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4 November 2025

Yield Components Analysis in Partially Interspecific Lines of Cotton and Irrigation-Nitrogen Effects

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1
Hellenic Agricultural Organization Demeter, Institute of Industrial and Forage Crops, GR-41335 Larissa, Greece
2
School of Agriculture, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece
3
Department of Viticulture and Oenology, Democritus University of Thrace, GR-66100 Drama, Greece
4
Directorate of Water Management of Thessaly, Decentralized Administration of Thessaly—Central Greece, GR-41335 Larissa, Greece
Appl. Sci.2025, 15(21), 11746;https://doi.org/10.3390/app152111746
This article belongs to the Section Agricultural Science and Technology
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Abstract

Cotton production in Mediterranean regions is increasingly constrained by limited water availability, making it essential to identify genotypes that can maintain yield under reduced irrigation. In this study, four partially interspecific cotton lines (Pa7) and the commercial cultivar Celia were evaluated under two nitrogen rates designed to test resource-use efficiency and three irrigation regimes across two growing seasons in Greece. A strip–split plot design with three replications was used, and field data were analyzed with ANOVA, stability indices, and multivariate tools (Additive Main Effects and Multiplicative Interaction—AMMI, and Genotype plus Genotype × Environment—GGE biplots). Results showed that moderate irrigation consistently ensured stable seed cotton yields, whereas a higher water supply increased the plant height without proportional yield benefits, while fertilizer supplied in the specific quantities showed a lower impact on yield stability. Genotype × environment interactions were highly significant: Celia confirmed its high stability, while line M3 combined good stability with favorable agronomic traits. Yield was strongly associated with boll weight and lint percentage, indicating their usefulness as indirect selection criteria. These findings highlight the agronomic potential of partially interspecific cotton lines and demonstrate that moderate irrigation can sustain productivity while reducing water inputs, contributing to a more efficient use of resources in cotton production under water-limited conditions. These results provide practical insights for breeding and water management strategies aiming to sustain cotton productivity under Mediterranean water-limited conditions.

1. Introduction

Cotton is one of the most important crops worldwide, and Greece is the leading cotton producer in the European Union [,]. In Mediterranean regions, cotton productivity is strongly influenced by water availability, making efficient irrigation strategies essential for sustainable production [,,]. Climate change and increasing water scarcity further challenge cotton cultivation, affecting both vegetative growth and reproductive development [,,,,]. Farmers often struggle to balance water supply with crop needs, highlighting the need for region-specific management practices.
Water management, including appropriate irrigation scheduling and drainage control, plays a critical role in maintaining yield while minimizing losses caused by drought or waterlogging [,,,]. Systems such as deficit irrigation and subsurface drip have shown promise in improving water-use efficiency, though their performance can vary depending on soil and climatic conditions [,,,]. Similarly, nitrogen availability is essential for cotton development. Both insufficient and excessive nitrogen can negatively affect growth and yield, suggesting that careful nutrient management is crucial for optimal results [,,,]. Previous studies have shown that genotypic effects and their interactions with environmental factors largely determine cotton performance under irrigation and fertilization regimes, but these interactions remain complex and environment-dependent [,].
Partially interspecific cotton lines, developed by crossing Gossypium hirsutum and G. barbadense with other species such as Hibiscus cannabinus, offer a promising approach to improving yield and fiber quality [,,,,,,]. These lines often exhibit high heritability for yield-related traits, which could make them more adaptable to environmental variability [,,,]. However, their agronomic performance under contrasting irrigation and nitrogen conditions has not been systematically studied. Moreover, genotype × environment interactions (G × E) remain a major challenge for breeders, as they can complicate the selection of consistently high-performing genotypes [,,,,,,].
However, the performance and stability of the partially interspecific Pa7 cotton lines under combined irrigation–nitrogen regimes remain unexplored in Mediterranean environments. Despite the promising potential of Pa7 lines, their agronomic performance under combined water and nitrogen limitations has not been thoroughly characterized. Considering the known effects of irrigation and nitrogen on cotton productivity, we hypothesized that moderate irrigation would enhance yield stability, and that seed-cotton yield would be closely associated with key traits such as boll weight and lint percentage.
In this study, we evaluated, for the first time, the performance and stability of four Pa7 partially interspecific cotton lines, compared to the commercial cultivar Celia, under three irrigation levels and two nitrogen rates. Our objectives were: (a) to assess the effect of these inputs on yield and its components, (b) estimate stability using Additive Main Effects and Multiplicative Interaction (AMMI), Genotype plus Genotype × Environment (GGE) biplots, and the Stability Index, and (c) identify traits most closely linked to yield, providing practical insights for sustainable cotton production under Mediterranean conditions.

