Financial assessment of alternative breeding goals using stand-level optimization and data envelopment analysis

ABSTRACT Choosing traits for breeding requires financial and genetic information. This study introduces a theoretically sound and empirically detailed assessment setup for analyzing two traits from the financial perspective: decay resistance and tree growth. The analysis is conducted in two stages: first (1), a stand-level optimization analysis is applied producing maximum bare land value with the presence of enhanced tree growth or decay resistance. Realized genetic improvement effects in growth were incorporated in the existing growth models by means of genetic multipliers. For decay resistance the genetic gain was based on an assumption of full resistance to decay. Then (2), applying the optimal solutions as outputs, and investments for enhanced tree growth and decay resistance as inputs in a data envelopment analysis, DEA efficient frontiers for three climatic regions, Southern, Central and Northern Finland were revealed. The results indicated that focusing on tree growth in Southern Finland would be more beneficial than in Central and Northern Finland. Further, the DEA showed that efficient frontiers are not identical among the climatic regions, suggesting that separate breeding goals would be applied, too.

[1]  H. Viitanen,et al.  Variation in the decay resistance and its relationship with other wood characteristics in old Scots pines , 2003 .

[2]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[3]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[4]  R. Olschewski,et al.  Optimizing joint production of timber and carbon sequestration of afforestation projects , 2010 .

[5]  T. Metcalfe,et al.  Stellar structure modeling using a parallel genetic algorithm for objective global optimization , 2002, astro-ph/0208315.

[6]  Risto Lahdelma,et al.  Data envelopment analysis: visualizing the results , 1995 .

[7]  R. Henry,et al.  Genetics of physical wood properties and early growth in a tropical pine hybrid , 2003 .

[8]  D. Neale,et al.  Forest tree genomics: growing resources and applications , 2011, Nature Reviews Genetics.

[9]  Ted L. Helvoigt,et al.  Data envelopment analysis of technical efficiency and productivity growth in the US Pacific Northwest sawmill industry , 2008 .

[10]  M. Ivković,et al.  Bio-economic Modelling as a Method for Determining Economic Weights for Optimal Multiple-Trait Tree Selection , 2010 .

[11]  William R. Wykoff,et al.  A Basal Area Increment Model for Individual Conifers in the Northern Rocky Mountains , 1990, Forest Science.

[12]  M. Izadifar,et al.  Application of genetic algorithm for optimization of vegetable oil hydrogenation process , 2007 .

[13]  Financial performance of using genetically improved regeneration material of Scots pine (Pinus sylvestris L.) in Finland , 2012, New Forests.

[14]  L. Valsta,et al.  An optimization model for Norway spruce management based on individual-tree growth models. , 1992 .

[15]  Avi Ostfeld,et al.  A coupled model tree-genetic algorithm scheme for flow and water quality predictions in watersheds , 2008 .

[16]  Using a production approach to estimate economic weights for structural attributes of Pinus radiata wood , 2013 .

[17]  A. D. Cameron,et al.  Genetic relationships between wood quality traits and diameter growth of juvenile core wood in Sitka spruce , 2013 .

[18]  A. Amirteimoori,et al.  Measuring the relative performance of forest management units: a chance-constrained DEA model in the presence of the nondiscretionary factor , 2019, Canadian Journal of Forest Research.

[19]  Luis A. Apiolaza,et al.  A DEA approach to assess the efficiency of radiata pine logs to produce New Zealand structural grades , 2013 .

[20]  M. Venäläinen,et al.  Genetic Parameters Regarding the Resistance of Pinus sylvestris Heartwood to Decay Caused by Coniophora puteana , 2002 .

[21]  R. Sturrock,et al.  Fungal decay of western redcedar wood products– a review , 2017 .

[22]  Olli Tahvonen,et al.  On the economics of optimal timber production in boreal Scots pine stands , 2013 .

[23]  Timo Pukkala,et al.  Use of depth-first search and direct search methods to optimize even-aged stand management: a case study involving maritime pine in Asturias (northwest Spain) , 2015 .

[24]  Joe Zhu,et al.  Primal-dual correspondence and frontier projections in two-stage network DEA models , 2019, Omega.

[25]  Michael Norman,et al.  Data Envelopment Analysis: The Assessment of Performance , 1991 .

[26]  K. Eerikäinen,et al.  Long-term impacts of forest management on biomass supply and forest resource development: a scenario analysis for Finland , 2015, European Journal of Forest Research.

