Long-term field performance of PHYA1 RNAi cotton cultivars reveals sustained yield advantage and improved fibre quality

Long-term field performance of PHYA1 RNAi cotton cultivars reveals sustained yield advantage and improved fibre quality | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Long-term field performance of PHYA1 RNAi cotton cultivars reveals sustained yield advantage and improved fibre quality Ibrokhim Abdurakhmonov, Zabardast Buriev, Mukhtor Darmanov, Yuldoshbek Muhammadov, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9260784/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Global demand for natural cotton fibre continues to increase, but the simultaneous improvement of fibre quality and yield remains a major challenge in cotton breeding. Here, we assess the 13-year field performance of PHYA1 RNA interference (RNAi)-derived cotton cultivars in Uzbekistan. In experimental-station trials, RNAi cultivars (‘Porloq-1–4’) showed a 14% yield advantage over four traditional cultivars (3.06 ± 0.45 versus 2.68 ± 0.37 t ha⁻¹, P < 0.001), together with longer fibre (1.22 ± 0.03 versus 1.14 ± 0.02 in, P < 0.001), lower micronaire (4.48 ± 0.26 versus 4.65 ± 0.21, P < 0.01) and higher fibre strength (31.7 ± 1.4 versus 30.8 ± 1.2 g tex⁻¹, P < 0.001). In regional production datasets spanning 13 administrative districts, RNAi cultivars also outperformed the traditional cultivar ‘Sulton’ in yield (3.16 ± 0.78 versus 2.95 ± 0.73 t ha⁻¹, P < 0.05) and maintained superior fibre quality. In an unweighted additive ANOVA of the regional five-genotype yield dataset, environment (zone × year) was the dominant source of variation (68.29%, P < 0.001), whereas genotype also contributed significantly (4.43%, P < 0.001). Multivariate analysis of combined HVI traits likewise identified significant genotype effects in both the experimental and regional panels, supporting consistent differences in fibre quality among genotypes. Stability analyses indicated that the RNAi cultivars were generally well adapted across environments, with ‘Porloq-2’ and ‘Porloq-4’ showing particularly consistent performance. Over 2013–2025, area-weighted RNAi yield increased from 2.81 to 4.22 t ha⁻¹, while fibre length increased from 1.21 to 1.25 in. Annual-mean correlation analyses showed no strong adverse yield–quality relationships in the RNAi group. Together, these results indicate that PHYA1 RNAi confers durable agronomic and fibre-quality advantages under long-term field conditions. Biological sciences/Plant sciences/Plant breeding Biological sciences/Genetics/Agricultural genetics Biological sciences/Genetics/Plant genetics Figures Figure 1 Figure 2 Main Cotton ( Gossypium hirsutum L.) is the world’s most important natural textile fibre, supporting the livelihoods of millions of farmers across more than 80 countries 1,2 . The global market increasingly demands longer, stronger, and finer fibres, while growers require high yields and early maturity to remain profitable under variable climatic conditions 3,4 . However, these traits are often negatively correlated in conventional breeding, making simultaneous improvement difficult 5–10 . Phytochromes are red/far‑red light photoreceptors that regulate numerous aspects of plant development, including yield 11 and fibre length 12–14 . The cotton phytochrome gene family comprises four subfamilies ( PHYA, PHYB, PHYC, PHYE ), with distinct subfamilies (e.g., PHYA , which is divided into two paralogues, PHYA1 and PHYA2) , resulting from a Malvaceae‑specific duplication 15 . Previous work identified a quantitative trait locus (QTL) for fibre length associated with PHYA1 14 . Using RNA interference (RNAi) to suppress PHYA1 expression, we developed transgenic cotton lines that exhibited increased fibre length, reduced micronaire, improved strength, and early maturity without yield penalty 16 . These effects were heritable and transferable via sexual crosses, leading to the development of the Porloq series of RNAi cotton cultivars, which were first commercially released in Uzbekistan in 2013 1,16,17 . Subsequent studies revealed that PHYA1 RNAi induces compensatory overexpression of other phytochrome genes and alters many key developmental and cell-wall pathway genes, including microRNA profiles associated with fibre development 18 . Some early biochemical and proteomic studies comparing PHYA1 RNAi genotypes with non-RNAi genotypes showed that PHYA1 RNAi also contributed to salt stress tolerance by reducing reactive oxygen species (ROS) levels and enhancing antioxidant enzyme activity 19 . While the initial agronomic benefits of PHYA1 RNAi were documented in controlled trials 1,16,20 , long‑term, multi‑environment performance data spanning a decade of commercial cultivation have not been systematically analysed. Such data are essential to assess the stability, adaptability, and sustainability of RNAi cultivars under real‑world farming conditions. Here we present a comprehensive analysis of yield and fibre quality for PHYA1 RNAi cultivars grown in Uzbekistan from 2013 to 2025, integrating experimental station trials and multi‑location farm data. We evaluate yield variation, stability, temporal trends, trait correlations, and heritability, and compare the performance of RNAi cultivars with that of traditional cultivars. Results Experimental station performance At the Qibray Experimental Station of the Centre of Genomics and Bioinformatics, Uzbekistan, the four RNAi cultivars (‘Porloq-1–4’) were evaluated alongside four traditional parental cultivars (‘AN Bayaut-2’, ‘S-6524’, ‘Toshkent-6’ and ‘Namangan-77’), which served as recurrent backgrounds for introgression of the RNAi construct 1,16 under replicated small-plot conditions. Trials were conducted in a randomised complete block design with four replications per cultivar; row spacing was 90 cm, row length was 11 m, and plot area was approximately 50 m². All agronomic practices were uniform across entries. Using unweighted mean ± s.d. across available observations (Table 1; Fig. 1A), the RNAi cultivars collectively showed higher yield than the traditional cultivars (3.06 ± 0.45 versus 2.68 ± 0.37 t ha⁻¹, P < 0.001, two-sided Welch’s t-test). Among the RNAi cultivars, ‘Porloq-2’ had the highest mean yield (3.24 ± 0.40 t ha⁻¹). Fibre quality data (Table 2; Fig. 1A) showed that the pooled RNAi cultivars had significantly lower micronaire than the traditional group (4.48 ± 0.26 versus 4.65 ± 0.21, P < 0.01), whereas among individual RNAi cultivars, this difference was significant only for ‘Porloq-2’ (4.26 ± 0.22, P < 0.001), exemplifying finer fibre. Fibre length was also greater in the RNAi group, with upper half mean length (UHM) of 1.22 ± 0.03 in compared with 1.14 ± 0.02 inches in the traditional cultivars (P < 0.001). Uniformity was modest but significantly higher in the RNAi cultivars (84.0 ± 1.0% versus 82.8 ± 1.7%, P < 0.01). Fibre strength (31.7 ± 1.4 versus 30.8 ± 1.2 g tex⁻¹, P < 0.001) and reflectance (Rd; 80.4 ± 1.2 versus 78.7 ± 1.9, P < 0.001) were also significantly higher in the RNAi group, whereas elongation did not differ significantly. Together, these results support stable expression of the RNAi-associated fibre-quality phenotype under experimental-station conditions. Table 1 | Area and yield summary for 2013–2025 data Location Cultivar Type n (Yield) Total Area (ha) Yield (t/ha) Experimental Station (Qibray) Porloq-1 RNAi 9 28.0 2.97±0.56 Porloq-2 RNAi 14 27.3 3.24±0.40*** Porloq-3 RNAi 11 12.8 2.76±0.35 Porloq-4 RNAi 12 21.2 3.21±0.38*** All RNAi RNAi 46 89.3 3.06±0.45*** AN Bayaut‑2 Traditional 7 0.14 2.57±0.14 S‑6524 Traditional 7 0.14 2.94±0.55 Toshkent‑6 Traditional 7 0.14 2.49±0.36 Namangan‑77 Traditional 7 0.14 2.73±0.11 All Traditional Traditional 28 0.56 2.68±0.37 Regional (Multi ‑ location) Porloq‑1 RNAi 45 132,820 3.04±0.96 Porloq‑2 RNAi 39 62,592 3.13±0.53 Porloq‑3 RNAi 3 390 3.30±0.56 Porloq‑4 RNAi 42 217,786 3.28±0.77* All RNAi RNAi 129 413,587 3.16±0.78* Sulton Traditional 133 2,019,298 2.95±0.73 Notes: Unweighted mean ± s.d. across available observations; n, number of variety-year (experimental) or variety-location-year (regional) observations with non-missing yield. Significance was tested against the pooled traditional comparator group (experimental station; 2.68 ± 0.37, n = 28) or ‘Sulton’ (regional; 2.95 ± 0.73, n = 133): *P < 0.05, **P < 0.01, ***P < 0.001 (two-sided Welch’s t-test). Table 2 | Fibre quality summary for 2013–2025 data Location Cultivar type n (HVI) MIC UHM (in) Unf (%) STR (g tex -1 ) ELO (%) Rd Experimental Station (Qibray) Porloq-1 RNAi 6 4.58±0.19 1.21±0.02*** 84.5±1.0** 32.8±1.7* 8.8±1.6 80.7±1.6* Porloq-2 RNAi 11 4.26±0.22*** 1.27±0.01*** 84.5±0.8*** 32.5±1.1*** 7.1±0.9 80.8±0.7*** Porloq-3 RNAi 9 4.57±0.24 1.20±0.03*** 83.0±0.9 31.0±1.2 7.9±1.1 80.3±1.3** Porloq-4 RNAi 10 4.56±0.22 1.20±0.02*** 84.0±0.8** 30.8±0.9 7.5±1.2 80.1±1.2* All RNAi RNAi 36 4.48±0.26** 1.22±0.03*** 84.0±1.0** 31.7±1.4*** 7.7±1.2 80.4±1.2*** AN Bayaut‑2 Traditional 6 4.78±0.14 1.12±0.01 83.8±1.0 31.1±1.1 7.2±1.3 79.4±1.9 S‑6524 Traditional 6 4.40±0.11 1.16±0.01 83.0±1.1 31.8±1.4 7.9±1.9 78.3±2.3 Toshkent‑6 Traditional 6 4.65±0.16 1.13±0.02 81.3±2.1 30.2±1.0 7.4±1.3 77.8±1.6 Namangan‑77 Traditional 6 4.78±0.21 1.13±0.01 82.9±1.1 30.2±1.0 7.1±1.5 79.3±1.6 All Traditional Traditional 24 4.65±0.21 1.14±0.02 82.8±1.7 30.8±1.2 7.5±1.4 78.7±1.9 Regional (Multi ‑ location) Porloq-1 RNAi 14 4.39±0.32* 1.23±0.03*** 83.5±1.5 33.2±2.2** 7.2±1.2 79.6±3.3 Porloq-2 RNAi 16 4.44±0.23* 1.24±0.03*** 84.3±1.2** 34.4±3.0*** 7.4±1.5 81.0±3.2* Porloq-3 RNAi 3 4.50±0.61 1.21±0.03* 83.3±1.3 32.5±3.9 9.1±3.1 79.4±1.9 Porloq-4 RNAi 15 4.64±0.38 1.21±0.03*** 83.9±1.5 31.5±2.0 6.7±1.8 80.7±3.2 All RNAi RNAi 48 4.49±0.34* 1.22±0.03*** 83.9±1.4*** 33.0±2.8*** 7.2±1.7 80.4±3.1* Sulton Traditional 104 4.60±0.12 1.12±0.02 83.1±0.7 31.1±1.8 7.2±1.1 79.0±1.4 Notes: Unweighted mean ± s.d. across available observations; n = number of HVI measurements. Significance was tested against the pooled traditional comparator group (experimental station) or against ‘Sulton’ (regional): *P < 0.05, **P < 0.01, ***P < 0.001 (two-sided Welch’s t-test). Regional multi-environmental performance Across 13 administrative districts representing five agro-climatic zones, the multi-location farm dataset (n = 1,421 variety-region-year observations, 2013–2025) provided a broad picture of RNAi cultivar performance under commercial farming conditions. Cultivars were planted by farmers according to local practice, using conventional 90 cm row spacing and standard agronomic management. Harvested seed cotton was stored, ginned and analysed by local HVI laboratories under the coordination of the Ministry of Agriculture of Uzbekistan (Tashkent). Area and yield data were recorded by administrative districts and cotton industry cluster companies, then submitted to the Ministry of Agriculture of Uzbekistan and the State Statistics Committee. Using unweighted mean comparisons across available observations (Table 1; Fig. 1B), the RNAi cultivars (‘Porloq-1–4’) maintained a modest but significant yield advantage over the widely grown traditional cultivar ‘Sulton’ (3.16 ± 0.78 versus 2.95 ± 0.73 t ha⁻¹, P < 0.05, two-sided Welch’s t-test). Fibre quality parameters were also generally superior in the RNAi group (Table 2; Fig. 1B): RNAi cultivars produced longer fibre (UHM 1.22 ± 0.03 versus 1.12 ± 0.02 in, P < 0.001), lower micronaire (4.49 ± 0.34 versus 4.60 ± 0.12, P < 0.05), higher uniformity (83.9 ± 1.4% versus 83.1 ± 0.7%, P < 0.001), stronger fibre (33.0 ± 2.8 versus 31.1 ± 1.8 g tex⁻¹, P < 0.001) and slightly higher reflectance (Rd; 80.4 ± 3.1 versus 79.0 ± 1.4, P < 0.05), whereas elongation did not differ significantly. Overall, these results indicate that the RNAi cultivars retained both a yield advantage and improved fibre quality under commercial regional production conditions. Genotype-by-environment and stability analyses To quantify the relative contributions of genotype and environment to regional yield variation, we analysed the regional yield dataset using an unweighted additive ANOVA with the five genotypes consistently represented across the dataset (‘Porloq-1–4’ and ‘Sulton’; Table 3). Because the regional dataset was sparse and unbalanced across genotype–zone–year combinations, environment was defined as zone × year, and a separate genotype × environment interaction term was not retained. Under this additive model, environment accounted for the largest proportion of the total variance (68.29%, P < 0.001), indicating a strong influence of agro-climatic and year-specific conditions on regional yield, whereas genotype explained 4.43% of the variance (P < 0.001). The remaining 27.82% was captured by the residual term. Thus, regional yield variation was driven primarily by environmental heterogeneity, while still supporting a significant overall genotype effect. Because the regional AMMI analysis (see below) was conducted on a reduced common panel excluding ‘Porloq-3’, we also fitted a corresponding additive ANOVA for the reduced four-genotype panel (‘Porloq-1’, ‘Porloq-2’, ‘Porloq-4’ and ‘Sulton’; Supplementary Table S1). This analysis gave the same overall pattern, with year explaining the largest share of variation (57.33%, P < 0.001), followed by zone (6.84%, P < 0.001) and genotype (3.62%, P < 0.001), supporting the robustness of the reduced panel used for stability analysis. Additive ANOVA of the balanced experimental-station panel (Qibray, 2013–2018) likewise showed a significant genotype effect on yield (F = 4.70, P < 0.001, η² = 46.1%), whereas the main effect of year was not significant (F = 0.69, P = 0.636, η² = 4.8%; Supplementary Table S2). Further, MANOVA of the combined HVI fibre traits also identified significant genotype effects in both the experimental and regional panels, supporting consistent multivariate differences in fibre quality among genotypes (Supplementary Table S3). Univariate ANOVA of experimental-station HVI traits (Supplementary Table S4) and regional HVI traits (Supplementary Table S5) broadly supported the MANOVA results, indicating significant genotype effects for several individual fibre traits. Table 3 | Additive ANOVA of regional yield (2013–2025) for five genotypes S ource Sum of Squares df Mean Square F-value P-value η² (%) Genotype 6.73 4 1.68 7.83 <0.001 4.43 Environment (zone × year) 103.71 64 1.62 7.55 <0.001 68.29 Residual 41.43 193 0.215 – – 27.82 Total 151.87 261 – – – 100.0 Note: η² was calculated as the proportion of the total sum of squares attributable to each source. Because the regional dataset was sparse and unbalanced across genotype–zone–year combinations, yield variation was analysed using an unweighted additive ANOVA, with environment defined as zone × year; a separate genotype × environment term was not retained. Stability was evaluated using Finlay–Wilkinson regression and additive main effects and multiplicative interaction (AMMI) analysis. For the experimental-station dataset, a balanced panel of eight genotypes evaluated at Qibray across six common years (2013–2018) supported a formal AMMI2 analysis (Table 4; Fig. 2A). The first two interaction principal components explained 67.6% and 21.6% of the genotype × year interaction sum of squares, respectively. Among the RNAi cultivars, ‘Porloq-2’ combined the highest mean yield with the lowest ASV, indicating both strong productivity and high temporal stability, whereas ‘Porloq-4’ also showed favourable stability. Because the archived