let me highlight that we have already collaborated with several of these countries at different stages of the methodological development process of SDG 241, especially Argentina, Brazil, Ecuador, and Mexico that contributed to the indicators methodology and its different aspects. We will have a good mix of participants with diverse backgrounds, including representatives from the national statistical offices, ministries of agriculture, ministry of environment and other institutions and organizations relevant to sustainability issues at the national level. In addition, we are also joined by FAO colleagues, both from the headquarters here in Rome and different Latin American offices. We hope that this training will be a great opportunity for you to enhance your understanding about the fundamental building blocks of SDG 241 and its policy use once it's get implemented at the national level. The training will be interactive, so we gradually in a phased manner will cover the different aspects of the indicator, that is its conceptual and methodological basis, scope and coverage, the data collection and analysis tools and processes or mechanisms for reporting it back to FAO. As we move along, we will take breaks for questions and discussion and try to answer the questions that you may have. We would like to extend thanks to Mr. Michael Rahija, the regional statistician and Ms. Alda Lisbeth Diaz-Caballo, advisor for calculation of SDG indicators, both from the FAO regional office for Latin America and Caribbean, for their contribution in supporting us with the organizational aspects of this training, especially in making the last minute arrangements for Spanish translations, we really appreciate it. Last but not the least, we would like to express our gratitude and profound appreciation to the respective participants who have made room in their busy schedule to attend this training in these extraordinary circumstances. We are expecting to have an active participation and constructive discussion throughout this training. Thank you very much once again. With this brief introduction, I will now leave the floor again to Stefania. Okay, thank you, Spandiar. I personally would like to thank Michael and Alda too for their precious support and collaboration for this event. Many details have been defined at the last minute and the teamwork has been priceless. I leave now the floor to Alda since she would like to say a few words. So, Alda, it's your turn. It's very good morning to all of you. Thank you for joining us today. This time, I will address you in the name of Michael and the whole team of the regional statistics. We have a project that is very important and it's the TCP3730. This project is about the improvement of the census and the agricultural survey for the calculation of the indicators of ODS. We will work with you all this time and, therefore, the importance of 241 as a framework for this training will allow us to move forward with the process we want. We need to support you in everything you need because we want you to do this measurement and to promote what agriculture is. We also want to encourage you all so that you can apply everything you learn and we want you to do this monitoring of the indicator. We will encourage you and we will support you so that you can work with us. We also encourage you to call the offices and countries and ask you to give us the support we are going to give you to do this calculation of 241. Thank you for being here. Later, you will see Michael Rajija. I am the leader I am supporting in this project, but he is our regional statistic that also guides us. Greetings and be very well. A success in this course. Thank you very much, Alda. So let me share my screen now. Okay. Can you confirm you see my screen? Yes. Okay. Okay. Thank you very much. So thanks to Spandayar and Alda for their introductory speeches. Let me give you immediately quickly through instructions that were already listed in the concept notes, but it's important to highlight again a few. So preferably use a PC or a laptop and not a mobile phone or a tablet. This is because the content sometimes could be heavy to follow, so it's important to have this screen and that you are comfortable in a silent place with no background noise or echo and that you have also a clear vision of your monitor. Please also turn off all the sound notifications like Skype, WhatsApp, emails, or any other. If you have connectivity issue, our voice breaks or video freeze close the other applications that might be open on your computer. If it doesn't work, also maybe check through your house or your office, wherever you are, if you can switch off some devices. You can access Zoom from all devices, both via a web browser or through the application, but we strongly recommend to download the application for a better experience. Zoom provides new versions of the application, so it's strongly recommended to check for updates to ensure that you have the new features and also to enhance the security of the application. To do so, you can open the application and click on your profile picture in the top right and then you check for updates. If there is a new version, Zoom will let you know we download and install it. For a better sound quality, please do not use your built-in computer microphone. Use a USB headset with integrated microphone or maybe a wired earphone and microphone, but maybe not a Bluetooth. If several participants use a unique microphone, please make sure who is speaking is close to the microphone. For future use, the sessions will be recorded and probably uploaded on the SDG webpage. So in case you don't want to show your Vsage, please keep your camera off even when you are talking. Let's go now through a few rules. Please follow the meeting in mute mode and click the unmute button only when you are given the floor. This is because today we are more than 100. In this moment, we are 127 participants and often can happen to have noise in the background that disturb the fingers. So we kindly ask also to have the camera switched off for not overloading the internet bandwidth. You can switch on the camera when speaking. The two icons are on the bottom left of the Zoom interface. Here it's the picture of the icons. Please ensure that the name of your country appears in the name box. To do this, you click on the dots appearing in the right hand corner or your image box.  If you have a question, write in the chat box that you have one and wait for the SDG 241 team to give you the floor. Unmute yourself. Please have your video on when you're taking the floor unless of course you don't want to and but please be ready to turn it off in case of poor connection. Speak loud and close to the microphone stating your country and your question. And when finished, mute back yourself and switch off the camera. You can also raise the hand virtually for requesting the floor. You need to look for this symbol. It is the raise hand function and you can find it in the participants menu. The floor will be passed to participants based on the order that appears on my screen. To the extent possible, of course. If many questions will be asked, we will consolidate them by subject and if still there are too many and we don't have time, we will answer them by email. Anyway, please be ensured that we will reply to all questions. As you know, we have requested only two to five lead representatives per country that are allowed to talk. We apologize, but this is needful for this kind of meetings with such big number of participants. So if you have a question but you are not among the lead representatives, we kindly ask you to coordinate yourself through other channel or through other application. From time to time, the SDG 241 team will ask questions as sort of quiz through the poll function in Zoom. So please don't hesitate to ask clarifications if something is not clear, since you will be asked to reply all questions. Finally, whatever issue you have, please write me, Stefania Bacci. You can use the private chat in Zoom. You can change it easily in the general chat. You just need to change the recipient's name. I will be happy to help you for any kinds of doubts, questions or technical issues. As you know, Interpretation is available in Spanish. You can select it in the bottom bar. If you prefer to follow the training Spanish, switch on the Spanish channel by simply clicking here as it is shown in this screenshot. You will hear the translation at 80% of the volume with the original speaker at 20%. So you can still hear tone and intonation for a better understanding. Please be reminded in the virtual meeting, audio quality may deteriorate unexpectedly and become insufficient for interpretation purposes. So our interpreters will indicate this verbally and resume interpretation as soon as the sound quality permits. Mrs. Jasmine Silva and Mrs. Andrea Lagarini are the two interpreters. You can see the word interpreter close to their names. Please pay attention to the icons. If you see the flags indicating their languages, it means that your Zoom application is not updated. So in this case, please update it immediately. You need to see the initials of the languages, not the flags. So that's all for now. Hope everything was clear. In case not, you know I am available through the chat. And thank you. And now let's start the training. So let me stop the sharing and I turn on my video again. So the agenda for today is quite concentrated. We are going to learn about some of the SDG indicators and we will concentrate of course on the two for one indicator. Specifically, we will see all the 11 subindicators that compose the three dimensions, the economic, the environmental and the social dimensions. Today we