Associations among nutrient concentration, silage fermentation products, in vivo organic matter digestibility, rumen fermentation and in vitro methane yield in 78 grass silages

Grass-clover silage constitutes a large part of ruminant diets in Northern and Western Europe, but the impact of silage quality on methane (CH4) production is largely unknown. This study was conducted to identify the quality attributes of grass silage associated with variation in CH4 yield. We expected that silage nutrient concentrations and silage fermentation products would affect CH4 yield, and that these factors could be used to predict the methanogenic potential of the si-lages. Round bales (n = 78) of grass and grass-clover silage from 37 farms in Norway were sampled, incubated, and screened for in vitro CH4 yield, i.e. CH4 production expressed on the basis of incubated organic matter (CH4-OM) and digestible OM (CH4-dOM) using sheep. Concentration of indigestible neutral detergent fiber (iNDF) was quantified using the in situ technique. The data were subjected to correlation and principal component analyses. Stepwise multiple regression was used to model methanogenic potential of silages. Among all investigated silage composition variables, neutral detergent fiber (aNDFom) and water-soluble carbohydrate (WSC) concentra-tions obtained the greatest correlations to CH4-OM (r =-0.63 and r = 0.57, respectively, P < 0.001), while concentration of iNDF negatively correlated with CH4-OM (r =-0.48, P < 0.001). In vivo organic matter digestibility (OMD) and concentration of ammonia-N (NH3-N) in silages were also correlated to CH4-OM (r = 0.44 and r =-0.32, P < 0.001 and P < 0.01, respectively). The stepwise regression using CH4-OM as response variable included aNDFom, WSC, iNDF, silage propionic acid and pH in descending order. The stepwise regression using CH4-dOM as response variable included WSC, aNDFom and iNDF in descending order. Among in vitro rumen short chain fatty acids (SCFA), molar proportion of butyrate was the most prominent in increasing CH4-OM and CH4-dOM (r = 0.23 and r = 0.36, P < 0.05 and P < 0.01, respectively), while molar proportion of propionate was the most prominent SCFA in reducing CH4-OM and CH4-dOM (r =-0.23 and r =-0.26, respectively, P < 0.05). Regression models that account for silage quality attributes can be used to predict CH4 yield from silages with a coefficient of determination (R-2) between 0.33 (CH4-dOM) and 0.65 (CH4-OM). In conclusion, concentration of WSC increased in vitro CH4-OM and CH4-dOM, while concentration of aNDFom and iNDF decreased CH4-OM and CH4-dOM in grass silages

Detection of lameness and mastitis pathogens in milk using visual and olfactory sensing

The objective of this project is to investigate feasibility of visual combined with olfactory sensing and multimodal collaborative intelligence for the perception of diseases, especially the contagious ones, among a population of dairy cattle. The idea is to develop artificially intelligent systems that can generate lowdimensional representations about presence of diseases by learning from visual and olfactory sensory inputs, which are high-dimensional and noisy. The idea of in-cooperating visual and olfactory intelligence is a brilliant one; this is because the olfactory intelligence of animals and insects are predominant over visual intelligence and that olfactory intelligence are currently barely decoded computationally, i.e, no computational models outperform the olfactory perceptional capability of moths being widely studied. This is because in contrast to high-resolution camera sensors reaching many mega pixels, state-of-the-art volatile organic compounds sensing arrays called electronic nose achieve only tens of pixels and can only sense ppm maybe ppb concentration level (1 000 000 to 1 000 times lower than insects). The invention of multi-layer and large artificial neural networks for attempting to encode of human visual perceptual intelligence in a computational manner has achieved breakthrough in high-performance artificial intelligence systems. Newer models often contain one or more architectural modules which encodes cognitive science findings such as memory, contrast, analogy, anticipation of consequences, reasoning, knowledge in physics. We are targeting the derivation of a heterogeneous deep architecture combining the visual and olfactory branches their collaborative intelligence. The scope of this project is to evaluate the potential ofthe proposed approach in a real world setup and to clarify technical challenges.publishedVersio

Factors associated with milking-to-milking variability in somatic cell counts from healthy cows in an automatic milking system

