René Gislum

Optimal Sample Size for Predicting Viability of Cabbage and Radish Seeds Based on near Infrared Spectra of Single Seeds

The effects of the number of seeds in a training sample set on the ability to predict the viability of cabbage or radish seeds are presented and discussed. The supervised classification method extended canonical variates analysis (ECVA) was used to develop a classification model. Calibration sub-sets of different sizes were chosen randomly with several iterations and using the spectral-based sample selection algorithms DUPLEX and CADEX. An independent test set was used to validate the developed classification models. The results showed that 200 seeds were optimal in a calibration set for both cabbage and radish data. The misclassification rates at optimal sample size were 8%, 6% and 7% for cabbage and 3%, 3% and 2% for radish respectively for random method (averaged for 10 iterations), DUPLEX and CADEX algorithms. This was similar to the misclassification rate of 6% and 2% for cabbage and radish obtained using all 600 seeds in the calibration set. Thus, the number of seeds in the calibration set can be reduced by up to 67% without significant loss of classification accuracy, which will effectively enhance the cost-effectiveness of NIR spectral analysis. Wavelength regions important for the discrimination between viable and non-viable seeds were identified using interval ECVA (iECVA) models, ECVA weight plots and the mean difference spectrum for viable and non-viable seeds.

Productivity and carbon footprint of perennial grass–forage legume intercropping strategies with high or low nitrogen fertilizer input

A three-season field experiment was established and repeated twice with spring barley used as cover crop for different perennial grass-legume intercrops followed by a full year pasture cropping and winter wheat after sward incorporation. Two fertilization regimes were applied with plots fertilized with either a high or a low rate of mineral nitrogen (N) fertilizer. Life cycle assessment (LCA) was used to evaluate the carbon footprint (global warming potential) of the grassland management including measured nitrous oxide (N2O) emissions after sward incorporation. Without applying any mineral N fertilizer, the forage legume pure stand, especially red clover, was able to produce about 15 t above ground dry matter ha(-1) year(-1) saving around 325 kg mineral Nfertilizer ha(-1) compared to the cocksfoot and tall fescue grass treatments. The pure stand ryegrass yielded around 3t DM more than red clover in the high fertilizer treatment. Nitrous oxide emissions were highest in the treatments containing legumes. The LCA showed that the low input N systems had markedly lower carbon footprint values than crops from the high N input system with the pure stand legumes without N fertilization having the lowest carbon footprint. Thus, a reduction in N fertilizer application rates in the low input systems offsets increased N2O emissions after forage legume treatments compared to grass plots due to the N fertilizer production-related emissions. When including the subsequent wheat yield in the total aboveground production across the three-season rotation, the pure stand red clover without N application and pure stand ryegrass treatments with the highest N input equalled. The present study illustrate how leguminous biological nitrogen fixation (BNF) represents an important low impact renewable N source without reducing crop yields and thereby farmers earnings.

Prediction of biogas yield and its kinetics in reed canary grass using near infrared reflectance spectroscopy and chemometrics.

A rapid method is needed to assess biogas and methane yield potential of various kinds of substrate prior to anaerobic digestion. This study reports near infrared reflectance spectroscopy (NIRS) as a rapid alternative method to the conventional batch methods for prediction of specific biogas yield (SBY), specific methane yield (SMY) and kinetics of biogas yield (k-SBY) of reed canary grass (RCG) biomass. Dried and powdered RCG biomass with different level of maturity was used for biochemical composition analysis, batch assays and NIRS analysis. Calibration models were developed using partial least square (PLS) regression from NIRS spectra. The calibration models for SBY (R(2)=0.68, RPD=1.83) and k-SBY (R(2)=0.71, RPD=1.75) were better than the model for SMY (R(2)=0.53, RPD=1.49). Although the PLS model for SMY was less successful, the model performance was better compared to the models based on chemical composition.

