The study sits at the intersection of two research traditions that have been converging for years: Educational Data Mining, a field devoted to extracting useful patterns from the digital traces students leave behind, and the broader movement to apply artificial intelligence to sustainable development. The authors frame their work explicitly in terms of institutional sustainability, arguing that a university’s ability to identify and support struggling students is not merely a pastoral concern but a structural one. Student success rates and the quality of education feed directly into institutional rankings, funding decisions, and reputations, which means that a reliable early-warning system has consequences far beyond any individual classroom.

To build their model, the researchers collected primary data from 200 university students using a structured questionnaire with Likert-scale items designed to measure learning behavior, social media usage, and psychological traits, alongside academic records used strictly for research purposes in accordance with institutional ethical guidelines. What distinguishes this dataset from many earlier efforts in the field is its breadth. Rather than focusing narrowly on grades and attendance, the team captured seven categories of potential influence on academic outcomes: demographic background, socioeconomic conditions, academic history, social and family environment, health and wellness, infrastructure and services available to the student, and time management and extracurricular activity. This holistic framing reflects a growing recognition in education research that performance is shaped by a dense web of factors extending well beyond the lecture hall.

With the dataset assembled, the researchers trained and compared several regression algorithms, each representing a different mathematical philosophy for mapping input variables to a predicted outcome. The contenders included a Decision Tree Regressor, which partitions the data through a series of branching questions; a Random Forest Regressor, which averages the outputs of many decision trees to reduce overfitting; and a Support Vector Regressor, which fits a function within a tolerance margin around the training points. They also deployed Elastic Net Regression, a hybrid technique that blends two regularization penalties — the L1 penalty of lasso regression and the L2 penalty of ridge regression — into a single objective function. Regularization is the mathematical equivalent of a discipline mechanism: it constrains the model’s coefficients so that the algorithm cannot chase noise in the training data, and the elastic net’s dual penalty allows it both to shrink irrelevant features toward zero and to handle situations where predictor variables are correlated with one another, a common condition in survey data where questions about family support, finances, and wellness inevitably overlap.

The verdict was unambiguous. The Elastic Net Regressor outperformed its competitors, achieving an R-squared value of 0.9961 and a root mean square error of 0.511. In plain terms, an R-squared approaching 1.0 means the model explains almost all of the variance in the observed academic outcomes, and an RMSE of roughly half a point indicates that its predictions deviate only modestly from actual performance values. Such near-ceiling accuracy on a dataset of this size will invite scrutiny — models that fit training data almost perfectly sometimes reflect overfitting rather than genuine predictive power — but the authors position the result as evidence that the relationships between academic performance and its determinants can be represented effectively by machine learning when the right features are measured and the right constraints are applied. The elastic net’s built-in feature selection, which pushes the coefficients of uninformative variables toward zero, is likely part of the reason it generalized better than the tree-based ensembles on this data.

Perhaps the most consequential aspect of the study is not the raw accuracy but the transparency layer the researchers built on top of it. Black-box predictions have long been a barrier to adopting machine learning in education, because administrators and educators are understandably reluctant to act on a number they cannot interrogate. Madaan and Sharma addressed this by applying two of the most widely used explainable AI frameworks: SHAP, or SHapley Additive exPlanations, which borrows concepts from cooperative game theory to distribute credit for each prediction among the input features, and LIME, or Local Interpretable Model-agnostic Explanations, which builds simple, locally faithful surrogate models around individual predictions. Together, these methods allow the system to show not just that a student is predicted to underperform, but which specific factors — a health issue, a family circumstance, a time-management pattern — are driving that forecast for that particular individual.

This interpretability matters because it converts prediction into action. A model that merely flags a student as at risk tells an advisor where to look; a model that says the risk is driven primarily by, say, inadequate access to campus services or excessive social media consumption tells the advisor what to do. The authors visualize the factors influencing academic performance graphically, and they describe the development of dashboards intended to support decision-making processes within higher education institutions. In operational terms, the envisioned workflow is straightforward: institutions feed student data into the model, the system computes predicted outcomes and their explanations, and staff use those outputs to detect struggling students early, offer timely interventions, and ultimately reduce dropout. The researchers argue that such a system can be beneficial across the sector, from resource-constrained colleges to large research universities.