experimental annual means contain one observation per genotype-year cell, AMMI was retained as the primary interaction-based stability framework for this panel, while the corresponding additive ANOVA is presented separately in Supplementary Tables S1–S2. For the regional dataset, a conventional five-genotype AMMI analysis was not retained because ‘Porloq-3’ was represented in too few regional observations for a robust common-panel analysis. We therefore constructed a reduced regional panel using ‘Porloq-1’, ‘Porloq-2’, ‘Porloq-4’ and ‘Sulton’, and calculated unweighted annual genotype means across locations for the years shared by all four genotypes. In this reduced regional AMMI2 panel, the first two interaction principal components explained 54.6% and 29.1% of the interaction sum of squares, respectively (Table 4; Fig. 2B). ‘Porloq-4’ had the smallest ASV in the regional panel, followed by ‘Sulton’ and ‘Porloq-2’, whereas ‘Porloq-1’ showed the largest interaction magnitude. These regional results should therefore be interpreted as a year-aggregated regional synthesis rather than as a full location-by-year AMMI analysis. Thus, the regional additive ANOVA and the reduced regional AMMI address complementary questions: the ANOVA partitions variation in the full reproducible regional yield dataset, whereas the AMMI summarises stability patterns in a reduced common-year panel suitable for interaction analysis. Table 4 | AMMI stability parameters from experimental and regional yield panels Genotype Type Mean yield (t/ha) ASV IPCA1 IPCA2 A. Experimental Station balanced panel (Qibray, 2013–2018) AN Bayaut-2 Traditional cultivar 2.57 0.480 0.077 0.416 Namangan-77 Traditional cultivar 2.73 0.100 -0.019 -0.080 Porloq-1 RNAi cultivar 3.03 4.537 1.452 0.077 Porloq-2 RNAi cultivar 3.43 0.160 -0.038 -0.109 Porloq-3 RNAi cultivar 2.65 1.051 -0.289 0.537 Porloq-4 RNAi cultivar 3.13 0.527 0.044 -0.509 S-6524 Traditional cultivar 3.10 2.800 -0.895 0.147 Toshkent-6 Traditional cultivar 2.55 1.144 -0.332 -0.479 B. Regional multi-location year-aggregated panel (common years across all four genotypes: 2013–2021 and 2023) Porloq-1 RNAi cultivar 3.11 2.040 -1.071 -0.341 Porloq-2 RNAi cultivar 3.14 1.288 0.616 -0.566 Porloq-4 RNAi cultivar 2.99 0.801 -0.152 0.748 Sulton Traditional cultivar 2.76 1.151 0.607 0.159 Note: Experimental AMMI was estimated from the balanced 8-genotype × 6-year panel. Regional AMMI was estimated from unweighted annual genotype means aggregated across locations for the reduced four-genotype panel, using only years shared by all four genotypes. Raw IPCA1 and IPCA2 scores are reported; biplot coordinates may differ by scaling convention. Temporal trends in yield and fibre quality Using area-weighted annual means from the regional datasets, we examined the evolution of yield and fibre quality over the 2013–2025 period. RNAi yield increased steadily from 2.81 t ha⁻¹ in 2013 to 4.22 t ha⁻¹ in 2025, closely mirroring the national trend but consistently exceeding traditional yields by an average of 0.23 t ha⁻¹ (range 0.11–0.36). Simultaneously, fibre length (UHM) of RNAi cultivars rose from 1.21 inches to 1.25 inches, while traditional UHM remained near 1.11 inches. Micronaire declined in both groups, but RNAi maintained a consistent advantage (0.2–0.3 units lower). Strength improved modestly for RNAi (from 31.1 to 33.2 g tex⁻¹) and changed little in the traditional group. Overall, these temporal patterns indicate that the yield advantage of the RNAi lines was maintained over time without deterioration in fibre quality, supporting partial alleviation of the conventional yield–quality trade-off. Trait correlations and heritability Correlation analysis based on unweighted annual means (Table 5) showed that, in the traditional cultivar Sulton, yield was most strongly associated with fibre strength, with a significant negative correlation between yield and STR (r = -0.74, P < 0.01). By contrast, correlations between yield and fibre length (r = -0.11) and between yield and micronaire (r = -0.25) were weak and not significant. In the pooled RNAi cultivars (‘Porloq-1–4’), none of the corresponding annual-mean correlations reached significance over the years with overlapping yield and fibre-quality data (n = 7): yield showed essentially no association with UHM (r = +0.01) or STR (r = +0.00), and a moderate but non-significant negative association with MIC (r = -0.69). Thus, under the unweighted annual-means framework, the strongest detectable relationship was the negative yield-strength correlation in the traditional group, whereas the RNAi group showed no significant evidence of an adverse yield–quality relationship (Table 5). Fig. 1C and D provide complementary observation-level summaries based on the merged unweighted dataset, whereas the formal correlation coefficients in Table 5 and Supplementary Table S6 were calculated from unweighted annual means. Table 5 | Pearson correlations between yield and fibre traits (2013–2025) Cultivar group n years Yield vs. UHM Yield vs. MIC Yield vs. STR Traditional (Sulton) 13 -0.11 -0.25 -0.74** RNAi (Porloq‑1–4) 7 +0.01 -0.69 +0.00 Note: Correlations were calculated from unweighted annual means across available observations. For the RNAi group, analyses were restricted to years with both yield and fibre-quality data in the regional datasets (n = 7 years), whereas Sulton had complete overlap across 2013–2025 (n = 13 years). Significance is indicated as P < 0.05, P < 0.01 and P < 0.001. Correlations among fibre-quality traits were also examined using unweighted annual means (Supplementary Table S6). In the traditional group, UHM was positively correlated with MIC (r = +0.61, P < 0.05) and STR was positively correlated with ELO (r = +0.62, P < 0.05), Unf (r = +0.77, P < 0.01) and Rd (r = +0.70, P < 0.01). In the RNAi group, the strongest positive association was between UHM and STR (r = +0.90, P < 0.01), indicating that longer fibres also tended to be stronger. A significant negative correlation was also observed between Unf and Rd (r = -0.83, P < 0.05) in the RNAi annual-mean dataset. Broad-sense heritability of fibre traits was estimated from the unweighted experimental-station RNAi data using a separate random-effects model (Table 6). Under this framework, UHM showed the highest heritability (H² = 0.80 ± 0.22), whereas MIC (0.43 ± 0.21) and STR (0.41 ± 0.21) showed more moderate estimates. These results indicate that fibre length remained the most strongly genotype-associated trait in the experimental RNAi panel, while micronaire and strength showed greater year-to-year and residual variation under the fitted model. Table 6 | Broad ‑ sense heritability (H²) of fibre traits in RNAi cultivars (experimental station) Trait σ²_G σ²_G×Y σ²_e H² (SE) UHM (inches) 0.0008 0.0000 0.0002 0.80 (0.22) MIC 0.0217 0.0121 0.0162 0.43 (0.21) STR (g tex -1 ) 0.8208 0.7067 0.4655 0.41 (0.21) Variance components were estimated from an unweighted random-effects model fitted to the experimental-station RNAi data. The SE values shown here are bootstrap approximations from the fitted variance-component model. Additional RNAi cultivars introduced after 2021 In addition to the four original RNAi cultivars (‘Porloq-1–4’), two newer RNAi lines, ‘Porloq-5’ and ‘Porloq-7’, were introduced during the later years of the study period. Both are elite selections derived from ‘Porloq-1’ through on-farm evaluation and multiplication. As summarised in Supplementary Table S7, the unweighted mean yield of ‘Porloq-5’ was 3.90 ± 0.72 t ha⁻¹ across three region×year observations, whereas ‘Porloq-7’ reached 4.08 ± 0.57 t ha⁻¹ across four observations. Although neither line differed significantly from ‘Sulton’ at P < 0.05, both showed strong performance under commercial conditions, indicating continued delivery of high-performing RNAi germplasm. Performance by agro-climatic zone Using the agro-climatic zone classification established in the 2021–2025 national study20, we examined yield and fibre quality of RNAi and traditional cultivars across five zones over the full 2013–2025 period (Supplementary Table S8). Under the unweighted analysis, RNAi cultivars showed numerically higher mean yield than Sulton in all zones, although none of the zone-specific yield differences was significant (P > 0.05). In contrast, UHM was significantly greater in the RNAi cultivars in every zone, with the biggest differences in the Fergana Valley, Desert Zone, Central Plains and Capital Region (P < 0.001) and a smaller but still significant difference in the Arid/Northern zone (P < 0.01). Fibre strength was also higher in the RNAi cultivars in the Desert Zone and Central Plains, whereas micronaire showed no significant zone-specific differences. Together, these results indicate that the most consistent zonal advantage of the RNAi cultivars was improved fibre length rather than significantly higher yield. Discussion Over a decade of cultivation, PHYA1 RNAi cotton cultivars combined improved fibre quality with stable yield performance across diverse environments in Uzbekistan. The AMMI analysis reinforces this conclusion while also clarifying the level of inference. In the experimental-station dataset, an eight-genotype × six-year balanced panel supported a formal AMMI2 analysis, in which ‘Porloq-2’ showed both the highest mean yield and the greatest stability among the RNAi cultivars, whereas ‘Porloq-4’ also performed consistently well. In contrast, the regional dataset required a reduced four-genotype common-year panel, because the full location-by-year matrix was too sparse for a robust AMMI analysis across all five genotypes. Framing the regional AMMI as a year-aggregated synthesis therefore preserves comparability while avoiding overinterpretation of incomplete multi-environment structure, which is especially important for cotton grown across the broad climatic and management diversity of Uzbekistan 20,21 . The superior fibre quality of the RNAi cultivars, particularly their consistently greater fibre length and generally stronger, finer fibre profile, directly addresses market demand for higher-value cotton. The increased fibre length relative to the traditional comparator is consistent with earlier genetic evidence implicating PHYA1 in fibre elongation 13,14 . The underlying mechanism likely involves altered phytochrome signalling 12 , with downstream effects on hormone-regulated developmental pathways, including auxin- and gibberellin-related processes 22,23 , as well as miRNA-mediated regulation of genes associated with fibre and cell-wall development 18 . Independent studies have further confirmed the distinctive structural and processing properties of Porloq fibres. Khudayberdieva et al. 24 reported that ‘Porloq-1’ and ‘Porloq-2’ show higher specific surface area, pore volume and crystallinity than ‘S-6524’, with ‘Porloq-2’ also showing denser packing and greater strength. Mamadjanova et al. 25 showed that ‘Porloq-2’ and ‘Porloq-4’ require modified preparation conditions for dyeing and yield improved whiteness, capillarity and tensile strength after optimised processing. Akhmedova et al. 26 likewise found that cotton-silk yarn blends containing ‘Porloq-1’ and ‘Porloq-2’ had greater strength and improved elastic properties. These independent studies therefore reinforce our field observations and suggest that the fibre-quality advantages of the RNAi-derived Porloq cultivars have practical value for downstream textile processing. Notably, during the 2021–2025 period, RNAi cultivars were well represented in the Desert Zone (Supplementary Table S8), a region characterised by salinity stress, water limitation and high temperature variability 20 . This deployment pattern aligns with farmers’ practical benefits of these cultivars in harsher production conditions, consistent with earlier physiological evidence indicating enhanced stress tolerance in PHYA1 RNAi cotton 19,20 . Despite these challenging conditions, the RNAi cultivars maintained clear fibre-quality advantages, particularly in fibre length, supporting their resilience in marginal production environments. The introduction of new RNAi lines, ‘Porloq-5’ and ‘Porloq-7’, after 2021 further underscores the technology’s continued agronomic potential. Both are elite selections derived from ‘Porloq-1’ through on-farm evaluation and subsequent multiplication, indicating that the underlying RNAi-associated phenotype can be retained during further selection. Under the unweighted analysis, mean yield during the 2021–2025 period was 3.90 ± 0.72 t ha⁻¹ for ‘Porloq-5’ and 4.08 ± 0.57 t ha⁻¹ for ‘Porloq-7’ (Supplementary Table S7). These values were numerically higher than the long-term mean of the original RNAi group, although neither cultivar differed significantly from ‘Sulton’ in the available Welch-test comparisons. The elevated yields observed in 2024–2025, as also reported for other traditional and Bt cotton cultivars, likely reflect in part the contribution of improved agronomic practices, including high-density planting under plastic mulch and drip irrigation 20 . Together, these observations are consistent with a productive interaction between genetic improvement and agronomic innovation, a pattern characteristic of sustainable intensification 20,27 . From a breeding perspective, these stability results are important because they show that the RNAi-associated yield and fibre-quality advantages were not confined to a narrow range of conditions. In the regional additive ANOVA, environmental variation explained a substantially larger proportion of yield variance than genotype, indicating strong modulation of performance by heterogeneous production conditions. Even so, genotype remained a significant source of variation, supporting the persistence of an overall RNAi-associated advantage across environments. The experimental AMMI analysis provides the clearest evidence for stable expression of this phenotype under a balanced multi-year design 28 , whereas the reduced regional AMMI indicates that the same pattern remains relevant under commercial farming conditions in Uzbekistan, albeit with a more limited inferential scope due to the sparse on-farm panel 21 . The correlation analyses provide additional context for how the RNAi phenotype relates to yield and fibre quality under long-term field conditions. In conventional Upland cotton breeding, simultaneous improvement of yield and fibre quality has often been constrained by unfavourable trait associations, including trade-offs among fibre strength, fibre length and other quality parameters 5–9 . Naoumkina and Kim 10 likewise highlighted the continuing difficulty of improving fibre quality while maintaining agronomic performance, underscoring the broader breeding relevance of the correlation patterns observed here. Against this background, the annual-mean correlations in our study are notable because the strongest significant relationship was observed not in the RNAi group but in the traditional cultivar Sulton, where yield was negatively correlated with fibre strength (r = -0.74, P < 0.01; Table 5). By contrast, the corresponding annual-mean correlations in the pooled RNAi cultivars were weak or non-significant, with essentially no association between yield and fibre length or strength, and only a moderate negative association with micronaire. Although these RNAi correlations should not be over-interpreted, given the limited number of overlapping years, they suggest that the RNAi-associated phenotype does not follow