Starch makes up roughly 60 to 70 percent of the wheat endosperm’s dry weight and is the single most important carbohydrate reserve in the grain. Its functionality is governed by a hierarchy of structural features: the ratio of the two polymers amylose and amylopectin, the molecular weight of those polymers, the branching pattern of the glucose chains, the way amylopectin double helices assemble into crystalline lamellae, and the overall morphology of the granules. Each of these levels influences gelatinization, hydration, retrogradation and other properties that determine whether a flour makes an airy loaf or a dense, gummy one. Because nitrogen controls how carbon is allocated during grain filling, researchers led by Md Najmol Hoque, Clair Keene and Shahidul Islam hypothesized that when nitrogen arrives relative to flowering could produce distinct, measurable changes across this entire structural hierarchy.

To test the idea, the team ran field trials during the 2022 and 2023 growing seasons at four locations in North Dakota and Minnesota — Carrington, Casselton, Prosper and Crookston — using a randomized complete block design with three commercially important hard red spring wheat cultivars: Faller, Frohberg and Glenn. Four nitrogen treatments were compared: an unfertilized control, 56 and 112 kilograms of nitrogen per hectare applied before planting, and a 168-kilogram-per-hectare split treatment that combined pre-plant and starter nitrogen with a post-anthesis top-dress of 33.6 kilograms per hectare during early grain filling. Grain was harvested at physiological maturity, milled in the laboratory, and the resulting flour subjected to an unusually comprehensive battery of analytical techniques spanning scanning electron microscopy, size-exclusion chromatography with multi-angle light scattering, X-ray diffraction, Fourier-transform infrared spectroscopy, proton nuclear magnetic resonance, differential scanning calorimetry and solvent retention assays.

The electron microscopy images told the first part of the story. Regardless of nitrogen treatment, the wheat endosperm displayed its characteristic bimodal starch granule population — large lenticular A-type granules and small spherical B-type granules — and nitrogen did not alter the intrinsic shape of individual granules. What changed was their surroundings. Under zero nitrogen, granules sat loosely packed and clearly separated with minimal matrix coverage, but as nitrogen supply increased, packing became progressively more compact and granules became increasingly embedded in a continuous protein matrix. The effect was most pronounced under the split treatment that included post-anthesis nitrogen, consistent with enhanced deposition of storage proteins around the granules during grain filling and a denser, more integrated endosperm microstructure.

The compositional data reinforced this picture. Total starch content declined steadily with increasing nitrogen, from 64.53 percent in the unfertilized control to 62.45 percent under the highest split treatment, presumably because greater nitrogen availability during grain filling favors protein deposition over carbon stored as starch. Yet starch damage during milling actually fell, from 5.08 percent to 4.62 percent, indicating that the denser endosperm matrix physically protected granules from fracture. Flour color shifted in parallel, becoming slightly darker and yellower as protein accumulation altered light scattering within the flour. These coordinated changes show that nitrogen reshapes not only how much starch the grain contains but also how mechanically robust that starch is when the grain meets the mill.

The most striking results concerned the starch polymers themselves. Amylose content followed a biphasic trajectory: it dropped from 26.33 percent in the control to 22.68 percent under the pre-plant 112-kilogram treatment, then partially recovered to 24.49 percent when post-anthesis nitrogen was added. Amylopectin moved in the mirror image, peaking at 77.32 percent under pre-plant nitrogen before easing back. Meanwhile, amylopectin molecular weight rose from 5.76 million daltons in the control to about 8.1 million daltons under nitrogen, while amylose molecular weight fell from 2.05 million to roughly 1.6 million daltons. In other words, pre-anthesis nitrogen pushed the starch toward an amylopectin-rich, high-molecular-weight architecture, whereas late-season nitrogen partially rebalanced the amylose-amylopectin ratio without dismantling the polymer architecture already established.

Structural analyses at the crystalline and molecular scales confirmed that these compositional shifts had real physical consequences. X-ray diffraction showed that all samples retained the characteristic A-type pattern of cereal starch, but relative crystallinity climbed from around 26 percent in unfertilized grain to as high as 33 percent under nitrogen, reflecting more ordered crystalline domains stabilized by the amylopectin-rich composition. Infrared spectroscopy tracked short-range molecular order in the granule’s outer regions and showed the same trend: the ratio associated with ordered structures rose while the amorphous indicator fell. Notably, the degree of branching, measured by nuclear magnetic resonance as the proportion of alpha-1,6 glycosidic linkages, continued to increase even after post-anthesis nitrogen — rising from roughly 2.4 to 2.8 percent in the control to 3.5 to 3.6 percent under the split treatment — whereas crystallinity and molecular order had already plateaued. Branching, it seems, remained responsive to nitrogen availability late into grain filling even after the major structural reorganization was complete.

