Journal of Crop Breeding

Extended Abstract
Background: Potato (Solanum tuberosum L.) is an important food crop that provides low-cost energy and high-quality protein. As a staple crop in global agriculture, potatoes hold significant nutritional value, particularly in developing countries. Ranked as the world’s third most important food crop after rice and wheat, potatoes serve as an important source of vitamins and essential minerals, playing a key role in global food security, especially in developing nations. In Iran, the cultivated area of potatoes spans approximately 81,000 hectares, with an average yield of 30.8 tons per hectare and an annual production of 2.5 million tons. Given the rising global food demand, it is essential to improve the yield and sustainability of this crop. Potato yield is a complex quantitative trait controlled by multiple genes and influenced by the genotype, environment, and their interaction (GE). Understanding the GE interaction is crucial for evaluating yield stability and genotype adaptability under varying conditions. This interaction can lead to rank changes among cultivars across different environments, in addition to a change in quantity, posing challenges for breeding programs. The G × E interaction has been extensively studied by biometricians, with numerous analytical methods developed to assess it. Various stability indices allow researchers to distinguish genotypes with high stability and adaptability from those suited only to specific environments. Nonparametric methods have been proposed as robust alternatives to parametric stability measures, particularly when dealing with outliers. Rank-based nonparametric methods enable reliable stability evaluations without stringent statistical assumptions. This study aimed to compare yield performance and analyze the G × E interaction in 25 potato hybrids and cultivars across five regions over 2 years to identify high-yielding and stable genotypes.
Methods: Twenty potato hybrids, along with five control cultivars (Savalan, Agria, Kaiser, Luca, and Satina), were evaluated in a randomized complete block design with three replications under varying climatic conditions in Ardabil, Khorasan Razavi, Karaj, Isfahan, and Hamedan. Tuber yield data were collected from the middle rows of each plot. Stability was assessed using nonparametric statistical methods, including Huehn’s indices (S_i⁽¹⁾, S_i⁽²⁾, S_i⁽³⁾, and S_i⁽⁶⁾), Thennaro’s statistics (NP₁, NP₂, NP₃, and NP₄), Sabaghnia’s indices (NS_i⁽¹⁾ and NS_i⁽²⁾), Katata’s stability parameters (σ_x and σ_my), Fox-rank stability statistic, and Rank-sum stability statistic.
Results: The results of the combined analysis of variance indicated that the main effects of genotype, year, location, and the two-way and three-way interactions between genotype, year, and location were significant at the 1% probability level. Dynamic stability was prioritized due to substantial differences in environments. Thus, methods that have a high correlation with the performance of genotypes were used in this study.  The stability statistics of Fox, the σ_my statistic, and the average stability rank were selected to select stable and high-yielding genotypes. Based on these statistics, hybrids 5, 1, and 8 were identified as the most stable and high-yielding genotypes, respectively. Hybrid 5 exhibited the highest yield (41.21 t/ha) among all tested cultivars and hybrids. However, other nonparametric correlation methods did not show a significant relationship with mean performance. For their practical application, performance plots and stability indices were used to select genotypes that simultaneously exhibit both stability and desirable performance. Based on the S_i⁽¹⁾ and S_i⁽²⁾ statistics, Hybrid 5 was identified as a stable and high-yielding genotype due to its optimal position in the plot. According to S_i⁽³⁾: Hybrids 5, 6, 3, and the Savalan cultivar; S_i⁽⁶⁾: Hybrids 17, 3, and 6; NP₁: Hybrids 17, 19, and 6; NP₂: Hybrids 17, 3, 6, 8, and the Savalan; NP₃ and NP₄: Hybrids 17 and 6; NS_i⁽¹⁾: Hybrids 9, 3, and the control cultivar Satina; and NS_i⁽²⁾: Hybrids 3 and 6, were identified as having above-average performance and general adaptability to different environments. Simple correlation coefficients using Spearman’s rank correlation were used to measure the relationship between the stability parameters. A hierarchical cluster analysis based on non-weighted values of genotypes was performed to understand the nature of relationships among the nonparametric methods.
Conclusion: Given the emphasis on dynamic stability, the methods of Fox, the σmy statistic, and the mean stability rank were selected as key criteria, leading to the identification of hybrids 5, 1, and 8 as the optimal hybrids. Due to the dynamic nature of these hybrids' stability, their performance is expected to improve under enhanced environmental conditions and with the optimized application of agricultural inputs.