The team’s central challenge was a familiar one in plant breeding: a genotype that thrives in one field may flop in another. This phenomenon, known as genotype by environment interaction, or GEI, arises because a plant’s genetic potential and the environment in which it grows do not act independently. Rainfall patterns, soil type, temperature, and season all shape how genes express themselves, and the resulting interplay can scramble the apparent rankings of breeding lines. A variety that looks like a champion in one trial may turn out to be a disappointment when farmers plant it elsewhere. Disentangling genuine genetic merit from environmental noise is therefore one of the most important tasks in modern crop improvement.

To tackle this problem, the researchers worked with two large populations of recombinant inbred lines, or RILs, which are genetically distinct lines created by crossing two parent varieties and then self-pollinating successive generations until each line is nearly genetically uniform. The first population, designated Population A, comprised 237 lines derived from a cross between GPU 28, a well-known and economically important Indian finger millet variety, and GE 1746. The second, Population B, comprised 201 lines from a cross between GPU 28 and GE 6635. GPU 28 has a documented track record of economic impact in Indian agriculture, making it a valuable donor parent for breeding programs seeking to combine its favorable traits with those of other germplasm lines.

The field evaluation was ambitious in scale and design. The two populations were tested across three environments over two cropping seasons in 2023, the Kharif or monsoon season and the Summer season, using an alpha lattice design with two replications. Alpha lattice designs are a form of incomplete block design that allows breeders to control field variability more effectively than simple randomized designs, improving the precision of yield estimates when large numbers of lines must be compared. This kind of multi-environment, multi-season testing is the gold standard for detecting how much of the observed yield variation comes from genetics, how much from environment, and how much from the interaction between the two.

The statistical backbone of the study came from two complementary analytical frameworks. The first, the Additive Main effect and Multiplicative Interaction model, universally known as AMMI, combines the classical analysis of variance with principal component analysis of the interaction term. In practical terms, AMMI first strips out the average effects of each genotype and each environment, then examines the residual interaction pattern to reveal which specific genotypes perform unusually well or poorly in which specific environments. The model can be visualized and interpreted through biplots, graphical representations that plot genotypes and environments in a shared space defined by the principal components of the interaction.

The second framework, the GGE biplot, takes a different but equally powerful approach. Rather than analyzing the interaction separately, the GGE biplot focuses on the genotype main effect plus the genotype by environment interaction effect, the two components that matter most when breeders want to know which line to recommend where. The GGE biplot has become a favorite tool among breeders because it can accomplish several tasks at once: it groups testing locations into so-called mega-environments that share the same best-performing genotypes, it identifies which locations are most discriminating and representative for testing purposes, and it reveals genotypes that combine high mean yield with high stability across sites.

The results of the AMMI analysis were unambiguous. All three sources of variation, the genotype main effects, the environment main effects, and the genotype by environment interaction, had statistically significant effects on grain yield. This triple significance confirms that both the choice of breeding line and the choice of growing environment matter, and that their interaction is large enough to influence which genotypes should be recommended to farmers. It also validates the multi-environment approach itself, because a significant interaction means that single-location trials would give misleading conclusions about overall performance.

The GGE biplot analysis then translated these statistical findings into actionable breeding information. By visualizing the yield data across environments, the researchers were able to delineate mega-environments, groups of locations where the same genotypes consistently came out on top, and to distinguish discriminating environments that effectively separate strong genotypes from weak ones. Most importantly, the analysis pinpointed specific lines that combined superior mean performance with stability across testing locations. In Population A, the standout genotypes were G229, G191, and G297, while in Population B the top performers were G130, G298, and G171. These lines represent the best of both worlds: they yield well on average and they do so reliably, without dramatic swings in performance from one environment to another.

Why does this matter beyond the breeding plot? Finger millet, known locally as ragi in India, is a nutritional powerhouse. Its grain is rich in calcium, iron, dietary fiber, and phenolic compounds with documented health-promoting properties, and recent reviews have highlighted its potential to contribute to food and nutritional security in a warming world. Millets generally require less water and fewer inputs than major cereals like rice and wheat, making them attractive candidates for climate-smart agriculture. Yet finger millet has historically received far less breeding investment than the big three cereals, which means that gains in yield and stability, even modest ones, can translate into meaningful improvements for smallholder farmers who depend on the crop.

