Comparison of methods for evaluating aggregate stability of mountain soils, A

August 1965.Includes bibliographical references (pages 44-47).Covers not scanned.Print version deaccessioned 2021.A comparison of three methods -- Middleton dispersion ratio, wet sieving, and waterdrop impact -- of testing aggregate stability was made. Soil samples were selected from 9 sites representing a wide variety of great soil groups in the Colorado Front Range of the Rocky Mountains at elevations ranging from 5,000 to 11,000 feet. Samples of the top of the A and B horizons were taken. A great variation in stability ranking was found by the different methods. The A and B horizons, although quite different from each other, gave similar responses to the different methods. There was a distinct tendency for the stability rankings of the B horizons to vary less than A horizons, with the different methods. The B horizon was more stable than the A horizon for all soils with the wet sieve test whereas with the waterdrop impact method only one soil had the B horizon more stable than the A horizon; the dispersion ratio test did not detect any significant (5% level) differences between the A and B horizons. The dispersion ratio was the least powerful test in terms of defining differences between soils; the use of 2-3 mm soil instead of <2 mm soil increased the distinguishing power somewhat. The waterdrop impact method was the most powerful test in terms of defining differences between soils. The wet sieve method was intermediate between the dispersion ratio and waterdrop impact methods

Accuracy of Genomic EBV Using an Evenly Spaced, Low-density SNP Panel in Broiler Chickens

Whole-genome or genomic selection is based on associations of large number of markers across the genome with phenotype but will require use of small SNP panels to be cost effective in chickens. The potential loss of accuracy of genotyping selection candidates with an evenly-spaced low-density marker panel and imputation of high-density SNP genotypes was evaluated in a commercial broiler chicken line. Several methods were used to estimate marker effects. The loss in accuracy was less than 5% for different methods and traits. Thus, genomic selection using evenly-spaced low-density marker panels is a cost-effective choice for implementation of genomic selection

Enhancing High-concentrated Wastewater Quality on Evaporation Rate from Five-Consecutive Oxidation Ponds as Located in Phetchaburi, Southerly Thailand

This research aimed to examine the environmental factors determining the rates of evaporation, a natural phenomenon contributing to the treatment of wastewater of 5-consecutive oxidation ponds of the King’s Royally Initiated Laem Phak Bia Environmental Research and Development Project. Data collected from the 17th of April to 30th of May 2019 by US Class A Evaporation Pan revealed that the sedimentation pond (Pond 1) has the highest rate, 7.22 mm d-1, the oxidation pond 1 (Pond 2), 5.70 mm d-1, the oxidation pond 3 (Pond 4), 5.56 mm d-1, the stabilization pond (Pond 5), mm d-1, the reference pond at 5.07 mm d-1 and the oxidation pond 2 (Pond 3), 3.59 mm d-1. Concluding the evaporation in domestic wastewater treatment plants is characterized by 1) heat generated from short and long wave radiation emitted by earth and the sun, 2) local wind profiles of the area affected the height differences of the roughness length, and 3) heat generated by the respiration and digestion process of microbial activities and other grey body contaminants. Presenting the day and night variations made for the analysis, the day evaporation was significantly higher resulted by the net radiation were accountable. Wind profile generated from the measurement of speeds and directions at two different sites at 3 and 10 m has explained for the roughness length heights over each pond as lower roughness height have cause the increased in the rates of evaporation in Pond 4 and 5 however, these processes were also suppressed by high ionic bonding molecules effected suggested by the high TDS and EC values. The vertical temperature profile has conveyed the movement in the heat flux that dominated an upward flux movement in Pond 1. This is the exothermic reaction from the digestion process have suggested that extra heat has been added

Estimated genomic heritabilities from the swine sample.

<p>  =  additive heritability,  =  dominance heritability, and  =  total heritability (or heritability in the broad sense).</p><p>Estimated genomic heritabilities from the swine sample.</p

Genomic additive and dominance relationships by Definitions I-VI for parent-offspring (3518 pairs), full-sibs (1441 pairs) and half-sibs (23,628 pairs) of the swine sample.

<p>Genomic additive and dominance relationships by Definitions I-VI for parent-offspring (3518 pairs), full-sibs (1441 pairs) and half-sibs (23,628 pairs) of the swine sample.</p

Six definitions of genomic additive and dominance relationships.

<p>Six definitions of genomic additive and dominance relationships.</p

Quantitative Genetics Model as the Unifying Model for Defining Genomic Relationship and Inbreeding Coefficient

<div><p>The traditional quantitative genetics model was used as the unifying approach to derive six existing and new definitions of genomic additive and dominance relationships. The theoretical differences of these definitions were in the assumptions of equal SNP effects (equivalent to across-SNP standardization), equal SNP variances (equivalent to within-SNP standardization), and expected or sample SNP additive and dominance variances. The six definitions of genomic additive and dominance relationships on average were consistent with the pedigree relationships, but had individual genomic specificity and large variations not observed from pedigree relationships. These large variations may allow finding least related genomes even within the same family for minimizing genomic relatedness among breeding individuals. The six definitions of genomic relationships generally had similar numerical results in genomic best linear unbiased predictions of additive effects (GBLUP) and similar genomic REML (GREML) estimates of additive heritability. Predicted SNP dominance effects and GREML estimates of dominance heritability were similar within definitions assuming equal SNP effects or within definitions assuming equal SNP variance, but had differences between these two groups of definitions. We proposed a new measure of genomic inbreeding coefficient based on parental genomic co-ancestry coefficient and genomic additive correlation as a genomic approach for predicting offspring inbreeding level. This genomic inbreeding coefficient had the highest correlation with pedigree inbreeding coefficient among the four methods evaluated for calculating genomic inbreeding coefficient in a Holstein sample and a swine sample.</p></div

Genomic additive and dominance relationships by Definitions I-VI for parent-offspring (239 pairs), full-sibs (48 pairs) and half-sibs (23,941) of the Holstein sample.

<p>Genomic additive and dominance relationships by Definitions I-VI for parent-offspring (239 pairs), full-sibs (48 pairs) and half-sibs (23,941) of the Holstein sample.</p

Statistical summary of diagonal values of additive and dominance relationships.

<p>Statistical summary of diagonal values of additive and dominance relationships.</p