As the Dutch government and dairy farming sector have given priority to reducing ammonia emission, the effect of diet on the ammonia emission from dairy cow barns was studied. In addition, the usefulness of milk urea content as an indicator of emission reduction was evaluated. An experiment was carried out with a herd of 55 to 57 Holstein-Friesian dairy cows housed in a naturally ventilated barn with cubicles and a slatted floor. The experiment was designed as a 3 x 3 factorial trial and repeated 3 times. During the experiment, cows were confined to the barn (no grazing) and were fed ensiled forages and additional concentrates. The default forage was grass silage. The nutritional experimental factors were: (1) rumen-degradable protein balance of the ration for lactating cows with 3 levels (0, 500, and 1000 g/cow per d), and (2) proportion of corn silage in the forage ration for lactating cows with 3 levels (0, 50, and 100%) of forage dry matter intake. Several series of dynamic regression models were fitted. One of these models explained emission of ammonia by the nutritional factors and the temperature; another model explained ammonia emission by the bulk milk urea content and the temperature. The ammonia emission from the barn increased when levels of rumen-degradable protein balance increased. Furthermore, at a given level of rumen-degradable protein balance, the emission of ammonia correlated positively with the corn silage content in the forage ration. However, this correlation was not causal, but was the result of interaction between corn silage proportion and intake of ileal digestible protein. The bulk milk urea content and the temperature correlated strongly with the ammonia emission from the barn; the selected model accounted for 76% of the variance in emission. It was concluded that the emission of ammonia from naturally ventilated dairy cow barns was strongly influenced by diet. The emission can be reduced approximately 50% by reducing the rumen-degradable protein balance of the ration from 1000 to 0 g/cow per d. The milk urea content is a good indicator of emission reduction.
The objective of this study was to quantify individual variation in daily milk yield and milking duration in response to the length of the milking interval and to assess the economic potential of using this individual variation to optimize the use of an automated milking system. Random coefficient models were used to describe the individual effects of milking interval on daily milk yield and milking duration. The random coefficient models were fitted on a data set consisting of 4,915 records of normal uninterrupted milkings collected from 311 cows kept in 5 separate herds for 1 wk. The estimated random parameters showed considerable variation between individuals within herds in milk yield and milking duration in response to milking interval. In the actual situation, the herd consisted of 60 cows and the automatic milking system operated at an occupation rate (OR) of 64%. When maximizing daily milk revenues per automated milking system by optimizing individual milking intervals, the average milking interval was reduced from 0.421 d to 0.400 d, the daily milk yield at the herd level was increased from 1,883 to 1,909 kg/d, and milk revenues increased from euro498 to euro507/d. If an OR of 85% could be reached with the same herd size, the optimal milking interval would decrease to 0.238 d, milk yield would increase to 1,997 kg/d, and milk revenues would increase to euro529/d. Consequently, more labor would be required for fetching the cows, and milking duration would increase. Alternatively, an OR of 85% could be achieved by increasing the herd size from 60 to 80 cows without decreasing the milking interval. Milk yield would then increase to 2,535 kg/d and milk revenues would increase to euro673/d. For practical implementation on farms, a dynamic approach is recommended, by which the parameter estimates regarding the effect of interval length on milk yield and the effect of milk yield on milking duration are updated regularly and also the milk production response to concentrate intake is taken into account.
Data of nitrogen fertilization experiments of 1934 - 1994 have been analysed, using models for N uptake and dry matter (DM) yield. Both models were affected by fertilizer level, soil type, soil organic matter content, grassland use, cutting frequency, grassland renovation, white clover content and the N content analysis (Crude Protein or total-N). Effects on Soil Nitrogen Supply (SNS), Apparent Nitrogen Recovery (ANR) and Nitrogen Use Efficiency (NUE) are discussed. Differences in SNS, ANR and NUE between sand and clay were small, SNS on poorly drained peat soil was 60 and 80 kg N per ha higher than on clay and sand, respectively, ANR on poorly drained peat soil was 7 and 10% lower. The NUE was similar on sand, clay and poorly drained peat. ANR was low at low N application levels, due to immobilization. ANR increased from 35% to 65% at application levels of 50 and 250 kg N per ha, respectively. At application levels of more than 250 kg N per ha, ANR decreased. NUE decreased from 45 to 29 kg DM per kg N with increasing N application levels of 0 and 550 kg per ha. It is suggested that for a good N utilization a minimum N application of 100 kg N per ha should be used. SNS increased by a mixed use of grazing and cutting with 27 and 40 kg N per ha for sand/clay and poorly drained peat respectively. ANR on sand decreased from 5 to 10% at applications of 200 and 500 kg N per ha and NUE decreased with 1-2 kg DM per kg N. The effect of grazing was stronger under pure grazing than with a mixed use of grazing and cutting. Increasing the cutting frequency from 3 to 8 cuts per year had no effect on SNS, increased ANR with 0-20% and decreased NUE with 4-7 kg DM per kg N. The positive effect of the higher ANR compensated the lower NUE at application levels of 400 kg N per ha. Changes in ANR over the last sixty years can be explained by changes in experimental conditions, experimental treatments and chemical analysis. Changes in NUE can be explained by a higher proportion of perennial ryegrass and genetic improvement.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.