New needs and training modalities for the sustainable transfer of know-how on food and agriculture statistics in the COVID-19 era 1

2.1 FAO new approach to capacity development

Since its foundation, FAO has been supporting Member Countries in strengthening their agricultural statistical systems and improving the skills of official statisticians, with capacity development having always been at the core of its interventions worldwide. Over time, FAO has enhanced its capacity development strategy to reach a more sustainable impact, implement the most advanced and up-to-date practices in the international development community and adapt to the changing needs of countries. FAO’s 2010 capacity development strategy[1] is underpinned by a series of principles inspired by the international debate on aid effectiveness, which is grounded in the Paris Declaration on Aid Effectiveness and the Accra Agenda for Action[2].

To make its capacity development approach more effective and sustainable, FAO has gradually shifted its focus from being the direct provider of “ad-hoc” training interventions to organizing programmes for transferring technical skills and know-how, playing a facilitating role, enabling coordination and collaboration, connecting actors and stakeholders, supporting national processes, and also targeting functional competencies, all to achieve longer-term impacts. Functional competencies include capacities relevant to individual and organizational effectiveness, such as leadership, advocacy and strategic planning, which are crucial for national decision makers and local actors to make the transformations needed towards longer term and sustainable outcomes. Functional competencies are a necessary complement to technical capacities, as they empower national actors such as Heads of National Statistics Offices (or other staff with managerial oversight) to effectively apply the new knowledge/skills and maximize the overall impact of the interventions. In this way, FAO’s new approach to capacity development aims to strike a better balance between technical and functional competencies, as both are crucial for ensuring that countries’capacities are enhanced in a sustainable way.

2.2 Integration of training and technical assistance

Training has been and will undoubtedly continue to be a fundamental pillar of a well-rounded capacity development programme in any organization. Nonetheless, despite improving and evolving over time, training still needs to be integrated with other types of capacity development in order to produce maximum impact. For instance, a common distinction is that between training and technical assistance. Whereas training is critical for imparting basic knowledge, getting participants to a basic level of understanding and forging common objectives and a common language, technical assistance tackles specific issues with a problem-solving approach, offering an opportunity to put theoretical knowledge into practice, test solutions and verify whether they are appropriate for the problem at hand.

The distinction between training and technical assistance is sometimes mistakenly understood to mean that the two approaches offer alternative pathways, when in fact they are complementary and mutually reinforcing. For example, training is often a prerequisite for implementing more hands-on activities, and therefore technical assistance is of little value unless recipients have already been armed with a basic understanding of the problem area. Conversely, the directness, flexibility and ability to cater for highly customized requirements, allows technical assistance to build on previous trainings by putting theory into practice, while overcoming some of the common limitations of training, such as the fact that it does not always convene the right people and at the right moment. Therefore, training and technical assistance are complementary.

2.3 Principles of the new FAO capacity development strategy

FAO capacity development interventions are now guided by the following principles[3]:

country-driven: provided only at the country’s request and designed collaboratively to ensure country ownership and leadership, for longer-term outcomes.

All these general principles apply equally to statistical capacity development and to statistical training, in particular. For instance, statistical trainings no longer focus preponderantly on technical competencies, but also on functional competencies that are useful for both technical staff, managerial staff and policy-makers, such as leadership, interpersonal communication, professional development, quality management and similar competencies.

2.4 Collaboration with training institutions on official statistics

Building partnerships and collaboration with training institutions and academic programmes is fundamental for broadening training in food and agriculture statistics. To this end, FAO actively collaborates with Regional Statistical Training Institutes, participates in the Global Network of Institutions for Statistical Training (GIST), and facilitates the mainstreaming of food and agricultural statistical training in academic programmes as demonstrated in the two concrete examples below.

3.1 New training needs arising from the SDGs

The global SDG indicator framework is the foundation of the 2030 Agenda’s follow-up and review process. It comprises 232 unique SDG indicators, whose purpose is to monitor progress towards the 169 SDG targets. Data submitted by national statistical institutions are validated and analyzed on an annual basis by the respective custodian agencies that prepare storylines for each indicator, which form the basic building blocks of the global SDG Progress Report. This Report, in turn, is the main document informing the annual High-Level Political Forum (HLPF), which is responsible for the follow-up and review of the 2030 Agenda.

