Our eight intervention questions

Ceres2030 brings together development and environmental economists, geographers, crop breeders from NGOs, research organizations and academia to explore the evidence around eight priority questions. Through the use of sophisticated tools and computing power, we are equipping researchers to make sense of hundreds of thousands of published papers, to synthesize this knowledge, identify gaps, and ensure decision-makers are working with the best possible analysis and information when they make decisions on investments in agriculture to end hunger (see the section Evidence synthesis for a detailed explanation of this process).

Our research teams are examining critical dimensions of food security. They are looking at the effectiveness of interventions to support farmers who live in drought or water-scare conditions. They are looking at the critical role of nutrition in livestock, a vital resource for a billion people. They are asking, “what does it really takes to store and sell crops to growing urban populations, and what are the factors that are shaping the entire food economy?” They are studying which policies and incentives work to encourage farmers to adopt sustainable farming practices, how organizations use a variety of approaches to support and seek out knowledge, and whether training programs that can offer employment opportunities for youth.

And they—76 researchers and information specialists from 23 countries—are doing all this vital work on a voluntary basis. The answers to these questions will be published, subject to peer review, in Nature Research Journals in 2020, and will be used to inform the cost model for SDG2.

How we assess the quality of evidence

Evidence syntheses, like scoping and systematic reviews, bring all the studies on a particular issue or intervention together to evaluate what they mean. It’s a process with specific steps designed to minimize bias and to ensure rigor and transparency, so that someone else could replicate the process and reach the same conclusion.

Each of the eight intervention research teams is supported by research synthesis experts. The first critical step is for the authors to create a protocol for each review. This is the roadmap setting out how the review is going to be done, how the reviewers will decide what studies or data to include or exclude in the review, and how those studies and data will be reviewed.

One particular issue facing agricultural research is that it has fewer randomized control trials than, say, medicine, and needs to be inclusive of many different kinds of evidence and data. This makes scientific appraisal more difficult. For this reason, we are taking a mixed-methods approach, which combines quantitative and qualitative evidence on complex and pressing questions, and which has been successful in previous agricultural systematic reviews. Groups with deep experience in mixed-methods reviews, such as the Campbell Collaboration and the Center for Evidence-Based Agriculture have worked closely with us to explore appropriate methods and offer expert advice.

Once each research team reached a consensus on the protocol, it was published—and it cannot be changed. Publishing ahead of doing the review protects its replicability and transparency. It also gives us a chance to share our work, what we are doing, and allow for scientific dissent as part of the process.

All the protocols have been uploaded to the OSF open-science platform. You can find links to these on each question pag

Using machine learning to find
policy interventions to end hunger

Given the amount of information available, relying only on keyword searching doesn’t work. If we want to understand the fullness of human knowledge, we need to incorporate new methods of discovery that account for the way we describe similar things in different ways.

Over the past decade, there have been enormous advances in artificial intelligence that enable computers to analyze the way we use language. This involves training a computer program to recognize relationships between words, so that it can capture the different ways people describe similar things.

We used machine learning and natural language processing (NLP) to create and analyze a preliminary dataset of ~50,000 articles and reports (2008-2018) about smallholder farmers from science journals and research and development organizations. We used a variety of search terms, such as small-scale food producers, rural farmers, and subsistence and contract farmers.

In order to increase coverage of materials published in low and middle income countries, we included the full table of contents from the African Journal of Biotechnology, African Journal of Agricultural Research, African Journal of Food, Agriculture, Nutrition and Development, African Crop Science Journal, Indian Journal of Agronomy, and the Indian Journal of Agricultural Economics.