Thinking of Investing in a Data Ecosystem for Sustainable Development?

September 27, 2022
Carmen Cañas, Jesus Melendez Vicente
Data Use, Explainer, IREX

Here are a Few Questions, Ideas, and Tools to Get You Started

Just like a natural ecosystem is the result of the interaction between certain organisms and the physical environment in which they live, data ecosystems are also the result of all the people (individuals), communities, and institutions interacting with each other in the way data is being produced, used, re-used, and/or shared. For international development stakeholders (everyone from national governments to international organizations, private sector, civil society as well as individuals and communities), harnessing the power of data for good has been a priority ever since the launch of the Sustainable Development Goals in 2015. From the onset of the Sustainable Development Goals agenda implementation, quality access and use of data was considered essential for decision-making, accountability, and for seeking solutions to complex and multifaceted development challenges. It was the start of the so-called “data revolution” for sustainable development. 

For initiatives aiming to harness the power of data for good, it is critical to start with a well defined problem and a thorough understanding of the data ecosystem around it. It is, therefore, very important for stakeholders driving such initiatives to have the tools and resources that will help them assess data ecosystems specific to a sector where the development challenge takes place or even specific to a nation for more effective and impactful policy making or strategic planning. Investing time to assess a data ecosystem will ensure the relevance, sustainability, and potential for scale of a solution to a development challenge. At the same time, data ecosystem assessments will help to close any significant data gaps and strengthen the ecosystem, enabling quality and timely data production, facilitating collaboration between stakeholders, increasing the use of data for decision-making, improving service delivery, building responsible and ethical data practices, and/or increasing government data transparency. 

For organizations thinking about conducting a data ecosystem assessment, there is an array of initiatives, tools, and approaches from which to choose. Based on our experiences deploying our own tools and supporting a variety of stakeholders, IREX and Development Gateway: An IREX Venture (DG) would like to share a few insights and questions to help you get started. 

Three Questions to Get You Started 

The number of tools, frameworks, and initiatives to help you understand, analyze, map, and integrate data ecosystems at various levels can seem daunting. Below, we have included a select list of tools you can use for your assessment. Each methodology has a unique set of outputs, focuses on different parts of a data ecosystem, and analyzes different types of ecosystems. Before selecting a tool, ask yourself the following questions:

Q1: What is the objective?

This is the most important question to ask at the beginning of the process. The success of an ecosystem assessment depends on having a clear understanding of the reason for conducting it. This is important for assessments led both internally and externally. We see two major motivations for leading these assessments:

  • To find possible solutions to a known problem, need, or interest – In this case, the assessment should be problem-driven and will help identify specific recommendations and investments that help find a solution to the known problem. Some notable examples are assessments to help identify possible data and digital investments, operationalizing strategic plans, or designing Monitoring and Evaluation (M&E) systems. A specific example illustrating this would be IREX and local actor D-Lab’s use of the Data Compass tool (as part of Data Zetu program) to help the Ministry of Health in Tanzania evaluate and sustain investments in public health care that included building capacities of rural health care facilities for more effective and systematic use of health data.
  •   To evaluate where an ecosystem stands in relation to other ecosystems – Some data ecosystem assessment tools have been designed around evaluative frameworks used to conduct multi-dimensional research on the state of a data ecosystem — typically — across several countries. A reason for using these tools would be to determine where an ecosystem fits relative to other ecosystems or an ideal state. Examples of tools that help this type of assessment are scorecards, benchmarks, or indexes, like the recently released Global Data Barometer.

Q2: At what level do you want to focus?

Data ecosystems are multilayered, and there are multiple levels at which an assessment can be led:

  • Country Level – These data ecosystem assessments are tools that provide a general landscape of a country. A country-level tool could be useful during investment or development planning. For example, a donor could use a tool that assesses the statistical capacities of a country to inform the design of a program or project that is data heavy. 
  • Sector Level – These data ecosystem tools help actors to better understand opportunities to harness data within a sector. It considers all actors involved in a sector, including the private sector, government, civil society organizations, nongovernmental organizations, and donors. For example, DG led a data and digital assessment in Guadalajara, Mexico in the open procurement sector. The objective of this sectoral assessment was to identify key areas of investments in open procurement from the perspective of multiple actors, including government contractors, civil society organizations working in the corruption sector, and government employees making purchases.
  • Organizations or Institutions Level – These data ecosystem assessments allow actors to understand the data ecosystem of a single organization or institution. For example, a social impact organization may want to understand the extent to which they are utilizing their data (i.e., data maturity level) in order to identify areas for improvement to become a data-driven and data-informed organization.
  • A Combination of Assessments at Multiple Levels – Sometimes multiple data ecosystem assessments are needed. For example, a country-level assessment can help a donor identify areas of investment, and a second assessment can allow us to drill down on a possible solution. This was the case of the Results Data Initiative Program led by DG in Malawi. First, an assessment was led to understand the data ecosystem of the agriculture sector in the country. During this assessment, we identified the need for a National Agriculture Data System as a key priority across all actors involved. During a second separate assessment, however, DG focused on the Ministry of Agriculture to design the National Agriculture Management Information System (NAMIS).

Q3: Which aspects of the ecosystem do you want to understand?

A data ecosystem consists of many different elements. Having clarity on which aspects of the ecosystem you want to assess will ensure that the final product responds to your needs. Below are some examples of aspects you could focus on during the assessment. This is an indicative list that can be used as a starting point, but you can add more or select multiple foci.

  • Available data – A key part of a data ecosystem is understanding what data exists. Additional information can be associated with available data, including data gaps; who produced the data; how it is collected; its infrastructure; how it is shared; the format in which it is collected; frequency of collection; interoperability(i.e., the ability of data systems to exchange and make use of information); data quality analysis; or the legal framework.
  • Data openness – Knowing the level of open data of a government can help governments and international organizations recognize areas for investment. The level of data openness can refer to multiple variables, including the format in which data is published; the quality and frequency of data published; and coverage or legal framework.
  • Data capacities and skills – Governments, donors, and civil society organizations need to have the capacity to produce and use available data. Depending on the assessment tool you select, you can assess statistical capacities, skills, access to training, or digital and data literacy.
  • Use and reuse of data – Leveraging data to make decisions to advance the implementation of the Sustainable Development Goals is one of the key premises of the data revolution. Each assessment tool will evaluate the use of data differently. Some examples include having a positive impact, using data for decision-making, or incentives or disincentives for data use. IREX recently helped the Millennium Challenge Corporation (MCC) to better understand data ecosystems within Lesotho’s healthcare sector with a focus on the use of data to understand key processes ranging from budgeting to maternal mortality rates; recommendations also helped inform investments in data ecosystems driven by MCC’s new compact plan for Lesotho.

Select List of Tools You Can Use for Your Assessment

IREX and DG has created a curated list of tools to be used for data ecosystem assessments. 

