Cashew breeders in India have identified a small group of varieties that produced consistently under changing growing conditions, offering a potential route toward more reliable nut harvests in coastal agriculture. The study evaluated 25 cashew varieties across six agricultural seasons and used two statistical approaches to separate genetic performance from environmental effects. The results highlighted BPP-8 and Vengurla-7 as especially promising for consistent nut yield, while other varieties performed strongly according to complementary measures of stability. The work addresses a practical problem facing perennial tree-crop production: a variety that performs well in one season or location may yield poorly when rainfall, temperature, soil conditions or other environmental factors change. By examining multiple seasons rather than relying on a single harvest, the researchers sought varieties that could serve as dependable parents in breeding programs aimed at sustainable production under a changing climate. Their analysis was conducted in coastal India, where cashew is an important crop and where long-term productivity depends on matching genetic material to variable field conditions.
The researchers combined an additive main effects and multiplicative interaction, or AMMI, model with genotype plus genotype-by-environment, known as GGE, biplot analysis. Both methods are designed for multi-environment trials, in which the same genotypes are tested under different conditions. In this setting, “genotype” refers to the inherited characteristics of a cashew variety, while “environment” can represent a season, location or other set of growing conditions. A genotype-by-environment interaction occurs when varieties respond differently to those conditions. For example, one variety may produce a high yield during a favorable season but lose its advantage during a stressful one, whereas another may maintain a more moderate but reliable yield. The distinction matters for breeders because average yield alone can conceal instability. A variety with the highest overall mean may not be the most useful choice if its performance fluctuates sharply. Stability analysis instead examines how consistently a variety behaves across environments and whether its performance is broadly adapted or suited to particular conditions.
In the pooled analysis, the study found statistically significant differences among the varieties, the environments and the variety-by-environment interactions for nut yield and related traits. The reported probability level was below 0.01, indicating that the observed sources of variation were unlikely to be explained by random differences alone under the model used. This result confirms that both inherited differences among the cashew materials and seasonal conditions contributed substantially to yield outcomes. It also shows why a multi-year trial was necessary. If the varieties had been assessed during only one season, the environmental component could have been mistaken for genetic superiority. Six seasons provided repeated observations with which to estimate the main effects of variety and environment and then analyze their interaction. For perennial crops such as cashew, where trees remain productive over many years, understanding this interaction is particularly important. Breeding decisions based on short-term results may favor varieties that are well matched to temporary conditions rather than those capable of maintaining performance over a broader range of seasons.
The AMMI model described the interaction through principal components, often abbreviated as interaction principal component axes, or IPCAs. These components summarize patterns in the complex matrix formed by varieties and environments. The first four significant components accounted for 53.79%, 29.35%, 12.22% and 3.57% of the genotype-by-environment interaction, respectively. Together, they captured the reported interaction structure in the data, allowing the researchers to compare varieties using stability parameters derived from the model. AMMI separates the overall mean, or additive, effects from the multiplicative interaction pattern. In practical terms, it first estimates how varieties and environments differ in their general effects and then uses the principal components to describe how particular varieties depart from those general expectations in particular environments. Varieties with smaller interaction effects are often considered more stable, although stability must be interpreted alongside yield. In this analysis, AMMI-based measures marked BPP-8, Vengurla-7 and Bhaskara as stable cashew nut yielders across the six years.
The second analytical framework, the GGE biplot, focused on the portion of variation associated with genotype and genotype-by-environment interaction. This approach is useful because breeders are primarily interested in distinguishing the genetic contribution to observed yield from environmental effects that cannot be inherited. The first principal component of the GGE interaction accounted for 79.9% of the contribution, followed by 11.7% from the second component. In a biplot, these components are represented graphically, allowing researchers to examine relationships among varieties and environments. The analysis identified K-22-1, Ullal-3 and Ullal-4 as stable nut yielders under the GGE criteria. The fact that the two methods did not produce an identical list is not necessarily contradictory. AMMI and GGE organize and interpret the data differently, and each can emphasize a different aspect of stability, adaptation or yield response. Considering their results together gives breeders a more detailed view than relying on either method alone.
Several additional biplot assessments provided information about how informative the tested seasons were and whether the environments could be grouped. The discriminativeness-versus-representativeness analysis identified Year 5 and Year 6 as the most discriminative environments. An environment is considered discriminative when it helps reveal meaningful differences among varieties; such seasons can be particularly valuable for testing and selection. Representativeness, by contrast, concerns how well an environment reflects the broader target conditions. The study also used a “which-won-where” biplot, a method that divides environments according to the varieties that perform best in them. This analysis identified BPP-8 as the sole winner among all environments and indicated the formation of one mega-environment. A mega-environment is a group of conditions in which the same genotype has the strongest relative performance. That finding suggests that BPP-8 had broad competitive performance across the environments included in the trial rather than winning only in a narrowly defined subset.
