Machine learning tracks grazing damage in Himalayan grassland soils

In the high-altitude bugyal grasslands of Uttarakhand’s Chakrata region, livestock grazing has left a measurable chemical and physical fingerprint in the soil, and a small set of routine soil measurements is enough for a machine learning model to read that fingerprint. A new open-access study, published in Discover Computing on 3 November 2026, reports that soil organic carbon fell by 40.7 percent between non-disturbed and highly disturbed plots, available nitrogen dropped by 34.5 percent and bulk density rose by 19.5 percent along the grazing gradient. When three supervised classifiers were trained on six soil parameters, a Random Forest model assigned each sample to the correct disturbance class in 86.3 percent of cross-validated cases, with soil organic carbon alone carrying more than a third of the classification signal.

The study, led by Aakash Kumar with colleagues including Gabriel Michael Filippelli, focused on two high-altitude sites, Moila Bugyal at roughly 2,600 metres and Koti Kanasar Bugyal at roughly 2,500 metres above sea level. Ninety composite soil samples were collected during a single pre-monsoon campaign in April 2026, with thirty samples in each of three disturbance classes: non-disturbed, moderately disturbed and highly disturbed. Field crews assigned each sampling location to a class before any laboratory work, using visual assessment of vegetation cover, trampling evidence and visible soil exposure at the one-square-metre quadrat scale. Each composite sample blended five subsamples taken from the top fifteen centimetres of soil, the biologically active surface layer most responsive to grazing, and sampling points were kept at least twenty metres apart to limit spatial dependence.

Six parameters were measured in every sample: soil organic carbon, available nitrogen, available phosphorus, available potassium, bulk density and soil pH. The statistical core of the paper is a two-way analysis of variance that tested the effects of site and disturbance class on each parameter, followed by Tukey’s post-hoc test to identify which pairs of classes differed. A permutational multivariate analysis of variance, or PERMANOVA, then asked whether the six parameters, taken together, discriminate between disturbance classes. The disturbance gradient explained 42.55 percent of the total multivariate soil variance, with a pseudo-F of 32.167 and a permutation-based p-value of 0.001, while site accounted for only 0.58 percent and the site-by-disturbance interaction for 1.31 percent, neither of which was significant. The authors note that this effect size is close to an order of magnitude above the variance typically explained by studied factors in ecological research, where syntheses of meta-analyses place the mean at roughly 2.5 to 5.4 percent.

The univariate results follow a consistent monotonic pattern. Soil organic carbon declined from 5.71 percent in non-disturbed plots to 4.64 percent under moderate disturbance and 3.39 percent under high disturbance, and Tukey’s test separated all three classes. Available nitrogen fell from 435.4 to 285.4 kilograms per hectare across the same gradient, available phosphorus dropped by 39.7 percent and available potassium by 36.4 percent. Bulk density climbed from 0.857 to 1.023 grams per cubic centimetre, a rise the authors interpret as the physical signature of hoof trampling. Soil pH increased only slightly, from 5.22 to 5.39, and although that change was statistically significant, the total shift of 0.17 units was smaller than the roughly 0.20-unit spread within each class, so pH resolved only the contrast between the two extreme classes.

That distinction between statistical significance and classification utility runs through the paper’s discussion. The authors point out that an F-test asks whether class means differ, while a classifier asks whether an individual sample’s value identifies its class. Because the within-class spread of pH exceeded the between-class separation, the class distributions overlapped almost completely, and pH contributed just 2.9 percent of the Random Forest importance, the lowest of the six parameters. Soil organic carbon, by contrast, was both the parameter with the largest proportional change and the most discriminative feature, accounting for 36.7 percent of the total importance. Available nitrogen ranked second at 21.8 percent and bulk density third at 21.4 percent, so those three measurements together carried 79.9 percent of the classification signal, while available phosphorus, available potassium and pH shared the remaining fifth.

The machine learning pipeline was deliberately simple. Three classifiers were implemented in Python using scikit-learn: Random Forest with 300 trees and a maximum depth of 20, a Support Vector Machine with a radial basis function kernel, and a Gradient Boosting Machine with 200 estimators. All were evaluated with five-fold stratified cross-validation, and performance was scored with overall accuracy and Cohen’s kappa, a metric that corrects for chance agreement. Random Forest reached 86.3 plus or minus 7.5 percent accuracy with a kappa of 0.795, the Support Vector Machine 85.2 percent and the Gradient Boosting Machine 82.0 percent, all three falling within the substantial-agreement band of the standard Landis and Koch benchmarks. The authors are explicit that these figures are optimistically biased, because the same cross-validation folds were used to compare candidate hyperparameter configurations and to report final performance, so the accuracies should be read as upper-bound estimates of generalisation.