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IREX

Development Gateway: an IREX Venture (DG) and IREX, in partnership with the Hewlett Foundation, are pleased to announce a new research program supporting data-driven decision-making in education in East and West Africa. This two-year, $300,000 project to map education data and decision ecosystems in Kenya and Senegal will focus on the variety of administrative, census, and survey data collected to implement and monitor primary and secondary education. The goal is to holistically understand the barriers to more effective data collection, sharing, interoperability, and use. By understanding the barriers, we can better design support for more robust education data ecosystems that drive better learning outcomes.

The Need for Education Data

This past June, The World Bank released its ​​State of Global Learning Poverty Report. The report shows that the COVID-19 Pandemic has significantly worsened learning outcomes and exacerbated an existing learning crisis. The report estimates that 70% of the world’s children are learning impoverished. At the same time, the report relies upon simulated models, which are less reliable than up-to-date official data. For example, the most recent UNESCO data from Kenya and Senegal was published in 2017.1 The Kenyan Ministry of Education, for example, directly cites data challenges in management, sharing, and interoperability.2 Inversely, in Senegal, there is limited research on the utility and availability of data for policy-making in education.3 Both countries exemplify the global challenge of effectively harnessing data for better outcomes in the education sector. Without education data, policymakers, education systems, and other stakeholders lack information to make informed decisions, monitor progress, and allocate resources efficiently or equally.4

“Yet for many [countries], data are currently incomplete, which makes monitoring difficult or impossible. It can also result in poorly-designed policies, leading to inefficient use of resources. Other challenges for countries faced with the new education agenda include inadequate funding for statistical activities, weak institutions, limited technical capacity, lack of adherence to international norms and standards, and insufficient coordination both at the national level and among national and international stakeholders.”5

The Data Revolution in Education

Addressing Data Gaps

There is a need for better, more accurate, more timely, and more interoperable data on education to help policymakers combat learning poverty. DG, together with education sector experts from IREX, will map the education data and decision ecosystems in Kenya and Senegal, focusing on primary and secondary education in order to more holistically understand the barriers to data sharing, interoperability, and use. By understanding the barriers to data use, we will allow for more targeted interventions for creating robust education data ecosystems for better learning outcomes. In addition to country-specific assessments, with actionable roadmap and investment recommendations, we will develop a white paper on education country data ecosystems, highlighting lessons from Kenya and Senegal, together with existing literature from around the globe.

“Partnering with the Hewlett Foundation on this exciting new program is an opportunity to support decision makers in Kenya and Senegal to understand, and subsequently remove, the barriers to gathering the data and information necessary to accurately address disparities in education. This program also represents a significant step in fulfilling a key goal of the IREX and Development Gateway partnership – to improve education systems and learning outcomes.”

 “At DG, we are eager to expand our work to the education sector, by bringing our combination of digital and data expertise, and experience partnering with governments globally. This program will give us the opportunity to go deeper than prior research in understanding the challenges governments face in building education data ecosystems that prioritize and support learning outcomes, while ensuring equitable access and quality of education opportunities across genders and geographies. We look forward to learning with and from our government partners, IREX colleagues, and the broader education data community.”

~Kristin Lord, President and CEO of IREX
Josh Powell, CEO of Development Gateway

The new program is an opportunity to kick-start our joint work in data for education. It builds on DG and IREX’s existing strategic partnership and is bolstered by IREX’s expertise in the education sector and DG’s experience with data and digital for development.

Footnotes
  1. http://uis.unesco.org/sites/default/files/documents/the-data-revolution-in-education-2017-en.pdf
  2. Ministry Of Education. (n.d.). National Education Sector Strategic Plan
    For The Period 2018 – 2022. Republic Of Kenya. https://assets.globalpartnership.org/s3fs-public/document/file/kenya-nessp-2018-2002.pdf?VersionId=tdCPzVW5gwJ1DODlRJsOWkwpP7BDDrKv.
  3. Ministère de l’Éducation nationale; Ministère de la Formation professionnelle et technique, de l’Apprentissage et de l’Artisanat; Ministère de l’Enseignement supérieur, de la Recherche et de l’Innovation; and Ministre de la Bonne Gouvernance et de la Protection de l’Enfance. (2018, August).
    Programme d’Amélioration de la Qualité, de l’Équité et de la Transparence-Education/Formation (PAQUET-EF) 2018-2030. Republique Du Senegal.
  4. http://uis.unesco.org/sites/default/files/documents/the-data-revolution-in-education-2017-en.pdf
  5. http://uis.unesco.org/sites/default/files/documents/the-data-revolution-in-education-2017-en.pdf
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In our final episode of the current series, I speak with Annie Kilroy, Senior Associate, and Fernando Ferreyra, DG’s Director of Software Development, about digital transformation and emerging technologies. The conversation centered on what has changed in the technology landscape in the past decade, our approach to digital transformation, and how to continue prioritizing users.

You can also listen to Data… for What?! on SpotifyStitcher, and Apple Podcasts.

Development Gateway: an IREX Venture (DG) has always been at the intersection between software and international development. In our new strategic plan, we highlight our evolving efforts to develop and deploy emerging technologies with an emphasis on sustainability, ethical innovation, and strong digital governance. Our experience co-creating with users, eye on ethical and sustainable technology, and our new drive to scale and innovate makes DG uniquely positioned to lead the digital development sector in the thoughtful implementation of new tools and approaches.

“The promise of digital transformation is to get more value for all of our efforts. Now, we need to be smart about how we invest there, how we look at these new technologies that are sometimes more disruptive, to understand if they actually fit”

Fernando Ferreyra Director of Software Development

What has Changed?

Since the start of our last strategy cycle, digital technologies have continued to expand exponentially. As smartphones become more accessible, the number of internet users continues to increase globally. With more users accessing the internet, governments are seeking to streamline their work and make more processes digital. In parallel, the COVID-19 Pandemic has accelerated the focus on digital transformation, as the need for e-government services, e-learning, and other modern methods for interacting with stakeholders has become a necessity. 

We have also seen over that time that some technologies have reached maturity. Cloud computing has become the industry standard and the price is no longer prohibitive. In the past, deploying technology would require the DG team to arrive at a government ministry with servers, cables, and everything needed to maintain a system to do work that can now be achieved through the Cloud in a matter of hours. The value proposition has been tremendous in terms of efficiency, and also presents opportunities to use tools like machine learning and analytics in a new way.

Our Approach: Centering the Users

One thing that will never change is a focus on the users, and understanding the context in which a tool will be deployed. Through our Custom Assessment Landscape Methodology our first step in any project has always been an assessment of the landscape in an effort to understand who will be using a system and what decisions it will facilitate. Then, co-designing and iterating with the stakeholders throughout the development process.

“We apply the CALM methodology that allows us to cast a really wide net and see things from a really detailed lens that really highlights not just the end users, but people that are affected by these systems as well.”

