Behaviour and Impact of Cow Slurry Beneath a Storage Lagoon: 1. Groundwater Contamination 1975–1982

The chemical and biological composition of groundwater sampled from a 76 m deep borehole situated 80 m from an unlined, earth-banked lagoon excavated in Upper Chalk and continuously used to store liquid cow manure was regularly monitored from August 1975 to January 1982 for signs of pollution. The lagoon became self-sealing 3 months after the start of filling in 1975, but was further enlarged and re-filled in autumn 1976, and emptied by dragline in September 1978 and 1980. No significant changes in groundwater composition were detected in the period August 1975 to December 1978. In January 1979, significant numbers of Escherichia coli and a large, rapid increase in concentrations of ammonium-nitrogen (NH₄-N), phosphorus (P), potassium (K), sodium (Na), chloride (Cl) and sulphate (SO4) were detected in the groundwater. Nutrient concentrations fluctuated at elevated levels throughout 1979 but decreased during 1980. Additional bacterial and chemical (NH₄-N, P and K) contamination was recorded in January 1981 and subsequent months. The contamination was considered to have occurred as a result of fissure flow through the unsaturated zone of the Upper Chalk following repeated disruption of the self-sealing layer during emptying of the lagoon by dragline in September 1978 and 1980. The results indicate that unlined, earth-banked slurry storage lagoons need to be carefully managed to avoid biological and chemical pollution of Chalk aquifers.     

Serum neutralizing antibody titers in dairy cattle administered an inactivated vesicular stomatitis virus vaccine

Two doses of a formalin-killed, cell culture-derived vesicular stomatitis virus (vsv)-New Jersey serotype vaccine were administered intramuscularly, 30 days apart, to all lactating and nonlactating cows in a 350-cow dairy herd. Serum specimens were obtained serially from 96 cows before vaccination and at 30, 52 and 80 days after vaccination and from 24 of these cows 175 days after vaccination. Serum neutralizing antibody titers to vsv-New Jersey serotype were determined from serum-dilution, plaque-reduction tests. Serum neutralizing antibody titers also were determined during the same period for 67 nonvaccinated heifers in the herd. Peak group geometric mean serum neutralizing antibody titers of 1:530.46 +/- 1.14 (group geometric mean titer log10, 2.725 +/- 0.055) developed 21 days after the second vaccination, but decreased to a low value of 1:65.36 +/- 1.38 (group geometric mean titer log10, 1.815 +/- 0.142) by 175 days after vaccination. The nonvaccinated group had no detectable antibody titer to vsv-New Jersey serotype throughout the study. All serum specimens from the vaccinates and controls were negative for heterologous reactivity to vsv-Indiana serotype.     

Using unsupervised learning techniques to assess interactions among complex traits in soybeans

Soybean yield components and agronomic traits are connected through physiological pathways that impose tradeoffs through genetic and environmental constraints. Our primary aim is to assess the interdependence of soybean traits by using unsupervised machine learning techniques to divide phenotypic associations into environmental and genetic associations. This study was performed on large scale, jointly analyzing 14 quantitative traits in a large multi-parental population designed for genetic studies. We collected phenotypes from 2012 to 2015 from a soybean nested association panel with 40 families of approximately 140 individuals each. Pearson and Spearman correlations measured phenotypic associations. A multivariate mixed linear model provided genotypic and environmental correlations. To evaluate relationships among traits, the study used principal component and undirected graphical models from phenotypic, genotypic, and environmental correlation matrices. Results indicate that high phenotypic correlation occurs when traits display both genetic and environmental correlations. In genetic terms, length of reproductive period, node number, and canopy coverage play important roles in determining yield potential. Optimal grain yield production occurs when the growing environment favors faster canopy closure and extended reproductive length. Environmental associations found among yield components give insight into the nature of yield component compensation. The use of unsupervised learning methods provides a good framework for investigating interactions among various quantitative traits and defining target traits for breeding.