Predicting CO2 production of lactating dairy cows from animal, dietary, and production traits using an international dataset.

Automated measurements of the ratio of concentrations of methane and carbon dioxide, [CH4]:[CO2], in breath from individual animals (the so-called "Sniffer-technique") and estimated CO2 production can be used to estimate CH4 production, provided that CO2 production can be reliably calculated. This would allow CH4 production from individual cows to be estimated in large cohorts of cows, whereby ranking of cows according to their CH4 production might become possible and their values could be used for breeding of low CH4 emitting animals. Estimates of CO2 production are typically based on predictions of heat production, which can be calculated from body weight (BW), energy-corrected milk yield, and days of pregnancy. The objectives of the present study were to develop predictions of CO2 production directly from milk production, dietary, and animal variables, and furthermore develop different models to be used for different scenarios, depending on available data. An international data set with 2,244 records from individual lactating cows including CO2 production and associated traits, as dry matter intake (DMI), diet composition, BW, milk production and composition, days in milk and days pregnant, was compiled to constitute the training data set. Research location and experiment nested within research location were included as random intercepts. The method of CO2 production measurement (respiration chamber (RC) or GreenFeed (GF)) was confounded with research location, and therefore excluded from the model. In total, 3 models were developed based on the current training data set: Model 1 ("Best Model"), where all significant traits were included, Model 2 ("On-Farm Model"), where DMI was excluded, and Model 3 ("Reduced On-Farm Model"), where both DMI and BW were excluded. Evaluation on test data sets either with RC data (n = 103), GF data without additives (n = 478) or GF data only including observations where nitrate, 3-nitrooxypropanol (3-NOP), or a combination of nitrate and 3-NOP were fed to the cows (GF+: n = 295), showed good precision of the 3 models, illustrated by low slope bias both in absolute values (-0.22 to 0.097) and in percentage (0.049 to 4.89) of mean square error (MSE). However, the mean bias (MB) indicated systematic over-prediction and under-prediction of CO2 production when the models were evaluated on the GF and the RC test data set, respectively. To address this bias, the 3 models were evaluated on a modified test data set, where the CO2 production (g/d) was adjusted by subtracting (where measurements were obtained by RC) or adding absolute MB (where measurements were obtained by GF) from evaluation of the specific model on RC, GF, and GF+ test data sets. By this modification, the absolute values of MB and MB as percentage of MSE became negligible. In conclusion, the 3 models were precise in predicting CO2 production from lactating dairy cows.

Reducing enteric methane emissions from dairy cattle: Two ways to supplement 3-nitrooxypropanol.

The aim of this work was to determine the effect of 3-nitrooxypropanol (3-NOP) on the enteric methane (CH4) emissions and performance of lactating dairy cows when mixed in with roughage or incorporated into a concentrate pellet. After 2 pretreatment weeks without 3-NOP supplementation, 30 Holstein Friesian cows were divided into 3 homogeneous treatment groups: no additive, 3-NOP mixed in with the basal diet (roughage; NOPbas), and 3-NOP incorporated into a concentrate pellet (NOPconc). The pretreatment period was followed by a 10-wk treatment period in which the NOPbas and NOPconc cows were fed 1.6 g of 3-NOP/cow per day. After the treatment period, a 2-wk washout period followed without 3-NOP supplementation. The CH4 emissions were measured using a GreenFeed unit (C-Lock Inc., Rapid City, SD) installed in a freestall with cubicles during the entire experimental period. On average for the total treatment period and compared with the no-additive group, CH4 production (g/d) was 28 and 23% lower for NOPbas and NOPconc, respectively. Methane yield (g/kg of dry matter intake) and methane intensity (g/kg of milk) were 23 and 24% lower for NOPbas, respectively, and 21 and 22% lower for NOPconc, respectively. No differences were found between NOPbas and NOPconc. Moreover, supplying 3-NOP did not affect total dry matter intake, milk production, or milk composition. The results of this experiment show that 3-NOP can reduce enteric CH4 emissions of dairy cattle when incorporated into a concentrate pellet and that this reduction is not different from the effect of mixing in 3-NOP with the basal diet (roughage). This broadens the possibilities for using 3-NOP in the dairy sector worldwide, as it is not always feasible to provide an additive mixed in with the basal diet.

Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction models.

Ruminant production systems are important contributors to anthropogenic methane (CH4) emissions, but there are large uncertainties in national and global livestock CH4 inventories. Sources of uncertainty in enteric CH4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH4 emission factors. There is also significant uncertainty associated with enteric CH4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF6) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes.

The effect of maize silage type on the performances and methane emission of dairy cattle.

