scholarly journals Rooting density differentiates wheat genotypes through Bayesian modeling

2016 ◽  
Author(s):  
Anton P. Wasson ◽  
Grace S. Chiu ◽  
Alexander B. Zwart ◽  
Timothy R. Binns
Keyword(s):  
Field Data ◽  
Nonlinear Modeling ◽  
Heritability Estimate ◽  
Root Traits ◽  
Phenotypic Traits ◽  
Bayesian Hierarchical ◽  
Wheat Genotypes ◽  
Field Samples ◽  
Rooting Density ◽  
Intensity Functions

AbstractWheat pre-breeders use soil coring and core-break counts to phenotype root architecture traits, with data collected on rooting density for hundreds of genotypes in small increments of depth. The measured densities are both large datasets and highly variable even within the same genotype, hence, any rigorous, comprehensive statistical analysis of such complex field data would be technically challenging. Traditionally, most attributes of the field data are therefore discarded in favor of simple numerical summary descriptors which retain much of the high variability exhibited by the raw data. This poses practical challenges: although plant scientists have established that root traits do drive resource capture in crops, traits that are more randomly (rather than genetically) determined are difficult to breed for. In this paper we develop a Bayesian hierarchical nonlinear modeling approach that utilizes the complete field data for wheat genotypes to fit anidealizedrelative intensity function for the root distribution over depth. Our approach was used to determineheritability: how much of the variation between field samples was purely random versus being mechanistically driven by the plant genetics? Based on the genotypic intensity functions, the overall heritability estimate was 0.62 (95% Bayesian confidence interval was 0.52 to 0.71). Despite root count profiles that were statistically very noisy, our Bayesian analysis led to denoised profiles which exhibited rigorously discernible phenotypic traits. The profile-specific traits could be representative of a genotype and thus can be used as a quantitative tool to associate phenotypic traits with specific genotypes.

Regional evaluation of wheat genotypes for grain protein level in the central west of New South Wales

1981 ◽  
Vol 21 (108) ◽  
pp. 68
Author(s):  
E Seif ◽  
LN Balaam
Keyword(s):  
Protein Level ◽  
New South ◽  
New South Wales ◽  
Heritability Estimate ◽  
Grain Protein ◽  
Wheat Protein ◽  
Wheat Genotypes ◽  
First Order ◽  
Laboratory Error ◽  
South Wales

The analysis of wheat protein data from ten homozygous genotypes grown at six locations in 4 years gave a heritability estimate of 50%, and another of 65% from a sub-set of genotypes of similar maturity. The first-order interactions, genotype x year and genotype x location, were small and nonsignificant. An examination of the variance of a genotype mean indicated that selection in regional testing programs could be based on data from as few as three trials. Laboratory error represented a large proportion of this variance, thus more than one laboratory determination will be necessary.

Plasticity and co‐variation of root traits govern differential phosphorus acquisition among 20 wheat genotypes

Oikos ◽  
2021 ◽  
Author(s):  
Xin‐Xin Wang ◽  
Jiaqi Zhang ◽  
Hong Wang ◽  
Zed Rengel ◽  
Hongbo Li
Keyword(s):  
Root Traits ◽  
Phosphorus Acquisition ◽  
Wheat Genotypes

Gas Turbine Health State Prognostics by Means of Bayesian Hierarchical Models

2019 ◽  
Author(s):  
Enzo Losi ◽  
Mauro Venturini ◽  
Lucrezia Manservigi
Keyword(s):  
Gas Turbine ◽  
Field Data ◽  
Health Management ◽  
Theoretical Background ◽  
Health State ◽  
Prediction Errors ◽  
Probabilistic Prediction ◽  
Future Health ◽  
Bayesian Hierarchical ◽  
Prediction Reliability

Abstract The prediction of the time evolution of gas turbine performance is an emerging requirement of modern prognostics and health management (PHM), aimed at improving system reliability and availability, while reducing life cycle costs. In this work, a data-driven Bayesian Hierarchical Model (BHM) is employed to perform a probabilistic prediction of gas turbine future health state thanks to its capability to deal with fleet data from multiple units. First, the theoretical background of the predictive methodology is outlined to highlight the inference mechanism and data processing for estimating BHM predicted outputs. Then, BHM is applied to both simulated and field data representative of gas turbine degradation to assess its prediction reliability and grasp some rules of thumb for minimizing BHM prediction error. For the considered field data, the average values of the prediction errors were found to be lower than 1.0 % or 1.7 % for single- or multi-step prediction, respectively.

