Criteria for using seedling performance potential tests

The concepts and principles in characterization of seedling performance potential are evaluated, the requirements of tests are discussed, and the guideline criteria presented in selected morphological and physiological tests are assessed. Further discussion follows concerning the development of proper concepts as a necessity for developing clear and operational devices, and problems in making predictions of survival and growth. When using a seedling performance potential test, the following features should be taken into an account:

basis of assessment; empirical or mechanistic

ability to accommodate changes in seedlings

prediction span

rules and scale of measurement

possibility to use in statistical quality control

applicability to different methods of characterization; single or sample sorting

design for site specific conditions

applicability for quality control during seedling raising in the nursery

suitability to repeated sampling

Clear practical requirements for a test are: yielding final results immediately, simple to understand and use at all levels of operation, inexpensive, accessible to all potential users, reliable, and of diagnostic value. Strong practicability of the performance potential tests require use of rigorous statistical analysis to find seedling tests with adequate prediction ability.     

Looking for the “silver bullet” -- can one test do it all?

Seedling quality tests should ideally fulfill several conceptual and practical requirements which are explored in this paper. Difficulties in making predictions of seedlings' field performance arise from several constraints that are discussed. Some proposed criteria are evaluated for quality testing methods, and some new reasons are considered. The reasons for seedling quality tests have to be clearly stated as tests have different purposes during nursery culture, at lifting, and pre-planting. Rooted cuttings (stecklings) and seedlings produced using embryogenesis (emblings) have additional quality requirements. Eleven rating criteria for seedling quality tests are proposed and the feasibility of using anyone of the 11 tests as a silver bullet are assessed using these criteria. New purposes for seedling testing will result from the forest management goals of genetic diversity, diversification in use of silvicultural systems, and climate change. Future tests may apply results of molecular genetics research on gene expression and genome mapping. Expert systems will become available which will integrate and build on existing data. Seedling morphology will remain the basis for stock type selection and an important characteristic of stock quality. Nursery production should aim for uniform planting stocks wherein the need for culling is minimized, but batch culling is possible. A silver bullet does not exist as no single test can be applied throughout the nursery culture, lifting and pre-planting stages for all species and conditions. However, continuing emphasis on quality has resulted in detailed characterization of the whole nursery growing environment, suggestions of major areas for improvement, and increased field performance. Future research efforts should focus on a better understanding of acclimatization of seedlings on the planting site.     

Tree species classification from fused active hyperspectral reflectance and LIDAR measurements

A new terrestrial laser system was tested for tree species classification. A dataset consisting of shape parameters of three boreal tree species was collected with Light Detection and Ranging (LIDAR) and integrated with an actively measured reflectance hyperspectra. Tree species were classified using parameters derived from reflectance spectra and point cloud shape distribution. Classification performance was tested with individual, paired, and mixed combinations of both reflectance and shape parameters. The best classification results were obtained with combined datasets consisting of two reflectance and two shape parameters. Of all tested classification parameter combinations, 67.5% were able to classify all trees with over 90% accuracy. The best reflectance spectrum bands for the examined species were located around 550 and 700 nm. The best shape parameters described the upper midsection or the tops of the trees. This study was a successful step in developing classification algorithms for integrated LIDAR and hyperspectral data.     

Method for predicting tension capacity of sawn timber considering slope of grain around knots

This study introduced a new parameter, the area reduction factor (ARF), to consider the effect of knots on the tension strength of timber. It is an improved version of the knot area ratio (KAR). ARF considers both the projected area of knots and the effect of the slope of grains around the knots. The tension capacity of a tested structural timber was predicted as a product of ARF, clear wood tension strength parallel to the grain, and the area of the cross section. ARF was determined as the minimum value obtained when a knot measurement window of 100 mm was slid along the plank. The prediction method was examined with 11 planks. The average ratio of the predicted capacity to the actual value was 1.11 with a coefficient of variation of 0.26. The average ratio obtained by using a KAR-based parameter, the clear wood area ratio (CWAR), was 2.15 with a coefficient of variation of 0.23. To study the reliability of ARF and CWAR as single parameters, the correlations of ARF and CWAR with the tension strength were determined for 57 planks. The coefficients of determination for ARF were slightly better than those for CWAR, although both of them seemed to be quite poor predictors of tension capacity when used alone. Therefore, a multiparameter model is preferred and should be a subject for further studies. The results of the structural tension tests conducted in this study have been presented in part at the 40th meeting of the International Council for Research and Innovation in Building and Construction: Working Commission W18 - Timber Structures (CIB-W18) in Bled, Slovenia, August 2007