Browsing by Author "Metges, Cornelia C."
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- ItemMethane prediction based on individual or groups of milk fatty acids for dairy cows fed rations with or without linseed(New York, NY [u.a.] : Elsevier, 2019) Engelke, Stefanie W.; Daş, Gürbüz; Derno, Michael; Tuchscherer, Armin; Wimmers, Klaus; Rychlik, Michael; Kienberger, Hermine; Berg, Werner; Kuhla, Björn; Metges, Cornelia C.Milk fatty acids (MFA) are a proxy for the prediction of CH4 emission from cows, and prediction differs with diet. Our objectives were (1) to compare the effect of diets on the relation between MFA profile and measured CH4 production, (2) to predict CH4 production based on 6 data sets differing in the number and type of MFA, and (3) to test whether additional inclusion of energy-corrected milk (ECM) yield or dry matter intake (DMI) as explanatory variables improves predictions. Twenty dairy cows were used. Four diets were used based on corn silage (CS) or grass silage (GS) without (L0) or with linseed (LS) supplementation. Ten cows were fed CS-L0 and CS-LS and the other 10 cows were fed GS-L0 and GS-LS in random order. In feeding wk 5 of each diet, CH4 production (L/d) was measured in respiration chambers for 48 h and milk was analyzed for MFA concentrations by gas chromatography. Specific CH4 prediction equations were obtained for L0-, LS-, GS-, and CS-based diets and for all 4 diets collectively and validated by an internal cross-validation. Models were developed containing either 43 identified MFA or a reduced set of 7 groups of biochemically related MFA plus C16:0 and C18:0. The CS and LS diets reduced CH4 production compared with GS and L0 diets, respectively. Methane yield (L/kg of DMI) reduction by LS was higher with CS than GS diets. The concentrations of C18:1 trans and n-3 MFA differed among GS and CS diets. The LS diets resulted in a higher proportion of unsaturated MFA at the expense of saturated MFA. When using the data set of 43 individual MFA to predict CH4 production (L/d), the cross-validation coefficient of determination (R2 CV) ranged from 0.47 to 0.92. When using groups of MFA variables, the R2 CV ranged from 0.31 to 0.84. The fit parameters of the latter models were improved by inclusion of ECM or DMI, but not when added to the data set of 43 MFA for all diets pooled. Models based on GS diets always had a lower prediction potential (R2 CV = 0.31 to 0.71) compared with data from CS diets (R2 CV = 0.56 to 0.92). Models based on LS diets produced lower prediction with data sets with reduced MFA variables (R2 CV = 0.62 to 0.68) compared with L0 diets (R2 CV = 0.67 to 0.80). The MFA C18:1 cis-9 and C24:0 and the monounsaturated FA occurred most often in models. In conclusion, models with a reduced number of MFA variables and ECM or DMI are suitable for CH4 prediction, and CH4 prediction equations based on diets containing linseed resulted in lower prediction accuracy. © 2019 American Dairy Science Association
- ItemMilk fatty acids estimated by mid-infrared spectroscopy and milk yield can predict methane emissions in dairy cows(Berlin ; Heidelberg : Springer, 2018-5-2) Engelke, Stefanie W.; Daş, Gürbüz; Derno, Michael; Tuchscherer, Armin; Berg, Werner; Kuhla, Björn; Metges, Cornelia C.Ruminant enteric methane emission contributes to global warming. Although breeding low methane-emitting cows appears to be possible through genetic selection, doing so requires methane emission quantification by using elaborate instrumentation (respiration chambers, SF6 technique, GreenFeed) not feasible on a large scale. It has been suggested that milk fatty acids are promising markers of methane production. We hypothesized that methane emission can be predicted from the milk fatty acid concentrations determined by mid-infrared spectroscopy, and the integration of energy-corrected milk yield would improve the prediction. Therefore, we examined relationships between methane emission of cows measured in respiration chambers and milk fatty acids, predicted by mid-infrared spectroscopy, to derive diet-specific and general prediction equations based on milk fatty acid concentrations alone and with the additional consideration of energy-corrected milk yield. Cows were fed diets differing in forage type and linseed supplementation to generate a large variation in both CH4 emission and milk fatty acids. Depending on the diet, equations derived from regression analysis explained 61 to 96% of variation of methane emission, implying the potential of milk fatty acid data predicted by mid-infrared spectroscopy as novel proxy for direct methane emission measurements. When data from all diets were analyzed collectively, the equation with energy-corrected milk yield (CH4 (L/day) = − 1364 + 9.58 × energy-corrected milk yield + 18.5 × saturated fatty acids + 32.4 × C18:0) showed an improved coefficient of determination of cross-validation R2 CV = 0.72 compared to an equation without energy-corrected milk yield (R2 CV = 0.61). Equations developed for diets supplemented by linseed showed a lower R2 CV as compared to diets without linseed (0.39 to 0.58 vs. 0.50 to 0.91). We demonstrate for the first time that milk fatty acid concentrations predicted by mid-infrared spectroscopy together with energy-corrected milk yield can be used to estimate enteric methane emission in dairy cows. © 2018, The Author(s).
- ItemProlonged Corrosion Stability of a Microchip Sensor Implant during In Vivo Exposure(Basel : MDPI, 2018) Glogener, Paul; Krause, Michael; Katzer, Jens; Schubert, Markus A.; Birkholz, Mario; Bellmann, Olaf; Kröger-Koch, Claudia; Hammon, Harald M.; Metges, Cornelia C.; Welsch, Christine; Ruff, Roman; Hoffmann, Klaus P.A microelectronic biosensor was subjected to in vivo exposure by implanting it in the vicinity of m. trapezii (Trapezius muscle) from cattle. The implant is intended for the continuous monitoring of glucose levels, and the study aimed at evaluating the biostability of exposed semiconductor surfaces. The sensor chip was a microelectromechanical system (MEMS) prepared using 0.25 µm complementary metal–oxide–semiconductor CMOS/BiCMOS technology. Sensing is based on the principle of affinity viscometry with a sensoric assay, which is separated by a semipermeable membrane from the tissue. Outer dimensions of the otherwise hermetically sealed biosensor system were 39 × 49 × 16 mm. The test system was implanted into cattle in a subcutaneous position without running it. After 17 months, the device was explanted and analyzed by comparing it with unexposed chips and systems. Investigations focused on the MEMS chip using SEM, TEM, and elemental analysis by EDX mapping. The sensor chip turned out to be uncorroded and no diminishing of the topmost passivation layer could be determined, which contrasts remarkably with previous results on CMOS biosensors. The negligible corrosive attack is understood to be a side effect of the semipermeable membrane separating the assay from the tissue. It is concluded that the separation has enabled a prolonged biostability of the chip, which will be of relevance for biosensor implants in general.