The team, led by Parviz Fasahat of the Sugar Beet Seed Institute in Karaj, together with colleagues from the Agricultural and Natural Resources Research Center of Khorasan Razavi, published the work in the journal Discover Plants. Their central hypothesis was that variation in dry matter across genotypes arises from underlying differences in photosynthetic efficiency, the allocation of sucrose to storage roots, and mineral ion homeostasis. If correct, dry matter could serve as a single, easily measured proxy for a suite of complex physiological processes that are otherwise difficult and expensive to assess, giving breeding programs a powerful screening tool.

The scale of the dataset lends the conclusion considerable weight. Dry matter percentage across the 66 cultivars ranged from 15.8 to 27.1 percent, with a mean of approximately 21.7 percent, a spread that reflects both substantial genetic diversity and the sensitivity of dry matter formation to environmental and management conditions. Root yield itself varied dramatically, from 49.7 to 140.8 tonnes per hectare with an average of 91.5 tonnes, while mean sugar content was 15.2 percent and white sugar content averaged 13.4 percent. Mineral impurities were similarly variable: sodium ranged from 0.7 to 11.9 milliequivalents per 100 grams, potassium from 2.2 to 7.2, and nitrogen from 0.3 to 7.1. This variability matters because excess sodium can induce salinity-like stress, elevated nitrogen shifts assimilate partitioning toward amino compounds rather than sugars, and fluctuating potassium levels influence osmotic adjustment and sugar translocation.

Using Spearman’s rank correlation on the pooled multi-year dataset, the researchers uncovered striking associations. Dry matter correlated strongly and positively with sucrose content (r = 0.88), white sugar content (r = 0.87), and the extraction coefficient of sugar (r = 0.78), the latter measuring how efficiently sucrose can actually be recovered during industrial processing. Moderate positive correlations appeared with sugar yield (r = 0.34) and white sugar yield (r = 0.54). On the negative side, dry matter was inversely associated with root yield (r = −0.22), sodium (r = −0.75), nitrogen (r = −0.31), and molasses sugar (r = −0.71), the fraction of sugar lost to crystallization because it remains trapped in impurity-laden molasses. Potassium (r = −0.05) and alkalinity (r = −0.09) showed weak, nonsignificant relationships, suggesting these traits are comparatively insensitive to dry matter variation.

Crucially, the team tested whether these relationships held up under different growing conditions rather than relying on a single season. Annual precipitation at the site ranged from 0.94 millimeters per day in 2022 to 1.75 in 2019, and maximum summer temperatures consistently exceeded 36 degrees Celsius, with relative humidity fluctuating considerably between seasons. Year-specific Pearson correlation analyses showed that the positive associations of dry matter with sugar content, white sugar content, and the extraction coefficient, together with its negative association with sodium, remained consistent across all six growing seasons. By contrast, the relationships between dry matter and yield-related traits, including root yield, sugar yield, and white sugar yield, varied substantially from year to year, indicating that productivity traits are far more environmentally sensitive than the technological quality attributes tied to dry matter.

To move beyond simple correlation, the researchers fitted separate linear mixed-effects models for each trait, treating dry matter as a fixed effect and cultivar as a random intercept to account for genetic variability. The models, estimated by maximum likelihood in SAS and validated with Akaike and Bayesian information criteria alongside residual diagnostics, confirmed the correlation picture. Dry matter exerted significant negative effects on root yield and sodium content, and highly significant positive effects on sugar yield, white sugar yield, sucrose content, white sugar content, and the extraction coefficient. Molasses sugar was negatively and significantly affected, while potassium and alkalinity showed no significant response. In other words, higher dry matter systematically improves the traits that matter in the factory while suppressing the impurities that erode them.

The correlation heatmap revealed further structure in the trait network. Sugar content, white sugar content, and extraction coefficient were tightly interlinked, with pairwise correlations exceeding 0.9, while sugar yield and white sugar yield moved together at r = 0.95. These synergies suggest that selecting for one quality trait is likely to bring parallel gains in the others. Conversely, the extraction coefficient showed strong negative associations with sodium, potassium, nitrogen, and molasses sugar, exposing the antagonism between recoverable sugar percentage and the compositional impurities that interfere with crystallization. Traits such as root yield, potassium, and alkalinity displayed generally weak correlations with most other parameters, meaning they can potentially be modified more independently in breeding schemes.

What do these statistical patterns mean mechanistically? The authors propose that high-dry matter genotypes allocate a greater proportion of photosynthates toward carbohydrate storage in the root, a process mediated by enhanced phloem loading and unloading through sucrose transporter proteins that regulate source-to-sink carbon flux. Such genotypes may show increased activity or expression of sucrose-phosphate synthase, invertases, and sucrose synthase, enzymes that promote sucrose loading and storage in root vacuoles. Meanwhile, the negative correlations with sodium and nitrogen point toward active ion homeostasis: efficient exclusion or compartmentalization of sodium ions through ion transporter families reduces toxicity and maintains osmotic balance, while lower nitrogen content signals a metabolic shift favoring carbon allocation to sucrose synthesis over amino acid biosynthesis. These adjustments likely improve both stress tolerance and extract purity.

