There is a paucity of knowledge on microbial community diversity and naturally occurring seasonal variations in agricultural soil. For this purpose the soil microbial community of a wheat field on an experimental farm in The Netherlands was studied by using both cultivation-based and molecule-based methods. Samples were taken in the different seasons over a 1-year period. Fatty acid-based typing of bacterial isolates obtained via plating revealed a diverse community of mainly gram-positive bacteria, and only a few isolates appeared to belong to the Proteobacteria and green sulfur bacteria. Some genera, such as Micrococcus, Arthrobacter, and Corynebacterium were detected throughout the year, while Bacillus was found only in July. Isolate diversity was lowest in July, and the most abundant species, Arthrobacter oxydans, and members of the genus Pseudomonas were found in reduced numbers in July. Analysis by molecular techniques showed that diversity of cloned 16S ribosomal DNA (rDNA) sequences was greater than the diversity among cultured isolates. Moreover, based on analysis of 16S rDNA sequences, there was a more even distribution among five main divisions, Acidobacterium, Proteobacteria, Nitrospira, cyanobacteria, and green sulfur bacteria. No clones were found belonging to the gram-positive bacteria, which dominated the cultured isolates. Seasonal fluctuations were assessed by denaturing gradient gel electrophoresis. Statistical analysis of the banding patterns revealed significant differences between samples taken in different seasons. Cluster analysis of the patterns revealed that the bacterial community in July clearly differed from those in the other months. Although the molecule-and cultivationbased methods allowed the detection of different parts of the bacterial community, results from both methods indicated that the community present in July showed the largest difference from the communities of the other months. Efforts were made to use the sequence data for providing insight into more general ecological relationships. Based on the distribution of 16S rDNA sequences among the bacterial divisions found in this work and in literature, it is suggested that the ratio between the number of Proteobacteria and Acidobacterium organisms might be indicative of the trophic level of the soil.

When analyzing Poisson-count data sometimes a lot of zeros are observed. When there are too many zeros a zero-inflated Poisson distribution can be used. A score test is presented to test whether the number of zeros is too large for a Poisson distribution to fit the data well.

Many cow-specific risk factors for clinical mastitis (CM) are known. Other studies have analyzed these risk factors separately or only analyzed a limited number of risk factors simultaneously. The goal of this study was to determine the influence of cow factors on the incidence rate of CM (IRCM) with all cow factors in one multivariate model. Also, using a similar approach, the probability of whether a CM case is caused by grampositive or gram-negative pathogens was calculated. Data were used from 274 Dutch dairy herds that recorded CM over an 18-mo period. The final dataset contained information on 28,137 lactations of 22,860 cows of different parities. In total 5,363 CM cases were recorded, but only 2,525 CM cases could be classified as gram-positive or gram-negative. The cow factors parity, lactation stage, season of the year, information on SCC from monthly test-day records, and CM history were included in the logistic regression analysis. Separate analyses were performed for heifers and multiparous cows in both the first month of lactation and from the second month of lactation onward. For investigating whether CM was caused by gram-positive or gram-negative pathogens, quarter position was included in the logistic regression analysis as well. The IRCM differed considerably among cows, ranging between 0.0002 and 0.0074 per cow-day at risk for specific cows depending on cow factors. In particular, previous CM cases, SCC in the previous month, and mean SCC in the previous lactation increased the IRCM in the current month of lactation. Results indicate that it is difficult to distinguish between gram-positive and gram-negative CM cases based on cow factors alone.

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The purpose of this study was to find the economically optimal period of first conception in gilts, addressing the issues of lifetime reproductive performance and expected herd life. A profit equation was used to combine the effects into one economic parameter. The data were from 14,910 gilts on 54 farms throughout The Netherlands. The average number of pigs born alive in the first litter increased with older age at conception. In the second litter a similar, but much smaller, effect was observed. Age at first conception had no effect on number of pigs born alive in the third or greater litter. Gilts bred at an older age had a shorter expected herd life than gilts bred at a younger age. Evaluating the reasons for culling revealed that infertility became a more important reason with increasing age at first conception. The proportion culled for infertility increased linearly from 18% at conception on d 200 to 24.5% at conception on d 300. Combining the effect of litter size and herd life led to the conclusion that the profit per gilt (sow) was not significantly affected by her age at first conception. It is concluded that the optimal economic age at first conception was considered to be approximately 200 to 220 d of age when the cost of housing and feed of the gilt from entry to first conception were taken into account.