1- University of Mohaghegh Ardabili
2- Horticulture Crops Research Department, Ardabil Agricultural and Natural Resources Research Centre, AREEO
2- Horticulture Crops Research Department, Ardabil Agricultural and Natural Resources Research Centre, AREEO
Abstract: (24 Views)
Extended Abstract
Background: Potato (Solanum tuberosum L.) is an important food crop that provides low-cost energy and high-quality protein. As a staple 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 a low-cost source of energy, high-quality protein, 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, improving the yield and sustainability of this crop is essential. Potato yield is a complex quantitative trait controlled by multiple genes and influenced by genotype, environment, and their interaction (G×E). Understanding G×E 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, posing challenges for breeding programs. 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 G×E interaction in 25 potato hybrids and cultivars across five regions over two 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. Hybrids and control cultivars were planted in two six-meter rows, with 75 cm row spacing and 25 cm plant spacing across all environments. Tuber yield data were collected from the middle rows of each plot. To assess stability, nonparametric statistical methods were applied, including Huehn’s indices (Si(1), Si(2), Si(3) and Si(6)), Thennaro’s statistics (NP1, NP2, NP3 and NP4), Sabaghnia’s indices (NSi(1) and NSi(2)) Katata’s stability parameters (σx and σmy), Fox-rank stability statistic and Rank-sum stability statistic.
Results: The results of the combined analysis of variance indicated that the two-way and three-way interaction between genotype, year, and location were significant at the 1% probability level. Due to substantial differences in climatic, soil, and agronomic conditions across test environments, dynamic stability was prioritized. So, methods that have a high correlation with the performance of genotypes were used. In order to select stable and high-yielding genotypes, the stability statistics of Fox, the σmy statistic and the average stability rank were selected. 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 Si(1) and Si(2) statistics, Hybrid 5 was identified as a stable and high-yielding genotype due to its optimal position in the plot. According to Si(3): Hybrids 5, 6, 3, and the Savalan cultivar; Si(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; NSi(1): Hybrids 9, 3, and the control cultivar Satina; and NSi(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 was used to measure the relationship between the stability parameters. To understand the nature of relationships among the nonparametric methods, a hierarchical cluster analysis based on non-weighted values of genotypes, was performed.
Conclusion: This study employed several stability statistics to evaluate genotypes in terms of performance, stability, or both. 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.
Background: Potato (Solanum tuberosum L.) is an important food crop that provides low-cost energy and high-quality protein. As a staple 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 a low-cost source of energy, high-quality protein, 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, improving the yield and sustainability of this crop is essential. Potato yield is a complex quantitative trait controlled by multiple genes and influenced by genotype, environment, and their interaction (G×E). Understanding G×E 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, posing challenges for breeding programs. 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 G×E interaction in 25 potato hybrids and cultivars across five regions over two 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. Hybrids and control cultivars were planted in two six-meter rows, with 75 cm row spacing and 25 cm plant spacing across all environments. Tuber yield data were collected from the middle rows of each plot. To assess stability, nonparametric statistical methods were applied, including Huehn’s indices (Si(1), Si(2), Si(3) and Si(6)), Thennaro’s statistics (NP1, NP2, NP3 and NP4), Sabaghnia’s indices (NSi(1) and NSi(2)) Katata’s stability parameters (σx and σmy), Fox-rank stability statistic and Rank-sum stability statistic.
Results: The results of the combined analysis of variance indicated that the two-way and three-way interaction between genotype, year, and location were significant at the 1% probability level. Due to substantial differences in climatic, soil, and agronomic conditions across test environments, dynamic stability was prioritized. So, methods that have a high correlation with the performance of genotypes were used. In order to select stable and high-yielding genotypes, the stability statistics of Fox, the σmy statistic and the average stability rank were selected. 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 Si(1) and Si(2) statistics, Hybrid 5 was identified as a stable and high-yielding genotype due to its optimal position in the plot. According to Si(3): Hybrids 5, 6, 3, and the Savalan cultivar; Si(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; NSi(1): Hybrids 9, 3, and the control cultivar Satina; and NSi(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 was used to measure the relationship between the stability parameters. To understand the nature of relationships among the nonparametric methods, a hierarchical cluster analysis based on non-weighted values of genotypes, was performed.
Conclusion: This study employed several stability statistics to evaluate genotypes in terms of performance, stability, or both. 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.
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
