Assessment of sulphur deficiency in commercial oilseed rape crops from plant analysis

2013 ◽  
Vol 152 (4) ◽  
pp. 616-633 ◽  
Author(s):  
X. SARDA ◽  
S. DIQUELOU ◽  
M. ABDALLAH ◽  
N. NESI ◽  
O. CANTAT ◽  
...  
Keyword(s):  
Oilseed Rape ◽  
Principal Component ◽  
Organic N ◽  
Plant Analysis ◽  
Nitrate Accumulation ◽  
Rainfall Gradient ◽  
N Supply ◽  
S Deficiency ◽  
N Metabolism ◽  
Highly Correlated

SUMMARYSulphur (S) is one of the six main macroelements required to sustain the growth of plants. Sources include soil, fertilizer and atmospheric deposition, which has been reduced by 85% over the last three decades. Risks of S deficiencies are now recognized in high S-demanding species such as Brassica napus L. With the aims of evaluating the risk of excessive or insufficient fertilization and identifying robust relationships that may be used as plant S status indicators, 57 commercial crops of oilseed rape were selected among contrasting soils and along a rainfall gradient that may affect soil S availability. Cultivation practices were investigated and the S and nitrogen (N) concentrations of soil, senescing leaves, stems and seeds were analysed. Despite an excessive organic N supply and large variation in S supply (from 0 to 112 kg S/ha), principal component analysis using 43 parameters indicated that seed yield was poorly related to N and S fertilization rates. While the N and protein-N concentrations in seeds were inversely related to oil and glucosinolate concentrations, they were linked to S and sulphate (SO42−) accumulation in the seeds. Sulphate concentrations in senescing leaves, stems or seeds could be deduced from total S concentrations, as they were positively and highly correlated. Sulphate accounted for on average 0·69 of total S in senescing leaves with minimum and maximum values of 0·007 and 0·94, which revealed conditions of limited and excess supply of S, respectively. This high variation of SO42− concentration in leaves can be interpreted as the result of its mobilization triggered by S deficiency, but cannot be used alone as an indicator of plant S status. A comparison with plants grown in controlled conditions under different S supplies suggests that the intensity of S starvation affects N metabolism, leading to NO3− (nitrate) accumulation. It is further suggested that dual evaluation of SO42− and NO3− concentrations in senescing leaves could be used at the vegetative stage as a field indicator to adjust S fertilization.

Critical sulfur concentrations in oilseed rape (Brassica napus) in relation to nitrogen supply and to plant age

1998 ◽  
Vol 38 (5) ◽  
pp. 511 ◽  
Author(s):  
A. Pinkerton
Keyword(s):  
Oilseed Rape ◽  
Seed Yield ◽  
Critical Values ◽  
Sand Culture ◽  
Culture Experiment ◽  
Total N ◽  
N Supply ◽  
S Deficiency ◽  
Time Required ◽  
Time Critical

Summary. Oilseed rape was grown in a sand culture experiment in a glasshouse to derive values for plant testing for the diagnosis of sulfur (S) deficiency and for the prediction of seed yield. Five rates of S, combined factorially with 4 rates of nitrogen (N), maintained constant throughout the experiment, were used to determine critical concentrations of S fractions and ratios (total S, St; sulfate-S, SO4; total N/total S, N/St; SO4/St). The most satisfactory indices of rapeseed S status for diagnosis or prediction were St and SO4. Whole shoots and youngest fully expanded leaves exhibited similar critical values in plants at the rosette stage, and critical values (St = 0.20–0.25%; SO4 = 230–460 mg/kg) changed little with time. Critical values for N/St changed with time, required 2 analyses, and gave no indication of the degree of deficiency when used to predict yield. Critical values of SO4/St depended on N supply, so 3 analyses were needed. It is argued that high critical values reported previously for prediction of seed yield have been obtained when there was a decline in soil-available S and plants relied on S taken up during early growth.

