Predicting enteric methane emission of dairy cows with milk Fourier-transform infrared spectra and gas chromatography-based milk fatty acid profiles.

The objective of the present study was to compare the prediction potential of milk Fourier-transform infrared spectroscopy (FTIR) for CH4 emissions of dairy cows with that of gas chromatography (GC)-based milk fatty acids (MFA). Data from 9 experiments with lactating Holstein-Friesian cows, with a total of 30 dietary treatments and 218 observations, were used. Methane emissions were measured for 3 consecutive days in climate respiration chambers and expressed as production (g/d), yield (g/kg of dry matter intake; DMI), and intensity (g/kg of fat- and protein-corrected milk; FPCM). Dry matter intake was 16.3 ± 2.18 kg/d (mean ± standard deviation), FPCM yield was 25.9 ± 5.06 kg/d, CH4 production was 366 ± 53.9 g/d, CH4 yield was 22.5 ± 2.10 g/kg of DMI, and CH4 intensity was 14.4 ± 2.58 g/kg of FPCM. Milk was sampled during the same days and analyzed by GC and by FTIR. Multivariate GC-determined MFA-based and FTIR-based CH4 prediction models were developed, and subsequently, the final CH4 prediction models were evaluated with root mean squared error of prediction and concordance correlation coefficient analysis. Further, we performed a random 10-fold cross validation to calculate the performance parameters of the models (e.g., the coefficient of determination of cross validation). The final GC-determined MFA-based CH4 prediction models estimate CH4 production, yield, and intensity with a root mean squared error of prediction of 35.7 g/d, 1.6 g/kg of DMI, and 1.6 g/kg of FPCM and with a concordance correlation coefficient of 0.72, 0.59, and 0.77, respectively. The final FTIR-based CH4 prediction models estimate CH4 production, yield, and intensity with a root mean squared error of prediction of 43.2 g/d, 1.9 g/kg of DMI, and 1.7 g/kg of FPCM and with a concordance correlation coefficient of 0.52, 0.40, and 0.72, respectively. The GC-determined MFA-based prediction models described a greater part of the observed variation in CH4 emission than did the FTIR-based models. The cross validation results indicate that all CH4 prediction models (both GC-determined MFA-based and FTIR-based models) are robust; the difference between the coefficient of determination and the coefficient of determination of cross validation ranged from 0.01 to 0.07. The results indicate that GC-determined MFA have a greater potential than FTIR spectra to estimate CH4 production, yield, and intensity. Both techniques hold potential but may not yet be ready to predict CH4 emission of dairy cows in practice. Additional CH4 measurements are needed to improve the accuracy and robustness of GC-determined MFA and FTIR spectra for CH4 prediction.

Economic and environmental effects of providing increased amounts of solid feed to veal calves.

Traditionally, veal calves receive most of their nutrients from milk replacer (MR). Nowadays, however, solid feed (SF; i.e., concentrates and roughages) increasingly substitutes for MR. Studies have shown that providing SF reduces different types of nonnutritive oral behaviors. The objective of this study was to assess the economic and environmental effects of substituting SF for MR in veal calf diets. With respect to environmental effects, we considered the emission of greenhouse gases and land occupation. Substitution rates were based on an experiment in which 160 calves were provided 2 mixtures of SF at 4 levels of dry matter (DM) intake. Mixtures of SF contained either 80% concentrates, 10% corn silage, and 10% straw on DM basis (C80) or 50% concentrates, 25% corn silage, and 25% straw (C50). The 4 levels of SF during the last 17 wk of the fattening period were 20, 100, 180, and 260 kg of DM SF. Additionally, provision of MR was adjusted to achieve equal rates of carcass gain. Substitution rates, representing the SF equivalent needed to substitute for 1 kg of DM MR, were 1.43 kg of DM for C80 and 1.61 kg of DM for C50. Economic effects were assessed based on prices and substitution rates of SF for MR and the possible penalty for carcass color. Environmental effects were assessed based on effects related to the production of feed ingredients, substitution rates, and changes in enteric methane emission and energy use for feed preparation. Costs of feeding SF needed to substitute for 1 kg of DM MR were €0.68 lower for C80 and €0.71 lower for C50, compared with the costs of feeding 1 kg of DM MR. When carcass color scores became too high, however, lower feeding costs were offset by lower revenues from meat. Emissions of greenhouse gases were hardly affected when SF intake was increased. In general, increased enteric methane emission were offset by lower emissions from feed production and energy use. Land occupation increased when intake of SF was increased, mostly because of the high land occupation associated with some concentrate ingredients. In conclusion, this study only showed a negative effect on land occupation when substituting SF for part of the MR in diets of veal calves. Effects on costs and greenhouse gas emissions were neutral or positive.