Annie Kilroy Senior Associate

At the same time, understanding the needs of the users requires a constant focus on responsibility and sustainability. Helping stakeholders select technology is not about the cutting edge, but rather what will work in a given context and be sustainable in the long term. This includes creating open source solutions, using technologies with strong documentation and support, prioritizing data protections and regular security updates, and planning for local ownership.

Where are We Going?

  • New and Emerging Technology – The digital development space is constantly changing, and there is a critical need for an experienced organization like DG to help the sector understand which tools are fit for purpose, and which are pure hype.
  • Bottom-Up and Top-Down Approaches – We will continue to work with organizations to marry guidance from global organizations like the UN or the WorldBank, with the bottom up approaches for what digital transformation looks like at the local level. 
  • Advisors and Implementers – In our new strategy, we see a role for ourselves as practitioners/implementers and also as advisors. Often, technology projects fail before they start because the funder does not understand the context, user needs, or, ultimately, the purpose. Playing an advisory role at the beginning of the technology process will allow us to influence digital development work at a greater scale and to have a clear avenue for informing the policy community.

Thank you so much for joining us on this exploration of our Strategic Plan. You can listen to all the episodes here. We will be back soon with a new season of Data… for What?! focused on data governance.

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Nigeria has the largest population and economy in sub-Saharan Africa (SSA). However, with more than 90% arable land and the largest agricultural output in SSA, Nigeria still relies on the import of staples such as maize and rice to meet food demand.1 

Basic statistics on hectares of cropland and major crops are outdated, if they exist at all. Availability of reliable and up-to-date land and crop usage data will help inform the government and private sector on how to allocate investments to strengthen Nigeria’s agriculture sector and, in particular, will help ensure that the availability and variety of fertilizer products meet market needs.

The VIFAA Innovation Fund

Development Gateway: An IREX Venture’s (DG’s) Visualizing Insights for Fertilizer in African Agriculture (VIFAA) Program2, funded by the Bill & Melinda Gates Foundation, launched an Innovation Fund to address data shortages relating to total crop production. Building on its track record of mapping land cover, Quantitative Engineering Design (QED)3 was selected to tackle the challenge of mapping Nigeria’s croplands. Through the Innovation Fund, DG and partners is using the resulting data and maps to answer two questions:

  • What is the total cropland under production in Nigeria?
  • What is the cropland under production by crop type?

Background

Historically, there have been attempts to map croplands and crop types across Nigeria; however, the immense size of Nigeria has made traditional mapping efforts cost prohibitive.4 Recent advancements in technology (i.e., satellite imagery) combined with machine learning have provided rapid and scalable access to high-resolution images of the ground that covers the globe.  These tools have been used to map large, industrial-scale croplands in developed countries.5 However, mapping initiatives using remote sensing imagery in tropical countries dominated by smallholder agriculture have not been as successful, largely due to dense cloud cover during the growing seasons and the size, complexity, and diversity of smallholder farm plots.

Mapping Methodology

QED’s high-level approach to land cover mapping is four-fold:

  • Survey: Generate training data by labeling satellite imagery and looking at the visual patterns in the landscape between different land uses (e.g., agriculture, urban, forests, etc.).
  • Model: Build and test multiple artificial intelligence (AI) models using the training data so that the computer learns from the training data to classify unknown areas across the country.
  • Validate: Create predictive maps with each model and assess each one’s performance. This is followed by refinement whereby steps 1-3 are repeated in selected areas requiring further improvement.
  • Statistics and visualization: Generate summary statistics based on predictive maps.

Surveying

QED developed Geosurvey, which is a software tool for efficiently collecting and labeling visual training data. This tool feeds labeled images of agricultural landscapes into a computer program, where trained surveyors assess each image and classify the features within each image (e.g., croplands or not). The process requires a team of skilled surveyors to identify visual indicators of agricultural features and learn patterns which can change depending on the cropping system and geographic region where the specific training data was developed to match the unique locations.

Due to the diversity of agricultural landscapes in Nigeria, conducting a single geosurvey for all of Nigeria would not provide the quality of training datasets required. This necessitated partitioning the country into six smaller, more homogenous, and sometimes overlapping agro-ecological zones to aid familiarity with region-specific fingerprints to the survey team. Using these dimensions, areas of interest (AOI) were identified. However, ecological subdivisions were found within these zones that affected surveyor calibrations such as in the northeast and the delta where croplands and agroforestry systems can subtly blend into the surrounding area, making differentiation difficult.

To overcome these challenges, a series of region-specific training materials were created. They consist of visual guides, videos, and interactive training sessions. To increase the quality and accuracy of the image labeling, QED developed peer review systems whereby each survey submission was reviewed by three different levels of experts. Using this system, the team was able to fully annotate over 10,000 square kilometers of Nigeria within the space of two months.

Modeling

In addition to the training datasets, QED also used a combination of publicly available high-resolution satellite imagery datasets, including Sentinel-2 which is a public satellite run by the European Space Agency. The satellite data is collected every five days (since 2015) to train the models to infer patterns and correlate with crop cycles between satellite imagery and the training datasets. The results are then extrapolated across Nigeria. Using high-performance computing, QED trained and evaluated more than 50,000 different models to produce cropland maps with an accuracy and precision of 85%.  

However, even with the level of detail provided by the Sentinel satellite imagery, the main rice and maize growing seasons occur during the rainy season, which is also the period when the availability of cloud-free imagery is scarce. With transfer learning, QED leveraged modeling and data gleaned from the mapping of other countries. Having the basic model built upon previously developed models allowed the team to spend more effort fine-tuning the models to suit local conditions.6

An outstanding question is the ability to differentiate land under fallow from land under active cropping. Attempting to model fallow lands is difficult, as fallow lands may be hard to differentiate from natural lands. Historically, cropland maps have rarely accounted for fallow cropland but, when factored in, the inaccuracies may be substantial. To improve crop yield estimates more broadly, it is vital to estimate the proportion of fallow cropland. QED will continue to review the literature on possible fallow land types and incorporate these types into the field validation surveys used by ground teams in Nigeria.

Validation

In addition to satellite-based verification, QED has been working with Nigerian agricultural experts for further validation of the maps, drawing on the expertise of on-the-ground experts and field staff. 

From April 2020, QED began collecting ground data in North Central Nigeria. During the wet season, the region grows mostly maize and rice. QED provided field tools, including mobile phones that were pre-authenticated and pre-loaded with data collection applications; navigation software; mobile data; and communication tools. In the field, surveyors followed predefined waypoints and collected a balanced mix of locations of croplands and non-croplands (e.g., roads, natural vegetation, fallow fields, buildings, etc.).

The survey tool contained data on land use class, present and historical fertilizer use, and the number of seasons under cultivation (if classified as cropland). For data classified as croplands, surveyors were required to include a photograph of the field for verification purposes. Using digital collection tools allowed for near real-time monitoring and feedback for the team. Data from more than 1,000 locations across the North  Central region of Nigeria was collected within one month. 