should pay a big attention to all that will be explained because it's the fundamental part of the whole training. So let's start immediately. The first session introduced the 21 SDG indicators that are under the FAO custodian ship. As from the year, you have the floor. Thank you very much. So let me just turn on my, share my screen. So is it okay? Yes, we can see it. Okay, so before we dive deep into the methodology of SDG two for one and start disentangling its intricacies step by step. In this very first presentation, I will give you an overview of the SDG indicators under FAO custodian ship. Of course, the focus will be on the progress that we have made until so far, both on the methodological and capacity development front. During the course of this presentation, I will introduce you to our future plans for capacity development. That is technical assistance and training, support to countries collection and reporting efforts to facilitate national, regional and global monitoring of FAO SDG indicators. Briefly, we will cover the following key points in this presentation. The 21 SDG indicators under FAO custodian ship and its current tier status will work on SDG indicators so far on various fronts that is methodological capacity development and support to data collection and reporting. Overview of our potential future lines of work in support of maximizing data reporting on SDGs. And lastly, the presentation will illustrate important resources that we have developed that is e-learning courses and website links, et cetera, where you will find additional and detailed information on FAO SDGs. So let me begin by giving you an overarching and holistic overview of the global indicator framework and the process that was adopted by United Nations for its implementation and operationalization at the national, regional and global levels. The global indicator framework comprises of 231 unique indicators and it was endorsed by the UN Journal Assembly in July, 2017. So in order to carry forward our see and manage this process, the United Nations Statistical Commission was made responsible for development and implementation of the SDG monitoring framework and in addition, an inter-agency and expert group on sustainable development goal indicators that is IAEG SDG was constituted to prepare initial proposal on the methodology and to oversee this work until 2030. The IAEG SDG has 28 countries as members, which represent their respective regions. An important point to note is that the process with IAEG SDG has fully led by countries with international organizations only serving as observers. Based on the level of methodological development and the availability of the data, the IAEG SDG has divided the indicators into three tiers. Tier one are the indicators for which are conceptually clear and international established methodology and standards are available and their data are regularly produced by more than 50% of the countries and of the population in every region where the indicator is relevant. Tier two are the indicators which are conceptually clear and international established methodology and standards are available but data is not regularly produced by countries that is less than 50% of the country or population in each region. And tier three, of course, the indicators for which neither international established methodology nor data exist so far. As of the 51st session of the United Nations Statistical Commission, the global indicator framework does not contain any tier three indicators. So in order to support the methodological development and monitoring process for each SDG indicator, a custodian UN agency was identified and was assigned the following responsibilities. Lead methodological development and documentation of the indicator support capacity of the countries to generate and disseminate national data, collect data from national sources, ensure its comparability and consistency and disseminate it at the global level. And lastly, to contribute to monitoring the progress at the global regional and national levels, for example, storylines and data for annual SDG reports and agency flagship presentations. The global indicators are a core set of metrics that all countries are invited to monitor and to report to custodian agencies like FAO. The key point is if national data are not produced, regional and global indicators cannot be produced and compiled. Another important point to be noted is that the global indicators can be complimented. This is very important, but not replaced with national or regional indicators. This is as per paragraph 75 of the United Nation resolution on 2030 agenda. And lastly, the global monitoring is based on data produced by countries with national statistical offices having a key coordinating role at the national level for international reporting. So even if the indicator estimates are produced by international organizations or custodian agencies, prior consultation validation and triangulation is needed with countries before it is published by the international agencies. We as FAO or custodian UN agency for 21 SDG indicator and a contributing agency for five others. These indicator obviously are related to food and agriculture space. In this capacity, FAO is supporting countries efforts in monitoring the 2030 agenda. The 21 SDG indicators are spread across the following six goals that include goal two, which is on food security, nutrition and sustainable agriculture. Goal five on gender equality. Goal six on use of water. Goal 12 on sustainable consumption and production. Goal 14 on oceans. And goal 15 on life on land. So as a custodian UN agency for the 21 SDG indicator, FAO mandated to or responsible for methodological development of the indicators, statistical capacity development of the countries, global data collection and its dissemination, global progress report and voluntary reviews submitted by the countries to FAO. And finally communication and advocacy on the 21 SDG indicators. In terms of FAO work on SDG indicators, back in 2015 of the 21 SDG indicators, 13 were tier three. This means that FAO had to develop new methodological proposals in consultation with countries and compile it with the IAEG SDG criteria for tier three reclassification. This was the case for indicator 2.3.1, 2.3.2, 2.4.1 and so on. For some other SDG indicators, FAO has also to develop new international definitions for the key concepts. For example, definition of small scale food producers, which is indicator 2.3.1 and 2.3.2 and definition of rural and urban areas, which is going to be used for desegregation of many SDG indicators, including those which are beyond FAO mandate. The work of course didn't stop at the methodological development stage, but continued at a fast pace, where in addition to methodological development, for all indicators under our custodianship, we developed improved data collection tools, guidelines and supporting material to facilitate countries report on the newly developed approved and endorsed methodologies. This slide summarizes the tier one SDG indicators, with the red being tier three, yellow tier two and green tier one. Now, as of November 2015, 13 indicators back then were tier three, five were tier two and only three were tier one. This meant a lot of our work back then was focused on methodological development of the indicators. Hence, given the intensity of the work involved, we as FAO realigned our work program both strategically and operationally to support the methodological development of tier three indicators. With the four years of steadfast technical work of cross-functional teams responsible for respective SDGs at FAO headquarters, while leveraging a participatory, consultative and inclusive process. And most importantly, with support from officials and experts from countries, international organizations, private sector and academia, we were able to establish methodological basis for all the remaining tier three indicators. As you may see in the matrix, currently none of the indicator remains as tier three. In parallel with the methodological development, lots of efforts were targeted to support countries to enable them start adopting, implementing and reporting data on the 21 indicators. This included testing of the methodologies in selected countries for finalization of its methodologies, development of e-learning courses, organization of country, regional and global training workshops, so as to build statistical capacity of the countries and development of comprehensive data and communication portal that serve as a one-stop shop for all the information on the 21 SDG indicators. Our new FAO vision for 2019 to 2030 is of course to scale up capacity development support to maximize country reporting. On the previous slide, I mentioned training workshop. The aim of these workshop, including this very virtual training that is progressive now, has been to invite countries to collaborate on testing of the new methods that were developed. Enlarge the pool of SDG. In 2011, basically it's South-South cooperation amongst countries, until so far, we have conducted training workshops in 2017 and between 2017 and 2020 that were participated by experts from 150 countries belonging to all regions of the world. The ultimate focus was obviously to increase the number of data points, that is the number of reporting countries. We have also worked on the capacity development front, whereby we developed and prepared e-learning courses for almost all the 21 SDG indicators that are now published and available freely online on FAO-dedicated SDG portal or webpage. These are excellent resources that will help you get acquainted with the indicator's methodology, data collection and reporting tools and