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Background: The genetic yield potential of groundnut (Arachis hypogaea L.) has been continuously challenged by several diseases including early leaf spot (ELS). Methods: In the current study, we evaluated groundnut mini core collections under artificial and natural disease epiphytotics in six environments to identify stable elite sources for ELS resistance and pod yield. Mixed model analysis was done to adequately capture the variance component as a result of genotype (G), environment (E) and G x E interaction (GEI). Result: Highly significant (p <0.001) effects for G and GEI on ELS and pod weight were observed. The parametric and non-parametric stability models ranked the genotype differently for their stability to ELS. The GGE biplot identified ICG 1519 as a stable genotype for the ELS resistance. For pod weight, ICG 8896 and ICG 7897 were consistently stable from all the stability models including the GGE biplot. ICG 9449 and ICG 4540 were identified as stable genotypes for both ELS and pod weight. These elite sources of ELS resistance identified in the current study will be useful in the development and deployment of groundnut varieties with resistance to ELS and high pod yielding potentials. [ABSTRACT FROM AUTHOR]

Increasing the maize production capacity to ensure food security is still the primary goal of global maize planting. The purpose of this study was to evaluate genotypes with high yield and stability in summer maize hybrids grown in the Huanghuaihai region of China using additive main effects and multiplicative interaction (AMMI) analysis and best linear unbiased prediction (BLUP) technique. A total of 18 summer maize hybrids with one check hybrid were used for this study using a randomized complete block design (RCBD) with three replicates at 74 locations during two consecutive years (2018–2019). A three-way analysis of variance (ANOVA) and an AMMI analysis showed that genotype (G), environment (E), year (Y) and their interactions were highly significant (p < 0.001) except G × E × Y for all evaluated traits viz., grain yield (GY), ear length (EL), hundred seed weight (HSW) and E × Y for hundred seed weight. The first seven interaction principal components (IPCs) were highly significant and explained 81.74% of the genotype by environment interaction (GEI). By comparing different models, the best linear unbiased prediction (BLUP) was considered the best model for data analysis in this study. The combination of AMMI model and BLUP technology to use the WAASB (weighted average of absolute scores from the singular value decomposition of the matrix of BLUP for GEI effects generated by linear mixed model) index was considered promising for similar research in the future. Genotypes H321 and Y23 had high yield and good stability, and could be used as new potential genetic resources for improving and stabilizing grain yield in maize breeding practices in the Huanghuaihai region of China. Genotypes H9, H168, Q218, Y303 and L5 had narrow adaptability and only apply to specific areas. The check genotype Z958 had good adaptability in most environments due to its good stability, but it also needs the potential to increase grain yield. Significant positive correlations were also found between the tested agronomic traits.

We study the efficiency properties of equilibria in general equilibrium economies with incomplete financial markets. We focus the analysis on economies with a finitely large number of agents and initial endowments close to a Pareto optimal one. Consider an economy with a Pareto optimal equilibrium allocation and a sufficiently small, but nonzero, amount of trade. If the matrix of the derivatives of the indirect utility functions with respect to prices has maximal rank (equal to the number of non-numeraire commodities), then locally all the economies have a unique, constrained Pareto-efficient equilibrium. To the contrary, pick a Pareto optimal initial endowment. Then, locally, there are open sets of economies with a unique, constrained Pareto-efficient equilibrium and other open sets of economies with a unique constrained Pareto-inefficient equilibrium. The key step of the analysis is based on the observation that, in some small open neighborhood of an economy with a Pareto optimal initial endowment, we can partially characterize constrained Pareto optimal equilibria as the optimal solution of a well-defined, strictly concave optimization problem. [ABSTRACT FROM AUTHOR]

Semi-empirical tight-binding-based quantum chemistry method (GFN2-xTB) has been done to study the influence of single metal (Li, Mg, Al, Fe, Ag) doping on the electronic and optical properties of monolayer corrugated graphitic carbon nitride (g-C3N4). Based on the calculated formation energy and population analysis, we showed that the metal atom is chemically bound to the g-C3N4 surface. The metal doping reduces the vertical ionisation potential and raises the electron affinity and Lewis acidity of g-C3N4. The metal-doped g-C3N4 systems have lower band gap than that of pristine g-C3N4 which is attributed to the electron donating from the metal atom to g-C3N4. Analysis of the lowest unoccupied molecular orbital and the highest occupied molecular orbital shows that for the Fe- and Ag-doped g-C3N4 systems the separation of photo-generated electron–hole pairs is efficient, resulting in the enhancement of photo-catalytic activity. [ABSTRACT FROM AUTHOR]

