Reference sample size for multiple regression in corn

Filho, Alberto Cargnelutti; Toebe, Marcos

doi:10.1590/s1678-3921.pab2020.v55.01400

Cited by 7 publications

(5 citation statements)

References 17 publications

(38 reference statements)

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“…Lyu et al (2021) described the importance of generating multiple models that could be compared to develop accurate evaluation measures that could contribute to the ecological aspects and sustainable economic development of pasture areas. Cargnelutti Filho and Toebe (2020) highlighted that when variables exhibit high correlations (as in the present research, Table 1), multiple models can generate adequate precision indicators and reduce their uncertainty coefficient. However, their applicability is worth noting.…”

Section: Resultssupporting

confidence: 50%

Prediction of Brachiaria decumbens forage biomass using structural characteristics

Conrado

Lopes

Cândido

et al. 2021

Chil. j. agric. res.

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Tools that generate models with good biomass predictive capacity are essential to maintain the sustainability of production systems. The objective was to analyze the relationship between forage biomass and structural variables and generate models to predict total forage biomass (TFB) and green leaf blade biomass (GLB). Irrigated pastures of Brachiaria decumbens Stapf 'Basilisk' were kept under rotational stocking with sheep (Ovis aries L.) The TFB, GLB, leaf area index (LAI), height (cm), and normalized difference vegetation index (NDVI) were evaluated. The experimental design was completely randomized with four replicates: ten and five cycles of defoliation management, respectively, were used to generate and validate the stages of the models. The best goodness of fit was obtained by nonlinear models for both TFB and GLB, which can be confirmed by high Spearman's correlations and significance (P < 0.0001). The path analysis showed low collinearity (42.60) between NDVI, LAI, and height; the high determination coefficient (R²) with values of 0.8421 and 0.7767 demonstrated their associations with TFB and GLB, respectively. Among the studied models to predict TFB and GLB, only exponentials using NDVI and power using LAI and height showed the best fit. In the validation stage, the models related to height exhibited the highest performance with 0.9531 (TFB) and 0.9638 (GLB) d-index, -2.3 (TFB) and -7.20 (GLB) bias, and 0.8532 (TFB) and 0.8932 (GLB) R². Only nonlinear models using height (cm) to predict TFB and GLB had the best practical application potential, thus ensuring efficiency in data collection.

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Section: Resultssupporting

confidence: 50%

Prediction of Brachiaria decumbens forage biomass using structural characteristics

Conrado

Lopes

Cândido

et al. 2021

Chil. j. agric. res.

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“…The analysis using SCIGRESS was based on multilinear regression (MLR), i.e., a traditional approach in QSAR [31]. According to Cargnelutti Filho et al [32], in MLR, the sample size depends on effect size, whereas the number of independent variables and recommendations for the number of chemicals are associated with the different criteria adopted by each researcher. Based on information from the theoretical model selection, Jenkins and Quintana-Asencio [30] tried to recommend the minimum N (understood as the number of samples) to correctly match a model to a data shape in regression models.…”

Section: Resultsmentioning

confidence: 99%

Analysis of Structure–Activity Relationships of Food-Derived DPP IV-Inhibitory Di- and Tripeptides Using Interpretable Descriptors

Hrynkiewicz,

Iwaniak,

Minkiewicz

et al. 2023

Applied Sciences

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This study aimed to analyze the structural requirements for di- and tripeptides exhibiting a DPP IV-inhibitory effect. The sequences of 46 di- and 33 tripeptides, including their bioactivity (IC50; μM), were implemented from the BIOPEP-UWM database, whereas modeling was performed using SCIGRESS Explorer: Version FJ 3.5.1 software. Models included 336 (dipeptide dataset) and 184 descriptors (tripeptide dataset). The values of the determination coefficient (R2) defining model reliability were 0.782 and 0.829 for di- and tripeptides, respectively. Based on the implemented descriptors, it was concluded that increased numbers of nitrogen atoms, as well as the methyl groups, are required for dipeptides to enhance the DPP IV-inhibitory effect. This was indicated by the presence of amino acids with an aliphatic side chain (e.g., Leu, Val, Ile) and an aromatic ring (Trp). In the case of tripeptides, a correlation was found between their molecular weight (MW) and studied bioactivity. A tripeptide with a molecular weight of up to 500 Da was found suitable for the sequence to act as the DPP IV inhibitor. Although there is still a gap in explaining the relations between the structural nature and the DPP IV-inhibitory activity of peptides, and certain issues related to this topic still remain unknown, the results are in line with those reported by other authors. Additionally, the suitability of the SCIGRESS tool in the QSAR analysis of peptides derived from foods can be confirmed. Interpretable descriptors enabled the achievement of more unequivocal results concerning the main structural factors affecting the DPP IV inhibition of di- and tripeptides.

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“…The influence of specific agronomic traits on sweet corn production, as revealed by the path diagram, underscores the importance of considering a range of factors in breeding and agronomic decision-making. By integrating insights from studies on nutrient uptake, genetic diversity, and environmental influences, breeders and agronomists can make informed decisions to optimize sweet corn production and enhance the performance of specific traits such as dry weight, ultimately contributing to the overall improvement of sweet corn varieties and agronomic practices (Sugiono, 2023;Filho & Toebe, 2020).…”

Section: Figure 1 Heatmap Data Of Correlationmentioning

confidence: 99%

Selection of Sweet Corn Inbred Lines by Agronomic Performance to Determining Hybrid Parents

Suyadi,

Arifin,

Kurniawan

2024

jpt

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The development of hybrid sweet corn varieties involves the selection of parental lines with specific traits to ensure good yield potential. The selection of inbred lines of sweet corn is a critical aspect of breeding programs aimed at developing improved varieties. This research will provide useful information of sweet corn inbreed lines have the potential to be developed as parent lines in the development of hybrid varieties. The study utilized 24 inbred lines (G1-G24) of sweet corn and employed a Randomized Complete Block Design (RCBD). Data analysis involved the use of analysis of variance with a 5% significance level, path and dendrogram analysis. Based on the analysis of 24 inbreed line of sweet corn, the potential for selecting parents based on quantitative traits influencing the outcome is evident in inbreed line G18, G12, G4, G23, and G20. Identification of these potential parents is essential for the genetic improvement of the population. The correlation heatmap data indicates a 41% correlation between the number of rows and cob stag length, and correlation between number of rows and ear diameter by 48%. The path diagram indicates that fresh weight, a crucial measure of crop yield, is strongly influenced by several agronomic traits, including plant height, ear length, ear diameter, dry weight, number of plants, row length, brix, and ear stalk. G24 can indeed be used as one of the parents with other group in hybrid breeding programs.

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Reference sample size for multiple regression in corn

Cited by 7 publications

References 17 publications

Prediction of Brachiaria decumbens forage biomass using structural characteristics

Prediction of Brachiaria decumbens forage biomass using structural characteristics

Analysis of Structure–Activity Relationships of Food-Derived DPP IV-Inhibitory Di- and Tripeptides Using Interpretable Descriptors

Selection of Sweet Corn Inbred Lines by Agronomic Performance to Determining Hybrid Parents

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