Data collection and analysis tools for food security and nutrition - HLPE e-consultation on the Report’s scope
During its 46th Plenary Session (14-18 October 2019), the Committee on World Food Security (CFS) adopted its four-year Programme of Work (MYPoW 2020-2023), which includes a request to the High-Level Panel of Experts on Food Security and Nutrition (CFS-HLPE) to produce a report on “Data collection and analysis tools” for food security and nutrition, to be presented at the 50th Plenary session of the CFS in October 2022 (to access the MYPoW, please click here).
The report, which will provide recommendations to the CFS workstream “Data collection and analysis tools”, will:
- Identify the barriers impeding quality data collection, analysis, and use in decision-making;
- Identify specific high priority gaps in data production and analysis not covered by ongoing initiatives;
- Highlight the benefits of using data and the opportunity costs of not using data for decisions;
- Illustrate initiatives that have encouraged evidence-based decisions in agriculture and food security across the public, private, and academic sectors as well as approaches that have not worked;
- Provide insights into how to ensure data collection and its utilization give voice to the people most affected by policies stemming from that data, including farmers and other food producers.
To implement this CFS request, the HLPE is launching an open e-consultation to seek views and comments on the following scope and building blocks of the report, outlined below.
Please note that in parallel to this scoping consultation, the HLPE is calling for interested experts to candidate to the Project Team for this report. The call for candidature is open until 28 February 2021. Read more here.
Draft scope of the HLPE Report on “Data collection and analysis tools for food security and nutrition” proposed by the HLPE’s Steering Committee
“Although it is widely recognized that sound decisions are based on good information and data, in many countries, particularly low and lower middle-income countries, the availability of timely and reliable rural, agricultural and food security statistics is largely lacking. Despite all efforts, most of these countries still do not conduct regular household and farm surveys, do not meet the minimum data requirements, lack sustainable data systems, and have insufficient capacity to analyze and use the data at their disposal.
Addressing the gap in quality data is also essential to monitor progress and understand where the world stands in achieving its shared goals - the SDGs. Custodian UN specialized agencies were identified for each SDG indicator to ensure that robust, global statistics were provided to measure progress in achieving the 2030 Agenda. However, the success of the SDGs rests largely upon strengthening data collection and statistical capacity-development at national level, including capacity building that strengthens coordination among national statistical offices.
In recent years, several initiatives[1] have begun to invest in strengthening national data systems through technical assistance, methodological innovation and research, and by supporting national capacity to collect, process, analyze, and use agricultural data. Yet, beyond these first steps, more needs to be done at the global level to support the process of laying the groundwork for informed decision making, setting standards for improved data-driven policies around food security and nutrition, and strengthening effective monitoring, review and follow- up to deliver SDG 2.
Of course, data sources are wide and varied and should be collected and utilized with an eye towards transparency, openness, and consistent with legal standards and relevant human rights principles.” (CFS MYPoW).
In particular, data-driven technology (e.g Internet of things) generates concerns of data privacy and farmer agency, especially when farm-specific production data are transferred to the private sector. Another important concern is the data and digital divide, which may further exacerbate rural inequalities remaining inaccessible to poor and food insecure farmers.
The objectives of this report are to identify challenges and gaps in collecting, analysing and using data in decision-making processes on food security and nutrition. The report will reflect on existing conceptual frameworks underpinning data collection, analysis and use on FSN. The report will also identify successful examples and initiatives for data collection and generation, including those that engage all food systems’ actors (such as workers, farmers and producers) and that contribute to the valorization of indigenous knowledge.
The report will explore how qualitative research methodologies (including case studies, lived experiences, traditional, indigenous and local knowledge) can provide evidence for a deeper understanding of FSN issues and on sustainable agriculture practices. The recommendations will help countries better collect and analyse data to monitor their progress towards SDG2, and other related SDGs. The report will specifically explore what data analysis and tools are needed to ensure that FSN policies address all the dimensions of food security, including, as identified by the HLPE, agency and sustainability (HLPE, 2020), and what are the specific challenges in measuring these two dimensions and the causalities of failures by achieving the FSN objectives. Particular attention will be devoted to challenges and solutions in empowering farmers, producers and workers in generating, accessing and using data and to data and information systems governance.