Copyright of Terra Latinoamericana is the property of Sociedad Mexicana de la Ciencia del Suelo A.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sunflower is the most important oil crop ranked as fourth edible oil in the world. The study was conducted in Northern Ethiopia during 2017-2019 cropping seasons using randomized completely block design with three replications. The objective was to decipher the genotype by environment interaction (GEI) in multi-environment trials (MET) and identify adaptable sunflower genotypes. Combined ANOVA, AMMI ANOVA and Eberhart and Rusell regression were analyzed, and GGE bi-plots, AMMI1 and AMMI2 bi-plots, Principal component Analysis (PCA), multi-trait genotype-ideotype distance index (MGIDI), correlation network plot for sunflower traits were sketched. AMMI stability measures, Best Linear Unbiased Prediction (BLUP) based indexes; parametric and non-parametric statistics were computed using R-statistical software. In the AMMI ANOVA the main effects of the environment (E) (54.18 % SS), genotype (G) (16.9 % SS) and GEI (23.50 % SS) were significant (p < 0.001). The genotypic Likely-hood Ratio Test revealed significant for all traits. The AMMI bi-plot and the GGE bi-plots selected G10 and G2 as the most adaptable genotypes. CV, HMGV, RPGV, HMRPGV, P i , GAI, KRS, S (3) and S (6) also identified G10 as the most stable genotype. Based on the MGIDI, G10 (MGIDI = 1.45) and G5 (MGIDI = 2.19) are selected and these genotypes are recommended for further cultivation in Tigray., Competing Interests: The authors are composition of researchers and instructor in different disciplines like crop production and protection. These authors contributed in different parts of the research planning, implementation, data collection, data analysis, manuscript drafting and editing. Every member contributed in the research is included in this manuscript. Hence, the authors declare that there are no conflicts of interest. Furthermore, this investigation received no specific fund from any funding agency, commercial or not-for-profit sectors., (© 2024 The Authors. Published by Elsevier Ltd.)

Each person describes unique patterns during gait cycles and this information can be extracted from live video stream and used for subject identification. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. In this paper, a method to enhance the appearance-based gait recognition method by also integrating features extracted from depth data is proposed. Two approaches are proposed that integrate simple depth features in a way suitable for real-time processing. Unlike previously presented works which usually use a short range sensors like Microsoft Kinect, here, a long-range stereo camera in outdoor environment is used. The experimental results for the proposed approaches show that recognition rates are improved when compared to existing popular gait recognition methods. [ABSTRACT FROM AUTHOR]

Determining the amount of genotype by environment interaction (GEI) is an important step in identifying high yielding and stable genotypes for cultivar development. Keeping in view the significance of genotype and environment, a multi environment study was conducted to evaluate the response of genotypes to different cropping systems and environments. The performance of vegetative growth traits of the 40 exotic bread wheat lines was assessed across seven different environments during 2015-16 and 2016-17 growing seasons. The trials were conducted using RCB design at four locations for two years; Research Farm, of the University of Agriculture, Peshawar; Agriculture Research Station, Buner; Agriculture Research Station, Baffa, Mansehra; and Barani Agriculture Research Station, Jarma, Kohat (one year only). Data were recorded on leaf area, plant height, biological yield and straw yield. Highest plant height was recorded for G02, G31 and G11. The exotic lines included in the study generally had more plant height in Peshawar as compared to other locations. The highest maximum mean for leaf area was observed for G17 and CSA while minimum was observed for G03 followed by G33 and G25. Among the seven environments, genotypes on average produced larger flag leaves having maximum leaf area in Peshawar while lowest was observed in Mansehra. For straw yield of the 40 genotypes across seven environments, the highest straw yield of 12547 kg ha-1 was produced at Mansehra in 2016-17 and the lowest yield of 2440 kg ha-1 was recorded at Buner in 2017-18. Overall, the genotypes included in the study had more biological yield in Mansehra as compared to other locations. Line G06 and G17 produced maximum biological yield. The lines with desirable characteristics can be used in hybridization programs to combine these characteristics in single line, or to transfer them to other high yielding varieties. [ABSTRACT FROM AUTHOR]

A pesar de la numerosa información científica sobre cambio climático mundial, no existen estudios que demuestren los efectos sobre la biodiversidad a menor escala. Por ello, utilizando 19 variables bioclimáticas, cinco de radiación solar, altitud, software especializado (MaxEnt) y coordenadas geográficas de presencia de cinco especies de flora categorizada verificadas en campo se modelaron sus nichos ecológicos actuales y proyectados a los cuatro escenarios futuros de emisiones (2050 y 2070). Se demostró que el de Buddleja coriacea disminuirá en más del 80 % por las variaciones futuras de precipitación y temperatura consecuencia del cambio climático, mientras que Carica candicans, Haplorhus peruviana, Kageneckia lanceolata y Weberbauerella brongniartioides se mantendrán e incrementarán, deduciéndose que sus categorías de amenaza derivan principalmente por actividad antropogénica. Estos modelos mejoran significativamente la comprensión del funcionamiento ecosistémico, otorgando información útil para diseñar políticas y acciones de conservación que orienten la gestión territorial hacia la estrategia de adaptación basada en ecosistemas.

