In a recent update of earlier estimates (1), the Food and Agriculture Organization (FAO) of the United Nations reported that more than one billion people now suffer malnutrition (2). Despite declines in food prices from their 2008 highs, local prices in many developing countries are still high by recent historical standards. Long-run trends in global food commodity prices are driven by differential rates of growth in the supply and demand for food crops, feed, and livestock products.

Is increased support needed for wheat disease research to avert crop losses from current and future strains?

Research-enabled growth in agricultural productivity is pivotal to sub-Saharan Africa’s overall economic growth prospects. Yet, investments in research and development (R&D) targeted to many national food and agricultural economies throughout Africa are fragile and faltering. To gain insight into what could be driving this trend, this article updates, summarizes and reassesses the published evidence on the returns to African agricultural R&D. Based on a compilation of 113 studies published between 1975 and 2014 spanning 25 countries, the reported internal rates of return (IRRs) to food and agricultural research conducted in or of direct consequence for sub-Saharan Africa averaged 42.3%py. In addition to the 376 IRR estimates, the corresponding 129 benefit-cost ratios (BCRs) averaged 30.1. Most (96.5%) of the returns-to-research evaluations are of publicly performed R&D, and the majority (87.6%) of the studies were published in the period 1990–2009. The large dispersion in the reported IRRs and BCRs makes it difficult to discern meaningful patterns in the evidence. Moreover, the distribution of IRRs is heavily (positively) skewed, such that the median value (35.0%py) is well below the mean, like it is for research done elsewhere in the world (mean 62.4%py; median 38.0%py). Around 78.5% of the evaluations relate to the commodity-specific consequences of agricultural research, while 5.5% report on the returns to an “all agriculture” aggregate. The weight of commodity-specific evaluation evidence is not especially congruent with the composition of agricultural production throughout Africa, nor, to the best that can be determined, the commodity orientation of public African agricultural R&D.

Digital agriculture is often heralded as the next major wave of innovation in food and agriculture. Driven by big data, the intention is that digital agriculture will transform the entire research and development pipeline across agricultural value chains throughout the developed and developing worlds. Yet, issues concerning data quality, interoperability, intellectual property ownership and data privacy present considerable challenges to this vision. Digital agriculture platforms, which support data sharing, analysis, interoperability, and public and private sector collaboration, are one approach to address this challenge, but as a new research domain, there is a lack of conceptual clarity around what constitutes a “digital agriculture platform”. Here, we use a “bottom up” and “top down” analysis approach to develop a taxonomy of the digital agriculture landscape. Then, we select a set of digital agriculture platforms for in‐depth analysis across a set of technical and use requirements. While digital agriculture will remain a constantly evolving landscape with varied technologies and application areas, this presents a first attempt to characterize this landscape and establish a common vocabulary for understanding digital agriculture platforms.