Cropland Statistics

Statistics have been generated for cropland areas under production across the various geopolitical regions and states in Nigeria (see table below). These statistics include total land area, estimated cropland area, and estimated cropland percentage, which can be compared against statistics from other sources. A further breakdown on regional and state statistics can be viewed via an interactive map.7 

Cropland Area Comparison Nigeria: 2020 vs. 2021

QED has updated the previous 2020 cropland map produced for Nigeria in order to reflect cropland estimates in 2021 and review any changes between the two years. To derive multi-year cropland predictions, QED employed Transfer Learning (TL), an AI technique which is used in scenarios with a large existing dataset and trained models and a smaller but related dataset from which new models are trained. Rather than collecting enough new data to build new models, the existing data and models are leveraged to develop the new models and are augmented with smaller sets of new data to provide additional data for the model to learn. In this case, QED used the cropland model developed for 2020 and adapted it to work with the dataset for 2021. Using this technique, the updated cropland map for Nigeria from the 2021 season was produced with slightly higher accuracy than from 2020.Developing the ability to map croplands across multiple years provided additional insights into national crop production dynamics. We estimate that arable land across Nigeria between 2020 and 2021 experienced a net increase of 1.34%. While numerically small as an absolute percentage (given the size of Nigeria), 1.34% equates to 12,225.19 km, roughly half of the total land area of Rwanda.These results demonstrate promise for more cost-effective updating of national-scale maps, if there is demand to regenerate them on a yearly subscription basis across multiple African countries.

Highlight: Comparisons estimate an increase of 1.34% in Nigeria’s national cropland production equal to +12,225.19 km.


We also noticed that many inter-annual differences occurred around the borders of natural areas. Many bandits (e.g., Boko Haram) use the forested areas as home bases. There appears to be consistent correlations between the regions known to have banditry and corresponding reductions in cropland.

Footnotes
  1. Chapin Metz, 1991.
  2. Learn more about the program at go.developmentgateway.org/vifaa
  3. For more information about QED please visit https://qed.ai.
  4. I.D. Hill et al 1978.
  5. Kussul et al 2017. Available at https://ieeexplore.ieee.org/abstract/document/7891032.
  6. Engage with the map: https://maps.qed.ai/map/ng_cp_preds.
  7.  The map is available at https://cropstats-ng.qed.ai
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Kassimou Issaka, agroéconomiste et directeur général de l’Agence territoriale de développement agricole (ATDA Pôles), donne un aperçu du travail de l’ATDA et explique comment la plateforme Cashew-IN développée par DG et CNFA soutiendra son travail.

Parlez-nous du ATDA Pôles en quelques mots.

L’ATDA4 est une organisation sous tutelle du Ministère de l’Agriculture, de l’Elevage et de la Pêche (MAEP) du Bénin qui a pour mission de mettre en œuvre la politique du Gouvernement en matière de promotion des filières porteuses spécifiques au Borgou Sud – Donga – Collines du Bénin. Elle est dotée de la personnalité morale et de l’autonomie financière et regroupe 16 communes avec pour siège Parakou.

L’ATDA4 a entre autres pour attribution d’élaborer, faire valider et conduire avec les acteurs-clés du territoire du pôle, les plans; de faciliter l’accès des acteurs des filières aux informations et innovations, de coordonner les projets de développement des filières agricoles intervenant dans le pôle; de coordonner les interventions des acteurs publics et privés sur les filières agricoles dans le pôle de développement.

Quelle est votre ambition, ces 5 prochaines années, pour la filière anacarde?

Pour la période 2019-2021 le Bénin ambitionnait de porter la production de noix brute de cajou à 300 000 tonnes et d’en transformer 50% localement, soit 150 000 tonnes. Actuellement, l’évaluation de la réalisation de cette ambition est en cours. Mais on peut déjà retenir que la production cible n’est pas atteinte et que les unités de transformation installées ont une capacité largement inférieure aux prévisions.

En 2021, le Gouvernement du Bénin a lancé l’étude de faisabilité du Programme National de Développement des Plantations et des Grandes Cultures (PNDPGC). La filière anacarde figure en bonne place dans ce programme. En effet, le Bénin projette d’implanter 500 000 ha de nouvelles plantations d’anacardiers d’ici cinq (05) ans et de transformer localement la totalité de sa production. Pour y parvenir, des unités de transformation ont été créées et un décret pris par le Gouvernement pour interdire l’exportation de la noix de cajou brute du Bénin à partir du 1er avril 2024.

Comment l’outil Cashew-IN peut vous aider à atteindre vos objectifs?

Avec l’outil Cashew-IN les zones potentielles d’extension de la production peuvent être connues sur le territoire national. Il en est de même de la facilitation du suivi de l’évolution des indicateurs de la filière. Les informations disponibles appuieront la prise de décision.

Cette interview a été modifiée pour des raisons de clarté et de style. 

Pour toutes questions ou suggestions, merci de vous rapprocher de Madame Constance Konan, ckonan@developmentgateway.org.  

Partenaires

USDA

Le ministère américain de l’agriculture est un département fédéral qui assure le leadership dans les domaines de l’alimentation, de l’agriculture, des ressources naturelles, du développement rural, de la nutrition et des questions connexes, sur la base de politiques publiques, des meilleures données scientifiques disponibles et d’une gestion efficace. Grâce à son projet PRO-Cashew, l’USDA s’efforce de stimuler la compétitivité des producteurs ouest-africains en améliorant l’efficacité et la qualité de la production et du commerce, et en travaillant à l’élaboration de politiques régionales plus cohérentes en matière de commerce et d’investissement.

CNFA

Cultivating New Frontiers in Agriculture est une organisation internationale de développement agricole spécialisée dans la conception et la mise en œuvre d’initiatives agricoles durables basées sur les entreprises. CNFA travaille avec des entreprises, des fondations, des gouvernements et des communautés pour établir des partenariats locaux et mondiaux personnalisés qui répondent à la demande croissante de nourriture dans le monde. Depuis notre création en 1985, CNFA a conçu et mis en œuvre des initiatives de développement agricole basées sur les entreprises afin de faciliter l’accès au marché, d’améliorer la compétitivité des entreprises agricoles, d’accroître la productivité et d’améliorer l’accès aux intrants et au financement dans 47 pays du monde.

 

Pour en savoir plus sur Cashew-IN, consultez notre liste de lecture du programme Cashew-IN sur YouTube.

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Voices of the Cashew Sector – Kassimou Issaka

September 12, 2022 Agriculture
Constance Konan, Aminata Camara Badji
Program

Since 2020, Development Gateway: an IREX Venture (DG) has partnered with Cultivating New Frontiers in Agriculture (CNFA) under the USDA West Africa PRO-Cashew project to develop the Cashew-IN data collection and analysis platform

The project has identified gaps in the data collection, storage, usage, and dissemination related to the cashew sector in all five of the implementing countries (Benin, Burkina Faso, Côte d’Ivoire, Ghana, and Nigeria). The project is now working to address these gaps through a multi-country cashew data management system (Cashew-IN) that will facilitate access to and use of data to improve decision-making for policymakers, farmers, and the private sector. The ultimate goal is to generate better market outcomes for cashew nuts in these countries.