Development of high-yielding dual-purpose sweetpotato (Ipomoea batatas [L.] Lam) genotypes is useful for cultivation in mixed crop-livestock farming systems. The objective of this study was to determine genotype-by-environment interaction (GEI) for storage root yield (SRY), vine yield (VY), root: vine ratio (RVR), total above-ground and below-ground biomass (TABGB) and root dry matter content (RDMC) among newly developed sweetpotato clones to select promising dual-purpose clones. Forty-five sweetpotato genotypes, of which 32 were newly developed candidate clones, were evaluated across six diverse environments in Rwanda. Additive main effects and multiplicative interaction (AMMI) and genotype and genotype-by-environment (GGE) biplot analyzes were used to analyze GEI. AMMI analysis revealed a relatively larger proportion of the GEI effect for SRY (31.21%), VY (37.17%), RVR (41.73%), TABGB (35.24%) and RDMC (36.23%) than for genotype and environmental effects, suggesting differential response of test genotypes across environments for the assessed traits. GGE-biplot analysis explained total variation of 72.98% for SRY, 83.26% for VY, 74.03% for RVR, 78.82% for TABGB and 56.78% for RDMC. Clone 21 with enhanced SRY and TABGB, and clone 42 for SRY had broad adaptation. For better VY, clone 733 was the best performer in environments E2 and E6 which are designated as mega-environment (MGE) 2, and clone 321 in E1, E3 and E4 (MGE 3). For RVR, clone 577 had best adaptation in E3, E4 and E5 (MGE 1), while clone 460 in E2 and E6 (MGE 2). Clones 42 and 381 were best performers for RDMC with specific adaptation in environments E3 and E1, respectively. [ABSTRACT FROM AUTHOR]

This study consists of a systematic review on the impacts generated by economic activities, wastewater treatment plants (wwtps) processes, and climatic factors (temperature and precipitation) on greenhouse gases (ghg) emissions, focusing first on the Bogotá River Basin and, subsequently, conducting a comparative analysis with Valle de México (Mexico) and Rímac River (Peru) basins. Since the main pollution source of the basins is wastewater discharges, wwtps have been implemented for mitigation. This research presents the diagnosis of the current state of wwtps, an analysis of climatological and meteorological factors, in addition to the possible health effects, applying the 2006 ipcc methodology. In the case of the Bogotá River Basin, the largest generation of ghgs comes from CH4 (82.163%) and N2O (76.059%), being its middle basin the most affected, according to results: mixed treatment (45.1%) with great influence on climate change; economic industrial activities with high impact (70.17%); and climatic factors such as low temperatures and average rainfall generating high concentration of GHG. In regard to the Valley of Mexico basins, the lower basin shows greater impact, as follows: aerobic treatment (62.11%), economic trading activities (56.10%), and high temperatures and rainfall. From the Rímac River Basin, in Peru, the middle basin reports greater impact, as follows: mixed treatment (49.4%), economic trading activities (59.2%), and climatic factors such as rainfall and high temperatures. The validation of the previous data was confirmed via the national ghg inventory reported by each country, showing that the treatment used, the type of economic activity and climate are variables that affect ghg emissions. It was also found that cardiovascular diseases are the most recurrent health issues caused by ghg emissions. [ABSTRACT FROM AUTHOR]

Many spoken Mandarin varieties allow for the oscillation of the co-verb gěi 'give' between the pre- and post-verbal positions. Much attention has been directed to semantic and syntactic factors that constrain its placement. However, language external factors remain under-researched. This study examines sociolinguistic including regional influences on the variable placement of the co-verb gěi in spoken Mandarin. Data is collected at both production and perception levels to investigate the regional and social distributions of the gěi-phrase. At the production level, the data suggests that substrate influences from a speaker's home vernacular play a crucial role. Results at the perception level remain inconclusive, and more research is required to unpack the complex role of sociolinguistic factors in such variation. [ABSTRACT FROM AUTHOR]

Copyright of Revista Mexicana de Ciencias Agrícolas is the property of Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This document exposes the current numbers of emissions generated by greenhouse gases (ghg). Likewise, a solution is sought for the elimination or reduction of these numbers. It focuses, specifically, on the air pollution rates, regarding urbanization and education as a starting point, as well as the conditions of modern life. From a current perspective, it analyzes some environmental policies, which have been applied by different governments in order to generate change. [ABSTRACT FROM AUTHOR]

