Browsing by Subject "Milk"

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The prediction of energy balance of dairy cows from animal, feed, and milk traits with special regard to milk fatty acids
(2017) Becher, Vera; Rodehutscord, Markus
The objective of the present study was to predict the energy balance (EB) of dairy cows from animal, feed, and milk traits. As the milk fatty acid (FA) profile is known to react to physiological conditions like an energy deficit, special regard was given to milk FA in order to identify new potential indicators for negative EB. Visiting six experimental stations in Germany, single milk samples were taken from dairy cows between their 6th and 133th day in milk to create a dataset covering a large spectrum of EB and a variety of practical diets. The milk composition was analyzed by mid-infrared spectrometry, and the milk FA profile via gas chromatography. Energy balance (MJ NEL/d), as response variable, was calculated by subtracting the cow’s energy requirements from energy intake. As candidate variables parity, day of lactation, dietary nutrient composition, milk yield, milk composition, and the milk FA profile were provided resulting in a pool of 62 potential predictors. The prediction of EB was performed in two different ways: first, an automated stepwise variable selection was performed with the whole variable pool (GLMs-N) and with FA only (GLMs-FA-N). As this method recently earned criticism, some other methods were also tested for a first variable selection: the regularized linear regression models Lasso, elastic net (ENET), adaptive Lasso (AdaLasso), and adaptive elastic net (ADAENET). As a machine learning method which also considers interactions and non-linear relationships random forests were also applied. The first variable selection was performed using a five-fold cross-validation which resulted in five models per selection method. All chosen effects were combined to one model (MODEL1) for each method, respectively. Following this, the individual effects of the MODEL1 were used for a forward selection based on the corrected Akaike Information Criterion (AICC) for further model reduction, resulting in MODEL2. Then, the non-significant effects were removed from the MODEL2, achieving the final MODEL3 for each method. The final models were validated using leave-one-out cross-validation. The models showed adequate correlations (r) between the predicted and the observed EB in leave-one-out cross-validation: although GLMs-FA-N had the lowest accuracy (r = 0.79), the result was still remarkable and showed how much information milk FA alone can provide. GLMs-N and AdaLasso performed best with r = 0.86 and 0.85 containing 21 and 18 predictors, respectively. However, other models like ADAENET achieved only slightly lower accuracy (r = 0.83) with only 6 predictors. The composition of the predictors was relatively similar in all models. All (except for GLMs-FA-N) contained days in milk, milk yield, C18:1c9, C15:0iso, and the ratio of omega-6 to omega-3 FA (n-6/n-3) as effects with the strongest impacts on the prediction. While milk yield, days in milk, and C18:1c9 mirrored physiologically obvious effects, the strong and positive impact of n-6/n-3 and C15:0iso was unexpected. The n-6/n-3 ratio might be physiologically connected to EB as might reflect the dietary forage-to-concentrate ratio which influences dietary energy content and thus EB. The importance of C15:0iso, a FA arising from microbial FA synthesis in the rumen, could not be explained satisfyingly. The nature of the potential physiological connections between EB and some FA like C15:0iso or n-6 or n-3 FA might require further research. The present study showed that it is possible to predict the cow’s EB from animal and milk traits with an adequate accuracy. As long as the diets have similar composition and not contain ingredients which strongly affect the milk FA profile, dietary effects have not to be taken into account. However, a practical application of the obtained models is not yet possible: First, as the dataset was relatively small (n = 248), it is not clear whether or not the models would perform adequately with independent datasets. Second, FA analysis by gas chromatography is very expensive. Third, even if gas chromatographic analysis were affordable for standard milk analysis, there are some highly variable, very low concentrated FA as predictors in the models, which might be prone to laboratory effects, and this could spoil the predictions. Although under criticism, automatic stepwise selection provided the best performing model and thus seems sufficient for practical issues like the one dealt with in the present study. However, the differences in accuracy between the applied methods were very small and as regularized linear regression methods, especially ENET and ADAENET, are supposed to deal better with highly correlated variables, it might be safer to use them with datasets containing highly correlated variables such as the one used in the present work.
