Plant field trials are an experimental toolkit with a remarkable history of success: they have driven our understanding of crops since 1856, facilitated the ‘green revolution’ that feeds over a billion people, and supported the rise of modern yield gains that both reduce our impact on the environment through reduced crop footprints and feed a rapidly growing global population.
New ways of conducting, measuring, and understanding field trials are therefore vital to the advancement of plant science. However, great opportunities in agronomy are being left unrealized through a general lack of coherence and coordination, driven by starvation-level funding.
The modest remedy we have attempted (Newman & Furbank 2021) is to take one of the few large field trial datasets in the world generated by public money, enrich it with satellite data, and liberate it for general use. By making thousands of field trials accessible and linking them to basic satellite sensor data, we have tried to highlight the remarkable power of open data to inform our understanding of plants.
Relatively little plant trial data is available in the public sphere. In contrast with the data available to private breeding companies, who enjoy sufficient resources, public trial data is often derived from disparate experiments conducted with different densities, conditions, inputs, funding levels, and quality. Such heterogeneity is not, in itself, a fundamental problem. However, failing to fully capture differences in data quality and experimental design constrains our ability to monitor and understand crops. In short, data management remains a poorly addressed issue that is holding back agronomy.
Compounding this problem is the under-utilization of an incredible resource. Satellite data are rarely used to categorize the environmental, vegetation, and weather patterns of field trials in either the developed or developing worlds. Despite the targeted development of vegetation monitoring to study landscape-scale processes, capture highly detailed weather patterns, and capture carbon flows through soil and photosynthetic fluxes, satellite data are almost never used to enrich field trials or open-air plant experiments. This represents an enormous missed opportunity.
For example, the entire eastern hemisphere has been covered for over a decade by the Himawari satellites: 16-band multispectral instrument that measure (amongst other parameters) land surface temperatures to within a degree K at constant 10-minute or 2.5-minute intervals. The potential for these instruments to routinely capture frost, drought, and heat stress patterns across field trials, using just one processed data stream of many, seems a remarkable loss.
It is hard to imagine any agronomist who would pass up the opportunity to examine the temperature profile of their fields in real time, using a direct measure of surface temperature. Yet it is the result of intrinsic and funding-level barriers to the use of satellite data in agronomy and plant science that archives of such data remain untouched.
Remote sensing data are also, for now, surprisingly universal in coverage, allowing their application to low-income and high income countries alike. Remote sensing data on developing world crops is often far better than those in, say, developed European countries simply as a result of lower cloud cover. However, barriers to access and use, and the diversion of talent to first-world problems, result in severe under-utilisation of these exciting resources.
A cornucopia of plant data are embedded within remote sensing archives. Satellites measure leaf area index, water use efficiency, soil moisture, ground temperature, air temperature, rainfall, aerosol density, incident light quality, the fraction of photosynthetically absorbed radiation, pollution, snow cover, flooding, frost extent, and even stubble burning events. The opportunities for plant research and agronomy are considerable and growing fast.
Our paper represents a low-resolution sample of the incipient power and opportunity of conducting plant science from space. It is also a call to arms for better plant data, and better plant science funding, to feed the growing world.