Environmental impact of replacing soybean meal with rapeseed meal in diets of finishing pigs.

The major impact of the livestock sector on the environment may be reduced by feeding agricultural co-products to animals. Since the last decade, co-products from biodiesel production, such as rapeseed meal (RSM), became increasingly available in Europe. Consequently, an increase in RSM content in livestock diets was observed at the expense of soybean meal (SBM) content. Cultivation of SBM is associated with high environmental impacts, especially when emissions related to land use change (LUC) are included. This study aims to assess the environmental impact of replacing SBM with RSM in finishing pig diets. As RSM has a lower nutritional value, we assessed the environmental impact of replacing SBM with RSM using scenarios that differed in handling changes in nutritional level. Scenario 1 (S1) was the basic scenario containing SBM. In scenario 2 (S2), RSM replaced SBM based on CP content, resulting in reduced energy and amino acid content, and hence an increased feed intake to realize the same growth rate. The diet of scenario 3 (S3) was identical to S2; however, we assumed that pigs were not able to increase their feed intake, leading to reduced growth performance. In scenario 4 (S4), the energy and amino acid content were increased to the same level of S1. Pig performances were simulated using a growth model. We analyzed the environmental impact of each scenario using life-cycle assessment, including processes of feed production, manure management, piglet production, enteric fermentation and housing. Results show that, expressed as per kg of BW, replacing SBM with RSM in finishing pig diets marginally decreased global warming potential (GWP) and energy use (EU) but decreased land use (LU) up to 12%. Between scenarios, S3 had the maximum potential to reduce the environmental impact, due to a lower impact per kg of feed and an increased body protein-to-lipid ratio of the pigs, resulting in a better feed conversion ratio. Optimization of the body protein-to-lipid ratio, therefore, might result in a reduced environmental impact of pig production. Furthermore, the impact of replacing SBM with RSM changed only marginally when emissions related to direct (up to 2.9%) and indirect LUC (up to 2.5%) were included. When we evaluated environmental impacts of feed production only, which implies excluding other processes along the chain as is generally found in the literature, GWP decreased up to 10%, including LUC, EU up to 5% and LU up to 16%.

Utilization of roughages and concentrates relative to that of milk replacer increases strongly with age in veal calves.

We aimed to investigate the feeding values of milk replacer (MR), roughage, and concentrates for veal calves in a paired-gain setting, thus avoiding any prior assumptions in feeding values and major differences in nutrient intakes. One hundred sixty male Holstein-Friesian calves at 2 wk of age and 45 ± 0.2 kg of body weight (BW) were included in the experiment. Calves were allocated to pens (5 calves per pen) and pens were randomly assigned to 1 of 4 solid feed (SF) levels: SF1, SF2, SF3, or SF4, respectively, and to 1 of 2 roughage-to-concentrate (R:C) ratios: 20:80 or 50:50. An adaptation period from wk 1 to 10 preceded the experimental period (wk 11 to 27). Total dry matter (DM) intake from SF was targeted to reach 20, 100, 180, and 260 kg of DM for SF1 to SF4, respectively, during the 16-wk experimental period, and increased with preplanned, equal weekly increments. Roughage was composed of 50% corn silage and 50% chopped wheat straw based on DM. The quantity of MR provided was adjusted every 2 wk based on BW to achieve similar targeted rates of carcass gain across treatments. The reduction in MR provided (in kg of DM) to realize equal rates of carcass gain with inclusion of SF (in kg of DM) differed between the R:C ratio of 50:50 (0.41 kg of MR/kg of SF) and the R:C ratio of 20:80 (0.52 kg of MR/kg of SF). As carcass gain unintentionally increased with SF intake, the paired-gain objective was not fully achieved. When adjusted for realized rates of carcass gain, calves fed an R:C ratio of 20:80 still required 10% less MR than calves fed an R:C ratio of 50:50 for equal rates of carcass gain, indicating that the utilization of SF for gain increased with concentrate inclusion. Averaged for the 16-wk experimental period, the feeding value of MR relative to that of concentrates and roughages was close to that predicted based on their respective digestible energy contents. Nevertheless, the feeding value of SF relative to that of MR increased substantially with age. Therefore, additivity in feeding values of these ration components cannot be assumed. The results of the current study may contribute to the development of new concepts for formulation of veal calf diets with substantial amounts of SF.

