Global change impacts on mineral nutritional quality of cereal grains: Coordinated datasets and analyses to advance a systems-based understanding

Authors: Miner, Grace; Stewart, Catherine; Delgado, Jorge; IPPOLITO, JAMES - The Ohio State University; MASON, R - Colorado State University; HALEY, SCOTT - Colorado State University; Guttieri, Mary; Ainsworth, Elizabeth - Lisa; McGrath, Justin; Beebout, Sarah

Submitted to: Field Crops Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/8/2024
Publication Date: 3/23/2024
Citation: Miner, G.L., Stewart, C.E., Delgado, J.A., Ippolito, J.A., Mason, R.E., Haley, S.D., Guttieri, M.J., Ainsworth, E.A., McGrath, J.M., Beebout, S.E. 2024. Global change impacts on mineral nutritional quality of cereal grains: Coordinated datasets and analyses to advance a systems-based understanding. Field Crops Research. 310. Article e109338. https://doi.org/10.1016/j.fcr.2024.109338.
DOI: https://doi.org/10.1016/j.fcr.2024.109338

Interpretive Summary: The effect of global climate change on agriculture’s ability to deliver nutritious crops is not well understood. Around the world, experiments are increasingly evaluating the effect of elevated CO2, changing temperatures, and water stress on crop yields, but impacts of these interactive factors on crop nutritional qualities such as protein and micronutrient concentrations are only rarely measured. Comparison of nutritional outcomes across studies is often hampered by differences in protocols and other ways in which research is performed. Based upon a review of the state of the science, a team of researchers from USDA and universities has developed a core set of six recommendations, or best practices, composed of six key inter-reliant methods, practices, tools, and data that should support experimental datasets. These recommendations seek to standardize and improve the outcomes of research seeking quantify the nutritional response of agriculture to climate change.

Technical Abstract: Global nutritional health outcomes are directly reliant on agroecosystem nutrient outputs. Appropriately, there is concern surrounding the impacts of a changing climate not only on crop yields, but also on crop nutritional quality (e.g., mineral nutrient concentrations). Quantifying the impacts and interactions of elevated CO2 concentration (e[CO2]), temperature, water, and edaphic factors on crop yields and mineral nutrition is critical, yet a systems-level understanding of these interactive factors is poorly developed. Datasets are needed to expand our mechanistic understanding, inform climate adaptation practices, and support nutritional planning at regional and global scales. Empirical data for climate impacts on crop nutritional quality remain scarce, with most research emerging from valuable, but geographically limited, Free-air CO2 Enrichment (FACE) experiments. Several FACE studies suggest that human nutrition will be adversely impacted by e[CO2]. Specific concerns center on observed declines in grain protein, iron (Fe) and zinc (Zn) concentrations due to already wide-spread human nutritional deficiencies in these nutrients. Unfortunately, the data reported for e[CO2]-induced shifts in mineral nutrition are incomplete, precluding the generalizability of results. As global change experiments expand to pursue pressing questions regarding interactive climate impacts on crop yields and nutritional content, it is imperative to interrogate the measurements, data standardization methods, and metadata needed for unifying comparison and synthesis. We frame this review around six key inter-reliant methods, tools, and practices that should support experimental datasets to inform questions of global change impacts on crop nutrition and aid in detecting genotypic differences in yield-adjusted mineral nutrient density. Improved mechanistic understanding of soil-plant-nutrient-grain-yield-climate interactions will help determine the climate adaptation solutions that can be supported via soil fertility, agronomic management, and plant breeding. This work is essential for developing modeling tools to support nutritional planning and to identify areas where agronomic management and breeding can minimize climate impacts on nutritional outcomes.