Search Results (140)

Breeding programs rely on genotype-by-environment interaction (GEI) to recommend cultivars for specific locations. GEI describes how different genotypes perform under varying environmental conditions. Several methods were proposed to assess adaptability and stability across environments. These methods utilize various statistical approaches like parametric and non-parametric regression, multivariate analysis techniques, and even Bayesian frameworks and artificial intelligence. The accessibility of environmental data through platforms like NASA POWER allows breeders to integrate this information into a breeding process. It has been done by using multi-omics integration models that combine data across various biological levels to create accurate predictive models. In the context of phenotypic adaptability and stability analysis, structural equation modeling (SEM) offers an interesting approach to integrating environmental covariates. This work aimed to propose a novel approach that integrates weather information into adaptability and stability analysis, combining SEM with the established Eberhart and Russell model. Additionally, a user-friendly applet, denoted ECERSEM-AdaptStab, was made available to perform the analysis. This approach utilized data from 12 cotton cultivar trials conducted across two growing seasons at 19 sites. This approach successfully integrated environmental covariates into a phenotypic adaptability and stability analysis of cotton cultivars. Specifically, the genotypes TMG 41 WS, IMA CV 690, DP 555 BGRR, BRS 286 and BRS 369 RF were recommended for favorable environments, while the genotypes TMG 43 WS, IMA 5675 B2RF, IMA 08 WS, NUOPAL, DELTA OPAL, BRS 335, and BRS 368 RF are more suitable for unfavorable environments. Full article

Banana (Musa spp.) is an important crop in the economies of many developing countries. In the north of Argentina, a subtropical region, banana plants grow in a suboptimal environment that limits yield because only one harvest per year is achieved. The objective of this work was to characterize the agroecological aptitude of Formosa, Argentina, for banana production through the behavior of three varieties of international use: Williams, Jaffa and Grand Naine, evaluated over five consecutive years. The three-way data analysis technique called Multiple Factor Analysis (MFA) was used for evaluating the varieties’ performances across cycles of production. The results allowed for inferring the existence of a genotype x environment interaction (GEI), corroborated by two-way factorial ANOVA. In order to determine how this suboptimal environment affected the development of each genotype of this perennial crop, Dual Multiple Factor Analysis (DMFA) was applied to jointly analyze the correlation structure between the traits that contributed to the performance of each variety in each year. The correlation structures between variables were different in each population and varied between years. All traits showed great variation between the years and genotypes, with the fruit peel thickness being the most discrepant throughout the years. However, Formosa appeared as a promising subtropical agroecological environment to produce banana because the varieties’ performances were acceptable for large-scale production systems. In addition to evaluating the adequate aptitude for cultivating banana in Formosa considering the significant effect of the GEIs, this research made a methodological contribution by proposing the use of three-way data analysis in Agronomy Science via MFA and DMFA. Full article

Recommended List (RL) is the UK plant variety recommendation system introduced in 1944 for supporting growers in making decisions on variety choices. The current RL system is heavily focused on single-trial analyses developed in the 1980s without making full use of information across varieties and trial sites. Given the statistical advances that have been developed and adopted elsewhere, it is timely to review and update the methods for data analysis in RL. In addition, threats from climate change challenge the prediction of variety performance in future environments. Better variety recommendations, particularly for matching varieties to specific environments can be achieved through the improved modeling of effects from genetics, environments, and genetic-by-environment interactions. Here, we evaluate grain yield data from 153 spring barley varieties that were trialed for RL from 2002 to 2019. Our results show that the current RL system produces poor and inconsistent predictions on variety performance across environments. Improvement in RL can be achieved by using mixed models that account for genetic relationships among varieties, and additional improvement is possible if genetic-by-environment interaction can be modeled accurately. We highlight the relevance and importance of genomics in both variety registration and recommendation. Full article