Influence of carbon supply on developmental kinetics of symbiotic and nonsymbiotic alfalfa cv. Algonquin through first flowering

1985 ◽  
Vol 63 (4) ◽  
pp. 847-849
Author(s):  
F. D. H. Macdowall
Keyword(s):  
Light Intensity ◽  
High Light Intensity ◽  
High Light ◽  
Organic N ◽  
Initial Growth ◽  
Carbon Supply ◽  
N Supply ◽  
N Metabolism ◽  
Light Inhibition ◽  
Kinetics Of

Biphasic first-order growth kinetics of NO3-supported or symbiotic seedlings of Medicago sativa L. cv. Algonquin were followed over a range of light intensities and at two concentrations of CO2. The initial, [Formula: see text]-supported growth coefficients ([Formula: see text] or relative growth rate) decreased with decreasing light intensity, but those for symbiotic growth showed relief from high light inhibition by passing through a maximum [Formula: see text] at an intermediate light intensity. In low light intensity (60 μE∙m−2∙s−1) the low initial growth coefficient persisted to 40 days in Hoagland's solution, or for 58 days symbiotically at which time the corresponding biomass was reached. At high light intensity (550 μE∙m−2∙s−1) the initial values of [Formula: see text] were insensitive to the enrichment of CO2 (1325 μL∙L−1), but after 27 days values of [Formula: see text] were enhanced by the raised CO2 concentration. The initial growth phase, which is N limited at a high C supply, was followed by a phase of growth that was C limited at a high N supply. The symbiotic N supply, unlike the combined N supply, was dependent only on the C supply because when the CO2 concentration was raised the acceleration of symbiotic seedling growth equalled the maximum on [Formula: see text] nutrition. The results support a hypothesis that the change in kinetic phase is controlled by developmental morphogenesis independent of N source and C supply and that a plant pool of organic N metabolites plays a role in the regulation of the N metabolism that is involved in the growth effects.

scholarly journals Nitrate accumulation, nitrogen balance and cation-anion ratio during the regrowth of perennial ryegrass.

1958 ◽  
Vol 6 (3) ◽  
pp. 211-221
Author(s):  
W. Dijkshoorn
Keyword(s):  
Dry Matter ◽  
Gradual Increase ◽  
Specific Yield ◽  
Nitrate Accumulation ◽  
Yield Level ◽  
N Supply ◽  
Rate Of Increase ◽  
N Metabolism ◽  
Critical Yield ◽  
The Relationship

The regrowth of ryegrass was harvested on various dates in a pot experiment. NO3 accumulation occurred until a specific yield level was reached which was critical in respect to the N supply. Below this critical yield the N percentage in the yield gradually declined as a result of the decreasing capacity of the dry matter to assimilate N. After the critical yield was exceeded NO3 disappeared from the leaves and no further N was taken up, so that the N percentage in the yield decreased more rapidly. At first there was a gradual increase in the cation-anion ratio as the grass aged. After the critical yield for N-depletion was exceeded there was a higher rate of increase. The relationship between cation-anion ratio and N status is connected with N metabolism.-From author's summary. (Abstract retrieved from CAB Abstracts by CABI’s permission)

scholarly journals Suitability Analysis of 17 Probiotic Type Strains of Lactic Acid Bacteria as Starter for Kimchi Fermentation

Foods ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1435
Author(s):  
Hee Seo ◽  
Jae-Han Bae ◽  
Gayun Kim ◽  
Seul-Ah Kim ◽  
Byung Hee Ryu ◽  
...  
Keyword(s):  
Lactic Acid ◽  
Lactic Acid Bacteria ◽  
Growth Rates ◽  
Principal Component ◽  
Fermented Foods ◽  
Suitability Analysis ◽  
Moderate Growth ◽  
Health Promoting ◽  
Highly Correlated ◽  
Type Strains

The use of probiotic starters can improve the sensory and health-promoting properties of fermented foods. This study aimed to evaluate the suitability of probiotic lactic acid bacteria (LAB) as a starter for kimchi fermentation. Seventeen probiotic type strains were tested for their growth rates, volatile aroma compounds, metabolites, and sensory characteristics of kimchi, and their characteristics were compared to those of Leuconostoc (Le.) mesenteroides DRC 1506, a commercial kimchi starter. Among the tested strains, Limosilactobacillus fermentum, Limosilactobacillus reuteri, Lacticaseibacillus rhamnosus, Lacticaseibacillus paracasei, and Ligilactobacillus salivarius exhibited high or moderate growth rates in simulated kimchi juice (SKJ) at 37 °C and 15 °C. When these five strains were inoculated in kimchi and metabolite profiles were analyzed during fermentation using GC/MS and 1H-NMR, data from the principal component analysis (PCA) showed that L. fermentum and L. reuteri were highly correlated with Le. mesenteroides in concentrations of sugar, mannitol, lactate, acetate, and total volatile compounds. Sensory test results also indicated that these three strains showed similar sensory preferences. In conclusion, L. fermentum and L. reuteri can be considered potential candidates as probiotic starters or cocultures to develop health-promoting kimchi products.