The study also connects to a lively international research conversation. Recent work has applied machine learning to academic performance prediction across a wide range of contexts — from graph neural networks that model peer influence in online question pools, to exercise-aware knowledge tracing systems that track what a student knows in real time, to comparative studies of classification and regression models among postgraduate public health students. Other teams have explored elastic net approaches in adjacent domains, including using elastic net regression to classify developmental language disorders, underscoring the method’s versatility when datasets contain many correlated predictors. What the Banasthali Vidyapith study adds to this landscape is the combination of a multi-dimensional survey-based feature set, a head-to-head algorithm comparison, and an explicit explainability pipeline, all framed within the sustainability agenda that increasingly shapes higher education policy.

There are, of course, important caveats. A model trained on 200 students at a single institution cannot simply be transplanted to a different university with a different student population, and the authors note that no datasets were generated or analyzed beyond the study itself, with code available on request. Survey-based measures of behavior and psychological traits are self-reported and therefore subject to well-known biases, and any system that predicts student outcomes raises ethical questions about labeling, privacy, and the risk that a low prediction becomes a self-fulfilling prophecy. The researchers are careful to state that academic records were used strictly for research purposes under institutional ethical guidelines, and the growing literature on explainable educational AI reflects a shared awareness that transparency is not just a technical nicety but an ethical requirement when algorithms touch people’s educational trajectories.

Even with those qualifications, the direction of travel is clear. As artificial intelligence matures from novelty to infrastructure in higher education, the institutions that thrive will be those that can turn data into timely, comprehensible, and humane action. A regression model that achieves an R-squared of 0.9961 while explaining its own reasoning through SHAP and LIME offers a glimpse of what that future could look like: not chatbots replacing teachers, but quiet analytical systems working in the background, flagging trouble before it becomes failure, and giving educators the evidence they need to intervene while intervention still matters. For the students who would otherwise have slipped through the cracks, that difference may be the most important number of all.

Subject of Research: Machine learning prediction of university student academic performance using regression models and explainable AI

Article Title: Predicting student academic performance using a regression model

Article References: Predicting student academic performance using a regression model. (n.d.). https://doi.org/10.1007/s43545-026-01838-2

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01838-2

Keywords: machine learning, educational data mining, academic performance prediction, elastic net regression, explainable AI, SHAP, LIME, higher education, student dropout, predictive analytics, sustainability, regression models

The classic model, proposed by Herbert Marsh in 1986, makes two elegant predictions. First, students who perform well in math tend to develop strong math self-concepts, because they compare their grades or test scores with those of peers—a so-called external, or social, comparison. This produces a positive within-domain effect. Second, a student who excels in math often feels comparatively worse at English, even with decent English grades, because the two abilities are contrasted internally. This dimensional comparison produces a negative between-domain effect: high math achievement actually suppresses verbal self-concept, and vice versa. Meta-analytic results have confirmed both patterns with striking consistency across countries, age groups, and school types, making the model one of the most robust findings in the discipline.

The trouble, as Wolff first argued in 2021, lies in how the model’s coefficients are interpreted. In the standard specification, a student’s math self-concept is regressed on both math achievement and verbal achievement. Researchers have traditionally labeled the math-achievement coefficient a pure social comparison effect. But dimensional comparison theory defines dimensional comparisons as contrasts between one’s own achievements in two domains. If that is what students actually do, then the verbal achievement coefficient in the classic model does not capture the contrast itself—it captures only the part of the contrast that remains after the shared variance between the two achievement measures is partitioned. The contrast, in other words, leaks into the within-domain coefficient, inflating it.

To fix this, Wolff proposed the difference I/E model, a reparameterization in which self-concept in one domain is predicted by achievement in that same domain plus the difference between achievement in that domain and achievement in the other. Algebraically, the two models contain exactly the same information: they fit the data identically and explain the same variance. The between-domain effects are equal in magnitude and differ only in sign. But the within-domain effect in the classic model equals the sum of the within- and between-domain effects in the difference model. Using meta-analytic estimates, the authors calculate that the genuine within-domain effect on math self-concept might be around β = .37 rather than .57, and on verbal self-concept around β = .29 rather than .46—shifting the ratio of within- to between-domain effects from roughly 2.8 to about 1.8.