Kassimou Issaka, Agroeconomist and General Manager of the Territorial Agency for Agricultural Development (ATDA Pôles),  gives an overview of ATDA Pôles’ work and explains how the Cashew-IN platform developed by CNFA and DG will support his work.

In a few words, what’s the ATDA Pôles?

The Territorial Agency for Agricultural Development is an organization under the Ministry of Agriculture, Livestock and Fisheries (MAEP) of Benin whose mission is to implement the government’s policy on the promotion of promising sectors specific to the Borgou Sud – Donga – Collines of Benin. It has a legal status and financial autonomy and groups together sixteen communes with its headquarters in Parakou.

The ATDA4’s responsibilities include developing, validating, and conducting plans with key actors in the cluster’s territory; facilitating access to information and innovations for stakeholders in the sectors; coordinating agricultural sector development projects in the cluster; and coordinating the interventions of public and private actors in the agricultural sectors in the development cluster. 

What is your ambition for the cashew sector in Benin throughout the next five years? 

From 2019-2021, Benin aimed to increase the production of raw cashew nuts to 300,000 tons and process 50% (i.e., 150,000 tons) locally. Currently, the evaluation of the achievement of this ambition is underway. However, it can already be noted that the target production has not been reached and the processing units installed have a capacity that is much lower than expected.

From 2021, the Government of Benin launched the feasibility study of the National Program for the Development of Plantations and Major Crops (PNDPGC). The cashew nut sector figures prominently in this program. Indeed, Benin plans to install 500,000 ha of new cashew plantations within five years and to process locally all of its production. To achieve this, processing units and a decree have been issued by the Government to prohibit the export of raw cashew nuts from Benin as of April 1, 2024.

How can the Cashew-IN tool help you achieve your objectives?

With the Cashew-IN platform, potential areas for production expansion can be identified in Benin. The same applies to facilitating the monitoring of the evolution of the sector’s indicators. The information will support proper decision-making.

This interview was edited for clarity and style. 

For any questions or comments, please reach out to Constance Konan at ckonan@developmentgateway.org

Partners

USDA

The U.S. Department of Agriculture is a federal department that provides leadership on food, agriculture, natural resources, rural development, nutrition, and related issues based on public policy, the best available science, and effective management. Through their PRO-Cashew project, USDA is working to boost the competitiveness of West African producers by improving efficiency and quality in production and trade, and by working to develop more coherent regional trade and investment policies.

CNFA

Cultivating New Frontiers in Agriculture is an international agricultural development organization that specializes in the design and implementation of sustainable, enterprise-based agricultural initiatives. We work with businesses, foundations, governments, and communities to build customized local and global partnerships that meet the world’s growing demand for food. Since its inception in 1985, CNFA has designed and implemented enterprise-based, agricultural development initiatives to facilitate market access, enhance agribusiness competitiveness, increase productivity, and improve access to inputs and financing in 47 countries around the world.

To learn more about Cashew-IN, check out our Cashew-IN program playlist on YouTube.

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Nigeria’s Changing Fertilizer Sector

September 7, 2022 Agriculture Vinisha Bhatia-Murdach, Scott Wallace
Data Use, Explainer

In the past decade, there has been over $7 billion worth of private-sector investments into Nigeria’s agriculture sector. Why, and what is the impact of these investments? What does it mean for Nigerian farmers? Just under 95% of those investments came from the fertilizer sector ($6.6 billion). Historically, fertilizer was considered the government’s turf where the Ministry of Agriculture was the buyer (through tenders or contracted suppliers), and the state governments were the primary customer. Thus, in most cases the private sector did not have a strong connection to the smallholder farmer and fertilizer was typically a scarce commodity when the rains began to fall.

But the market in Nigeria is changing. Throughout the past decade, the government ceased announcing fertilizer tenders. During this period, the private sector invested in large-scale urea nitrogen plants in the country (urea is a nitrogen-based product which is one of the three key nutrients — phosphorus and potassium being the other two needed for plant growth). This has transitioned Nigeria from being a net importer of nitrogen-based fertilizers to becoming a net exporter.

Domestic urea plant capacity has grown from approximately 500,000 MT per annum to current capacity of over 6,000,000 MT per annum. In the past, fertilizer consumption would spike around the national election cycle as the supply would be tied to government issued tenders. Since 2016, nitrogen consumption has realized a consistent increase in domestic consumption peaking at 1,303,423 in 2021 as shown in the chart below. 

Recent Timeline of Nigeria's Fertilizer Sector

Nigeria Privatizes the National Fertilizer Company of Nigeria (NAFCON) urea plant to the company Notore

This plant was idle for over a decade prior to privatization

Nigeria hosts Africa Fertilizer Summit
Notore begins production with annual output of 250,000 to 500,000 MT
Nigerian Ministry of Agriculture discontinues fertilizer tenders
Indorama constructs a 1.4 million MT urea plant… (a $1.2 billion investment from the private sector)
Nigeria becomes a net exporter of nitrogen fertilizer
The King of Morocco and the Nigerian President sign an agreement on discounted pricing of phosphorus imports based fertilizer products targeting 1 million MT
Dangote constructs two train, 2.8 million MT urea plant ($2.5 billion investment)

First train began production in 2021; second train began production in  2022

Federal Government of Nigeria (FGN) bans use of foreign currency for importation of NPK
Over 15 Nigerian private sector businesses invest in NPK blending plants to address Nigeria’s changing market dynamics
Indorama constructs second 1.4 million MT urea plant ($1.2 billion investment)

A second train (i.e., a second plant in the same facility) started production in April 2021

2005
2006
2009
2012
2013-2016
2016
2017
2017-2021
2018
2018 - 2021
2018 - 2021

As the timeline highlights, the roles of the private sector and the public sector are shifting. Since the 1960s and through recent times, the government has been heavily involved in the supply of fertilizer to farmers through tenders, subsidies, and importations. During this time, the private sector was often considered a middle-man that did not add value in the supply chain.

Challenges that stemmed from the reliance on government-managed tenders are now changing. Since 2014, Nigeria has become a net exporter and global powerhouse in the production of urea. With the private sector investing in the natural gas reserves to produce urea, the role of the public sector and the private sector is transitioning to a more efficient marketplace that better addresses the needs of Nigeria’s farmers. In regards to urea, Nigeria’s farmers are no longer burdened with the constraints around availability and accessibility and domestic urea consumption has continued to increase since 2015. 

