Environmental factors influencing maturation and release of ascospores of Venturia pirina in Victoria, Australia

2001 ◽  
Vol 52 (8) ◽  
pp. 825 ◽  
Author(s):  
O. N. Villalta ◽  
W. S. Washington ◽  
G. M. Rimmington ◽  
W. E. MacHardy
Keyword(s):  
Linear Equations ◽  
Management Program ◽  
Data Sets ◽  
Regression Equations ◽  
Degree Days ◽  
Night Time ◽  
Cumulative Percentage ◽  
Pear Scab ◽  
Linear Relationships ◽  
Venturia Pirina

The influence of moisture, light, and temperature on Venturia pirina ascospore maturation and discharge was studied during 1992–99 in 2 pear-growing regions in Victoria. In each year and site, mature ascospores were trapped over a 3-month period beginning a few days before or at the green-tip stage of pear tree development in early September and ending in late November, with the majority of ascospores ((>80%) trapped between green-tip and petal-fall. Ascospore discharge was associated with rain and dew, with 90–98% of the season’s total number of ascospores trapped during rain events and 2–10% trapped during dew events in the 12 data sets examined. Most ascospores were trapped (82.5– 99.9%) during daytime (0600–1800 hours). The 0.1–17.5% of ascospores detected during night time (1900–0500 hours) were trapped mainly within 1–3 h of dawn or dusk. There were linear relationships between the logit of cumulative percentage of ascospore maturation and temperature accumulation (above 0 degree-days), calculated both daily and for days with >= 0.2 mm of rainfall. Six linear regression equations were formulated with 10 years of field data and using the 2 methods of accumulating degree-days, to predict the cumulative percentage of matured ascospores. Predictions were compared with additional field and laboratory observations not used in the formulation of the linear equations. The importance of the temperature-based linear equations is discussed in relation to the prediction of pear scab ascospore maturity for use in a pear scab management program.

scholarly journals Pear Scab: Components of Potential Ascospore Dose and Validation of an Ascospore Maturity Model

Plant Disease ◽  
2000 ◽  
Vol 84 (6) ◽  
pp. 681-683 ◽  
Author(s):  
Robert A. Spotts ◽  
Louis A. Cervantes ◽  
Franz J. A. Niederholzer
Keyword(s):  
Linear Regression ◽  
Leaf Litter ◽  
Original Model ◽  
Infected Leaf ◽  
Data Sets ◽  
Maturity Model ◽  
Linear Regression Equation ◽  
Degree Days ◽  
Pear Scab ◽  
Venturia Pirina

Variables necessary to calculate potential ascospore dose (PAD) for Venturia pirina, the causal agent of pear scab, were measured: area of individual leaves averaged 20.0 cm2, number of lesions per infected leaf ranged from 1.8 to 2.2, 13.5 to 41.7% of lesions were fertile (contained pseudothecia), number of pseudothecia per fertile lesion ranged from 15.2 to 24.4, and number of asci per pseudothecium averaged 131.5. The proportions of orchard floor covered with leaves (leaf litter density) were 0.34 and 0.67 for mowed and nonmowed orchards, respectively. Incidence of infected leaves ranged from 0.1 to 32.3%. Calculated PAD ranged from 1,114 to 708,320 ascospores per m2 in a commercial and an unsprayed orchard, respectively. For validation of a previously published model relating ascospore maturity to degree-days with a 0°C base, a linear regression equation was developed using 11 data sets collected between 1981 and 1998. The slope of the line was not different (P = 0.951) from that of the original model.

An evaluation of the Hennessy Grading Probe for measuring fat depth in beef carcasses

1989 ◽  
Vol 29 (6) ◽  
pp. 781 ◽  
Author(s):  
DL Hopkins
Keyword(s):  
Linear Equations ◽  
Data Sets ◽  
Regression Equations ◽  
Data Set ◽  
General Validity ◽  
Measurement Analysis ◽  
Beef Carcasses ◽  
The Right ◽  
The Relationship ◽  
Methods Of Measurement

Fat depth at the P8 site on the rump was measured by the cut-and-measure (CM) technique and with the Hennessy Grading Probe (HGP) on 2501 beef carcasses at 1 abattoir over a 12-month period. CM measurements that differed by more than 1 mm between the right and left sides of the carcass were discarded. A subsequent data set of 1850 carcasses was randomly divided so that 2 models could be developed to assess the general validity of the relationship between the 2 methods of measurement. Analysis of measurements of the left side of the carcasses of these 2 subsamples showed the data were not normally distributed. Removal of outliers at the 95% confidence level and also measurements at both extremes of the data range improved the symmetry of the sets of data. From each adjusted data set, regression equations were developed to predict CM measurements from HGP measurements. Linear equations were adequate for predicting CM measurements from HGP measurements, and curvilinear analysis did not improve the predictions. Compared with the curvilinear equations, the linear equations resulted in smaller differences between the 2 data sets for the predicted CM measurements over a range of HGP measurements.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

scholarly journals Framework for Reconstruction of Pseudo Quad Polarimetric Imagery from General Compact Polarimetry