Potatoes (Solanum tuberosum L.) are an important plant crop, whose yield may vary significantly depending on pedo-climatic conditions and genotype. Therefore, the analysis of the genotype × environment interaction (GEI) is mandatory for the setup of high-yielding and stable potato genotypes. This research evaluated the tuber yield (t ha⁻¹) and yield characteristic of nine potato cultivars over 3 years and 4 organic farms in Poland by additive main effects and multiplicative interactions (AMMIs) and genotype plus genotype environment interaction (GGE) biplot analyses. The results of these analyses indicated significant differentiation of tuber yield among genotypes in individual environments. It was found that the environment (E, where E = L (localization) × Y (year)), genotype (G) and GEI, but not replication, significantly affected tuber yield. The AMMI analysis showed that the environment factor explained the most considerable part of tuber yield variations (52.3%), while the GEI and G factors explained a much lower part of the variations. The AMMI and GGE analyses identified five cvs.: Twister (46.4 t ha⁻¹), Alouette (35.8 t ha⁻¹), Kokra (34.8 t ha⁻¹), Levante (33.1 t ha⁻¹), and Gardena (30.4 t ha⁻¹), as leading cultivars in the studied organic farms due to their high productivity coupled with yield stability. The statistical measure Kang (YS_i) showed that these cvs. can be considered as adaptable to a wide range of organic environments. In the case of morphological traits of tubers (tuber shape and depth of tuber eyes), the most important factor influencing both these traits was genotype (G). Influence of other factors, like localization (L), year (Y), and all interactions (double and triple), were much less significant or insignificant. In case of taste and non-darkening of tuber flesh, the main effects which significantly affected the values of these traits were genotype (G) and localization (L). We observed that cooking type can vary depending on the year (Y) and the localization (L). Full article

This study aimed to rigorously evaluate the impact of tuned mass dampers (TMDs) on structural response under seismic excitation. By strategically placing TMDs at various levels within the structures, the research sought to determine their effectiveness in mitigating structural movement. A single-degree-of-freedom (SDOF) system incorporating TMDs was utilized to model structures of 10, 13, and 16 stories, each configured with TMDs at different heights. The structures were subjected to near-fault earthquakes to assess the efficacy of TMDs in reducing structural response. The findings revealed significant enhancements in structural performance when TMDs were optimally positioned. Specifically, a 50% reduction in both acceleration and displacement, alongside a 65% decrease in maximum drift, underscored the effectiveness of TMD deployment. Furthermore, the study demonstrated that distributing multiple TMDs along the height of the structure provided superior drift control. Notably, positioning TMDs within the upper one-third of the structure yielded the most pronounced improvements in acceleration, displacement, and maximum drift. Finally, the research indicates that the strategic incorporation of TMDs can significantly enhance the seismic resilience of structures. The results highlight the substantial benefits of TMDs in optimizing acceleration, displacement, and drift, thereby affirming their critical role in seismic design and retrofitting strategies. Full article

Brassica carinata is an important and native oilseed crop in Ethiopia. The seed oil from B.carinata attracts global attention for its various industrial applications, mainly due to its high erucic acid levels and its superior agronomic traits. Since the demand for high erucic acid from oilseed brassica has been increasing in the world market due to its wider applications in bio-industries, the breeding target of B. carinata has recently been focused on enhancing its erucic acid. Several high erucic acid B. carinata genotypes have been screened from the pre-breeding activities. Such genotypes, however, need to be tested for their stable performance, for their erucic acid level, and other desirable traits under different environments. The aim of this study was to identify high erucic acid B. carinata genotypes with stable performance in multiple desirable traits. Thirty-two B. carinata genotypes were grown in a randomized complete block design with three replications at three locations for two years. The genotypes were evaluated for nine desirable traits related to seed oil quality (erucic acid and oil content), seed yield, and other agronomic traits. The results showed that the proportion of genotype by environment interaction (GEI) was clearly observed in erucic acid, which led to a stability and mean performance analysis for selecting the most stable and best-performing genotypes for the desired traits. For such an analysis, we used the multi-trait stability index (MTSI) along with the weighted average of absolute score BLUPs (WAASB). As revealed from the MTSI, five genotypes (G13, G18, G10, G22 and G5) were identified as the most stable in erucic acid, oil content, seed yield, and other agronomic traits. The selected genotypes showed on average 45.7% erucic acid, 3185 kg ha⁻¹ seed yield and 45.1% oil content with 4.3%, 25.8% and 6.9% positive selection gain, respectively. The negative selection gain of phenological traits and the plant height of the selected genotypes revealed their early maturity and their lower probability of being affected by lodging. Our findings demonstrated MTSI can be used to select high erucic acid B. carinata with a set of desirable traits, which would facilitate breeding efforts in developing novel and high erucic acid B. carinata varieties. Our results also showed that MTSI is an effective tool for selecting genotypes across different environments due to its unique ability to select multiple traits simultaneously. Full article

