scholarly journals Modeling depth of drainage ditches in forested peatlands in Finland

2020 ◽  
Vol 26 (2) ◽  
Author(s):  
Hannu Hökkä ◽  
Leena Stenberg ◽  
Ari Laurén
Keyword(s):  
Linear Models ◽  
Bank Erosion ◽  
Important Variable ◽  
Forest Growth ◽  
Drainage Ditches ◽  
Inventory Data ◽  
Mixed Linear Models ◽  
Bed Slope ◽  
Ditch Cleaning ◽  
Average Development

Drainage ditches have been dug in peatlands and paludified forests in order to enhance forest growth in an area of 4.7 M ha in Finland. Because of peat subsidence, bank erosion, sedimentation, and ingrowth of vegetation ditches deteriorate with time. In this study the shallowing of ditch depth over time was investigated on the basis of country-wide peatland inventory data measured repeatedly up to four times. Mixed linear models were constructed separately for original ditches and maintained ditches (cleaned once or twice). After 20 years the ditches were 20-30 cm shallower than right after the digging. Time since digging was the most important variable explaining the shallowing for both original and maintained ditches. Other variables explaining the ditch shallowing were the digging method (excavator, plow), ditch bed slope, location, and peat layer thickness. The average development of maintained and original excavator ditches was very similar. The results can be used in assessing decision making concerning ditch cleaning.

Inheritance and Correlation Analysis of Main Agronomic Traits in Mungbean (Vigna radiata L.) by Mixed Linear Models

2013 ◽  
Vol 38 (4) ◽  
pp. 624-631
Author(s):  
Chang-You LIU ◽  
Bao-Jie FAN ◽  
Zhi-Min CAO ◽  
Yan WANG ◽  
Zhi-Xiao ZHANG ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Vigna Radiata ◽  
Linear Models ◽  
Agronomic Traits ◽  
Mixed Linear Models

scholarly journals A Method for Assessing Extent and Sources of Heterogeneity of Residual Variances in Mixed Linear Models

1990 ◽  
Vol 73 (6) ◽  
pp. 1612-1624 ◽  
Author(s):  
J.L. Foulley ◽  
D. Gianola ◽  
M. San Cristobal ◽  
S. Im
Keyword(s):  
Linear Models ◽  
Mixed Linear Models ◽  
Residual Variances

scholarly journals Application of Mixed Linear Models in the Analysis of Road Surface Features

2015 ◽  
Vol 54 (1) ◽  
pp. 101-109 ◽  
Author(s):  
Jurgita Židanavičiūtė ◽  
Audrius Vaitkus
Keyword(s):  
Linear Models ◽  
Road Surface ◽  
Linear Regression Models ◽  
Mixed Linear Models ◽  
Time Intervals ◽  
Surface Strength ◽  
The Road ◽  
Unequally Spaced ◽  
Units Of Analysis ◽  
The Impact

The data were collected by researchers at the Road Research Institute, in a study investigating the impact of differentfactors on road surface strength. In this statistical analysis, we apply linear mixed models (LMMs) to clustered longitudinal data, inwhich the units of analysis (points in the road) are nested within clusters (sample of four different road segments), and repeatedmeasures of road strength in these different points are collected over time with unequally spaced time intervals. The data arebalanced – each cluster has the same number of units, which are measured at the same number of time points. Because of correlateddata and different clusters in which data could be correlated, linear regression models are not appropriate here, and therefore linearmixed models are applied.

scholarly journals Stool and ruminal microbiome components associated with methane emission and feed efficiency in Nelore beef cattle.

2021 ◽  
Author(s):  
Bruno G.N. Andrade ◽  
Haithem Afli ◽  
Flavia A. Bressani ◽  
Rafael R. C. Cuadrat ◽  
Priscila S. N. de Oliveira ◽  
...  
Keyword(s):  
Methane Emission ◽  
Feed Efficiency ◽  
Linear Models ◽  
Anthropogenic Activities ◽  
Mixed Linear Models ◽  
Human Welfare ◽  
Feed Composition ◽  
Fecal Microbiome ◽  
Treatment Groups ◽  
The Impact

Abstract Background: The impact of extreme changes in weather patterns in the economy and human welfare are some of the biggest challenges that our civilization is facing. From the anthropogenic activities that contribute to climate change, reducing the impact of farming activities is a priority, since it is responsible for up to 18% of greenhouse gases linked to such activities. To this end, we tested if the ruminal and fecal microbiome components of 52 Brazilian Nelore bulls, belonging to two treatment groups based on the feed intervention, conventional and by-products based diet, could be used in the future as biomarkers for methane emission and feed efficiency in bovine.Results: We identified a total of 5,693 Amplicon Sequence Variants (ASVs) in the Nelore bulls microbiomes. Differential abundance (DA) analysis with the ANCOM approach identified 30 bacterial and 15 archaea ASVs as DA among treatment groups. Association analysis using Maaslin2 and Mixed Linear Models indicated that bacterial ASVs are linked to the residual methane emission (RCH4) and Residual Feed Intake (RFI) phenotypes, contributing to the host’s phenotypic variation, suggesting their potential as targets for interventions and/or biomarkers.Conclusion: Feed composition induced significant differences in abundance and richness of ruminal and fecal microbial populations. The diet based on industrial byproducts applied to our treatment groups influenced the microbiome diversity of bacteria and archaea, but not of protozoa. Different ASVs were associated with RCH4 emission and RFI in both ruminal and fecal microbiomes. While ruminal ASVs are expected to directly influence RCH4 emission and RFI, the relation of fecal taxa, such as Alistipes and Rikenellaceae (gut group RC9), with these traits might also be associated with host health due to their link to anti-inflammatory compounds, and these have the potential to be used as accessible biomarkers for these complex phenotypes.

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