What was missing until now was a direct empirical test. The new research delivers one across six pre-registered studies. In the first, the team reanalyzed PISA 2000 data from 55,582 students in 26 countries—the same dataset Marsh and K.T. Hau used in their landmark 2004 cross-cultural validation. The logic was subtle. If the classic model’s within-domain effect already contains a between-domain component, then explicitly adding the other domain’s achievement as a predictor should increase the within-domain effect, because the shared contribution is now being counted twice. Conversely, in the difference model, adding the achievement difference should decrease the within-domain effect, because the dimensional component is being removed from it. Both patterns appeared exactly as predicted, and the changes scaled with the strength of the between-domain effects: in the Netherlands, where the negative between-domain effect on math self-concept was strong, the within-domain effect jumped by more than a quarter of a standard unit when the classic model was fully specified.

Study 2 brought in something the models alone cannot provide: students’ own reports of the comparisons they make. Nearly 950 German ninth- and tenth-graders rated how they judged their math and German achievement relative to classmates and relative to each other. When these explicit ratings were added to the models, the results were decisive. Controlling for social comparison ratings shrank the within-domain effects in both model versions while leaving the between-domain effects untouched. Controlling for dimensional comparison ratings reduced all four effects in the classic model, but only the between-domain effects in the difference model. Strikingly, the explicit comparison ratings were stronger predictors than the objective grades themselves, and the within-domain effects dropped by more than 60 percent—up to 85 percent in the difference model—once the ratings were accounted for.

Study 3 attacked the question from a third angle using moderator variables. Two beliefs—the conviction that math and verbal abilities are negatively related, and the perceived dissimilarity of the two subjects—should, on theoretical grounds, strengthen dimensional comparisons but have no reason to affect social comparisons. In the classic model, both beliefs significantly amplified all four effects, including the supposedly social within-domain effects. In the difference model, they amplified only the between-domain effects. This is precisely the signature one would expect if the classic model’s within-domain coefficients are contaminated by dimensional comparison processes, and it echoes earlier findings that had puzzled researchers who lacked the difference-score framework to interpret them.

The remaining three studies asked whether relative achievement information—the difference between grades in two subjects—is actually salient to students when they evaluate themselves. University students studying to become teachers reported relying on the difference between their math and German grades more than twice as strongly as on their grade in the other subject alone when reflecting on their school-day self-concepts. A replication with 229 secondary school students assessing their current self-concepts showed the same pattern, albeit with smaller effect sizes. In a deliberately exploratory sixth study, the authors posed the same forced choice to ChatGPT-5 across 145 prompts per domain; the language model chose the achievement difference in every single case, reasoning that the contrast between grades offers a more direct index of relative strength. The authors are careful to note that LLM responses reflect patterns in training data rather than human cognition, but the unanimous preference is at least consistent with the conceptual plausibility of difference-based self-evaluation.

The implications ripple outward. Prior studies that labeled the classic model’s within-domain effects as social comparison effects now require qualification, since those coefficients bundle together social and dimensional processes. Group comparisons—across ages, countries, ability levels, or teacher knowledge—may have drawn premature conclusions when between-domain effects differed alongside within-domain effects. Intervention research faces a parallel challenge: programs designed to boost self-concept in one domain can inadvertently depress it in another, as one study found when a math-values intervention lowered students’ German values. Promisingly, an intervention that explicitly taught students about dimensional comparisons raised math self-concept without harming verbal self-concept, with effects lasting at least six months.

The authors stop short of declaring the difference model superior. Both specifications encode identical information, and neither directly isolates psychological mechanisms; the difference model’s real contribution is interpretive transparency, making a theoretically grounded contrast explicit rather than hidden in algebraic cross-talk. Their practical recommendation is pragmatic: researchers can keep estimating the classic model within a single statistical framework, then use modern software to decompose the within-domain effects along difference-score lines. What the six studies establish is that the field’s most trusted coefficients deserve a more cautious reading—and that a 40-year-old model still has the capacity to surprise the scientists who know it best.

Subject of Research: The role of social and dimensional comparisons in academic self-concept formation within the internal/external frame of reference model

Article Title: Revisiting the Role of Social and Dimensional Comparisons in the Internal/External Frame of Reference Model: A Systematic Comparison of the Classic and the Difference I/E Model

Article References: Revisiting the Role of Social and Dimensional Comparisons in the Internal/External Frame of Reference Model: A Systematic Comparison of the Classic and the Difference I/E Model. (n.d.). https://doi.org/10.1007/s10648-026-10212-w

Image Credits: AI Generated

DOI: 10.1007/s10648-026-10212-w

Keywords: academic self-concept, internal/external frame of reference model, dimensional comparisons, social comparisons, educational psychology, difference I/E model, achievement, self-concept formation, PISA, meta-analysis, regression models, student motivation

The research, conducted on computed tomography images of 409 living individuals, 226 males and 183 females, represents one of the largest datasets assembled for this purpose in a North Indian population. The choice of imaging modality matters enormously. Unlike studies based on cadavers, where soft tissue shrinks and distorts after death, CT scans capture the true relationship between the bony framework and the overlying soft tissues in living people. The authors measured seventeen metrical parameters, spanning soft tissue depths, linear distances, and angles, and then constructed regression models to predict the various dimensions of the external nose from measurements that can be taken directly on a skull.