[27]  K. Chin,et al.  Thermal treatment of wood using vegetable oils: A review , 2018, Construction and Building Materials.

[28]  Matthias Ehrgott,et al.  Uncertain Data Envelopment Analysis , 2018, Eur. J. Oper. Res..

[29]  D. Garrick,et al.  Breeding objectives for three silvicultural regimes of radiata pine , 2001 .

[30]  Olli Tahvonen,et al.  Applying a process-based model in Norway spruce management , 2012 .

[31]  Mika Lehtonen,et al.  Reusing legacy FORTRAN in the MOTTI growth and yield simulator , 2005 .

[32]  R. Wardlaw,et al.  EVALUATION OF GENETIC ALGORITHMS FOR OPTIMAL RESERVOIR SYSTEM OPERATION , 1999 .

[33]  P. Mathur,et al.  Economic weights for sow productivity traits in nucleus pig populations , 2006 .

[34]  J. Karhu,et al.  Genetically improved reforestation stock provides simultaneous benefits for growers and a sawmill, a case study in Finland , 2018 .

[35]  L. Apiolaza,et al.  Incorporating economic weights into radiata pine breeding selection decisions , 2015 .

[36]  Magnus Thor,et al.  Reaction zone and periodic increment decrease in Picea abies trees infected by Heterobasidion annosum s.l. , 2010 .

[37]  S. Chang,et al.  Carbon sequestration and uneven-aged management of loblolly pine stands in the southern USA: a joint optimization approach , 2012 .

[38]  M. Powell,et al.  Developing breeding objectives for radiata pine structural wood production. I. Bioeconomic model and economic weights , 2006 .

[39]  C. Kole,et al.  Economic Importance, Breeding Objectives and Achievements , 2011 .

[40]  Yong Li,et al.  Optimizing emission inventory for chemical transport models by using genetic algorithm , 2010 .

[41]  Vivian W. Y Tam,et al.  Life-cycle cost analysis of green-building implementation using timber applications , 2017 .

[42]  S. Liong,et al.  Peak-flow forecasting with genetic algorithm and SWMM , 1995 .

[43]  H. Mäkinen,et al.  Including variation in branch and tree properties improves timber grade estimates in Scots pine stands , 2018 .

[44]  K. Hyytiäinen,et al.  Effects of initial stand states on optimal thinning regime and rotation of Picea abies stands , 2006 .

[45]  R. Banker Estimating most productive scale size using data envelopment analysis , 1984 .

[46]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[47]  Genetic parameters for wood quality traits and resistance to the pathogens Heterobasidion parviporum and Endoconidiophora polonica in a Norway spruce breeding population , 2016, European Journal of Forest Research.

[48]  R. Mäkipää,et al.  The impact of a short-term carbon payment scheme on forest management , 2018 .

[49]  G. Swedjemark,et al.  Genotypic variation in susceptibility following artificial Heterobasidion annosum inoculation of Picea abies clones in a 17-year-old field test , 2004 .

[50]  John S. Liu,et al.  Research fronts in data envelopment analysis , 2016 .

[51]  Jardar Lohne,et al.  High-rise Timber Buildings as a Climate Change Mitigation Measure – A Comparative LCA of Structural System Alternatives , 2016 .

[52]  J. Partanen,et al.  Highly heritable heartwood properties of Scots pine: possibilities for selective seed harvest in seed orchards , 2011 .

[53]  Josef Jablonsky,et al.  A robust data envelopment analysis model with different scenarios , 2017 .

[54]  J. Hynynen,et al.  Realised and projected gains in growth, quality and simulated yield of genetically improved Scots pine in southern Finland , 2016, European Journal of Forest Research.

[55]  D. Herms,et al.  The Dilemma of Plants: To Grow or Defend , 1992, The Quarterly Review of Biology.

[56]  J. Koricheva,et al.  Regulation of Woody Plant Secondary Metabolism by Resource Availability: Hypothesis Testing by Means of Meta-Analysis , 1998 .

[57]  Taraneh Sowlati,et al.  The development of a timber allocation model using data envelopment analysis , 2005 .

[58]  Anssi Ahtikoski,et al.  Optimizing stand management involving the effect of genetic gain: preliminary results for Scots pine in Finland , 2013 .

[59]  StepheN D. VerryN Breeding for wood quality — a perspective for the future* , 2008 .