Scientists Map How Environment Shapes the Medicinal Power of Kalmegh

Kalmegh, the bitter herb known scientifically as Andrographis paniculata, has long been prized in traditional medicine systems across South Asia, and modern pharmacology has increasingly validated its reputation. The plant’s therapeutic punch comes from a family of diterpenoid compounds, most famously andrographolide, alongside neo-andrographolide and 14-deoxy-11,12-didehydroandrographolide, which together display anti-inflammatory, hepatoprotective, antimicrobial and anticancer activities. Yet for all its promise, kalmegh has remained a difficult crop to breed deliberately, because the traits that matter most to pharmaceutical buyers, herb yield and diterpenoid content, do not behave consistently from one field to the next. A new multi-location study published in Plant Biosystems has now tackled that problem head-on, using sophisticated statistical tools to separate genuine genetic superiority from the noise of environmental variation.

The research, led by Mythri B C and Channayya Hiremath of the CSIR-Central Institute of Medicinal and Aromatic Plants with collaborators across India, evaluated twenty-three kalmegh genotypes during the rainy season of 2024. The team planted the genotypes in triplicate under a randomized complete block design at four environmentally diverse sites: Bangalore, Hyderabad, Lucknow and Pantanagar. This spread of locations was deliberate. Southern, central and northern Indian growing conditions differ in rainfall, temperature and soil characteristics, and those differences can dramatically reshape both how much biomass a plant produces and which secondary metabolites it accumulates in its leaves and stems. By testing the same genetic material in all four environments simultaneously, the researchers could ask a deceptively simple question with profound commercial implications: which genotypes are genuinely excellent, and which merely happen to excel in one lucky location?

The answer required disentangling three statistical effects that are often confounded in single-site trials. The first is the genotype effect, the intrinsic genetic capacity of each line. The second is the environment effect, the overall push or pull that each location exerts on all genotypes grown there. The third, and most troublesome, is the genotype-by-environment interaction, often abbreviated GEI, which describes how the relative ranking of genotypes changes across locations. Analysis of variance revealed significant effects of genotype, environment and GEI for all five measured traits: fresh herb yield, dry herb yield, andrographolide content, neo-andrographolide content and 14-deoxy-11,12-didehydroandrographolide content. In practical terms, this means that no breeder can simply pick the best kalmegh line from one trial and expect it to dominate everywhere. The interaction term is large enough that it actively reshuffles the league table from site to site.

To visualize and quantify this reshuffling, the team deployed two of the most widely used frameworks in modern plant breeding: the AMMI model and the GGE biplot. AMMI, which stands for Additive Main Effects and Multiplicative Interaction, works by first removing the average genotype and environment effects through standard analysis of variance, then applying principal component analysis to the leftover interaction matrix. The resulting interaction principal component axes, or IPCAs, compress a sprawling table of genotype-by-location performance into a few interpretable dimensions. The GGE biplot takes a complementary approach, focusing on the genotype main effect plus the genotype-by-environment interaction, the two components that matter when a breeder wants to know which cultivar to recommend for which region. Plotting genotypes and environments together in the same bi-plot space allows researchers to identify winning genotypes for each environment, detect groups of locations that behave similarly, and assess how stable each genotype is around its mean performance.

Beyond these graphical tools, the researchers computed quantitative stability metrics, including the modified AMMI Stability Value, known as MASV, and a simultaneous selection index, abbreviated SI. These indices condense the interaction information into single numbers per genotype, making it easier to rank candidates that combine high performance with low sensitivity to environmental fluctuation. The MASV approach, refined in earlier work on Indian breeding programs, penalizes genotypes that carry large interaction scores on the significant IPCA axes, while the selection index integrates yield and stability into a joint criterion. Together with the AMMI and GGE analyses, these metrics provided a multi-angle assessment that guards against the quirks of any single statistical model, a concern that has been debated extensively in the agronomy literature comparing AMMI and GGE methodologies.

The headline finding is a study in nuance: no single genotype was superior for all five traits. Instead, the analysis carved the germplasm into specialists. Genotypes G1 and G18 emerged as stable, high performers for both fresh and dry herb yield, meaning farmers growing them across the tested regions could expect dependable biomass without wild swings from site to site. For the flagship molecule andrographolide, the compounds most associated with the plant’s anti-inflammatory and hepatoprotective credentials, genotypes G16 and G17 delivered stable content across environments. Meanwhile, G8 proved the most reliable source for the other two diterpenoids tracked in the study, neo-andrographolide and 14-deoxy-11,12-didehydroandrographolide. This pattern of trait-specific stability is not a disappointment but a roadmap: it tells breeders exactly which lines to cross or combine in breeding programs aimed at different end goals, whether that goal is raw herbal biomass for the nutraceutical trade or maximized diterpenoid concentration for standardized pharmaceutical extracts.