the same adverse yield-quality relationships often reported in conventional cotton germplasm 5–8,29 . Correlations among fibre-quality traits further support this interpretation. In the RNAi group, fibre length (UHM) was strongly and positively correlated with fibre strength (STR; r = +0.90, P < 0.01; Supplementary Table S6), indicating that longer fibres also tended to be stronger. This pattern is noteworthy because fibre length and strength have often proven difficult to improve simultaneously in cotton breeding 7–9,29–33 . By contrast, the traditional group showed a different correlation structure, including a positive association between UHM and MIC and several strong positive correlations among STR, ELO, Unf and Rd. Together, these results suggest that the RNAi-associated fibre phenotype is not simply an extension of the conventional trait relationships observed in the traditional comparator, but instead reflects a distinct trait architecture consistent with earlier evidence linking PHYA1 to fibre elongation and fibre-quality improvement 13,16,18 . Heritability estimates from the unweighted experimental-station RNAi data indicate that fibre length remained the most strongly genotype-associated trait in the RNAi panel. Broad-sense heritability was highest for UHM (H² = 0.80 ± 0.22), whereas MIC (0.43 ± 0.21) and STR (0.41 ± 0.21) were more moderate (Table 6). Thus, although the RNAi-associated fibre phenotype was not uniformly highly heritable across all traits, fibre length remained the most stable and genetically structured component under the fitted model, supporting its value as a target for further selection. These estimates are broadly consistent with the range reported for cotton fibre traits in previous genetic studies, although the UHM estimate in our RNAi panel lies toward the upper end of published values. In elite Upland germplasm, Campbell et al. 29 reported broad-sense heritability values of 0.35–0.68 for fibre length, strength and micronaire, whereas Fang et al. 30 found values of 0.68–0.87 in a random-mated recombinant inbred population. Studies in additional mapping populations have likewise shown that fibre-quality heritability is often moderate to high, but variable among traits and genetic backgrounds 31–33 . In this context, the relatively high heritability of UHM in the present study, together with the more moderate values for MIC and STR, suggests that the fibre-length component of the PHYA1 RNAi phenotypes was more consistently expressed across years than the corresponding effects on other quality traits. This interpretation is consistent with earlier evidence linking PHYA1 to fibre elongation and fibre-quality improvement 1,13,16 , and with downstream regulatory studies implicating miRNA-mediated control 18 and stress-responsive physiological effects in the broader RNAi phenotype 19 . From a breeding perspective, this trait-specific stability is important because it suggests that fibre length may provide the most reliable selection target within the RNAi-derived quality profile. A limitation of this study is that the regional dataset was observational, sparse and unbalanced across genotype–zone–year combinations, rather than generated from a fully replicated multi-environment experimental design. Accordingly, the regional analyses should be interpreted with appropriate caution, particularly for interaction structure and stability inference from incomplete multi-environment panels 21 . At the same time, this data set provides an important real-world perspective by capturing cultivar performance under commercial farming conditions, including farmer deployment choices across contrasting agro-climatic zones. In that sense, the regional analysis complements the balanced experimental-station trials by showing how the RNAi cultivars performed under natural production settings and during practical technology adoption. The consistency of the observed phenotypic patterns across years, together with the concordant evidence from the experimental-station trials, therefore, supports the overall robustness and practical relevance of the main conclusions. Future work should investigate the molecular basis of the apparent stability of the RNAi-associated phenotype across diverse environments and evaluate whether PHYA1 RNAi can be combined effectively with other biotechnological traits, including Bt-based insect resistance, to further enhance productivity under sustainable intensification 20,27 . Methods Plant materials The PHYA1 RNAi cotton cultivars (‘Porloq-1–7’) were developed by crossing a transgenic PHYA1 RNAi donor line in the Coker-312 background with Uzbek commercial cultivars, as described previously 1,16 . The traditional Uzbek cultivars ‘AN Bayaut-2’, ‘S-6524’, ‘Toshkent-6’ and ‘Namangan-77’ were used as recurrent parental backgrounds for backcross introgression of the RNAi hairpin construct from stable Coker-312 donor genotypes. In the regional trial dataset, the widely grown traditional cultivar ‘Sulton’ served as the main control genotype. Experimental station field trials Trials were conducted at the experimental farm of the Centre of Genomics and Bioinformatics (Qibray district, Tashkent region, Uzbekistan) from 2013 to 2025. The trials were designed to compare cultivar performance under uniform field conditions. Cultivars were arranged in a randomised complete block design with four replications. Each plot consisted of five rows, each 11 m long, with 90 cm row spacing, giving a plot area of approximately 50 m² and a total area of about 200 m² per cultivar. Agronomic practices, including planting date, seed rate, fertilisation and irrigation, were kept uniform across entries. Yield and fibre-quality data were collected by the responsible departments and experimental-station staff, reviewed in annual internal laboratory meetings, and archived at the Centre. Regional multi-location field trials No specific replicated plot trials were established for the regional dataset. Instead, RNAi and traditional cultivars were grown by farmers under conventional production conditions (90 cm row spacing, standard agrotechnology) across 13 administrative districts representing five agro-climatic zones of Uzbekistan. Cultivar choice was determined by farmer deployment and local production practice rather than by experimental assignment. After harvest, seed cotton was transported to ginning factories, stored, ginned and analysed in local HVI laboratories under the coordination of the Ministry of Agriculture of Uzbekistan (Tashkent). For each cultivar × region × year combination, planted area (ha) and yield (t ha⁻¹) were recorded by administrative districts and cotton industry cluster companies, then submitted to the Ministry of Agriculture of Uzbekistan and the State Statistics Committee. Yield and fibre-quality measurements Seed cotton yield (t ha⁻¹) was recorded at harvest. Fibre-quality parameters, micronaire (MIC), upper half mean length (UHM, inches), uniformity (Unf, %), strength (STR, g tex⁻¹), elongation (ELO, %), and reflectance (Rd), were measured by high-volume instrumentation (HVI) using standard procedures 34 . Sampling for HVI analysis followed a scale-dependent protocol. For experimental-station plots, seed cotton was collected from a representative set of plants within each plot (typically 20–30 plants), bulked, ginned, and subsampled to obtain a 20–30 g fibre sample for HVI analysis. For selection-scale field plots (1–6 ha), seed cotton was sampled randomly from the central part of each plot, bulked, ginned, and a 20–30 g fibre subsample was submitted for analysis. For commercial fields (≥30 ha), seed cotton delivered to collection points or cotton clusters was baled, and fibre samples were taken from each bale using a sampling spear, coded, and delivered to the regional HVI laboratory of the SIFAT Centre (now incorporated into the Inspection for the Control of the Agro-industrial Complex under the Cabinet of Ministers of the Republic of Uzbekistan (Agroinspection). From 2012 to 2018, HVI measurements were performed using a Uster HVI-900 instrument, and from 2019 to 2025 using a Uster HVI-1000. Before measurement, all samples were conditioned under standard atmospheric conditions (65 ± 2% relative humidity, 20 ± 2 °C) for at least 24 h, and instruments were calibrated using standard reference samples 34 . Statistical analysis All analyses were performed in R (v4.3.1) and Python (v3.9). For descriptive regional summaries and temporal trend analyses, area-weighted means were calculated using planted area for each cultivar–environment combination as weights 35 . However, for the descriptive summaries presented in Tables 1 and 2, values are reported as unweighted mean ± standard deviation across available observations, so that the displayed means, dispersion estimates and pairwise tests are internally consistent. For the regional yield dataset, yield variation was analysed using an unweighted additive ANOVA. Because the dataset was sparse and unbalanced across genotype–zone–year combinations, genotype was treated as a fixed factor and environmental structure was represented in two complementary ways. In the primary regional analysis (Table 3), environment was defined as zone × year, and effect sizes (η²) were calculated as the proportion of the total sum of squares attributable to each source 36 . All statistical analyses, including variance-component estimation, were implemented in R using the lme4 package where appropriate 37 . A separate genotype × environment interaction term was not retained because the regional panel did not provide a sufficiently balanced factorial structure for robust estimation of that interaction across all genotype–zone–year combinations. In the supporting analysis of the reduced four-genotype regional panel used for AMMI (Supplementary Table S1), yield variation was also evaluated using an additive ANOVA with genotype, zone and year as fixed effects. A separate additive ANOVA was fitted to the balanced experimental-station panel (Qibray, 2013–2018) with genotype and year as fixed factors. Pairwise comparisons in Tables 1 and 2 were conducted using two-sided Welch’s t-tests 38 to account for unequal variances. In the experimental-station sections, each RNAi cultivar and the pooled RNAi group were compared against the pooled traditional comparator group. In the regional sections, comparisons were made against ‘Sulton’. Significance is indicated as P < 0.05, P < 0.01 and P < 0.001. Stability was evaluated using Finlay–Wilkinson regression28 and the additive main effects and multiplicative interaction (AMMI) framework 39 . Genotype stability patterns were visualised using AMMI2 biplots based on the first two interaction principal component axes. For the experimental-station dataset, AMMI2 was estimated from the balanced panel of eight genotypes across the six common years 2013–2018. For the regional dataset, ‘Porloq-3’ was excluded because it was represented in too few observations for a reliable common-panel AMMI analysis. The reduced regional AMMI2 analysis was therefore based on ‘Porloq-1’, ‘Porloq-2’, ‘Porloq-4’ and ‘Sulton’, using unweighted annual genotype means aggregated across locations and restricted to the years shared by all four genotypes (2013–2021 and 2023). Genotype and year main effects were removed from the genotype × year matrices, singular value decomposition was applied to the interaction residual matrix, and the AMMI stability value (ASV) was calculated from IPCA1 and IPCA2 using the standard weighted formulation 40,41 . Finlay–Wilkinson regression coefficients were estimated by regressing genotype yield on the environmental index, defined as the mean yield across all genotypes in each environment 28 . For time-series analysis, annual area-weighted means of yield and fibre traits were calculated separately for RNAi and traditional groups. For the formal correlation analyses reported in Table 5 and Supplementary Table S6, Pearson correlations were calculated from unweighted annual means across available observations. For the RNAi group, these analyses were restricted to years with overlapping yield and fibre-quality data. Heritability estimation. Broad-sense heritability (H²) for fibre traits was estimated from the experimental-station RNAi data using an unweighted random-effects model fitted by restricted maximum likelihood (REML) with the lme4 package 37 . The model was: where is the random effect of genotype, is the random effect of year, is the genotype × year interaction, and is the residual error. Variance components were used to calculate: Because the archived experimental-station RNAi dataset effectively contains one observation per genotype × year cell for the fitted traits, these heritability estimates should be interpreted as model-based approximations under the available design. Standard errors were obtained by bootstrap approximation from the fitted variance-component model. All significance tests were two-sided with . Means are reported as mean ± standard deviation unless otherwise noted. Declarations Acknowledgements We thank Prof. Khakimjon Khamidov, who supervised field trials of the cotton cultivars at the Centre of Genomics and Bioinformatics and all former and current postdoctoral researchers, PhD students and research assistants (Komronbek Mirzayokubov, Ulmas Sobitov, Tokhir Norov, Obid Turaev, Ozod Turaev, Jurabek Norbekov, Farhod Radjabov, Dilshod Usmanov, Fakhriddin Kushanov and Saidakhmad Omarov) for their contributions to experimental-station field management and field-trial support. We are grateful to Prof. Shodmon E. Nomozov, breeder of the ‘Sulton’ cultivar, for providing and verifying the regional Sulton dataset. We also thank all cotton farmers who contributed to data collection and to the adoption of the technology under commercial field conditions. Author contributions I.Y.A. conceived the study, performed the analyses and wrote the first draft of the manuscript. Z.T.B., S.E.S. and A.A. contributed to data curation, data analysis and manuscript revision. M.M.D., Y.A.M., S.I.M., I.E.B., A.B.M., N.N.K., A.B.I., H.S.R., A.M. and K.A.U. coordinated and conducted the experimental-station field trials, data collection and analyses, and contributed to field selection and seed propagation. I.Y.A., Z.T.B., R.R.A., A.A.T. and M.M.D. coordinated the regional cultivation and commercialisation of the RNAi cultivars. All authors reviewed, edited and approved the final manuscript. Funding This work was supported by research and commercialisation grants I5-FQ-0-89870 (2014–2015), I-2015-6-15/2 (2015–2016), IFA-2017-5-6 (2017–2018), FA-F-5-021 (2017–2020), FA-F-5-025 (2017–2020) and FA-A-QX-2018-393 (2018–2020) from the Foundation for Supporting Science and Innovation of the Ministry of Higher Education, Science and Innovations of the Republic of Uzbekistan. Competing interests The authors declare no competing interests. Ethical Approval The study did not involve human participants, human tissue, or animal experiments. All data were anonymised and aggregated at the regional level to protect privacy. AI declaration In accordance with Nature Portfolio policies, authors also declare the use of AI-assisted tools for final language, typo and formatting corrections. