scholarly journals DRY-FIELD (TEGALAN) AGROFORESTRY SYSTEMS AS MINIATURE NATURE FOREST IN OUTSIDE FOREST AREA ON BULU - GIRIPURWO VILLAGE, GIRIMULYO DISTRICT, KULONPROGO

2015 ◽  
Vol 2 (1) ◽  
pp. 213
Author(s):  
Chandra Nur Triwiyanto ◽  
Priyono Suryanto ◽  
Budiadi _
Keyword(s):  
Mixed Models ◽  
Forest Ecosystems ◽  
Mixed Model ◽  
Agroforestry Systems ◽  
Natural Forest ◽  
Equation Modeling ◽  
Intensive Management ◽  
Forest Trees ◽  
Exponential Equation ◽  
Selection Of

<p>A dry-field (tegalan) is one of the subsystems of classical agroforestry, where there is no intensive management of both the spacing and the selection of the type that is considered. Dry-field (tegalan) agroforestry subsystems have a state that resembles natural forest ecosystems. It became an opportunity execution of research related to the stands composition of dry-field (tegalan) and forest nature. The purpose of this study is to determine the pattern of developing agroforestry in Bulu and the relation of the natural forest. This research was conducted in Bulu-Giripurwo Village, Girimulyo District, Kulonprogo. Data was collected at 36 sample plots representing 3 strata. The first stratum had an area of &lt; 1000 m2, the second 1,000 m2–2,000 m2, and the third &gt; 2,000 m2. K-Means Cluster Analysis and exponential equation modeling was used to analyze the dry-field (tegalan). The results of this study indicate that there were three models of management of cultivated land in Bulu; the Mixed Model, Model MPTS dominant plants, and forest trees dominant model. Mixed models had the stand equation Y = 3.39 x 2.7128-0,026X with an R2 of 0.798. The dominant MPTS plan models had the stand equation 3,155 x 2.7128-0,021X with an R2 of 0.770. The dominant of forest trees models has stands equation Y = 3.182 x 2.7128-0,024X with R2 of 0.706. These results demonstrate agroforestry modeling subsystem dry-field (tegalan) has characteristics resembling natural forest indicated from the value of R2 that characterized so close with the equation of uneven-aged forest model.</p><p><br /><strong>Keywords</strong>: Agroforestry, dry-field (tegalan), natural forest</p>

scholarly journals Using mixed models to combine smooth size-selection and catch-comparison curves over hauls

2003 ◽  
Vol 60 (4) ◽  
pp. 448-459 ◽  
Author(s):  
R J Fryer ◽  
A F Zuur ◽  
N Graham
Keyword(s):  
Mixed Models ◽  
Fixed Effects ◽  
Mixed Model ◽  
Random Effect ◽  
Categorical Variable ◽  
Size Selection ◽  
Research Survey ◽  
Different Levels ◽  
Selection Of ◽  
Comparison Data

Parametric size-selection curves are often combined over hauls to estimate a mean selection curve using a mixed model in which between-haul variation in selection is treated as a random effect. This paper shows how the mixed model can be extended to estimate a mean selection curve when smooth nonparametric size-selection curves are used. The method also estimates the between-haul variation in selection at each length and can model fixed effects in the form of the different levels of a categorical variable. Data obtained to estimate the size-selection of dab by a Nordmøre grid are used for illustration. The method can also be used to provide a length-based analysis of catch-comparison data, either to compare a test net with a standard net or to calibrate two research survey vessels. Haddock data from an intercalibration exercise are used for illustration.

scholarly journals A general framework for prediction in penalized regression

2020 ◽  
pp. 1471082X1989686
Author(s):  
Alba Carballo ◽  
Maria Durban ◽  
Göran Kauermann ◽  
Dae-Jin Lee
Keyword(s):  
Mixed Models ◽  
General Framework ◽  
Mixed Model ◽  
Penalized Regression ◽  
Low Rank ◽  
Mixed Model Approach ◽  
Alternative Approach ◽  
Predicted Values ◽  
Selection Of ◽  
Model Approach

