Search

Gait recognition is the process of identifying a person from a distance based on their walking patterns. However, the recognition rate drops significantly under cross-view angle and appearance-based variations. In this study, the effectiveness of the most well-known gait representations in solving this problem is investigated based on deep learning. For this purpose, a comprehensive performance evaluation is performed by combining different modalities, including silhouettes, optical flows, and concatenated image of the Gait Energy Image (GEI) head and leg region, with GEI itself. This evaluation is carried out across different multimodal deep convolutional neural network (CNN) architectures, namely fine-tuned EfficientNet-B0, MobileNet-V1, and ConvNeXt-base models. These models are trained separately on GEIs, silhouettes, optical flows, and concatenated image of GEI head and leg regions, and then extracted GEI features are fused in pairs with other extracted modality features to find the most effective gait combination. Experimental results on the two different datasets CASIA-B and Outdoor-Gait show that the concatenated image of GEI head and leg regions significantly increased the recognition rate of the networks compared to other modalities. Moreover, this modality demonstrates greater robustness under varied carrying (BG) and clothing (CL) conditions compared to optical flows (OF) and silhouettes (SF). Codes available at https://github.com/busrakckugurlu/Different-gait-combinations-based-on-multi-modal-deep-CNN-architectures.git [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In response to the growing population and increasing demand for cattle products, enhancing sorghum forage yield is essential for ensuring food security. This study aimed to identify stable genotypes with high forage yields and key yield traits for sorghum breeding programs. Ninety-five forage sorghum lines were evaluated under five distinct climatic conditions over two years (2020–2021), revealing significant genotype × environment interaction (GEI) effects for 14 agronomic traits. Two BLUP-based mixed model stability methods, weight average absolute score based on BLUP (WAASB) and the multi-trait stability index (MTSI), were employed for stability analysis. Three genotypes, G90 (424B), G80 (382B) and G3 (349B) were identified stable and high yielding for forage yield based on WAASB based methods. The MTSI, a novel simultaneous selection index, effectively selected genotypes based on multiple agro-morphological traits, except for the leaf-to-stem ratio. Genotypes G81, G90, G80, and G89 were identified as desirable based on the MTSI. The strength and weakness plot is highlighted as a valuable graphical tool for identifying and selecting genotypes based on trait strengths and weaknesses. Among these, G90 (424B) and G80 (382B) stood out as superior, excelling in both forage yield and early maturity, as determined by WAASB based methods and MTSI method. These genotypes warrant further comprehensive investigation across diverse environments and show significant potential for future breeding programs. [ABSTRACT FROM AUTHOR]

The current study was conducted at four different locations in India in order to evaluate the genetic diversity and stability of 38 genotypes of onion (Allium cepa L.) under various climatic circumstances. The genetic diversity was evaluated through cluster analysis which divided thirty-eight genotypes into two major clusters (cluster I and cluster II) each of which had 20 and 18 genotypes. As per Additive Main-effects and Multiplicative Interaction (AMMI) model, five genotypes from cluster I, namely Accession (Acc.) 1217, 1209, Bhima Shweta, 1639, and 1640 were shown to have greater adaptation, with regressive values of 0.14, 0.74, 1.51, 2.11, and 2.17, respectively and marketable yields ranging from 176.23–227.31 q/ha. The mean performance of genotypes in individual clusters revealed that the cluster I and cluster II genotypes showed maximum marketable yield, total yield and average bulb weight. Based on the findings of the assessment study, these stable genotypes could be utilised as parent plants in future breeding programmes to develop high-yielding onion varieties that not only showcase stability but also possess widespread adaptability, thereby enhancing overall onion crop performance. [ABSTRACT FROM AUTHOR]

Diverse rice germplasm comprising 112 genotypes was evaluated for yield traits across three environments. Pooled and environmentwise analysis of variance revealed heterogeneity in the data and significant environment interactions for all the yield traits. As per AMMI (additive main effects and multiplicative interaction) and GGE (genotype and genotype x environment interaction) biplots, the influence of environment was significant and varying on all the component yield traits including grain yield and was not significant in case of flowering date. Dry season at Maruteru in 2014–15 (E1) was the most discriminative and representative environment for favourable plant growth in terms of plant height, panicle number and panicle length. None of the environments represented ideal environment for the favourable expression of grain number while all the environments were equally informative for thousand grain weight and grain yield. Panicle number, grain number and thousand grain weight were contributing to grain yield across the environments. Three genotypes Panthdhan 12, Konark and Udaygiri were the most stable genotypes for grain yield with favourable combination of associated yield genes for all the traits, viz. 1000 grain weight, the number of grains per panicle, the number of filled grains per panicle, productive tillers and plant height with higher yield, and grouped in one cluster. Genotyping using previously reported markers revealed that favourable alleles of yield genes associated with the number of productive tillers were predominantly found followed by alleles for the number of grains/filled grains per panicle correlating with the superior phenotypic value of the respective trait. The information on association of yield stability with reported yield genes from this study is useful in marker-assisted breeding studies for yield improvement and can be confirmed with various sets of genotypes under multi-environment testing. The identified superior genotypes are potential components in future breeding programmes and the development of stable adaptable varieties. The present study suggests that yield stability could be effectively achieved with targeted improvement of component yield traits associated with favourable alleles. [ABSTRACT FROM AUTHOR]

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 (YSi) 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). [ABSTRACT FROM AUTHOR]