The novelties of the global SDG indicator framework, particularly with respect to its predecessor the Millennium Development Goals (MDG) indicator framework, have been well documented[10]. However, the question of how the novel nature and expanded scope of the SDG indicator framework has impacted training needs has not been thoroughly examined. The first key distinction between the MDG indicators and SDG indicators is the sheer size of the current framework compared to its predecessor. The 232 unique indicators dwarf the roughly 60 MDG indicators, outnumbering them almost four to one. In addition, a staggering proportion of the SDG indicators were initially completely new, and addressing new statistical domains not traditionally covered by official statistics. As a result, almost two fifths of the SDG indicators were initially classified in the “Tier III category”[11], not yet having an internally recognized methodology for their compilation. In the case of the SDG indicators under FAO custodianship, the proportion of indicators initially categorized as Tier III indicators was even higher, i.e. over 60 percent (13 out of 21).

The implications of these two changes alone on the training needs of countries are difficult to overstate. Countries were already struggling to produce many of the 60 MDG indicators (for instance, almost no country itself had been able to produce MDG indicator 1.9 on the “Proportion of population below minimum level of dietary energy consumption”), when suddenly they were confronted with an additional 88 Tier III indicators whose methodology was still in the making and which eventually they would have to become not only familiar with, but also able to implement in practice.

While all remaining Tier III indicators were effectively upgraded by the 2020 Comprehensive Review[12], after an arduous process of methodological development and approval under the aegis of the IAEG-SDG[13], the unmet training needs of countries are still manifest today (June 2021), when over 40 percent (97 out of 232) of global SDG indicators remain in the Tier II category, meaning that the majority of countries are still unable to produce them. From another perspective, for 9 out of 17 Goals, the proportion of countries and territories with available data is 60 percent or less[14]. In such cases, by definition it is difficult or often impossible to calculate regional and global aggregates and thus properly monitor progress at global or regional level.

Aside from the quantitative and qualitative leap represented by the expanded number of SDG indicators, in general, and the large proportion of them beginning their life with a non-established methodology, another key structural change with respect to the MDGs is the fact that the SDG indicators require the involvement of a wider spectrum of data providers – even entities that may not have hitherto been considered part of the National Statistical System (NSS). This requires the NSO to play a leading role in coordinating the NSS and in ensuring that data provided by other entities comply with national and international quality standards. Most of the NSOs in the world are not prepared to provide data stewardship to the many data producers within the NSS, and need to be trained to perform these important new functions.

3.2 New training needs due to the COVID-19 outbreak

The COVID-19 outbreak at the beginning of 2020 has had a major impact not only on the socio-economic fabric of countries around the world, but also on the activities of the global statistical community. In particular, the COVID-19 outbreak has affected statistical activities at international levels in many ways:

Social distancing measures and lockdowns have caused a disruption in data sproduction at country level. In several countries, most ongoing or planned data collections such as surveys and censuses have been put on hold. It is difficult to predict when these activities will resume, but it is very likely that official national data on many SDG indicators, including those under FAO custodianship, as well as basic statistics, will be available with a delay, or not be available at all for 2020 and 2021 in many countries.

Of course, there are big disparities by income group in the capacity of countries to cope with the impact of COVID relying on their own resources. According to the UNSD/WB NSO assessments of COVID impact [16] conducted during 2020 and reproduced in Fig. 1 below, only few high-income countries were in need of any kind of support, while two-thirds or more of upper middle-income countries reported that they required technical assistance, training and financial support. In particular, 80 percent of low and lower middle income countries; 78 percent of upper middle income countries, and 23 of high income countries indicated that more training is needed. For low and lower-middle income countries, most voiced a stronger need for every type of support.

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The surveys also highlighted that only 58 percent of NSOs indicated that the office has adequate ICT facilities for remote training of staff and enumerators. In spite of these infrastructural issues, remote training of NSO staff has become widespread in countries of all income levels, although low and lower-middle income countries lagged somewhat behind.