The study is careful to acknowledge its constraints. All trials were conducted at a single location, so the findings speak to overall associations across a large multi-year dataset rather than formally quantifying genotype-by-environment interaction, and the pooled analysis was a deliberate choice given that objective. Even so, the interannual variation in rainfall, temperature, and humidity at the site was real and substantial, which makes the stability of the quality-related correlations all the more convincing. The authors suggest that the next steps should include transcriptomic profiling of sucrose transporter and ion channel genes, enzyme activity assays for carbon metabolism, and ionomic analyses to unravel the genotype-specific regulatory networks that drive dry matter variation. Recent genomic work on nearly a thousand Beta vulgaris germplasms has already confirmed extensive genetic diversity underlying dry matter and sucrose traits, with complex marker-trait associations pointing to multifactorial control.

For breeders, the practical message is that dry matter content deserves a more prominent place in selection programs than it has traditionally received. Because it is cheap and fast to measure, yet tracks the physiological processes governing sucrose accumulation and impurity regulation more faithfully than biomass traits do, it could serve as an early-generation screening criterion for genotypes with enhanced sugar quality and processing efficiency. The trade-off with root yield, though real, was modest and environmentally variable, whereas the quality benefits were robust across every season tested. In an era when sugar factories demand ever-higher extractable purity and growers face increasingly erratic climates, a single root measurement that integrates carbon metabolism and ion regulation may prove to be one of the most valuable indicators in the sugar beet breeder’s toolkit.

Subject of Research: Dry matter content as an integrative physiological and quality indicator in sugar beet

Article Title: Dry matter content as a key indicator of physiological and quality traits in sugar beet

Article References: Fasahat, P., Rezaei, J., Babaee, B., Yousefabadi, V.-A., & Sadeghzadeh Hemayati, S. (2026). Dry matter content as a key indicator of physiological and quality traits in sugar beet. Discover Plants, 3(1), Article 402. https://doi.org/10.1007/s44372-026-00865-w

Image Credits: AI Generated

DOI: 10.1007/s44372-026-00865-w

Keywords: sugar beet, dry matter, sucrose content, plant physiology, ion homeostasis, sugar yield, breeding, correlation analysis, mixed-effects models, root quality, molasses sugar, source-sink dynamics

The research, published in the Indian Journal of Genetics and Plant Breeding, evaluated 20 sugarcane clones across three distinct crop cycles: Plant I, Plant II, and the ratoon crop. The team, led by D. Adilakshmi and D. Purushotama Rao, combined classical joint analysis of variance with two of the most sophisticated frameworks in modern plant breeding statistics: the AMMI model, which decomposes genotype-by-environment interaction into interpretable components, and the REML/BLUP-based WAASB approach, which blends mixed-model prediction with a weighted average of absolute scores from the interaction. The goal was simple to state and hard to achieve: find clones that deliver both high cane and sugar yield and dependable stability across the seasonal rollercoaster of successive crop cycles.

The statistical verdict was unambiguous. For every character measured, the analysis revealed highly significant effects of genotype, cropping year, and the interaction between them at the one percent probability level. In plain terms, the clones genuinely differed from one another, the years genuinely differed from one another, and crucially, the clones responded differently to different years. That interaction term is what makes sugarcane breeding so treacherous, because it means no single clone can be crowned the winner on average performance alone. A clone’s rank in one crop cycle is no guarantee of its rank in the next, and breeders who select only on mean yield risk promoting varieties that shine in trials and disappoint in farmers’ fields.

To separate genuine genetic signal from environmental noise, the researchers estimated a suite of genetic parameters for each trait. Cane yield emerged as the standout, showing the highest broad-sense heritability in the study at 0.42, alongside a genetic coefficient of variation of 11.74 percent, a selection accuracy of 0.87, and a ratio of coefficients of variation greater than one. Each of these numbers tells a story. Heritability of 0.42 means that a meaningful fraction of the observed variation in cane yield is genetic rather than environmental, so selection on this trait will actually move the population. The genetic coefficient of variation quantifies how much exploitable genetic diversity exists, and the CV ratio above one indicates that genetic variation outweighs environmental error, a favorable sign for direct genetic improvement. Selection accuracy above 0.70 was recorded for all characters, meaning the statistical models were predicting true genetic values with high confidence across the board.

Not every trait proved equally cooperative. The Genotype-by-Environment Interaction Ratio, a measure of how much of a trait’s variation is driven by environmental sensitivity rather than stable genetic expression, was highest for the number of millable canes at 0.56 and for brix percentage, a key indicator of sugar content, at 0.52. These traits, in other words, are the weather vanes of the sugarcane plant, swinging substantially with the seasons. Meanwhile, commercial cane sugar yield, abbreviated CCS yield, posted a genotypic coefficient of variation of 13.28 percent, the highest in the study, marking it as the trait with the greatest potential for genetic gain. For breeders, this creates a strategic tension: the traits that matter most for sugar production carry the most exploitable variation, but some of the component traits are also the most environmentally fickle, demanding stability-aware selection methods rather than simple averages.