There are two main approaches to carrying out prediction in the context of penalized regression: with low-rank basis and penalties or through the smooth mixed models. In this article, we give further insight in the case of P-splines showing the influence of the penalty on the prediction. In the context of mixed models, we can connect the new predicted values to the observed values through a joint normal distribution, which allows us to compute prediction intervals. In this work, we propose an alternative approach, called the extended mixed model approach, that allows us to fit and predict data simultaneously. The methodology is illustrated with two real datasets, one of them on aboveground biomass and the other on monthly sulphur dioxide ([Formula: see text]) levels in a selection of monitoring sites in Europe.

scholarly journals Gradient boosting for linear mixed models

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Colin Griesbach ◽  
Benjamin Säfken ◽  
Elisabeth Waldmann
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Selection Procedure ◽  
Classification Theory ◽  
Gradient Boosting ◽  
Random Structure ◽  
Boosting Algorithm ◽  
The One ◽  
Biased Estimates ◽  
Selection Of

Abstract Gradient boosting from the field of statistical learning is widely known as a powerful framework for estimation and selection of predictor effects in various regression models by adapting concepts from classification theory. Current boosting approaches also offer methods accounting for random effects and thus enable prediction of mixed models for longitudinal and clustered data. However, these approaches include several flaws resulting in unbalanced effect selection with falsely induced shrinkage and a low convergence rate on the one hand and biased estimates of the random effects on the other hand. We therefore propose a new boosting algorithm which explicitly accounts for the random structure by excluding it from the selection procedure, properly correcting the random effects estimates and in addition providing likelihood-based estimation of the random effects variance structure. The new algorithm offers an organic and unbiased fitting approach, which is shown via simulations and data examples.

scholarly journals Selection of coffee progenies for resistance to nematode Meloidogyne paranaensis in infested area

2014 ◽  
Vol 14 (2) ◽  
pp. 94-101 ◽  
Author(s):  
Sonia Maria Lima Salgado ◽  
Juliana Costa de Rezende ◽  
José Airton Rodrigues Nunes
Keyword(s):  
Genetic Variability ◽  
Coffea Arabica ◽  
Mixed Model ◽  
High Rate ◽  
Germplasm Bank ◽  
Genotypic Association ◽  
Coffee Plants ◽  
Plagiotropic Branches ◽  
Mixed Model Methodology ◽  
Selection Of

The purpose of this study was to select Coffea arabica progenies for resistance to M. paranaensis in an infested coffee growing area using Henderson's mixed model methodology. Forty-one genotypes were selected at the Coffee Active Germplasm Bank of Minas Gerais, and evaluated in regard to stem diameter, number of plagiotropic branches, reaction to the nematode, and yield per plant. There was genetic variability among the genotypes studied for all the traits evaluated, and among the populations studied for yield and reaction to the nematode, indicating possibilities for obtaining genetic gains through selection in this population. There was high rate of genotypic association between all the traits studied. Coffee plants of Timor Hybrid UFV408-01 population, and F3 progenies derived from crossing Catuaí Vermelho and Amphillo MR 2161 were the most promising in the area infested by M. paranaensis.

scholarly journals Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 362
Author(s):  
Ioannis Spyroglou ◽  
Jan Skalák ◽  
Veronika Balakhonova ◽  
Zuzana Benedikty ◽  
Alexandros G. Rigas ◽  
...  
Keyword(s):  
Time Series ◽  
Random Effects ◽  
Mixed Models ◽  
Mixed Model ◽  
Repeated Measurements ◽  
Time Series Models ◽  
Additional Time ◽  
Biotic Stresses ◽  
Viable Solution ◽  
Long Time

Plants adapt to continual changes in environmental conditions throughout their life spans. High-throughput phenotyping methods have been developed to noninvasively monitor the physiological responses to abiotic/biotic stresses on a scale spanning a long time, covering most of the vegetative and reproductive stages. However, some of the physiological events comprise almost immediate and very fast responses towards the changing environment which might be overlooked in long-term observations. Additionally, there are certain technical difficulties and restrictions in analyzing phenotyping data, especially when dealing with repeated measurements. In this study, a method for comparing means at different time points using generalized linear mixed models combined with classical time series models is presented. As an example, we use multiple chlorophyll time series measurements from different genotypes. The use of additional time series models as random effects is essential as the residuals of the initial mixed model may contain autocorrelations that bias the result. The nature of mixed models offers a viable solution as these can incorporate time series models for residuals as random effects. The results from analyzing chlorophyll content time series show that the autocorrelation is successfully eliminated from the residuals and incorporated into the final model. This allows the use of statistical inference.