Predicting soil organic carbon at field scale using a national soil spectral library

Visible and near infrared diffuse reflectance (vis-NIR) spectroscopy is a low-cost, efficient and accurate soil analysis technique and is thus becoming increasingly popular. Soil spectral libraries are commonly constructed as the basis for estimating soil texture and properties. In this study, partial least squares regression was used to develop models to predict the soil organic carbon (SOC) content of 35 soil samples from one field using (i) the Danish soil spectral library (2688 samples), (ii) a spiked spectral library (a combination of 30 samples selected from the local area and the spectral library, 2718 samples) and (iii) three sub-sets selected from the spectral library. In an attempt to improve prediction accuracy, sub-sets of the soil spectral library were made using three different sample selection methods: those geographically closest (84 samples), those with the same landscape and parent material (96 samples) and those with the most alike spectra to spectra from the field investigation (100 samples). These sub-sets were used to develop three calibration models and in predictions of SOC content. The results showed that the geographically closest model, which used the fewest number of samples, gave the lowest root mean square error of prediction (RMSEP) of 0.19% and the highest ratio of performance to deviation (RPD) of 3.7, followed by the spiked library, same parent material, the spectral library and the most alike spectra. The spiked library model also gave a low RMSEP value of 0.19% and high RPD value of 3.7% and performed markedly better than the model without spiking, despite using 30 samples for library spiking. The accuracy of the model developed using a sub-set from a spectral library was highly dependent on geographical location, soil parent material and landscape.

Viability Prediction of Ricinus cummunis L. Seeds Using Multispectral Imaging

The purpose of this study was to highlight the use of multispectral imaging in seed quality testing of castor seeds. Visually, 120 seeds were divided into three classes: yellow, grey and black seeds. Thereafter, images at 19 different wavelengths ranging from 375–970 nm were captured of all the seeds. Mean intensity for each single seed was extracted from the images, and a significant difference between the three colour classes was observed, with the best separation in the near-infrared wavelengths. A specified feature (RegionMSI mean) based on normalized canonical discriminant analysis, were employed and viable seeds were distinguished from dead seeds with 92% accuracy. The same model was tested on a validation set of seeds. These seeds were divided into two groups depending on germination ability, 241 were predicted as viable and expected to germinate and 59 were predicted as dead or non-germinated seeds. This validation of the model resulted in 96% correct classification of the seeds. The results illustrate how multispectral imaging technology can be employed for prediction of viable castor seeds, based on seed coat colour.

Use of Multispectral Imaging in Varietal Identification of Tomato

Multispectral imaging is an emerging non-destructive technology. In this work its potential for varietal discrimination and identification of tomato cultivars of Nepal was investigated. Two sample sets were used for the study, one with two parents and their crosses and other with eleven cultivars to study parents and offspring relationship and varietal identification respectively. Normalized canonical discriminant analysis (nCDA) and principal component analysis (PCA) were used to analyze and compare the results for parents and offspring study. Both the results showed clear discrimination of parents and offspring. nCDA was also used for pairwise discrimination of the eleven cultivars, which correctly discriminated upto 100% and only few pairs below 85%. Partial least square discriminant analysis (PLS-DA) was further used to classify all the cultivars. The model displayed an overall classification accuracy of 82%, which was further improved to 96% and 86% with stepwise PLS-DA models on high (seven) and poor (four) sensitivity cultivars, respectively. The stepwise PLS-DA models had satisfactory classification errors for cross-validation and prediction 7% and 7%, respectively. The results obtained provide an opportunity of using multispectral imaging technology as a primary tool in a scientific community for identification/discrimination of plant varieties in regard to genetic purity and plant variety protection/registration.

Use of partial least squares discriminant analysis on visible-near infrared multispectral image data to examine germination ability and germ length in spinach seeds

Because of the difficulties in obtaining homogenous germination of spinach seeds for baby leaf production, the possibility of using partial least squares discriminant analysis (PLS‐DA) on features extracted from multispectral images of spinach seeds was investigated. The objective has been to discriminate between different seed sizes, as well as to predict germination ability and germ length. Images of 300 seeds including small, medium, and large seeds were taken, and the seeds were examined for germination ability and germ length. PLS‐DA loadings plots were used to reduce the multidimensional image features to a few important features. The PLS‐DA prediction resulted in an independent test set not only providing discrimination of seed size but also demonstrating how germination ability and germ length vary according to seed size. The result indicated that larger seeds had both a significantly higher germination potential and germ length compared with smaller seeds. The variable importance for projection method showed that the near infrared (NIR) wavelength region is important for germination predictability. However, the PLS‐DA model did not improve when only the NIR region was used. Copyright

Quantification of SOC and Clay Content Using Visible Near-Infrared Reflectance–Mid-Infrared Reflectance Spectroscopy With Jack-Knifing Partial Least Squares Regression