The Paris Agreement is an important milestone in international climate policy and the associated negotiations of the United Nations Climate Summit under the United Nations Framework Convention on Climate Change (UNFCCC). Article 6 of the Paris Agreement is a supplementary mechanism—regarding carbon markets—to reduce greenhouse gases and achieve the UNFCCC's main objective of stabilizing greenhouse gas concentrations. The 2021 United Nations Climate Summit in Glasgow (COP26) played a key role in the pursuit of this goal. Via an overview of Article 6 negotiations in past COP summits and a systematic review and qualitative meta‐synthesis of the literature leading up to COP26, I identify three arguments involving Articles 6.2, 6.4, and 6.8 behind the selected literature. I substantiate these arguments based on the meta‐evaluation of the existing literature and propose a three‐stage meta‐evaluation of the arguments on Article 6 (carbon markets) of the Paris Agreement in the context of COP26 to ascertain if the negotiations were successful in leading to requisite agreements on Article 6. I conclude that future qualitative research should focus on Articles 6.4 and 6.8 of the Paris Agreement and examine the outcomes of future climate debates to see how ideas will flow through the negotiations regarding these two articles to pursue the goal of the UNFCCC under the Paris Agreement. Related Articles: Jihyung Joo, Jihyung, Jouni Paavola, and James Van Alstine. 2023. "The Divergence of South Korea's Emissions Trading Scheme (ETS) from the EU ETS: An Institutional Complementarity View." Politics & Policy 51(6): 1155–73. https://doi.org/10.1111/polp.12566. Nunes Silva, Carlos. 2012. "Policy and Evidence in a Partisan Age: The Great Disconnect—By Paul Gary Wyckoff." Politics & Policy 40(3): 541–43. https://doi.org/10.1111/j.1747‐1346.2012.00363.x. Robles, Pedro, and Daniel J. Mallinson. 2023. "Catching up with AI: Pushing toward a Cohesive Governance Framework." Politics & Policy 51(3): 355–72. https://doi.org/10.1111/polp.12529. [ABSTRACT FROM AUTHOR]

Gait recognition, a biometric identification method, has garnered significant attention due to its unique attributes, including non-invasiveness, long-distance capture, and resistance to impersonation. Gait recognition has undergone a revolution driven by the remarkable capacity of deep learning to extract complicated features from data. An overview of the current developments in deep learning-based gait identification methods is provided in this work. We explore and analyze the development of gait recognition and highlight its uses in forensics, security, and criminal investigations. The article delves into the challenges associated with gait recognition, such as variations in walking conditions, viewing angles, and clothing as well. We discuss about the effectiveness of deep neural networks in addressing these challenges by providing a comprehensive analysis of state-of-the-art architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms. Diverse neural network-based gait recognition models, such as Gate Controlled and Shared Attention ICDNet (GA-ICDNet), Multi-Scale Temporal Feature Extractor (MSTFE), GaitNet, and various CNN-based approaches, demonstrate impressive accuracy across different walking conditions, showcasing the effectiveness of these models in capturing unique gait patterns. GaitNet achieved an exceptional identification accuracy of 99.7%, whereas GA-ICDNet showed high precision with an equal error rate of 0.67% in verification tasks. GaitGraph (ResGCN+2D CNN) achieved rank-1 accuracies ranging from 66.3% to 87.7%, whereas a Fully Connected Network with Koopman Operator achieved an average rank-1 accuracy of 74.7% for OU-MVLP across various conditions. However, GCPFP (GCN with Graph Convolution-Based Part Feature Polling) utilizing graph convolutional network (GCN) and GaitSet achieves the lowest average rank-1 accuracy of 62.4% for CASIA-B, while MFINet (Multiple Factor Inference Network) exhibits the lowest accuracy range of 11.72% to 19.32% under clothing variation conditions on CASIA-B. In addition to an across-the-board analysis of recent breakthroughs in gait recognition, the scope for potential future research direction is also assessed. [ABSTRACT FROM AUTHOR]

The current study investigates how genotype by environment interaction (GEI) complicates superior cultivar selection in onion breeding. Employing a randomized block design with three replications, we assessed GEI effects on onion yield and identified adaptable, stable genotypes. The sensitivity of onion genotypes to environmental changes significantly influences yield and quality, necessitating adaptability across diverse climatic conditions. We conducted multilocation trials evaluating 28 onion genotypes across four distinct locations, each representing varying environmental factors. Through phylogenetic analysis, genotypes were categorized into four clusters: cluster I (3), cluster II (4), cluster III (15), and cluster IV (6). Rigorous assessment of yield performance, employing additive main effects and multiplicative interaction models, particularly the AMMI model, revealed five genotypes demonstrating remarkable stability and potential across diverse environmental conditions: RO-1626, RO-1623, RO-1639, RO-1625, and RO-1627. Notably, genotypes from cluster II exhibited the highest marketable yield (277.06 q/ha) and total yield (295.66 q/ha), indicating adaptability to varied environmental conditions. These findings hold promise for breeding high-yielding onion varieties resilient to diverse environments, ensuring stability, adaptability, and quality in cultivation.

