Articles containing the keyword 'mixed models'

Models for individual-tree basal area growth were constructed for Scots pine (Pinus sylvestris L.), pubescent birch (Betula pubescens Ehrh.) and Norway spruce (Picea abies (L.) Karst.) growing in drained peatland stands. The data consisted of two separate sets of permanent sample plots forming a large sample of drained peatland stands in Finland. The dependent variable in all models was the 5-year basal area growth of a tree. The independent tree-level variables were tree dbh, tree basal area, and the sum of the basal area of trees larger than the target tree. Independent stand-level variables were stand basal area, the diameter of the tree of median basal area, and temperature sum. Categorical variables describing the site quality, as well as the condition and age of drainage, were used. Differences in tree growth were used as criteria in reclassifying the a priori site types into new yield classes by tree species. All models were constructed as mixed linear models with a random stand effect. The models were tested against the modelling data and against independent data sets.

• Hökkä, E-mail: hh@mm.unknown
• Alenius, E-mail: va@mm.unknown
• Penttilä, E-mail: tp@mm.unknown

Semiparametric models, ordinary regression models and mixed models were compared for modelling stem volume in National Forest Inventory data. MSE was lowest for the mixed model. Examination of spatial distribution of residuals showed that spatial correlation of residuals is lower for semiparametric and mixed models than for parametric models with fixed regressors. Mixed models and semiparametric models can both be used for describing the effect of geographic location on stem form.

• Kangas, E-mail: ak@mm.unknown
• Korhonen, E-mail: kk@mm.unknown

Much of forestry data is characterized by a longitudinal or repeated measures structure where multiple observations taken on some units of interest are correlated. Such dependencies are often ignored in favour of an apparently simpler analysis at the cost of invalid inferences. The last decade has brought to light many new statistical techniques that enable one to successfully deal with dependent observations. Although apparently distinct at first, the theory of Estimating Functions provides a natural extension of classical estimation that encompasses many of these new approaches. This contribution introduces Estimating Function Theory as a principle with potential for unification and presents examples covering a variety of modelling issues to demonstrate its applicability.

• Schabenberger, E-mail: os@mm.unknown
• Gregoire, E-mail: tg@mm.unknown

The widespread cork oak (Quercus suber L.) mortality and reduced afforestation /regeneration are causing an overall reduction in cork production. To enhance trees’ growth and vitality, afforestation techniques using fertirrigation were tested. The main objective was the promotion of trees’ growth on new dense plantations using minimum water requirements until reaching productive forests. The experimental plot – Irricork – was installed in 2017 in a ≈1 ha stand with 14 years’ age cork oaks summer-fertirrigated since plantation. Four fertirrigation treatments were applied during fertirrigation campaigns. Radial growth, meteorological parameters and fertirrigation volume were measured every 15–30 days over four years. It was observed that weather, tree size, debarking and trees’ intra-competition had a significant effect on radial increments. Fertirrigation significantly enhanced growth during summer drought and decoupled increments from air vapor pressure deficit constraints. There was a linear relationship between trees’ radial increments and fertirrigation volume up to 140 m³ week^–1. Above this value, increments were smoother. In conclusion, summer fertirrigation of 140 m³ week^–1 efficiently enhanced the radial growth of trees with 50–75 circumference at breast height, under the particular edaphoclimatic conditions of the stand. This study showed to be, therefore, promising in the use of efficient fertirrigation the enhance cork oaks’ radial growth.