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Most UN Agencies have immediately organized a series of interventions to mitigate the most unsettling effects of the pandemic on the statistical programmes and activities at country-level, including the provision of financial support, equipment and infrastructure support, technical assistance and training. As can be seen in Fig. 2 below, generally speaking over 40 percent of NSOs reported that their staff participated in remote trainings amidst the COVID-19 pandemic, with the highest concentration of these trainings focused on data collection. Trainings on data analysis, processing and dissemination also saw a high attendance, with some variation across countries of different income groups.

FAO has developed an integrated set of complementary actions in response to these challenges:

In order to evaluate the impact of the outbreak on food security/food access and their causes, FAO has planned to conduct repeated, rapid assessments of food insecurity using the Food Insecurity Experience Scale (FIES). The FIES[17] is the basis of one of the official indicators to monitor SDG target 2.1, i.e. SDG indicator 2.1.2 (prevalence of moderate or severe food insecurity). The FIES module has been adapted to capture the impact of the COVID-19 pandemic on households’ability to access food, slightly modifying the reference period and wording of the FIES questions to make them more effective in monitoring food insecurity trends in relation to the pandemic. Moreover, specific questions have been added in order to isolate the impact of COVID-19 from that of other concomitant causes of households’ inability to access food. Several countries have already contacted FAO requesting remote training and technical support to adapt their FIES module to assess the impact of the COVID-19 pandemic and make it fit for an emergency situation, as well as for using alternative modalities of data collection (phone or web interviews).

Other solutions could, however, be envisaged and tested: i) substantially simplifying existing questionnaires that could be administered through simplified phone interviews for household-based farms and through web applications for the non-household sector; ii) applying innovative methods for reducing the sample size in order to limit the number of face-to-face interviews, while maintaining sufficient levels of representativeness of the sample; iii) developing specific protocols for remotely organising efficient training of supervisors/enumerators; and v) analysing the possibility of using unexplored existing national data sources. All these solutions would of course imply a minimum availability of conditions of accessibility and communication at country level. The guidelines prepared on the basis of the literature review and case studies in pilot countries will be the basis for organizing training programmes and providing technical support to countries in the use of alternative data collection modes and data sources.

The traditional educational background of professional statisticians may no longer be sufficient to deal effectively and proficiently with the new types of data sources and the new analytical and computational requirements that should be essential components of the skill-set of a contemporary statistician.

Firstly, Big data are becoming pervasive in today’s society, with commercial entities, government agencies and academic institutions all seeming progressively interested in capturing and analyzing them. Big data are not usually collected through planned processes and consequently cannot be the object of traditional data handling approaches. In order to analyse Big data, it is necessary to explore large, complex and seemingly unrelated sets of raw data, looking for significant correlations and new and unanticipated connections among them, using artificial intelligence (AI)-driven data analytics platforms, powerful enough to handle data sets of huge dimensions. Machine learning, which is an automated process that extracts data patterns using AI algorithms, is the way in which Big data are converted into a new type of knowledge. Big data analytics can be defined as the iterative and exploratory process of using advanced technologies and analytical techniques to reveal critical information, such as hidden and/or meaningful data patterns, trends and associations. Moreover, the need to integrate different data sources, the widespread use of geospatial information, and the growing application of new technologies, such as cloud computing, are all developments that have led to the emergence and rapid propagation of a new job profile, the “data scientist”. Data science involves the computational aspects of carrying out a complete data analysis, including acquisition, management, manipulation, visualization, and transformation of Big data into usable information and knowledge. This typically involves some mixture of statistics and large-scale computing. More and more universities today are incorporating data science into their statistics curricula, providing an opportunity for students to learn a new approach to statistical thinking, practice and computation[19].

These kinds of professionals are still generally rare in National Statistical Offices, especially those of developing countries. Their skill-set is also almost impossible to acquire solely through short-term training courses or training-on-the-job. Possible solutions are therefore for NSOs to recruit a higher proportion of data scientists – properly trained in accredited Universities - or to encourage young staff to follow post-graduate programmes in this area. Eventually, when these new kinds of professionals have reached a critical mass within the organization, the most efficient way of employing their skills is to create mixed units that combine traditional statisticians and data scientists to facilitate the application of traditional and new approaches for addressing the statistical challenges faced by the organization from different perspectives.