The logic of regression-based prediction is central to the method. In forensic facial approximation, an artist or anthropologist needs to know, for example, how far the tip of the nose, the pronasale, projects forward from a given bony landmark, or how wide the nostrils will be relative to the width of the nasal aperture. Traditional guidelines, some dating back to the pioneering work of Mikhail Gerasimov in the mid-twentieth century, offered rough anatomical rules of thumb. Subsequent decades brought a wave of proposed relationships between hard and soft tissue dimensions, tested and debated in populations from Scotland to Brazil, Korea, and Japan. The persistent problem is population specificity: nasal form varies substantially across human groups, shaped in part by climatic adaptation, and a prediction equation derived from one population may mislead when applied to another.

By building their models from CT data of living North Indian adults, Singh and Purkait provide equations tailored to the population most likely to be encountered in their medico-legal context. The seventeen parameters include soft tissue depths at key landmarks, which allow the reconstruction artist to place the nasal tip, the alar margins, and the bridge with quantified confidence rather than guesswork. Regression analysis, when its assumptions are properly tested, offers a defensible statistical bridge between what the skull preserves and what the living face displayed. Each predicted dimension narrows the space of possible noses, and by extension, the space of possible identities.

But metrics alone cannot capture everything that makes a face recognizable. The study therefore went beyond measurement, cataloguing forty morphological attributes of the nose, features such as the shape of the bridge, the form of the tip, and the character of the nostrils, to identify which nasal features occur most frequently in the studied population and, crucially, which bony traits tend to accompany which soft tissue traits. This dual approach yields two distinct working methods, each suited to a different state of the recovered skull.

The first method, called the most frequent nasal features method, is designed for the worst-case scenario: a skull in which the fragile nasal bones are missing entirely. In such cases, the reconstruction relies on the population-level frequency of nasal features, essentially building a statistically typical nose consistent with the surviving anatomy of the midface. The second method, the characteristic association method, applies when the nasal bones are partially broken or intact. Here the reconstruction becomes more individualized, because specific configurations of the bony nose are associated with characteristic configurations of the soft tissue nose. A particular shape of the nasal aperture or bridge, in other words, carries information about the overlying soft form that a fully denuded skull cannot provide.

The workflow that emerges from the study is a hybrid one. The nasal framework is first constructed using the metrical parameters, establishing the quantitative skeleton of the soft nose: its projection, width, and height, anchored to the bony landmarks. The reconstruction is then refined morphologically, adjusting the shape of the tip, the alae, and the contour of the bridge to match the most probable features or the characteristic associations indicated by the bone. This layering of quantitative prediction and morphological judgment is designed to increase the likelihood that the reconstructed face resembles the antemortem face, which is the ultimate measure of success in facial approximation.

The significance of the work lies partly in its scale and partly in its pragmatism. Previous systematic reviews of nasal prediction techniques have catalogued dozens of methods, from simple proportion rules to three-dimensional statistical shape models derived from cone-beam CT. Many were tested on small samples or on populations far removed from the case at hand. Studies in Korean, Japanese, Brazilian, and Turkish populations have each produced population-specific databases of facial soft tissue thickness and nasal dimensions, underscoring that no single universal formula exists. The Indian subcontinent, with its enormous population and substantial medico-legal caseload, has been comparatively underrepresented, and the new study adds a substantial, ethically approved dataset to the global record.

The method also reflects a broader shift in forensic anthropology from two-dimensional profile estimation toward fully three-dimensional reconstruction. Early techniques predicted nose projection in profile only, leaving the artist to improvise the width and the shape of the tip. Three-dimensional imaging now allows every dimension to be modeled simultaneously, and the resulting reconstructions can be rendered digitally, compared against missing-persons photographs, and adjusted iteratively. The authors’ combination of seventeen metrical predictors with forty morphological attributes is an attempt to systematize what has long been an art guided by incomplete science, giving practitioners a repeatable protocol rather than a set of divergent personal heuristics.