Assessing genetic variation for heat stress tolerance in Indian bread wheat genotypes using morpho-physiological traits and molecular markers

2016 ◽  
Vol 15 (6) ◽  
pp. 539-547 ◽  
Author(s):  
P. Sharma ◽  
S. Sareen ◽  
M. Saini ◽  
Shefali
Keyword(s):  
Genetic Diversity ◽  
Genetic Variation ◽  
Heat Stress ◽  
Molecular Markers ◽  
Heat Tolerance ◽  
Polymorphic Information Content ◽  
Physiological Traits ◽  
Phenotypic Traits ◽  
Phenotypic Data ◽  
Wheat Genotypes

AbstractHeat stress greatly limits the productivity of wheat in many regions. Knowledge on the degree of genetic diversity of wheat varieties along with their selective traits will facilitate the development of high yielding, stress-tolerant wheat cultivar. The objective of this study were to determine genetic variation in morpho-physiological traits associated with heat tolerance in 30 diverse wheat genotypes and to examine genetic diversity and relationship among the genotypes varying heat tolerance using molecular markers. Phenotypic data of 15 traits were evaluated for heat tolerance under non-stress and stress conditions for two consecutive years. A positive and significant correlation among cell membrane stability, canopy temperature depression, biomass, susceptibility index and grain yield was shown. Genetic diversity assessed by 41 polymorphic simple sequence repeat (SSR) markers was compared with diversity evaluated for 15 phenotypic traits averaged over stress and non-stress field conditions. The mean polymorphic information content for SSR value was 0.38 with range of 0.12–0.75. Based on morpho-physiological traits and genotypic data, three groups were obtained based on their tolerance (HHT, MHT and LHT) levels. Analysis of molecular variance explained 91.7% of the total variation could be due to variance within the heat tolerance genotypes. Genetic diversity among HHT was higher than LHT genotypes and HHT genotypes were distributed among all cluster implied that genetic basis of heat tolerance in these genotypes was different thereby enabling the wheat breeders to combine these diverse sources of genetic variation to improve heat tolerance in wheat breeding programme.

scholarly journals Differentiating Wheat Genotypes by Bayesian Hierarchical Nonlinear Mixed Modeling of Wheat Root Density

2017 ◽  
Vol 8 ◽  
Author(s):  
Anton P. Wasson ◽  
Grace S. Chiu ◽  
Alexander B. Zwart ◽  
Timothy R. Binns
Keyword(s):  
Wheat Root ◽  
Root Density ◽  
Bayesian Hierarchical ◽  
Wheat Genotypes

Growth of aflatoxigenic moulds and aflatoxin formation in Brazil nuts

2008 ◽  
Vol 1 (2) ◽  
pp. 127-137 ◽  
Author(s):  
P. Johnsson ◽  
M. Lindblad ◽  
A. Thim ◽  
N. Jonsson ◽  
E. Vargas ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Field Data ◽  
Aflatoxin Production ◽  
Processing Plant ◽  
Production Chain ◽  
Brazil Nuts ◽  
Field Samples ◽  
Aspergillus Nomius ◽  
The Relationship ◽  
And Storage