4.1 The 2019 FAO SDG Capacity Assessment: Analysis of training needs

The Statistical Capacity Assessment for the FAO-relevant SDG indicators was conducted to provide insights about the strengths and weaknesses of member countries’ national statistical systems regarding their capacity to monitor and report the 21 SDG indicators under FAO custodianship. By the closing date of February 2020, 114 countries had answered the questionnaire, yielding a global response rate of 58 percent[20].

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The Assessment aimed to provide insights about the strengths and weaknesses of the national statistical systems worldwide, concerning their capacity to monitor and report the SDG indicators under FAO custodianship, with the underlying intent of assisting FAO in designing more targeted interventions and mobilizing resources to support countries in producing the relevant SDG indicators.

The survey questionnaire was organized in various sections to collect information on the national coordination mechanisms for SDG reporting, current food and agriculture data availability, plans for filling data gaps, and needs for technical and financial support. The target respondents were the National Coordinator for SDG Monitoring or the SDG focal point nominated by the Director General of the National Statistics Office in each country.

Overall, the survey results showed that the majority of countries do not regularly conduct most of the key data collection activities that provide the main data sources to compile the SDG indicators related to food and agriculture. In addition, most data collection activities not carried out recently are not generally planned to be conducted in the near future. As a result, the expectation is that these countries will not be able to compile many SDG indicators that primarily rely on such data in the short and medium-term. It is important to highlight that this situation, assessed before the onset of the COVID-19 pandemic, can only have deteriorated due to the lockdowns and related restrictions imposed by the pandemic, as documented in the 2020 UNSD/WB survey of NSOs examined in the previous section.

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Another key finding of the 2019 FAO assessment was that most countries seem to have established the required mechanisms for coordinating SDG monitoring and reporting, yet many were still not able to identify an indicator-specific national focal point for most of the SDG indicators under FAO custodianship. This is a major impediment to effective reporting that explains many of the existing data gaps, as well as certain discrepancies between the data available in the global SDG database and the information reported by countries. The absence of indicator-specific focal points not only suggests shortcomings in national coordination mechanisms, but, crucially, hampers the potential effectiveness of training programmes. In this scenario, countries will typically nominate an ad-hoc participant to a training initiative organized by a custodian agency, who is not necessarily the person that will continue to be responsible for coordinating the national reporting of the indicator in the future, therefore potentially underutilizing the knowledge gained.

The final critical finding of the assessment was that 75 percent of countries reported that they would require some form of assistance to produce SDG indicators under FAO custodianship. This is not all too surprising considering that the statistical capacity of developing countries is typically constrained by the low availability of resources (internal or external), and therefore depends substantially on external capacity development support (both training and technical assistance) to maintain currently used data sources, enhance the quantity and quality of data required for the publication of SDG indicators, and strengthen inter-institutional coordination. The dependency on external assistance and additional resources is an important indication of the capacity of countries’statistical systems. Figure 3 shows that in general, at least 40 percent of the countries requested support for producing and compiling a given SDG indicator. This figure exceeds 55 percent in certain cases.

One evident limitation of the 2019 FAO Statistical Capacity Assessment is the fact that the questionnaire did not distinguish the principal modality of capacity development, i.e. training and/or technical assistance. On the contrary, relevant questions only explicitly mentioned technical assistance rather than training, to the extent that, in hindsight, technical assistance was to some degree conflated with capacity development in the broader sense.

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Despite this, if we accept the premise that questions regarding technical assistance may have probed into broader capacity development needs that included training, it may be possible to infer training needs more specifically from the specific type of assistance requested. Figure 4, for instance, shows the number of requests for assistance for producing and/or compiling the SDG indicators under FAO custodianship, disaggregated by type of assistance. As can be seen, assistance on the methodology for compiling the indicator gathered the highest overall number of requests, which is revealing because this is precisely the type of support, within the typology of Fig. 4, which is best served by a focused training programme rather than technical assistance missions, according to what has been explained in section I.b above. Another type of assistance within the typology of Fig. 4 that could also potentially benefit from training is the analysis and interpretation of the indicator, which comes in third place based on the number of requests. By contrast, developing new surveys or other data sources, adapting existing sources as well as fostering institutional coordination on data reporting, are all activities that are more effectively supported by a technical assistance stricto sensu, in which an FAO officer can work side-by-side with the national counterpart and flesh out the most appropriate solution.