2. Materials and Methods

2.1. Experimental Conditions

Field experiments were conducted over two consecutive growing seasons (2006 and 2007) at the experimental farm of Aristotle University of Thessaloniki [], located in Northern Greece (40°32′9.32″ N, 22°59′6.17″ E; 10 m above sea level). The site has a Mediterranean climate, characterized by hot, dry summers and mild, wet winters. Conducting the experiment over two natural years allowed us to capture the interannual climate variability typical of the Mediterranean region, which cannot be simulated reliably within a single season. Daily maximum and minimum temperatures, along with rainfall, were recorded throughout both seasons (Figure 1) to provide detailed climatic context. The soil at the site is a calcareous loam (Typic Xerorthent). Pre-sowing soil analyses of the 0–30 cm layer were conducted to characterize fertility. Soil properties measured were:
Figure 1. Monthly average of maximum and minimum air temperatures (°C) and rainfall (mm) for 2006 and 2007 in Thessaloniki, Greece.
  • 2006: pH 7.71; organic matter 1.15%; nitrate nitrogen (N-NO3) 8.4 ppm; available phosphorus (Olsen method) 9.8 ppm; potassium 135 ppm.
  • 2007: pH 8.0; organic matter 2.63%; N-NO3 53.2 ppm; phosphorus 16.2 ppm; potassium 189 ppm.
Both experiments were conducted in the same field to ensure comparability, with notable differences in the soil chemical properties observed between 2006 and 2007.

2.2. Genetic Materials

The study evaluated four partially interspecific cotton lines (Pa7 generation), developed from crosses between G. hirsutum (4S, Coker 310) and G. barbadense (B403, Carnak), followed by pollination with Hibiscus cannabinus L. pollen []. The commercial cultivar Celia (M5) was planted in the border rows to maintain uniform growth conditions for the experimental lines. Celia was included as a check, serving to evaluate the performance, stability, and adaptability of the interspecific lines under the various environmental conditions. Table 1 presents the pedigree and coding of the lines (M1–M4) evaluated during the 2006 and 2007 growing seasons.
Table 1. Pedigree of Plant Material (M1–M4).

2.3. Experimental Design

A strip–split plot factorial design with three replications was implemented to evaluate the combined effects of irrigation, nitrogen fertilization, and genotype performance. Irrigation treatments were arranged in strips, while genotypes and nitrogen levels were randomly assigned within these strips, maintaining a full factorial structure. Each plot consisted of seven rows, 8.0 m in length and spaced 1.0 m apart, with data collected from the five central rows. The two border rows, planted with the commercial cultivar Celia, maintained uniform growth conditions. A summary of all experimental factors and their combinations—Genotype (G), Fertilization (F), Irrigation level (I), and Year (Y)—is presented in Table S1. A schematic field layout is shown in Figure S1, illustrating the strip–split plot structure with irrigation as the main plot, fertilization as the subplot, and genotype as the sub–subplot factor.
The strip–split plot design was chosen to allow for the efficient assessment of multiple factors while maintaining replication and randomization within manageable field plots. Irrigation levels were set to reflect the low, moderate, and high-water supplies typical of Mediterranean cotton systems, and the two nitrogen (fertilization) rates were selected to represent sub-optimal and sufficient nutrient availability. This setup enabled the direct comparison of genotype performance under defined water and nutrient conditions, and facilitated the evaluation of their adaptability and stability across the experimental environments.
Year × Irrigation combinations were defined as distinct environments, following the framework of Stratilakis and Goulas [], which reduces uncontrolled interactions and facilitates a more precise evaluation of genotype × environment effects. Irrigation strips were randomized across years, and the “environment” factor (Year × Irrigation) was treated as a fixed effect in the ANOVA. Six environments were established across the two growing seasons, corresponding to the three irrigation levels in each year: I1Y1 (E1), I1Y2 (E2), I2Y1 (E3), I2Y2 (E4), I3Y1 (E5), and I3Y2 (E6).
This experimental arrangement allowed for an unbiased comparison of genotypes and fertilization treatments while enabling the estimation of the main effects and interactions through analysis of variance (ANOVA), providing a robust framework for evaluating performance under contrasting irrigation regimes.