The present study aimed at gaining more knowledge of the growth of aflatoxigenic moulds and aflatoxin production in Brazil nuts in relation to humidity conditions and storage time. For this purpose, the growth of aflatoxigenic moulds and the increase in aflatoxin levels in Brazil nuts was studied in the laboratory at temperature and humidity conditions that are relevant for the Amazon region. Fresh unprocessed Brazil nuts in shell were inoculated with an aflatoxin producing strain of Aspergillus nomius previously isolated from Brazil nuts. The nuts were stored at 27 °C in combination with 97, 90 or 80% surrounding relative humidity in a respirometer for up to 3 months. The General Linear Model (GLM) was used for evaluation of the effect of water activity and time on aflatoxigenic mould levels and on aflatoxin levels, as well as the relationship between mould and aflatoxin levels. During storage at the highest relative humidity (97%) aflatoxin formation occurred rapidly, whereas storage at 90% relative humidity resulted in slower aflatoxin formation. At the lowest relative humidity (80%), aflatoxin formation occurred sporadically during storage. The increase in mould and aflatoxin levels along the production chain is also described, using field data collected in the state of Para, Brazil. The growth of aflatoxigenic moulds and aflatoxin formation increased rapidly between 40-90 days following collection of the nuts, before the nuts reached the final drying stage at the processing plant. In addition, a logistic regression model predicting the probability that the European legislative limit of 4 µg/kg for aflatoxins in nuts will be exceeded in relation to colony counts of either one selected aflatoxigenic mould strain (laboratory experiments) or of a mixture of aflatoxigenic strains (field data) was developed. The probability that total aflatoxin levels will exceed the European legislative limit of 4 µg/kg increased rapidly from approx. 30% to above 80% for both experimental and field samples at mould levels between 2 and 3 log cfu/g.

scholarly journals Biopore-Induced Deep Root Traits of Two Winter Crops

Agriculture ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 634
Author(s):  
Ning Huang ◽  
Miriam Athmann ◽  
Eusun Han
Keyword(s):  
Soil Depth ◽  
Root Traits ◽  
Rooting Depth ◽  
Crop Species ◽  
Deep Root ◽  
Rooting Density ◽  
Arable Fields ◽  
Soil Monolith ◽  
Winter Crops

Deeper root growth can be induced by increased biopore density. In this study, we aimed to compare deep root traits of two winter crops in field conditions in response to altered biopore density as affected by crop sequence. Two fodder crop species—chicory and tall fescue—were grown for two consecutive years as preceding crops (pre-crops). Root traits of two winter crops—barley and canola, which were grown as subsequent crops (post-crops)—were measured using the profile wall and soil monolith method. While barley and canola differed greatly in deep root traits, they both significantly increased rooting density inside biopores by two-fold at soil depths shallower than 100 cm. A similar increase in rooting density in the bulk soil was observed below 100 cm soil depth. As a result, rooting depth significantly increased (>5 cm) under biopore-rich conditions throughout the season of the winter crops. Morphological root traits revealed species-wise variation in response to altered biopore density, in which only barley increased root size under biopore-rich conditions. We concluded that large-sized biopores induce deeper rooting of winter crops that can increase soil resource acquisition potential, which is considered to be important for agricultural systems with less outsourced farm resources, e.g., Organic Agriculture. Crops with contrasting root systems can respond differently to varying biopore density, especially root morphology, which should be taken into account upon exploiting biopore-rich conditions in arable fields. Our results also indicate the need for further detailed research with a greater number of species, varieties and genotypes for functional classification of root plasticity against the altered subsoil structure.

scholarly journals Soil coring at multiple field environments can directly quantify variation in deep root traits to select wheat genotypes for breeding

2014 ◽  
Vol 65 (21) ◽  
pp. 6231-6249 ◽  
Author(s):  
A. P. Wasson ◽  
G. J. Rebetzke ◽  
J. A. Kirkegaard ◽  
J. Christopher ◽  
R. A. Richards ◽  
...  
Keyword(s):  
Root Traits ◽  
Wheat Genotypes ◽  
Deep Root ◽  
Multiple Field

scholarly journals Bayesian forward modelling of cosmic shear data

2021 ◽  
Vol 502 (2) ◽  
pp. 3035-3044
Author(s):  
Natalia Porqueres ◽  
Alan Heavens ◽  
Daniel Mortlock ◽  
Guilhem Lavaux
Keyword(s):  
Power Spectrum ◽  
Power Spectra ◽  
Density Field ◽  
Matter Distribution ◽  
Bayesian Hierarchical ◽  
Matter Power Spectrum ◽  
Field Samples ◽  
Dark Matter Distribution ◽  
Cosmic Shear ◽  
Non Gaussian

