Category: Funding

David Lobell of Stanford University, whose work we have featured here in the past, has a think-piece out called “Principles and priorities for one CGIAR.” For the uninitiated…

OneCGIAR is a dynamic reformulation of CGIAR’s partnerships, knowledge and physical assets – building on an energized, interconnected, and diverse global talent pool. It aims to drive major progress in key areas where innovation is needed to deliver on the SDGs by 2030, anchored in more unified governance, institutions, country engagement, and funding.

And yes, there’s a hashtag. ((But is it all too little, too late?))

Anyway, David thinks the new, improved OneCGIAR should focus on just two things. The first is kind of obvious:

…continued investment in breeding, a longstanding strength of the system. Progress on flagship crops such as wheat and rice will be needed, especially in the face of climate change. For example, maintenance breeding to protect against evolving diseases and pests will be ever more important, as will finding varieties that can withstand heat extremes. Equally important, however, will be to expand work on the many other crops that are grown by poor farmers throughout the world. Historically, these “orphan” crops have received far less attention than major internationally traded crops, but many compelling reasons exist to expand efforts on these crops, including (i) their potential value in addressing micronutrient deficiency (“hidden hunger”), for which less progress has been made than for calorie deficiency in many regions (Gödecke et al., 2018); (ii) the ability of nitrogen-fixing legumes to reverse soil degradation in a cost-effective way (Vanlauwe et al., 2019), a critical need for improving productivity and fertilizer responsiveness in many smallholder fields; (iii) the ability of many orphan crops, such as pigeonpea, cowpea, and cassava, to withstand increasingly frequent extreme heat and drought conditions, and (iv) the prospects that new technologies like genomic selection and gene editing will dramatically reduce the cost of working on orphan crops, especially given recent progress in sequencing many of their genomes (Dawson et al., 2019).

Nice to see orphan crops being highlighted in this way, as we often do here. And of course the international genebanks underpin the CGIAR’s breeding, although David doesn’t mention them. David’s proposed second priority is perhaps more surprising:

…precision agronomy, often also referred to as site-specific or digital agronomy. In food insecure regions, productivity gains from improved management are often far greater than from improved genetics. Yet spurring adoption of a new seed has typically been easier than a new set of practices (Stevenson et al., 2019). Part of the reason is that the ideal management depends a lot on local soil, weather, plot history, and economic conditions, and many “best-practice” recommendations fail to deliver profits for a large fraction of farmers (Jayne et al., 2018).

However, new technologies will help to much more quickly diagnose the major needs at subnational and even field scales. For example, spectrometers can be used to rapidly measure soil deficiencies (Viscarra Rossel and Bouma, 2016), photos from mobile phones can be used to diagnose canopy stresses (see Fig. 1), and satellite imagery can be used to identify fields most likely to benefit from specific inputs or practices (Jain et al., 2019). These existing examples, many of which involve CGIAR scientists, are just the proverbial tip of the iceberg. As the different data streams grow and become integrated, it is plausible that every smallholder in the world could have access to recommendations with a high probability of boosting yields and profits. Although some of this can be achieved by the private sector, my view is that a major investment by CGIAR, along with national partners, would help to ensure that poor farmers can quickly benefit from these technologies.

Again, this is an area that we have occasionally discussed here, in particular the usefulness of high-resolution spatial datasets. ((I’ve unashamedly used my mother-in-law as a guinea pig when exploring some of these.))

I haven’t seen any replies to David’s suggestions yet, but I’m sure they’re in the works and we’ll be on the lookout for them.