The research objective is to explore Chile's entrepreneurial landscape by assessing individual characteristics and institutional factors through a 'pillars' framework and compare it against Colombia, and Brazil; to identify socioeconomic, individual, and institutional differences using the Global Entrepreneurship Index (GEI); to apply bottleneck approach to highlight areas requiring policy intervention. GEI features individual and institutional stage variables in a method where every variable collaboratively interacts, incorporating 14 foundational elements and three sub - indexes: attitudes, abilities, and aspirations. Ranking 18th on the GEI globally and the best in Latin America, Chile excels in key entrepreneurial pillars, showcasing strengths in innovation and a robust entrepreneurial culture. Brazil closely rivals Chile in competition and networking, emphasizing political and economic influence. Colombia surpasses Chile in internationalization and growth-stimulating policies but faces challenges like historical conflicts and wealth distribution. This study identified areas where immediate policy intervention may be necessary by examining Chiles's entrepreneurial ecosystem. The Penalty for Bottleneck (PFB) technique identif ied the weakest pillars highlighting process innovation, competition, and internationalization. The primary component identified as a bottleneck for resource allocation is Process Innovation, accounting for 73% of the allocation, followed by Competition at 23%. The findings show that allocating more resources to process innovation may improve greatly the overall GEI score. [ABSTRACT FROM AUTHOR]

Context. Micronutrient enrichment of pearl millet (Pennisetum glaucum (L.) R.Br.), an important food source in arid and semi-arid Asia and Africa, can be achieved by using stable genotypes with high iron and zinc content in breeding programs. Aims. We aimed to identify stable expression of high grain iron and zinc content in pearl millet lines across environments. Methods. In total, 29 genotypes comprising 25 recombinant inbred lines (RILs), two parental lines and two checks were grown and examined from 2014 to 2016 in diverse environments. Best performing genotypes were identified through genotype + genotype × environment interaction (GGE) biplot and additive main-effects and multiplicative interaction (AMMI) model analysis. Key results. Analysis of variance showed highly significant (P < 0.01) variations. The GGE biplot accounted for 87.26% (principal component 1, PC1) and 9.64% (PC2) of variation for iron, and 87.04% (PC1) and 6.35% (PC2) for zinc. On the basis of Gollob's F validation test, three interaction PCs were significant for both traits. After 1000 validations, the real root-mean-square predictive difference was computed for model diagnosis. The GGE biplot indicated two winning RILs (G4, G11) across environments, whereas AMMI model analysis determined 10 RILs for iron (G12, G23, G24, G7, G15, G13, G25, G11, G4, G22) for seven for zinc (G14, G15, G4, G7, G11, G4, G26) as best performers. The most stable RILs across environments were G12 for iron and G14 for zinc. Conclusions. High iron and zinc lines with consistent performance across environments were identified and can be used in the development of biofortified hybrids. Implications. The findings suggest that AMMI and GGE, as powerful and straightforward techniques, may be useful in selecting better performing genotypes. [ABSTRACT FROM AUTHOR]

Gait recognition, a biometric identification method, has garnered significant attention due to its unique attributes, including non-invasiveness, long-distance capture, and resistance to impersonation. Gait recognition has undergone a revolution driven by the remarkable capacity of deep learning to extract complicated features from data. An overview of the current developments in deep learning-based gait identification methods is provided in this work. We explore and analyze the development of gait recognition and highlight its uses in forensics, security, and criminal investigations. The article delves into the challenges associated with gait recognition, such as variations in walking conditions, viewing angles, and clothing as well. We discuss about the effectiveness of deep neural networks in addressing these challenges by providing a comprehensive analysis of state-of-the-art architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms. Diverse neural network-based gait recognition models, such as Gate Controlled and Shared Attention ICDNet (GA-ICDNet), Multi-Scale Temporal Feature Extractor (MSTFE), GaitNet, and various CNN-based approaches, demonstrate impressive accuracy across different walking conditions, showcasing the effectiveness of these models in capturing unique gait patterns. GaitNet achieved an exceptional identification accuracy of 99.7%, whereas GA-ICDNet showed high precision with an equal error rate of 0.67% in verification tasks. GaitGraph (ResGCN+2D CNN) achieved rank-1 accuracies ranging from 66.3% to 87.7%, whereas a Fully Connected Network with Koopman Operator achieved an average rank-1 accuracy of 74.7% for OU-MVLP across various conditions. However, GCPFP (GCN with Graph Convolution-Based Part Feature Polling) utilizing graph convolutional network (GCN) and GaitSet achieves the lowest average rank-1 accuracy of 62.4% for CASIA-B, while MFINet (Multiple Factor Inference Network) exhibits the lowest accuracy range of 11.72% to 19.32% under clothing variation conditions on CASIA-B. In addition to an across-the-board analysis of recent breakthroughs in gait recognition, the scope for potential future research direction is also assessed.

The research objective is to explore Chile's entrepreneurial landscape by assessing individual characteristics and institutional factors through a 'pillars' framework and compare it against Colombia, and Brazil; to identify socio-economic, individual, and institutional differences using the Global Entrepreneurship Index (GEI); to apply bottleneck approach to highlight areas requiring policy intervention. GEI features individual and institutional stage variables in a method where every variable collaboratively interacts, incorporating 14 foundational elements and three sub-indexes: attitudes, abilities, and aspirations. Ranking 18th on the GEI globally and the best in Latin America, Chile excels in key entrepreneurial pillars, showcasing strengths in innovation and a robust entrepreneurial culture. Brazil closely rivals Chile in competition and networking, emphasizing political and economic influence. Colombia surpasses Chile in internationalization and growth-stimulating policies but faces challenges like historical conflicts and wealth distribution. This study identified areas where immediate policy intervention may be necessary by examining Chiles's entrepreneurial ecosystem. The Penalty for Bottleneck (PFB) technique identified the weakest pillars highlighting process innovation, competition, and internationalization. The primary component identified as a bottleneck for resource allocation is Process Innovation, accounting for 73% of the allocation, followed by Competition at 23%. The findings show that allocating more resources to process innovation may improve greatly the overall GEI score.