2.4. Fertilizer and Irrigation Application

Three irrigation levels were established for each growing season: 233 mm (I1), 333 mm (I2), and 433 mm (I3) in 2006 (Y1), and 321 mm (I1), 421 mm (I2), and 521 mm (I3) in 2007 (Y2) (Table 2). Recent studies on local agronomic recommendations and irrigation scheduling in Greece provide additional context and support for the methodology applied in this study, highlighting practices relevant to cotton water management under Mediterranean conditions [,]. The higher irrigation volumes in 2007 were applied to offset the increased evapotranspiration associated with hotter summer temperatures while maintaining a consistent 100 mm spacing between treatments to ensure comparability across levels. To secure adequate soil moisture for germination, the first two irrigations after sowing were delivered as artificial rainfall, with subsequent irrigations supplied via drip irrigation. Table 2 provides the detailed irrigation schedule and amounts.
Table 2. Irrigation Levels and Rainfall (2006 & 2007).
Nitrogen fertilization was applied at two levels: (a) 40 kg N ha−1 as a base application before sowing in mixed nitrate/ammonium form, and (b) 80 kg N ha−1 as the 40 kg N ha−1 base application before sowing in mixed nitrate/ammonium form plus 40 kg N ha−1 in nitrate form during crop growth. Pre-sowing soil analyses in 2007 revealed deficiencies in micronutrients, prompting the addition of boron (20 kg ha−1) and a foliar zinc fertilizer to correct these limitations, whereas no amendments were necessary in 2006. These adjustments ensured sufficient nutrient availability for optimal crop development and allowed for a controlled assessment of genotype performance under varying water and nitrogen regimes.
A plant growth regulator, mepiquat chloride, was applied at 150 mL/100 L water in 2006 and 300 mL/100 L in 2007, reflecting differences in crop growth response due to seasonal climatic conditions.

2.5. Crop Management

For the 2007 sowing, seeds were screened to remove damaged or infertile ones, whereas in 2006, untreated seeds were used. Sowing was carried out on 25 April 2006 and 4 May 2007, and harvesting was performed manually in two stages in both years: early October (4–6) and mid-November (16). Throughout the growing seasons, recommended agronomic practices for cotton yield trials were followed to ensure optimal crop development.
Standard integrated pest management (IPM) practices were followed. Registered insecticides and fungicides were applied only when necessary to maintain pest-free plots, and weeds were controlled using a combination of mechanical weeding and selective herbicide applications, avoiding excessive chemical inputs. At crop maturity, chemical defoliation was conducted using a mixture of ethephon and cyclanilide to facilitate harvesting.

2.6. Measurements

During the crop growth period, the following agronomic traits were recorded:
Plant height (cm): the vertical distance from the base of the plant (soil level) to the tip of the main stem.
Seed-cotton yield (t ha−1): the total weight of harvested cotton, including both lint (fiber) and seeds, before ginning.
First-pick cotton yield (%): the percentage of total cotton harvested during the first picking. Cotton is usually harvested in multiple rounds due to staggered boll maturation, with the first pick generally producing the highest quality and largest portion of the yield.
Proportion of First-Pick Yield (%) = (First-pick yield/Total yield) ×100
Boll weight (g): the weight of a single mature cotton boll containing fibers and seeds. Ten naturally opened bolls from the central part of the plants were randomly collected to determine boll weight.
Lint percentage (%): the proportion of usable fiber (lint) obtained from a given amount of raw cotton after ginning.

2.7. Statistical Analysis

A combined ANOVA treated the irrigation levels as distinct environments under a fixed-effects model, following Steel et al. [], and utilizing IBM SPSS version 29 (IBM Corp, Armonk, NY, USA). Partial eta squared (η2) was calculated for all factors and interactions in the ANOVA. Trait correlations were evaluated using Pearson’s correlation coefficient, following the method described by Steel et al. [], and visualized using JMP 18 statistical software (Statistical Discovery LLC, Cary, NC, USA). Statistical analysis was conducted at a significance threshold of p < 0.05.

2.8. Stability Index

A Stability Index (SI) was calculated as ( x ¯ / s ) 2 , where x ¯ is the mean and (s) the standard deviation for each trait []. Higher SI values indicate greater stability across environments. Mean seed-cotton yield (t ha−1) and SI were calculated for each genotype across the irrigation × fertilization (I × F) treatments. Treatment-level 95% confidence intervals (CIs) for mean seed-cotton yield were estimated using the standard error of the mean and the t-distribution at a 95% confidence level. Error bars in figures represent these 95% CIs (± t ha−1).