Long-term investments in urea have for the first time created matching incentives between private sector, public sector, and the farmer. According to Nigeria’s Fertilizer Technical Working Group, private-sector domestic sales of urea have a higher return on investment compared to exports. Thus, substantial investments are taking place in the supply chain to strengthen farmer access and product availability. For the Nigerian farmers, this means one of the primary fertilizer products — urea with 46% nitrogen — is now readily available throughout the country. 

The sudden change in the availability of domestic urea also created an incentive for private-sector entrepreneurs to invest in domestic NPK blending plants and further drove the Federal Government of Nigeria to encourage utilization of this nitrogen product to support domestic NPK production through the Presidential Fertilizer Initiative. However, rapid growth in domestic NPK production presents new challenges and obstacles to overcome for the  public sector, private sector, farmers, and other key stakeholders. There is a dire need to understand what these obstacles are and how to take advantage of this new and dynamic market. 

This is where DG, IFDC, and our partners come in. The Visualizing Insights on Fertilizer for African Agriculture (VIFAA) program is bringing together needed data from a variety of actors in the public and private fertilizer sector with the goal of understanding how to use this data to make informed decisions. This includes supporting future investments by the private sector and addressing policies and regulations by the public sector to ensure that fertilizer products are able to improve soil nutrients, farmer profitability, and ultimately to support an increase in agricultural productivity. 

In 2021, VIFAA launched the Nigeria fertilizer dashboard to support the nation’s agriculture sector. This dashboard features 14 indicators including price, availability, and a plant directory. In 2021 and 2022, VIFAA and their partners have also invested in new technologies to develop cropland under production maps using geo-spatial mapping and artificial intelligence to build a better understanding of changes in cropping patterns.

Stay tuned for future blogs about Nigeria’s changing market.


Cover photo by Markus Winkler on Unsplash

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Farmer-Centric Data Governance Assessment: A New Paradigm For LMICs

September 6, 2022 Agriculture
Jonathan van Geuns
Data Use

Why a New User-Centric Paradigm is Imperative

The benefits of digital agriculture are prolific. Digitization makes it possible to trace supply chains, treat plants precisely, or obtain data on the status of soil. Digitization holds great promise to modernize the agriculture sector, including by increasing access to finance and helping to combat climate challenges. Data-led efforts are critical, particularly when a lack of access to reliable data threatens humanity. The value of data is unquestionable.

While much data is generated by farmers, it generally is owned and controlled by others. Farmers rely on external resources, like weather data and proprietary tools, that provide specific advisories inferred from data. Yet farmers increasingly lose control over their data and the benefits derived from it. Other dangers exist, with intermediation, opaque data value chains, and dominant market mechanisms. The rapidly increasing data extraction is of great concern, especially in light of current data governance practices, insufficient regulatory guidance, and other capacity restraints. As a result, some farmers are becoming reluctant to share data, also placing them in the position of having to forgo access to beneficial services. 

Agriculture’s potential is limited by dominant monopolies and asymmetries of power and information (i.e., when some groups have access to more information than others). The current data economy — certainly at the level of global, big data initiatives — is defined by a paradigm of extraction and a lack of consideration of equity, whereby individuals and communities go unrecognized beyond their use as data points and producers. This has implications on the success of development programs, particularly for economic inclusion and growth.

Reflecting on these issues, many questions arise. For example: 

  • Who controls the data and what does farm data ownership mean? 
  • Is it in the interests of smallholders to provide data to firms that share or sell the data with other agribusinesses, banks, or insurers? 
  • Would it be preferable to retain control and make decisions about how data is used? 
  • What level of participation should we expect from farmers and farmer groups in data governance?

Why User-Centric Data Governance?

How data is governed informs what products, services, and insights are used and shapes program strategy. Empowering farmers with more control over their data is critical to improve and protect their livelihoods.

User-centric approaches to data governance place farmers, as beneficiaries, at the center of data initiatives which reduces the negative effects of centralized power. User-centric models grant farmers and their communities greater agency over their inputs and outputs and meaningful participation shaped by their immediate needs.

Models like data collaboratives, data commons, data cooperatives, and decentralized data fiduciaries hold the potential to strengthen the position of farmers as equals in future developments. These evolving approaches transition control of data and safeguards against privacy invasion, data misuse, opacity, and other harms. Models can also generate better data sharing opportunities, counter data fragmentation, increase data quality, and identify avenues for innovation. 

Farmer-centric, participatory models hold the potential to shift the hegemonic paradigm toward long-term, sustainable, equitable, trustworthy, and more successful solutions. Greater participation in the creation and implementation of data governance can bring about greater societal and economic equity and contribute towards increased public confidence in the use of data. 

Given the momentum of digital agriculture and investment strategies, it is crucial that equitable models of data governance are placed within the ecosystem as soon as possible. There is a pressing need to unpack these practices, learn about their challenges, and think about measuring their impact. This study attempts to move beyond the theory covered by others to explore the practical implementation in LMICs, their elements of success, with a focus on agriculture. 

Who Benefits from Farmer-Centric Data Governance?

  • Farmers gain more control and agency over their data, more equality, meaningful participation and representation, bargaining power, alignment with interest, and access to new markets and opportunities.
  • Agribusiness, tech providers, governments and development organizations, benefit, among others, from:
    • better and more consistent, reliable,  recent, higher quality data, enhanced data access and availability, greater data sharing opportunities, enhanced data management, and decreased data fragmentation;
    • greater efficiency and productivity, (public) service design and delivery,  decision making, situational awareness and response;
    • improved mediation and formalized relationships, communication, transparency, meaningful feedback and nurtured trust; and,
    • improved reputation, public relations, legal and privacy compliance, responsibility and meaningful corporate social responsibility;
    • increased knowledge creation and transfer, research opportunities, value creation and new avenues for innovation.

About the Study

The objective of this study is to showcase opportunities of user-centric models and the enabling environment needed for implementation. In partnership with USAID, BMGF, and DAI, the study conducted by Development Gateway  and Athena Infonomics aims to:

  • raise awareness around the current data economy and implications of the status quo;
  • identify user-centric data governance models and mechanisms, particularly in LMICs;
  • place farmer-centric data governance approaches on the radar of primary stakeholders;
  • demonstrate purpose, value, and benefits (as well as challenges) of these practices; and
  • identify if support for implementation is needed, for whom, and in what form.

 

Below we introduce a primer of user-centric, participatory data governance. These practices are not exclusive or exhaustive and often seek different objectives. You might already employ a number of these approaches.

Data collaboratives promote collaboration between diverse organizations, harness their collective capacities and insights and enhance access to wider institutional data which would be outside their purview. Data collaboratives provide organizations with an option to decide rules of data exchange. This also provides a way of gaining trust and confidence between stakeholders. The model can be used as a term to describe initiatives where private sector data is combined and shared with a third party who manages access to it, usually to make proprietary or siloed data available to inform research or public sector decisions. Data collaboratives need responsible, tenable data stewards to empower members or the general public.