Those structural differences translated directly into function. Differential scanning calorimetry revealed that gelatinization onset, peak and conclusion temperatures all rose with nitrogen, peaking under the pre-plant 112-kilogram treatment — peak gelatinization temperatures climbed from the low 60s Celsius in unfertilized grain to as high as 71.7 degrees — before easing back under the split treatment. Gelatinization enthalpy followed the same pattern, rising from about 10.5 to 12.3 joules per gram in the control to nearly 17 to 18 joules per gram under pre-plant nitrogen, meaning more energy was needed to disrupt the more highly ordered double-helical and crystalline domains. Hydration behaved differently: swelling power and solubility increased progressively with every increment of nitrogen and kept rising under post-anthesis application, with swelling jumping from 13.15 percent to 19.92 percent and solubility from 16.60 percent to 26.59 percent. Water solvent retention capacity of the flour rose while sodium bicarbonate retention fell, the latter consistent with the reduced starch damage. Late nitrogen, in short, primarily made starch more water-accessible rather than more thermally resistant.

Multivariate statistics tied the whole system together. Principal component analysis of the twelve cultivar-by-treatment combinations showed that the first two components captured 76.9 percent of the total variation, with the dominant axis describing a transition from less ordered to more ordered starch — crystallinity, molecular order, thermal stability and hydration traits all loading together on one side, and total starch, damaged starch and amylose molecular weight on the other. Correlation analysis confirmed that relative crystallinity and infrared-derived order tracked gelatinization temperatures and enthalpy, while swelling and solubility correlated with amylopectin content and structural order. Cultivar mattered too: Frohberg showed the highest gelatinization temperatures and hydration traits, Glenn maintained the highest crystallinity across treatments, and Faller exhibited the greatest branching response, with genotype-by-environment interactions significant for composition and hydration but not for polymer molecular weight, which proved more stable across environments.

The practical implications are considerable. Because pre-anthesis nitrogen chiefly governs composition, crystallinity and thermal behavior, while post-anthesis nitrogen preferentially enhances branching and hydration, fertilizer scheduling could become a tool for tailoring starch functionality to specific end uses — a harder-cooking, more thermally stable starch for one product, a more water-absorbing flour for another — rather than simply maximizing protein content. The authors caution that responses varied in magnitude across site-years that differed in temperature and rainfall, and that future work integrating starch biosynthetic enzyme activity and gene expression will be needed to explain the regulatory mechanisms. But the central message stands: the starch in a wheat kernel is not a fixed commodity. It is a dynamic structure that farmers can, in effect, redesign from the field — one carefully timed application of nitrogen at a time.

Subject of Research: Effects of pre- and post-anthesis nitrogen fertilization on starch structure and functionality in hard red spring wheat

Article Title: Differential regulation of starch structure-function relationships by pre- and post-anthesis nitrogen in hard red spring wheat

Article References: Hoque, M. N., Keene, C., & Islam, S. (2026). Differential regulation of starch structure-function relationships by pre- and post-anthesis nitrogen in hard red spring wheat. Food Chemistry: X, 39, Article 104525. https://doi.org/10.1016/j.fochx.2026.104525

Image Credits: AI Generated

DOI: 10.1016/j.fochx.2026.104525

Keywords: wheat, starch, nitrogen fertilization, post-anthesis nitrogen, amylose, amylopectin, crystallinity, gelatinization, hard red spring wheat, grain filling, food chemistry, flour quality

The research team, led by Jared Lile with Jennifer Lachowiec, Jason D. Fiedler of the USDA Agricultural Research Service, and Jason P. Cook, mined twelve years of historical quality data from the Montana State University Spring Wheat Breeding Program, spanning 2010 through 2022. In total, 842 lines were phenotyped, including 24 check varieties and 818 elite experimental lines of hard red spring wheat, a market class that accounted for roughly a quarter of total United States wheat production in 2024. Hard red spring wheat is prized for its high grain protein content and gluten strength, and its market price depends directly on how well the flour performs in the mill, the mixer, and the oven.

The twelve traits under scrutiny spanned the full quality spectrum, from milling characteristics such as flour yield and ash content, through dough properties like mix time, mix water absorption, and mix tolerance, to baking outcomes including bake mixing time, bake water absorption, and loaf volume. Measurements followed American Association of Cereal Chemists protocols, using near-infrared reflectance instruments for protein determination, a Brabender Quadrumat Sr. mill for flour yield, a Mixograph for dough rheology, and standardized bake tests for loaf volume. Single kernel hardness was captured separately with a single-kernel characterization system. This depth of phenotyping, sustained over more than a decade, is what gave the study its statistical power.