processes at your own convenience. Here are some other SDG indicators for which the courses are already available online. Now we are currently in process of finalizing the e-learning courses on indicator 1231, which is Global Food Losses and 1471 on value added for sustainable fisheries. One of the key features of these e-learning courses is that we have added recently a feature which if once you complete these courses successfully you will be awarded a course completion certificate. All the information on the e-learning courses can be accessed using this link. Going forward at FAO, we will continue to work closely and in collaboration with our member states to pursue and implement our future activities, particularly those focused on capacity development that include further work on various methodological aspects of the indicator and its testing, that is data desegregation techniques for casting, now casting small area estimation to facilitate reporting on the SDGs. Secondly, in collaboration with member countries to carry out data gap assessment at the national level. Thirdly, to further strengthen our engagement with the next stakeholders, particularly those on the alignment of national and regional indicators framework with SDG framework. In doing so, we believe it will reduce data collection and reporting burden on the countries that already face resource constraints. Another key area of work would be to provide support in further development and implementation of new data collection tools, including alternative data sources and new means of data collection, like using cell phones and computer assisted web interviews, especially given a COVID-19 pandemic, which has not only slowed down, but in some cases altogether start the face-to-face data collection due to travel restrictions. We will continue to provide capacity development through various modalities, including through virtual trainings to support countries in adoption, implementation and reporting of FAO SDG indicators. And lastly, we aim to provide technical assistance in improving the analysis and use of FAO SDG indicators in making informed decisions and evidence-based policies at the national level. So for general information on SDGs, please don't hesitate to write to the office of the chief statistician. That is a unit responsible to coordinate the SDG work of FAO at the global, regional and national level using the first email address given here. For matters related to SDG 241, you can always reach out to us using the dedicated email address given here. So thank you very much. Stefania, the floor is yours. Thanks for the introduction to the SDG and the FAO work. I would like first to ask everybody, please pay attention not to turn on your microphones because we have heard sometimes some people talking. So I am muting you. I'm sorry, but I need to, but please pay attention because for the interpreters, this can be a problem. So I found that you are introduced as the 21 SDG indicators in FAO. But in this training, we know we are focusing only in one, which is the SDG 241, proportional agriculture area and the productive and sustainable agriculture. So if we don't have any questions so far, I would pass immediately the floor to him again. I have already seen some questions in your registration phase, but I prefer not to answer them now because they are all very specific. So we will go through them after Aspanaya has introduced more concepts. So Aspanaya, the floor is yours again. So thank you very much, Stefania. Can you hear me? Yes, yes. Okay, perfect. So in this presentation, we will unfold or cover SDG indicator 2.4.1, which is proportion of agriculture area under productive and sustainable agriculture. The core objective of this three days virtual training are two. First and foremost, I will walk you through the SDG indicator 2.4.1 conceptual and methodological basis. It's compilation and interpretation. Tomorrow we will focus on the tools and instruments developed both for collecting and reporting data on the indicator. You will get to know about the survey questionnaire and related document. SDG 241 in the context of agris survey program and 50 by 2030 initiative and also the FAO data collection questionnaire as an instrument used by FAO to collect data from member countries. And on the third day, we will discuss with you the data gaps and your concrete plans in the short, medium and long term to collect data on the indicator in order to bridge those gaps. And lastly, an overall aim of this training is also to unite or assemble key stakeholders at the country level. Those who are responsible for collecting and reporting data representatives from the national statistical office and also those responsible for using the data produced for evidence based policies at the national or submission level. That is the representatives from Ministry of Agriculture and other relevant institutions. So to contextualize as highlighted in the in the previous presentation at FAO, we developed global public goods, that is methodology standards and classifications and coordination consultation and close partnership with key stakeholders at all levels. So to give you some historical perspective in early 2016, the FAO strategic program on sustainable agriculture and global strategy to improve agriculture and rural statistics joined forces to develop the pioneer methodology for the then tier three STG indicator 2.4.1 to measure progress towards target 2.4. Now, as many of you may know, defining and measuring sustainable agriculture, which is a multi-dimensional concept is challenging as it is complex and country specific. And thus, we have had several attempts in the past 50 years since 1970 has never been done before. Given the multi-dimensionality of the sustainability concept, FAO initiated a global discussion to deliberate the fundamental questions as to what sustainability means in the context of agriculture sector. What are the fundamental building blocks of sustainability? What are the economic, social and environmental factors that affect and in turn get affected by sustainability in agriculture, both in an inter-temporal and inter-spatial way? What schematic aspects to keep as part of the framework and what to let go of? How to strike a balance between the different sustainability issues faced by different regions and countries? How it will be measured and monitored consistently over time using a framework and data collection tools that are universal that is applicable to both developing and developed countries? As you will find out in the course of this training, the methodology that we have developed for SCG 2.41 is simple and involves basic arithmetic rules that are used to describe the sustainability assessment of the country once the data has been collected, cleaned, processed and analyzed. The approved and endorsed methodology of SCG 2.41 is a result of long participatory and consultative process that involve discussion with and contribution of thematic and subject matter experts, statisticians, policy makers and researchers from country institutions and offices, ministry of agriculture, international organization, civil society, private sector and academia on the very issue I mentioned earlier. The reason behind us involving key stakeholders with diverse background was to make this indicator owned by everybody, especially countries. The current methodology of 2.41 embodies these principles that is it's universal, policy relevant and practical. The way the methodology of this multi-dimensional indicator is designed and you will see that as we progress during this training is simple, logical and practical. This was to ensure sustainability of the indicator monitoring over time and country level. So SCG 2.41 has five targets. The target that we are interested in today is target 2.4, which is written in extensive here. As you can see, like many other SCG targets, this target is a very complex one. We highlighted in red some of the key aspects that needs to be captured as we try to measure progress to this target. Sustainability, resilience, productivity, production, environmental consideration, that is climate change, soil quality, et cetera. All these diverse aspects in one single target. Clearly, this would require that an approach that captures these different dimensions or aspect. The indicator that was submitted to IAEG SCG and was approved in March 2015, is proportion of agriculture area under productive and sustainable agriculture. The indicator is now tier two, which means that the methodology of the indicator has now been approved and endorsed in the October 2018 meeting of IAEG SCG, with further refinements in the biodiversity subindicator endorsed in November 2019. So the methodology for this indicator has been established. However, in general data is not yet available or partially available. The formula we propose to measure the indicator is very simple and straightforward. It is area under productive and sustainable agriculture divided by the agriculture land area. So let us focus on the denominator first, the agriculture land area. It is defined as arable land plus permanent crops and permanent meadows and pastures. It's a well-known and established concept that is collected by statistical bodies in countries and compiled internationally via questionnaire by FAO and disseminated regularly through FAO stack. The issue obviously is with the numerator of the formula. How do we measure area under productive and sustainable agriculture? What is clear from the description of the target, which I illustrated on the previous slide, we have to look at sustainability across all its dimension, that is economic, social and environmental. Meaning the agricultural land area under