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 (YSi) 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).

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. [ABSTRACT FROM AUTHOR]

Objective: To know the current state of research on the evaluation of the Carbon Footprint (CF) of coffee and its value chain. Methodology: A systematized review of literature from international bibliographic databases was conducted, identifying five aspects; 1) place of assessment, 2) scope of the assessment, 3) methodology used, 4) result of the CF, and 5) functional unit of measurement considered. Results: Sixteen articles from 16 countries were reviewed, evaluating different scopes of the coffee production chain. Six different methodologies were applied, including international standards and specialized software. The calculated CF ranges from 0.12 kg/CO2eq to 14.61 kg/CO2eq depending on the scope of the research in the coffee production chain. Limitations: Scarce collection of publications obtained in the systematized search process. Conclusions: There is a need to increase research on the subject in small-scale production areas, since most of the methodologies and applications have focused on areas where the grain production and technification are concentrated, leaving aside other production areas that require attention with respect to their production processes. [ABSTRACT FROM AUTHOR]

Gait recognition is a promising biometric modality due to its non-invasive nature and difficulty to disguise. However, the performance still lacks compared to other, well established biometric modalities. This paper presents results of our study on gait recognition, focusing on the comparison between Gait Energy Images (GEI) and Gait Entropy Images (GEnI) under various conditions. Different methodologies are explored, including deep learning techniques and Vision Transformers (ViTs), for feature extraction and classification. The popular CASIA–B dataset is used to evaluate the performance across different walking conditions and entropy measures. The effectiveness of gait recognition systems in accurately identifying individuals is shown, thus highlighting the potential of GEnI in enhancing the recognition performance under varying conditions. [ABSTRACT FROM AUTHOR]

The three cultivars of Gayo Arabica coffee (GAC) are distributed widely in the Gayo Highlands, Aceh Province, Indonesia, between 900 and 1,700 m above sea level (masl). The difference in altitude affects the yield and quality of coffee, and Arabica coffee has biennial bearing characteristics, so there are always annual yield fluctuations. This study aimed to determine the yield performance and stability level of the three GAC cultivars, Gayo 1, Gayo 2, and Gayo 3 (G3), across six different environments. The study used a randomized complete block design with three replications. The six environmental conditions are the combinations of two altitudes (900 and 1,500 masl) and 3 years of production (2019, 2020, and 2021). The estimation of coffee yields is based on the fruit value, which is calculated by the number of productive branches per tree, nodes per branch, berries per node, and the weight of a single cherry. An investigation of the genotype-by-environmental interaction using a combined analysis of variance and the yield performance and stability analysis performed using the Eberhart and Russell method, AMMI (additive main effects and multiplicative interaction) and GGE (genotype + genotype × environment) biplot analysis. Results showed that the yield performance of GAC was significantly affected by the interaction between genotype and environment. G3 was an ideal cultivar because it had high yield performance, was stable, and could adapt to broader environments in Gayo Highland. Therefore, G3 deserves high priority for Arabica coffee development in that region. [ABSTRACT FROM AUTHOR]

Coffee yield is a complex quantitative trait whose expression is determined by genotype, environment, and genotype by environment interaction (GEI). Seventeen Arabica coffee genotypes were evaluated in twelve environments in south Ethiopia to analyze the extent of GEI effect on Arabica coffee yield and to identify the highest yielding and stable genotypes. The experiment was established using RCBD with three replications at each location. The results revealed significant differences among genotypes, environments, and GEI, with an explained proportion of variation of 3.26%, 71.74%, and 17.52%, respectively. According to GGE-biplot and AMMI models, G7, G16, G5, G14, G15, G12, G11, and G13 were identified as high-yielding genotypes. Likewise, E7, E1, E5, E3, and E11 were classified as favorable environments. According to the AMMI and GGE biplot models and various stability indices (AMMI stability value, cultivar superiority index, and yield stability index), G16 (AW7705) was the highest bean yielder in tons per hectare and the most stable genotype. Therefore, AW7705 was discovered to be a promising candidate, which can be considered desirable genotypes in further tests, to releasing a new variety for Arabica coffee growing regions of southern Ethiopia and other similar agro-ecologies elsewhere. G7 (AW105) was the most yielding but an unstable genotype and can be considered in the specific target environments. Since the environment and GEI explained significant variations in coffee yield, testing genotypes in diverse environments prior to deciding on any variety to use under wider agro-ecology is recommended for coffee breeders to identify stable and high-yielding coffee varieties. [ABSTRACT FROM AUTHOR]