The findings do not mean that a single variety will eliminate the risks associated with climate variability, nor do they establish that the identified genotypes will perform identically in every cashew-growing region. The experiment was conducted in coastal India, and the conclusions are tied to the varieties, seasons and field conditions represented in the study. Environmental interactions can change across soils, rainfall regimes, temperatures, management systems and geographic regions. The researchers’ objective was more specific: to identify stable materials that could be used as parents in a breeding program. In that context, BPP-8 and Vengurla-7 emerged as ideal candidates for consistent nut yield based on the multi-year stability analysis, while Bhaskara, K-22-1, Ullal-3 and Ullal-4 were also identified by particular analytical measures. Further breeding and evaluation would be needed to determine how these materials transmit yield, stability and other desirable traits to their offspring.
By linking field experimentation with quantitative analysis, the study provides a framework for selecting cashew varieties under uncertainty. The approach can help breeders avoid choosing solely on the basis of the largest average harvest and instead identify genetic materials that combine productivity with dependable performance. That balance is increasingly relevant as agricultural environments become less predictable and growers seek varieties that can withstand variation without sacrificing yield. The researchers reported that the datasets generated and analyzed were included in the submitted manuscript, supporting further examination of the results. Their work, carried out with field facilities and financial support involving Odisha University of Agriculture and Technology and the ICAR Directorate of Cashew Research, adds multi-season evidence to efforts to develop climate-resilient cashew. The immediate outcome is a shortlist of promising genotypes and a clearer picture of how they responded across six seasons. The broader significance lies in showing how AMMI and GGE biplot methods can turn complex variety-by-season data into practical guidance for perennial-crop breeding.
The percentage contributions reported for the AMMI interaction components describe how the observed variety-by-season pattern was distributed among statistical axes; they do not represent percentages of total nut yield, nor do they measure heritability. The first axis captured the largest share of interaction, but the additional significant axes were also retained because smaller components can contain structured responses that matter when varieties react differently to particular seasons. This distinction is important when interpreting stability: a genotype positioned near the overall interaction center may show limited deviation from expected performance, while a genotype with a larger interaction score may nevertheless be valuable if that response is associated with high yield in a clearly defined set of conditions.
The differing selections produced by AMMI and GGE therefore illustrate why stability is a multidimensional breeding target rather than a single numerical property. AMMI evaluates interaction after modeling additive variety and environment effects, whereas GGE emphasizes genotype performance and its interaction with the environment. Their outputs can support different decisions: a breeder seeking broad consistency may prioritize materials repeatedly identified as stable, while a breeder targeting a specific production zone may value a genotype that performs exceptionally under a recognizable environmental pattern. In either case, yield and stability need to be considered together, because a consistently low-yielding variety would not meet the practical objective of improving production.
For breeding use, the reported genotypes should be viewed as sources of testable genetic material rather than finished recommendations for every coastal farm. Their value as parents will depend on the performance of progeny and on whether desirable yield behavior is inherited alongside other important characteristics. The study’s emphasis on changing environmental conditions makes subsequent validation especially relevant: independent trials could examine whether the same rankings persist under additional seasons or sites and could test how management affects the interaction pattern. Such work would help distinguish broadly useful stability from stability specific to the trial conditions. It could also clarify whether the identified mega-environment remains coherent when the target region is expanded. The present results consequently offer both a shortlist for crossing and a statistical basis for designing the next stage of evaluation.
Subject of Research: Multi-season genetic stability and genotype-by-environment interaction in coastal Indian cashew varieties
Article Title: Additive Main Effects and Multiplicative Interactions Analysis for Nut Yield in Cashew Grown in Coastal India
Article References: Sethi, K., Dash, M., Suvadra, J. S., Panda, P. K., Tripathy, P., S. Mohana, G., Eradasappa, E., & Adiga, J. D. (2026). Additive Main Effects and Multiplicative Interactions Analysis for Nut Yield in Cashew Grown in Coastal India. Indian Journal of Genetics and Plant Breeding. https://doi.org/10.1007/s44489-026-00045-w
Image Credits: AI Generated
DOI: 10.1007/s44489-026-00045-w
Keywords: Cashew, AMMI analysis, GGE biplot, Nut yield, Plant breeding, Genotype-environment interaction, Climate resilience, Agricultural genetics, Additive, Main, Effects, Multiplicative
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Scienmag. “Cashew Study Identifies Stable Varieties for Climate-Resilient Coastal Farming.” Scienmag, 28 August 2026, https://scienmag.com/cashew-study-identifies-stable-varieties-for-climate-resilient-coastal-farming/. Accessed 28 August 2026.
Scienmag. “Cashew Study Identifies Stable Varieties for Climate-Resilient Coastal Farming.” Scienmag. August 28, 2026. https://scienmag.com/cashew-study-identifies-stable-varieties-for-climate-resilient-coastal-farming/
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Tags: AdditiveAgricultural geneticsAMMI analysisAMMI and GGE biplot methodsCashewCashew breeding for climate resilienceclimate resiliencecoastal agriculture stabilityEffectsenvironmental impact on cashew productiongenetic selection for climate adaptabilitygenetic stability in nut yieldsgenotype-environment interactionGGE biplotIndian cashew varietieslong-term productivity in coastal farmingMainmulti-season crop performanceMultiplicativeNut yieldplant breedingreliable cashew varieties for changing climatesstatistical analysis in crop breedingsustainable perennial crop cultivation