productive and sustainable agriculture will be the agriculture area of those agriculture holdings that satisfy the sustainability criteria for all the sub indicators selected across all the three dimensions of SDG 241. Here are the steps that were used in the methodological development of SDG 241. We discussed and chose the scale of assessment for SDG 241 and the choice made for 241 was to adopt a bottoms up approach whereby we selected farm or agriculture holding level sustainability that in turn is aggregated to the national level. Then we determined the scope of activities of the holding to be covered by this indicator and the choice made for 241 was to cover crops and livestock activities. We reviewed the dimension to be covered and we decided to stick to the classical dimension of sustainability that is economic, social, environmental in the sustainability assessment. Let me add here that in the beginning of the process when we embarked on the development of the indicator methodology, we selected five dimension that included in addition to the three already mentioned two other dimensions that were institutional and governance and resilience. However, later on it was decided to integrate resilience with economic, environmental and social dimension and drop the governance on institutional dimension as we were exclusively focused on farm level assessments. We then zoomed inside the dimension into what we call teams or aspect and in turn selected the sub indicators that are needed to measure the progress within each team. Then we established sustainability criteria also known as thresholds or cutoff points for each sub indicator to classify the farms and the agriculture areas it owns or operates by assigning it red, yellow or green statuses which we call the traffic light approach. Then the next decision was selection of the data collection instrument for collecting and reporting data on the indicator and the choice made for 241 was to use farm survey as a unique data collection tool. We also discussed to decide on the periodicity or frequency for collecting and reporting data on SDG 241 and it was decided that the periodicity will be set at three years and finally we discussed the modality for reporting the indicator for this we developed both a dashboard where all the 11 sub indicators or teams are presented in one chart where each sub indicator is illustrated separately by sustainability status and an aggregate SDG indicator that can be calculated directly from the dashboard. The principles that were used to develop the indicator first the policy relevant action ability. We wanted to make sure that every sub indicator selected as part of SDG 241 framework had a meaning for the policy makers and thus provided information based on which informed decisions can be taken to improve the situation at the country level. Meaning the sub indicators must be easily understood the primary reason as to why these are selected and the results easily interpreted by the policy makers. For example to answer the question is agriculture sustainability decreased and why and which policy needs to be implemented to address these issues. Secondly universality and comparability are fundamental we are in SDG process a universal process thus we needed to make sure that the indicator is valid everywhere. It must be relevant for all countries across the globe both developing and developed. Measureability and cost effectiveness were very high in our mind as we were trying to find a right balance between an ideal indicator from the subject matter perspective and one that can be measured consistently with a reasonable cost. The affordability of the indicator in terms of data collection and reporting was our top priority. And finally the minimum cross correlation amongst the sub indicators. So in selecting a limited set of themes and sub indicators efforts were made to reduce cross correlation between different sub indicators. Obviously high cross correlation between sub indicators would imply that two or more sub indicators captured the same sustainability theme. In this case the inclusion of one single indicator instead of several would be sufficient to adequately measure agriculture sustainability performances. All these decisions and choices obviously had an implication for the choice of the sub indicator for the different dimensions the choice of sustainability criteria for each sub indicator and the level of sophistication in data collection. With regard to measurement scope as we are interested in assigning agriculture areas sustainability statuses the basic unit of observation and measurement are farms or agriculture holdings with focus on those that primarily produce crops and or livestock or its mix to check as to whether these are economically feasible environment friendly and socially acceptable. So we include both intensive and extensive agriculture holdings as long as their primary activities are crops livestock or a mix of both. These may include holding that produce both food and not food products and crops grown for fodder or for energy purposes. The secondary activities are considered like aquaculture agroforestry are considered if and only if these activity take place on the agriculture area of the holding. So we consider only on holdings that produce crops and livestock on top of these if there are some minor activities which are performed by the holding those will be also part of the scope of the indicator. What is out of scope are the holdings which are exclusively focused on aquaculture and or agroforestry. We exclude from the scope production from gardens, backyards and hobby farms. Food harvested from the wild is excluded from the scope and lastly common areas or common lands that are not exclusively managed by the agriculture holding and nomadic pastoralism is also not part of the scope of the SDG indicator. The periodicity or reporting frequency of the indicator is set at three due to various considerations. First the SDG indicator 2 for 1 measure progress toward more productive and sustainable agriculture and for many sub indicators selected it is unlikely that their values will change from one to the other. Secondly the three-year data collection and reporting will enable countries to have at least three data points on the indicator before 2030. This will also in turn help them to make a historical trend to assess their performances over time and for international agencies to compare as to how countries are doing with each other or having a three-year periodicity will reduce data collection and reporting burden on the countries. As mentioned earlier in the 2 for 1 methodology is designed whereby information is collected through farm service sustainability assessments are made at the farm level and the final results are expressed as a national value. However this methodology is scale-independent and can be adopted for any geographical level though any introduction of additional stratification variables will certainly have implication for the sample size and thus the cost of data collection. So in order to further enrich the analysis for making informed national policies the indicator can be disaggregated at sub-national level and also according to different farm types as to whether the agriculture holding is a household or non-household holding, whether it is focused on crops livestock or a mix of both and whether the farm is using water to irrigate its agriculture area or not. The other stratification variables that the country may consider are size of farm and gender of the holder of the agriculture holding. Now as mentioned earlier the indicator is multi-dimensional this slide presents a table or matrix that includes everything that we need to know about this indicator. Towards the extreme left you can see that the indicator cut across the three dimensions are economic, social and environmental. Within each dimension we have a team for instance as you can see within the economic dimension we have three themes or aspects and corresponding three sub indicators that are used to measure progress within those teams. Likewise we have five themes in the environmental dimension and three in the social dimension. So in total we have 11 themes and corresponding 11 sub indicators. This decision was of course in relation to the measurability and cost effectiveness as the list of issues and themes and the sub indicators to measure and monitor sustainability is much longer that could be considered or captured however there was a feeling that capturing 11 in total was a very good step forward. One other important consideration to take note of is that we have developed a universal framework that covers the entire spectrum of agriculture confronting sustainability issues that varies from one country to another or one region to another within the same country or one type of agriculture production system to another that is household and thus not all the sub indicators are applicable to all kinds of farming system. So as you can see within the social dimension team 9 and sub indicator 9 which is decent work and the sub indicators wage rate in agriculture is only applicable to farm that higher unskilled labor. And similarly the sub indicator 10 which is food insecurity experience scale which is indicator 2.1.2 is only applicable to household farms. In addition we have a different reference period for some of the sub indicators like say for example for profitability team and the net farm income sub indicator we have a reference period of three years and so as well for prevalence of soil degradation and variation in water availability we said the reference period three years. We will explain the reference period as we move into each sub indicator separately