Fully automated on-line analysis equipment is available for analysis of somatic cell count (SCC) at every milking in automatic milking systems. In addition to results from on-line cell counters (OCC), an array of additional cow-level and quarter-level factors considered important for udder health are recorded in these systems. However, the amount of variability in SCC that can be explained by available data is unknown, and so is the proportion of the variability that may be due to physiological or normal variability. Our aim was to increase our knowledge on OCC as an indicator for disturbances in udder health by assessing the variability in OCC in cows free from clinical mastitis. The first objective was to evaluate how much of the variability in OCC could be explained by different potential sources of variability, including intramammary infection (IMI) status (assessed by bacterial culture of quarter milk samples). The second objective was to evaluate the repeatability of the OCC sensor used in our study and the agreement between OCC values and SCC measured in a dairy herd improvement (DHI) laboratory. A longitudinal study was performed in the research herd of the Norwegian University of Life Sciences from January 5th 2016 to May 22nd 2017. Data from 62,471 milkings from 173 lactations in 129 cows were analyzed. We used ln-transformed OCC values (in 1000 cells/ml) as the outcome (lnOCC) in linear mixed models, with random intercepts at cow-level and lactation-level within cow. We were able to explain 15.0% of the variability in lnOCC with the following fixed effects: lactation stage, parity, milk yield, OCC in residual milk from the previous milking, inter-quarter difference between the highest and lowest conductivity, season, IMI status, and genetic lineage. When including the random intercepts, the degree of explanation was 55.2%. The individual variables explained only a small part of the total variability in lnOCC. We concluded that physiological or normal variability is probably responsible for a large part of the overall variability in OCC in cows without clinical mastitis. This is important to consider when using OCC data for research purposes or in decision-support tools. Sensor repeatability was evaluated by analyzing milk from the same sample multiple times. The coefficient of variation was 0.11 at an OCC level relevant for detection of subclinical mastitis. The agreement study showed a concordance correlation coefficient of 0.82 when comparing results from the OCC with results from a DHI laboratory.publishedVersio

Milk-flow data collected routinely in an automatic milking system: an alternative to milking-time testing in the management of teat-end condition?

Abstract Background Having a poor teat-end condition is associated with increased mastitis risk, hence avoiding milking machine settings that have a negative effect on teat-end condition is important for successful dairy production. Milking-time testing (MTT) can be used in the evaluation of vacuum conditions during milking, but the method is less suited for herds using automatic milking systems (AMS) and relationships with teat end condition is poorly described. This study aimed to increase knowledge on interpretation of MTT in AMS and to assess whether milk-flow data obtained routinely by an AMS can be useful for the management of teat-end health. A cross-sectional study, including 251 teats of 79 Norwegian Red cows milked by AMS was performed in the research herd of the Norwegian University of Life Sciences. The following MTT variables were obtained at teat level: Average vacuum level in the short milk tube during main milking (MTVAC), average vacuum in the mouthpiece chamber during main milking and overmilking, teat compression intensity (COMPR) and overmilking time. Average and peak milk flow rates were obtained at quarter level from the AMS software. Teat-end callosity thickness and roughness was registered, and teat dimensions; length, and width at apex and base, were measured. Interrelationships among variables obtained by MTT, quarter milk flow variables, and teat dimensions were described. Associations between these variables and teat-end callosity thickness and roughness, were investigated. Results Principal component analysis showed clusters of strongly related variables. There was a strong negative relationship between MTVAC and average milk flow rate. The variables MTVAC, COMPR and average and peak milk flow rate were associated with both thickness and roughness of the callosity ring. Conclusions Quarter milk flow rate obtained directly from the AMS software was useful in assessing associations between milking machine function and teat-end condition; low average milk flow rates were associated with a higher likelihood of the teat having a thickened or roughened teat-end callosity ring. Since information on milk flow rate is readily available from the herd management system, this information might be used when evaluating causes for impaired teat-end condition in AMS

Factors associated with milking-to-milking variability in somatic cell counts from healthy cows in an automatic milking system

Fully automated on-line analysis equipment is available for analysis of somatic cell count (SCC) at every milking in automatic milking systems. In addition to results from on-line cell counters (OCC), an array of additional cow-level and quarter-level factors considered important for udder health are recorded in these systems. However, the amount of variability in SCC that can be explained by available data is unknown, and so is the proportion of the variability that may be due to physiological or normal variability. Our aim was to increase our knowledge on OCC as an indicator for disturbances in udder health by assessing the variability in OCC in cows free from clinical mastitis. The first objective was to evaluate how much of the variability in OCC could be explained by different potential sources of variability, including intramammary infection (IMI) status (assessed by bacterial culture of quarter milk samples). The second objective was to evaluate the repeatability of the OCC sensor used in our study and the agreement between OCC values and SCC measured in a dairy herd improvement (DHI) laboratory. A longitudinal study was performed in the research herd of the Norwegian University of Life Sciences from January 5th 2016 to May 22nd 2017. Data from 62,471 milkings from 173 lactations in 129 cows were analyzed. We used ln-transformed OCC values (in 1000 cells/ml) as the outcome (lnOCC) in linear mixed models, with random intercepts at cow-level and lactation-level within cow. We were able to explain 15.0% of the variability in lnOCC with the following fixed effects: lactation stage, parity, milk yield, OCC in residual milk from the previous milking, inter-quarter difference between the highest and lowest conductivity, season, IMI status, and genetic lineage. When including the random intercepts, the degree of explanation was 55.2%. The individual variables explained only a small part of the total variability in lnOCC. We concluded that physiological or normal variability is probably responsible for a large part of the overall variability in OCC in cows without clinical mastitis. This is important to consider when using OCC data for research purposes or in decision-support tools. Sensor repeatability was evaluated by analyzing milk from the same sample multiple times. The coefficient of variation was 0.11 at an OCC level relevant for detection of subclinical mastitis. The agreement study showed a concordance correlation coefficient of 0.82 when comparing results from the OCC with results from a DHI laboratory