Data marketplaces are platforms where data providers (sellers) and data consumers (buyers) can meet, match, and trade their respective data assets and requirements. Marketplaces are emerging as new intermediaries and increasingly play a vital role in the data economy. Acting as an intermediary, they may increase willingness to share data. Farmers may also have access to aggregated platform data on key market trends derived from data supplied by its users. Marketplaces empower smallholders, especially in LMICs, by generating and sharing data on prices, transaction history, and demand. Data marketplaces can assist in overcoming economic and environmental challenges, optimize productivity, and cut costs.

Data commons pool and share data as a resource, with a high degree of community ownership and leadership. This approach can address power imbalances by democratizing access to and availability of data. They can be created with a variety of data and governance structures. A prerequisite is that they be stewarded responsibly (for which many refer to Ostrom’s principles). In science communities, research data is often pooled to increase the impact of data held by individuals. The discourse revolves around the learnings of open access and application of new forms of management. 

 Data fiduciaries are a type of steward acting as an intermediary that manages access to data between individuals and data collectors based on a duty of care. A data fiduciary can be used to create a trusted environment between various stakeholders and assist in overcoming power imbalances. Mediation of data relationships through a data fiduciary allows for a representative control model which enables individuals to have more control over how data is used and shared.

Indigenous data governance shifts access and control of data away from governments and others directly to Indigenous Peoples. Considering how withholding information has been used as a vector of control, these approaches illustrate how important data sovereignty can be to self-determination. Here, data stewardship entails governance on behalf of (and by a community in) the entire data lifecycle. The mainstream open data and FAIR data principles discourse have been faulted for ignoring historical contexts and power differentials and propose safeguards in the CARE Principles.

Data cooperatives are voluntary communal pooling by individuals of their personal data for mutual economic, social and cultural benefit, and aspirations of a group through a united, jointly-owned, and democratically controlled autonomous association. The model works when stakeholders can be given an equal opportunity in organization and management and have a collective interest. Data cooperatives grant farmers more control over their data; they can manage, curate, and protect access to their data. At the same time, it offers an innovative approach to foster direct engagement and represent interests. Cooperatives come in many forms as they evolve out of the needs of their members.

General Actionable Principles

  1. User-centric models should be integrated into digital agriculture technology programs. These models have immense potential to shift the current paradigms of information imbalances to benefit farmers, communities, and societies. User-centric models can empower individuals to gain more control and ownership over their data, create individual or collective agency and negotiation power, and protect against data misuse. 
  2. Farmer-centric data governance approaches pursue more consistent and higher quality data sharing, interoperability, and defragmentation. Its impact is dependent on whether design, deployment, and implementation are made collaboratively, building on a foundation of trust. This creates true incentives and can overcome the increasing unwillingness of farmers to share data.
  3. Meaningful participation must strongly tie inputs and outputs of farmers to data governance. Farmers and their communities should have visibility into practices and avenues for voting, inquiry, redress, and rectification. Feedback should contribute to decision-making and strategic direction. This requires close monitoring to ensure not only that these requirements are met but also sustained throughout the entire engagement lifecycle.
  4. Data stewards play a crucial role as trusted intermediaries between farmers, data collectors, and data generators. Including a steward enables more access to data pooled by farmers, data that can be shared for broader social benefit, and for a better bargaining position. More research is needed to conclude exact fiduciary relationships and roles of data stewardship.
  5. Trust needs to be fostered. The need to establish and maintain trust is foundational for success in any engagement, if digital AgTech and data are to transform agri-food networks. Efforts that integrate data and data collection products must clearly explain what activities are being undertaken, their benefits, privacy measures, and the process as to how questions and concerns are addressed and resolved; all of this needs to be included not only at the start of an engagement, but with a plan to maintain these conversations and services throughout.
  6. Center local context and culture to determine outcomes, implications, and impact when considering models. Farmers are not homogenous, and their needs vary even on a local level. Women and Indigenous Communities often lack representation and agency. User-centric models may very well help, but should not be rolled out in a generic fashion. Context helps define and decide which approaches are most appropriate. Even the best-fit model can bring harm if it is seen to compete with or supplant existing and trusted community dynamics. 
  7. Prioritize existing practices in the communal governance and their research in LMICs, rather than approaches that strongly reflect Anglophone, Western concepts. Data and information sharing in agriculture often happens via less formal constructs. Language barriers constrict examining resources on experiences outside the expected avenues. Communities often know best how to organize and govern resources. Research and implementation need to follow systematic observation of diverse communities with different vantage points in multiple case studies.
  8. User-centric models are not a panacea ‘one-size-fits all’ solution. Data governance applications tend to be hybrid, iterative, and adaptive. The practical implementation is a lot more ambiguous and variable than theory often suggests. A meaningful farmer-centric data ecosystem can only be built on a range of models and mechanisms. Multi-level governance is an important component of successful engagement. It is important not to get carried away by any hype, instead focus on identifying exactly what is needed.
  9. More research is needed to identify training and capacity building requirements and financial sustainability. The data governance models showcased here are the most prominent examples in an expanding field of opportunities with a newly emerging set of organizations specialized in different avenues of data governance. There is a need to develop data governance skills training aimed at farmers and the development community to assess and capitalize on user-centric data governance opportunities.
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DG at the African Green Revolution Forum (AGRF)

September 1, 2022 Agriculture
Victoria Blackham, Charlene Migwe-Kagume, Seember Ali
Launch, News/Events

Representatives from Development Gateway: an IREX Venture (DG) will be attending the African Green Revolution Forum (AGRF) from September 5-9 in Kigali, Rwanda to highlight two projects: the Visualizing Insights on African Agriculture (VIFAA) project and the Farmer-Centric Data Governance Models project.

Expanding VIFAA

Visualizing Insights on Fertilizer for African Agriculture (VIFAA) was originally designed as a four-year program to holistically address the supply, demand, and use of fertilizer data in sub-Saharan Africa (SSA). VIFAA focuses on better aligning the fertilizer data supply with information needs in Kenya, Nigeria, and Ghana, bringing together disparate data from existing sources and investing in innovations for filling priority data gaps. In 2022, the program was expanded to include five additional countries: Ethiopia, Malawi, Mozambique, Senegal, and Zambia.  

Launching the New AfricaFertilizer.org 

 AfricaFertilizer.org will provide fertilizer data on 25 additional SSA countries in addition to integrating the current data dashboards for Nigeria, Ghana, and Kenya. Users of AfricaFertilizer.org will have the ability to customize data visualizations for cross-country comparison and access timely information on fertilizer markets. Within the platform, trade, consumption, production, and price data will be available. 

AfricaFertilizer.org will display the recently launched Africa Fertilizer Watch dashboard, a visualization of how the Russian invasion of Ukraine has impacted the fertilizer markets of 10 countries in Eastern and Southern Africa. The Africa Fertilizer Watch dashboard also includes indicators tracking overall market risk, affordability, availability, and distribution of fertilizer. Russia’s ongoing attacks in Ukraine have caused severe fertilizer shortages as most countries in SSA rely on fertilizer imports for growing crops. This new dashboard provides transparency for decision-makers to better navigate supply chain disruptions and ensure fertilizer availability. 