The pharmacological stakes are considerable. Andrographolide and its labdane diterpenoid relatives have attracted intense research interest for applications ranging from upper respiratory infection treatment, supported by randomized clinical evaluations of kalmegh extracts, to cardioprotection, with experimental studies showing that andrographolide can mitigate adverse cardiac remodeling after myocardial infarction by enhancing the Nrf2 signaling pathway. Synthetic chemists have also used andrographolide as a scaffold for deriving new ent-labdane diterpene derivatives with cytotoxic activity against cancer cell lines, and computational screens have explored kalmegh phytochemicals as antiviral candidates. Every one of these downstream applications depends on a reliable, standardized supply of raw material with consistent diterpenoid profiles, which is precisely what unstable, environment-sensitive cultivars fail to deliver. A batch of kalmegh grown in one region might meet pharmacopoeial thresholds for andrographolide while the same genotype grown elsewhere falls short, creating quality-control headaches for manufacturers and pricing uncertainty for farmers.

This is why the genotype-by-environment framework matters beyond the academic exercise. Multi-environment trial analysis, once the exclusive domain of staple crop breeding, has become essential for medicinal and aromatic plants whose value lies in specific secondary metabolites rather than calories. Previous work by some of the same research groups has applied GGE biplot methods to lemongrass clone selection and to geranium morphology and chemistry, and an earlier study by Hiremath and colleagues specifically examined andrographolide and neo-andrographolide stability in kalmegh using AMMI and GGE approaches. The current study extends that lineage by adding a fourth environment, a broader genotype panel and the third diterpenoid, 14-deoxy-11,12-didehydroandrographolide, giving a more complete picture of the crop’s chemical and agronomic variability across India’s diverse kalmegh-growing regions.

The authors are careful to flag the limits of their conclusions, and that caution is scientifically important. Because the trial covered a single rainy season, the stability parameters they report reflect spatial variation only. Season-to-season weather fluctuation, including the vagaries of monsoon timing and intensity, can introduce additional interaction effects that a one-year snapshot cannot capture. Before any of the identified genotypes, G1, G18 for yield, G16 and G17 for andrographolide, or G8 for the other diterpenoids, can be released for commercial cultivation, the team emphasizes that multi-year, multi-location evaluation is required to confirm that their stability holds across time as well as space. This is a standard but frequently skipped step in medicinal crop improvement, and its explicit inclusion here signals a rigorous path from statistical identification to practical deployment.

Even so, the study delivers an immediately usable asset to the kalmegh value chain. Breeders now have named, stable genotypes to use as parents for combining high yield with high diterpenoid content; farmers and cultivation companies have shortlisted candidates matched to their regions; and phytochemical suppliers have evidence-based guidance on where to source material with consistent active compound levels. The work also demonstrates how modern statistical genetics, from AMMI’s interaction principal components to the visual intuition of GGE biplots and the numerical discipline of stability indices, can be brought to bear on a traditional medicinal plant that has historically been cultivated with little formal breeding. As global demand for evidence-based botanical therapeutics continues to grow, the marriage of classical field trials with quantitative genetics offers a template for upgrading other underbred medicinal crops, ensuring that the chemistry that makes these plants valuable can be reproduced reliably, harvest after harvest, field after field.

Subject of Research: Genotype-by-environment interaction analysis of herb yield and diterpenoid content in the medicinal plant Andrographis paniculata (kalmegh)

Article Title: Unravel genotype by environment interaction for herb yield and important diterpenoids to enhance the therapeutic potential of kalmegh (Andrographis paniculata, Acanthaceae): AMMI and GGE biplot-based approach

Article References: B C, M., S, G. P., Likitha, A. G., Hiremath, C., T M, A. K., A C, J., Gupta, N., Upadhyay, R. K., & Shanker, K. (2026). Unravel genotype by environment interaction for herb yield and important diterpenoids to enhance the therapeutic potential of kalmegh (Andrographis paniculata, Acanthaceae): AMMI and GGE biplot-based approach. Plant Biosystems, 160(5), Article 268. https://doi.org/10.1007/s44473-026-00283-6

Image Credits: AI Generated

DOI: 10.1007/s44473-026-00283-6

Keywords: kalmegh, Andrographis paniculata, andrographolide, diterpenoids, genotype-by-environment interaction, AMMI, GGE biplot, multi-environment trials, medicinal plant breeding, stability analysis, herb yield, phytochemistry