in my next presentation. As I said the hardest choice for us was to limit the framework of SEG 241 to 11 teams and sub indicators and we have a series of expert discussions in meetings consultations literature review have shown that sustainability is so complex that in general there is a much longer list of issues that can be considered to capture sustainability in agriculture. In this slide you can see some issues that are considered important but are not captured so we still recommend countries to consider these themes and these aspects if these are relevant in their national or sub national context in order to assess their sustainability of agriculture at a national level or sub national level. One critical aspect that we will discuss in detail as part of each sub indicator in the next presentation was developing the development of a threshold or sustainability criteria that will be used to assign sustainability statuses to each agriculture holding and the agriculture area that its own operates or manage. Briefly thresholds or sustainability criteria are national policy based or international targets or science based absolute or relative values or levels below or above which for each sub indicator the farm is assigned sustainability status. So for each sub indicator criteria to assess sustainability levels have been developed. Now in order to capture the concept of continuous progress towards sustainability a traffic light approach was devised in which three sustainability levels are considered for each sub indicator. Green is considered as desirable yellow acceptable and red unsustainable. The traffic light approach acknowledges the trade-offs between the trade-offs existing between sustainability dimensions and themes and the need to find an acceptable balance between them. Each sub indicator as I already mentioned is assessed at the level of agriculture holding and thereafter the sustainability level is associated with the agriculture land area of that particular agriculture holding and then it is aggregated at the national level. Recollecting from the previous slide the reporting of SDG 241 can be done at various levels using both a dashboard approach and an aggregate indicator. Let me clarify what we require from countries is to report on the dashboard and aggregate indicator at the national level. What makes the dashboard approach more appealing is that it helps visualize the performances across the dimensions as well as across independent themes and sub indicators separately. This makes the dashboard policy relevant and actionable for policy makers as it gives them the tool to quickly check at a single glance where the major problem lies. Where to put in the emphasis what policies needs to be put in place and resources directed to address it to improve the situation. An added advantage of the dashboard is that it allows the possibility of combining data from different data sources. The final aggregate indicator 241 is derived from the dashboard at the country level and the final number is a result of the sub indicator that has recorded the highest sustainability and sustainability performance. This can be easily done either using the formula below or by looking at the dashboard and checking as to which indicators amongst the 11 the highest level of unsustainability that is the highest level of red at the country level. As you can see from this dashboard it is fairly obvious that with a single look you can see that amongst the 11 sub indicators profitability theme or net form income is the one that has recorded the highest level of unsustainability and hence the aggregate value of sg241 would be 40%. This may change over time as countries start monitoring sg241. The performances of countries over time can be measured by the change in the proportion of agriculture area that is unsustainable which is the maximum unsustainability level achieved across the 11 sub indicators shown on the previous slide and in this case an increase in the value of sg241 unsustainable or red over time will indicate further degradation while a decrease in this value will indicate improvement. We said in the beginning that policy relevance is very important consideration while we were developing the framework for sg241 and in this respect the dashboard approach is really interesting as it provides a structured and transparent framework to measure and report on sustainable agriculture it allows focus on main issues related to sustainability and encourage discussion by linking it to policy actions and lastly it drives the policy towards agricultural sustainability issues with focus on interventions at various levels and additionally it is the dashboard is easy to interpret in terms of the extent to which the country agriculture is far from being productive and sustainable and easy to prioritize identify and prioritize the area that require attention. I will stop here Perfect. So in the previous presentation as Tiffania mentioned we learned about the conceptual and methodological basis of sg241 that is its scope coverage themes, sub indicators periodicity reporting etc in this session we will go through the 11 themes and 11 sub indicators of sg241 in turn particularly focusing on the rationale for including the themes and the sub indicators the data items required to construct the sub indicators and the sustainability criteria developed to assign the agriculture holding and its agriculture land area red, green or yellow statuses. So as highlighted in my previous presentation I will just define using the simple formula which is area under productive and sustainable agriculture divided by agriculture land area now let us focus on the denominator which is based on foul land use classes a search countries have been providing national level statistics and Bolivia the relevant FAO state questionnaire to FAO very importantly the same land use classes are collected by census which automatically addresses the issue of common land in other words the agriculture census does focus on farms only just like 241 and exclude common land along the lines of sg241 so we focus on agriculture land area well established and well known concept which is derived by adding crop land and land under permanent models and pastures now for estimation of the agriculture land area and these classes we adhere to the system of environmental economic account for agriculture forestry and fisheries and world census of agriculture 2020 its standards and classification systems another important point to take note of is that the land tenor of agriculture particularly from 241 point of view the scope include the entire agriculture land area which is owned and operated by the holding or the agriculture land area which is rented in or land which is borrowed for free or occupied or common land managed by the holding exclusively now as you can see from this very simple example parcel parcel 1 which is composed of field 1 and field 2 its owned and used by agriculture holding so this will be part of the scope of 241 parcel 2 is again owned and used so this will be part of the scope of SDG 241 parcel 4 though its not owned by the agriculture holding but its rented in so it will be included as part of the scope of 241 while parcel 3 which is owned by the holding but its rented out to another agriculture holding and hence this will be excluded as for that particular agriculture holding is concerned so if you know the parcel 3 will be considered as part of the sustainability assessment of the other holding which is renting it in so I hope this is clear the indicator framework this slide illustrates once again the framework of SDG 241 the three dimensions economic environmental and social and you know the fact that some indicators are applicable only to a certain kind of form types and some indicators have different reference period and I will explain it to you as to why we are using different reference period from indicator while for other we are using last calendar year so before going into the detail of respective sub indicator let me provide you with the generic steps that will be used to estimate each sub indicator so once relevant qualitative information is collected through agriculture service and thereafter checked, cleaned validated and stored on computer as excel spreadsheets it must then be transformed into appropriate quantitative primary variables that are in turn used to construct the 11 sub indicators of 241 a set of scripts, routines and procedures typically carried out with statistical software such as STATA, SPSS or R are applied to the survey data for constructing the primary variables that will in turn be combined to construct each sub indicator so the economic dimension so the first dimension within the framework of 241 is economic of which the first the theme is land productivity and the sub indicator is from output value per hectare this indicator is applicable to other form types and when I say all form types remember in the previous presentation I showed you the different typologies of the farm okay so we have household farms and non household farms we have holdings which are focused only on crops holding only specialized in livestock related activities and holding which produce a mix of both life crops and livestock and then the third typology is as to whether this holding is using water to irrigate its agriculture land area the reference period for this sub indicator is last calendar year now let me explain as to what we mean by land productivity in context of this sub indicator as all of you may know land productivity is a measure of agriculture value of outputs obtained on a given area of land for a given period of time at farm level land productivity reflects technology and