• The performance of the hybrids was primarily affected by the environment, planting dates, and their interactions. • The magnitudes of the GEI sum of squares were more than five times that of the hybrid sum of squares for the grain yield. • The stability of hybrids did not show the same trend across environments among the planting dates. • AMMI, Pi, GGE, YSI, and ASV analysis methods effectively identified stable and high-yielding hybrids. • Dealing with GEI is a valuable strategy to optimize resources in maize breeding programs. Maize is one of the most important field crops for feed, food, and raw material in South Africa. It is the main staple food for the majority of the people impoverished households in South Africa. Maize is susceptible to drought, and erratic rainfall and extreme climate variation are major maize production constraints in South Africa. This study aimed to determine the influence of planting dates on the grain yield stability of six maize genotypes in five environments for three (2019/20 to 2021/22) consecutive seasons. The additive main effect and multiplicative interaction (AMMI) model indicated that the environment contributed 59.20 % and 47.76 % of the total variation in grain yield at planting dates one (PD1) and two (PD2), respectively. The magnitudes of the genotype x environment interaction sum of squares were 5.20 for PD1 and 12.30 for PD2 times that of the genotype (genotype) sum of squares for the grain yield (GEI). The first principal component accounted for 53.14 % (PD1) and 47.76 % (PD2) of the total variation in the dataset. The AMMI genotype superiority measure (Pi), genotype and genotype by environment interaction (GGE), yield stability index, and AMMI stability value analyses identified genotypes DKC75–65BR and DKC78–45BRGEN as stable and high yielding across environments and planting dates. Genotype DKC71–44B was the least stable at both planting dates. Bothaville (E7) for PD1 and Coligny 2021/22(E13) for PD2 had high yield potential and were favorable environments, whereas Potchefstroom 2019/20 and 2021/22 were the least favorable environments for PD1 and PD2, respectively. [ABSTRACT FROM AUTHOR]

Rice is the staple food of almost half of the world’s population, impacting nutrition especially in children, pregnant women, and nursing mothers. Because the traits were quantitatively inherited, they are affected by changes in location and year. A RBD with three replications was used to identify superior and stable high-zinc rice genotypes in Uttar Pradesh, India. Grain zinc content (GZC) is negatively correlated with grain yield using genetic association study. There was a significant G × E interaction (GEI) and V16 and V21 for GYP and V9, V2 and V10 for GZC were identified as stable based on the AMMI model and bi-plot. V11, V5, V21 for grain yield per plant (GYP) and for GZC, V14, and V10 are found to be stable and common in all AMMI stability parameters. V6, V13 and V5 for GYP and V10, V8 and V2 for GZC were identified as stable based on the mean vs. WAASB bi-plot. V21 for GYP and V4 for GZC was the highest yielder and widely adaptable based on WAASBY scores. V13 for GYP and V1 for GZC were all-time winners. V13 and V1 have the highest predicted mean for GYP and GZC, respectively, based on BLUP. V6, V21and V13 were identified as stable and selected based on the multi-trait stability index (MTSI). These selected genotypes selected through BLUP-based stability methods, MTSI, and strength and weakness plots make it easier to evaluate and select genotypes for varietal recommendations and future Zn-fortified rice breeding studies. [ABSTRACT FROM AUTHOR]

Stem rust disease (UG99) caused by the fungus Puccinia graminis f. sp. Tritici has a negative significant impact on the world's wheat production. Host-plant resistance is the most efficient and practical strategy used for managing this disease, hence, breeding for stem rust resistance continues to be an integral part of the genetic improvement of wheat. Twenty-one wheat genotypes including advanced breeding lines, released varieties and a susceptible check (Morocco) were evaluated for their response to stem rust disease across 6 environments in Ethiopia. Genotypes (G), environment (E), and Genotype x environment interactions (GEI) were examined by biplot which partitioned the main effect into G, E, and GEI with significant levels (P ≤ 0.001) being obtained from final rust severity data. The results revealed that genotypes had contributed 58.92% of resistance variation followed by GEI and E with 28.68% and 3.97% of the total effects, respectively. AMMI biplot and GGE biplot techniques enabled us to identify two stable genotypes (G12 and G60) based on their performance across diverse environments, while G31 and G52 showed specifically good resistance performance on Debrezeit and Kulmsa testing environments. The GGE biplot method also identified Lay-Gaint as the ideal test environment with a high representative and better discriminative power for selecting durable rust-resistant genotypes, while Sali was the least desirable test environment. In addition, the GGE biplot analysis identified two distinct mega-environments (ME1 and ME2) for rust severity in Ethiopia. [ABSTRACT FROM AUTHOR]

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

Resumen La utilización de factores de emisión por defecto y de forma homogénea en todo el país en la estimación de las emisiones de óxido nitroso (N2O) derivadas de la aplicación de fertilizantes sintéticos en los Inventarios de Gases de Efecto Invernadero de Argentina, no permiten reflejar las características productivas locales y sus diferencias regionales. Los objetivos de este trabajo fueron: a) desagregar la información de los datos de nitrógeno (N) sintético aplicado por región, cultivo y clima y b) analizar y sintetizar el estado de situación de la investigación argentina en el desarrollo factores de emisión (FE) locales. Para el primer objetivo se desarrolló una metodología para desagregar el dato de N aplicado por jurisdicción y cultivo. Para el segundo, se realizó una recopilación de trabajos científicos publicados en revistas nacionales e internacionales. Se logró desagregar el dato de aplicación de N por jurisdicción y cultivo mediante el cruzamiento de distintas fuentes de información: el 36,7% del N aplicado a nivel nacional se concentra en la Provincia de Buenos Aires, el 26,3 % en Córdoba, el 12% en Santa Fe y el 7,3% en Entre Ríos. El maíz (Zea mays L.) y trigo (Triticum aestivum L.) son los cultivos con mayor aplicación de N (33,9 y 23,3%, respectivamente). Por otro lado, encontramos 11 trabajos que permiten proponer FE para estimar emisiones directas de N2O a partir de datos experimentales, y 15 que permiten estimar las fracciones de volatilización o lixiviación que resultan en emisiones indirectas de N2O. Los valores promedio estimados y reportados a partir de la bibliografía fueron: 0,0124 kg N2O-N (kg N)-1 (FE directas de N2O), 0,081 kg N volatilizado (kg N)-1 (fracción de volatilización) y 0,244 kg N (kg N)-1 (fracción de lixiviación). Estos valores promedio difieren en hasta un 25% con los utilizados actualmente en el Inventario Nacional.