2021 ◽  
Vol 13 (3) ◽  
pp. 530
Author(s):  
Junjun Yin ◽  
Jian Yang
Keyword(s):  
Linear Equations ◽  
Estimation Method ◽  
Data Sets ◽  
Reconstruction Procedure ◽  
Component Decomposition ◽  
Reconstruction Methods ◽  
Sar Imagery ◽  
Key Aspects ◽  
Non Linear Equations ◽  
Reconstruction Model

Pseudo quad polarimetric (quad-pol) image reconstruction from the hybrid dual-pol (or compact polarimetric (CP)) synthetic aperture radar (SAR) imagery is a category of important techniques for radar polarimetric applications. There are three key aspects concerned in the literature for the reconstruction methods, i.e., the scattering symmetric assumption, the reconstruction model, and the solving approach of the unknowns. Since CP measurements depend on the CP mode configurations, different reconstruction procedures were designed when the transmit wave varies, which means the reconstruction procedures were not unified. In this study, we propose a unified reconstruction framework for the general CP mode, which is applicable to the mode with an arbitrary transmitted ellipse wave. The unified reconstruction procedure is based on the formalized CP descriptors. The general CP symmetric scattering model-based three-component decomposition method is also employed to fit the reconstruction model parameter. Finally, a least squares (LS) estimation method, which was proposed for the linear π/4 CP data, is extended for the arbitrary CP mode to estimate the solution of the system of non-linear equations. Validation is carried out based on polarimetric data sets from both RADARSAT-2 (C-band) and ALOS-2/PALSAR (L-band), to compare the performances of reconstruction models, methods, and CP modes.

Incorporating Additional Tree and Environmental Variables in a Lodgepole Pine Stem Profile Model

1993 ◽  
Vol 8 (3) ◽  
pp. 86-90
Author(s):  
John C. Byrne
Keyword(s):  
Environmental Variables ◽  
Pinus Contorta ◽  
Lodgepole Pine ◽  
Linear Equations ◽  
The United States ◽  
National Forest ◽  
Data Sets ◽  
Tree Form ◽  
Northern Rocky Mountains ◽  
Profile Model

Abstract A new variable-form segmented stem profile model is developed for lodgepole pine (Pinus contorta) trees from the northern Rocky Mountains of the United States. I improved estimates of stem diameter by predicting two of the model coefficients with linear equations using a measure of tree form, defined as a ratio of dbh and total height. Additional improvements were obtained by fitting this model to individual national forest data sets. Other tree and environmental variables tested but found of little use in improving stem profile estimates were crown ratio, habitat series, elevation, slope percent, and aspect. West. J. Appl. For. 8(3):86-90.

scholarly journals Morphometry of Rhinomugil Corsula (Hamilton, 1822) From Sitakunda Coast of the Bay of Bengal, Bangladesh

2021 ◽  
Vol 49 (2) ◽  
pp. 229-241
Author(s):  
Syeda Ismat Ara ◽  
Mohammad Ali Azadi ◽  
Munira Nasiruddin ◽  
Aftab Hossain
Keyword(s):  
Bay Of Bengal ◽  
The Body ◽  
Head Length ◽  
Regression Equations ◽  
Morphometric Characters ◽  
Total Length ◽  
Linear Relationships ◽  
Investigation Period ◽  
R Value ◽  
Rhinomugil Corsula

A total of 65 specimens of Rhinomugil corsula (Hamilton, 1822), measuring from 8.1 cm to 28.9 cm in total length, collected from the Sitakunda coast of the Bay of Bengal, were used for the morphometric analysis during the period between March 2016 and February 2018. Twenty seven morphometric characters were selected and studied during the investigation period. The regression equations – both arithmetic and logarithmic – between the total length (TL) and 21 morphometric characters, and head length (HL) with five morphometric characters related to the head, were determined. The value of coefficient of correlation ‘r’ for each relationship was calculated and t-test for each ‘r’ value was also done. The relationships of the various measurements of the body with the total length – and head length with the five relevant characters -of R. corsula from the Sitakunda coast of the Bay of Bengal showed linear relationships, which were highly significant (P<0.01). The ranges of ‘b’ values 0.967 to 1.346 in case of the relationships between TL and 21 relevant characters, whereas 0.906 to 1.236 in case of the relationships between HL and 5 relevant characters. These values differ insignificantly (P>0.01) with typical value b=1 indicating isometric relationships among the characters. Bangladesh J. Zool. 49 (2): 229-241, 2021

COMPARISON OF OBSERVATIONS AND MACROCLIMATIC MODEL ESTIMATES OF MONTHLY WINTER SOIL TEMPERATURES AT OTTAWA

1985 ◽  
Vol 65 (1) ◽  
pp. 109-122 ◽  
Author(s):  
L. M. DWYER ◽  
H. N. HAYHOE
Keyword(s):  
Snow Cover ◽  
Predictive Models ◽  
Regression Models ◽  
Stepwise Regression ◽  
Regression Coefficients ◽  
Data Sets ◽  
Regression Equations ◽  
Grass Cover ◽  
Partial Regression ◽  
Soil Temperatures