production processes for a given agro ecological condition in a broader sense an increase in the level of land productivity enables higher production per unit of land now land productivity is driven by combination of multiple factors which include climate soil quality topography, land use and its management but in addition to this the land productivity varies also in time this variability in land productivity occurs at different time scales from seasonal to interanual in response to variability in rainfall and many other weather or climate related factors in the context of 241 we use the same classical approach to estimate land productivity that is first the farm output value in local currency unit is estimated which is then divided by the agriculture land area measured in hectares and lastly the farm productivity is then compared with the farm output value of hectare of the distribution of the farms that are part of the sample so for this indicator for estimating the farm output value of hectare we have the following formula which I explained on the previous slide too so we estimate the farm output value and divided by the agriculture land area which is estimated in hectares so for us to estimate the value of output what we need is physical quantities of the products produced by the agriculture holding and the farm get prices of those agriculture products so in case of a typical agriculture holding we would collect information on five main crops and its byproducts produced in the last calendar year five main livestock raised or produced and its products produced by the holding in the last calendar year and the fact if the farm is producing other unformed products as secondary activities then its quantities and farm get prices secondly we would need agriculture land area of the farm which is the denominator of the indicator we need to categorize farms by different types which I explained on the previous slide and then we in turn will compare the given farm output to the farm by the distribution of the farm selected as part of the sample to arrive at the thresholds so let me walk you through the steps as to how we are going to do it here is an example of typical crops byproduct produced by a holding the actual list of crops and byproduct of course will vary from one country to another and from one agriculture holding to another depending on which region of the country this farm is located so this is just a mere example so what we would need is we would need the quantity of maybe two or three or four maximum five crops produced by the agriculture holding and then the byproducts of these crops produced by the holding so both the quantities and the farm get prices now this slide summarizes the list of other on-farm products and activities that the holding may be carrying out as secondary activities alongside its primary activities this list is taken from international standard industrial classification revision 4 or Isaac revision 4 now depending on whether these other on-farm activities or commodities contribute to the farm revenues if yes then it must be included in the estimation of farm output value per hectare or else if the farm is not engaged in these activities then it can be ignored now the very first step for us is to categorize farms by different types which I explained to you earlier as part of the stratification which is recommended by FAO now why we are recommending the stratification for different farm typologies the reason basically is to compare likes with likes so we want to compare same agriculture holdings with similar agriculture holdings in terms of their productivity performance so as we have three typologies as part of the recommendation which is household and non-household then we have crops, livestock and mixed and then as to whether this holding is using water for irrigation or not we arrive at 12 different combinations of farm typologies so let's say for example first typology it could be a crop producing agriculture holding in the household sector and it's using water for irrigation or it could be a household farm focused on livestock activities with irrigation available or it could be a non-lifestock a non-household farm with livestock activity with irrigation facilities so again let me reiterate the purpose behind us categorizing farms by these different typologies is to make sure that basically we compare apples with apples and oranges with oranges as an example for a given farm the output value at farm level is estimated by first multiplying the quantities of each crop livestock and its products or byproducts if available by its respective farm get prices these measures are expressed in local currency units and represents the numerator of the farm output value per hectare for that particular farm and is then divided by the farm agriculture area to estimate the land productivity for that particular farm so this one I just explained this is an example which has been taken from the pilot tests that we conducted in Bangladesh last year so here you have the crops produced by given agriculture holding so this is holding identification number and these are the different crops these are the quantity of crops these are the farm get prices just by simple multiplication of these two we arrive at the farm output value for that particular crop we add up all these to arrive at the total farm output value per hectare has been calculated for all farms mind you we have categorized farms by different types then each category of farms are ordered from lowest to highest in terms of their farm output value per hectare so we order it from lowest to highest and then we try to estimate the farm output value per hectare corresponding to the 90th percentile identified using this very simple formula ok so we will have basically different distribution for each one type and then we order the farms by productivity from lowest to highest then we identify the 90th percentile using this formula which is nothing but 0.92 total number of observations or total number of farms is part of the sample for that particular category so the 90th percentile in this case is estimated to be 600 now as I mentioned to you earlier we have established thresholds ok we have established thresholds for each sub indicator for us to assign the farm and its agricultural area sustainability status so the way we have drafted or the way we have developed the thresholds for this particular sub indicator we have two thresholds which are derived from the 90th percentile so first we multiply the 90th percentile with a 2 third to estimate the productivity which is which is 2 third from the 90th percentile and then we estimate 1 third of the 90th percentile to estimate the second threshold based on these two thresholds then we will see as to whether a given farm output value per hectare is whether above the 2 third between these two or less than the 1 third to for us to assign the green, yellow and red statuses so these two thresholds are then used to assign as I mentioned red, green and yellow sustainability statuses to the farm and the agriculture area that it owns or operates so in general the sustainability status of agriculture holding is determined depending on whether or not the farm output value per hectare is above below or between the threshold set for the category of farm to which it belongs so the green is if the farm output value per hectare is equal to or greater than the value corresponding to the 2 third of the 90th percentile estimated for the distribution of categories of farm to which this farm belongs so the farm output value per hectare is above 2 third it will be classified as green if the farm output value per hectare is between 2 third and 1 third then it will be classified as yellow and if the farm output value per hectare is less than 1 third of the 90th percentile then it will be classified as as red so this is an example again from the Bangladesh tests that we carried out back in 2018 and 19 so for each category of farm so this is the first category crop household irrigated the 90th percentile is estimated to be 600 the corresponding 2 third of the 90th percentile is 400 and 1 third of the 90th percentile is 200 and likewise we have estimated for another typology of farm and then for the third one so taking the same example from Bangladesh so the land activity for holding one is estimated to be 900 it belongs to crop household sector and irrigated the 90th percentile value from the distribution is estimated to be 600 2 third of the 90th percentile is 400 and 1 third is 200 so in this case 900 is above the 2 third of the 90th percentile hence this farm will be classified as green for the second one the productivity of the farm is estimated to be 300 the 2 third and 1 third threshold are depicted here it falls within these two values so 300 falls within 533 and 267 hence it will be classified as yellow and the third one is 200 and it is below the 1 third of the 90th percentile and hence it will be classified as red so then what we do is we aggregate the agriculture area associated with farms that were classified as green, yellow and red to estimate the proportion of agriculture area for this particular sub indicator so and that's it so let's go into the next sub indicator within the economic dimension of SDG 241 as you may be aware an important part of sustainability in agriculture is the economic liability of the farm which is driven to a larger extent by its productivity in the context of 241 profitability is measured using the net farm income that the farm is able to earn from farming operations now availability and use of information on farm economic performance measured using profitability will support better decision making both at the