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

Abstract

ContextGlobal 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 of elevated CO2 concentrations, elevated temperature, drought stress, edaphic factors, and agronomic management on crop yields and mineral nutrition is critical, yet a systems-level understanding of these interactive factors is poorly developed, limiting our ability to effectively target solutions. Empirical data for climate impacts on crop nutritional quality remain scarce, with much of the research emerging from valuable, but geographically limited, Free-air CO2 Enrichment (FACE) experiments, several of which suggest that human nutrition will be adversely impacted by e[CO2]. Specific concerns center on observed declines in grain protein, iron, and zinc concentrations due to already wide-spread human nutritional deficiencies in these nutrients. ObjectivesAs global change experiments expand to pursue questions regarding interactive climate impacts on crop yields and nutritional quality, it is imperative to interrogate the measurements, data standardization, and metadata needed for unifying synthesis. The data reported for shifts in crop nutritional quality are often incomplete, precluding the generalizability and comparability of results. MethodsWe frame this review around six inter-reliant methods, tools, and practices to support maximally useful experimental datasets to inform questions of global change impacts on crop nutrition and aid in detecting genotypic differences in mineral nutrient density. The bulk of the data and discussion centers on wheat (Triticum aestivum L.) due to the central role this crop plays in human nutrition and sustained biofortification efforts. ResultsTo permit experimental comparability and synthesis, datasets should (1) clearly delineate analytical methods and standards and (2) link mean nutrient concentrations with the covariate of yield. (3) Multi-year, multi-location data is required to identify genotypes with significant deviations in nutrient concentrations, with (4) data normalized for yield within appropriate analytical frameworks. (5) Inclusion of data on soil properties, weather, and abiotic and biotic stresses as well as (6) agronomic practices and nutrient management is essential for understanding global change impacts on nutritional outcomes. ConclusionsCoordinated, multi-dimensional data will permit the syntheses and meta-analyses needed to identify and quantify climate impacts on nutrition. ImplicationsThis work is essential to effectively target nutritional solutions, to develop modeling tools to support nutritional planning, and to identify areas where agronomic management and breeding can minimize climate impacts on nutritional outcomes.