2.9. Multi-Environment Evaluation Using AMMI and GGE Biplots

Genotype × environment interactions (GEIs) were analyzed using AMMI (Additive Main Effects and Multiplicative Interaction) and GGE (Genotype + Genotype × Environment) biplot models [,]. AMMI combines ANOVA for the genotype and environment main effects with PCA of GEI and generates biplots via singular value decomposition (SVD) on a double-centered G × E matrix [,], with low PC1 values indicating higher stability across environments []. The biplot concept, introduced by Gabriel [] (details: http://www.ggebiplot.com/concept.htm, accessed on 1 October 2025), visually represents both genotypes and environments, and in breeding studies, “testers” can also include traits or markers. GGE biplots capture the genotype main effects and GEI using a G × E matrix from which environment main effects are removed, enabling the visualization of genotype performance and stability; genotypes and environments closer to the ideal point are considered most desirable. Analyses were conducted with PB Tools version 1.4 (free edition, IRRI, Laguna, Philippines).

3. Results

3.1. Combined ANOVA

The combined ANOVA (Table 3) revealed significant differences among environments for all measured traits. Among the three factors (Environment, Genotype, Fertilization), fertilization did not significantly affect the traits. The combination of Environment with Genotype was significant for each of the traits studied, while the combination with Fertilization resulted in significant differences only in the case of the two traits (Plant height and Boll weight). The other two-way and three-way combinations had no significant effect on any of the traits. Nevertheless, the unique contribution (partial η2) in the traits studied of the Environment varied in the range 0.309–0.897, suggesting a very large effect compared to the rest of the significant factors or their combination. An exception represents the trait Lint percentage, where Genotype had the highest contribution. The environment × Genotype (E × G) interaction also explained a significant amount of the variance for each of the traits (partial η2 > 0.259) studied. Environment × Fertilization (E × F) interaction explained a large part of the variance in the case of boll weight (partial η2 = 0.164), while for plant height (partial η2 = 0.091), a medium contribution was observed.
Table 3. Combined ANOVA for plant height (cm), seed-cotton yield (t ha−1), first-pick yield (%), boll weight (g), and lint percentage (%) across artificial/considered environments over all years within the three irrigation levels. Mean squares (m.s.) and effect sizes (Partial η2) are presented.

3.2. Stability Index (SI)

Table 4 presents the Stability Index (SI) values for all genotypes across the tested environments. The commercial cultivar Celia (M5) consistently showed the highest stability across all traits, particularly for lint percentage, confirming its reliability under varying irrigation levels. Among the partially interspecific lines, M3 exhibited intermediate to high stability across several traits, indicating good adaptability. In contrast, M1, M2, and M4 displayed lower stability values, reflecting greater sensitivity to environmental variation and genotype × environment interactions.
Table 4. Stability Index (SI) values for each genotype and trait across environments. Traits: plant height (cm), seed-cotton yield (t ha−1), first-pick yield proportion (%), boll weight (g), and lint percentage (%). Higher SI values indicate greater stability across environments.
Analysis across individual environments (Tables S2 and S3) confirmed the overall stability patterns observed in Table 4. The proportion of first-pick yield was highly variable, particularly in E2 and E4, whereas boll weight and lint percentage remained relatively consistent across environments. Environments E1, E3, and E5 generally showed lower and more uniform Stability Index (SI) values, indicating reduced sensitivity of genotypes to environmental fluctuations. Within this context, the commercial cultivar Celia (M5) maintained consistently high stability across all traits and environments, whereas the partially interspecific lines exhibited greater variation. Notably, M3 combined intermediate to high stability with satisfactory agronomic performance, highlighting its potential for wider cultivation under Mediterranean cotton-growing conditions. Overall, the Stability Index results confirmed that Celia (M5) was the most stable genotype, followed by M3, which combined adaptability and satisfactory yield levels.

3.3. Trait Correlations

Correlation analysis (Figure 2) revealed significant interdependence among traits. Plant height was negatively correlated with all other traits (r = −0.45 to −0.85), whereas seed-cotton yield was strongly positively correlated with boll weight (r = 0.88) and moderately with first-pick proportion (r = 0.66) and lint percentage (r = 0.58). These patterns suggest that indirect selection focusing on traits strongly associated with yield, such as boll weight and lint percentage, could effectively improve cotton productivity.
Figure 2. Scatterplot matrix displaying pairwise correlations among the analyzed parameters. Correlation coefficients are provided in the upper triangle within the probability-sized circles, while the lower triangle includes scatterplots (black dots) with fitted regression lines (red) and 95% shaded density ellipses illustrating the clustering of data points.

