Quantitative validation and comparison of a range of forest growth model types |
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| Affiliation: | 1. University of Canterbury, Private bag 4800, Chirstchurch, New Zealand;2. Forest Research, New Zealand;1. Sustainable Forest Management Research Institute, University of Valladolid - INIA, Avda. Madrid, s/n, 34004, Palencia, Spain;2. Departamento de Producción Vegetal y Recursos Forestales, ETS de Ingenierías Agrarias. Universidad de Valladolid, Palencia, Spain;3. Department of Agroforestry Sciences, Escuela Técnica Superior de Ingeniería, University of Huelva, Campus Universitario de La Rábida, Palos de la Frontera, 21819, Huelva, Spain;4. Departamento de Sistemas y Recursos Naturales, Escuela Técnica Superior de Ingeniería de Montes, Forestal y del Medio Natural, Universidad Politécnica de Madrid, Ciudad Universitaria, s/n, 28040, Madrid, Spain;5. Department of Silviculture and Forest Management, INIA, Forest Research Centre, Ctra.A Coruña, Km 7.5, 28040 Madrid, Spain;1. Irstea, UR EMGR, Centre de Grenoble, F-38402 St-Martin-d’Hères, France;2. CIRAD, UPR BSEF, F-34398 Montpellier, France;3. Irstea, UR LISC, F-63172 Aubière, France;4. INRA, UMR AMAP, F-34000 Montpellier, France;1. Beijing Research Center for Information Technology in Agriculture, Beijing, China;2. School of Information Science and Technology of Beijing Forestry University, Beijing, China;3. National Engineering Research Center for Information Technology in Agriculture, Beijing, China;4. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing, China;5. Beijing Engineering Research Center of Agricultural Internet of Things, Beijing, China;6. Fujian Provincial Department of Forestry, Fuzhou, China;1. Forest Dynamics and Management, INIA-CIFOR, Crtra Coruña Km 7.5, 28040 Madrid, Spain;2. Sustainable Forest Management Research Institute, University of Valladolid and INIA, Av. Madrid 44, Palencia 34004, Spain;3. Chair of Forest Growth and Yield Science, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Hans-Carl-v.-Carlowitz-Platz 2, 85354 Freising, Germany;4. INRAE, University of Bordeaux, BIOGECO, F-33610 Cestas, France;5. Department of Silviculture, Warsaw University of Life Sciences-SGGW, Nowoursynowska 159, 02776 Warsaw, Poland;6. Departament of Plant Production and Forest Resources, Higher Technical School of Agricultural Engineering of Palencia, University of Valladolid, Spain;7. Institute of Forest Biology and Silviculture, Vytautas Magnus University, Studentų 11, Akademija, 53361 Kaunas Distr., Lithuania;8. Laboratory of Functional Ecology, Institute of Biology, University of Neuchâtel, Rue Emile-Argand 11, 2000 Neuchâtel, Switzerland;9. University of Natural Resources and Life Sciences Vienna, Department of Forest and Soil Sciences, Institute of Forest Growth, Austria;10. Earth and Life Institute, Université catholique de Louvain, Croix du Sud 2, Box L7.05.09, BE-1348 Louvain–La–Neuve, Belgium;11. Latvian State Forest Research Institute Silava, Rigas 111, Salaspils, LV 2169, Latvia;12. CEFE UMR 5175, CNRS - Université de Montpellier - Université Paul-Valéry Montpellier - EPHE, 1919 Route de Mende, F-34293 Montpellier Cedex 5, France;13. Department for Innovation in Biological Agro-food and Forestry System (DIBAF), University of Tuscia, Via San Camillo De Lellis, SNC, 01100 Viterbo, Italy;14. Department of Forest Ecology and Silviculture, Faculty of Forestry, University of Agriculture in Krakow al, 29 Listopada 46 31-425 Kraków, Poland;15. Bavarian State Institute of Forestry (LWF), Hans-Carl-von-Carlowitz-Platz 1, D-85354 Freising, Germany |
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| Abstract: | Predictions from a range of model types (simplified process-based, a statistical state space, statistical difference, and a hybrid model) were compared to 969 measurements of forest growth across an environmental gradient. The models compared were 3-PG, CANTY, CanSPBL(1.2), and CanSPBL(water). The study made an objective comparison and validation of model types, with the main criterion for comparison being each model's ability to match actual historical measurements of forest growth in an independent data set. A number of stand level forest growth variables were compared including basal area, mean top height, and stocking over 14,058 ha of plantation-grown Pinus radiata in south-eastern New Zealand. Stand variable predictions at 195 permanent plot locations covering a range of elevations from 0 to 660 m were highly correlated with field estimates derived from plot data. The hybrid model CanSPBL(water) on average was the most accurate model in the study where predictions of stocking, basal area, and mean top height were 96%, 96%, and 96% efficient. The statistical-difference equation model CanSPBL(1.2) was equally efficient but on average 3% less accurate and slightly more biased in predictions suggesting that the hybrid model explained differences in growth due to differences water availability and soil type. The process-based model 3-PG predicted stocking and basal area 89% and 88% efficiently. Finally, the statistical state-space model CANTY predicted stocking, basal area, and mean top height 96%, 87%, and 87% efficiently. Results quantify the amount of precision that can be expected from the three model types, and suggest that each approach has strengths and weaknesses. |
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