The study also carries methodological significance for the broader plant breeding community. The authors note that their findings demonstrate the utility of AMMI and GGE models in selecting finger millet genotypes with broad adaptation and provide a statistical basis for genotype recommendation in targeted environments. In other words, the same analytical pipeline can be applied to other crops and other regions, helping breeders everywhere make more defensible decisions about which lines to advance and which testing sites to prioritize. The work builds on a rich statistical literature stretching back to the 1960s, when stability parameters were first formalized, and on decades of AMMI and GGE applications in crops ranging from rice and chickpea to wheat and soybean.

For the finger millet research community, the identification of stable, high-yielding RILs from two large mapping populations opens several doors. Because these lines are genetically characterized descendants of known parents, they can serve not only as candidate varieties but also as material for genetic mapping studies aimed at locating the genes underlying yield stability itself. The populations were originally developed partly to study grain iron content, another trait of nutritional importance, suggesting that the same lines could eventually support breeding for both productivity and nutritional quality. As climate variability intensifies and global interest in millets continues to grow, studies like this one show how classical field breeding, when paired with modern statistical tools, can quietly deliver the resilient crop varieties that food security will demand.

Subject of Research: Genotype by environment interaction and yield stability analysis in finger millet recombinant inbred line populations

Article Title: Ameliorating the Stability and Yield Potential in Finger Millet (Eleusine Coracana (L) Gaertn) Through Genotype × Environment Interaction Studies

Article References: Chetana, Nagaraja, T. E., Meenakshi, J., Vinutha, D. N., Manjunatha, M., Kavya, S., Bhat, S., Tilak, I. S., & Madhusudhana, R. (2026). Ameliorating the Stability and Yield Potential in Finger Millet (Eleusine Coracana (L) Gaertn) Through Genotype × Environment Interaction Studies. Indian Journal of Genetics and Plant Breeding, 86(1), 17-28. https://doi.org/10.1007/s44489-026-00004-5

Image Credits: AI Generated

DOI: 10.1007/s44489-026-00004-5

Keywords: finger millet, genotype by environment interaction, AMMI analysis, GGE biplot, recombinant inbred lines, yield stability, plant breeding, mega-environments, grain yield, climate resilience, multi-environment trials, Eleusine coracana

The research team, led by Salman Saleem, Musab Imtiaz, Mubashir Ahmed Khan, Tayyub Hussain and Usman Saleem, evaluated nineteen newly developed single-cross maize hybrids alongside a commercial check variety across eight locations over two autumn growing seasons. The trial sites spanned Islamabad, Mardan, Faisalabad, Sahiwal, Vehari, Chiniot, Nowshera and Pakpattan, capturing a wide spectrum of altitude, latitude and macro-climatic conditions that reflect the true range of maize production environments in Pakistan. The hybrids themselves were bred from eighteen yellow-kernelled inbred lines at the Maize, Sorghum and Millet Program within the Crop Sciences Institute, and the check used for comparison was the widely grown commercial hybrid 30Y87 from Pioneer Seeds.

At the heart of the study lies one of the most consequential phenomena in modern plant breeding: the genotype-by-environment interaction, commonly abbreviated as GEI. This interaction describes how the relative performance of different genetic lines changes as environmental conditions change. A hybrid carrying genes well suited to a hot, dry location may underperform a different hybrid in a cooler, wetter valley, and the rankings can flip entirely from one year to the next. When GEI is strong, selecting winners from a single location or a single season becomes misleading, because the apparent superiority may simply reflect a fortunate match between one genotype and one set of conditions rather than genuine genetic merit.

The combined analysis of variance revealed just how dominant these interactions are in Pakistani maize. Of the total variability in grain yield observed across the trials, the genotype-by-environment interaction accounted for a striking 72.60 percent, dwarfing the contributions of genotype alone at 13.40 percent and environment alone at 8.82 percent. In practical terms, this means that where and when a hybrid is grown matters far more to its observed yield than the hybrid’s average genetic potential in isolation. The GE interaction was decomposed into fifteen significant principal components, with the first explaining 24.7 percent of the interaction variability, the second 19.2 percent, and the first five together capturing 78.5 percent of the total.

To make sense of such a complex interaction structure, the researchers turned to two complementary statistical frameworks that have become workhorses of multi-environment trial analysis. The first, the Additive Main Effects and Multiplicative Interaction model, or AMMI, integrates classical analysis of variance with principal component analysis, allowing the additive contributions of genotypes and environments to be separated from the multiplicative interaction terms. Expressed graphically as biplots, AMMI results position genotypes along axes representing mean yield and interaction scores, so that a breeder can see at a glance which entries combine high productivity with low sensitivity to environmental fluctuation. The AMMI Stability Value, derived from the first two principal component scores, further quantifies stability into a single ranking metric.