Nitrous oxide (N2O) emission estimation derived from the application of synthetic fertilizers using default emission factors and in a homogeneous way throughout the country for the Inventories of Greenhouse Gases of Argentina do not reflect local productive characteristics and the regional differences. The objectives of this study were a) to disaggregate synthetic nitrogen (N) data by region, crop and weather, b) to analyze and synthesize the state of the art of research in Argentina about the development of local N2O emission factors (FE) for synthetic fertilizers application. For the first objective, a methodology was developed that consists of multiplying the average N dose applied per region by the area sown per crop. For the second, a compilation of scientific papers published in national and international journals was carried out. It was possible to disaggregate the N application data by district and crop by crossing different sources of information: 36.7% of N at country level is applied in the Province of Buenos Aires, 26.3% in Córdoba, 12% in Santa Fe and 7.3% in Entre Ríos. Corn (Zea mays L.) and wheat (Triticum aestivum L.) are the crops with the highest application of N (33.9 and 23.3% respectively). Concerning the state of the art of local research, we found 11 papers that allow us to propose FE for direct N2O emissions, and 15 that allow us to estimate volatilization or leaching fractions that result in indirect N2O emissions. The average values estimated and reported from the literature were 0.0124 kg N2O-N (kg N)-1(direct N2O emission factors), 0.081 kg N volatilized (kg N)-1(volatilization fraction) and 0.244 kg N (kg N)-1 (leaching fraction). Compared to the values used in the National Inventory, the difference is up to 25%. [ABSTRACT FROM AUTHOR]

Salinity is one of the major abiotic stress factors that limit the productivity of crops, including wheat, in many regions of the world. Therefore, the priority in wheat breeding, to ensure global food security, is developing varieties that are adapted to saline environments. In situ, evaluation of wheat genotypes can provide valuable information on the performance of different genotypes under natural saline conditions and can help to identify the most salt-tolerant genotypes. To ensure an accurate evaluation of the performance of twenty-seven wheat genotypes under different environments, the trial was conducted on two different soil types (solonetz and chernozem) in two growing seasons. AMMI analysis shows that the environmental factor had the largest share (55.15%) in the variation of grain yield, where soil type had a dominant effect. Genotypes Renesansa, Harmonija, and Bankut 1205 achieved a high grain yield on both soil types. However, among the mentioned genotypes, the genotype Harmonija showed the highest tolerance to salinity. A significant proportion of the genotype and environment interaction (GEI; 25.89%) shows that there is a change in the ranking of genotypes across environments. According to the AMMI1 biplot, the genotypes Renesansa and Harmonija were distinguished by high grain yield and high stability. The environment Chernozem 2015/2016 had the greatest contribution to the GEI and the highest grain yield, while Solonetz 2017/2018 was characterized by the highest stability and the lowest grain yield. According to the AMMI2 biplot, genotype Harmonija achieved high stability in unfavourable environmental conditions that characterized the environment Solonetz 2017/2018.

The expected gains in the productivity and sustainability of cotton cultivars across environmental spectra may face many challenges. To address this issue, a set of 572 cotton accessions, including 284 F1 hybrids along with their parental lines and checks, were evaluated and compared across multiple test environments. Popular biometrical tools viz; best linear unbiased prediction (BLUP), additive main effects and multiplicative interaction (AMMI) and genotype main effects and genotype × environment interaction effects (GGE) were utilized for reliable estimation graphical depiction of GEI effects. The first two interaction coordinates as principal components in BLUP, AMMI, and GGE biplots explained the maximum proportion of GEI regarding seed cotton yield. The contrasting environments did not cluster together, thus revealing their varying influences on the genotypes. A set of 44 F1 hybrids featuring higher stability, 46 with higher adaptability, and 5 F1 hybrids exhibited higher stability, adaptability, and highest seed cotton yield across the investigated environments simultaneously. F1 hybrids of cotton were concluded as more stable and adaptable regarding seed cotton yield across environments. Accordingly, switching from line to hybrid breeding could enable a cotton breeding program to address the broader issue of growing this crop in a wide range of target environments.