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the content of the published work. Data availability Cultivar‑level yield, area, and fibre quality data are provided as Supplementary Data Files 1–4. Code Availability The code used for statistical analyses and figure generation is available from the corresponding author upon reasonable request. Analyses were conducted using R (v4.3.1) and Python (v3.9) with publicly available packages. References Abdurakhmonov, I. Y. et al. RNA interference for functional genomics and improvement of cotton. Front. Plant Sci. 7 , 202 (2016). Sunilkumar, G., Campbell, L. M., Puckhaber, L., Stipanovic, R. D. & Rathore, K. S. Engineering cottonseed for use in human nutrition by tissue-specific reduction of toxic gossypol. Proc. Natl Acad. Sci. USA 103 , 18054–18059 (2006). Phalan, B., Onial, M., Balmford, A. & Green, R. E. Reconciling food production and biodiversity conservation: land sharing and land sparing compared. Science 333 , 1289–1291 (2011). Garnett, T. et al. Sustainable intensification in agriculture: premises and policies. 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Sharifi, P., Aminpanah, H., Erfani, R. & Abbasian, A. Evaluation of genotype × environment interaction in rice based on AMMI model in Iran. Rice Sci. 24 , 173–180 (2017). Additional Declarations There is NO Competing Interest. Supplementary Files 2NPFinalSupplementaryInformation.doc Supplementary Information Data1cleaned.csv Dataset 1 Data2cleanedlong.csv Dataset 2 Data3cleanedlong.csv Dataset 3 Data4cleaned.csv Dataset 4 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Bioinformatics","correspondingAuthor":false,"prefix":"","firstName":"Abdusattor","middleName":"","lastName":"Abdukarimov","suffix":""}],"badges":[],"createdAt":"2026-03-29 19:55:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9260784/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9260784/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106067258,"identity":"1ec606a8-40b1-4709-a4ca-948786b2744a","added_by":"auto","created_at":"2026-04-03 05:47:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":129613,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eYield and fibre-quality performance of RNAi and control cotton groups across experimental-station and regional datasets. \u003c/strong\u003eA, Pooled RNAi cultivars versus pooled traditional cultivars at the experimental station. B, Pooled RNAi cultivars versus ‘Sulton’ in the regional dataset. Bars show unweighted mean ± s.e.m. across available observations. Y axes are plotted on a log10 scale for visual comparability, but statistical tests were performed on the original scale. Pairwise differences were assessed using two-sided Welch’s t-tests; different letters indicate P \u0026lt; 0.05. C, Yield versus an overall fibre-quality index in the merged unweighted observation-level dataset; the index was calculated from standardised MIC, UHM, Unf, STR, ELO and Rd values. D, Pearson correlation heat maps for yield and fibre-quality traits in the same dataset, shown separately for RNAi and traditional/Sulton groups.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9260784/v1/f48b0dd58a0c57c036e5a01e.jpg"},{"id":106095383,"identity":"8c6788d6-fa3a-4281-9ca0-d12ab1e5ffad","added_by":"auto","created_at":"2026-04-03 11:47:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65228,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAMMI2 biplots showing genotype stability for cotton yield. A\u003c/strong\u003e, Balanced experimental-station panel (Qibray, 2013–2018; eight genotypes). B, Reduced regional common-year panel (2013–2021 and 2023; four genotypes), analysed without area weighting after exclusion of ‘Porloq-3’ because of sparse representation. Filled circles denote genotypes and open triangles denote years. Genotypes located near the origin show greater stability, whereas those farther from the origin show stronger genotype × environment interaction. Percentages on the axes indicate the variance explained by IPCA1 and IPCA2.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9260784/v1/00eea8b001c23d7159a708fb.jpg"},{"id":106415053,"identity":"38eacda6-d7a0-445b-82bc-8c9bc6dd3898","added_by":"auto","created_at":"2026-04-08 10:32:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1786095,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9260784/v1/041da85a-89c2-4137-be33-91d783c4c513.pdf"},{"id":106067259,"identity":"731d6928-b856-4f90-b59f-9c8788aa585d","added_by":"auto","created_at":"2026-04-03 05:47:54","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":138240,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"2NPFinalSupplementaryInformation.doc","url":"https://assets-eu.researchsquare.com/files/rs-9260784/v1/e807b2feafe606e2a0604ed2.doc"},{"id":106094498,"identity":"87022406-6407-49a1-9c6d-d0689a2e3b91","added_by":"auto","created_at":"2026-04-03 11:42:45","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5358,"visible":true,"origin":"","legend":"Dataset 1","description":"","filename":"Data1cleaned.csv","url":"https://assets-eu.researchsquare.com/files/rs-9260784/v1/f113fcd6c6d03ba0865a18fc.csv"},{"id":106067261,"identity":"5474f98e-ff03-42a8-989d-eeb28f0b10e9","added_by":"auto","created_at":"2026-04-03 05:47:54","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":7836,"visible":true,"origin":"","legend":"Dataset 2","description":"","filename":"Data2cleanedlong.csv","url":"https://assets-eu.researchsquare.com/files/rs-9260784/v1/e4ffac40c51fef8b489bd424.csv"},{"id":106067263,"identity":"b417c97f-4156-40d7-9bfe-889087f4acc4","added_by":"auto","created_at":"2026-04-03 05:47:54","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":26325,"visible":true,"origin":"","legend":"\u003cp\u003eDataset 3\u003c/p\u003e","description":"","filename":"Data3cleanedlong.csv","url":"https://assets-eu.researchsquare.com/files/rs-9260784/v1/b66030c47d5a2aa7bd8d1abd.csv"},{"id":106067264,"identity":"965a34ae-066e-4e32-977b-f29e1280bfb7","added_by":"auto","created_at":"2026-04-03 05:47:54","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":10713,"visible":true,"origin":"","legend":"\u003cp\u003eDataset 4\u003c/p\u003e","description":"","filename":"Data4cleaned.csv","url":"https://assets-eu.researchsquare.com/files/rs-9260784/v1/01d559136eb019b4fe0a905a.csv"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Long-term field performance of \u003ci\u003ePHYA1\u003c/i\u003e RNAi cotton cultivars reveals sustained yield advantage and improved fibre quality","fulltext":[{"header":"Main","content":"\u003cp\u003eCotton (\u003cem\u003eGossypium hirsutum\u003c/em\u003e L.) is the world\u0026rsquo;s most important natural textile fibre, supporting the livelihoods of millions of farmers across more than 80 countries\u003csup\u003e1,2\u003c/sup\u003e. The global market increasingly demands longer, stronger, and finer fibres, while growers require high yields and early maturity to remain profitable under variable climatic conditions\u003csup\u003e3,4\u003c/sup\u003e. However, these traits are often negatively correlated in conventional breeding, making simultaneous improvement difficult\u003csup\u003e5\u0026ndash;10\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePhytochromes are red/far‑red light photoreceptors that regulate numerous aspects of plant development, including yield\u003csup\u003e11\u003c/sup\u003e and fibre length\u003csup\u003e12\u0026ndash;14\u003c/sup\u003e. The cotton phytochrome gene family comprises four subfamilies (\u003cem\u003ePHYA, PHYB, PHYC, PHYE\u003c/em\u003e), with distinct subfamilies (e.g., \u003cem\u003ePHYA\u003c/em\u003e, which is divided into two paralogues, \u003cem\u003ePHYA1\u003c/em\u003e and \u003cem\u003ePHYA2)\u003c/em\u003e, resulting from a Malvaceae‑specific duplication\u003csup\u003e15\u003c/sup\u003e. Previous work identified a quantitative trait locus (QTL) for fibre length associated with \u003cem\u003ePHYA1\u003c/em\u003e\u003csup\u003e14\u003c/sup\u003e. Using RNA interference (RNAi) to suppress \u003cem\u003ePHYA1\u003c/em\u003e expression, we developed transgenic cotton lines that exhibited increased fibre length, reduced micronaire, improved strength, and early maturity without yield penalty\u003csup\u003e16\u003c/sup\u003e. These effects were heritable and transferable via sexual crosses, leading to the development of the Porloq series of RNAi cotton cultivars, which were first commercially released in Uzbekistan in 2013\u003csup\u003e1,16,17\u003c/sup\u003e. Subsequent studies revealed that \u003cem\u003ePHYA1\u003c/em\u003e RNAi induces compensatory overexpression of other phytochrome genes and alters many key developmental and cell-wall pathway genes, including microRNA profiles associated with fibre development\u003csup\u003e18\u003c/sup\u003e. Some early biochemical and proteomic studies comparing \u003cem\u003ePHYA1\u003c/em\u003e RNAi genotypes with non-RNAi genotypes showed that \u003cem\u003ePHYA1\u003c/em\u003e RNAi also contributed to salt stress tolerance by reducing reactive oxygen species (ROS) levels and enhancing antioxidant enzyme activity\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWhile the initial agronomic benefits of \u003cem\u003ePHYA1\u003c/em\u003e RNAi were documented in controlled trials\u003csup\u003e1,16,20\u003c/sup\u003e, long‑term, multi‑environment performance data spanning a decade of commercial cultivation have not been systematically analysed. Such data are essential to assess the stability, adaptability, and sustainability of RNAi cultivars under real‑world farming conditions. Here we present a comprehensive analysis of yield and fibre quality for \u003cem\u003ePHYA1\u003c/em\u003e RNAi cultivars grown in Uzbekistan from 2013 to 2025, integrating experimental station trials and multi‑location farm data. We evaluate yield variation, stability, temporal trends, trait correlations, and heritability, and compare the performance of RNAi cultivars with that of traditional cultivars.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eExperimental station performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the Qibray Experimental Station of the Centre of Genomics and Bioinformatics, Uzbekistan, the four RNAi cultivars (\u0026lsquo;Porloq-1\u0026ndash;4\u0026rsquo;) were evaluated alongside four traditional parental cultivars (\u0026lsquo;AN Bayaut-2\u0026rsquo;, \u0026lsquo;S-6524\u0026rsquo;, \u0026lsquo;Toshkent-6\u0026rsquo; and \u0026lsquo;Namangan-77\u0026rsquo;), which served as recurrent backgrounds for introgression of the RNAi construct\u003csup\u003e1,16\u0026nbsp;\u003c/sup\u003eunder replicated small-plot conditions. Trials were conducted in a randomised complete block design with four replications per cultivar; row spacing was 90 cm, row length was 11 m, and plot area was approximately 50 m\u0026sup2;. All agronomic practices were uniform across entries. Using unweighted mean \u0026plusmn; s.d. across available observations (Table 1; Fig. 1A), the RNAi cultivars collectively showed higher yield than the traditional cultivars (3.06 \u0026plusmn; 0.45 versus 2.68 \u0026plusmn; 0.37 t ha⁻\u0026sup1;, P \u0026lt; 0.001, two-sided Welch\u0026rsquo;s t-test). Among the RNAi cultivars, \u0026lsquo;Porloq-2\u0026rsquo; had the highest mean yield (3.24 \u0026plusmn; 0.40 t ha⁻\u0026sup1;).\u003c/p\u003e\n\u003cp\u003eFibre quality data (Table 2; Fig. 1A) showed that the pooled RNAi cultivars had significantly lower micronaire than the traditional group (4.48 \u0026plusmn; 0.26 versus 4.65 \u0026plusmn; 0.21, P \u0026lt; 0.01), whereas among individual RNAi cultivars, this difference was significant only for \u0026lsquo;Porloq-2\u0026rsquo; (4.26 \u0026plusmn; 0.22, P \u0026lt; 0.001), exemplifying finer fibre. Fibre length was also greater in the RNAi group, with upper half mean length (UHM) of 1.22 \u0026plusmn; 0.03 in compared with 1.14 \u0026plusmn; 0.02 inches in the traditional cultivars (P \u0026lt; 0.001). Uniformity was modest but significantly higher in the RNAi cultivars (84.0 \u0026plusmn; 1.0% versus 82.8 \u0026plusmn; 1.7%, P \u0026lt; 0.01). Fibre strength (31.7 \u0026plusmn; 1.4 versus 30.8 \u0026plusmn; 1.2 g tex⁻\u0026sup1;, P \u0026lt; 0.001) and reflectance (Rd; 80.4 \u0026plusmn; 1.2 versus 78.7 \u0026plusmn; 1.9, P \u0026lt; 0.001) were also significantly higher in the RNAi group, whereas elongation did not differ significantly. Together, these results support stable expression of the RNAi-associated fibre-quality phenotype under experimental-station conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 | Area and yield summary for 2013\u0026ndash;2025 data\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCultivar Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en (Yield)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Area (ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYield (t/ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperimental Station (Qibray)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003ePorloq-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eRNAi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.97\u0026plusmn;0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003ePorloq-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eRNAi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.24\u0026plusmn;0.40***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003ePorloq-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eRNAi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.76\u0026plusmn;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003ePorloq-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eRNAi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.21\u0026plusmn;0.38***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll RNAi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRNAi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e46\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e89.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.06\u0026plusmn;0.45***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eAN Bayaut‑2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eTraditional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.57\u0026plusmn;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eS‑6524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eTraditional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.94\u0026plusmn;0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eToshkent‑6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eTraditional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.49\u0026plusmn;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNamangan‑77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eTraditional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.73\u0026plusmn;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll Traditional\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraditional\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.68\u0026plusmn;0.37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegional (Multi\u003c/strong\u003e\u003cstrong\u003e‑\u003c/strong\u003e\u003cstrong\u003elocation)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003ePorloq‑1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eRNAi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e132,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.04\u0026plusmn;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003ePorloq‑2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eRNAi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e62,592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.13\u0026plusmn;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003ePorloq‑3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eRNAi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.30\u0026plusmn;0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003ePorloq‑4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eRNAi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e217,786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.28\u0026plusmn;0.77*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll RNAi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRNAi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e129\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e413,587\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.16\u0026plusmn;0.78*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSulton\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraditional\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e133\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2,019,298\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.95\u0026plusmn;0.73\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: Unweighted mean \u0026plusmn; s.d. across available observations; n, number of variety-year (experimental) or variety-location-year (regional) observations with non-missing yield. Significance was tested against the pooled traditional comparator group (experimental station; 2.68 \u0026plusmn; 0.37, n = 28) or \u0026lsquo;Sulton\u0026rsquo; (regional; 2.95 \u0026plusmn; 0.73, n = 133): *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001 (two-sided Welch\u0026rsquo;s t-test).