3.4. AMMI and GGE Analyses

AMMI and GGE biplot analyses (Figure 3 and Figures S2–S5) explained a large proportion of total variation and revealed significant differences among genotypes in stability and adaptability across environments. The first two principal components (PC1 and PC2) captured most of the variation, allowing for an assessment of genotype performance under the tested irrigation and fertilization conditions.
Figure 3. Stability analysis of seed-cotton yield (t ha−1) using multivariate methods: (a) AMMI adaptation map; (b) AMMI1 biplot; (c) environmental stability GGE biplot; (d) genotypic stability GGE biplot; and (e) “which-won-where” GGE biplot. Percent variance explained by PC1 and PC2 is indicated in each plot. Genotypes positioned near the ideal point represent high stability and superior agronomic performance.

3.4.1. Seed-Cotton Yield (AMMI and GGE Biplots)

For overall seed-cotton yield, both the AMMI adaptation map and AMMI1 biplot (Figure 3) indicated that G5 (M5, Celia) consistently achieved the highest yield and demonstrated superior stability across all environments. Partially interspecific lines G3 (M3), G1 (M1), and G2 (M2) showed moderate yields with stable performance, whereas G4 (M4) exhibited lower stability. The six environments were clustered into two groups: E1, E3, E5, and E2, E4, E6. The GGE biplot confirmed that E1, E3, and E5 were the most stable environments, and the “which-won-where” analysis highlighted G5 (M5) as the most desirable genotype across all conditions.

3.4.2. Plant Height Stability

For plant height (Figure S2), AMMI and GGE analyses revealed that G2 (M2) and G4 (M4) were the most stable genotypes, followed by G1 (M1), which exhibited the greatest height across environments. Environments E6, E2, and E4 showed minimal variation in height, while E1 and E5 exhibited greater differences. The GGE biplot positioned E3 near the ideal environment center, with G2 (M2), G4 (M4), and G1 (M1) located close to the ideal genotype, indicating favorable adaptation.

3.4.3. First-Pick Yield Proportion

Regarding the proportion of first-pick yield (Figure S3), AMMI analysis identified G3 (M3) and G5 (M5) as the most productive genotypes. Environment clustering was similar to yield, with E1, E3, E5 forming one group and E2, E4, E6 another. GGE biplot analysis confirmed the stability of E1, E3, and E5, highlighting G5 (M5) as the most desirable genotype, closely followed by G3 (M3), with both showing adaptability across all environments.

3.4.4. Boll Weight Stability

For boll weight (Figure S4), G5 (M5) consistently demonstrated the highest values across environments, while the other genotypes maintained a stable average performance. The environmental clustering was consistent with previous traits, and GGE analysis confirmed E1, E3, and E5 as the most stable. G5 (M5) adapted well to most environments, whereas G1 (M1), G2 (M2), and G3 (M3) performed best in E4.

3.4.5. Lint Percentage Stability

Finally, for lint percentage (Figure S5), G5 (M5) achieved the highest values in all environments, with the other genotypes showing stable intermediate performance. Environmental grouping and GGE analysis patterns were consistent with those observed for boll weight, confirming the superior stability of E1, E3, and E5 and the broad adaptability of G5 (M5).

3.5. Integrated Stability Overview

The integrated ranking of genotypes based on the AMMI, GGE, and SI outcomes confirmed that Celia (M5) exhibited the most stable performance across environments, with consistently high seed-cotton yield and superior lint percentage (see Table S4 for full SI values). Line M3 combined moderately high yield with relatively stable performance across environments, highlighting its adaptability under Mediterranean cotton-growing conditions.
Treatment-level analysis indicated that intermediate irrigation combined with balanced nitrogen supply (I2F1, I2F2) produced the most consistent yield responses, with mean yields around 3.0–3.1 t ha−1 and lower variation across replicates (Table S5).
Mean trait data for each genotype across all six environments are provided (Table S6), supporting the observed stability patterns.

