Canonical Correlation between Soil Attributes and Foliar of Conilon Coffee Trees

Fonseca, Abel Souza da; Lima, Julião Soares de Souza; Dardengo, Maria Christina Junger Delôgo; Silva, Samuel de Assis; Xavier, Alexandre Cândido

doi:10.9734/jeai/2019/v35i330208

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…Multi‐environment trials often generate data on several traits, and these data should be exploited. In breeding trials (as well as in many other areas), indirect selection helps geneticists and breeders to select superior genotypes (Ferrari et al, 2018; Fonseca, Lima, Dardengo, Silva, & Xavier, 2019; Gediya et al, 2019; Lopes Costa, Melo, & Oliveira Mano, 2019; Meira et al, 2017; Olivoto, de Souza, et al, 2017; Olivoto, Nardino, et al, 2017; Santos et al, 2018); thus, any tool that facilitates this work is welcome. metan provides useful functions for implementing biometrical models easily.…”

Section: The Metan Packagementioning

confidence: 99%

metan: An R package for multi‐environment trial analysis

2020

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1. Multi-environment trials (MET) are crucial steps in plant breeding programs that aim at increasing crop productivity to ensure global food security. The analysis of MET data requires the combination of several approaches including data manipulation, visualization and modelling. As new methods are proposed, analysing MET data correctly and completely remains a challenge, often intractable with existing tools. 2. Here we describe the metan R package, a collection of functions that implement a workflow-based approach to (a) check, manipulate and summarize typical MET data; (b) analyse individual environments using both fixed and mixed-effect models; (c) compute parametric and nonparametric stability statistics; (d) implement biometrical models widely used in MET analysis and (e) plot typical MET data quickly.3. In this paper, we present a summary of the functions implemented in metan and how they integrate into a workflow to explore and analyse MET data. We guide the user along a gentle learning curve and show how adding only a few commands or options at a time, powerful analyses can be implemented. 4. metan offers a flexible, intuitive and richly documented working environment with tools that will facilitate the implementation of a complete analysis of MET datasets. K E Y W O R D Sadditive main effect and multiplicative interaction, biometry, genotype-environment interaction, GGE biplot, multi-environment trials, R software, stability, statistics

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Section: The Metan Packagementioning

confidence: 99%

metan: An R package for multi‐environment trial analysis

2020

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“…Olivoto, Nardino, et al, 2017; T. Olivoto, Souza, et al, 2017; Ferrari et al, 2018; Santos et al, 2018; Fonseca, Lima, Dardengo, Silva, & Xavier, 2019; Gediya et al, 2019; Lopes Costa, Melo, & Oliveira Mano, 2019); thus, any tool that facilitates this work is welcome. provides useful functions for implementing biometrical models easily.…”