Associations among nutrient concentration, silage fermentation products, in vivo organic matter digestibility, rumen fermentation and in vitro methane yield in 78 grass silages

Grass-clover silage constitutes a large part of ruminant diets in Northern and Western Europe, but the impact of silage quality on methane (CH4) production is largely unknown. This study was conducted to identify the quality attributes of grass silage associated with variation in CH4 yield. We expected that silage nutrient concentrations and silage fermentation products would affect CH4 yield, and that these factors could be used to predict the methanogenic potential of the silages. Round bales (n = 78) of grass and grass-clover silage from 37 farms in Norway were sampled, incubated, and screened for in vitro CH4 yield, i.e. CH4 production expressed on the basis of incubated organic matter (CH4-OM) and digestible OM (CH4-dOM) using sheep. Concentration of indigestible neutral detergent fiber (iNDF) was quantified using the in situ technique. The data were subjected to correlation and principal component analyses. Stepwise multiple regression was used to model methanogenic potential of silages. Among all investigated silage composition variables, neutral detergent fiber (aNDFom) and water-soluble carbohydrate (WSC) concentrations obtained the greatest correlations to CH4-OM (r = −0.63 and r = 0.57, respectively, P < 0.001), while concentration of iNDF negatively correlated with CH4-OM (r = −0.48, P < 0.001). In vivo organic matter digestibility (OMD) and concentration of ammonia-N (NH3-N) in silages were also correlated to CH4-OM (r = 0.44 and r = −0.32, P < 0.001 and P < 0.01, respectively). The stepwise regression using CH4-OM as response variable included aNDFom, WSC, iNDF, silage propionic acid and pH in descending order. The stepwise regression using CH4-dOM as response variable included WSC, aNDFom and iNDF in descending order. Among in vitro rumen short chain fatty acids (SCFA), molar proportion of butyrate was the most prominent in increasing CH4-OM and CH4-dOM (r = 0.23 and r = 0.36, P < 0.05 and P < 0.01, respectively), while molar proportion of propionate was the most prominent SCFA in reducing CH4-OM and CH4-dOM (r = −0.23 and r = −0.26, respectively, P < 0.05). Regression models that account for silage quality attributes can be used to predict CH4 yield from silages with a coefficient of determination (R2) between 0.33 (CH4-dOM) and 0.65 (CH4-OM). In conclusion, concentration of WSC increased in vitro CH4-OM and CH4-dOM, while concentration of aNDFom and iNDF decreased CH4-OM and CH4-dOM in grass silages

Associations among nutrient concentration, silage fermentation products, in vivo organic matter digestibility, rumen fermentation and in vitro methane yield in 78 grass silages

Grass-clover silage constitutes a large part of ruminant diets in Northern and Western Europe, but the impact of silage quality on methane (CH4) production is largely unknown. This study was conducted to identify the quality attributes of grass silage associated with variation in CH4 yield. We expected that silage nutrient concentrations and silage fermentation products would affect CH4 yield, and that these factors could be used to predict the methanogenic potential of the silages. Round bales (n = 78) of grass and grass-clover silage from 37 farms in Norway were sampled, incubated, and screened for in vitro CH4 yield, i.e. CH4 production expressed on the basis of incubated organic matter (CH4-OM) and digestible OM (CH4-dOM) using sheep. Concentration of indigestible neutral detergent fiber (iNDF) was quantified using the in situ technique. The data were subjected to correlation and principal component analyses. Stepwise multiple regression was used to model methanogenic potential of silages. Among all investigated silage composition variables, neutral detergent fiber (aNDFom) and water-soluble carbohydrate (WSC) concentrations obtained the greatest correlations to CH4-OM (r = −0.63 and r = 0.57, respectively, P < 0.001), while concentration of iNDF negatively correlated with CH4-OM (r = −0.48, P < 0.001). In vivo organic matter digestibility (OMD) and concentration of ammonia-N (NH3-N) in silages were also correlated to CH4-OM (r = 0.44 and r = −0.32, P < 0.001 and P < 0.01, respectively). The stepwise regression using CH4-OM as response variable included aNDFom, WSC, iNDF, silage propionic acid and pH in descending order. The stepwise regression using CH4-dOM as response variable included WSC, aNDFom and iNDF in descending order. Among in vitro rumen short chain fatty acids (SCFA), molar proportion of butyrate was the most prominent in increasing CH4-OM and CH4-dOM (r = 0.23 and r = 0.36, P < 0.05 and P < 0.01, respectively), while molar proportion of propionate was the most prominent SCFA in reducing CH4-OM and CH4-dOM (r = −0.23 and r = −0.26, respectively, P < 0.05). Regression models that account for silage quality attributes can be used to predict CH4 yield from silages with a coefficient of determination (R2) between 0.33 (CH4-dOM) and 0.65 (CH4-OM). In conclusion, concentration of WSC increased in vitro CH4-OM and CH4-dOM, while concentration of aNDFom and iNDF decreased CH4-OM and CH4-dOM in grass silages.publishedVersio