Abstract A total of 125 soil samples were collected from a Danish field varying in soil texture from sandy to loamy. Visible near-infrared reflectance (Vis-NIR) and mid-infrared reflectance (MIR) spectroscopy combined with chemometric methods were used to predict soil organic carbon (SOC) and clay contents. The main objective of this study was to find the best model for predicting SOC and clay content in the sampled field using Vis-NIR, MIR, and the combination of Vis-NIR and MIR and using different model development techniques. The secondary objectives were (i) to use iterations of calculation to find the optimal number of replicates for MIR measurements based on the root mean square error of cross validation (RMSECV) and (ii) to apply partial least squares regression in combination with jack-knifing (JK) to identify the most important part of spectral variables and the best model for predicting SOC and clay content. The study showed that with repeated MIR measurements it was possible to improve RMSECV by 20%. The optimal number of repeated MIR measurements was between 3 and 4 for SOC and clay content. Comparing all the prediction results, the combination of MIR and Vis-NIR with the partial least squares regression–JK technique resulted in the lowest prediction errors (RMSECVsoc of 0.35% and RMSECVclay of 1.05%). The average uncertainties of laboratory measurements were 0.39% and 1.86% for SOC and clay contents, respectively. All models had acceptable and—to a large extent—comparable margins of error. Partial least squares regression with JK simplified and enhanced the interpretation of the developed models because of a reduction in the number of variables used in the models.

Classification of Viable and Non-Viable Spinach (Spinacia Oleracea L.) Seeds by Single Seed near Infrared Spectroscopy and Extended Canonical Variates Analysis

Near-infrared (NIR) reflectance spectroscopy is a common non-destructive method for predicting seed quality parameters, such as moisture, oil, carbohydrates and protein content. Furthermore, variations in absorbance between germinating and non-germinating seeds have been shown in single seed studies. Spinach (Spinacia oleracea L.) is the major crop in vegetable seed production in Denmark and two seed lots with viability percentages of 90% and 97% were chosen for examination by single seed NIR spectroscopy. Lipids play a major role in both ageing and germination. During accelerated ageing, lipid peroxidation leads to deterioration of cell membranes and contributes in that way to reducing seed viability of the seed sample. These biochemical changes may be the reason for a clear grouping between aged and non-aged seeds when performing the extended canonical variates analysis (ECVA). Assigning the difference of scatter corrected absorbance spectra from aged and non-aged seeds also lead to CH2, CH3 and HC=CH structures, which are some of the functional groups in lipids. In the ECVA plot, there was a clear difference between seeds with and without a pericarp. Evaluating the spectra, the pattern of peaks was almost similar, but the intensity was different in the absorption band at 1350 nm. The number of misclassified seeds ranged from 1.7% to 10.5% and it was lowest in seeds with a pericarp. This indicates the influence of the pericarp during germination, which is in accordance with earlier studies of spinach seeds. Single seed NIR and ECVA classification are potential methods for the prediction of seed viability.

Cultivar and row distance interactions in perennial ryegrass

Abstract To gain information about how widening of the row distance influences seed yields in first-year perennial ryegrass, experiments with four row distances in three types of perennial ryegrass were conducted at the University of Aarhus, Faculty of Agricultural Sciences. Perennial ryegrass was undersown at 12-, 24-, 36-, or 48-cm row distance in a cover crop of spring barley. The seeding rate in perennial ryegrass was 6 kg seeds ha−1 regardless of row distance. Although increasing the row distance from 12 to 48 cm had a negative effect on the yield component number of reproductive tillers, the yield was not affected in the first-year seed production in three perennial ryegrass cultivars. Regardless of row distance the seed rate was 6 kg ha−1 and hence in-row plant density in autumn and spring was higher at 48 compared with 12 cm; however, in all three cultivars the highest number of reproductive tillers was recorded at 12-cm row distance. Row distance affected seed yields of only the diploid amenity cultivar ‘Allegro’, where a row distance of 48 cm reduced the seed yield compared with 12- and 24-cm row distance. When data from the three cultivars were merged there was a positive correlation between the seed yield and seed weight (r=0.72***), whereas the correlation between seed yield and the number of reproductive tillers was negatively correlated (r= − 0.49***). This may reflect choice of cultivars in the experiment with the tetraploid forage cultivar ‘Tivoli’ having the lowest number of reproductive tillers, highest seed weight, and highest seed yield, and the diploid amenity cultivar ‘Allegro’ having the highest number of reproductive tillers, lowest seed weight, and the lowest seed yield. When the three cultivars were merged, there was a positive and highly significant correlation between seed weight and seed yield (r=0.72***). In contrast, there was no correlation between seed weight and seed yield when data were analysed for the individual cultivars. This suggests a cultivar-dependent relationship between seed weight and seed yield and furthermore between number of reproductive tillers and seed yield.