Remote Sensing and Machine Learning in Cropland Mapping

In Nigeria and Ghana, basic statistics on hectares of cropland and major crops are outdated, if they exist at all. Availability of reliable and up-to-date land and crop usage data will help inform the government and private sector on how to allocate investments to strengthen the agriculture sector, in particular ensuring that the availability and variety of fertilizer products meet market needs.

Through VIFAA’s innovation fund, DG generated comprehensive cropland under production maps for Nigeria in collaboration with machine learning and satellite imagery experts, Quantitative Engineering Design (QED.ai). The updated map provides information on: 

  • the total land under production across all crops in Nigeria, 
  • what percentage is under production versus uncultivated, and
  • how production breaks down by state and geopolitical zone. 

The dashboard and use of innovative tools to fill data gaps are resulting in better-informed decisions across Nigeria’s fertilizer sector.  In 2021, DG expanded this work to map all croplands and calculate cropland estimates across Ghana

Launching the Farmer-Centric Data Governance Models Report

While at AGRF, DG is also invited to launch the Farmer-Centric Data Governance Report, which was researched and prepared in partnership with DAI, USAID, and BMGF. 

Background on Farmer-Centric Data Governance Models 

While new technologies and opportunities for data have given rise to more accurate predictions and profitability, farmers are increasingly losing control of their data and the benefits derived from it. How data is governed informs which products, services, and insights are used and shapes programmatic strategy. For farmers, the increase of data collection, storage, and use by third parties is of great concern in regards to ownership and governance, insufficient regulatory guidance as well as literacy and capacity restraints. Farmers may be reluctant to share their data, particularly if it will be used and owned by someone else, which may position the farmer to have to forgo access to beneficial digital services. 

The agricultural sector’s potential is limited by dominant monopolies and asymmetries of power and information (in other words, when certain groups have access to more information than others). The current data economy — certainly at the level of global, big data initiatives — is defined by a paradigm of extraction and a lack of consideration of equity, whereby individuals and communities go unrecognized beyond their use as data points and producers. Empowering farmers with more control over their data is critical to improve and protect their lives and livelihoods. Employing data governance approaches grant farmers greater agency over their contributions and meaningful participation shaped by their immediate need, thereby, rebalancing power asymmetries in profound ways. 

Our Objective

This report presents our findings and recommends new ways of approaching data governance. It aims to: 

  • raise awareness around the current data economy and implications; 
  • identify user-centric data governance approaches (in particular in LMICs); 
  • put farmer-centric data governance approaches on the radar; and
  • demonstrate purpose, value, benefits, and challenges of these practices.

About the Partners for VIFAA

International Fertilizer Development Center (IFDC) – As an independent non-profit organization, IFDC works throughout Africa and Asia to increase soil fertility and develop inclusive market systems. Combining science-backed innovations, an enabling policy environment, holistic market systems development, and strategic partnerships, the organization bridges the gap between identifying and scaling sustainable agricultural solutions, resulting in improved household food security and enriched family livelihoods around the world. Using an inclusive approach, IFDC employs locally driven solutions that are environmentally sound and impact oriented that bring change at local, regional, and national levels. More at https://ifdc.org

AfricaFertilizer.org (AFO) – the premier source for fertilizer statistics and information in Africa. It is hosted by IFDC and supported by several partners, key among them being the International Fertilizer Association (IFA), Argus Media, and the Bill & Melinda Gates foundation through Development Gateway under the VIFAA program. Since 2009, AFO has been collecting, processing, and publishing fertilizer production, trade, and consumption statistics for the main fertilizer markets in sub-Saharan Africa. AFO has an extensive network of fertilizer industry players in the main fertilizer trade corridors and maintains key information on the major producers, their production facilities and capacities, importers/suppliers, and various distribution channels. More at  https://africafertilizer.org.

Athena Infonomics – is a data-driven global consultancy that combines  social science research methods and ICT tools to drive innovation in policies, processes and programs in global development. They play a supportive role for the written components and research behind the Farmer-Centric Data Governance Models Project. More at https://www.athenainfonomics.com

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You can also listen to Data… for What?! on SpotifyStitcher, and Apple Podcasts.

DG has worked in agriculture since 2015, but it was only in our last strategy cycle that Agriculture was specifically elevated as a strategic area of focus. That decision was largely shaped by several projects, where our experience as data experts allowed us to support partners to map agricultural data ecosystems, collate and unify data sources, and support specific sector policy and service delivery objectives using data and digital tools. Additionally, we saw agriculture as an under-served sector in data and technology, as compared for example to health and education. We felt that we had a unique contribution to make in agriculture at the beginning of our 2018-2021 strategy, and based on the rapid growth and successes of our work, are continuing to scale our work in agriculture through this new strategy.

Building Trust in Data

Our work in agriculture has made clear that, with the right partnerships, our expertise in data and technology does not require us to become experts in the sector in order to add value. Our success has come from our expertise in data and digital combined with our ability to build trust between partners. For example, through Visualizing Insights on Fertilizer for African Agriculture (VIFAA) program, we worked with Africafertilizer.org and other partners to combine and then validate separate datasets from the private sector, government, and civil society. That collated data has been visualized on dashboards to provide a fuller picture of the entire sector. 

We have also built trust in the cashew and seed sector supply chains through improved data governance including MoU’s and data sharing agreements. A focus on improved data governance has allowed us to reduce fragmentation and data silos, which in turn has made data beneficial for decision makers in the private sector as well as those making policy decisions. 

What is the Future of Data & Digital in Agriculture?

There is tremendous opportunity for innovation in the agriculture sector, but a few that we are particularly interested in are:

  • Ethical Use of Remote sensing and Machine Learning – We have seen significant advances in remote sensing and machine learning, which we have harnessed to provide cropland maps in Nigeria and Ghana for the first time since the 1970s. Innovation can drive down costs and allow for more consistent data collection, which we have seen clearly through the cropland mapping.
  • Responsible Data Use – In general, we have increased our focus on responsible data use and data governance and this focus will include the agricultural sector as well. This means looking at data sharing agreements between stakeholders, but also increasingly looking at data governance models that protect smallholder farmers and include them more directly.
  • Creating Partnerships and Combined Approaches – We have also seen that there are many paths to impacting smallholder farmers. Historically we have focused more on upstream policy interventions and support to the private sector that have the trickle-down effect of helping the farmer. As we move further into digital innovations, we anticipate that there will be more opportunities for DG to work directly with smallholder farmers.

We are taking a two week break from the podcast to take this conversation about our work in agriculture to in-person presentations and conversations at AGRF. If you are there, please connect with DG! Otherwise, we will be back in a few weeks for our final episode in this series where we will discuss digital transformation.

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