Section: The Packagementioning

confidence: 99%

metan: an R package for multi-environment trial analysis

Olivoto

Lúcio

2020

Preprint

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10 1. Multi-environment trials (MET) are crucial steps in plant breeding programs that aim 11 increasing crop productivity to ensure global food security. The analysis of MET data 12 requires the combination of several approaches including data manipulation, visualization, 13 and modeling. As new methods are proposed, analyzing MET data correctly and 14 completely remains a challenge, often intractable with existing tools. 15 2. Here we describe the metan R package, a collection of functions that implement a 16 workflow-based approach to (a) check, manipulate and summarise typical MET data; (b) 17 analyze individual environments using both fixed and mixed-effect models; (c) compute 18 parametric and non-parametric stability statistics; (c) implement biometrical models 19 widely used in MET analysis; and (d) plot typical MET data quickly.203. In this paper, we present a summary of the functions implemented in metan and how 21 they integrate into a workflow to explore and analyze MET data. We guide the user 22 along a gentle learning curve and show how adding only a few commands or options at 23 a time, powerfull analyzes can be implemented. 24 4. metan offers a flexible, intuitive, and richly documented working environment with tools 25 that will facilitate the implementation of a complete analysis of MET data sets. 26 Key-words: AMMI, biometry, genotype-environment interaction, GGE biplot, multi-27 environment trials, R software, stability, statistics 28 29In 50 years the world average of cereal yields has increased by 64%, from 1.68 to 30 2.76 t ha −1 . In the same period, the total production of cereals has raised from 1.305 × 10 9 to 31 3.6 × 10 9 t, an increase of 175%, while the cultivated area increased by only 7.9% in the same 32 period (FAOSTAT, 2019). These unparallel increases have been possible due to the improved 33 cultivation techniques in combination with superior cultivars. For maize, for example, 50% 34 of the increase in yield was due to breeding (Duvick, 2005). Plant breeding programs have 35 been developing new cultivars for adaptation to new locations, management practices, or 36 growing conditions, in a clear and crucial example of exploitation of genotype-vs-environment 37 interaction (GEI). 38The breeders' desire to modeling the GEI appropriately has led to the development of 39 the so-called stability analyses, which includes ANOVA-based methods (Yates ) and some methods that combines 43 different statistical techniques, such as the Additive Main Effect and Multiplicative Interaction, 44 AMMI, (Gauch, 2013), and Genotype plus Genotype-vs-Environment interaction, GGE, (Yan 45 & Kang, 2003). Then, it is no surprise that scientific production related to multi-environment 46 trial analysis has been growing fast in the last decades. A bibliometric survey in the SCOPUS 47 database revealed that in the last half-century (1969-2019) 6590 documents were published 48 in 902 sources (Journals, books, etc.) by 19.351 authors. In this period, the number of 49 publications has been increased on av...

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“…Therefore, the variables' weighting should be carried out after the normalization [50] (pp. [64][65]. Both the weights' construction elements take the values from the interval of [0, 1].…”

Section: R|mentioning

confidence: 99%

“…It is worth noting that it is a relatively rarely used tool in the context of agriculture, specifically in organic agriculture. One may mention here the study of A.S. da Fonseca et al [64], which aimed to identify the linear dependencies between chemical properties and nutrients in the leaf tissue in seed coffee using the canonical analysis. S. Zabolotnyy et al [65], employing the canonical analysis, investigated the relationship between determinants of efficiency and agricultural holdings' financial situation.…”

mentioning

confidence: 99%

Identification of the Conditions for Organic Agriculture Development in Polish Districts—An Implementation of Canonical Analysis

2020

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Organic farming in Poland has been developing dynamically since the accession to the EU. However, there are considerable differences in the level of organic farming development in particular regions. Therefore, it is vital to identify the primary factors and conditions for this development and their importance considering their spatial distribution. The presented paper aims to estimate the relationships between the level of organic agriculture development and selected conditions of financial and environmental character. The investigation is based on primary data for 2017 retrieved from the Local Data Bank of the Main Statistical Office and Agricultural and Food Quality Inspection database for all Poland districts. In order to achieve this objective, a multivariate statistical analysis method—the canonical correlation was applied. The analysis was preceded by constructing the composite indices of organic agriculture development and its conditions based on a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method and correlation analysis between the indices developed by the authors. The performed canonical analysis showed two statistically significant canonical variables with relatively high values of the canonical correlation (0.74 and 0.59). It proves that the degree of explaining the variability of one set of variables through linear relationships (referring to the level of organic farming development), by the second input data set (describing the determinants of the development), by successive pairs of canonical variables is high. This means that the created model describes relatively well the considered data sets.

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Canonical Correlation between Soil Attributes and Foliar of Conilon Coffee Trees

Cited by 5 publications

References 11 publications

metan: An R package for multi‐environment trial analysis

metan: An R package for multi‐environment trial analysis

metan: an R package for multi-environment trial analysis

Identification of the Conditions for Organic Agriculture Development in Polish Districts—An Implementation of Canonical Analysis

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