4. Discussion

4.1. Environmental and Genotypic Effects

Since yield characteristics are expected to vary across environments [,], the data were analyzed considering each environment (E) as a combination of Year × Irrigation levels, capturing both seasonal and water regime effects. Supplemental irrigation and water deficits have previously been reported to influence yield components [], while the combined nitrogen and water application generally enhances yields and water use efficiency [,]. Recent studies in dry and semi-arid regions indicate that deficit irrigation can maintain acceptable cotton yields while improving water productivity [,].
Our ANOVA results confirmed significant effects of Environment and Genotype on all traits, indicating that different genotypes have distinct physiological responses to water and nutrient availability, rather than all responding uniformly [,,]. Moderate irrigation consistently provided stable yields, whereas excessive water did not lead to proportional yield increases. After a certain threshold, additional water primarily promotes vegetative growth, reducing the allocation of assimilates to boll development. This trade-off between biomass accumulation and reproductive output is particularly relevant for Mediterranean cotton production, where over-irrigation can decrease resource-use efficiency. Consequently, high irrigation increased plant height, but this likely diverted assimilates from boll formation, explaining the observed negative correlation between plant height and yield [,,].
Boll weight was strongly genetically controlled, with minor E × G interactions, while plant height and first-pick proportion were influenced by both the genotype and environment [,,,]. Traits under strong genetic control, such as boll weight therefore provide more predictable outcomes under variable conditions and can be exploited in selection and management strategies [,,,,]. Balanced nitrogen management remains important, as appropriate N levels enhance boll number, seed-cotton yield, and first-pick proportion while minimizing losses and environmental impact [,], reflecting the role of N in optimizing reproductive organ development and photosynthate partitioning.

4.2. Trait Stability and G × E Interactions

4.2.1. Environmental and Management Effects on Stability

Stability analysis revealed that first-pick proportion varied more across environments than boll weight and lint percentage [,,,,]. This indicates that early harvest traits are more sensitive to environmental fluctuations and management practices, requiring targeted stability strategies. Intermediate irrigation environments showed higher variability in first-pick proportion, whereas boll weight and lint percentage remained relatively stable, highlighting that traits under strong genetic control buffer environmental variability [,,,]. Moderate irrigation again produced the most stable performance across genotypes and growing seasons, demonstrating that intermediate water supply mitigates stress fluctuations while preventing excessive vegetative growth.

4.2.2. Genotypic Responses and G × E Patterns

The commercial cultivar Celia consistently exhibited the highest stability indices, especially for lint percentage, whereas interspecific lines showed more variable responses [,]. This indicates that cultivar choice can strongly influence yield stability and should be considered alongside genotype × environment interactions. Significant G × E interactions for seed-cotton yield, first-pick proportion, and other traits [,] highlight the need to consider both genetic and environmental factors in cultivar recommendations. Boll weight and lint percentage emerged as reliable traits for indirect selection, while first-pick proportion may require targeted management under specific irrigation regimes. As expected, stability tends to be higher for traits with qualitative inheritance, while quantitative traits are more influenced by environmental variability [,,,,,,,,,,,,,].
Trait correlations showed plant height negatively associated with other traits, whereas seed-cotton yield correlated strongly with boll weight and moderately with first-pick proportion and lint percentage [,,,,]. Excessive vegetative growth may divert resources from reproductive organs, affecting yield, although this effect can vary depending on genotype and environmental context [,], emphasizing the importance of managing vegetative vigor. Conversely, boll weight, lint percentage, and first-pick proportion consistently aligned with seed-cotton yield, supporting their use in selection and management.
Optimizing nitrogen and irrigation improved seed-cotton yield via enhanced growth and nitrogen use efficiency [,]. This improvement likely stems from better photosynthate production and allocation to reproductive structures. Although nitrogen did not significantly affect seed-cotton yield in this study, soils with lower initial N may show different responses, highlighting the need to explore genotype × nitrogen × water interactions under diverse soil conditions. Lint percentage varied between genotypes with significant E × G interactions [,], and irrigation–fertilization combinations affected genotypic responses [,]. Although nitrogen did not significantly affect the seed-cotton yield in this study, soils with lower initial N may show different responses, highlighting the need to explore genotype × nitrogen × water interactions in diverse soils. Yield differences among genotypes were more pronounced in certain environments, with low-to-moderate irrigation producing yields comparable to higher irrigation levels [,]. Plant height responded positively to water availability and showed significant E × G interactions [,,,].