The second framework, the GGE biplot, is built on genotype main effects plus the genotype-by-environment interaction, and it excels at answering questions that AMMI alone addresses less directly. By constructing a polygon around the genotypes positioned farthest from the origin, the which-won-where analysis partitions the testing locations into mega-environments, each dominated by a different winning hybrid. A companion biplot, ranking genotypes against concentric circles around an ideal entry, identifies which hybrid comes closest to the theoretical combination of maximum mean yield and perfect stability. A third view, based on the average environment coordination, orders hybrids by their distance from the stability axis.

The results were unambiguous. Two hybrids, designated G10 and G6, achieved overall mean grain yields exceeding 13,000 kilograms per hectare while simultaneously showing principal component scores close to zero and favourable stability rankings, marking them as broad adapters capable of performing well across all eight environments and both years. Meanwhile, G13 and the commercial check G2 emerged as the most stable genotypes, maintaining good and consistent average yields even though their yields fell short of the top performers. In the GGE ranking biplot, G10 sat closest to the concentric circles around the ideal genotype, and the authors recommend it as the single best performer overall. In contrast, G18, despite ranking second in raw yield at 13,352 kilograms per hectare, displayed a large stability value and a wide spread across the biplot, marking it as specifically adapted rather than broadly reliable.

The which-won-where analysis carried particular significance for breeding strategy. Sixteen environments were partitioned into six sectors, with four identified as repeatable mega-environments, because individual locations tended to fall within the same sector in both trial years. This repeatability, observed for example among environments in Chiniot, Faisalabad, Sahiwal and Pakpattan, suggests genuine geographical adaptation zones rather than random year-to-year noise. For breeders, the implication is profound: locations that behave similarly across years can be grouped, redundant test sites can be pruned, and breeding programs can target specific mega-environments with hybrids tailored to their conditions, dramatically improving the efficiency of multi-environment testing.

The environmental evaluation component of the GGE analysis added yet another layer of insight. Environments differed substantially in their discriminating ability, visualised as vector length on the biplot. Mardan in both years proved the most effective location for separating genotypes, displaying the longest vectors, while sites such as Nowshera and Islamabad showed weaker discriminating power. Choosing test locations with strong discriminating ability and good representativeness of the target production region is a critical but often overlooked aspect of breeding program design, and this study demonstrates how biplot methods can guide those decisions empirically rather than by intuition.

Perhaps the most important methodological lesson from the work is that AMMI and GGE biplots are most powerful when used together. The two frameworks occasionally diverge in their assessment of individual genotypes, as they weight genotype main effects and interaction terms differently, and relying on either alone risks a skewed picture. By cross-validating candidates through AMMI stability values, mean yield rankings, and multiple GGE biplot views, the researchers converged on recommendations that are robust to the quirks of any single statistical model. The selected hybrids, particularly G10 and G6, are now candidates for incorporation into crop improvement programs aimed at developing broadly adapted, climate-resilient and high-yielding maize varieties for Pakistan’s farmers. As climate variability intensifies and demand for maize as food, feed and industrial raw material continues to climb, the ability to breed hybrids that yield heavily everywhere rather than brilliantly in one place may prove one of the most valuable tools in the agricultural toolkit.

Subject of Research: Genotype-by-environment interaction and yield stability analysis of maize hybrids using AMMI and GGE biplot methods in Pakistan

Article Title: Exploitation of genotype-by-environment interactions in maize hybrids at multiple locations in Pakistan using AMMI and GGE Biplots

Article References: Saleem, S., Imtiaz, M., Khan, M. A., Hussain, T., & Saleem, U. (2026). Exploitation of genotype-by-environment interactions in maize hybrids at multiple locations in Pakistan using AMMI and GGE Biplots. Discover Agriculture, 4(1), Article 287. https://doi.org/10.1007/s44279-026-00758-2

Image Credits: AI Generated

DOI: 10.1007/s44279-026-00758-2

Keywords: maize, genotype-by-environment interaction, AMMI model, GGE biplot, yield stability, multi-environment trials, plant breeding, Pakistan, Zea mays, grain yield, mega-environments, hybrid selection