Relationship of total bacterial and somatic cell counts with milk production and composition multivariate analysis

This study was realized to evaluate the monthly production, composition and quality of milk (total and defatted dry extract, lactose, fat and protein) and their relation to somatic cell count (SCC) and total bacterial count (TBC) using multivariate statistical analyses. The data are from a dairy farm for the period of two years (from January 2015 to December 2016). The SCC and TBC variables were transformed to somatic cell score (SCS) and log10 (LogTBC). Factor analysis, discriminant analysis and cluster analysis were used. Through factor analysis, it was found two factors that together explained 69.5% of the total data variation. The first factor represented the inverse relationship between lactose versus fat and protein content, while the second factor represented the inverse relationship among monthly milk yield versus SCS and LogTBC. The discriminant analysis identified that lactose and protein contents and SCS were the variables that had the greatest participation in the separation of the groups formed by the cluster analysis. The groups differed mainly by the monthly production of milk, composition and SCS. Finally, there are important multivariate relations between the variables milk production, composition and quality.(AU)

Relationship of total bacterial and somatic cell counts with milk production and composition multivariate analysis

This study was realized to evaluate the monthly production, composition and quality of milk (total and defatted dry extract, lactose, fat and protein) and their relation to somatic cell count (SCC) and total bacterial count (TBC) using multivariate statistical analyses. The data are from a dairy farm for the period of two years (from January 2015 to December 2016). The SCC and TBC variables were transformed to somatic cell score (SCS) and log10 (LogTBC). Factor analysis, discriminant analysis and cluster analysis were used. Through factor analysis, it was found two factors that together explained 69.5% of the total data variation. The first factor represented the inverse relationship between lactose versus fat and protein content, while the second factor represented the inverse relationship among monthly milk yield versus SCS and LogTBC. The discriminant analysis identified that lactose and protein contents and SCS were the variables that had the greatest participation in the separation of the groups formed by the cluster analysis. The groups differed mainly by the monthly production of milk, composition and SCS. Finally, there are important multivariate relations between the variables milk production, composition and quality.

Estimation of herbicide bioconcentration in sugarcane (Saccharum officinarum L. ) / Modelagem de bioconcentração de herbicidas em cana (Saccharum officinarum L. )

Sugarcane is an important crop for sugar and biofuel production in Brazil. Growers depend greatly on herbicides to produce it. This experiment used herbicide physical-chemical and sugarcane plant physiological properties to simulate herbicide uptake and estimate the bioconcentration factor (BCF). The (BCF) was calculated for the steady state chemical equilibrium between the plant herbicide concentration and soil solution. Plant-water partition coefficient (sugarcane bagasse-water partition coefficient), herbicide dilution rate, metabolism and dissipation in the soil-plant system, as well as total plant biomass factors were used. In addition, we added Tebuthiuron at rate of 5.0kg a.i. ha-1 to physically test the model. In conclusion, the model showed the following ranking of herbicide uptake: sulfentrazone > picloram >tebuthiuron > hexazinone > metribuzin > simazine > ametryn > diuron > clomazone > acetochlor. Furthermore, the highest BCF herbicides showed higher Groundwater Ubiquity Score (GUS) index indicating high leaching potential. We did not find tebuthiuron in plants after three months of herbicide application.(AU)

A cana de açúcar é uma cultura importante para produção de açúcar e biocombustíveis no Brasil e exige elevada utilização de herbicidas. Utilizamos modelo matemático para ajudar na compreensão da absorção de herbicida dessa cultura. Propriedades físico-químicas dos herbicidas e propriedades fisiológicas das plantas de cana foram usados para estimar a absorção e também o fator de bioconcentração, bioconcentration factor (BCF), calculado para o equilíbrio químico entre a concentração do herbicida na planta e na solução do solo. O coeficiente de partição planta/água, a taxa de diluição de herbicida, o metabolismo e a dissipação no sistema solo-planta e biomassa total das plantas foram adicionados ao modelo. O herbicida tebuthiuron aplicado ao solo na dose de 5,0kg ha-1 i.a. foi utilizado para testar o modelo. A absorção dos herbicidas mostrada pelo modelo indicou em ordem o seguinte: sulfentrazone> picloram> tebuthiuron> hexazinone> metribuzin> simazina> ametryn> diuron> clomazone> acetochlor. Esses herbicidas com alto índice (BCF) também apresentaram alto índice de potencial de lixiviação para água subterrânea "Groundwater Ubiquity Score" (GUS). Tebuthiuron não foi encontrado nas plantas após três meses de aplicação.(AU)