For the families of missing persons, the stakes of this technical work are profoundly human. A reconstructed face displayed in the media may be the last realistic chance that someone recognizes a lost relative, and the nose sits at the center of that recognition. By quantifying the relationship between the skull and the soft tissue nose in a large living sample, and by offering clear decision rules for skulls in different states of preservation, Singh and Purkait have given forensic practitioners a more rigorous toolkit for turning anonymous remains back into a person with a face, a name, and, ultimately, an identity.

Subject of Research: Forensic facial reconstruction of the nose from skull measurements

Article Title: A proposed morphological and metric method for three-dimensional nasal reconstruction

Article References: Singh, S., & Purkait, R. (2026). A proposed morphological and metric method for three-dimensional nasal reconstruction. International Journal of Legal Medicine. https://doi.org/10.1007/s00414-026-03959-y

Image Credits: AI Generated

DOI: 10.1007/s00414-026-03959-y

Keywords: forensic anthropology, facial reconstruction, nose prediction, computed tomography, regression models, soft tissue depth, nasal aperture, morphological traits, North Indian population, International Journal of Legal Medicine, proposed, morphological

The research focused on Sabi sheep and Matebele goats, two hardy genotypes that dominate Zimbabwe’s communal production systems. Sabi sheep are fat-tailed, hair-covered animals prized for their fertility and resistance to local diseases and parasites, with mature weights typically ranging from 35 to 45 kilograms. Matebele goats, native to Matabeleland and parts of Midlands province, are medium- to large-framed meat animals whose mature females can reach 35 to 55 kilograms, standing roughly 65 centimeters at the withers. Despite their numerical dominance—the national goat population stands near four million, while sheep numbers have climbed by about 28 percent to roughly 700,000 head—these breeds have received little systematic attention in genetic improvement programs, largely because communal flocks lack structured recording, controlled mating, or performance evaluation.

That gap matters because body weight is a fundamental indicator in livestock management. It signals growth, market readiness, health status, and suitability as a replacement breeding female. In many communal settings, however, weighing equipment is simply unavailable, making indirect estimation from linear body measurements a practical necessity. Previous studies have confirmed strong associations between live weight and traits such as heart girth, body length, and withers height in small ruminants worldwide, but the relationships are known to shift with sex, age, and physiological state. What remained poorly understood—and what the new study set out to quantify—was how maternal age class alters the structural relationship between body dimensions and body mass, and which traits carry genuine predictive weight at each stage of maturity.

The team conducted a cross-sectional observational study at Matopos Research Station in Bulawayo Province, a site with sweet veld rangeland and low annual rainfall below 450 millimeters. Using purposive sampling from institutional flocks of 124 Matebele goats and 98 Sabi sheep, the researchers selected only healthy, non-pregnant, non-lactating females with body condition scores between 3 and 4 on a five-point scale, and with documented birth records confirmed by dental examination where necessary. The final sample comprised 20 young (2-year-old) Matebele does, 40 mature (4-year-old) does, 26 young Sabi ewes, and 27 mature ewes. Animals aged 3 and 5 years were deliberately excluded to maintain a clear physiological contrast between early reproductive maturity—when skeletal growth is still under way—and peak skeletal and reproductive maturity, when frame size has stabilized. All measurements were taken by a single person to eliminate inter-observer variation, and the animals were managed under a standardized semi-extensive regime with uniform grazing, supplementation, vaccination, and parasite control.

Four morphometric traits were recorded alongside body weight: heart girth, the circumference of the chest; body length, measured from the ear to the tail along a straight backbone; withers height; and rump height. The statistical toolkit combined classical and more sophisticated approaches. Descriptive statistics and Pearson’s correlations established phenotypic associations, while stepwise multiple linear regression built predictive models. Multicollinearity among the inter-correlated skeletal traits was assessed using variance inflation factors, with variables exceeding the conventional threshold of 10 excluded to preserve model stability. Crucially, the team also applied path analysis, a technique that decomposes simple correlations into direct and indirect components, revealing whether a trait truly influences body weight on its own or merely travels in the company of other, more influential measurements. Model performance was validated through cross-validated coefficients of determination, root mean square error, and mean absolute error, with all analyses performed in IBM SPSS Statistics version 29.0 under ethical approval from the Zimbabwe Open University Animal Research Ethics Committee.

