Current issue: 53(1)
Under compilation: 53(2)
The genus Betula L. is composed of several species, which are difficult to distinguish in the field on the basis of morphological traits. The aim of this study was to evaluate the taxonomic importance of using visible + near infrared (Vis + NIR) spectra of single seeds for differentiating Betula pendula Roth and Betula pubescens Ehrh. Seeds from several families (controlled crossings of known parent trees) of each species were used and Vis + NIR reflectance spectra were obtained from single seeds. Multivariate discriminant models were developed by Orthogonal Projections to Latent Structures – Discriminant Analysis (OPLS-DA). The OPLS-DA model fitted on Vis + NIR spectra recognized B. pubescens with 100% classification accuracy while the prediction accuracy of class membership for B. pendula was 99%. However, the discriminant models fitted on NIR spectra alone resulted in 100% classification accuracies for both species. Absorption bands accounted for distinguishing between birch species were attributed to differences in color and chemical composition, presumably polysaccharides, proteins and fatty acids, of the seeds. In conclusion, the results demonstrate the feasibility of NIR spectroscopy as taxonomic tool for classification of species that have morphological resemblance.
This study aimed at establishing calibrations to predict nutrient concentrations of solid moose (Alces alces L.) rumen content using near infrared spectroscopy (NIRS), as an alternative to expensive chemical analyses. NIR reflectance spectra of 148 dry pulverized samples were recorded. The scanned samples were then analyzed for crude protein, available protein, microbial nitrogen (N), ash, acid-detergent fiber (ADF), neutral detergent fiber (NDF) and lignin contents following standard chemical analysis procedures. The calibration models were derived by Orthogonal Projection to Latent Structure (OPLS) and validated using external prediction sets. The calibration models accurately predicted crude protein, available protein and ash contents (R2 = 0.99, 0.96, and 0.92, prediction error = 0.39, 0.72 and 0.53% dry matter, respectively) while NDF (R2 = 0.92; prediction error = 2.23% dry matter) and ADF (R2 = 0.89; prediction error = 1.94% dry matter) were predicted with sufficient accuracy and that of microbial-N (R2 = 0.81; prediction error = 1.25 mg yeast-RNA g–1 dry matter) and lignin (R2 = 0.84; prediction error = 1.05% dry matter) were acceptable. The ratio of performance to deviation values were > 3.0 for crude protein and available protein, between 3.0 and 2.5 for ADF, NDF and lignin, and 2.32 for microbial-N; attesting the robustness of the calibration models. It can be concluded that NIR spectroscopy offers a quick and inexpensive procedure for prediction of nutrient concentrations of solid rumen contents in wild herbivores.
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.
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.
Land degradation is widespread and a serious threat affecting the livelihoods of 1.5 billion people worldwide of which one sixth or 250 million people reside in drylands. Globally, it is estimated that 10–20% of drylands are already degraded and about 12 million ha are degraded each year. Driven by unsustainable land use practices, adverse climatic conditions and population increase, land degradation has led to decline in provision of ecosystem services, food insecurity, social and political instability and reduction in the ecosystem’s resilience to natural climate variability. Several global initiatives have been launched to combat land degradation, including rehabilitation of degraded drylands. This review aimed at collating the current state-of-knowledge about rehabilitation of degraded drylands. It was found that the prospect of restoring degraded drylands is technically promising using a suite of passive (e.g. area exclosure, assisted natural regeneration, rotational grazing) and active (e.g. mixed-species planting, framework species, maximum diversity, and use of nurse tree) rehabilitation measures. Advances in soil reclamation using biological, chemical and physical measures have been made. Despite technical advances, the scale of rehabilitation intervention is small and lacks holistic approach. Development of process-based models that forecast outcomes of the various rehabilitation activities will be useful tools for researchers and practitioners. The concept of forest landscape restoration approach, which operates at landscape-level, could also be adopted as the overarching framework for rehabilitation of degraded dryland ecosystems. The review identified a data gap in cost-benefit analysis of rehabilitation interventions. However, the cost of rehabilitation and sustainable management of drylands is opined to be lower than the losses that accrue from inaction, depending on the degree of degradation. Thus, local communities’ participation, incorporation of traditional ecological knowledge, clear division of tasks and benefits, strengthening local institutions are crucial not only for cost-sharing, but also for the long-term success of rehabilitation activities.