• Camilo-Alves, MED – Mediterranean Institute for Agriculture, Environment and Development & CHANGE – Global Change and Sustainability Institute, Institute for Advanced Studies and Research, University of Evora, Pólo da Mitra, Ap. 94, 7006-554 Évora, Portugal https://orcid.org/0000-0001-5156-172X E-mail: calves@uevora.pt
• Nunes, Department of Plant Science, School of Science and Technology, University of Evora, Pólo da Mitra, Ap. 94, 7006-554 Évora, Portugal https://orcid.org/0000-0002-6144-3484 E-mail: jain@uevora.pt
• Poeiras, MED – Mediterranean Institute for Agriculture, Environment and Development & CHANGE – Global Change and Sustainability Institute, Institute for Advanced Studies and Research, University of Evora, Pólo da Mitra, Ap. 94, 7006-554 Évora, Portugal https://orcid.org/0000-0002-6049-807X E-mail: apcp@uevora.pt
• Ribeiro, Department of Plant Science, School of Science and Technology, University of Evora, Pólo da Mitra, Ap. 94, 7006-554 Évora, Portugal https://orcid.org/0000-0001-7242-5866 E-mail: jmrpr@uevora.pt
• Dinis, https://orcid.org/0000-0001-6984-1033 E-mail: dinis.cati@gmail.com
• Barroso, MED – Mediterranean Institute for Agriculture, Environment and Development & CHANGE – Global Change and Sustainability Institute, and Department of Plant Science, School of Science and Technology, University of Evora, Pólo da Mitra, Ap. 94, 7006-554 Évora, Portugal https://orcid.org/0000-0002-0160-3845 E-mail: jmmb@uevora.pt
• Vaz, MED – Mediterranean Institute for Agriculture, Environment and Development & CHANGE – Global Change and Sustainability Institute, and Department of Biology, School of Science and Technology, University of Evora. Pólo da Mitra, Ap. 94, 7006-554 Évora, Portugal https://orcid.org/0000-0002-3368-757X E-mail: mvaz@uevora.pt
• Almeida-Ribeiro, ICT – Institute of Earth Sciences and Department of Plant Science, School of Science and Technology, University of Evora, Pólo da Mitra, Ap. 94, 7006-554 Évora, Portugal https://orcid.org/0000-0002-0160-3845 E-mail: nmcar@uevora.pt

The present research focuses on the productivity of energy wood chipping operations at several sites in Italy. The aim was to assess the productivity and specifically the effect attributed to the operator in the chipping of wood biomass. The research included 172 trials involving 67 operators across the country that were analysed using a mixed model approach, in order to assess productivity, and to isolate the operator effect from other potential variables. The model was constructed using different predictors aiming to explain the variability due to the machines and the raw-materials. The final model included the average piece weight of raw material chipped as well as the power of the machine. The coefficients of determination (R²) were 0.76 for the fixed part of the model, and 0.88 when the effects due to the operators were included. The operators’ performance compared to their peers was established, and it was compared to a subjective classification based on the operator’s previous experience. The results of this study can help to the planning and logistics of raw material supply for bioenergy, as well as to a more effective training of future forest operators.

• Mola-Yudego, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland; NIBIO Norwegian Institute of Bioeconomy Research, P.O. Box 115, 1431 Ås, Norway http://orcid.org/0000-0003-0286-0170 E-mail: blas.mola@uef.fi
• Picchi, CNR IVALSA, Via Madonna del Piano 10, I-50019 Sesto Fiorentino, Italy E-mail: picchi@ivalsa.cnr.it
• Röser, Forest Feedstocks Group, FPInnovations, Vancouver, British Columbia, Canada E-mail: dominik.roser@fpinnovations.ca
• Spinelli, CNR IVALSA, Via Madonna del Piano 10, I-50019 Sesto Fiorentino, Italy E-mail: spinelli@ivalsa.cnr.it

• Repola, Finnish Forest Research Institute, Rovaniemi Research Unit, Eteläranta 55, FI-96300 Rovaniemi, Finland E-mail: jaakko.repola@metla.fi

• Jutras, Département des sciences du bois et de la forêt, Université Laval, Ste-Foy, Québec, G1K 7P4, Canada E-mail: sj@nn.ca
• Hökkä, Finnish Forest Research Institute, Rovaniemi Research Station, P.O. Box 16, FIN 96301 Rovaniemi, Finland E-mail: hannu.hokka@metla.fi
• Alenius, Finnish Forest Research Institute, Rovaniemi Research Station, P.O. Box 16, FIN 96301 Rovaniemi, Finland E-mail: va@nn.fi
• Salminen, Finnish Forest Research Institute, Rovaniemi Research Station, P.O. Box 16, FIN 96301 Rovaniemi, Finland E-mail: hs@nn.fi

• Ihalainen, University of Joensuu, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: marjut.ihalainen@joensuu.fi
• Salo, The Finnish Forest Research Institute, Joensuu Research Centre, P.O. Box 68, FIN-80101 Joensuu, Finland E-mail: ks@nn.fi
• Pukkala, University of Joensuu, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: tp@nn.fi