Lettuce is an important cool-temperature crop, and its principal abiotic stress is low heat tolerance. Lettuce production has become more challenging in the context of global warming changes. Hence, the main objective of this research was to investigate the relationship between stability and lettuce heat tolerance. Field and greenhouse trials were run in 2015 (summer) and 2016 (fall and spring). The environments were composed of a combination of season and place (field, glass, and plastic greenhouse), and the assessed genotypes were BRS Leila and Mediterrânea, Elisa, Everglades, Simpson, and Vanda. Statistical analysis showed a significant effect (p < 0.05) of environments (E), genotypes (G), and the GEI. BRS Leila, Elisa, and BRS Mediterrânea showed the greatest means to the first anthesis in suitable environments (milder temperatures). Among these cultivars, BRS Mediterrânea was the most stable and adapted to hot environments. The environmental conditions studied in this research, mainly high temperatures, could become a reality in many lettuce-producing areas. Therefore, the results can help indicate and develop lettuce varieties with greater heat tolerance. Full article

Maize (Zea mays L.) is an important cereal and is affected by climate change. Therefore, the production of climate-smart maize is urgently needed by preserving diverse genetic backgrounds through the exploration of their genetic diversity. To achieve this, 96 maize inbred lines were used to screen for phenotypic yield-associated traits and grain quality parameters. These traits were studied across two different environments (Anand and Godhra) and polymorphic simple sequence repeat (SSR) markers were employed to investigate the genetic diversity, population structure, and trait-linked association. Genotype–environment interaction (GEI) reveals that most of the phenotypic traits were governed by the genotype itself across the environments, except for plant and ear height, which largely interact with the environment. The genotypic correlation was found to be positive and significant among protein, lysine and tryptophan content. Similarly, yield-attributing traits like ear girth, kernel rows ear⁻¹, kernels row⁻¹ and number of kernels ear⁻¹ were strongly correlated to each other. Pair-wise genetic distance ranged from 0.0983 (1820194/T1 and 1820192/4-20) to 0.7377 (IGI-1101 and 1820168/T1). The SSRs can discriminate the maize population into three distinct groups and shortlisted two genotypes (IGI-1101 and 1820168/T1) as highly diverse lines. Out of the studied 136 SSRs, 61 were polymorphic to amplify a total of 131 alleles (2–3 per loci) with 0.46 average gene diversity. The Polymorphism Information Content (PIC) ranged from 0.24 (umc1578) to 0.58 (umc2252). Similarly, population structure analysis revealed three distinct groups with 19.79% admixture among the genotypes. Genome-wide scanning through a mixed linear model identifies the stable association of the markers umc2038, umc2050 and umc2296 with protein, umc2296 and umc2252 with tryptophan, and umc1535 and umc1303 with total soluble sugar. The obtained maize lines and SSRs can be utilized in future maize breeding programs in relation to other trait characterizations, developments, and subsequent molecular breeding performances for trait introgression into elite genotypes. Full article

Durum wheat is one of the main crops in the Mediterranean region, which is characterized as the hotspot of climate change, with large year-to-year weather fluctuations. Although chemical input reduction in agriculture is strongly demanded, as well as healthy food, there is still a lack of stable and high-yielding crop varieties specifically adapted for organic conditions. This study evaluates the performance of fifteen durum wheat varieties in terms of suitability for organic farming in central Italy and assesses the impact of the genotype–environment interaction (GEI) on productive and quality traits. Variety performance was evaluated in field experiments over four successive seasons. In addition, a genotypic diversity analysis of 38 microsatellites associated with traits important for organic farming was performed. The AMMI (additive main effects and multiplicative interaction) stability analysis revealed that the best and most stable genotype regarding quality traits, such as thousand-kernel weight, protein content, and test weight was the ancient variety, Senatore Cappelli. The most stable and high yield was determined for the Fuego, Iride, and Mv-Pelsodur genotypes. Moreover, SSR markers that could be used for plant breeding, targeting organic farming systems based on molecular markers and GEI results, were identified. Full article

Genotype by environment interaction (GEI) is a complex problem that complicates the barley selection and breeding process. The knowledge of the relationship between cereal phenology and climatic data is important for understanding GEI and the physiological pathways responsible for the interaction effect. The grain yield of twenty winter barley genotypes in six environments was observed. Factors influencing the variability were analyzed using a linear mixed model. The partial least squares regression (PLSR) model was applied to determine the most relevant environmental variables in certain stages of development that explained GEI effects. Biplot with environmental variables explained 43.7% of the GEI. The barley was generally the most sensitive to the environmental conditions (relative humidity, maximum temperature and its variation, sun hours, and precipitation) during the anthesis and filling stage (May) which caused GEI. Temperature variables did not show significance only in the vegetative phase. Different genotypes responded differently to environmental factors. Genotypes NS-525, NS-589, and J-103 were highlighted as widely adaptable, and Zaječar was a suitable and reliable location for yield testing. The GEI information presented in this paper can be useful in traditional plant breeding and future breeding programs through molecular research of crop developmental genes and examination of physiological processes in two-row barley. Full article

