A global dataset on agronomic and genetic biofortification of wheat for zinc, iron and protein concentration

Hidden hunger, caused by the insufficient intake of vitamins and essential micronutrients, along with the high concentrations of antinutritional factors, remains a pervasive global issue. A recent global analysis estimates that 65% and 46% of the global population suffer from inadequate iron (Fe) and zinc (Zn) intake, respectively. Epidemiological studies indicate that at least one in five individuals is at risk of Zn deficiency, partially attributed to diets with low Zn and high phytate concentrations. Wheat provides ~35–40% of the caloric intake of the global population. However, it is inherently deficient in key micronutrients such as Zn and Fe. Wheat production on nutrient deficient soil further decreases micronutrient concentrations in grains. Excessive application of phosphorus (P) fertilizers in Zn-deficient soils exacerbate the problem by elevating grain phytate concentrations. Phytate, an antinutritional factor, binds to Zn and Fe, thereby reducing their bioavailability in human diet. Addressing the dual challenge of improving both grain yield and nutritional quality is thus a critical global priority. The data set presented here is derived from the systemic global literature search following the Preferred Reporting Items for SystematicReviews and Meta-Analyses (PRISMA-S) guidelines (See figure below). A comprehensive search was conducted using Web of Science, CAB Direct, Scopus, PUBMED, and Google Scholar from September 2023 to February 2024. The search terms for Web of Science, CAB Direct, Scopus, and PUBMED included: (“Micronutrient” OR “Zinc Application” OR “Iron Application” OR “Ferti*”) AND (“Wheat” AND “Grain Zinc” OR “Grain Iron” AND “Grain Yield” OR “Produ*”). In Google Scholar, the search string used was: “Bread Wheat” OR “Durum Wheat” AND “Grain Zn” OR “Grain Fe” OR “Mineral” AND “Grain Protein.” Boolean operators “AND” and “OR” were used to combine the keywords, and asterisks (*) were included to account for multiple keyword variants. An additional literature search was also conducted on genotype by environment (G×E) interactions and heritability to understand the genetic and environmental influence on grain Zn, Fe and protein concentrations in wheat. Data from tables were recorded directly, whereas figures such as bar charts and graphs were read using WebPlotDigitizer (https://automeris.io/WebPlotDigitizer/). Data on site characteristics including the site name, geographic coordinates, year of study, rainfall received in the season, soil clay, sand and silt, soil pH, soil organic carbon (SOC), total nitrogen, available phosphorus and potassium, Zn and Fe concentrations in the soil were extracted from each study. Missing soil information was retrieved from Soil Grids (https://soilgrids.org/) using the reported GPS coordinates of experimental site. In addition, soil type data based on the World Reference Base (WRB) were also extracted from SoilGrid. Information on crop management including irrigation, inoculation, and the nitrogen, phosphorus, potassium, Zn and Fe application rates, grain yield, grain concentrations of Zn, Fe, protein and phytate were also extracted from each study. Information on wheat varieties, their release dates, and habit were extracted from the Wheat Atlas http://wheatatlas.org/varieties. This information was verified by cross-checking with documents from each country. All the data were then organized in Microsoft Excel and processed to facilitate visualization and formal statistical analyses.