The effect of feeding and visiting behavior on methane and hydrogen emissions of dairy cattle measured with the GreenFeed system under different dietary conditions.

The objectives were to investigate the effect of feeding and visiting behavior of dairy cattle on CH4 and H2 production measured with voluntary visits to the GreenFeed system (GF) and to determine whether these effects depended on basal diet (BD) and 3-nitrooxypropanol (3-NOP) supplementation. The experiment involved 64 lactating dairy cattle (146 ± 45 DIM at the start of trial; mean ± SD) in 2 overlapping crossover trials, each consisting of 2 measurement periods. Cows within block were randomly allocated to 1 of 3 types of BD: a grass silage-based diet consisting of 30% concentrates and 70% grass silage (DM basis); a grass silage and corn silage mixed diet consisting of 30% concentrates, 42% grass silage, and 28% corn silage (DM basis); or a corn silage-based diet consisting of 30% concentrates, 14% grass silage, and 56% corn silage (DM basis). Each type of BD was subsequently supplemented with 0 and 60 mg 3-NOP/kg of DM in one crossover or 0 and 80 mg 3-NOP/kg of DM in the other crossover. Diets were provided in feed bins that automatically recorded feed intake and feeding behavior, with additional concentrate fed in the GF. All visits to the GF that resulted in a spot measurement of both CH4 and H2 emission were analyzed in relation to feeding behavior (e.g., meal size and time interval to preceding meal) as well as GF visiting behavior (e.g., duration of visit). Feeding and GF visiting behavior were related to CH4 and H2 production measured with the GF, in particular the meal size before a GF measurement and the time interval between a GF measurement and the preceding meal. Relationships between gas production and both feeding and GF visiting behavior were affected by type of BD as well as 3-NOP supplementation. With an increase of the time interval between a GF measurement and the preceding meal, CH4 production decreased with 0 mg 3-NOP/kg of DM but increased with 60 and 80 mg 3-NOP/kg of DM, whereas type of BD did not affect these relationships. In contrast, CH4 production increased with 0 mg 3-NOP/kg of DM but decreased with 60 and 80 mg 3-NOP/kg of DM upon an increase in the size of the meal preceding a GF measurement. With an increase of the time interval between a GF measurement and the preceding meal, or with a decrease of the size of the meal preceding a GF measurement, H2 production decreased for all treatments, although the effect was generally somewhat stronger for 60 and 80 mg 3-NOP/kg of DM than for 0 mg 3-NOP/kg of DM. Hence, the timing of GF measurements next to feeding and GF visiting behavior are essential when assessing the effect of dietary treatment on the production of CH4 and H2 in a setting where a spot-sampling device such as a GF is used and where the measurements depend on voluntary visits from the cows.

Long-term effects of 3-nitrooxypropanol on methane emission and milk production characteristics in Holstein-Friesian dairy cows.

The objective was to determine the long-term effect of 3-nitrooxypropanol (3-NOP) on CH4 emission and milk production characteristics from dairy cows receiving 3-NOP in their diet for a full year, covering all lactation stages of the dairy cows. Sixty-four late-lactation Holstein-Friesian cows (34% primiparous) were blocked in pairs, based on expected calving date, parity, and daily milk yield. The experiment started with an adaptation period of 1 wk followed by a covariate period of 3 wk in which all cows received the same basal diet and baseline measurements were performed. Directly after, cows within a block were randomly allocated to 1 of 2 dietary treatments: a diet containing on average 69.8 mg 3-NOP/kg DM (total ration level, corrected for intake of nonsupplemented GreenFeed bait) and a diet containing a placebo. Forage composition as well as forage-to-concentrate ratio altered with lactation stage (i.e., dry period and early, mid, and late lactation). Diets were provided as a total mixed ration, and additional bait was fed in GreenFeed units (C-Lock Inc.), which were used for emission measurements. Supplementation of 3-NOP did not affect total DMI, BW, or BCS, but resulted in a 6.5% increase in the yields of energy-corrected milk and fat- and protein-corrected milk (FPCM). Furthermore, milk fat and protein as well as feed efficiency were increased upon 3-NOP supplementation. Overall, a reduction of 21%, 20%, and 27% was achieved for CH4 production (g/d), yield (g/kg DMI), and intensity (g/kg FPCM), respectively, upon 3-NOP supplementation. The CH4 mitigation potential of 3-NOP was affected by the lactation stage dependent diet to which 3-NOP was supplemented. On average, a 16%, 20%, 16%, and 26% reduction in CH4 yield (g/kg DMI) was achieved upon 3-NOP supplementation for the dry period, and early, mid, and late-lactation diets, respectively. The CH4 mitigation potential of 3-NOP was affected by the length of 3-NOP supplementation within a lactation stage dependent diet and by variation in diet composition within a lactation stage dependent diet as a result of changes in grass and corn silage silos. In conclusion, 3-NOP reduced CH4 emission from cows receiving 3-NOP for a year, with a positive effect on production characteristics. The CH4 mitigation potential of 3-NOP was influenced by diet type, diet composition, and nutrition value, and the efficacy of 3-NOP appeared to decline over time but not continuously. Associated with changes in diet composition, increased efficacy of 3-NOP was observed at the start of the trial, at the start of a new lactation, and, importantly, at the end of the trial. These results suggest that diet composition has a large effect on the efficacy of 3-NOP, perhaps even larger than the week of supplementation after first introduction of 3-NOP. More studies are needed to clarify the long-term effects of 3-NOP on CH4 emission and to further investigate what influence variation in diet composition may have on the mitigation potential of 3-NOP.