Mastitis alert preferences of farmers milking with automatic milking systems.

The aim of this study was to assess farmers' preferences for the performance characteristics of mastitis detection systems. Additionally, we looked at whether certain groups of farmers could be distinguished with specific preferences. Farmers' opinions concerning mastitis detection systems, as well as general information about the farm and the farmer, were investigated with a standard questionnaire. The second part of the questionnaire was specifically aimed at elucidating preferences. Definitions of time windows and performance parameters, such as sensitivity and specificity, were incorporated into characteristics of a detection system (attributes) that reflect farmers' daily experience. Based on data from 139 farmers, we concluded that, on average, they prefer a clinical mastitis detection system that produces a low number of false alerts, while alerting in good time and with emphasis on the more severe cases. These 3 attributes were evaluated as more important than the 3 other attributes, representing the costs of the detection system, the number of missed cases, and how long before clinical signs alerts need to be given. Variation in importance per attribute, however, was high, denoting that farmers' preferences differ considerably. Although some significant relationships were found between farm characteristics and attributes, no clear groups of farmers with specific preferences could be distinguished. Based on these results, we advise making detection systems adaptable for the farmers to satisfy their preferences and to match the circumstances on the farm. Furthermore, these results support that for evaluation of detection algorithms comparisons have to be made at high levels of specificity (e.g., 99%) and time windows have to be kept small (preferably no more than 24 h).

The relationship between milking interval and somatic cell count in automatic milking systems.

The aim of this study was to explore whether, during automatic milking, milking interval or its variation is related to somatic cell count (SCC), even when corrected for effects of production, lactation stage, and parity. Data on milking interval and production level were available from the automatic milking systems of 151 farms. Data on SCC, parity, and lactation stage were derived from dairy herd improvement records of the same farms. Mainly due to incomplete records, data of 100 farms were used in the final analysis. For every cow, only 1 test day was used in the final analysis. Milking interval, the coefficient of variation of milking interval, production rate, the difference in production rate between short- and long-term, parity, days in milk, and some biologically relevant interactions were used in a linear mixed model with farm as random variable to assess their association with log10-transformed SCC. None of the interactions was significantly related to SCC, whereas all main effects were, and thus, stayed in the final model. The effect of milking interval was, although significant, not very strong, which shows that the effect of milking interval on SCC is marginal when corrected for the other variables. The variation in milking intervals was positively related with SCC, showing that the variation in milking interval is even more important than the milking interval itself. In the end, this study showed only a limited association between milking interval and SCC when milking with an automatic milking system.

Somatic cell count assessment at the quarter or cow milking level.

The aim was to investigate whether on-line somatic cell count (SCC) assessment, when combined with electrical conductivity (EC), should be implemented at the udder quarter or at the cow level. Data were collected from 3 farms with automatic milking systems, resulting in 3,191 quarter milkings used in the analyses. Visual observations of foremilk and quarter milk samples for laboratory SCC analysis were used to define 2 gold standards. One was based on visual observation only and the other was based on a combination of visual observation and SCC (using a reference value of 500,000 cells/mL), which means that a quarter milking must have visually abnormal milk as well as an increased SCC to be categorized positive. On-line SCC assessment took place at the quarter level during the first part of the milking. Composite cow level samples were used for laboratory SCC analysis and to compare the performance of SCC assessment at quarter and cow levels. The EC at the quarter level was measured by in-line sensors of the automatic milking system. Alerts for SCC indicators were calculated based on straightforward reference values. Alerts for EC were based on straightforward reference values, or on interquarter ratios. The latter was calculated by dividing the value of a given quarter by the average value of the 2 lowest quarters of that milking. The EC and SCC indicators were combined with either a Boolean "and" or "or" function. Receiver operating characteristic curves were used to visually present results using different threshold values. Sensitivity, specificity, and success rate at the quarter level and false alert rate per 1,000 cow milkings were used to compare indicators at given sensitivity or specificity levels. Quarter level SCC assessment was superior to cow level assessment (transformed partial area under the curve=0.70 vs. 0.62) when combined with EC measurement at quarter level. When aiming for the same sensitivity level (e.g., 50%) with all visual abnormal milk as the gold standard, more false alerts were generated with cow level assessment (137 per 1,000 cow milkings) compared with quarter level SCC assessment (75 per 1,000 cow milkings). As a comparison, using EC alone resulted in 292 false alerts per 1,000 cow milkings in the same situation. Therefore, it is concluded that quarter level SCC assessment was superior to cow level assessment when combined with EC measurement at quarter level.

Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction.

The objective was to develop and validate a clinical mastitis (CM) detection model by means of decision-tree induction. For farmers milking with an automatic milking system (AMS), it is desirable that the detection model has a high level of sensitivity (Se), especially for more severe cases of CM, at a very high specificity (Sp). In addition, an alert for CM should be generated preferably at the quarter milking (QM) at which the CM infection is visible for the first time. Data were collected from 9 Dutch dairy herds milking automatically during a 2.5-yr period. Data included sensor data (electrical conductivity, color, and yield) at the QM level and visual observations of quarters with CM recorded by the farmers. Visual observations of quarters with CM were combined with sensor data of the most recent automatic milking recorded for that same quarter, within a 24-h time window before the visual assessment time. Sensor data of 3.5 million QM were collected, of which 348 QM were combined with a CM observation. Data were divided into a training set, including two-thirds of all data, and a test set. Cows in the training set were not included in the test set and vice versa. A decision-tree model was trained using only clear examples of healthy (n=24,717) or diseased (n=243) QM. The model was tested on 105 QM with CM and a random sample of 50,000 QM without CM. While keeping the Se at a level comparable to that of models currently used by AMS, the decision-tree model was able to decrease the number of false-positive alerts by more than 50%. At an Sp of 99%, 40% of the CM cases were detected. Sixty-four percent of the severe CM cases were detected and only 12.5% of the CM that were scored as watery milk. The Se increased considerably from 40% to 66.7% when the time window increased from less than 24h before the CM observation, to a time window from 24h before to 24h after the CM observation. Even at very wide time windows, however, it was impossible to reach an Se of 100%. This indicates the inability to detect all CM cases based on sensor data alone. Sensitivity levels varied largely when the decision tree was validated per herd. This trend was confirmed when decision trees were trained using data from 8 herds and tested on data from the ninth herd. This indicates that when using the decision tree as a generic CM detection model in practice, some herds will continue having difficulties in detecting CM using mastitis alert lists, whereas others will perform well.

Discriminating between true-positive and false-positive clinical mastitis alerts from automatic milking systems.

Automatic milking systems (AMS) generate alert lists reporting cows likely to have clinical mastitis (CM). Dutch farmers indicated that they use non-AMS cow information or the detailed alert information from the AMS to decide whether to check an alerted cow for CM. However, it is not yet known to what extent such information can be used to discriminate between true-positive and false-positive alerts. The overall objective was to investigate whether selection of the alerted cows that need further investigation for CM can be made. For this purpose, non-AMS cow information and detailed alert information were used. During a 2-yr study period, 11,156 alerts for CM, including 159 true-positive alerts, were collected at one farm in The Netherlands. Non-AMS cow information on parity, days in milk, season of the year, somatic cell count history, and CM history was added to each alert. In addition, 6 alert information variables were defined. These were the height of electrical conductivity, the alert origin (electrical conductivity, color, or both), whether or not a color alert for mastitic milk was given, whether or not a color alert for abnormal milk was given, deviation from the expected milk yield, and the number of alerts of the cow in the preceding 12 to 96 h. Subsequently, naive Bayesian networks (NBN) were constructed to compute the posterior probability of an alert being truly positive based only on non-AMS cow information, based on only alert information, or based on both types of information. The NBN including both types of information had the highest area under the receiver operating characteristic curve (AUC; 0.78), followed by the NBN including only alert information (AUC=0.75) and the NBN including only non-AMS cow information (AUC=0.62). By combining the 2 types of information and by setting a threshold on the computed probabilities, the number of false-positive alerts on a mastitis alert list was reduced by 35%, and 10% of the true-positive alerts would not be identified. To detect CM cases at a farm with an AMS, checking all alerts is still the best option but would result in a high workload. Checking alerts based on a single alert information variable would result in missing too many true-positive cases. Using a combination of alert information variables, however, is the best way to select cows that need further investigation. The effect of adding non-AMS cow information on making a distinction between true-positive and false-positive alerts would be minor.

On-farm quantification of sustainability indicators: an application to egg production systems.