A critical comparison of stability models is essential to give an insight to breeders for developing relatively stable wheat cultivars. A multi-environment trial (MET) was conducted to assess the pattern of genotype by environment interaction (GEI) effects on yield using multiple stability models viz. Additive Main effect and Multiplicative Interaction (AMMI), GGE biplot analysis and stability parameters. Eighty-one wheat genotypes were evaluated during three consecutive years (2013/14, 2014/15 and 2015/16) across nine environments (sites x year combination) in Khyber Pakhtunkhwa, Pakistan. Graphical stability approaches such as AMMI and GGE provided almost similar results for identifying the high-yielding and stable wheat genotypes. The AMMI analysis identified G-58 and G-79, whereas GGE biplot identidified G-79 as the most stable and high yielding genotype. Numerical stability parameters like Eberhart and Russell's model and Francis coefficient of variation (CV) declared G-79 as top-ranked genotype while Shukla stability value (σi²) and Wrick' s ecovalence (Wi) identified G-80 (check cultivar Janbaz), G-52 and G-79 as leading wheat genotypes based on grain yield. Spearman's rank correlation revealed significant positive correlations of AMMI stability value (ASV) with CV, σi² with Wi, and Wi with CV indicating that these parameters could invariably be used for identifying stable wheat genotypes depending upon the nature of the experiment, breeding material, and the complexity of data. Different stability models identified G-79 as high yielding and stable genotype and thus could be recommended for commercialization in the province of Khyber Pakhtunkhwa, Pakistan. Furthermore, stability parameters can supplement the use of AMMI and GGE biplot analysis to get more credible and reliable scrutiny of wheat genotypes in METs. [ABSTRACT FROM AUTHOR]

Despite the numerous scientific information on global climate change, there are no studies that show effects on biodiversity on a smaller scale. Therefore, using 19 bioclimatic variables, five solar radiation, altitude, specialized software (MaxEnt) and geographical coordinates of the presence of five species of categorized flora verified in the field, their current ecological niches were modeled and projected to the four future emission scenarios (2050 and 2070) showing that Buddleja coriacea will decrease by more than 80% due to future variations in precipitation and temperature due to climate change, while Carica candicans, Haplorhus peruviana, Kageneckia lanceolata and Weberbauerella brongniartioides will remain and increase, deducing that Its threat categories derive mainly from anthropogenic activity. These models significantly improve the understanding of ecosystem functioning, providing useful information to design conservation policies and actions that guide territorial management towards the Ecosystem-based Adaptation strategy. [ABSTRACT FROM AUTHOR]

Copyright of Bistua: Revista de la Facultad de Ciencias Básicas is the property of Facultad de Ciencias Basicas de la Universidad de Pamplona and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Wildfires destroy thousands of hectares of vegetation every year in Chile, a phenomenon that has steadily increased over time, both in terms of the number of fires and the area affected. Since 1985 until 2016 have occurred 1,476 wildfires severe in intensity (> 200 ha), that burned a total of about 1,243,407 ha of vegetation, and an average of 40,000 ha affected per year. Depending on the type and intensity of the fire, there are different levels of severity with which the fire affects the vegetation, a variation that is crucial for the estimation GEI in the event. The purpose of this research was to analyze the burn severity of Rufina wildfires occurred in 1999, in the VI Region of L. G. B. O’Higgins in Chile, south of the capital Santiago, using Landsat 5 TM and Landsat 7 ETM+ imagery, including in the analysis the estimated greenhouse gases emitted in relation to with the vegetation and burn severity. Burn severity was estimated through the Normalized Burn Ratio (dNBR) and GEI with the equation proposed by IPCC in 2006, which was adjusted with the combustion efficiency coefficients proposed by De Santis et al. (2010). The results show that around 16,783 ha were affected by fires of different severity and the native forest and tree plantations were affected by high severity. The ton of GEI for each level of burn severity and type of vegetation was estimated, being carbon dioxide (CO2 ) the main GEI emitted to the atmosphere in the fire. The highest emissions occurred in the areas of grasslands and scrublands, with high severity, with values ranging between 186 and 170 t/ha respectively

Each individual describes unique patterns during their gait cycles. This information can be extracted from the live video stream and used for subject identification. In appearance based recognition methods, this is done by tracking silhouettes of persons across gait cycles. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. When such sensors are used for gait recognition, existing RGB appearance based methods can be extended to get a substantial gain in recognition accuracy. In this paper, this is accomplished using information fusion techniques that combine features from extracted silhouettes, used in traditional appearance based methods, and the height feature that can now be estimated using depth data. The latter is estimated during the silhouette extraction step with minimal additional computational cost. Two approaches are proposed that can be implemented easily as an extension to existing appearance based methods. An extensive experimental evaluation was performed to provide insights into how much the recognition accuracy can be improved. The results are presented and discussed considering different types of subjects and populations of different height distributions. [ABSTRACT FROM AUTHOR]