On the genetic side, the team used genotyping-by-sequencing to profile all 842 lines, ultimately retaining 21,958 single nucleotide polymorphisms after filtering and imputation against the Chinese Spring wheat reference genome. Broad-sense heritability, calculated with the Cullis method suited to unbalanced datasets, proved high for nearly every trait: all but one exceeded 0.60, with only wheat ash content falling to 0.39. That combination of high heritability and polygenic architecture, meaning many genes of small effect, makes end-use quality traits ideal candidates for genomic selection, which models the entire genome simultaneously rather than tracking a handful of major loci as marker-assisted selection does.

The researchers compared two broad analytical frameworks. The first was a conventional two-step approach: best linear unbiased estimators were computed for each line across all environments, and those summary values then served as response variables for seven genomic prediction models, including ridge regression BLUP, Bayesian ridge regression, Bayesian LASSO, BayesA, BayesB, BayesC, and reproducing kernel Hilbert space models. In five-fold cross-validation repeated over 100 cycles, mean predictive abilities ranged from 0.4 to 0.7, with mix tolerance and flour yield performing best and the low-heritability wheat ash content performing worst. Strikingly, the choice of model mattered far less than the trait itself; within any given trait, the seven models were nearly indistinguishable.

The second framework was a single-step reaction norm model, an extension of the GBLUP approach that feeds raw phenotypic observations and marker data into one unified model. Its key innovation is an explicit genotype-by-environment interaction term, built from covariance matrices that combine the genomic relationship matrix with environmental incidence matrices. This allows the model to predict how a specific line will perform in a specific environment, mimicking a scenario in which a breeder phenotypes only a few check varieties in a trial and predicts the rest. In cross-validation, the median predictive ability within location-year combinations tracked the heritability of each trait almost exactly, with bake time, flour yield, mix time, and single kernel hardness approaching 0.8.

The team also tested whether incorporating known major genes as fixed effects could sharpen predictions. Wheat end-use quality is strongly influenced by seed protein families, including glutenins, gliadins, puroindolines, purothionins, avenin-like proteins, purinins, triticins, and amylase-trypsin inhibitors. Markers within one megabase of these genes were pulled out of the random-effect marker matrix and treated as fixed effects, either individually, as principal components of the candidate marker set, or as markers discovered by genome-wide association studies run within each training fold. None of these strategies improved predictive ability, a result the authors attribute to the fact that the genome-wide models were already capturing the effects of these loci implicitly.

One cautionary finding deserves wide attention. When the researchers included GWAS markers identified on the full dataset, spanning both training and validation material, predictive ability jumped by roughly one standard deviation across all traits. That apparent gain was an artifact of data leakage, sometimes called the inside trading effect, in which information from the validation set contaminates model fitting. The demonstration is a timely reminder for the genomics community that benchmarking practices matter: fixed-effect markers must be identified strictly within training data, or reported accuracies will be silently inflated.

The most demanding test was leave-one-year-out validation, in which all experimental lines from an entire year were withheld and predicted from the remaining eleven years of data. Using a predictive ability threshold of 0.3 as the boundary between useful and uninformative predictions, five traits, namely bake time, loaf volume, mix time, mix tolerance, and single kernel hardness, cleared the bar in all thirteen years. Wheat ash content was the only trait that consistently fell short. In the forward prediction of 2022, the most recent year, the reproducing kernel Hilbert space model showed a discernible edge for grain and flour protein content, though a gap in genotyping for the 2021 preliminary yield trial lines likely dampened overall performance in that final year.

The practical implications reach well beyond Montana. Because the two-step and single-step methods answer different questions, one estimating a line’s overall breeding value across environments, the other predicting its phenotype in a specific environment, breeders can deploy them at different stages of the pipeline. By selecting on genomic predictions before expensive quality testing is possible, programs can increase selection intensity for end-use quality and accelerate genetic gain, ensuring that the bread wheat reaching global markets continues to meet the exacting standards that millers, bakers, and consumers demand. As training datasets accumulate and reference genomes improve, the authors expect these approaches to become routine tools for keeping one of the world’s most important staple crops a step ahead.

Subject of Research: Genomic selection models for predicting end-use quality traits in hard red spring wheat breeding

Article Title: Evaluating reaction norm and fixed effect models for genomic selection of end-use quality in wheat

Article References: Lile, J., Lachowiec, J., Fiedler, J. D., & Cook, J. P. (2026). Evaluating reaction norm and fixed effect models for genomic selection of end-use quality in wheat. Theoretical and Applied Genetics, 139(10), Article 293. https://doi.org/10.1007/s00122-026-05394-4

Image Credits: AI Generated

DOI: 10.1007/s00122-026-05394-4

Keywords: genomic selection, wheat, end-use quality, hard red spring wheat, reaction norm model, genotype-by-environment interaction, fixed effects, GWAS, predictive ability, breeding, baking quality, GBLUP