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 | Fibre quality summary for 2013\u0026ndash;2025 data\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCultivar type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en (HVI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUHM (in)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnf (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSTR (g tex\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eELO (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 624px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperimental Station (Qibray)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003ePorloq-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eRNAi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e4.58\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.21\u0026plusmn;0.02***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e84.5\u0026plusmn;1.0**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e32.8\u0026plusmn;1.7*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e8.8\u0026plusmn;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e80.7\u0026plusmn;1.6*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003ePorloq-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eRNAi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e4.26\u0026plusmn;0.22***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.27\u0026plusmn;0.01***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e84.5\u0026plusmn;0.8***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e32.5\u0026plusmn;1.1***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e7.1\u0026plusmn;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e80.8\u0026plusmn;0.7***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003ePorloq-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eRNAi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e4.57\u0026plusmn;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.20\u0026plusmn;0.03***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e83.0\u0026plusmn;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e31.0\u0026plusmn;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e7.9\u0026plusmn;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e80.3\u0026plusmn;1.3**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003ePorloq-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eRNAi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e4.56\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.20\u0026plusmn;0.02***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e84.0\u0026plusmn;0.8**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e30.8\u0026plusmn;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e7.5\u0026plusmn;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e80.1\u0026plusmn;1.2*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll RNAi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRNAi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.48\u0026plusmn;0.26**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.22\u0026plusmn;0.03***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e84.0\u0026plusmn;1.0**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e31.7\u0026plusmn;1.4***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.7\u0026plusmn;1.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e80.4\u0026plusmn;1.2***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eAN Bayaut‑2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eTraditional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e4.78\u0026plusmn;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.12\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e83.8\u0026plusmn;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e31.1\u0026plusmn;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e7.2\u0026plusmn;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e79.4\u0026plusmn;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eS‑6524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eTraditional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e4.40\u0026plusmn;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.16\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e83.0\u0026plusmn;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e31.8\u0026plusmn;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e7.9\u0026plusmn;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e78.3\u0026plusmn;2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eToshkent‑6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eTraditional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e4.65\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.13\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e81.3\u0026plusmn;2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e30.2\u0026plusmn;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e7.4\u0026plusmn;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e77.8\u0026plusmn;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eNamangan‑77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eTraditional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e4.78\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.13\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e82.9\u0026plusmn;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e30.2\u0026plusmn;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e7.1\u0026plusmn;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e79.3\u0026plusmn;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eAll Traditional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eTraditional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e4.65\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.14\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e82.8\u0026plusmn;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e30.8\u0026plusmn;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e7.5\u0026plusmn;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e78.7\u0026plusmn;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 624px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegional (Multi\u003c/strong\u003e\u003cstrong\u003e‑\u003c/strong\u003e\u003cstrong\u003elocation)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003ePorloq-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eRNAi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e4.39\u0026plusmn;0.32*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.23\u0026plusmn;0.03***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e83.5\u0026plusmn;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e33.2\u0026plusmn;2.2**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e7.2\u0026plusmn;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e79.6\u0026plusmn;3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003ePorloq-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eRNAi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e4.44\u0026plusmn;0.23*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.24\u0026plusmn;0.03***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e84.3\u0026plusmn;1.2**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e34.4\u0026plusmn;3.0***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e7.4\u0026plusmn;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e81.0\u0026plusmn;3.2*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003ePorloq-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eRNAi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e4.50\u0026plusmn;0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.21\u0026plusmn;0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e83.3\u0026plusmn;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e32.5\u0026plusmn;3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e9.1\u0026plusmn;3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e79.4\u0026plusmn;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003ePorloq-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eRNAi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e4.64\u0026plusmn;0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.21\u0026plusmn;0.03***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e83.9\u0026plusmn;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e31.5\u0026plusmn;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e6.7\u0026plusmn;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e80.7\u0026plusmn;3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eAll RNAi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eRNAi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e4.49\u0026plusmn;0.34*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.22\u0026plusmn;0.03***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e83.9\u0026plusmn;1.4***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e33.0\u0026plusmn;2.8***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e7.2\u0026plusmn;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e80.4\u0026plusmn;3.1*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eSulton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eTraditional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e4.60\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.12\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e83.1\u0026plusmn;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e31.1\u0026plusmn;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e7.2\u0026plusmn;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e79.0\u0026plusmn;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: Unweighted mean \u0026plusmn; s.d. across available observations; n = number of HVI measurements. Significance was tested against the pooled traditional comparator group (experimental station) or against \u0026lsquo;Sulton\u0026rsquo; (regional): *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001 (two-sided Welch\u0026rsquo;s t-test).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegional multi-environmental performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross 13 administrative districts representing five agro-climatic zones, the multi-location farm dataset (n = 1,421 variety-region-year observations, 2013\u0026ndash;2025) provided a broad picture of RNAi cultivar performance under commercial farming conditions. Cultivars were planted by farmers according to local practice, using conventional 90 cm row spacing and standard agronomic management. Harvested seed cotton was stored, ginned and analysed by local HVI laboratories under the coordination of the Ministry of Agriculture of Uzbekistan (Tashkent). Area and yield data were recorded by administrative districts and cotton industry cluster companies, then submitted to the Ministry of Agriculture of Uzbekistan and the State Statistics Committee. Using unweighted mean comparisons across available observations (Table 1; Fig. 1B), the RNAi cultivars (\u0026lsquo;Porloq-1\u0026ndash;4\u0026rsquo;) maintained a modest but significant yield advantage over the widely grown traditional cultivar \u0026lsquo;Sulton\u0026rsquo; (3.16 \u0026plusmn; 0.78 versus 2.95 \u0026plusmn; 0.73 t ha⁻\u0026sup1;, P \u0026lt; 0.05, two-sided Welch\u0026rsquo;s t-test). Fibre quality parameters were also generally superior in the RNAi group (Table 2; Fig. 1B): RNAi cultivars produced longer fibre (UHM 1.22 \u0026plusmn; 0.03 versus 1.12 \u0026plusmn; 0.02 in, P \u0026lt; 0.001), lower micronaire (4.49 \u0026plusmn; 0.34 versus 4.60 \u0026plusmn; 0.12, P \u0026lt; 0.05), higher uniformity (83.9 \u0026plusmn; 1.4% versus 83.1 \u0026plusmn; 0.7%, P \u0026lt; 0.001), stronger fibre (33.0 \u0026plusmn; 2.8 versus 31.1 \u0026plusmn; 1.8 g tex⁻\u0026sup1;, P \u0026lt; 0.001) and slightly higher reflectance (Rd; 80.4 \u0026plusmn; 3.1 versus 79.0 \u0026plusmn; 1.4, P \u0026lt; 0.05), whereas elongation did not differ significantly. Overall, these results indicate that the RNAi cultivars retained both a yield advantage and improved fibre quality under commercial regional production conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenotype-by-environment and stability analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo quantify the relative contributions of genotype and environment to regional yield variation, we analysed the regional yield dataset using an unweighted additive ANOVA with the five genotypes consistently represented across the dataset (\u0026lsquo;Porloq-1\u0026ndash;4\u0026rsquo; and \u0026lsquo;Sulton\u0026rsquo;; Table 3). Because the regional dataset was sparse and unbalanced across genotype\u0026ndash;zone\u0026ndash;year combinations, environment was defined as zone \u0026times; year, and a separate genotype \u0026times; environment interaction term was not retained. Under this additive model, environment accounted for the largest proportion of the total variance (68.29%, P \u0026lt; 0.001), indicating a strong influence of agro-climatic and year-specific conditions on regional yield, whereas genotype explained 4.43% of the variance (P \u0026lt; 0.001). The remaining 27.82% was captured by the residual term. Thus, regional yield variation was driven primarily by environmental heterogeneity, while still supporting a significant overall genotype effect.\u003c/p\u003e\n\u003cp\u003eBecause the regional AMMI analysis (see below) was conducted on a reduced common panel excluding \u0026lsquo;Porloq-3\u0026rsquo;, we also fitted a corresponding additive ANOVA for the reduced four-genotype panel (\u0026lsquo;Porloq-1\u0026rsquo;, \u0026lsquo;Porloq-2\u0026rsquo;, \u0026lsquo;Porloq-4\u0026rsquo; and \u0026lsquo;Sulton\u0026rsquo;; Supplementary Table S1). This analysis gave the same overall pattern, with year explaining the largest share of variation (57.33%, P \u0026lt; 0.001), followed by zone (6.84%, P \u0026lt; 0.001) and genotype (3.62%, P \u0026lt; 0.001), supporting the robustness of the reduced panel used for stability analysis.