The yield and protein performance in a soybean genotype result from its interaction with the prevailing environmental conditions. This makes selecting the best genotypes under varied target production environments more complex. This study’s objectives were to determine protein content and protein stability of 30 elite soybean genotypes in major soybean-growing areas of Uganda, assess the yield performance and stability in soybeans and determine the relationship between the protein content and grain yield in soybeans. The genotypes were planted in a randomized complete block design of three replications for six seasons across eight locations in Uganda. Genotype and genotype-by-environment (GGE) biplot analyses classified the test locations into three mega-environments for soybean protein and grain yields. Genotype NII X GC 20.3 had the highest mean protein content of 43.0%, and BSPS 48A-9-2 and BSPS 48A-28 were superior for the mean grain yield (1207 kg ha−1). Bulindi was the most discriminating and representative test environment for soybean yield. A weak and negative correlation (r = −0.1**, d.f. = 29) was detected between the protein content (%) and yield (kg ha−1). The highest-yielding genotypes BSPS 48A-9-2, BSPS 48A-31, and Nam II × GC 44.2 are recommended for further evaluation under farmers’ production conditions for selection and release as new soybean varieties in Uganda.

Finger millet (Eleusine coracana (L.) Gaertn. subsp. coracana ) production has become stagnant over the years and one of the possible ways to increase the production can be spread of widely adaptable high yielding cultivars. Five national finger millet cultivars were grown in randomized complete block design at ICAR-Vivekananda Institute of Hill Agriculture for six consecutive years to evaluate the grain yield stability. The grain yield data were subjected to AMMI and GGE biplot techniques for assessing the stability and patterns of GE interaction in finger millet National cultivars. The combined ANOVA showed that finger millet grain yield was significantly affected by environment, which explained 54.67% of the total treatment (G+E+GE) variation, whereas the G and GEI accounted for 10.38% and 34.96%, respectively. The partitioning of GEI sum of squares using AMMI analysis indicated that the first two PCAs were highly significant. The first IPCA axis (IPCA1) accounted for 50.3% of the G×E interaction sum of squares. The second IPCA axis accounted for 38.2% of the interaction sum of squares. Both represented a total of 88.5% variation. AMMI 1 biplot indicated the general adaptation of genotype HR 374 across the environments, whereas the other genotypes showed specific adaptation to one or other environments. GGE-biplot graphical analysis further confirmed the results and revealed that HR 374 as an ideal genotype in terms of high yield and stability followed by RAU 8 as desirable genotype. In our research, both of AMMI and biplot models were successful in assessing the performance of genotypes and the selection of best genotype was identical in both of them.

A field experiment was conducted during 2014 and 2015 using seven released triticale varieties in Wag-Lasta marginal high lands. Triticale (Triticosecale) is a hybrid of wheat (Triticum) and rye (Secale). The objective of the trial was to select adaptable and high yielding triticale variety for Wag-Lasta marginal high lands. Randomized complete block design with three replications and Plot size of 2.5 m length, 1.2 m width, 0.2 m row spaces were used. Phonological, agronomic and grain yield Data were taken. The combined analysis of variance showed a significant difference among the test varieties, location, and variety by location (GEI) interaction for most of the traits. Mean squares of Location was highly significantly (P

Genotype-by-environment interaction (GEI) is often a great challenge for breeders since it makes the selection of stable or superior genotypes more difficult. In order to reduce drawbacks caused by GEI and make the selection for wheat quality more effective, it is important to properly assess the effects of genotype, environment, and GEI on the trait of interest. In the present study, GEI patterns for the selected quality and mixograph traits were studied using the Additive Main Effects and Multiplicative Interaction (AMMI) model. Two biparental wheat populations consisting of 145 and 175 RILs were evaluated in six environments. The environment was the dominant source of variation for grain protein content (GPC), wet gluten content (WGC), and test weight (TW), accounting for approximately 40% to 85% of the total variation. The pattern was less consistent for mixograph traits for which the dominant source of variation has been shown to be trait and population-dependent. Overall, GEI has been shown to play a more important role for mixograph traits compared to other quality traits. Inspection of the AMMI2 biplot revealed some broadly adapted RILs, among which, MG124 is the most interesting, being the prevalent “winner” for GPC and WGC, but also the “winner” for non-correlated trait TW in environment SB10.