1. On-farm quantification of sustainability indicators (SI) is an effective way to make sustainable development measurable. The egg production sector was used as a case study to illustrate this approach. 2. The objective was to select SI for economic, ecological and societal issues, and to analyse the performance on selected SI of different production systems. 3. For the case study, we compared 4 egg production systems, characterised by differences in the housing systems which are most common in the Netherlands: the battery-cage system, the deep-litter system with and without outdoor run, and the aviary system with outdoor run. 4. Based on a clear set of criteria, we selected SI for animal welfare, economics, environmental impact, ergonomics and product quality. 5. We showed that on-farm quantification of SI was an appropriate method to identify the strengths and weaknesses of different systems. 6. From this analysis it appears that the aviary system with outdoor run is a good alternative for the battery-cage system, with better scores for the aviary system on animal welfare and economics, but with worse scores on environmental impact.

Risk factors for Salmonella enteritidis infections in laying hens.

Contamination with SE is an important threat to food safety in egg production. Various risk factors exist for infection with and spreading of SE on a farm. A data set of regularly collected blood samples from hens at the end of lay was available for analysis. Data included information about infection with SE, date of sampling, housing system and flock size and whether there were hens of different ages on the farm or in the house. By using the mentioned data set, our objective was to identify risk factors associated with SE infection in laying hens. Multiple logistic regression was used to assess the contribution of different variables. Results showed that bigger flocks increased the chance of infection with SE in all housing systems. The system with the lowest chance of infection was the cage system with wet manure. An outdoor run increased the chance of infection only at farms with all hens of the same age. The presence of hens of different ages on a farm was a risk factor for deep litter systems only. This resulted in the highest chance of infection for a deep litter system on a farm with hens of different ages. On a farm with all hens of the same age, however, a deep litter system did not increase the chance of infection with SE compared with a cage system. The main risk factors associated with SE infection, therefore, were flock size, housing system, and farm with hens of different ages.

Prediction of ammonia emission from dairy barns using feed characteristics part II: Relation between urinary urea concentration and ammonia emission.

Emission of NH3 from dairy barns can be reduced substantially by changing the cows' diet. Emission of NH3 is reduced most effectively when dietary changes result in a reduction of urinary urea concentration. The objective of this research was to predict NH3 emission from dairy barns for various diets, using feed characteristics, and climate, barn, and slurry related parameters. Model results were validated using experimental data. Cows were fed one of nine diets, which was a combination of three rumen degradable protein balances and one of three roughage compositions. Each diet was repeated once. Measured parameters included herd, diet, urine, slurry, barn and climate characteristics, and emission of NH3 from the barn. For a wide range of diets and barn conditions, observed NH3 emission from a dairy barn can be predicted accurately using a combination of existing nutrition-emission models. Accuracy of prediction improved considerably, however, when observed emissions during four diet treatments were omitted due to suspected technical failure of the emission measurement equipment. Results also show that NH3 emissions in common practical situations will range from about 3.3 to 16.3 kg per cow per 190 d. To reduce NH3 emission in practice, farmers should maximize the diet's grass content, and at the same time, minimize its rumen degradable protein balance level. Currently, however, farmers need additional information to compose such a low-emission diet, which should fulfill also the intestine digestible protein and net energy-lactation requirements of a cow.

Prediction of ammonia emission from dairy barns using feed characteristics part 1: Relation between feed characteristics and urinary urea concentration.

Urinary urea concentration is an important predictor of NH3 emission from dairy barns. To reduce urinary urea concentration, accurate and precise prediction of urea concentration for different feeding regimes is a prerequisite. The objective of this research, therefore, was to predict urinary urea concentration of a cow using feed characteristics. To compute urinary urea concentration of a cow, we predicted: urine volume; urinary N excretion, using a regression or a mechanistic model; and the relationship between urinary urea concentration and urinary N concentration, which was derived from experimental data. Model results were validated using experimental data. Cows were fed one of nine diets, which was a combination of one of three rumen-degradable protein balances, and one of three roughage compositions. Each diet was repeated once. Measured parameters included herd, diet, and urine characteristics. Observed urinary urea concentration can be predicted with reasonable accuracy from existing models to predict urine volume and urinary N excretion using feed characteristics. The regression model predicted N excretion slightly better than the mechanistic model. In addition, input parameters required for the regression model are recorded at each dairy farm in The Netherlands. This regression model, therefore, can be used by animal nutritionists and producers to determine diets that result in a reduced NH3 emission.