Articles containing the keyword 'sorting'

The particle size distribution affects several properties of the soil, thus, the ability to define the texture type of the soil as accurately as possible in field conditions is essential. The soil particle size classification devised by Atterberg (1912) is used in Finnish forestry. The study is based on a small laboratory material. The correlation between some characteristics of the soil particle size distribution, field capacity and cation exchange capacity were determined.

The particle size characteristics such as the relative proportion of different particle sizes, average particle size (Md) and parameters depicting the degree of sorting were determined. The relative proportion of soil particles below 0.06 mm correlated best with both field capacity and cation exchange capacity. Similarly, the average particle size and the degree of sorting correlated well with the field capacity and the cation exchange capacity.

The use of sorting characteristics is not well-suited to the type of soil sample material containing a high proportion of particles of varying size as was used in this material. Such characteristics are probably more easily applicable to the fine sand and sand sediments which are predominant in Finnish forest soils. The most useful particle size distribution characteristics in soils having a great variation in particle sizes were the average particle size and the relative proportion of silt and clay. Thus, the nutrient and water status of the soil can be predicted to some extent by examining the percentage of silt and clay, average particle size and the degree of sorting.

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• Sepponen, E-mail: ps@mm.unknown

Larix sibirica Ledeb. is one of the promising timber species for planting in the boreal ecosystem; but poor seed lot quality is the major hurdle for production of sufficient quantity of planting stocks. Here, we evaluated the potential of Near Infrared (NIR) Spectroscopy for sorting viable and non-viable seeds, as the conventional sorting technique is inefficient. NIR reflectance spectra were collected from single seeds, and discriminant models were developed with Orthogonal Projections to Latent Structure – Discriminant Analysis (OPLS-DA). The computed model predicted the class membership of filled-viable, empty and petrified seeds in the test set with 98%, 82% and 87% accuracy, respectively. When two-class OPLS-DA model was fitted to discriminate viable and non-viable (empty and petrified seeds combined), the predicted class membership of test set samples was 100% for both classes. The origins of spectral differences between non-viable (petrified and empty) and viable seeds were attributed to differences in seed moisture content and storage reserves. In conclusion, the result provides evidence that NIR spectroscopy is a powerful non-destructive method for sorting non-viable seeds of Larix sibirica; thus efforts should be made to develop on-line sorting system for large-scale seed handling.

• Farhadi, Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, P.O. Box 49, SE-230 53 Alnarp, Sweden E-mail: mostafa.farhadi@slu.se
• Tigabu, Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, P.O. Box 49, SE-230 53 Alnarp, Sweden E-mail: mulualem.tigabu@slu.se
• Odén, Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, P.O. Box 49, SE-230 53 Alnarp, Sweden E-mail: per.oden@slu.se

A large quantity of non-viable (empty, insect-attacked and shriveled) seeds of Juniperus polycarpos (K. Koch) is often encountered during seed collection, which should be removed from the seed lots to ensure precision sowing in the nursery or out in the field. The aims of this study were to evaluate different modelling approaches and to examine the sensitivity of the change in detection system (Silicon-detector in the shorter vis-a-vis InGsAs-detector in the longer NIR regions) for discriminating non-viable seeds from viable seeds by Near Infrared (NIR) spectroscopy. NIR reflectance spectra were collected from single seeds, and discriminant models were developed by Partial Least Squares – Discriminant Analysis (PLS-DA) and Orthogonal Projection to Latent Structures – Discriminant Analysis (OPLS-DA) using the entire or selected NIR regions. Both modelling approaches resulted in 98% and 100% classification accuracy for viable and non-viable seeds in the test set, respectively. However, OPLS-DA models were superb in terms of model parsimony and information quality. Modelling in the shorter and longer wavelength region also resulted in similar classification accuracy, suggesting that prediction of class membership is insensitive to change in the detection system. The origins of spectral differences between non-viable and viable seeds were attributed to differences in seed coat chemical composition, mainly terpenoids that were dominant in non-viable seeds and storage reserves in viable seeds. In conclusion, the results demonstrate that NIR spectroscopy has great potential as seed sorting technology to upgrade seed lot quality that ensures precision sowing.

• Daneshvar, Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, P.O. Box 49, SE-230 53, Alnarp, Sweden; (permanent address) Department of Natural Resources, Gonbad Kavous University, Shahid Fallahi Street, P.O. Box 163, Gonbad, Iran E-mail: abolfazl.daneshvar@slu.se
• Tigabu, Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, P.O. Box 49, SE-230 53, Alnarp, Sweden E-mail: mulualem.tigabu@slu.se
• Karimidoost, Agriculture and Natural Resources Research Center of Golestan Province, Beheshti Ave. P.O. Box 4915677555, Gorgan, Iran E-mail: karimidoost@yahoo.com
• Oden, Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, P.O. Box 49, SE-230 53, Alnarp, Sweden E-mail: per.oden@slu.se

• Edlund, SLU, Department of Forest Products and Markets, P.O. Box 7060, SE-750 07 Uppsala, Sweden E-mail: jacob.edlund@spm.slu.se
• Warensjö, SLU, Department of Forest Products and Markets, P.O. Box 7060, SE-750 07 Uppsala, Sweden E-mail: mw@nn.se