4.2.3. Multivariate Stability Analysis (AMMI, GGE, SI)

AMMI and GGE biplot analyses confirmed significant G × E interactions, separating genotypes based on adaptability [,]. For plant height, G2 (M2) and G4 (M4) were most stable, while G5 (M5) was shortest but still performed well for other traits, suggesting that stability in one trait may not compromise overall productivity. Seed-cotton yield was highest and most stable for G5 (M5), whereas G3 (M3) showed a favorable first-pick proportion. Environments E1, E3, and E5 provided consistent conditions that allowed for genotypes to express their potential, while E2, E4, and E6 imposed water-stress conditions that amplified G × E effects.
The Stability Index complemented biplot analyses, confirming G2 (M2) and G4 (M4) as most stable for plant height, G5 (M5) as the highest and most stable for seed-cotton yield, and G3 (M3) balancing stability and productivity for first-pick proportion. Among the new Pa7 lines, M3 emerged as a promising interspecific line combining genetic potential and plasticity across varying irrigation regimes.
The integrated ranking of genotypes based on the AMMI, GGE, and Stability Index (SI) outcomes confirmed that Celia (M5) exhibited the highest stability across environments, consistently achieving superior lint percentage and boll weight. Line M3 combined moderately high yield with relatively stable performance, highlighting its adaptability under Mediterranean cotton-growing conditions. These findings are consistent with previous reports showing that stable cotton genotypes can maintain high yield across variable environments [], and that optimized nitrogen management enhances yield and stability under semi-arid conditions []. Treatment-level analysis further indicated that intermediate irrigation combined with balanced nitrogen supply (I2F1, I2F2) produced the most consistent yield responses, supporting observations from similar Mediterranean and semi-arid studies [,]. Collectively, these results suggest that genotype selection based on stability indices, combined with tailored irrigation and fertilization management, can effectively mitigate the impact of environmental fluctuations and maximize cotton productivity in water-limited regions.

4.3. Practical and Breeding Implications

In broader terms, these results have several implications. For producers, moderate irrigation provides stable yields while reducing water use and costs. Optimized nitrogen management enhances productivity and environmental efficiency. For breeding programs, lines like M3 and others with strong genetic control for traits such as boll weight and lint percentage offer reliable options for selection, especially under water-limited conditions. For sustainability and policy, these findings support strategies aligned with EU CAP and Mediterranean water management policies [], showing that moderate irrigation can achieve economic and environmental goals.
Although the experiments were conducted in 2006–2007, the findings remain relevant today because the environmental conditions resemble comparable climatic stresses currently observed in the Mediterranean. These data provide a valuable baseline for long-term breeding and irrigation management, offering actionable insights into cotton yield stability under limited water supply. Using modern analytical approaches such as AMMI, GGE biplots, and Stability Index analyses, we accurately assessed the genotype performance, adaptability, and stability. In addition, integrating UAV-based digital phenotyping enables the precise, high-throughput monitoring of growth and yield traits, enhancing future studies of genotype responses under irrigation gradients [,].
Stable and promising genotypes were identified, with moderate irrigation levels sufficient to maintain high yields by optimizing the balance between vegetative growth and reproductive output. Traits under strong genetic control, such as boll weight and lint percentage, emerged as reliable indicators for indirect selection. The interspecific line M3 consistently combined productivity and stability across environments, highlighting its potential for breeding programs aimed at water-limited regions.