Methane mitigation potential of 3-nitrooxypropanol in lactating cows is influenced by basal diet composition.

The objective of this study was to investigate whether the CH4 mitigation potential of 3-nitrooxypropanol (3-NOP) in dairy cattle was affected by basal diet (BD) composition. The experiment involved 64 Holstein-Friesian dairy cows (146 ± 45 d in milk at the start of trial; mean ± SD) in 2 overlapping crossover trials, each consisting of 2 measurement periods. Cows were blocked according to parity, d in milk, and milk yield, and randomly allocated to 1 of 3 diets: a grass silage-based diet (GS) consisting of 30% concentrates and 70% grass silage (DM basis), a grass silage- and corn silage-mixed diet (GSCS) consisting of 30% concentrates, 42% grass silage, and 28% corn silage (DM basis), or a corn silage-based diet (CS) consisting of 30% concentrates, 14% grass silage, and 56% corn silage (DM basis). Two types of concentrates were formulated, viz. a concentrate for the GS diet and a concentrate for the CS diet, to meet the energy and protein requirements for maintenance and milk production. The concentrate for the GSCS diet consisted of a 50:50 mixture of both concentrates. Subsequently, the cows within each type of BD received 2 treatments in a crossover design: either 60 mg of 3-NOP/kg of DM (NOP60) and a placebo with 0 mg of 3-NOP/kg of DM (NOP0) in one crossover or 80 mg of 3-NOP/kg of DM (NOP80) and NOP0 in the other crossover. Diets were provided as total mixed ration in feed bins, which automatically recorded feed intake. Additional concentrate was fed in the GreenFeed system that was used to measure emissions of CH4 and H2. The CS diets resulted in a reduced CH4 yield (g/kg DMI) and CH4 intensity (g/kg milk). Feeding 3-NOP resulted in a decreased DMI. Milk production and composition did not differ between NOP60 and NOP0, whereas milk yield and the yield of major components decreased for NOP80 compared with NOP0. Feed efficiency was not affected by feeding 3-NOP. Interactions between BD and supplementation of 3-NOP were observed for the production (g/d) and yield (g/kg DMI) of both CH4 and H2, indicating that the mitigating effect of 3-NOP depended on the composition of the BD. Emissions of CH4 decreased upon 3-NOP supplementation for all BD, but the decrease in CH4 emissions was smaller for GS (-26.2% for NOP60 and -28.4% for NOP80 in CH4 yield) compared with both GSCS (-35.1% for NOP60 and -37.9% for NOP80 for CH4 yield) and CS (-34.8% for NOP60 and -41.6% for NOP80 for CH4 yield), with no difference between the latter 2 BD. Emissions of H2 increased upon 3-NOP supplementation for all BD, but the H2 yield (g/kg DMI) increased 3.16 and 3.30-fold, respectively, when NOP60 and NOP80 were supplemented to GS, and 4.70 and 4.96 fold, respectively, when NOP60 and NOP80 were supplemented to CS. In conclusion, 3-NOP can effectively decrease CH4 emissions in dairy cows across diets, but the level of CH4 mitigation is greater when supplemented in a corn silage-based diet compared with a grass silage-based diet.

Machine learning to further improve the decision which boar ejaculates to process into artificial insemination doses.