With this e-consultation, the HLPE Steering Committee is seeking your feedback. In particular, you are invited to:
- Share your comments on the objectives and content of this report as outlined above.
- Share good practices and successful experiences on how to:
- Improve quantitative and qualitative data collection and analysis;
- Address capacity gaps of local institutions, farmers’, producers’ and workers’ organizations in generating, sharing and analysing good quality data data, as well as in using data to inform decision-making in food systems;
- Address capacity gaps at country level to generate and use data in policy-making processes, monitoring and reporting related to SDG2; including with respect to financial resources, human resources, data management, legislation and the enabling environment and FSN governance.
- Share the most recent references that should be considered in this report.
- Provide feedback on the following questions, to guide the development of the report:
- What data do countries need for more effective decision-making for food security and nutrition and to inform policies for the transformation of food systems?
- What are the gaps and barriers in national and international data production and use with respect to FSN? What type of data will be most useful in measuring food security dimensions such as “agency” and “sustainability”?
- What are the current national and international processes for the collection, processing, analysis, and use of reliable and accurate agricultural and food security and nutrition statistics? What are the main gaps, challenges and inequalities in existing processes?
- What are the policies that countries need to strengthen their capacity to collect, process, analyze and use quality qualitative and quantitative data to achieve the 2030 Agenda goals? What policy areas should countries prioritize to strengthen their data and information systems (education, technology, finance, participation, etc.)?
- What are the financing needs and the financial mechanisms and tools that should be established to allow all countries to collect, analyse and use FSN data?
- What are the most promising new developments with respect to innovation and information and communication technology, including artificial intelligence, in data collection, analysis and sharing that could be applied to food security and nutrition?
- How can agricultural census, rural and household surveys, earth observation and other big data be used to improve food security and nutrition policies and outcomes? How integrated and coordinated are these to provide needed reliable and timely data for food security and nutrition policies and interventions?
- What are some of the risks inherent in data-driven technologies for FSN? How can these risks be mitigated? What are some of the issues related to data privacy, access and control that should be carefully considered?
- What are the actual capacities of countries in monitoring the achievement of the SDGs, what are the capacity development needs, especially with respect to data for SDG2? What capacity development is necessary to ensure collection, analysis, monitoring and reporting of data on food security and nutrition at national and regional levels? How to ensure data harmonization at all levels?
- What are the gaps with respect to data collection and analysis tools for FSN vis-à-vis existing initiatives and programmes?
- How can the international community together with governments ensure data and information systems governance for FSN? Which mechanism or organization should ensure good governance of data and information systems? How to regulate and mitigate potential conflicts between public and private ownership of data?
[1] The Global Strategy to Improve Agricultural and Rural Statistics at FAO (GSARS), the World Bank’s Living Standard Measurement Study’s Integrated Surveys on Agriculture (LSMS-ISA), FAO’s AGRISurvey programme and the 50x2030 Initiative to Close Agricultural Data Gaps.
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Regarding the need for data on food security and nutrition from development agronomy advisor perspective concentrating on food production, I have two major concerns.
- The first is the financial resources needed to accurately collect any data. Most host countries have a very limited tax base to support civil services. Thus, data collections must be aware of these financial limits and not expect data collection to exceed what is reasonable possible with the financial resources available. If you do insist in more comprehensive data collection than is financially feasible then the quality of the data will quickly deteriorate as numerators will fill in guesses of what the data is. Unfortunately, most scientist like to collect as much data as they can. Please review the following webpage:
https://smallholderagriculture.agsci.colostate.edu/financially-suppressed-economy-2/
https://smallholderagriculture.agsci.colostate.edu/financially-stalled-governments/
- My second concern is really a major oversight in evaluating agronomic innovations intended to promote food security and improved nutrition for smallholder communities. We tend to jump quickly from small plot research to extension/education without considering if the innovation is operationally feasible. That is, is there enough labor or contract mechanization to implement most innovation is the timely manner needed to take full advantage of the innovation. This also a community variable and not an individual farmer variable as the casual labor pool and contract mechanization is shared by multiple farmers. Who within agriculture development projects is responsible to collect and analysis data on available labor and access to mechanization, etc.? I think it falls into an administrative void between the agronomists and social scientists. At some point we much recognize that most agronomic innovations tend to me more labor intensive than the indigenous practices but do not make certain that labor is readily available within a smallholder community. We just keep badgering smallholders on the importance of early planting. Most smallholder farmers are very labor short along with the dietary calories to fuel that labor.