scholarly journals Drought Stress Detection in Juvenile Oilseed Rape Using Hyperspectral Imaging with a Focus on Spectra Variability

2020 ◽  
Vol 12 (20) ◽  
pp. 3462
Author(s):  
Wiktor R. Żelazny ◽  
Jan Lukáš
Keyword(s):  
Drought Stress ◽  
Hyperspectral Imaging ◽  
Oilseed Rape ◽  
Treatment Effects ◽  
Vegetation Index ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Stress Detection ◽  
Mean Values ◽  
Standard Deviations

Hyperspectral imaging (HSI) has been gaining recognition as a promising proximal and remote sensing technique for crop drought stress detection. A modelling approach accounting for the treatment effects on the stress indicators’ standard deviations was applied to proximal images of oilseed rape—a crop subjected to various HSI studies, with the exception of drought. The aim of the present study was to determine the spectral responses of two cultivars, ‘Cadeli’ and ‘Viking’, representing distinctive water management strategies, to three types of watering regimes. Hyperspectral data cubes were acquired at the leaf level using a 2D frame camera. The influence of the experimental factors on the extent of leaf discolorations, vegetation index values, and principal component scores was investigated using Bayesian linear models. Clear treatment effects were obtained primarily for the vegetation indexes with respect to the watering regimes. The mean values of RGI, MTCI, RNDVI, and GI responded to the difference between the well-watered and water-deprived plants. The RGI index excelled among them in terms of effect strengths, which amounted to −0.96[−2.21,0.21] and −0.71[−1.97,0.49] units for each cultivar. A consistent increase in the multiple index standard deviations, especially RGI, PSRI, TCARI, and TCARI/OSAVI, was associated with worsening of the hydric regime. These increases were captured not only for the dry treatment but also for the plants subjected to regeneration after a drought episode, particularly by PSRI (a multiplicative effect of 0.33[0.16,0.68] for ‘Cadeli’). This result suggests a higher sensitivity of the vegetation index variability measures relative to the means in the context of the oilseed rape drought stress diagnosis and justifies the application of HSI to capture these effects. RGI is an index deserving additional scrutiny in future studies, as both its mean and standard deviation were affected by the watering regimes.

Implementation of Reduce AI-NN Model for Highly Accurate Blood Pressure Measurement

2020 ◽  
Author(s):  
Jerry Lin ◽  
Rajeev Kumar Pandey ◽  
Paul C.-P. Chao
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Measurement Accuracy ◽  
Blood Pressure Measurement ◽  
Principal Component ◽  
Data Sets ◽  
British Hypertension Society ◽  
The Neural Network ◽  
Pca Method ◽  
Highly Correlated

Abstract This study proposes a reduce AI model for the accurate measurement of the blood pressure (BP). In this study varied temporal periods of photoplethysmography (PPG) waveforms is used as the features for the artificial neural networks to estimate blood pressure. A nonlinear Principal component analysis (PCA) method is used herein to remove the redundant features and determine a set of dominant features which is highly correlated to the Blood pressure (BP). The reduce features-set not only helps to minimize the size of the neural network but also improve the measurement accuracy of the systolic blood pressure (SBP) and diastolic blood pressure (DBP). The designed Neural Network has the 5-input layer, 2 hidden layers (32 nodes each) and 2 output nodes for SBP and DBP, respectively. The NN model is trained by the PPG data sets, acquired from the 96 subjects. The testing regression for the SBP and DBP estimation is obtained as 0.81. The resultant errors for the SBP and DBP measurement are 2.00±6.08 mmHg and 1.87±4.09 mmHg, respectively. According to the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS) standard, the measured error of ±6.08 mmHg is less than 8 mmHg, which shows that the device performance is in grade “A”.