Following the same typology of capacity development needs, and the premise that training would be a more effective delivery modality for supporting countries with the methodology for compiling the indicators and with the analysis and interpretation of the indicator, it is also possible to distinguish the intensity of training needs across continents. Figure 5 reveals some differences regarding the type of assistance requested across continents. In Africa and Oceania, for instance, the combined requests for methodological support and support on the analysis and interpretation of the indicator account for over 50 percent of requests overall, pointing to a key role for training in the continent. In the Americas and Asia, the same proportion is 46 percent, whereas in Europe it is only 38 percent, indicating a relatively smaller role for training in this region compared to technical assistance.

In the absence of specific questions distinguishing between training and technical assistance needs, it is also possible to approximate this distinction based on a different typology of support needs, this time by phase of data collection. These phases are broadly based on the Generic Statistical Business Process Model (GSBPM). Figure 6 shows which phases of data collection processes require the most/least support.

The question then becomes which of these phases is more amenable to training and which to technical assistance. In our view, training would be most effective as a capacity development modality in the early preparatory phases as well as late phases in the GSBPM cycle, whereas technical assistance would be most appropriate for the intermediate phases that require a more hands-on approach. Therefore, we see training as being more effective for supporting countries in the specification of needs, design, analysis, dissemination, archiving and evaluation phases, whereas technical assistance as more appropriate for the process, building and collection phases. With this admittedly somehow coarse distinction, the overall number of requests for support by countries that could be addressed through training slightly exceeds the number of requests that could be addressed via technical assistance.

All these result are particularly useful for planning purposes, as capacity development programmes can be better tailored with regard to their scope, modality and regional preferences.

Before developing the 2019 Statistical Capacity Assessment, FAO carefully reviewed other recent studies of statistical capacity assessment to determine the degree to which some information could already be derived therefrom, and therefore reduce the extent of FAO’s questionnaire accordingly, in the interest of relieving countries’response burden. One could broadly distinguish between two main types of assessments:

On the one hand, there are general “outcome-level” assessments that are meant to monitor the results of capacity development efforts, and therefore can provide some evidence of unmet training and/or technical assistance needs. Such assessments include the World Bank’s Statistical Capacity Indicator Dashboard[21], the Open Data Watch’s Open Data Inventory[22], as well as the still-to-be developed SDG indicator 17.18.1 “Statistical capacity indicator for Sustainable Development Goals”. Such type of outcome-level assessment can be useful in performing comparisons across countries and therefore in selecting countries to be targeted for capacity development initiatives. However, they are less suitable for clarifying the precise type of support that is appropriate for a given capacity gap. On the other hand, there are “input-level” assessments, which seek not only to provide aggregate scores and country rankings, but also to elucidate the underlying drivers and factors constraining the capacity of national statistical systems to produce data. Like the aforementioned FAO 2019 Statistical Capacity Assessment, such assessments may be able to indicate not only the unmet capacity development needs in general, but also those needs that are better served by training compared to other types of support. Such assessments include the African Development Bank’s (AfDB) “Country Assessment of Agricultural Statistical Systems in Africa”[23], ESCAP’s 2017 “Report on the Capacity Screening of Economic Statistics in Asia and the Pacific”[24], PARIS21’s “Measuring Statistical Capacity Development” reports[25], as well as the UN Statistical Division’s “Assessment of the Statistical Capacity of National Statistical Systems to Compile Global SDG Indicators”[26].

Despite the number of such assessments, none of them seemed to be sufficiently focused on agriculture- and food-related statistics and/or SDG indicators. They are too generic or not comprehensive enough to extract lessons about the compilation the FAO-relevant SDG indicators. On the other hand, meaningful results can only be extracted with a large country coverage, as well as consistent information coming from various data sources. Bearing these two considerations in mind, FAO deemed it necessary to develop a standard questionnaire addressed to all member countries in order to ensure international comparability.

The downside of this approach is that the reporting burden on countries continues to increase instead of decrease, which may provoke criticism of international organizations. Clearly, there is a need to reconsider the approach whereby individual international organizations undertake their own statistical capacity assessments, likely increasing the burden of respondent countries and international organizations themselves. There are also cases where assessments are carried out, but their results are not published or not published in a way that can be informative to other organizations, further limiting their potential value.