ABSTRACT We present a Bayesian hierarchical modelling approach to infer the cosmic matter density field, and the lensing and the matter power spectra, from cosmic shear data. This method uses a physical model of cosmic structure formation to infer physically plausible cosmic structures, which accounts for the non-Gaussian features of the gravitationally evolved matter distribution and light-cone effects. We test and validate our framework with realistic simulated shear data, demonstrating that the method recovers the unbiased matter distribution and the correct lensing and matter power spectrum. While the cosmology is fixed in this test, and the method employs a prior power spectrum, we demonstrate that the lensing results are sensitive to the true power spectrum when this differs from the prior. In this case, the density field samples are generated with a power spectrum that deviates from the prior, and the method recovers the true lensing power spectrum. The method also recovers the matter power spectrum across the sky, but as currently implemented, it cannot determine the radial power since isotropy is not imposed. In summary, our method provides physically plausible inference of the dark matter distribution from cosmic shear data, allowing us to extract information beyond the two-point statistics and exploiting the full information content of the cosmological fields.

scholarly journals Deciphering the change in root system architectural traits under limiting and non-limiting phosphorus in Indian bread wheat germplasm

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0255840
Author(s):  
Palaparthi Dharmateja ◽  
Manjeet Kumar ◽  
Rakesh Pandey ◽  
Pranab Kumar Mandal ◽  
Prashanth Babu ◽  
...  
Keyword(s):  
Root System ◽  
Bread Wheat ◽  
Root Length ◽  
Phosphorus Uptake ◽  
Root Traits ◽  
System Architectures ◽  
Wheat Genotypes ◽  
Uptake Efficiency ◽  
Wide Range ◽  
Use Efficiency

The root system architectures (RSAs) largely decide the phosphorus use efficiency (PUE) of plants by influencing the phosphorus uptake. Very limited information is available on wheat’s RSAs and their deciding factors affecting phosphorus uptake efficiency (PupE) due to difficulties in adopting scoring values used for evaluating root traits. Based on our earlier research experience on nitrogen uptake efficiency screening under, hydroponics and soil-filled pot conditions, a comprehensive study on 182 Indian bread wheat genotypes was carried out under hydroponics with limited P (LP) and non-limiting P (NLP) conditions. The findings revealed a significant genetic variation, root traits correlation, and moderate to high heritability for RSAs traits namely primary root length (PRL), total root length (TRL), total root surface area (TSA), root average diameter (RAD), total root volume (TRV), total root tips (TRT) and total root forks (TRF). In LP, the expressions of TRL, TRV, TSA, TRT and TRF were enhanced while PRL and RAD were diminished. An almost similar pattern of correlations among the RSAs was also observed in both conditions except for RAD. RAD exhibited significant negative correlations with PRL, TRL, TSA, TRT and TRF under LP (r = -0.45, r = -0.35, r = -0.16, r = -0.30, and r = -0.28 respectively). The subclass of TRL, TSA, TRV and TRT representing the 0–0.5 mm diameter had a higher root distribution percentage in LP than NLP. Comparatively wide range of H’ value i.e. 0.43 to 0.97 in LP than NLP indicates that expression pattern of these traits are highly influenced by the level of P. In which, RAD (0.43) expression was reduced in LP, and expressions of TRF (0.91) and TSA (0.97) were significantly enhanced. The principal component analysis for grouping of traits and genotypes over LP and NLP revealed a high PC1 score indicating the presence of non-crossover interactions. Based on the comprehensive P response index value (CPRI value), the top five highly P efficient wheat genotypes namely BW 181, BW 103, BW 104, BW 143 and BW 66, were identified. Considering the future need for developing resource-efficient wheat varieties, these genotypes would serve as valuable genetic sources for improving P efficiency in wheat cultivars. This set of genotypes would also help in understanding the genetic architecture of a complex trait like P use efficiency.

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