This paper investigates a novel method for gait gender recognition using a visual camera sensor. Numerous recent studies have focused on visual gait analysis involving a single gait energy image (GEI), yet these methods lose temporal movement information, which results in inferior accuracy under complex conditions. Therefore, in this paper, we introduce a new subGEI map by calculating from fewer frames than a gait cycle, extract the synthetic optical flow of multi subGEIs as temporal features, and utilize a two-stream CNN to combine use the GEI and the optical flow information for further gait analysis. In addition, this paper adopts three kinds of CNN networks, namely, VGG-16, Inception-V3, and a newly designed CNN and develops three types of subGEIs for comparison experiments. The experimental results on gait dataset CASIA show that: 1) the two-stream network produces better performance than that of the CNN-based methods; 2) the combination of temporal and spatial features significantly improves visual gait performance; and 3) the visual based approaches have great advantages than traditional sensors-based methods. [ABSTRACT FROM AUTHOR]

Genotype by environment (GE) interaction complicates selection process of desirable genotypes. Therefore, newly developed germplasm or genotypes needs to be evaluated in multi-environment trials (METs) to know yield potential and adaptability. This experiment was designed to interpret GE interaction using 55 F5:8 bread wheat recombinant inbred lines (RILs) with five checks. Experimental material was planted using alpha lattice design in two replicates at Peshawar (E1and E5), Kohat (E2 and E6), Sarai Naurang (E3 and E7) and Dera Ismail Khan (E4 and E8) (Khyber Pakhtunkhwa) during 2014/15 and 2015/16. Locations in each year were considered as independent environments. Pooled ANOVA revealed significant interaction due to GE for days to heading, days to maturity, grain filling duration, grain growth rate, grains spike-1, 1000-grain weight and grain yield. Averaged over eight environments, wheat RIL's 44, 55, and 25 were desirable for days to heading, days to maturity, grain filling duration, grain growth rate, grains spike-1 and grain yield. Mean grain yield ranged from 1613 to 2471 kg ha-1 across environments. Among the tested environments, Sarai Naurang (E3 and E7) was identified as highly productive environment. The RIL25 had superior performance for various traits. Wheat RIL25 produced maximum grain yield in E2 (3123 kg ha-1), E3 (3665 kg ha-1) and E7 (3256 kg ha-1), thus emerged as promising line with wider adaptation. Conversely, RIL24 had higher grain yield in E1 (2800 kg ha-1); RIL1 in E4 (2884 kg ha-1); RIL58 in E5 (2370 kg ha-1); RIL7 in E6 (3200 kg ha-1) and RIL53 in E8 (2339 kg ha-1) suggesting their specific adaptability to respective environments. Grain yield was significantly correlated with days to heading, grain growth rate and grains spike-1. Keeping in view the significance of GE interaction, it would be worthwhile to carryout stability analysis for identifying high yielding RILs with suitable stability. [ABSTRACT FROM AUTHOR]

A set of 286 recombinant inbred lines (RILs) along with the parents and a popular wheat variety in India were grown for two consecutive years at three locations belonging to the two major wheat growing zones of India and evaluated for four grain quality traits. Rare recombinants with high trait value appeared for protein content (PC), thousand-kernel weight (TKW), sedimentation value (SV), and kernel hardness (KH). The magnitude of environmental effects was more pronounced than genotypic effects and genotype-environment interaction (GEI). The cumulative contribution of environment and GEI components to the total variance was highest in the expression of PC followed by TKW, SV, and KH. The top five percent (14 RILs) of genotypes with high trait value were subjected to Eberhart and Russell (1966) (ER), genotype and genotype-environment (GGE) and additive main effects and multiplicative interaction (AMMI) stability models. Five RILs were identified as stable in all the three stability models. RIL61 with 38.8%, RIL101 with 8.9%, RIL226 with 26.1% superiority over check variety were the most stable genotypes in all the three stability models for PC, TKW and KH, respectively. RIL113 was found to be stable genotype in ER and GGE models, whereas, RIL231 was the most stable genotype in AMMI and GGE models in the expression of SV. These common stable genotypes with high trait value identified through ER, AMMI and GGE models could be potential donors in active breeding programs to develop high yielding wheat varieties with improved PC, TKW, SV and KH. [ABSTRACT FROM AUTHOR]

The uniformity of genotypes are significant for crop breeding program decisions to improve new varieties. The AMMI (Additive main effects and multiplicative interaction) analysis and Genotype x Environment Interaction (GEI) is make to estimation grain yield and understands GxE interaction patterns by researches as differential ranking of variety yields in multi-environment trials. Therefore, fifteen barley advanced line and six national cultivars and four foreign varieties (registered in abroad) were used in the study. The experiments were performed according to a complete randomized block design with four replications at five environments during two years. The stability and superiority of genotypes for yield and other traits were determined using AMMI and GGE biplot analysis. Factors (G, GE, and GEI) were found to be highly significant (P < 0.01) for grain yield. AMMI analysis indicated that the major contributions to treatment sum of squares were environments (98.52%), GE (0.45%) and genotypes (1.02%), respectively, suggesting that grain yield of genotypes were effected environmental conditions. The GGE biplot indicated that PCA 1 axes (Principal component) was significant as P<0.01 and supplied to 49.36% of complete GxE interaction. The AMMI indicated that G8 and G23 desirable and stabile genotypes for grain yield in multienvironment. Moreover, E2 and E5 (irrigated environments) were high yielding, while E3 (drought stress) low yielding as forecast. On the other hand, GGE biplot indicated that three group were occurred among traits, first group (GY: grain yield, CC: crude cellulose, CD: cold damage), second group (PC: , HW: hectoliter weight, TGW: thousand grain weight, SH: seed humidity), third group (LOD: lodging, PH: plant height, HT: heading time). Moreover: the study showed that G3, G6, G7, G8, G13 and G21 were the best genotypes both grain yield and other traits. The results of AMMI model and GGE biplot indicated that G8 is suitable to recommend for release and G23 desirable origin for yield stability and G7 valuable source for quality to use in barley breeding program. [ABSTRACT FROM AUTHOR]