The identification of high-yielding and stable genotypes for cultivation across differential production regions is among the key breeding objectives in cowpea improvement programs. This study was aimed to determine genotype-by-environment interaction (GEI) for grain yield to select high-yielding and stable cowpea genotypes for production in South Africa and identical agro-ecologies, and for cultivar development. Fifty cowpea genotypes were tested for grain yield across seven environments of South Africa using a 10 × 5 alpha lattice design replicated three times, during the 2019/2020 and 2020/2021 planting seasons. Grain yield data were subjected to analysis of variance (ANOVA), additive main effects and multiplicative interaction (AMMI) and the genotype-by-environment interaction (GGE) biplot analyses. ANOVA and AMMI showed significant genotype, environment and GEI effects. High grain yield was recorded for genotypes G35 (0.47 t ha−1), G1 (0.45 t ha−1) and G47 (0.43 t ha−1) across test environments. AMMI stability values (ASV); identified Acc-Cowp44 as the most stable genotype across all sites, recording the lowest ASV of 0.03. The comparison view of GGE biplot revealed Acc-Cowp29, Acc-Cowp38 and Acc-Cowp5 as ideal genotypes, possessing high grain yield of 0.19, 0.47 and 0.36 t ha−1, respectively. The identified genotypes are recommended for production and inclusion in subsequent breeding activities.

Purpose – This paper aims to investigate and examines Sweden's overall entrepreneurship performance (ecosystem) by applying the Global Entrepreneurship Index (GEI) while benchmarking the entrepreneurial ecosystem of Sweden with that of Finland and Norway. Design/methodology/approach – In terms of subindices, pillars and component factors, this research analyzes the entrepreneurial ecosystem of Sweden using the GEI supplemented by the Penalty for Bottleneck (PFB) approach utile for identification of bottlenecks. In addition, the Swedish ecosystem is benchmarked against its Finnish and Norwegian counterparts drawing on data collected between 2015 and 2018. Findings – Using data drawn from the GEI, Sweden manifests a strong entrepreneurial ecosystem with a GEI score of 72.7 out of 100. However, fledgling start-up skills, insufficient human capital, and slow and erratic growth undercut otherwise solid entrepreneurial aspirations drawing on well-developed institutional variables. On a macrolevel, Sweden evinces greater capacity for entrepreneurship and innovation than either Norway or Finland but, on a microlevel, several discontinuities manifest in terms of subindices, pillars and component factors to the advantage of Norway and/or Finland and, conversely, to the detriment of Sweden. Practical implications – Policymakers should fund a mix of programs and institute regulatory reforms designed to promote entrepreneurial systemic development in Sweden by remediating entrepreneurial gaps depressing GEI scores. Crucial policy interventions are required to accrete start-up skills and human capital and engender high growth. Incremental funding of 47% over current levels budgeted to buoy entrepreneurial activity are mandated for Sweden to approach its GEI potential. Originality/value – Insights are derived from extracting data drawn from a new methodology for gauging entrepreneurial activity incorporating individual and institutional variables into a single model that combines PFB and GEI analysis with a view to identifying, through the PFB approach and weak aspects of Sweden's entrepreneurial performance.

ABSTRACTThe performance of genotypes under diverse environments can be used to determine their adaptability and stability. However, information on the performance of coffee genotypes in various environmental conditions is limited. Thus, this study’s objectives were to estimate genotype by environment interaction (GEI), evaluate the mean performance and stability of 16 fruity flavored coffee genotypes in eight specialty coffee growing environments in western Ethiopia, and assess the magnitude of correlations among different stability parameters. The experiment was laid out in a randomized complete block design (RCBD) with two replications. For coffee yield, data were recorded and a combined analysis of variance and stability analysis were performed. Additive main effect and multiplicative interaction (AMMI) analysis revealed that genotypes, environments, and GEI showed highly significant differences (P

Horse gram yellow mosaic virus (HgYMV) disease is one of the major biotic constraints in horse gram production. Development and deployment of cultivars resistant to HgYMV disease are considered as the most eco-friendly and sustainable approach to mitigate the production losses caused by the disease. However, for easy acceptance of such HgYMV disease-resistant cultivars by farmers, they should be in high yielding background. Under these premises, nine HgYMV disease-resistant genotypes including two checks were field-evaluated in triplicated randomized complete block design (RCBD) to identify those that exhibit stable grain yield plant-1 across four (two locationtwo years combination) environmrnts during 2020 and 2021 late rainy seasons. Additive Main effects and Multiplicative Interaction (AMMI) model was used to detect and characterize genotype × environment interaction (GEI). Genotype + Genotype × environment (GGE) bi-plot was used to visually (subjective criterion) interpret GEI patterns of genotypes and identify those that are stable across four environments. AMMI Stability Value (ASV) and Stability Index (SI) were used as objective criteria to assess relative stability of genotypes. A simple statistic, namely, yield relative to environment maximum (YREM) was also used to detect cross-over GEI and to quantify genotypes' attainable grain yield loss attributable to crossover GEI. The genotypes differed significantly and displayed significant GEI for grain yield. GEIsignal explained over 50 per cent of total SS due to GEI. AMMI 2 model family was adequate to explain detected variation attributable to GEI. One genotype namely, 'Palem 2' was found highly stable across four test environments based on three criteria, namely GGE bi-plot, ASV and SI with high mean grain yield plant-1. 'Palem 2' with unit YREM is likely to maintain its high grain yield potential across temporal environments without reduction in grain yield even in the presence of cross over GEI. [ABSTRACT FROM AUTHOR]