Multivariate analyses on heavy metal binding fractions of river sediments in Southern Taiwan

2000 ◽  
Vol 42 (7-8) ◽  
pp. 193-199 ◽  
Author(s):  
K. C. Yu ◽  
C. Y. Chang ◽  
L. J. Tsai ◽  
S. T. Ho
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Organic Matter ◽  
Metal Binding ◽  
River Sediments ◽  
Principal Component ◽  
Fe Oxides ◽  
Data Points ◽  
Southern Taiwan ◽  
Highly Correlated

This study depicts the amounts of heavy metals (Cu, Zn, Pb, Cr, Co, and Ni) bound to four geochemical compositions of sediments (carbonates, Mn oxides, Fe oxides, and organic matters), and the correlations between various geochemical compositions and their heavy-metal complexes. Hundreds of data, obtained from sediments of five main rivers (located in southern Taiwan), were analyzed by using multivariate analysis method. Among the four different geochemical compositions, the total amount of the six heavy metals bound to organic matter is the highest. Zn is easily bound to various geochemical compositions, especially carbonates in sediments of the Yenshui river and the Potzu river (i.e., the heavily heavy-metal polluted sediments); Cr, Pb, and Ni are mainly bound to both Fe oxides and organic matter; Cu has high affinity to organic matter. By performing principal component analyses, the data points of organic matter and both Pb and Cu associated with organic matter cluster together in sediments ofthe Peikang, the Potzu, and the Yenshui rivers, which indicates both Pb and Cu might be discharged from the same pollution sources in these rivers. Moreover, correlations between any two binding fractions of heavy metal associated with Fe oxides in different rivers are not consistent, which indicates some factors including the binding sites of Fe oxides, the extent of heavy metal pollution, binding competitions between heavy metals may affect the amounts of heavy metals bound to Fe oxides. Furthermore, it should be noted that the amount of Pb bound to Fe oxides is highly correlated with the amount of Fe oxides in sediments of the Peikang, the Potzu, and the Yenshui rivers.

scholarly journals Effect of Site Specific Nitrogen Management on Seed Nitrogen—A Driving Factor of Winter Oilseed Rape (Brassica napus L.) Yield

Agronomy ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1364 ◽  
Author(s):  
Remigiusz Łukowiak ◽  
Witold Grzebisz
Keyword(s):  
Oilseed Rape ◽  
Field Experiments ◽  
Winter Oilseed Rape ◽  
Brassica Napus L ◽  
N Accumulation ◽  
Yield Increase ◽  
N Supply ◽  
Site Diversity ◽  
Rate Interaction ◽  
N Pool

It has been assumed that the management of both soil and fertilizer N in winter oilseed rape (WOSR) is crucial for N accumulation in seeds (Nse) and yield. This hypothesis was evaluated based on field experiments conducted in 2008/09, 2009/10, 2010/11 seasons, each year at two sites, differing in soil fertility, including indigenous N (Ni) supply. The experimental factors consisted of two N fertilizers: N and NS, and four Nf rates: 0, 80, 120, 160 kg ha−1. Yield, as governed by site × Nf rate interaction, responded linearly to Nse at harvest. The maximum Nse (Nsemax), as evaluated by N input (Nin = Ni + Nf) to WOSR at spring regrowth, varied from 95 to 153 kg ha−1, and determined 80% of yield variability. The basic reason of site diversity in Nsemax was Ni efficiency, ranging from 46% to 70%, respectively. The second cause of Nse variability was a shortage of N supply from + 9.5 soil to −8.8 kg ha−1 to the growing seeds during the seed filling period (SFP). This N pool supports the N concentration in seeds, resulting in both seed density and a seed weight increase, finally leading to a yield increase.