The development of stable and adaptable new cultivars are based only on positive results obtained from the interaction between the genotype and the environment. Therefore, the study aimed to test the stability and general adaptability of promising barley lines in terms of grain yield and traits in multi-environments. For this purpose, twelve barley genotypes were used in the study. The trials were carried out with four replications in a random design at seven environments in years 2012-13 and 2013- 14. The superior and stable genotypes were identified with GGE biplot and AMMI (Additive main effects and multiplicative interaction) models. The AMMI analysis showed that the major treatment sum of squares was affected by environments (80.6%), GE (14.0%) and genotypes (5.4%), respectively. On the other hand, the first two principal component axes (PCA 1 and PCA 2) contributed to the complete interaction with 88.1%, whereas, PCA 3 and PCA 4 axes only with 12.0%. The GGE biplot indicated that G4 is adaptable for all environments, while Altikat, G2 and G3 showed specific adaptation to E1, E3 and E5, G6, G7 and G8 to E6, respectively. According to both techniques, G2, G3, G6, G7, G8 and Altikat were the best genotypes with high yield, whereas G4 was the best with high yield, and stable and general adaptation. The results of biplot indicated that G4 (ARUPO /K8755//MORA/3/CERISE/SHYRI//ALEL I/4/CANELA/5/HART-BAR) was recommended for release and it was released as HEVSEL in 2017. On the other hand; G7 and G6 were protected as genetic material to use as parent in breeding programs for yield stability and quality respectively. [ABSTRACT FROM AUTHOR]

The present study contains a data analysis that links the amount of organics in the wastewater treatment plant a in the basin of the Bogotá river and its incidence in the generation of greenhouse gases during the process of depuration. A series of technical consideration regarding the generations of greenhouse gases among different treatment plants are mentioned. Accordingly with the characteristics of the system employed in the wastewater treatment is determined which is the one generating a higher amount of emissions accounting the actual status of each plant. To achieve so, the level 1 methodology of the IPCC for greenhouse emissions is employed. Based on the results is determined which of the treatment systems used in the wastewater treatment plants are producing most of the greenhouse gases emissions. [ABSTRACT FROM AUTHOR]

The objective was to select productive and stable E. grandis families across contrasting sites in Brazil and Uruguay. Survival and growth of 130 open pollinated families were evaluated three years after planting. Survival ranged from 28 to 89% and mean annual increment from 20.5 to 48.4 m3ha- y-1. Low productivity in one site was attributed to unusually low rainfall and in the other, because of Cylindrocladium leaf disease. Heritability among families and the correlation family by environment were intermediate. Genetic pairwise correlations ranged from 0.03 to 0.81 across sites. The lowest genetic correlation between sites was observed where Cylindrocladium leaf disease occurs. Selection of top families across sites allowed selecting the best ones in each site. However, the effect of genotype-environment interactions was observed where climatic conditions are not adequate to the E. grandis, favoring the occurrence of Cylindrocladium disease. [ABSTRACT FROM AUTHOR]

A field experiment was conducted during 2014 and 2015 using seven released triticale varieties in Wag-Lasta marginal high lands. Triticale (Triticosecale) is a hybrid of wheat (Triticum) and rye (Secale). The objective of the trial was to select adaptable and high yielding triticale variety for Wag-Lasta marginal high lands. Randomized complete block design with three replications and Plot size of 2.5 m length, 1.2 m width, 0.2 m row spaces were used. Phonological, agronomic and grain yield Data were taken. The combined analysis of variance showed a significant difference among the test varieties, location, and variety by location (GEI) interaction for most of the traits. Mean squares of Location was highly significantly (P < 0.01) different for all traits considered except spike length. Varieties were also highly significantly (P < 0.01) different from each other except biomass. Mean squares for variety by location interaction effects were highly significant (p < 0.01) for days to heading, days to maturity and significant (p < 0.05) for spike length and biomass; indicated the differential response of the varieties to test location for these traits. However, grain yield was not significantly affected by interaction effect that varieties were performed nearly similar in these test locations. Based on the data analysis and field evaluation variety Logawshibo followed by Dilfakare gave the highest grain yields of 30.32 qt/ha and 27.43 qt/ha, respectively. Thus, these varieties are recommended for Wag-Lasta marginal highlands and similar environment of the region. Moreover, these varieties should be demonstrated and popularized for the users. [ABSTRACT FROM AUTHOR]