Whereas a capacity assessment will inevitably require the gathering of information specific for each international organization, there is a lot of information that could be common for all or almost all agencies. Specific information includes qualitative information on the national statistical infrastructure, fleshed out in a way that sheds light on the real capabilities of the countries in implementing the specific indicators, with reference to specific definitions, classifications, standards and data sources. By contrast, there is a plethora of general information that could be useful for all agencies, including information on the main census/survey instruments in place, how they are organized and what is their frequency, the structure of the National Statistical System, the role of the National Statistical Office, the presence of an SDG reporting coordination body and NSO SDG focal point.

All this points to the need for a much more coordinated approach among international organizations in carrying out statistical capacity assessments. One straightforward solution would be to determine a common core questionnaire that would be administered with an agreed regularity (for example every three years), to be complemented by a system of rotating modules that would collect agency-specific information at specific intervals. The common core questionnaire will itself greatly reduce the reporting burden on countries, whereas the rotating modules sent at specific intervals will further accommodate countries by ensuring that a large number of similar requests are not addressed to National Statistical Systems during the same reporting period. In the past, FAO presented this approach to the Global Network of Institutes for Statistical Training, but unfortunately it did not gain enough traction at the time. We also note that the UN Statistical Division’s statistical capacity assessment to some extent approximates the requirements of the common core assessment described above, although it is probably too generic to serve this purpose in its current format and would therefore require to be further adapted.

5.1 Combination of various training modalities and tools

New complex global challenges, such as the COVID-19 pandemic, all call for innovative learning methodologies and delivery solutions, involving multiple stakeholders within and across countries. These include experience-sharing events and live interviews, self-paced e-learning courses, mobile responsive programmes, technical webinars and online tutored blended learning programmes, that are relevant – now more than ever – to maximize statistical capacity development support, foster countries’ ownership, and ensure large-scale uptake and impact. In essence, it is the combination of different learning modalities, appropriately blended into a coherent learning programme, which is likely to have the greatest impact, with one modality reinforcing the strengths and dampening the limitations of another.

The strategic choice of FAO in the specific selection of innovative learning solutions is based on each solution’s advantages in: 1) reaching a wider target audience and giving access to training to geographically dispersed communities, 2) having a greater impact, bearing in mind that training can be made available to anyone, anywhere and at any time and can thus be integrated in a “blended” manner, in combination with other methods; and 3) having a greater return on investment, allowing to reach thousands of individuals throughout the world therefore, resulting in a highly cost-effective method.

A key training tool that responds to all the three criteria above is the e-learning course[27]. E-learning materials provide “just in time” resources suitable for both self-study and on-the-job training, where individuals learn at their own pace, anywhere and anytime. This can be a particularly useful attribute, for example, in conflict and post-conflict zones, where accessing decision-makers and other stakeholders raises even greater challenges. In addition, e-learning resources can be easily integrated with other training modalities. Regarding the return on investment, developing e-learning programmes is indeed more expensive than preparing classroom materials or organizing training-of-trainers’events – especially when multimedia or highly interactive methods are adopted. However, this initial cost is more than offset by the substantially lower delivery costs for e-learning as compared to traditional face-to-face trainings, which require printing of materials, instructor time, and participants travel and job time lost to attend classroom sessions. In addition, while traditional methods can reach a very limited number of individuals per year, e-learning can reach a theoretically unlimited number of individuals throughout the world, therefore providing a highly cost-effective method.

For the above-mentioned reasons, FAO has designed and developed multilingual SDG Indicators e-learning courses, offered free of charge as global public goods through the FAO e-learning Academy (e-learning.fao.org), both to contribute to the implementation of the SDG 2030 Agenda and to concretely support member countries in the collection, analysis, monitoring and reporting of the SDG indicators under FAO custodianship. These courses (see the complete list in Annex 1) explain the relevance of the SDG indicator for the SDG target, describe step-by-step the compilation methodology agreed at international level, and guide countries in the collection analysis, interpretation, monitoring and reporting of each SDG indicator, under FAO custodianship.