Variability of the protein and energy values of European dried distillers´ grains with solubles for ruminants
(2013) Westreicher Kristen, Edwin; Rodehutscord, Markus
The increasing demand of energy together with the implementation of the European Program for the use of energy from renewable sources are favourable scenarios to increment the ethanol production in the coming years in the EU. Ethanol production yields dried distillers´ grains with soluble (DDGS) as the main by-product, a valuable feedstuff for ruminants. A great number of publications mainly form USA and Canada has demonstrated the great variability of the feed value of corn-DDGS, the main by-product from ethanol production in these countries. In the EU, different and diverse technological conditions predominate and little was investigated to evaluate the feed value of DDGS. The variability of feeding value in conjunction with expected increase of DDGS production demands for further and more specific characterization of this by-product in the EU. Therefore, a project was conceived to characterize the chemical composition and evaluate the protein and energy value for ruminants of DDGS from different European countries. Thirteen samples of DDGS originating from wheat, corn, barley, and blends of different substrates were used. In the first study, the objective was to characterize variations in the composition and nutritive value of DDGS, and to estimate the undegradable crude protein (UDP) in DDGS. The rumen degradation of crude protein (CP) was determined using the nylon bag technique. Samples were incubated for 0, 1, 2, 4, 8, 16, 32, and 72 h, and in situ degradation kinetics were determined. UDP was estimated using a passage rate of 8 %/h. In vitro gas production was measured to estimate the metabolizable energy (ME), net energy for lactation (NEL) and in vitro digestibility of organic matter (IVDOM). Chemical profiles varied among samples (in g/kg dry matter (DM) ± standard deviation, the values were 310 ± 33 CP, 86 ± 37 ether extract, 89 ± 18 crude fibre, 408 ± 39 neutral detergent fibre, 151 ± 39 acid detergent fibre, and 62 ± 31 acid detergent lignin), as well as in protein fractions according to the Cornell Net Carbohydrate and Protein System (in g/kg CP, the values were 161 ± 82 for fraction A, 24 ± 11 for fraction B1, 404 ± 105 for fraction B2, 242 ± 61 for fraction B3, and 170 ± 87 for fraction C). ME, NEL (MJ/kg DM) and IVDOM (%), also varied among samples: 12.1 ± 0.59, 7.3 ± 0.39, and 72.5 ± 4.30, respectively. The in situ rapidly degradable CP fraction (a) varied from 10.2 to 30.6%, and the potentially degradable fraction (b) averaged to 66.8%. UDP varied from 8.6 to 62.6% of CP. This first study suggests significant variations in composition and nutritive value among different sources of DDGS. UDP could be predicted on the basis of analysed CP fractions, but the accuracy of UDP prediction improved upon the inclusion of neutral-detergent insoluble nitrogen, explaining 94% of the variation in the UDP values. To conclude, chemical protein fractions may be used to predict the UDP values of DDGS and the variability in the protein fractions of DDGS should be considered when formulating diets for dairy cows. To provide additional information on the nutritional value of DDGS, a second study was carried out to determine and compare the in situ ruminal degradation of CP and amino acids (AAs) of DDGS and to characterize the in vitro pepsin-pancreatin solubility of CP (PPS) from dietary DDGS (d-DDGS) and DDGS residue (DDGS-r) obtained after 16-h ruminal incubation. The rumen degradation of AAs and CP was determined using nylon bag incubations in the rumen of cows. Lysine and methionine content of d-DDGS varied from 1.36 to 4.00 and 1.34 to 1.99 g/16 g N, respectively. The milk protein score (MPS) of d-DDGS was low and ranged from 0.36 to 0.51, and lysine and isoleucine were estimated to be the most limiting AAs in d-DDGS and DDGS-r. DDGS-r contained slightly more essential AAs than did the d-DDGS. Rumen degradation of CP after 16 h varied from 44% to 94% between DDGS samples. Rumen degradation of lysine and methionine ranged from 39% to 90% and from 35% to 92%, respectively. Linear regressions showed that ruminal degradation of individual AAs can be predicted from CP degradation. The PPS of d-DDGS was higher than that of DDGS-r and it varied from 70% to 89% and from 47% to 81%, respectively. There was no significant correlation between the PPS of d-DDGS and PPS of DDGS-r (R2 = 0.31). The estimated intestinally absorbable dietary protein (IADP) averaged 21%. Moderate correlation was found between the crude fibre content and PPS of DDGS-r (R2 = 0.43). This study suggests an overestimation of the contribution of UDP of DDGS to digestible protein supply in the duodenum in currently used protein evaluation systems. More research is required and recommended to assess the intestinal digestibility of AAs from DDGS. Finally, in a third study, three sources of DDGS were evaluated in diets of mid-lactating dairy cows on milk production and milk composition and on digestibility in sheep. DDGS from wheat, corn and barley (DDGS1), wheat and corn (DDGS2) and wheat (DDGS3) were studied and compared with a rapeseed meal (RSM). RSM and DDGS were characterized through in situ CP degradability. Nutrient digestibility was determined in sheep. Twenty-four multiparous cows were used in a 4 × 4 Latin square design with 28-day periods. Treatments included total mixed rations containing as primary protein sources RSM (control), DDGS1 (D1), DDGS2 (D2) or DDGS3 (D3). RSM contained less rapidly degradable CP (fraction a), more potentially degradable CP (fraction b) and more UDP than the three DDGS. In vivo organic matter digestibility of RSM was similar to DDGS. Calculated NEL was lower for RSM (7.4 MJ/kg DM) than for DDGS, which averaged 7.7 MJ/kg DM. Cows? dry matter intake did not differ between diets (21.7 kg/d). Cows fed D1 yielded more milk than those fed D3 (31.7 vs. 30.4 kg/d); no differences were found between control and DDGS diets (31.3 vs. 31.1 kg/d). Energy-corrected milk was similar among diets (31.2 kg/d). Diets affected neither milk fat concentration (4.0%) nor milk fat yield (1.24 kg/d). Milk protein yield of control cows (1.12 kg/d) was significantly higher than D3 (1.06 kg/d) but not different from D1 and D2 (1.08 kg/d each). Feeding DDGS significantly increased milk lactose concentration (4.91%) compared to control (4.81%). DDGS can be a suitable feed compared to RSM and can be fed up to 4 kg dry matter per day in rations of dairy cows in mid-lactation. To conclude, DDGS is a suitable feedstuff for ruminants in terms of chemical composition, energy and protein value. However, the variability should be considered when included in diets of ruminants, especially in animals with high performance. For this purpose, prediction approaches initated in this study should be further developed into tools for routine application for rapid DDGS evaluation and estimation of feed values. These approaches might also be usefull for the evaluation of other feed protein sources and taked into consideration for practical feeding and diets formulation.