\u003c/p\u003e\n\u003cp\u003eAdditive ANOVA of the balanced experimental-station panel (Qibray, 2013\u0026ndash;2018) likewise showed a significant genotype effect on yield (F = 4.70, P \u0026lt; 0.001, \u0026eta;\u0026sup2; = 46.1%), whereas the main effect of year was not significant (F = 0.69, P = 0.636, \u0026eta;\u0026sup2; = 4.8%; Supplementary Table S2). Further, MANOVA of the combined HVI fibre traits also identified significant genotype effects in both the experimental and regional panels, supporting consistent multivariate differences in fibre quality among genotypes (Supplementary Table S3). Univariate ANOVA of experimental-station HVI traits (Supplementary Table S4) and regional HVI traits (Supplementary Table S5) broadly supported the MANOVA results, indicating significant genotype effects for several individual fibre traits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 | Additive ANOVA of regional yield (2013\u0026ndash;2025) for five genotypes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003cstrong\u003eource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026eta;\u0026sup2; (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eEnvironment (zone \u0026times; year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e103.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e7.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e68.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e41.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e27.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e151.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: \u0026eta;\u0026sup2; was calculated as the proportion of the total sum of squares attributable to each source. Because the regional dataset was sparse and unbalanced across genotype\u0026ndash;zone\u0026ndash;year combinations, yield variation was analysed using an unweighted additive ANOVA, with environment defined as zone \u0026times; year; a separate genotype \u0026times; environment term was not retained.\u003c/p\u003e\n\u003cp\u003eStability was evaluated using Finlay\u0026ndash;Wilkinson regression and additive main effects and multiplicative interaction (AMMI) analysis. For the experimental-station dataset, a balanced panel of eight genotypes evaluated at Qibray across six common years (2013\u0026ndash;2018) supported a formal AMMI2 analysis (Table 4; Fig. 2A). The first two interaction principal components explained 67.6% and 21.6% of the genotype \u0026times; year interaction sum of squares, respectively. Among the RNAi cultivars, \u0026lsquo;Porloq-2\u0026rsquo; combined the highest mean yield with the lowest ASV, indicating both strong productivity and high temporal stability, whereas \u0026lsquo;Porloq-4\u0026rsquo; also showed favourable stability. Because the archived experimental annual means contain one observation per genotype-year cell, AMMI was retained as the primary interaction-based stability framework for this panel, while the corresponding additive ANOVA is presented separately in Supplementary Tables S1\u0026ndash;S2.\u003c/p\u003e\n\u003cp\u003eFor the regional dataset, a conventional five-genotype AMMI analysis was not retained because \u0026lsquo;Porloq-3\u0026rsquo; was represented in too few regional observations for a robust common-panel analysis. We therefore constructed a reduced regional panel using \u0026lsquo;Porloq-1\u0026rsquo;, \u0026lsquo;Porloq-2\u0026rsquo;, \u0026lsquo;Porloq-4\u0026rsquo; and \u0026lsquo;Sulton\u0026rsquo;, and calculated unweighted annual genotype means across locations for the years shared by all four genotypes. In this reduced regional AMMI2 panel, the first two interaction principal components explained 54.6% and 29.1% of the interaction sum of squares, respectively (Table 4; Fig. 2B). \u0026lsquo;Porloq-4\u0026rsquo; had the smallest ASV in the regional panel, followed by \u0026lsquo;Sulton\u0026rsquo; and \u0026lsquo;Porloq-2\u0026rsquo;, whereas \u0026lsquo;Porloq-1\u0026rsquo; showed the largest interaction magnitude. These regional results should therefore be interpreted as a year-aggregated regional synthesis rather than as a full location-by-year AMMI analysis. Thus, the regional additive ANOVA and the reduced regional AMMI address complementary questions: the ANOVA partitions variation in the full reproducible regional yield dataset, whereas the AMMI summarises stability patterns in a reduced common-year panel suitable for interaction analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 | AMMI stability parameters from experimental and regional yield panels\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean yield (t/ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIPCA1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIPCA2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 624px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA. Experimental Station balanced panel (Qibray, 2013\u0026ndash;2018)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eAN Bayaut-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTraditional cultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNamangan-77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTraditional cultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003ePorloq-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRNAi cultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003ePorloq-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRNAi cultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003ePorloq-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRNAi cultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003ePorloq-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRNAi cultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eS-6524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTraditional cultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eToshkent-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTraditional cultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 624px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB. Regional multi-location year-aggregated panel (common years across all four genotypes: 2013\u0026ndash;2021 and 2023)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003ePorloq-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRNAi cultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-1.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003ePorloq-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRNAi cultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003ePorloq-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRNAi cultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eSulton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTraditional cultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Experimental AMMI was estimated from the balanced 8-genotype \u0026times; 6-year panel. Regional AMMI was estimated from unweighted annual genotype means aggregated across locations for the reduced four-genotype panel, using only years shared by all four genotypes. Raw IPCA1 and IPCA2 scores are reported; biplot coordinates may differ by scaling convention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTemporal trends in yield and fibre quality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing area-weighted annual means from the regional datasets, we examined the evolution of yield and fibre quality over the 2013\u0026ndash;2025 period. RNAi yield increased steadily from 2.81 t ha⁻\u0026sup1; in 2013 to 4.22 t ha⁻\u0026sup1; in 2025, closely mirroring the national trend but consistently exceeding traditional yields by an average of 0.23 t ha⁻\u0026sup1; (range 0.11\u0026ndash;0.36). Simultaneously, fibre length (UHM) of RNAi cultivars rose from 1.21 inches to 1.25 inches, while traditional UHM remained near 1.11 inches. Micronaire declined in both groups, but RNAi maintained a consistent advantage (0.2\u0026ndash;0.3 units lower). Strength improved modestly for RNAi (from 31.1 to 33.2 g tex⁻\u0026sup1;) and changed little in the traditional group. Overall, these temporal patterns indicate that the yield advantage of the RNAi lines was maintained over time without deterioration in fibre quality, supporting partial alleviation of the conventional yield\u0026ndash;quality trade-off.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrait correlations and heritability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analysis based on unweighted annual means (Table 5) showed that, in the traditional cultivar Sulton, yield was most strongly associated with fibre strength, with a significant negative correlation between yield and STR (r = -0.74, P \u0026lt; 0.01). By contrast, correlations between yield and fibre length (r = -0.11) and between yield and micronaire (r = -0.25) were weak and not significant. In the pooled RNAi cultivars (\u0026lsquo;Porloq-1\u0026ndash;4\u0026rsquo;), none of the corresponding annual-mean correlations reached significance over the years with overlapping yield and fibre-quality data (n = 7): yield showed essentially no association with UHM (r = +0.01) or STR (r = +0.00), and a moderate but non-significant negative association with MIC (r = -0.69). Thus, under the unweighted annual-means framework, the strongest detectable relationship was the negative yield-strength correlation in the traditional group, whereas the RNAi group showed no significant evidence of an adverse yield\u0026ndash;quality relationship (Table 5). Fig. 1C and D provide complementary observation-level summaries based on the merged unweighted dataset, whereas the formal correlation coefficients in Table 5 and Supplementary Table S6 were calculated from unweighted annual means.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 | Pearson correlations between yield and fibre traits (2013\u0026ndash;2025)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCultivar group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYield vs. UHM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYield vs. MIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYield vs. STR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eTraditional (Sulton)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-0.74**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eRNAi (Porloq‑1\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e+0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e+0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Correlations were calculated from unweighted annual means across available observations. For the RNAi group, analyses were restricted to years with both yield and fibre-quality data in the regional datasets (n = 7 years), whereas Sulton had complete overlap across 2013\u0026ndash;2025 (n = 13 years). Significance is indicated as P \u0026lt; 0.05, P \u0026lt; 0.01 and P \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003eCorrelations among fibre-quality traits were also examined using unweighted annual means (Supplementary Table S6). In the traditional group, UHM was positively correlated with MIC (r = +0.61, P \u0026lt; 0.05) and STR was positively correlated with ELO (r = +0.62, P \u0026lt; 0.05), Unf (r = +0.77, P \u0026lt; 0.01) and Rd (r = +0.70, P \u0026lt; 0.01). In the RNAi group, the strongest positive association was between UHM and STR (r = +0.90, P \u0026lt; 0.01), indicating that longer fibres also tended to be stronger. A significant negative correlation was also observed between Unf and Rd (r = -0.83, P \u0026lt; 0.05) in the RNAi annual-mean dataset.\u003c/p\u003e\n\u003cp\u003eBroad-sense heritability of fibre traits was estimated from the unweighted experimental-station RNAi data using a separate random-effects model (Table 6). Under this framework, UHM showed the highest heritability (H\u0026sup2; = 0.80 \u0026plusmn; 0.22), whereas MIC (0.43 \u0026plusmn; 0.21) and STR (0.41 \u0026plusmn; 0.21) showed more moderate estimates. These results indicate that fibre length remained the most strongly genotype-associated trait in the experimental RNAi panel, while micronaire and strength showed greater year-to-year and residual variation under the fitted model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6 | Broad\u003c/strong\u003e\u003cstrong\u003e‑\u003c/strong\u003e\u003cstrong\u003esense heritability (H\u0026sup2;) of fibre traits in RNAi cultivars (experimental station)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sigma;\u0026sup2;_G\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sigma;\u0026sup2;_G\u0026times;Y\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sigma;\u0026sup2;_e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u0026sup2;\u003cem\u003e\u0026nbsp;\u003c/em\u003e(SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eUHM (inches)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.80 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.0217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.0121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.0162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.43 (0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eSTR (g tex\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.8208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.7067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.4655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.41 (0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eVariance components were estimated from an unweighted random-effects model fitted to the experimental-station RNAi data. The SE values shown here are bootstrap approximations from the fitted variance-component model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional RNAi cultivars introduced after 2021\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to the four original RNAi cultivars (\u0026lsquo;Porloq-1\u0026ndash;4\u0026rsquo;), two newer RNAi lines, \u0026lsquo;Porloq-5\u0026rsquo; and \u0026lsquo;Porloq-7\u0026rsquo;, were introduced during the later years of the study period. Both are elite selections derived from \u0026lsquo;Porloq-1\u0026rsquo; through on-farm evaluation and multiplication. As summarised in Supplementary Table S7, the unweighted mean yield of \u0026lsquo;Porloq-5\u0026rsquo; was 3.90 \u0026plusmn; 0.72 t ha⁻\u0026sup1; across three region\u0026times;year observations, whereas \u0026lsquo;Porloq-7\u0026rsquo; reached 4.08 \u0026plusmn; 0.57 t ha⁻\u0026sup1; across four observations. Although neither line differed significantly from \u0026lsquo;Sulton\u0026rsquo; at P \u0026lt; 0.05, both showed strong performance under commercial conditions, indicating continued delivery of high-performing RNAi germplasm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerformance by agro-climatic zone\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the agro-climatic zone classification established in the 2021\u0026ndash;2025 national study20, we examined yield and fibre quality of RNAi and traditional cultivars across five zones over the full 2013\u0026ndash;2025 period (Supplementary Table S8). Under the unweighted analysis, RNAi cultivars showed numerically higher mean yield than Sulton in all zones, although none of the zone-specific yield differences was significant (P \u0026gt; 0.05). In contrast, UHM was significantly greater in the RNAi cultivars in every zone, with the biggest differences in the Fergana Valley, Desert Zone, Central Plains and Capital Region (P \u0026lt; 0.001) and a smaller but still significant difference in the Arid/Northern zone (P \u0026lt; 0.01). Fibre strength was also higher in the RNAi cultivars in the Desert Zone and Central Plains, whereas micronaire showed no significant zone-specific differences. Together, these results indicate that the most consistent zonal advantage of the RNAi cultivars was improved fibre length rather than significantly higher yield.