Non-destructive testing (NDT) is a crucial method for detecting damages in concrete structures. Structural damage can lead to functional changes, necessitating a range of damage detection techniques. Non-destructive methods enable the pinpointing of the location of the damage without causing harm to the structure, thus saving both time and money. Damaged structures exhibit alterations in their static and dynamic properties, primarily stemming from a reduction in stiffness. Monitoring these changes allows for the determination of the failure location and severity, facilitating timely repairs and reinforcement before further deterioration occurs. A systematic approach to damage detection and assessment is pivotal for fortifying structures and preventing structural collapse, which can result in both financial and human losses. In this study, we employ image processing to categorize damaged beams based on their crack growth and propagation patterns. We also utilize support vector machine (SVM) and k-nearest neighbor (KNN) methods to detect the type, location, and extent of failures in reinforced concrete beams. To provide context and relevance for the laboratory specimens, we will compare our findings to the results from controlled experiments in a controlled laboratory setting. Full article

Shear strength prediction in FRP-bonded reinforced concrete beams is crucial for ensuring structural integrity and safety. In this extensive investigation, advanced machine learning algorithms are harnessed to achieve precise shear strength predictions for rectangular RC beams reinforced with FRP sheets. The aim of this research is to enhance the accuracy and reliability of shear strength estimation, providing valuable insights for the design and assessment of FRP-strengthened structures. The primary contributions of this study lie in the meticulous comparison of various machine learning algorithms, including Xgboost, Gradient Boosting, Random Forest, AdaBoost, K-nearest neighbors, and ElasticNet. Through comprehensive evaluation based on predictive performance, the most suitable model for accurately estimating the shear strength of FRP-reinforced rectangular RC beams is identified. Notably, Xgboost emerges as the superior performer, boasting an impressive R² value of 0.901. It outperforms other algorithms and demonstrates the lowest RMSE, MAE, and MAPE values, establishing itself as the most accurate and reliable predictor. Furthermore, a sensitivity analysis is conducted using artificial neural networks to assess the influence of input variables. This additional research facet sheds light on the critical factors shaping shear strength outcomes. The study, as a whole, represents a substantial contribution to advancing the development of accurate and dependable prediction models. The practical implications of this work are far-reaching, particularly for engineering applications in the realm of structures reinforced with FRP. The findings have the potential to transform the approach to the design and assessment of such structures, elevating safety, efficiency, and performance to new heights. Full article

One of the most important effects of climatic changes is increasing temperatures and expanding water deficit stress in tropical and subtropical regions. As the fourth most important cereal crop, barley (Hordeum vulgare L.) is crucial for food and feed security, as well as for a sustainable agricultural system. The present study investigates 56 promising barley genotypes, along with four local varieties (Norooz, Oxin, Golchin, and Negin) in four locations to identify high-yielding and adapted genotypes in the warm climate of Iran. Genotypes were tested in an alpha lattice design with six blocks, which were repeated three times. Traits measured were the number of days to heading and maturity, plant height, thousand kernels weight, and grain yield. A combined analysis of variance showed the significant effects of genotypes (G), environments (E), and their interaction (GEI) on all measured traits. Application of the additive main-effect and multiplicative interaction (AMMI) model to the grain yield data showed that GEI was divided into three significant components (IPCAs), and each accounted for 50.93%, 30.60%, and 18.47%, respectively. Two selection indices [Smith–Hazel (SH) and multiple trait selection index (MTSI)] identified G18, G24, G29, and G57 as desirable genotypes at the four test locations. Using several BLUP-based indices, such as the harmonic mean of genotypic values (HMGV), the relative performance of genotypic values (RPGV), and the harmonic mean of the relative performance of genotypic values (HMRPGV), genotypes G6, G11, G22, G24, G29, G38, G52, and G57 were identified as superior genotypes. The application of GGE analysis identified G6, G24, G29, G52, and G57 as the high-yielding and most stable genotypes. Considering all statistical models, genotypes G24, G29, and G57 can be used, as they are well-adapted to the test locations in warm regions of Iran. Full article