Validation of NBudget for estimating soil N supply in Australia's northern grains region in the absence of soil test data

Soil Research ◽  
2017 ◽  
Vol 55 (6) ◽  
pp. 590 ◽  
Author(s):  
David F. Herridge
Keyword(s):  
Soil Water ◽  
Organic N ◽  
Soil N ◽  
Limited Information ◽  
Available N ◽  
Support Tool ◽  
Grain Yields ◽  
N Supply ◽  
Nitrate N ◽  
Winter Crop

Effective management of fertiliser nitrogen (N) inputs by farmers will generally have beneficial productivity, economic and environmental consequences. The reality is that farmers may be unsure of plant-available N levels in cropping soils at sowing and make decisions about how much fertiliser N to apply with limited information about existing soil N supply. NBudget is a Microsoft (Armonk, NY, USA) Excel-based decision support tool developed primarily to assist farmers and/or advisors in Australia’s northern grains region manage N. NBudget estimates plant-available (nitrate) N at sowing; it also estimates sowing soil water, grain yields, fertiliser N requirements for cereals and oilseed crops and N2 fixation by legumes. NBudget does not rely on soil testing for nitrate-N, organic carbon or soil water content. Rather, the tool relies on precrop (fallow) rainfall data plus basic descriptions of soil texture and fertility, tillage practice and information about paddock use in the previous 2 years. Use is made of rule-of-thumb values and stand-alone or linked algorithms describing, among other things, rates of mineralisation of background soil organic N and fresh residue N. Winter and summer versions of NBudget cover the 10 major crops of the region: bread wheat, durum, barley, canola, chickpea and faba bean in the winter crop version; sorghum, sunflower, soybean and mung bean in the summer crop version. Validating the winter crop version of NBudget estimates of sowing soil nitrate-N against three independent datasets (n=65) indicated generally close agreement between measured and predicted values (y=0.91x+16.8; r2=0.78). A limitation of the tool is that it does not account for losses of N from waterlogged or flooded soils. Although NBudget also predicts grain yields and fertiliser N requirements for the coming season, potential users may simply factor predicted soil N supply into their fertiliser decisions, rather than rely on the output of the tool. Decisions about fertiliser N inputs are often complex and are based on several criteria, including attitudes to risk, history of fertiliser use and costs. The usefulness and likely longevity of NBudget would be enhanced by transforming the current Excel-based tool, currently available on request from the author, to a stand-alone app or web-based tool.

scholarly journals Quality Control and Peak Finding for Proteomics Data Collected from Nipple Aspirate Fluid by Surface-Enhanced Laser Desorption and Ionization

2003 ◽  
Vol 49 (10) ◽  
pp. 1615-1623 ◽  
Author(s):  
Kevin R Coombes ◽  
Herbert A Fritsche ◽  
Charlotte Clarke ◽  
Jeng-neng Chen ◽  
Keith A Baggerly ◽  
...  
Keyword(s):  
Quality Control ◽  
Mahalanobis Distance ◽  
Control Sample ◽  
Principal Component ◽  
Nipple Aspirate Fluid ◽  
Proteomics Data ◽  
Proteinchip Array ◽  
Control Procedures ◽  
Highly Correlated ◽  
Novel Algorithms

Abstract Background: Recently, researchers have been using mass spectroscopy to study cancer. For use of proteomics spectra in a clinical setting, stringent quality-control procedures will be needed. Methods: We pooled samples of nipple aspirate fluid from healthy breasts and breasts with cancer to prepare a control sample. Aliquots of the control sample were used on two spots on each of three IMAC ProteinChip® arrays (Ciphergen Biosystems, Inc.) on 4 successive days to generate 24 SELDI spectra. In 36 subsequent experiments, the control sample was applied to two spots of each ProteinChip array, and the resulting spectra were analyzed to determine how closely they agreed with the original 24 spectra. Results: We describe novel algorithms that (a) locate peaks in unprocessed proteomics spectra and (b) iteratively combine peak detection with baseline correction. These algorithms detected ∼200 peaks per spectrum, 68 of which are detected in all 24 original spectra. The peaks were highly correlated across samples. Moreover, we could explain 80% of the variance, using only six principal components. Using a criterion that rejects a chip if the Mahalanobis distance from both control spectra to the center of the six-dimensional principal component space exceeds the 95% confidence limit threshold, we rejected 5 of the 36 chips. Conclusions: Mahalanobis distance in principal component space provides a method for assessing the reproducibility of proteomics spectra that is robust, effective, easily computed, and statistically sound.

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