micro and micro levels so since performance measure drives behavior better information on performance can alter behavior and decision making by the government and producers both in the large scale commercial farming or the non household sector and medium and small scale subsistence agriculture or household farms so the dimension is economic the theme is profitability the coverage again is all farm types and the reference period is last three calendar years now I will explain to you as to why we are resorting to three calendar years instead of last calendar year which was the case with productivity sub indicator so the SDG 241 we provide two options or two approaches to countries on how to report on this sub indicator so one is a more sophisticated approach which we recommend to countries to put in place and the second one is a simpler approach or simplified approach which is based on farmers declaration about about the economic liability or profitability of his or her agriculture holding now using the sophisticated approach the more data demanding approach okay the net farm income is calculated using the formula which is given below net farm income and each variable which is which constitute the formula is described in turn so CR is total farm cash receipts including direct program payments so I will now explain direct program payments because it was one of the question so the farm cash receipts what does it mean cash receipt means you produce something and then you sell a specified quantity in the market at a certain price and and and you know those are your cash receipts because you know you receive cash and you record that now the direct program program payments which I will show you or maybe let me explain it to you directly these are the payments which are directly given by the government to the farm incentivizing him to produce you know produce more so these are payments by the government or support payment by the government to ensure a minimum level of production of a certain commodity because of it could be because of many reasons because of food security because of ensuring employment in agriculture sector so the direct program payments are direct monetary assistance given by the government to the farmers to encourage production in agriculture then we have income in kind which is yk then we have total operating expenses after rebates this includes labor cost as well depreciation and then we have value of inventory change of agriculture produce so how much was the agriculture produce in the beginning of the year how much it is at the end of the year so whether there are from storage facilities or whether if it is a livestock then what was the value of the livestock at the beginning of the year and what was the value of the livestock at the end of the year so we need to estimate the age sex profile of the livestock at the beginning of the period and at the end of the period for us to estimate the value of inventory change so this is more of sophisticated approach which is offered by FAO and this is adopted from Statistics Canada it is primarily recommended for use by countries where the financial records the farm financial records are adequately maintained on daily, weekly, monthly or yearly basis in general large scale commercial farms or non-household sector maintain detailed financial record using which the net farm income can be calculated so what we require so I explained earlier so the value of output is total farm cash receipts plus direct program payments from the government to the farmer to encourage the production plus income in kind if there is any and change in inventory so for farm cash receipts what we need is quantity into farm gate prices for crops livestock and other farm activities or products the same variables which were used to estimate the productivity for the previous sub indicator so until this point we are in sync in terms of our data with the indicator on productivity then direct program payments which I explained to you what it means income in kind it could be income which is received by the agriculture holding in terms of commodities and then value of inventory change as I explained to you we usually estimate value of inventory change for livestock producers as towards the value of the livestock at the beginning of the air how many of the livestock were bought between the air how many were sold how many were slaughtered how many got dead and then we estimate the balance at the end of the air to estimate this value of inventory change on the cost price so this is the information which is in addition to what is required by the productivity indicator so here we require labor expenses both cash wages and in kind fertilizer expenses pesticide expenses tool expenses electricity cost for feeding animal irrigation cost taxes depreciation charges and others so this sophisticated approach if someone is interested we have I have provided a link here you can simply click there and go there for you to understand all these all these variables and data items as to what does it constitute now then I mentioned that apart from the sophisticated approach which is applicable to household large scale commercial farm which keeps good financial records of their operation throughout the air we offer two simplified approaches so the first simplified approaches to be used when detailed data are not available at the farm level so this is better adopted to the small holder or the household sector in this case what we need is output quantity and farm get prices again of crops, livestock and products and byproducts whether it's marketed or self consumed irrespective then we need operating expenses including input quantity and its market prices and then output quantity and farm get prices of other and farm commodities for this option we ignore depreciation and value of inventory change so this is the first simplified option and the third option which is a second simplified option which is proposed is respond and declaration on agriculture holding profitability over the last three calendar years as to whether this holding particular holding was economically making profits in the past three years the second simplified option is basically part of the SDG indicator survey questionnaire that we will show you tomorrow which is a more straightforward way of us assessing as to whether this farm is economically viable now how we assign the green yellow and red status to a given agriculture holding so the farm is labeled as green or classified as green if its profitability is above zero for all past three consecutive years it's classified as yellow if its profitability is above zero for at least one of the past three consecutive years and it is classified as green if its profitability is below zero for all three past consecutive years so just to exemplify in this case this particular farm was profitable in two out of three years hence it is classified as yellow the farm was profitable in three out of three years and is classified as green and the farm was unprofitable or has profitability below zero in all three past consecutive years and hence it is considered as unsustainable now I understand that if you use this more sophisticated approach which is adopted from statistics Canada you will have to collect this information on early basis for three consecutive years for you to be able to assign sustainability statuses to these farms hence instead of this more sophisticated approach we recommend countries to use this second simplified option whereby an agriculture holding himself declare as to whether his holding was profitable in the past three years this is in relation to reduce the data collection burden at the country level so in terms of in terms of the data that we have collected using the Bangladesh tests what we what we do is then we combine the farms and its agriculture area which is classified as green yellow and red we divide it by the total nationally representative agriculture area to arrive at the proportion of agriculture area which is classified as green yellow and red so the third and the final sub indicator in the economic dimension is the risk mitigation mechanism is of course economic the theme is resilience the coverage is all form types and the reference period for this sub indicator is is the last calendar calendar year resilience has emerged as a key factor in sustainability resilience encompass absorptive, anticipatory and adoptive capacities and refer to the properties of the system that allow farms to deal with external shocks and stresses to persist and continue to be well functioning in the sense of providing stability predictability security and other to the agriculture so in the context of 241 we considered the following external shocks so drought flood pest attack or market shock drought as you may know is a prolonged period of normally low rainfall leading to shortages of water flood is an overflow of large amount of water beyond its normal levels especially over what is normally dry land pest a destructive insect or other animal that attacks crops food livestock EDC and then market shocks any demand or supply shocks that alter the price matching equilibrium in the market price reduction for the commodity produced by the holding or increase in input prices etc is a shock coping mechanism or mitigation strategy we we propose three features access to insurance that is preventive which is preventive protection measure to protect the holding against external shocks access to credit both from formal and informal sources such as bank relatives or local money lenders and the fact if the farm or the agriculture holding is diversified and when I say diversified it means that the share of a single agriculture commodity