The Role of Ectomycorrhizal Symbiosis in the Resistance of Forests to Water Stress

2000 ◽  
Vol 29 (1) ◽  
pp. 63-69 ◽  
Author(s):  
Jean Garbaye
Keyword(s):  
Water Stress ◽  
Sustainable Management ◽  
Fine Roots ◽  
Forest Ecosystems ◽  
Water Status ◽  
Forest Trees ◽  
Direct Effects ◽  
Ectomycorrhizal Symbiosis ◽  
Protection Mechanisms

Forest trees live in enforced symbiosis with specialized fungi that form composite organs (ectomycorrhizas) with fine roots. This paper examines how this association contributes to the water status of trees and how it plays a major role in the protection mechanisms by which trees and forest stands resist drought-induced water stress. It shows how ectomycorrhizal symbiosis has both direct effects (at the uptake level) and indirect effects (at the regulation level) on the water status of trees. The facts presented are discussed in terms of forest adaptation to changing environmental conditions and the practical consequences for the sustainable management of forest ecosystems.

The Effect of E-Service Quality on Football Fan Satisfaction and Fan Loyalty toward the Websites of Their Favorable Football Teams

2016 ◽  
Vol 12 (1) ◽  
pp. 43-57 ◽  
Author(s):  
Javad Khazaei Pool ◽  
Ali Dehghan ◽  
Hadi Balouei Jamkhaneh ◽  
Akbar Jaberi ◽  
Maryam Sharifkhani
Keyword(s):  
Service Quality ◽  
Structural Equation ◽  
Marketing Research ◽  
Strong Support ◽  
Equation Modeling ◽  
Valuable Insight ◽  
Electronic Service ◽  
Sports Club ◽  
Insight Into ◽  
Selection Of

The purpose of the current study was to examine the effect of electronic service quality on fan satisfaction and fan loyalty in the online environment. Selection of three hundred and fifty-six fans of a famous sports club was through random sampling using the club's website. AMOS used structural equation modeling for data analysis. Results provided strong support on the effect of electronic service quality (E-S-QUAL) on fan satisfaction and fan loyalty toward the website of their favorable football teams. Business enterprises have well researched e-service quality and loyalty. However, limited research exists in the sports context. This paper provides valuable insight into the measurement of e-service quality and fan loyalty in the sport and offers a foundation for future marketing research.

scholarly journals Optimal models in the yield analysis of new flax cultivars

2018 ◽  
Vol 98 (4) ◽  
pp. 897-907
Author(s):  
Gaofeng Jia ◽  
Helen M. Booker
Keyword(s):  
Mixed Models ◽  
Statistical Power ◽  
Mixed Model ◽  
Trial Data ◽  
Residual Error ◽  
Yield Data ◽  
Symmetry Model ◽  
Compound Symmetry ◽  
Future Data ◽  
Variety Performance

Multi-environment trials are conducted to evaluate the performance of cultivars. In a combined analysis, the mixed model is superior to an analysis of variance for evaluating and comparing cultivars and dealing with an unbalanced data structure. This study seeks to identify the optimal models using the Saskatchewan Variety Performance Group post-registration regional trial data for flax. Yield data were collected for 15 entries in post-registration tests conducted in Saskatchewan from 2007 to 2016 (except 2011) and 16 mixed models with homogeneous or heterogeneous residual errors were compared. A compound symmetry model with heterogeneous residual error (CSR) had the best fit, with a normal distribution of residuals and a mean of zero fitted to the trial data for each year. The compound symmetry model with homogeneous residual error (CS) and a model extending the CSR to higher dimensions (DIAGR) were the next best models in most cases. Five hundred random samples from a two-stage sampling method were produced to determine the optimal models suitable for various environments. The CSR model was superior to other models for 396 out of 500 samples (79.2%). The top three models, CSR, CS, and DIAGR, had higher statistical power and could be used to access the yield stability of the new flax cultivars. Optimal mixed models are recommended for future data analysis of new flax cultivars in regional tests.

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