The structure of the errors is arguably more informative than their number. In the pooled out-of-fold confusion matrix for the Random Forest, 75 of 90 samples were correctly classified, and every misclassification occurred between adjacent classes. Three non-disturbed samples were predicted as moderately disturbed and two moderately disturbed samples as non-disturbed, while five moderately disturbed samples were predicted as highly disturbed and five highly disturbed samples as moderately disturbed. No non-disturbed sample was ever assigned to the highly disturbed class, and no highly disturbed sample was ever assigned to the non-disturbed class. The authors read this as the expected behaviour when ordinal categories are imposed on an underlying ecological continuum: the classifier was uncertain only where the gradient is genuinely continuous, and the partial overlap of the moderately disturbed confidence ellipse with both end-member ellipses in the principal component analysis mirrors the same pattern.

The ordination reinforces the multivariate story. Principal component analysis resolved 68.4 percent of the total variance on the first two components, and the first component separated the three disturbance classes along an axis loaded positively by soil organic carbon, available nitrogen, available phosphorus and available potassium and negatively by bulk density. The 95 percent confidence ellipses of the non-disturbed and highly disturbed classes did not overlap, while the moderately disturbed class occupied an intermediate position and overlapped partially with both. Pearson correlations showed the four nutrient and carbon parameters positively intercorrelated, with the strongest association between soil organic carbon and available nitrogen, and all four negatively correlated with bulk density, the strongest being the organic carbon and bulk density pair.

The authors argue that the three dominant indicators are not redundant measurements of a single process but the measurable expressions of three coupled pathways of degradation. Bulk density registers the mechanical pathway, responding directly to hoof pressure and to soil wetness at the moment of trampling. Available nitrogen registers the biological pathway, integrating litter return, microbial biomass and mineralisation turnover. Soil organic carbon registers the integrative pathway, depleted by both of the preceding processes and replaced only slowly in cold soils, which is why it encodes disturbance history rather than current-season condition. Because the three operate on different timescales, the authors suggest, measuring all three yields information about the temporal depth of disturbance and not merely its presence, although they flag this interpretation as a hypothesis generated by cross-sectional data that would require a chronosequence or grazing-exclusion experiment to test.

The practical payoff is a proposed three-parameter monitoring protocol built on soil organic carbon, available nitrogen and bulk density, all determinable with inexpensive methods that require little more than wet oxidation, distillation, a core sampler, an oven and a balance. The authors argue that such a protocol could retain most of the classification signal while substantially reducing analytical cost in Himalayan settings where laboratory facilities are limited. They are equally clear about the limits of the evidence. The dataset is a single-season snapshot from two bugyals within one sub-district, vegetation cover was assessed visually rather than quantified, no microbial variables were measured, and no environmental covariates such as temperature, precipitation, elevation or aspect were recorded. Spatial independence was addressed by design rather than by a formal test such as Moran’s I. The authors therefore present the results as a well-characterised baseline for the Chakrata bugyals rather than a validated regional model, and they identify multi-season sampling, additional sites across the Garhwal and Kumaon Himalaya, coupled plant-soil measurements and integration with satellite-derived indices as the necessary next steps towards conservation planning and adaptive grazing management in the Indian Himalayan Region.

Subject of Research: Machine learning classification of grazing-induced soil degradation in Himalayan alpine grasslands

Article Title: Machine learning-based assessment of grazing-induced soil changes in Himalayan alpine grasslands

Article References: Kumar, A., Thapa, V., Vijayvargiya, Y., Trivedi, S., Umre, A., Agrawal, R., Kumar, A., Bhutani, S., Pandey, J. K., & Filippelli, G. M. (2026). Machine learning-based assessment of grazing-induced soil changes in Himalayan alpine grasslands. Discover Computing, 29(1), Article 842. https://doi.org/10.1007/s10791-026-10747-0

Image Credits: AI Generated

DOI: 10.1007/s10791-026-10747-0

Keywords: Himalayan grasslands, grazing disturbance, soil organic carbon, machine learning, Random Forest, bulk density, soil nitrogen, PERMANOVA, Uttarakhand, bugyals, soil monitoring, alpine ecosystems