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOver a decade of cultivation, \u003cem\u003ePHYA1\u0026nbsp;\u003c/em\u003eRNAi cotton cultivars combined improved fibre quality with stable yield performance across diverse environments in Uzbekistan. The AMMI analysis reinforces this conclusion while also clarifying the level of inference. In the experimental-station dataset, an eight-genotype × six-year balanced panel supported a formal AMMI2 analysis, in which ‘Porloq-2’ showed both the highest mean yield and the greatest stability among the RNAi cultivars, whereas ‘Porloq-4’ also performed consistently well. In contrast, the regional dataset required a reduced four-genotype common-year panel, because the full location-by-year matrix was too sparse for a robust AMMI analysis across all five genotypes. Framing the regional AMMI as a year-aggregated synthesis therefore preserves comparability while avoiding overinterpretation of incomplete multi-environment structure, which is especially important for cotton grown across the broad climatic and management diversity of Uzbekistan\u003csup\u003e20,21\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe superior fibre quality of the RNAi cultivars, particularly their consistently greater fibre length and generally stronger, finer fibre profile, directly addresses market demand for higher-value cotton. The increased fibre length relative to the traditional comparator is consistent with earlier genetic evidence implicating \u003cem\u003ePHYA1\u003c/em\u003e in fibre elongation\u003csup\u003e13,14\u003c/sup\u003e. The underlying mechanism likely involves altered phytochrome signalling\u003csup\u003e12\u003c/sup\u003e, with downstream effects on hormone-regulated developmental pathways, including auxin- and gibberellin-related processes\u003csup\u003e22,23\u003c/sup\u003e, as well as miRNA-mediated regulation of genes associated with fibre and cell-wall development\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIndependent studies have further confirmed the distinctive structural and processing properties of Porloq fibres. Khudayberdieva et al.\u003csup\u003e24\u003c/sup\u003e reported that ‘Porloq-1’ and ‘Porloq-2’ show higher specific surface area, pore volume and crystallinity than ‘S-6524’, with ‘Porloq-2’ also showing denser packing and greater strength. Mamadjanova et al.\u003csup\u003e25\u003c/sup\u003e showed that ‘Porloq-2’ and ‘Porloq-4’ require modified preparation conditions for dyeing and yield improved whiteness, capillarity and tensile strength after optimised processing. Akhmedova et al.\u003csup\u003e26\u003c/sup\u003e likewise found that cotton-silk yarn blends containing ‘Porloq-1’ and ‘Porloq-2’ had greater strength and improved elastic properties. These independent studies therefore reinforce our field observations and suggest that the fibre-quality advantages of the RNAi-derived Porloq cultivars have practical value for downstream textile processing.\u003c/p\u003e\n\u003cp\u003eNotably, during the 2021–2025 period, RNAi cultivars were well represented in the Desert Zone (Supplementary Table S8), a region characterised by salinity stress, water limitation and high temperature variability\u003csup\u003e20\u003c/sup\u003e. This deployment pattern aligns with farmers’ practical benefits of these cultivars in harsher production conditions, consistent with earlier physiological evidence indicating enhanced stress tolerance in \u003cem\u003ePHYA1\u003c/em\u003e RNAi cotton\u003csup\u003e19,20\u003c/sup\u003e. Despite these challenging conditions, the RNAi cultivars maintained clear fibre-quality advantages, particularly in fibre length, supporting their resilience in marginal production environments.\u003c/p\u003e\n\u003cp\u003eThe introduction of new RNAi lines, ‘Porloq-5’ and ‘Porloq-7’, after 2021 further underscores the technology’s continued agronomic potential. Both are elite selections derived from ‘Porloq-1’ through on-farm evaluation and subsequent multiplication, indicating that the underlying RNAi-associated phenotype can be retained during further selection. Under the unweighted analysis, mean yield during the 2021–2025 period was 3.90 ± 0.72 t ha⁻¹ for ‘Porloq-5’ and 4.08 ± 0.57 t ha⁻¹ for ‘Porloq-7’ (Supplementary Table S7). These values were numerically higher than the long-term mean of the original RNAi group, although neither cultivar differed significantly from ‘Sulton’ in the available Welch-test comparisons. The elevated yields observed in 2024–2025, as also reported for other traditional and Bt cotton cultivars, likely reflect in part the contribution of improved agronomic practices, including high-density planting under plastic mulch and drip irrigation\u003csup\u003e20\u003c/sup\u003e. Together, these observations are consistent with a productive interaction between genetic improvement and agronomic innovation, a pattern characteristic of sustainable intensification\u003csup\u003e20,27\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFrom a breeding perspective, these stability results are important because they show that the RNAi-associated yield and fibre-quality advantages were not confined to a narrow range of conditions. In the regional additive ANOVA, environmental variation explained a substantially larger proportion of yield variance than genotype, indicating strong modulation of performance by heterogeneous production conditions. Even so, genotype remained a significant source of variation, supporting the persistence of an overall RNAi-associated advantage across environments. The experimental AMMI analysis provides the clearest evidence for stable expression of this phenotype under a balanced multi-year design\u003csup\u003e28\u003c/sup\u003e, whereas the reduced regional AMMI indicates that the same pattern remains relevant under commercial farming conditions in Uzbekistan, albeit with a more limited inferential scope due to the sparse on-farm panel\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe correlation analyses provide additional context for how the RNAi phenotype relates to yield and fibre quality under long-term field conditions. In conventional Upland cotton breeding, simultaneous improvement of yield and fibre quality has often been constrained by unfavourable trait associations, including trade-offs among fibre strength, fibre length and other quality parameters\u003csup\u003e5–9\u003c/sup\u003e. Naoumkina and Kim\u003csup\u003e10\u003c/sup\u003e likewise highlighted the continuing difficulty of improving fibre quality while maintaining agronomic performance, underscoring the broader breeding relevance of the correlation patterns observed here. Against this background, the annual-mean correlations in our study are notable because the strongest significant relationship was observed not in the RNAi group but in the traditional cultivar Sulton, where yield was negatively correlated with fibre strength (r = -0.74, P \u0026lt; 0.01; Table 5). By contrast, the corresponding annual-mean correlations in the pooled RNAi cultivars were weak or non-significant, with essentially no association between yield and fibre length or strength, and only a moderate negative association with micronaire. Although these RNAi correlations should not be over-interpreted, given the limited number of overlapping years, they suggest that the RNAi-associated phenotype does not follow the same adverse yield-quality relationships often reported in conventional cotton germplasm\u003csup\u003e5–8,29\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eCorrelations among fibre-quality traits further support this interpretation. In the RNAi group, fibre length (UHM) was strongly and positively correlated with fibre strength (STR; r = +0.90, P \u0026lt; 0.01; Supplementary Table S6), indicating that longer fibres also tended to be stronger. This pattern is noteworthy because fibre length and strength have often proven difficult to improve simultaneously in cotton breeding\u003csup\u003e7–9,29–33\u003c/sup\u003e. By contrast, the traditional group showed a different correlation structure, including a positive association between UHM and MIC and several strong positive correlations among STR, ELO, Unf and Rd. Together, these results suggest that the RNAi-associated fibre phenotype is not simply an extension of the conventional trait relationships observed in the traditional comparator, but instead reflects a distinct trait architecture consistent with earlier evidence linking \u003cem\u003ePHYA1\u003c/em\u003e to fibre elongation and fibre-quality improvement\u003csup\u003e13,16,18\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHeritability estimates from the unweighted experimental-station RNAi data indicate that fibre length remained the most strongly genotype-associated trait in the RNAi panel. Broad-sense heritability was highest for UHM (H² = 0.80 ± 0.22), whereas MIC (0.43 ± 0.21) and STR (0.41 ± 0.21) were more moderate (Table 6). Thus, although the RNAi-associated fibre phenotype was not uniformly highly heritable across all traits, fibre length remained the most stable and genetically structured component under the fitted model, supporting its value as a target for further selection.\u003c/p\u003e\n\u003cp\u003eThese estimates are broadly consistent with the range reported for cotton fibre traits in previous genetic studies, although the UHM estimate in our RNAi panel lies toward the upper end of published values. In elite Upland germplasm, Campbell et al.\u003csup\u003e29\u003c/sup\u003e reported broad-sense heritability values of 0.35–0.68 for fibre length, strength and micronaire, whereas Fang et al.\u003csup\u003e30\u003c/sup\u003e found values of 0.68–0.87 in a random-mated recombinant inbred population. Studies in additional mapping populations have likewise shown that fibre-quality heritability is often moderate to high, but variable among traits and genetic backgrounds\u003csup\u003e31–33\u003c/sup\u003e. In this context, the relatively high heritability of UHM in the present study, together with the more moderate values for MIC and STR, suggests that the fibre-length component of the \u003cem\u003ePHYA1\u003c/em\u003e RNAi phenotypes was more consistently expressed across years than the corresponding effects on other quality traits. This interpretation is consistent with earlier evidence linking \u003cem\u003ePHYA1\u003c/em\u003e to fibre elongation and fibre-quality improvement\u003csup\u003e1,13,16\u003c/sup\u003e, and with downstream regulatory studies implicating miRNA-mediated control\u003csup\u003e18\u003c/sup\u003e and stress-responsive physiological effects in the broader RNAi phenotype\u003csup\u003e19\u003c/sup\u003e. From a breeding perspective, this trait-specific stability is important because it suggests that fibre length may provide the most reliable selection target within the RNAi-derived quality profile.\u003c/p\u003e\n\u003cp\u003eA limitation of this study is that the regional dataset was observational, sparse and unbalanced across genotype–zone–year combinations, rather than generated from a fully replicated multi-environment experimental design. Accordingly, the regional analyses should be interpreted with appropriate caution, particularly for interaction structure and stability inference from incomplete multi-environment panels\u003csup\u003e21\u003c/sup\u003e. At the same time, this data set provides an important real-world perspective by capturing cultivar performance under commercial farming conditions, including farmer deployment choices across contrasting agro-climatic zones. In that sense, the regional analysis complements the balanced experimental-station trials by showing how the RNAi cultivars performed under natural production settings and during practical technology adoption. The consistency of the observed phenotypic patterns across years, together with the concordant evidence from the experimental-station trials, therefore, supports the overall robustness and practical relevance of the main conclusions. Future work should investigate the molecular basis of the apparent stability of the RNAi-associated phenotype across diverse environments and evaluate whether \u003cem\u003ePHYA1\u003c/em\u003e RNAi can be combined effectively with other biotechnological traits, including Bt-based insect resistance, to further enhance productivity under sustainable intensification\u003csup\u003e20,27\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003ePlant materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003ePHYA1\u003c/em\u003e RNAi cotton cultivars (\u0026lsquo;Porloq-1\u0026ndash;7\u0026rsquo;) were developed by crossing a transgenic \u003cem\u003ePHYA1\u003c/em\u003e RNAi donor line in the Coker-312 background with Uzbek commercial cultivars, as described previously\u003csup\u003e1,16\u003c/sup\u003e. The traditional Uzbek cultivars \u0026lsquo;AN Bayaut-2\u0026rsquo;, \u0026lsquo;S-6524\u0026rsquo;, \u0026lsquo;Toshkent-6\u0026rsquo; and \u0026lsquo;Namangan-77\u0026rsquo; were used as recurrent parental backgrounds for backcross introgression of the RNAi hairpin construct from stable Coker-312 donor genotypes. In the regional trial dataset, the widely grown traditional cultivar \u0026lsquo;Sulton\u0026rsquo; served as the main control genotype.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperimental station field trials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrials were conducted at the experimental farm of the Centre of Genomics and Bioinformatics (Qibray district, Tashkent region, Uzbekistan) from 2013 to 2025. The trials were designed to compare cultivar performance under uniform field conditions. Cultivars were arranged in a randomised complete block design with four replications. Each plot consisted of five rows, each 11 m long, with 90 cm row spacing, giving a plot area of approximately 50 m\u0026sup2; and a total area of about 200 m\u0026sup2; per cultivar. Agronomic practices, including planting date, seed rate, fertilisation and irrigation, were kept uniform across entries. Yield and fibre-quality data were collected by the responsible departments and experimental-station staff, reviewed in annual internal laboratory meetings, and archived at the Centre.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegional multi-location field trials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo specific replicated plot trials were established for the regional dataset. Instead, RNAi and traditional cultivars were grown by farmers under conventional production conditions (90 cm row spacing, standard agrotechnology) across 13 administrative districts representing five agro-climatic zones of Uzbekistan. Cultivar choice was determined by farmer deployment and local production practice rather than by experimental assignment. After harvest, seed cotton was transported to ginning factories, stored, ginned and analysed in local HVI laboratories under the coordination of the Ministry of Agriculture of Uzbekistan (Tashkent). For each cultivar \u0026times; region \u0026times; year combination, planted area (ha) and yield (t ha⁻\u0026sup1;) were recorded by administrative districts and cotton industry cluster companies, then submitted to the Ministry of Agriculture of Uzbekistan and the State Statistics Committee.