The content of the SDG Indicators courses is gender-inclusive, culturally sensitive, engaging, and rich in interactive elements, examples, relevant case-studies, step-by-step walk-throughs, guided tutorials, hands-on activities and interactive case-based scenarios. The content is developed following a logical learning sequence that is aligned with current state-of-the-art adult learning methodologies, theories and strategies, thus offering a learning experience with a clear purpose, with realistic objectives, measurable outcomes, and a learning pathway that matches the targeted professional profiles.

As mentioned above, a key advantage of e-learning resources is that they are easily integrated with other training modalities[28], primarily through the following mechanisms: i) asynchronous self-paced e-learning for Internet delivery and downloadable format; ii) synchronous Internet-based e-learning courses led by subject matter experts (tutors) and facilitators and iii) asynchronous self-paced e-learning and face-to-face training. As such, FAO is developing a number of targeted capacity development activities by combining and integrating the SDG Indicator e-learning courses. In some instances, in order to ensure the same level of basic knowledge and skills, participants in training events have been requested to take the online self-paced e-learning courses in advance, as a pre-requisite for their participation in a training event, therefore offering a combination of online and face-to-face approaches. In another instances, the self-paced e-learning courses are integrated with online blended learning programmes, specific peer-to-peer activities and assignment, online topic-based discussions, Q&A fora and live sessions. Such blended learning programmes increase experience sharing and social interaction between participants, while framing the challenges to the national context. They have therefore proven to be very successful in providing an individualized learning experience and in supporting national processes.

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A third example are online technical webinars based on the FAO e-learning courses. These live sessions have proven particularly successful in providing “just-in-time” training opportunities, once again based on a combination between live events and online self-paced courses. Furthermore, a growing number of academic institutions have included the FAO SDG indicator e-learning courses in their curricula. In particular, the Universities of Buenos Aires (Argentina) and Parma (Italy) have integrated the FAO e-learning courses in the programme of their joint International Master in Food Technology (MITA), thus offering a new combination of training methodologies merging formal university education and online self-paced courses.

In order to respond to the specific learning needs and maximize the impact of training initiatives, there are a number of good practices and lessons learnt that should be taken into account to the extent possible. Firstly, the training intervention should be tailored and targeted to country-specific needs. A good way of doing this is by conducting multi-stakeholder and multi-disciplinary learning needs assessments, in close collaboration with the target audience groups; collaboratively designing the learning intervention based on the country needs and developing the content capitalizing on local expertise, involving country institutions in the design, development and delivery. It is also useful to deliver training interventions in the local language or benefiting from the support of a local institution for the translation, as well as incorporating both local specificities and challenges, as well as regional and global perspectives.

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The current COVID-19 context has also highlighted the usefulness of short, mobile-compatible and responsive courses that allow for “learning-on-the-go”, anytime and anywhere, providing a much-coveted flexibility to in-service professionals who wish to upgrade their competences. For similar reasons, offering an offline, downloadable version of the courses, to enable access even in low bandwidth and poor-connectivity areas, should become standard practice across international organizations.

Last but not least, when it comes to organizing live events – whether virtual or physical – the importance of correctly timing the intervention with respect to the practical implementation or bottleneck at hand, cannot be overemphasized. To some extent, this issue is related to the concept of supply versus demand-driven interventions. In the past, supply-driven interventions were not only based on what was available, rather than on what was really needed by countries, but they also paid little regard to whether the training was appropriately timed with respect to the data cycle of each country. For instance, the impact of a training on new census of agriculture guidelines would be incomparably higher if the training took place during the early preparatory phases of a new census, rather than five years before the next census is planned. This is therefore another critical dimension to consider in the planning and tailoring of training interventions.

Up to now, the cumulative total number of learners of the FAO SDG Indicator e-learning courses is 21,300, of which one fifth are learners of the general introductory course, and the other four fifths are learners of different SDG indicator courses, as shown in the graph below. The e-learning course on SDG indicator 2.1.2 – Prevalence of Moderate and Severe Food Insecurity – appears to be the most sought after (see Fig. 7), whereas the course on 12.3.1.a – Food Loss Index – appears to have gathered the fewest learners. However, a proper comparison between the number of learners across SDG indicators should also take into account the different release dates of each course, available in Annex 1. For instance, the e-learning course on 12.3.1.a was released a full two-and-a-half years after the course on 2.1.2, owing to the much later date at which the methodology of 12.3.1.a was definitively approved by the IAEG-SDG.