This also impact nutrition as most smallholder farmers are seriously undernourished with access to only about half 4000 kcal, they need for a full day of agronomic field work needed to manual produce enough to food to meet family food security needs. The result is the need to concentrate on high calorie foods to optimize economic or production opportunities and preventing adopting a more diversified diet needed for good health.
There is an easily observable proxy for operational limitations. That is timing of field operations in which for manual agricultural communities’ basic crop establishment extends for some eight weeks, well beyond a time when innovations are valid. It is essentially we recognize that this delay in not discretionary but determined by limited operational resources to manage the land and needs to be addresses as such. Also, one needs to note the success of the green revolution in paddy lands of Asia was not only the result of improved rice yields developed by IRRI working with various national programs, but more important would be the concurrent shift from water buffalo to power tillers to establish the crop. This more than halved the crop establishment period. Thus, while technology substantially increased the paddy yield potential it did not assist the farmers establish their paddy in a timely manner. The farmers did this themselves and since the development community wasn’t involved the impact of the shift to power tillers is virtually overlooked by development and hinders the effort to advance the agriculture of Africa and other developing countries.
Thus, while I am skeptical about the financial resources available to collect quality data, I would like to see an effort to take a detailed look at the timing of agronomic operations and address the operational limits smallholder farmers face. I think this would lead to increased importance of mechanization allowing a major impact on crop management, production and ultimately quality of diet for smallholder farmers and their families. Please review the following webpages and links within each:
https://webdoc.agsci.colostate.edu/smallholderagriculture/OperationalFeasibility.pdf
Thank you.
1. ¿Qué datos necesitan los países para una toma de decisiones más eficaz para la SAN y para fundamentar las políticas para la transformación de los sistemas alimentarios?
Respuesta (R). Considero pertinente sugerir a este foro de consulta electrónica abierta que se avoque, de forma preliminar, a elaborar un ejercicio sobre Operacionalización de Variables (OV) en relación con la Seguridad Alimentaria y Nutricional (SAN), actualizada como consecuencia de la incidencia de la pandemia del COVID-19.
Conceptualmente la OV es un proceso metodológico que consiste en descomponer deductivamente las variables que inciden en la expresión de un determinado problema de investigación o constructo, que en este caso es la SAN, que reviste un grado significativo de complejidad; desagregándolo desde lo más general a lo más específico; es decir, subdividiendo dicho constructo en dimensiones, variables o subvariables e indicadores o ítems. El propósito de la OV es convertir un concepto abstracto en uno empírico, susceptible de ser medido a través de la aplicación de un instrumento. La OV es un proceso que variará de acuerdo al tipo de constructo que se vaya a analizar; no obstante, las variables deben estar claramente definidas y convenientemente categorizadas. Se consideran incompletos aquellos protocolos e instrumentos cuyo nivel de OV sea muy vago.
Teóricamente la SAN es un constructo dada la imposibilidad de realizar su medición directa como variable compleja toda vez que se trata de una entidad hipotética que requiere un marco teórico sólido para su definición en términos de sus dimensiones, variables o subvariables e indicadores en los que se disgrega. En tal sentido, propongo que se elabore un esquema que permita indicar las principales dimensiones implícitas en la SAN, completando las variables e indicadores que las conforman.
Como dimensiones propongo que se sigan utilizando las definidas por FAO-FIDA-PMA (2015); es decir: A. Disponibilidad de alimentos; B. Accesibilidad física y económica a los alimentos; C. Salud de las personas y D. Estabilidad en el suministro / acceso permanente a los alimentos. Adicionalmente, sería pertinente incorporar como dimensión la Institucionalidad y las políticas públicas, necesarias para dinamizar la eficacia y eficiencia de las otras dimensiones y del mismo informe que elaborará el equipo técnico a designar por la GANESAN. Una ves definidas estas cinco dimensiones, correspondería establecer las variables e indicadores en que estarían desagregadas cada una de estas dimensiones. De esta manera, sería posible dar una adecuada respuesta a esta pregunta y a otras indicadas en este borrador.
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