Maize is the staple food crop for millions of people in sub-Saharan Africa. Iron (Fe) and zinc (Zn) deficiency is a significant health risk that mainly affects low-income populations who rely solely on maize-based diets. This problem can be alleviated by developing micronutrient-rich maize grain. The aim of this study was to determine the adaptation and performance of hybrids for Fe and Zn concentration and grain yield under low soil nitrogen (N) and optimal conditions. Eighteen hybrids derived from lines and testers with low, medium, and high Fe and Zn concentration were grown during the summer rainy seasons of 2017 and 2018 at three locations under low and optimal N conditions. There were significant genotype and environment effects for grain yield, and Fe and Zn concentration, but the genotype by environment interaction effects were the largest, accounting for between 36% and 56% of variation under low N conditions. Low N levels significantly reduced grain yield, and Fe and Zn concentration. Hybrids G1, G2, G4, G7, G10, G11, and G16 were relatively stable, with relatively high mean Fe and Zn concentrations, and low additive main effects and multiplicative interaction (AMMI) stability values and iron stability index (FSI) and zinc stability index (ZSI) under low N conditions. These genotypes can be considered for production under low N stress conditions. Two environments (E4 and E3) were identified for good discriminatory power for genotype performance in terms of Fe and Zn content, respectively. Stable and high-yielding genotypes with high Fe and Zn concentration can be used as biofortified hybrids, which can contribute to a sustainable solution to malnutrition in the region, especially under low N conditions. [ABSTRACT FROM AUTHOR]

Baby corn is gaining preference as a vegetable across different parts of India but the availability of varieties specific to baby corn purposes is limited. To increase production, it is essential to study the variability and stability of genotypes that can be used for baby corn purposes across different environments. Assessment of genetic variability among 12 baby corn genotypes for nine quantitative traits revealed that days to 50% silking and cob weight are essential traits for increasing baby corn yield. Environment plays a predominant role in determining the stable performance of genotypes. AMMI and GGE biplot analysis provided a clear idea of genotype and environment interaction. The results of the present study showed the influence of environments on the evaluated genotypes across seasons. Which- won- where biplot revealed that pre kharif season was better for higher baby corn yield and also the genotype GAYMH-4 was stable across the three evaluated environments with respect to baby corn yield without husk.

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.

Biometrics are the body measurements and calculations related to individuals. Biometrics validation is used as a form of identification of individual. Gait recognition system is one of the most advanced technology that people have been working on for a while now that takes center stage in the field of biometrics. Compared to the other types of existing systems of biometric recognition such as fingerprint detection, iris-scanning systems etc., Gait Recognition system ensures no human intervention. This paper focuses on recognition based on a person's gait. Every person has a distinct gait pattern that is unique to every other person. To train the model CASIA-B dataset has been used. The dataset includes 124 subjects where each sample has undergone Gait Energy Image extraction. Samples with clothing and baggage have been included which changes the silhouette of the person. Therefore, the model has been trained for a wider application where people wear different type of clothing and carry-ons. A Convolutional Neural Network consisting of 8 layers has been trained which performs well on both samples of dataset and an accuracy of 95.45% was obtained on dataset not involving layers of clothing and accuracy of 91.80% was obtained for the sample with clothing and baggage. [ABSTRACT FROM AUTHOR]

Wheat is one of the products with the widest cultivation area and adaptation ability in the world. Although the heritability of the variety is important in grain yield, the environment and genotype x environment interaction is significantly effective. This study was carried out in 6 different environments. According to the Additive Main Effect and Multiplication Interaction (AMMI) model; genotype, environment and genotype x environment interaction were found to be effective on grain yield, respectively, by 9.40%, 56.79% and 16.28%. It has been determined that, there is a positive relationship between grain yield and the number of spike per square meter and the number of grain in each spike. In the study, 3 mega environments were formed. G19 in the first mega environment, G13 in the second mega environment and G21, G22 in the third mega environment came to the fore. G9, G13 and G19 were seen as hopeful lines in the grain yield. Also, E5 was the ideal environment and, G19 had the highest adaptability and grain yield. It has been concluded that G19 may be a candidate for variety because genotypes that are stable in different environmental conditions are preferred by the producers. [ABSTRACT FROM AUTHOR]

Research on forest phenology is an important parameter related to climate and environmental changes. An optical camera was used as a near-earth remote sensing satellite device to obtain forest images, and the data of Green excess index (GEI) in the images were calculated, which was fitted with the seasonal variation curve of GEI data by double Logistic method and normalization method. LSTM and GRU deep learning models were introduced to train and test the GEI data. Moreover, the rationality and performance evaluation of the deep learning model were verified, and finally the model predicted the trend of GEI data in the next 60 days. Results showed: In the aspects of forest phenology training and prediction, GRU and LSTM models were verified by histograms and autocorrelation graphs, indicating that the distribution of predicted data was consistent with the trend of real data, LSTM and GRU model data were feasible and the model was stable. The differences of MSE, RMSE, MAE and MAPE between LSTM model and GRU model were 0.0014, 0.013, 0.008 and 5.26%, respectively. GRU had higher performance than LSTM. The prediction of LSTM and GRU models about GEI data for the next 60 days both showed a trend chart consistent with the change trend of GEI data in the first half of the year. GRU and LSTM were used to predict GEI data by deep learning model, and the response of LSTM and GRU deep learning models in forest phenology prediction was realized, and the performance of GRU was better than that of LSTM model. It could further reveal the growth and climate change of forest phenology in the future, and provide a theoretical basis for the application of forest phenology prediction. [ABSTRACT FROM AUTHOR]