Knowledge assessment tests are used consistently throughout the FAO SDG-indicator e-learning courses to reinforce learning, increase active participation, increase retention and memorization of concepts ad principles, increase interactivity and provide learners with an opportunity to practice their understanding of the indicators and their interpretation. As such, the courses now include short tests at defined intervals throughout the course, as well as a final general evaluation. Passing the final scenario-based performance evaluation with a score of 75 percent or higher provides learners with an official certification granted by FAO through the Digital Badges system, which certifies the acquisition of competencies that match the professional profiles required by countries. The chart below illustrates the number of such Badges hitherto issued across the different SDG indicator e-learning courses. These numbers should also be qualified taking into consideration not only the different release dates of the courses themselves, but also the fact that the Badge certification system was in many cases introduced after the initial release date. For example, while the course on SDG indicator 2.4.1 appears to have the greatest number of certified learners, the proportion of certified learners out of the total number of students is greater for SDG indicator 14.b.1, given that this course has fewer overall learners.

In addition, the first pie chart below illustrates the geographic distribution of the learners of the FAO SDG indicator courses. As can be seen, the greatest share of learners are from Africa (31%), followed by Asia (25%), Europe (23%), North and South America (both 10%) and Oceania (1%). The second pie chart below illustrates the institutional background of learners. Learners from academia (22%) and from government institutions (20%) account for the largest share of learners, followed by an encouraging share of 18% for private individuals without any particular organizational affiliation. Learners from international organizations (16%), non-governmental organizations (12%), the private sector (6%) and other types of institutions (6%) account for the remaining share of users of the FAO SDG indicator e-learning courses. The varied geographical and institutional origin of learners attests not only to the ability of FAO’s SDG indicator e-learning courses to reach a broader audience, but also to the wider appeal of the courses well beyond their primary intended target group of government officials.

5.4 Training targeted to different users of statistics

Because statistics are only relevant if they are widely used, an integrated statistical capacity development programme should also aim to build not only the technical and function capacities of data producers, but also the data literacy of data users. This is an area that is generally more difficult to tackle given the variety of possible data users and the level of investment required to build data processing and analytical skills among all potential users.

For this reason, training that targets data users tends to focus on building the knowledge of the various user segments on the statistical products that are more relevant to their needs. For advanced data users who are already skilled in quantitative analysis and modelling (e.g. researchers), training focuses on promoting and facilitating access to micro-level data assets. To this end, in October 2020 FAO conducted a webinar targeting research centers and academia to promote its Food and Agriculture Microdata Catalogue (FAM) and train participants on how to use it. Launched in 2019, the FAM now has an inventory of over 950 micro datasets, collected through farm and household surveys, which contain information related to agriculture, food security, and nutrition.

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This webinar significantly increased the number of users requesting access to the FAM, as well as the number of papers produced based on FAM data. FAO also increasingly works with national statistical offices to build their own capacity to disseminate their micro-data assets and engage with advanced users to train them on using these data assets.

For more intermediated data users, who are generally more interested in accessing existing indicators, data visualizations, tables and statistics-derived analytical products, training is generally part of the capacity development activities aiming at support users in customizing their own tables or reports (e.g. policy or programme analysts, monitoring and evaluation specialists, data repackaging specialists). For example, all FAO SDG indicator e-learning courses have a component meant to train data users on interpreting the indicators and using them for decision-making.

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In addition, all technical support activities provided to national statistical agencies on data production (e.g. conduct of agriculture surveys or censuses, food loss assessments, production of food security indicators…) now include a training component to assist statistical agencies in better communicating results and products to data users. In some cases, there may even be targeted training sessions with data users to discuss these products and how they can be used in decision-making process. Moreover, some training and capacity development activities also bring together users and data producers to train and support them on the development and dissemination of analytical reports (e.g. Voluntary National Reviews, National report of the state of food security…).

Finally, for more basic users who are only interested in the latest statistical results and analytical reports (e.g. media, general public), training activities focus on building the capacity of national statistical agencies in writing effective communication briefs or press-releases.