produced or activity carried out is not greater than 66% in the total way of production of the holding or the revenues of the holding so basically we use these three mechanisms to assess as to whether the agriculture holding is resilient in terms of managing its risk so I mentioned to you the formula for allocation is very simple all you have to do is to take the total value of production of a given commodity and divide it by total value of production of the farm if it is greater than 66% this means that the farm is reliant on a single commodity for for its revenues or for its production and hence in case of a market shock or in case of a market shock the farm revenues or production is very much susceptible because it is relying on one single source of revenue or one single source of production for its operations but if it is less than 66% this means that the farm is producing at least two commodities in proportions whereby if they are the best attack or if there is market failure for one commodity there is still another one on which the farm can rely for its to sustain itself so the way the red, yellow and green statuses are assigned to the farm and its agriculture area we assess as to whether the farm has access to you know these three mitigation mechanisms so if the farm has access to or at least two of the three mitigation mechanisms it is classified as as green if it has access to one of the three mitigation mechanism then it is classified as yellow and if the farm has access to none of these mechanisms so if it is if it is a highly specialized farm growing only one crop it has no access to credit it has no access to insurance then this means that it is not resilient enough to survive in case of market failure or any external shocks related to climate or weather so based on the results as you can see here from the table the share of single commodity and the total value of output of a farm is 76% so this means that the farm is not diversified but so you know it is given a value zero it has access to credit so it has a value one it has been checked to insurance it has a value one so two out of three the farm is considered as desirable secondly the share of a single commodity less than 66% 2 is 33% 4 is 34% so this means that the farm is relying on three agriculture commodities for its revenues or its production hence it is one it does not have access to credit or insurance only it fulfills only one mitigation criteria it is classified as acceptable and then you know the third farm for which it is you know single commodity producer farm 100% specialized in one kind of agriculture commodities it doesn't have access to any other mitigation mechanism and hence it is considered as unsustainable or read now the information for for diversification because basically we are estimating a ratio based on what is the total value what is the contribution of a single commodity within the total revenue of the farm in a given time period this is the same information which is which can be used not only for the productivity indicator and profitability indicator but for this indicator as well so the same information is used here again to provide data on this indicator the only additional information required for this indicator is to have question as to whether this farmer has access to credit or access to insurance again lastly basically what we do is we estimate the we assign farms and the agriculture area that it owns manages and operates green yellow and red statuses we divide the respective green and yellow and red by nationally representative samples to calculate these proportions we stop here for today tomorrow first thing we start with the quits related to this subject and then we move forward with the other two dimensions Asfandiar for your information we have got a lot of compliments for this session the participant really appreciated it and they said that your presentation were fantastic a lot of them were saying this so really congratulations Asfandiar Thanks to the participant for their very engaged you know I really enjoyed the interaction during this session I really enjoyed it so for me not many things would be maybe if there is still some unclarity on my behalf then please do write to us and we will be very happy to reward to you because of the shortage of time I may not have given proper explanation or maybe not detailed enough explanation and explain the point to you so please write to us I mean we will happily reward to you We have also other questions as I mentioned before Asfandiar from the registration but I mean we will analyze all of them later and we will answer at the right moment Thank you all of you for this first day and we look forward to meet again you tomorrow same time Have a nice rest of the day Bye-bye to all


For this example, again, we have two machines. We'll assume machine A has a single core processor, and machine B has a quad core processor, and we're going to look at how that processor affects a set of programs. And this set of programs has a workload that varies from 100% on the CPU to 100% on the memory. While chances are that doesn't actually mimic any real-world programs, we'll allow us to see how a multi-core processor affects the execution time of a problem, depending on what's available for the processor to parallelize. So our multi-core processor isn't going to be able to affect our memory operations. Those won't get any faster just by having multiple cores, but it will be able to affect our CPU. And for this exercise, we will simply assume that all of our CPU instructions are perfectly parallelizable, and we don't have to worry about any of them stalling and waiting for other instructions to complete. So in the first case, machine A spends 100% of its time on the CPU instructions. And I've got a single core processor, so it runs all of those in serial, and then spends 0% of its time on the memory. Machine B is still spending 100% of its time on those CPU instructions, but those are going to run faster because it's got the quad-core processor instead of the single-core processor. So we know that the quad-core processor will finish those instructions in one-fourth as much time as the single-core processor did. And accordingly, the machine B is still spending 0% of its time on the memory. So now I have 25%, which tells me that machine B requires 25% as much time to accomplish the same amount of work that machine A did. Alternatively, I can put them into a relative performance ratio, and I can see that machine B is four times faster than machine A. So with program number two, we're spending 75% of the operations involve the CPU and the other 25% involve memory. So again, machine A doesn't see any speed up because we're using it as the baseline. Machine B takes one-fourth as much time to run the CPU instructions because it's got four processors to run those instructions. Machine A spends 25% of its time running the memory instructions. Again, for a total of 100% of the time, machine B also spent that same amount of time running the memory instructions. But now I get a different result. So here I have three-fourths times one-fourth, which will give me three-sixteenths and one-fourth or four-sixteenths. So this will give me seven-sixteenths. So it takes machine B a little less than half as much time to run this task than it took machine A. Or again, I can put this into a relative performance ratio and say that machine B is 16-sevenths times faster, so slightly more than two times faster than machine A. Again, the same thing for number three. But this time our ratios of CPU to memory have changed again. So 50% of the operations are CPU operations. And again, machine B spends one-quarter as much time as machine A did on those. And the other 50% of the operations are memory operations. And increasing the number of CPUs doesn't affect those. So this time I have 50% plus one-eighth. Gives me five-eighths. Or I could say that machine B is eight-fifths times faster than machine A. So machine B is still faster than machine A for this task, but it's by an increasingly smaller amount. We've gone from four times faster to a little over two times faster to 1.6 times faster. So I've moved our results from parts one through three up here to make room for questions four and five. So for part four, machine A is again the same sort of thing. This time it spends 25%. It's time running the CPU instructions and the other 75% running the memory instructions. So it's still spending 100% of its time on the task. Machine B has that same 25% of the instructions. And again, they still take one-fourth as much time on machine B. And again, the quad-core processor doesn't affect the execution time of the memory instructions. So I add this together. I've got three-fourths and one-sixteenth, which gives me 13-sixteenths. Or I can say that machine B is 16-sixteenths times faster than machine A. And lastly for number five, machine A spends none of its time running CPU instructions and all of its time running memory instructions, where machine B runs that same 0% of CPU instructions but runs them in one-fourth as much time and spends the remaining 100% of its time running the memory instructions and ends up taking just as much time as machine A did. So once none of our instructions are affected by parallelization then the multi-core processor doesn't actually affect the execution time. It's just as long to solve this problem on machine B as it did on machine A. So as the CPU load of the program decreases, the benefit that we get from using machine B decreases accordingly. We'll see some more with the next topic when we're looking at Amdahl's Law where he starts seeing limits to how much improvement we can get by just focusing on one part.