This is precisely where the WAASB framework earns its keep. The method, implemented through the metan R package developed by Tiago Olivoto and colleagues, computes for each genotype a weighted average of the absolute scores of the interaction principal component axes derived from AMMI analysis, then combines that stability measure with mean performance into a single superiority index. The weighted average of absolute scores, extended across environments with the WAASB parameter, penalizes clones that fluctuate wildly between crop cycles and rewards those that hold their performance steady. When the researchers projected the results onto WAASB and BLUP biplots, four clones rose clearly above the pack: 2018A 6, 2018A 157, 2018A 88, and 2018A 133. These lines combined high cane yield, superior sugar recovery, and consistent stability across all three crop cycles, outperforming the commercial standard varieties that served as benchmarks in the trial.

The biplots also delivered a verdict on the environments themselves. Plant II, the second plant crop cycle, emerged as the most discriminative environment, meaning it did the best job of separating strong clones from weak ones. This finding has practical consequences for breeding programs with limited resources: if a program can only afford to evaluate clones in a subset of crop cycles, the second plant crop offers the sharpest lens for identifying genetic differences. A discriminative environment amplifies the differences among genotypes, exposing weaknesses that milder environments might mask. Conversely, the ratoon cycle, with its depleted root systems and different seasonal physiology, tests a different dimension of performance, and the study’s design deliberately spanned all three cycles to capture the full picture of a clone’s character.

The three top-performing clones, 2018A 6, 2018A 157, and 2018A 88, outperformed the commercial standards for both cane yield and sugar yield characters, a double achievement that is rarer than it sounds. Cane yield measures the raw tonnage of stalks delivered to the mill, while sugar yield reflects how much recoverable sugar that tonnage actually contains. A clone can pile up biomass with watery stalks and fail the sugar test, or produce sweet but slender canes and fail the tonnage test. Clones that beat the standards on both fronts simultaneously are the genuine articles, and their identification across three crop cycles gives breeders confidence that the advantage is not a one-season fluke. The researchers recommend advancing these selections to multi-location evaluation, the next gate on the long road to commercial release.

What makes this study resonate beyond the sugarcane fields of Andhra Pradesh is its methodological lesson. The integration of diversified crop cycles with REML/BLUP and WAASB analysis transformed a messy, interaction-dominated dataset into a clear ranking of genetic merit. Mixed-model methods have swept through breeding programs for coffee, papaya, soybean, wheat, chickpea, and sugar beet in recent years, and this study extends that momentum to one of agriculture’s most complex crops. The approach acknowledges a fundamental truth of quantitative genetics: the phenotype a breeder observes is a blend of genotype, environment, and their interaction, and only statistical models that explicitly disentangle these components can predict which genotypes will succeed where it counts.

For a crop that supplies roughly three-quarters of the world’s sugar and an increasing share of bioenergy feedstocks, the stakes of getting selection right are enormous. Sugarcane’s long breeding cycle, its polyploid genome, and its multi-year harvest rhythm make it one of the slowest and most expensive major crops to improve, which is why every gain in selection efficiency compounds over decades. By demonstrating that clones 2018A 6, 2018A 157, 2018A 88, and 2018A 133 hold their yield and sugar advantages across plant and ratoon cycles alike, the Andhra Pradesh team has given sugarcane breeders a shortlist worth watching and a statistical playbook worth copying. As seasonal variability intensifies under a changing climate, the ability to breed for broad seasonal adaptation, not just peak performance, may prove to be the difference between varieties that endure and varieties that fade.

Subject of Research: Genotype-by-environment stability analysis of sugarcane clones across crop cycles using REML/BLUP and WAASB methods

Article Title: Assessment of High-Yielding and Stable Sugarcane Clones Using REML/BLUP and WAASB Strategies Across Diversified Crop Cycles for Broad Seasonal Adaptation

Article References: Adilakshmi, D., Padmavathi, P. V., Ramana Murthy, K. V., Chendra Sekhar, V., Mukunda Rao, C., & Purushotama Rao, D. (2026). Assessment of High-Yielding and Stable Sugarcane Clones Using REML/BLUP and WAASB Strategies Across Diversified Crop Cycles for Broad Seasonal Adaptation. Indian Journal of Genetics and Plant Breeding, 86(1), 79-92. https://doi.org/10.1007/s44489-026-00007-2

Image Credits: AI Generated

DOI: 10.1007/s44489-026-00007-2

Keywords: sugarcane, plant breeding, REML/BLUP, WAASB, genotype-by-environment interaction, stability analysis, ratoon crop, heritability, sugar yield, AMMI, crop cycles, India