Application of Multivariate Data Analyses in Waste Management

Böhm, Katharina; Smidt, E.; Tintner, Johannes

doi:10.5772/53975

Cited by 14 publications

(8 citation statements)

References 88 publications

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“…Nonetheless, drought tolerance is not often discussed as an independent character by plant breeders because tolerance mechanisms can be fairly general and polygenic in nature [ 38 ]. However, multivariate analysis techniques can be used to explore relationships, classification and parameter prediction within complex data sets as the conclusions are more realistic, meaningful and accurate [ 39 ]. Among the multivariate techniques, the robust hierarchical co-cluster (RHCOC) approach produces a far lower clustering error rate than the conventional hierarchical clustering approaches in presence of outlying observations in the dataset [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis of Morpho-Physiological Traits Reveals Differential Drought Tolerance Potential of Bread Wheat Genotypes at the Seedling Stage

Mohi-Ud-Din

Hossain

Rohman

et al. 2021

Plants

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Drought is one of the foremost environmental stresses that can severely limit crop growth and productivity by disrupting various physiological processes. In this study, the drought tolerance potential of 127 diverse bread wheat genotypes was evaluated by imposing polyethylene glycol (PEG)-induced drought followed by multivariate analysis of several growth-related attributes. Results showed significant variations in the mean values of different morpho-physiological traits due to PEG-induced drought effects. Correlation analysis revealed that most of the studied traits were significantly correlated among them. The robust hierarchical co-clustering indicated that all the genotypes were clustered into four major groups, with cluster 4 (26 genotypes) being, in general, drought-tolerant followed by cluster 1 (19 genotypes) whereas, cluster 2 (55 genotypes) and 3 (27 genotypes) being drought-sensitive. Linear discriminant analysis (LDA) confirmed that around 90% of the genotypes were correctly assigned to clusters. Squared distance (D2) analysis indicated that the clusters differed significantly from each other. Principal component analysis (PCA) and genotype by trait biplot analysis showed that the first three components accounted for 71.6% of the total variation, with principal component (PC) 1 accounting for 35.4%, PC2 for 24.6% and PC3 for 11.6% of the total variation. Both PCA and LDA revealed that dry weights, tissue water content, cell membrane stability, leaf relative water content, root-shoot weight ratio and seedling vigor index played the most important discriminatory roles in explaining drought tolerance variations among 127 wheat genotypes. Our results conclude that the drought-tolerant and -sensitive wheat genotypes identified in this study would offer valuable genetic tools for further improvement of wheat productivity in arid and semi-arid regions during this time of unpredictable climate change.

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Section: Introductionmentioning

confidence: 99%

Multivariate Analysis of Morpho-Physiological Traits Reveals Differential Drought Tolerance Potential of Bread Wheat Genotypes at the Seedling Stage

Mohi-Ud-Din

Hossain

Rohman

et al. 2021

Plants

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“…Multivariate data analysis allows us to handle huge data sets in order to discover such hidden data structures which contributes to a better understanding and easier interpretation. There are many multivariate data analysis techniques available [ 19 ] ranging from the classic inferential statistical method regression model (Poisson) [ 20 , 21 , 22 ] to new models based ANN machine learning algorithms as deep learning or random forest [ 16 , 23 , 24 ]. Most of the studies on MSW management in Europe are concentrated on the cities areas and used every type of methods to predict MSW generation.…”

Section: Background Of Multivariate Analysis On Msw Management In mentioning

confidence: 99%

Variables Influencing per Capita Production, Separate Collection, and Costs of Municipal Solid Waste in the Apulia Region (Italy): An Experience of Deep Learning

Fasano

Addante

Valenzano

et al. 2021

IJERPH

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Municipal solid waste (MSW) must be managed to reduce its impact on environmental matrices and population health as much as possible. In particular, the variables that influence the production, separate waste collection, and costs of MSW must be understood. Although many studies have shown that such factors are specific to an area, the awareness of these factors has created opportunities to implement operations to enable more effective and efficient MSW management services, and to specifically respond to the variables that have the most impact. The deep learning approaches used in this study are effective in achieving this goal and can be used in any other territorial context to ensure that the organizations that deal with these issues are more aware and create useful plans to promote the circular economy. Our findings indicate the important influence of number of rooms in a residential buildings and construction years on MSW production, the combination of services such as municipal collection centers and door-to-door service for separate MSW collection and the characteristics of the residential buildings in the municipalities on MSW management costs.

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“…Multivariate analysis is a method commonly adopted to assess the associations between a broad range of variables, and also to locate genotypic variability among complex data sets [ 29 ]. Principal component analysis (PCA) is an incredibly beneficial tool for dissecting the correlation between traits, as well as their interactions, and determining the genotypic performance in crop plants.…”

Section: Introductionmentioning

confidence: 99%

Dissecting the Relationship between Root Morphological Traits and Yield Attributes in Diverse Rice Cultivars under Subtropical Condition

Kaysar

Sarker

Monira

et al. 2022

Life

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Understanding the link between root morphological traits and yields is crucial for improving crop management. To evaluate this link, a pot experiment was conducted in the net house of the Department of Agronomy, Bangladesh Agricultural University, Mymensingh, Bangladesh during the boro(dry season irrigated) rice growing season of 2019–20. Thirteen cultivars, named BRRI dhan29, BRRI dhan58, BRRI dhan67, BRRI dhan74, BRRI dhan81, Binadhan-8, Binadhan-10, Hira-2, Tej gold, SL8H, Jagliboro, Rata boro, and Lakhai, were used following a completely randomized design (CRD) with three replications. The cultivars were screened for root number (RN), root length (RL), root volume (RV), root porosity (RP), leaf area index (LAI), total dry matter (TDM), and grain yield (GY). A considerable variation in root traits, LAI, and TDM were found among the studied cultivars, and the highest GY (26.26 g pot−1)was found for Binahan-10. Thirteen cultivars were grouped into three clusters using hierarchical cluster analysis, where clusters 1, 2, and 3 assembled with 3, 5, and 5 cultivars, respectively. Considering all of the studied traits, Cluster 3 (Binadhan-10, Hira-2, BRRI dhan29, BRRI dhan58, and Tejgold) showed promise, followed by Cluster 2 (BRRI dhan81, BRRI dhan67, SL8H, BRRI dhan74, and Binadhan-8). Principal component analysis (PCA) revealed that the RV, RDW, RFW, TDM, and GY are effective traits for rice cultivation.

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Application of Multivariate Data Analyses in Waste Management

Cited by 14 publications

References 88 publications

Multivariate Analysis of Morpho-Physiological Traits Reveals Differential Drought Tolerance Potential of Bread Wheat Genotypes at the Seedling Stage

Multivariate Analysis of Morpho-Physiological Traits Reveals Differential Drought Tolerance Potential of Bread Wheat Genotypes at the Seedling Stage

Variables Influencing per Capita Production, Separate Collection, and Costs of Municipal Solid Waste in the Apulia Region (Italy): An Experience of Deep Learning

Dissecting the Relationship between Root Morphological Traits and Yield Attributes in Diverse Rice Cultivars under Subtropical Condition

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