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Food and agriculture is the largest global industry, at $7.8Tn annual value, and is also the least digitized industry. As a consequence, the inefficiencies in this industry are staggering: yield gaps below potential are 20-70% worldwide, and of the crops that are produced, 20-50% are lost from the time of harvest up to consumption. Where some frame the challenges in agriculture as “grow more with less,” a more useful analysis is around risk and uncertainty. In emerging markets, lack of geospatial data makes it difficult to recommend improved seeds or fertilizers for particular locales, therefore risky to make operating loans, impossible to accurately price crop insurance, and ultimately poses challenges in making contracts for delivery to processors that bring ag products into the food system. In developed markets, the ever increasing demands around immediacy, transparency, quality, crop novelty and food safety are straining the capacity of growers and processors to keep up. We have come to see this as a challenge in developing predictions joining both buyers and sellers around a shared set of facts on harvest timing, total yield, and post harvest quality. While these challenges have been met historically from government agencies and marketing boards reporting seasonal and regional forecasts, in many instances these are insufficient for making critical operational decisions on short timescales. In this talk, we will present a new set of measurements and analytical tools that enable unprecedented granularity in predictions to reduce risk and uncertainty in the food and ag supply chain, with special attention to applications that have potential to be economically self-sustaining. 

Abstract Stomatal response to environmental conditions forms the backbone of all ecosystem and carbon cycle models, but is largely based on empirical relationships. Evolutionary theories of stomatal behaviour are critical for guarding against prediction errors of empirical models under future climates. Longstanding theory holds that stomata maximise fitness by acting to maintain constant marginal water use efficiency over a given time horizon, but a recent evolutionary theory proposes that stomata instead maximise carbon gain minus carbon costs/risk of hydraulic damage. Using data from 34 species that span global forest biomes, we find that the recent carbon‐maximisation optimisation theory is widely supported, revealing that the evolution of stomatal regulation has not been primarily driven by attainment of constant marginal water use efficiency. Optimal control of stomata to manage hydraulic risk is likely to have significant consequences for ecosystem fluxes during drought, which is critical given projected intensification of the global hydrological cycle.