\u003cbr\u003e\u003cstrong\u003eYield and fibre-quality measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeed cotton yield (t ha⁻\u0026sup1;) was recorded at harvest. Fibre-quality parameters, micronaire (MIC), upper half mean length (UHM, inches), uniformity (Unf, %), strength (STR, g tex⁻\u0026sup1;), elongation (ELO, %), and reflectance (Rd), were measured by high-volume instrumentation (HVI) using standard procedures\u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSampling for HVI analysis followed a scale-dependent protocol. For experimental-station plots, seed cotton was collected from a representative set of plants within each plot (typically 20\u0026ndash;30 plants), bulked, ginned, and subsampled to obtain a 20\u0026ndash;30 g fibre sample for HVI analysis. For selection-scale field plots (1\u0026ndash;6 ha), seed cotton was sampled randomly from the central part of each plot, bulked, ginned, and a 20\u0026ndash;30 g fibre subsample was submitted for analysis. For commercial fields (\u0026ge;30 ha), seed cotton delivered to collection points or cotton clusters was baled, and fibre samples were taken from each bale using a sampling spear, coded, and delivered to the regional HVI laboratory of the SIFAT Centre (now incorporated into the Inspection for the Control of the Agro-industrial Complex under the Cabinet of Ministers of the Republic of Uzbekistan (Agroinspection).\u003c/p\u003e\n\u003cp\u003eFrom 2012 to 2018, HVI measurements were performed using a Uster HVI-900 instrument, and from 2019 to 2025 using a Uster HVI-1000. Before measurement, all samples were conditioned under standard atmospheric conditions (65 \u0026plusmn; 2% relative humidity, 20 \u0026plusmn; 2 \u0026deg;C) for at least 24 h, and instruments were calibrated using standard reference samples\u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were performed in R (v4.3.1) and Python (v3.9). For descriptive regional summaries and temporal trend analyses, area-weighted means were calculated using planted area for each cultivar\u0026ndash;environment combination as weights\u003csup\u003e35\u003c/sup\u003e. However, for the descriptive summaries presented in Tables 1 and 2, values are reported as unweighted mean \u0026plusmn; standard deviation across available observations, so that the displayed means, dispersion estimates and pairwise tests are internally consistent.\u003c/p\u003e\n\u003cp\u003eFor the regional yield dataset, yield variation was analysed using an unweighted additive ANOVA. Because the dataset was sparse and unbalanced across genotype\u0026ndash;zone\u0026ndash;year combinations, genotype was treated as a fixed factor and environmental structure was represented in two complementary ways. In the primary regional analysis (Table 3), environment was defined as zone \u0026times; year, and effect sizes (\u0026eta;\u0026sup2;) were calculated as the proportion of the total sum of squares attributable to each source\u003csup\u003e36\u003c/sup\u003e. All statistical analyses, including variance-component estimation, were implemented in R using the lme4 package where appropriate\u003csup\u003e37\u003c/sup\u003e. A separate genotype \u0026times; environment interaction term was not retained because the regional panel did not provide a sufficiently balanced factorial structure for robust estimation of that interaction across all genotype\u0026ndash;zone\u0026ndash;year combinations. In the supporting analysis of the reduced four-genotype regional panel used for AMMI (Supplementary Table S1), yield variation was also evaluated using an additive ANOVA with genotype, zone and year as fixed effects. A separate additive ANOVA was fitted to the balanced experimental-station panel (Qibray, 2013\u0026ndash;2018) with genotype and year as fixed factors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePairwise comparisons in Tables 1 and 2 were conducted using two-sided Welch\u0026rsquo;s t-tests\u003csup\u003e38\u003c/sup\u003e to account for unequal variances. In the experimental-station sections, each RNAi cultivar and the pooled RNAi group were compared against the pooled traditional comparator group. In the regional sections, comparisons were made against \u0026lsquo;Sulton\u0026rsquo;. Significance is indicated as P \u0026lt; 0.05, P \u0026lt; 0.01 and P \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003eStability was evaluated using Finlay\u0026ndash;Wilkinson regression28 and the additive main effects and multiplicative interaction (AMMI) framework\u003csup\u003e39\u003c/sup\u003e. Genotype stability patterns were visualised using AMMI2 biplots based on the first two interaction principal component axes. For the experimental-station dataset, AMMI2 was estimated from the balanced panel of eight genotypes across the six common years 2013\u0026ndash;2018. For the regional dataset, \u0026lsquo;Porloq-3\u0026rsquo; was excluded because it was represented in too few observations for a reliable common-panel AMMI analysis. The reduced regional AMMI2 analysis was therefore based on \u0026lsquo;Porloq-1\u0026rsquo;, \u0026lsquo;Porloq-2\u0026rsquo;, \u0026lsquo;Porloq-4\u0026rsquo; and \u0026lsquo;Sulton\u0026rsquo;, using unweighted annual genotype means aggregated across locations and restricted to the years shared by all four genotypes (2013\u0026ndash;2021 and 2023). Genotype and year main effects were removed from the genotype \u0026times; year matrices, singular value decomposition was applied to the interaction residual matrix, and the AMMI stability value (ASV) was calculated from IPCA1 and IPCA2 using the standard weighted formulation\u003csup\u003e40,41\u003c/sup\u003e. Finlay\u0026ndash;Wilkinson regression coefficients were estimated by regressing genotype yield on the environmental index, defined as the mean yield across all genotypes in each environment\u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFor time-series analysis, annual area-weighted means of yield and fibre traits were calculated separately for RNAi and traditional groups. For the formal correlation analyses reported in Table 5 and Supplementary Table S6, Pearson correlations were calculated from unweighted annual means across available observations. For the RNAi group, these analyses were restricted to years with overlapping yield and fibre-quality data.\u003c/p\u003e\n\u003cp\u003eHeritability estimation. Broad-sense heritability (H\u0026sup2;) for fibre traits was estimated from the experimental-station RNAi data using an unweighted random-effects model fitted by restricted maximum likelihood (REML) with the lme4 package\u003csup\u003e37\u003c/sup\u003e. The model was:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"225\" height=\"21\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u003cbr\u003e\u0026nbsp;where\u0026nbsp;\u003cimg width=\"15\" height=\"19\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1026\" alt=\"image\"\u003eis the random effect of genotype,\u0026nbsp;\u003cimg width=\"12\" height=\"21\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1027\" alt=\"image\"\u003eis the random effect of year,\u0026nbsp;\u003cimg width=\"44\" height=\"22\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1028\" alt=\"image\"\u003eis the genotype \u0026times; year interaction, and\u0026nbsp;\u003cimg width=\"24\" height=\"21\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1029\" alt=\"image\"\u003eis the residual error. Variance components were used to calculate:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"175\" height=\"19\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1030\" alt=\"image\"\u003e\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBecause the archived experimental-station RNAi dataset effectively contains one observation per genotype \u0026times; year cell for the fitted traits, these heritability estimates should be interpreted as model-based approximations under the available design. Standard errors were obtained by bootstrap approximation from the fitted variance-component model. All significance tests were two-sided with\u0026nbsp;\u003cimg width=\"61\" height=\"19\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1031\" alt=\"image\"\u003e. Means are reported as mean \u0026plusmn; standard deviation unless otherwise noted.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Prof. Khakimjon Khamidov, who supervised field trials of the cotton cultivars at the Centre of Genomics and Bioinformatics and all former and current postdoctoral researchers, PhD students and research assistants (Komronbek Mirzayokubov, Ulmas Sobitov, Tokhir Norov, Obid Turaev, Ozod Turaev, Jurabek Norbekov, Farhod Radjabov, Dilshod Usmanov, Fakhriddin Kushanov and Saidakhmad Omarov) for their contributions to experimental-station field management and field-trial support. We are grateful to Prof. Shodmon E. Nomozov, breeder of the \u0026lsquo;Sulton\u0026rsquo; cultivar, for providing and verifying the regional Sulton dataset. We also thank all cotton farmers who contributed to data collection and to the adoption of the technology under commercial field conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI.Y.A. conceived the study, performed the analyses and wrote the first draft of the manuscript. Z.T.B., S.E.S. and A.A. contributed to data curation, data analysis and manuscript revision. M.M.D., Y.A.M., S.I.M., I.E.B., A.B.M., N.N.K., A.B.I., H.S.R., A.M. and K.A.U. coordinated and conducted the experimental-station field trials, data collection and analyses, and contributed to field selection and seed propagation. I.Y.A., Z.T.B., R.R.A., A.A.T. and M.M.D. coordinated the regional cultivation and commercialisation of the RNAi cultivars. All authors reviewed, edited and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by research and commercialisation grants I5-FQ-0-89870 (2014\u0026ndash;2015), I-2015-6-15/2 (2015\u0026ndash;2016), IFA-2017-5-6 (2017\u0026ndash;2018), FA-F-5-021 (2017\u0026ndash;2020), FA-F-5-025 (2017\u0026ndash;2020) and FA-A-QX-2018-393 (2018\u0026ndash;2020) from the Foundation for Supporting Science and Innovation of the Ministry of Higher Education, Science and Innovations of the Republic of Uzbekistan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study did not involve human participants, human tissue, or animal experiments. All data were anonymised and aggregated at the regional level to protect privacy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn accordance with Nature Portfolio policies, authors also declare the use of AI-assisted tools for final language, typo and formatting corrections. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the content of the published work.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCultivar‑level yield, area, and fibre quality data are provided as Supplementary Data Files 1\u0026ndash;4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe code used for statistical analyses and figure generation is available from the corresponding author upon reasonable request. Analyses were conducted using R (v4.3.1) and Python (v3.9) with publicly available packages.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdurakhmonov, I. Y. et al. RNA interference for functional genomics and improvement of cotton. \u003cem\u003eFront. Plant Sci.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 202 (2016). \u003c/li\u003e\n\u003cli\u003eSunilkumar, G., Campbell, L. M., Puckhaber, L., Stipanovic, R. D. \u0026amp; Rathore, K. S. Engineering cottonseed for use in human nutrition by tissue-specific reduction of toxic gossypol. \u003cem\u003eProc. Natl Acad. Sci. USA\u003c/em\u003e \u003cstrong\u003e103\u003c/strong\u003e, 18054\u0026ndash;18059 (2006). \u003c/li\u003e\n\u003cli\u003ePhalan, B., Onial, M., Balmford, A. \u0026amp; Green, R. E. 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Crop Sci.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 1837\u0026ndash;1844 (2011). \u003c/li\u003e\n\u003cli\u003eSharifi, P., Aminpanah, H., Erfani, R. \u0026amp; Abbasian, A. Evaluation of genotype \u0026times; environment interaction in rice based on AMMI model in Iran. \u003cem\u003eRice Sci.\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 173\u0026ndash;180 (2017). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9260784/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9260784/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Global demand for natural cotton fibre continues to increase, but the simultaneous improvement of fibre quality and yield remains a major challenge in cotton breeding. Here, we assess the 13-year field performance of \u003ci\u003ePHYA1\u003c/i\u003e RNA interference (RNAi)-derived cotton cultivars in Uzbekistan. In experimental-station trials, RNAi cultivars (‘Porloq-1–4’) showed a 14% yield advantage over four traditional cultivars (3.06 ± 0.45 versus 2.68 ± 0.37 t ha⁻¹, P \u003c 0.001), together with longer fibre (1.22 ± 0.03 versus 1.14 ± 0.02 in, P \u003c 0.001), lower micronaire (4.48 ± 0.26 versus 4.65 ± 0.21, P \u003c 0.01) and higher fibre strength (31.7 ± 1.4 versus 30.8 ± 1.2 g tex⁻¹, P \u003c 0.001). In regional production datasets spanning 13 administrative districts, RNAi cultivars also outperformed the traditional cultivar ‘Sulton’ in yield (3.16 ± 0.78 versus 2.95 ± 0.73 t ha⁻¹, P \u003c 0.05) and maintained superior fibre quality. In an unweighted additive ANOVA of the regional five-genotype yield dataset, environment (zone × year) was the dominant source of variation (68.29%, P \u003c 0.001), whereas genotype also contributed significantly (4.43%, P \u003c 0.001). Multivariate analysis of combined HVI traits likewise identified significant genotype effects in both the experimental and regional panels, supporting consistent differences in fibre quality among genotypes. Stability analyses indicated that the RNAi cultivars were generally well adapted across environments, with ‘Porloq-2’ and ‘Porloq-4’ showing particularly consistent performance. Over 2013–2025, area-weighted RNAi yield increased from 2.81 to 4.22 t ha⁻¹, while fibre length increased from 1.21 to 1.25 in. Annual-mean correlation analyses showed no strong adverse yield–quality relationships in the RNAi group. Together, these results indicate that \u003ci\u003ePHYA1\u003c/i\u003e RNAi confers durable agronomic and fibre-quality advantages under long-term field conditions.","manuscriptTitle":"Long-term field performance of PHYA1 RNAi cotton cultivars reveals sustained yield advantage and improved fibre quality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 05:47:50","doi":"10.21203/rs.3.rs-9260784/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c91add7d-c99d-459d-9115-9b7092d52afa","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65387927,"name":"Biological sciences/Plant sciences/Plant breeding"},{"id":65387928,"name":"Biological sciences/Genetics/Agricultural genetics"},{"id":65387929,"name":"Biological sciences/Genetics/Plant genetics"}],"tags":[],"updatedAt":"2026-04-08T09:58:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 05:47:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9260784","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9260784","identity":"rs-9260784","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}