Prediction of nutrient digestibility and energy concentrations in fresh grass using nutrient composition

Improved nutrient utilization efficiency is strongly related to enhanced economic performance and reduced environmental footprint of dairy farms. Pasture-based systems are widely used for dairy production in certain areas of the world, but prediction equations of fresh grass nutritive value (nutrient digestibility and energy concentrations) are limited. Equations to predict digestible energy (DE) and metabolizable energy (ME) used for grazing cattle have been either developed with cattle fed conserved forage and concentrate diets or sheep fed previously frozen grass, and the majority of them require measurements less commonly available to producers, such as nutrient digestibility. The aim of the present study was therefore to develop prediction equations more suitable to grazing cattle for nutrient digestibility and energy concentrations, which are routinely available at farm level by using grass nutrient contents as predictors. A study with 33 nonpregnant, nonlactating cows fed solely fresh-cut grass at maintenance energy level for 50 wk was carried out over 3 consecutive grazing seasons. Freshly harvested grass of 3 cuts (primary growth and first and second regrowth), 9 fertilizer input levels, and contrasting stage of maturity (3 to 9 wk after harvest) was used, thus ensuring a wide representation of nutritional quality. As a result, a large variation existed in digestibility of dry matter (0.642-0.900) and digestible organic matter in dry matter (0.636-0.851) and in concentrations of DE (11.8-16.7 MJ/kg of dry matter) and ME (9.0-14.1 MJ/kg of dry matter). Nutrient digestibilities and DE and ME concentrations were negatively related to grass neutral detergent fiber (NDF) and acid detergent fiber (ADF) contents but positively related to nitrogen (N), gross energy, and ether extract (EE) contents. For each predicted variable (nutrient digestibilities or energy concentrations), different combinations of predictors (grass chemical composition) were found to be significant and increase the explained variation. For example, relatively higher R(2) values were found for prediction of N digestibility using N and EE as predictors; gross-energy digestibility using EE, NDF, ADF, and ash; NDF, ADF, and organic matter digestibilities using N, water-soluble carbohydrates, EE, and NDF; digestible organic matter in dry matter using water-soluble carbohydrates, EE, NDF, and ADF; DE concentration using gross energy, EE, NDF, ADF, and ash; and ME concentration using N, EE, ADF, and ash. Equations presented may allow a relatively quick and easy prediction of grass quality and, hence, better grazing utilization on commercial and research farms, where nutrient composition falls within the range assessed in the current study.

Fat composition of organic and conventional retail milk in northeast England

This study of UK retail milk identified highly significant variations in fat composition. The survey, conducted over 2 yr replicating summer and winter, sampled 22 brands, 10 of which indicated organic production systems. Results corroborate earlier farm-based findings considering fat composition of milk produced under conventional and organic management. Organic milk had higher concentrations of beneficial fatty acids (FA) than conventional milk, including total polyunsaturated fatty acids (PUFA; 39.4 vs. 31.8 g/kg of total FA), conjugated linoleic acid cis-9,trans-11 (CLA9; 7.4 v 5.6 g/kg of FA), and α-linolenic acid (α-LN; 6.9 vs. 4.4 g/kg of FA). As expected, purchase season had a strong effect on fat composition: compared with milk purchased in winter, summer milk had a lower concentration of saturated fatty acids (682 vs. 725 g/kg of FA) and higher concentrations of PUFA (37.6 vs. 32.8 g/kg of FA), CLA9 (8.1 vs. 4.7 g/kg of FA), and α-LN (6.5 vs. 4.6 g/kg of FA). Differences identified between sampling years were more surprising: compared with that in yr 2, milk purchased in year 1 had higher concentrations of PUFA (37.5 vs. 32.9 g/kg of FA), α-LN (6.0 vs. 5.1 g/kg of FA), and linoleic acid (19.9 vs. 17.5 g/kg of FA) and lower concentrations of C16:0 and C14:0 (332 vs. 357 and 110 vs. 118 g/kg of FA, respectively). Strong interactions were identified between management and season as well as between season and year of the study. As in the earlier farm studies, differences in fat composition between systems were greater for summer compared with winter milk. Large between-year differences may be due to changes in weather influencing milk composition through forage availability, quality, and intake. If climate change predictions materialize, both forage and dairy management may have to adapt to maintain current milk quality. Considerable variation existed in milk fat composition between brands.