Current artificial insemination (AI) laboratory practices assess semen quality of each boar ejaculate to decide which ones to process into AI doses. This decision is aided with two, world-wide used, motility parameters that come available through computer assisted semen analysis (CASA). This decision process, however, still results in AI doses with variable and sometimes suboptimal fertility outcomes (e.g., small litter size). The hypothesis was that the decision which ejaculates to process into AI doses can be improved by adding more data from CASA systems, and data from other sources, in combination with a data-driven model. Available data consisted of ejaculates that passed the initial decision, and thus, were processed into AI doses and used to inseminate sows. Data were divided into a training set (6793 records) and a validation set (1191 records) for model development, and an independent test set (1434 records) for performance assessment. Gradient Boosting Machine (GBM) models were developed to predict four fertility phenotypes of interest (gestation length, total number born, number born alive, and number of stillborn piglets). Each fertility phenotype was considered as a numeric and as a binary outcome parameter, totaling to eight different fertility phenotypes. Data used to further improve the decision process originated from four sources: 1) CASA information, 2) boar ejaculate information, 3) breeding value estimations, and 4) weather information. These data were used to create seven prediction sets, where each new set added parameters to the ones included in the previous set. The GBM models predicted fertility phenotypes with low correlations (for numeric phenotypes) and area under the curve values (for binary phenotypes) on the test data. Hence, results demonstrated that a combination of more data and GBM did not enable further improvement of the AI dose quality checks, resulting in the rejection of our hypothesis. However, our study revealed parameters affecting boar ejaculate fertility which were not used in today's decision process. These parameters (listed in the top 10 in at least four GBM models) included one parameter associated with boar ejaculate information, two with breeding value estimations, five with CASA information, and one with weather information. These parameters, therefore, should be further investigated for their potential value when assessing the quality of boar ejaculates in daily routine AI doses processing.

Behavioral Ecology of Captive Species: Using Bibliographic Information to Assess Pet Suitability of Mammal Species.

Which mammal species are suitable to be kept as pet? For answering this question many factors have to be considered. Animals have many adaptations to their natural environment in which they have evolved that may cause adaptation problems and/or risks in captivity. Problems may be visible in behavior, welfare, health, and/or human-animal interaction, resulting, for example, in stereotypies, disease, and fear. A framework is developed in which bibliographic information of mammal species from the wild and captive environment is collected and assessed by three teams of animal scientists. Oneliners from literature about behavioral ecology, health, and welfare and human-animal relationship of 90 mammal species are collected by team 1 in a database and strength of behavioral needs and risks is assessed by team 2. Based on summaries of those strengths the suitability of the mammal species is assessed by team 3. Involvement of stakeholders for supplying bibliographic information and assessments was propagated. Combining the individual and subjective assessments of the scientists using statistical methods makes the final assessment of a rank order of suitability as pet of those species less biased and more objective. The framework is dynamic and produces an initial rank ordered list of the pet suitability of 90 mammal species, methods to add new mammal species to the list or remove animals from the list and a method to incorporate stakeholder assessments. A model is developed that allows for provisional classification of pet suitability. Periodical update of the pet suitability framework is expected to produce an updated list with increased reliability and accuracy. Furthermore, the framework could be further developed to assess the pet suitability of additional species of other animal groups, e.g., birds, reptiles, and amphibians.

Overall welfare assessment of laying hens: comparing science-based, environment-based and animal-based assessments.

To increase the validity of evaluations and facilitate expansion and maintenance of assessment systems, we constructed a database of studies on the welfare of laying hens around the world. On the basis of this database, we devised a science-based welfare assessment model. Our model includes measurements, levels and weightings based on the scientific studies in the database, and can clarify the advantages and disadvantages of housing systems for laying hens from the viewpoint of the five freedoms. We also evaluated the usefulness of our model by comparing it with environment-based Animal Needs Index (ANI), another science-based model called FOWEL, and animal-based measurements. Our model showed that freedom from injury, pain and disease, and freedom from discomfort were more secure in the cage system, while non-cage systems scored better for natural behavior and freedom from fear and distress. A significant strong-positive correlation was found between the animal-based assessment and the total scores of ANI (rs = 0.94, P < 0.05), FOWEL (rs = 0.99, P < 0.05) or our model (rs = 0.99, P < 0.05), which indicate that these different approaches to welfare assessment may be used almost interchangeably to 'measure' a common property ('overall laying hen welfare'). However, assessments using our model and FOWEL were more sensitive than ANI and can be applied to cage systems, which suggest that our model and FOWEL may have added value.