Copyright of Terra Latinoamericana is the property of Sociedad Mexicana de la Ciencia del Suelo A.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Different seed priming treatments are widely used in order to improve the nutritional status of wheat, as well as to improve its grain yield and yield- related traits. The present study aimed to evaluate the impact of seed priming with zinc oxide nanoparticles (ZnO NPs) on the yield related traits, such as, field emergence, plant height, spike length and grain yield per plant of four winter wheat genotypes (Triticum aestivum L.) during two vegetation seasons of 2018/2019 and 2019/2020. The seeds of each wheat genotypes were primed with different concentrations of ZnO NPs (0 mg L−1, 10 mg L−1, 100 mg L−1 and 1000 mg L−1) for 48 h in a dark box by continuous aeration and were sown in soil pots with 60–70% moisture content until full maturity. The additive main effects and multiplicative interaction (AMMI) models were used to study the genotype environment effects. The results indicated that the plants response to ZnO nanoparticles significantly increased all of the observed traits of the wheat, while its maximum rates reduced the traits of the wheat. The AMMI analysis revealed the very complex nature of the variation observed in the trial and showed the significant effect of the G×E interaction, in which the first main component was significant for all components.

Copyright of Archivos Latinoamericanos de Nutrición is the property of Sociedad Latinoamericana de Nutricion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

El metano (CH4) contribuye al cambio climático y su fuente antropogénica más importante es la fermentación entérica del ganado. A nivel continental el Panel Intergubernamental de Cambio Climático establece factores de emisión entérica de CH4 por defecto (Tier I); pero las decisiones y evaluación de la mitigación necesitan estimaciones más exactas. Así, el objetivo fue calcular la emisión de metano entérico de hatos de vacas Holstein-Friesian en Querétaro, México con datos de pesaje de leche (Tier II). Las curvas de lactación se modelaron con la función gamma incompleta por lactancia y hato. Un total de 11,092 lactancias del 2007 tuvieron un rendimiento ajustado a 305 días (d) de 9,985 kg leche. La curva de lactancia fue diferente entre primíparas y multíparas; la duración de la lactancia fue 365±21 días. Durante la lactancia, la emisión entérica para primíparas y multíparas a 305 días fue 165.7±3.8 y 181.6±8.3 kg CH4 vaca-1; a 365 d fue de 201.8±4.6 y 223.7±11.0 kg CH4 vaca-1 (P

In this paper, the effectiveness of biometric verification system has been achieved with template protection technique. Gait images of person are used; features are extracted from average gait images using Principle Component Analysis (PCA) for dimensionality reduction. At enrollment phase, three gait energy images (GEI)-upper images of person are used. The most reliable component for each person is needed to find and encoded it through Bose Chaudhuri Hocquenghem (BCH) encoding scheme to generate code word. A secrete binary string and hash value are generated for each person and stored in the database along with encoded template and position of strong reliable component. At verification phase, GEI-lower, slow and fast gait images are used. Those features are only selected from original features based on the position of strong reliable component and are used to generate code word and which is decoded using BCH decoder. The hash value is generated and compared with the hash value which was generated in the enrollment phase. If these two hash values are the same then the person is verified otherwise the person is not verified. Also comparison with other recently proposed studies is done to show the effectiveness of the proposed scheme.

The prevalence of Iron (Fe) defficiency in Indonesia is around 31 – 63.5 %. High Fe content rice lines had been developed to overcome the problem. This study was aimed to explore the effect of genotype (G) and genotype × environment inter-action (GEI) on yield of 21 high Fe content rice genotypes under 5 irrigated field environments. The research was conducted at DS 2011 in 2 locations and DS 2012 in 3 locations following randomized complete block design with three replications in each location. Combined analysis of variance showed genotype x environment inter-action at 1% probability level, where G and GEI captured totally 88.8% of total variability. There were two Mega-environments constructed, i.e. Mega-E1 that contained environments of trials in dry season 2011 (E4 and E5) with the winner of G12 (BP9474C-1-1-B) and Mega-E2 that contained environments of trials in dry season 2012 (E1, E2, and E3) with the winner of G3 (A69-1). E1 (Subang, DS 2012), E2 (Karawang, DS 2012), and E3 (Indramaru, DS 2012) had good discriminativeness and representasiveness for yield trait of high Fe content rice lines. Mean performance and stability of genotypes indicated that G3 (A69-1; average 6.72 t ha-1 ) was highly stable with high yield.