5. Conclusions

The evaluation of four partially interspecific cotton lines (Pa7) and the commercial cultivar Celia showed that yield and stability were mainly influenced by environmental conditions, while nitrogen fertilization had only minor effects. Moderate irrigation consistently provided stable yields, whereas increasing water beyond this level brought no substantial gains. This indicates that efficient water use can be achieved without compromising productivity—an essential aspect of sustainable cotton production under Mediterranean conditions.
Celia maintained high stability, while among the Pa7 lines, M3 stood out for combining satisfactory yield with consistent performance across environments. Boll weight and lint percentage proved to be reliable traits for selection in breeding programs.
Overall, the study demonstrates that integrating moderate irrigation practices with improved genetic material can enhance productivity, reduce resource consumption, and support the long-term sustainability of cotton cultivation under limited water availability. These findings are particularly relevant for Greece and other Mediterranean regions, where cotton remains a key component of the rural economy, and water-efficient production is increasingly critical for both profitability and environmental stewardship.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/app152111746/s1, Table S1. Summary of the experimental factor combinations [(Genotype (G), Fertilization (F), Irrigation level (I), and Year (Y)] used in the study and the structure of the strip–split plot design. Each irrigation strip (I1–I3) included two nitrogen fertilization subplots (F1, F2) and five genotypes (M1–M4 and Celia). Data were collected from the five central rows of each plot, with Celia planted in the two outer border rows; Table S2: Stability Index (SI) for each genotype and trait across six environments and three irrigation levels. Higher SI values indicate greater trait stability across environments. Traits: plant height (cm), seed cotton yield (t ha−1), first-pick yield proportion (%), boll weight (g), and lint percentage (%); Table S3: Trait Stability Index (SI) across six environments (E1–E6) for the three irrigation levels. Traits include plant height (cm), seed cotton yield (t ha−1), first-pick yield proportion (%), boll weight (g), and lint percentage (%). Higher SI values indicate greater stability of the trait across environments; Table S4: Qualitative ranking of genotypes based on combined stability evidence (AMMI, GGE, and SI analyses). Stability interpretations were derived from AMMI and GGE biplot positions and numerical SI values (Table 4). Lower deviation and higher SI indicate greater stability and performance consistency. Although stability was evaluated for all traits, lint percentage exhibited the highest consistency across environments and is presented here as a representative indicator; Table S5: Mean seed-cotton yield (t ha−1) and Stability Index (SI) across irrigation × fertilization (I × F) treatments. Error bars represent 95% confidence intervals (±t ha−1). Higher SI values indicate greater performance consistency across treatments; Table S6: Mean trait values across six environments (E1–E6). Summary of mean values for key agronomic traits by genotype: Plant height (cm), seed-cotton yield (t ha−1), first-pick yield (%), boll weight (g), and lint percentage (%). Figure S1: Schematic layout of the strip–split plot design used in the field experiment. Irrigation treatments (I1 = full, I2 = moderate, I3 = low) were arranged in strips, each containing two nitrogen subplots (F1 = 40 kg N ha−1; F2 = 80 kg N ha−1). Each subplot comprised seven rows (8 m long; 0.96 m row spacing), with data collected from the five central rows (Pa7 lines M1–M4 and M5: Celia) and the two outer rows planted with Celia to reduce border effects. The experiment was conducted in three replications per year, enabling estimation of main effects and interactions among irrigation, fertilization, and genotype; Figure S2: Stability analysis of plant height (cm) using: (a) AMMI adaptation map; (b) AMMI1 biplot; (c) environmental stability GGE biplot; (d) genotypic stability GGE biplot; and (e) “which-won-where” GGE biplot. The percentage of variance explained by PC1 and PC2 is indicated in each plot. Genotypes located close to the ideal point exhibit stable plant height across environments; Figure S3: Stability analysis of the proportion (%) of first-pick cotton yield using: (a) AMMI adaptation map; (b) AMMI1 biplot; (c) environmental stability GGE biplot; (d) genotypic stability GGE biplot; and (e) “which-won-where” GGE biplot. Variance explained by PC1 and PC2 is shown in each plot. Higher proximity to the ideal point denotes genotypes with superior early yield stability; Figure S4: Stability analysis of boll weight (g) across environments. Subfigures (a–e) correspond to AMMI and GGE visualizations as described above. Variance explained by PC1 and PC2 is shown in each plot. Genotypes near the ideal point demonstrate consistently high boll weight and strong adaptability; Figure S5: Stability analysis of lint percentage (%) using: (a) AMMI adaptation map; (b) AMMI1 biplot; (c) environmental stability GGE biplot; (d) genotypic stability GGE biplot; and (e) “which-won-where” GGE biplot. Variance explained by PC1 and PC2 is shown in each plot. Genotypes positioned close to the ideal point indicate stable and favorable fiber quality across environments.

Author Contributions

Conceptualization, V.G.; methodology, V.G.; validation, V.G.; investigation, V.G., E.B. and C.G.I.; statistical analysis, A.S., A.K. and V.G.; data curation, V.G.; writing—original draft preparation, V.G., E.B., A.K. and C.G.I.; writing—review and editing, E.B. and C.G.I.; visualization, A.S. and V.G.; supervision, V.G.; project administration, V.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

All raw data and the statistical scripts used for ANOVA, AMMI, and GGE analyses are available from the corresponding author upon reasonable request. Supplementary tables and figures provide additional visual and ranking information to ensure transparency.

Conflicts of Interest

The authors declare no conflicts of interest.

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