The results revealed a striking age-dependent pattern. Body weight rose consistently with age in both species—Matebele does increased from an average of 23.30 kilograms at two years to 27.90 kilograms at four, while Sabi ewes climbed from 28.12 to 35.96 kilograms over the same interval. More tellingly, the phenotypic correlations between weight and body measurements strengthened markedly with maturity. In young Matebele does, weight correlated only weakly with heart girth (r = 0.31), withers height (r = 0.20), and rump height (r = 0.18), while body length showed a moderate significant association (r = 0.39). In mature does, by contrast, heart girth correlations soared to 0.80, body length to 0.70, and withers height to 0.69, all highly significant. A similar maturation effect appeared in the sheep: heart girth correlations rose from a moderate 0.55 in young ewes to a strong 0.73 in mature animals, while associations with other traits remained comparatively weak.

The regression models told an equally nuanced story. For young Matebele does, rump height emerged as the best single predictor, explaining 71 percent of the variation in body weight—evidence that in still-growing animals, skeletal frame development dominates the determinants of mass. Young Sabi ewes required a two-trait model combining heart girth and rump height, which achieved a coefficient of determination of 0.66. In mature animals, heart girth reclaimed center stage: for Matebele does, heart girth and rump height jointly explained 69 percent of weight variation, while for mature Sabi ewes heart girth alone accounted for 53 percent. Cross-validated coefficients tracked the raw values closely, and error estimates remained low (root mean square errors between 1.37 and 1.73), indicating that the parsimonious models generalized well without overfitting. Adding further traits yielded only marginal gains, a finding with real practical value in settings where every extra measurement costs time.

Path analysis added the deepest insight, exposing why naive correlations can mislead. In young Matebele does, body length displayed the largest direct effect on weight (0.75), but this was substantially offset by a negative indirect effect of -0.36 mediated through other traits, leaving only a modest overall correlation. Rump height, despite its strong regression performance, showed a negligible direct effect of -0.03, with its predictive power flowing almost entirely through indirect pathways linked to frame development. In mature animals of both species, however, heart girth’s dominance was unambiguous. Mature Matebele does showed a direct effect of 0.55 supplemented by positive indirect contributions of 0.25, and mature Sabi ewes an even larger direct effect of 0.69 with minimal indirect mediation—figures that closely matched the observed correlations. Withers height and body length contributed almost nothing directly in mature animals; their apparent associations with weight were essentially echoes of their relationship with chest circumference.

The biological interpretation is coherent with established growth physiology. The thoracic region integrates skeletal structure, muscle deposition, and visceral organ mass, making heart girth a comprehensive proxy for overall body size once growth plates close and proportions stabilize. In younger animals, where skeletal elongation and tissue differentiation proceed at different rates, dimensions are not yet proportionally aligned with mass, and height-related traits gain temporary importance as markers of frame development. The authors note that their cross-sectional design captures two physiological snapshots rather than continuous growth trajectories, and they recommend longitudinal studies with larger, more diverse populations—potentially incorporating body condition scoring and advanced modeling—to refine age-specific equations further. Still, the practical prescription is clear. For resource-limited smallholder systems without scales, a heart girth measurement with an inexpensive tape offers a robust, biologically meaningful estimate of live weight in mature animals, with rump height improving accuracy in growing stock. Embedding such simple morphometric tools into structured breeding programs, the researchers argue, could finally give Zimbabwe’s overlooked indigenous genotypes a route into evidence-based selection, better feeding and health decisions, and sustainable genetic improvement.

Subject of Research: Prediction of body weight from morphometric traits in indigenous Sabi sheep and Matebele goats across maternal age classes

Article Title: Influence of maternal age-class and prediction of body weight from morphometric traits in indigenous small ruminant genotypes

Article References: Assan, N., Tada, O., Mwareya, N., & Musasira, M. (2026). Influence of maternal age-class and prediction of body weight from morphometric traits in indigenous small ruminant genotypes. BMC Agriculture, 2(1), Article 19. https://doi.org/10.1186/s44399-026-00045-x

Image Credits: AI Generated

DOI: 10.1186/s44399-026-00045-x

Keywords: body weight prediction, heart girth, morphometric traits, indigenous sheep, indigenous goats, Sabi sheep, Matebele goats, path analysis, regression models, smallholder livestock, Zimbabwe, maternal age class