Estimates of monthly soil temperatures under short-grass cover across Canada using a macroclimatic model (Ouellet 1973a) were compared to monthly averages of soil temperatures monitored over winter at Ottawa between November 1959 and April 1981. Although the fit between monthly estimates and Ottawa observations was generally good (R for all months and depths 0.10, 0.20, 0.50, 1.00 and 1.50 m was 0.90), it was noted that midwinter estimates were generally below observed temperatures at all soil depths. Data sets used in the development of the original Ouellet (1973a) multiple regression equations were collected from stations across Canada, many of which have reduced snow cover. It was found that the buffering capability of the snow cover accumulated at Ottawa during the winter months was underestimated by the pertinent partial regression coefficients in these equations. The coefficients were therefore modified for the Ottawa station during the winter months. The resultant regression models were used to estimate soil temperature during the winters of 1981–1982 and 1982–1983. Although the Ottawa-based models included fewer variables because of the smaller data base available from a single site, comparisons of model estimates and observations were good (R = 0.84 and 0.91) and midwinter estimates were not consistently underestimated as they were using the original Ouellet (1973a) model. Reliable monthly estimates of soil temperatures are important since they are a necessary input to more detailed predictive models of daily soil temperatures. Key words: Regression model, snowcover, stepwise regression, variable selection

Proteogenomic Analysis of the Venturia pirina (Pear Scab Fungus) Secretome Reveals Potential Effectors

2014 ◽  
Vol 13 (8) ◽  
pp. 3635-3644 ◽  
Author(s):  
Ira R. Cooke ◽  
Dan Jones ◽  
Joanna K. Bowen ◽  
Cecilia Deng ◽  
Pierre Faou ◽  
...  
Keyword(s):  
Pear Scab ◽  
Venturia Pirina

scholarly journals Individual Tree Volume Equations for Red Pine in Michigan

1997 ◽  
Vol 14 (2) ◽  
pp. 53-58 ◽  
Author(s):  
Gary W. Fowler
Keyword(s):  
Red Pine ◽  
Data Sets ◽  
Regression Equations ◽  
Independent Data ◽  
Data Set ◽  
Individual Tree ◽  
Standing Trees ◽  
Pine Stands ◽  
Volume Estimates ◽  
Volume Equations

Abstract New total, pulpwood, sawtimber, and residual pulpwood cubic foot individual tree volume equations were developed for red pine in Michigan using nonlinear and multiple linear regression. Equations were also developed for Doyle, International 1/4 in., and Scribner bd ft volume, and a procedure for estimating pulpwood and residual pulpwood rough cord volumes from the appropriate cubic foot equations was described. Average ratios of residual pulpwood (i.e., topwood, cubic foot or cords) to mbf were developed for 7.6 and 9.6 in. sawtimber. Data used to develop these equations were collected during May-August 1983-1985 from 3,507 felled and/or standing trees from 27 stands in Michigan. Sixteen and 11 stands were located in the Upper and Lower Peninsulas, respectively. All equations were validated on an independent data set. Rough cord volume estimates based on the new pulpwood equation were compared with contemporary tables for 2 small cruise data sets. The new equations can be used to more accurately estimate total volume and volume per acre when cruising red pine stands. North. J. Appl. For. 14(2):53-58.

scholarly journals Modeling the Soil Response to Rainstorms after Wildfire and Prescribed Fire in Mediterranean Forests

Climate ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 150
Author(s):  
Manuel Esteban Lucas-Borja ◽  
Giuseppe Bombino ◽  
Bruno Gianmarco Carrà ◽  
Daniela D’Agostino ◽  
Pietro Denisi ◽  
...  
Keyword(s):  
Linear Regression ◽  
Prescribed Fire ◽  
Linear Equations ◽  
Model Performance ◽  
Fire Risk ◽  
Mediterranean Forests ◽  
Regression Equations ◽  
Soil Response ◽  
Runoff Volume ◽  
Scs Cn

The use of the Soil Conservation Service-curve number (SCS-CN) model for runoff predictions after rainstorms in fire-affected forests in the Mediterranean climate is quite scarce and limited to the watershed scale. To validate the applicability of this model in this environment, this study has evaluated the runoff prediction capacity of the SCS-CN model after storms at the plot scale in two pine forests of Central-Eastern Spain, affected by wildfire (with or without straw mulching) or prescribed fire and in unburned soils. The model performance has been compared to the predictions of linear regression equations between rainfall depth and runoff volume. The runoff volume was simulated with reliability by the linear regression only for the unburned soil (coefficient of Nash and Sutcliffe E = 0.73–0.89). Conversely, the SCS-CN model was more accurate for burned soils (E = 0.81–0.97), also when mulching was applied (E = 0.96). The performance of this model was very satisfactory in predicting the maximum runoff. Very low values of CNs and initial abstraction were required to predict the particular hydrology of the experimental areas. Moreover, the post-fire hydrological “window-of-disturbance” could be reproduced only by increasing the CN for the storms immediately after the wildfire. This study indicates that, in Mediterranean forests subject to the fire risk, the simple linear equations are feasible to predict runoff after low-intensity storms, while the SCS-CN model is advisable when runoff predictions are needed to control the flooding risk.

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