Plasma-activated water to foster sustainable agriculture: Evidence and quest for the fundamentals

Societal Impact Statement: An increasing world population facing limited natural resources poses a global challenge to food security. This challenge is increasing due to climate change, which in turn is strongly affected by the food system that accounts, according to the Food and Agriculture Organization (FAO), for one-third of the global greenhouse gas emissions. To make food production sustainable, a new model of agriculture with responsible use of natural resources and minimum use of agrochemicals must be implemented. This approach fosters the development of new technologies, such as that based on plasma-activated water, whose effects, perspectives, and quest for fundamentals are discussed herein. Summary: Applications based on non-thermal atmospheric plasmas - commonly referred to as cold plasmas - to the food system are emerging as effective technologies that may contribute to reconciling the increasing demand for food with the need to minimize its environmental impact. In particular, the technology based on the so-called plasma-activated water (PAW) has proven effective in promoting plant germination, growth, and resistance to biotic and abiotic stresses. Several interpretations have been proposed for the various beneficial effects played by PAW on plants, and the quest for the fundamental processes underlying them is ongoing. In this contribution, we present and discuss the approach based on calcium signaling, a ubiquitous and versatile signal transduction mode that has recently been demonstrated to be involved in PAW sensing by plants. The plant Ca2+-mediated response is related to the mixture of reactive chemical species, as confirmed by selective plasma irradiation. Directions for future research are discussed, with a special focus on the link between Ca2+ signaling and plant responses to PAW/cold plasma. The potential offered by an interdisciplinary approach, combining real-time monitoring of intracellular Ca2+ changes with machine learning algorithms, is pinpointed.