To examine whether type of maize silage is important for milk production performances, maize silage LG30224 (LG) was compared with Falkone (FA), the latter having a 4.0% points lower rumen NDF digestibility and 19 g/kg dry matter (DM) more starch. To bridge the lower energy content of FA, a third treatment was involved by adding maize meal (MM) in a ratio of 92/8 on DM (FA+MM). Maize and grass silage were fed ad libitum in a ratio of 65/35 on DM basis. Concentrates were supplemented individually to meet energy and protein requirements. The experiment was set up as a Latin square with three groups of nine Holstein cows during three periods of 3 weeks. In the last 2 weeks of each period, DM intake (DMI) and milk performances were measured. Each group included one cannulated cow to study effects on rumen fermentation. During the last 4 days of each period, two cows from each group were placed in gas exchange chambers to measure nutrient digestibility and methane production. Total DMI was higher (p < 0.05) for FA+MM (20.8 kg/day) than for FA (20.3 kg/day), while DMI for LG was intermediate (20.6 kg/day). Treatment did not affect milk production nor composition, whereas fat-protein-corrected milk was higher for LG (30.5 kg/day) and FA+MM (30.3 kg/day) than for FA (29.9 kg/day). The ration did not affect pH nor volatile fatty acid composition in the rumen. Further, total tract digestibility of OM, crude protein, NDF and starch did not differ among treatments. The ration with LG gave higher methane production per day and per kg NDF intake than both rations with FA, but the difference was not significant when expressed per kg DMI or FPCM. Thus, maize silage type is of little importance for milk production if energy and physical structure requirements are met.

Application of biochar to anaerobic digestion versus digestate: Effects on N emissions and C stability.

Research attention is growing for biochar as amendment for anaerobic digestion (AD), as it may improve both the AD process and digestate properties. In this study, two biochars (from insect frass or the woody fraction of green waste, both pyrolyzed at 450 °C) were added (5 % w/w) during semi-continuous AD of organic kitchen waste and chicken manure. Biochar was mixed either during the AD process or to the digestate post-treatment. The impact of biochar addition on the AD process, NH3 and greenhouse gas emissions, as well as C and N mineralization following soil application, was examined. Biochar did not affect the biogas yield, but did lead to a reduction in NH3 levels in the biogas and NH4+-N sorption onto the biochars, reducing the risk of AD inhibition. N sorbed as NH4+-N onto the biochar was more available for mineralization than N sorbed as NH3. N sorption on biochar-amended digestates did not result in trade-offs when these digestates were applied to soil: the sorbed N was not released as NH3 or N2O after soil application and was not released as mineral N in either the short or medium term. The C-rich biochars increased the OC content of the digestate, which enhanced its value as soil improver. Additionally, mixing biochar with digestate decreased the soil CO2 emissions up to 33 % when it was added after AD; no priming effects were observed. These findings highlight the potential benefits of incorporating biochar into AD systems and the subsequent application of biochar-amended digestate in agricultural practices.

Screening tests for N sorption allow to select and engineer biochars for N mitigation during biomass processing.

Biochar amendment during biomass processing can improve those processes and products, and reduce the emissions of greenhouse gases and NH3, resulting in ecologic and economic benefits. The potential positive effects of biochar are related to NH4+-N and NH3 sorption, which in turn are depending on different biochar characteristics. By knowing the relationship between biochar characteristics and NH4+-N and NH3 sorption, biochar production can be steered towards a higher N sorption or existing biochars can be selected for targeted applications for high N sorption. Therefore, this study aims to develop fast screening tests to estimate the potential for both NH4+-N and NH3 sorption of biochar, before application in biomass processing. Further, the effects of feedstock, pyrolysis temperature, biochar characteristics and biochar treatments on N sorption are studied. The results show that NH4+-N sorption varied between 0 and 1.54 mg NH4+-N/g fresh biochar and was highest for manure-based biochars with a high nutrient content and cation exchange capacity, produced at lower temperatures (300-450 °C). For some biochars, the feedstock itself had a higher NH4+-N sorption than the biochar. Grinding and washing increased the NH4+-N sorption. In addition, a general linear model was proposed to predict the NH4+-N sorption based on three chemical characteristics· NH3 sorption varied between 0 and 100 % of the negative control and showed a linear positive relationship with the NH4+-N sorption, moisture retention factor and cation exchange capacity. Pyrolysis temperature and feedstock type did not significantly affect NH3 sorption. NH4+-N and NH3 desorption varied amongst biochar type.

Biochar amendment to cattle slurry reduces NH3 emissions during storage without risk of higher NH3 emissions after soil application of the solid fraction.

Cattle slurry storage is a major source of gaseous N emissions. The aim of this study was to evaluate the effects of biochar, clinoptilolite and elemental sulfur (S°) on (1) NH3 and greenhouse gas emissions during storage of cattle slurry and (2) after soil application of the enriched solid fractions; and (3) on the agronomic quality of the solid and liquid fractions. In the first phase, biochar was added to the slurry (10 g L-1); subsequently in the second phase, clinoptilolite (50 g L-1), S° (1 g L-1) and 40 g L-1 extra biochar were added. Gaseous emissions were monitored by a semi-continuous multi-gas analyzer and the agronomic quality of solid and liquid fractions was assessed after separation. The enriched solid fractions were applied to soil to study the effects on gaseous emissions, N and C mineralization. Amendment of biochar reduced NH3 emissions during cattle slurry storage by 12% during the first 7 days. Extra amendment of biochar, clinoptilolite and S° in combination with biochar resulted in a decrease of NH3 emissions of approximately 20%. The N sorbed from the slurry by the biochar was not released as NH3 during soil application of the solid fractions and was not released as mineral N in the short term (within 28 days). A short-term positive priming effect of biochar on the C mineralization of manure and biochar-manure mixture applied to soil was observed. The biochar-enriched solid fractions contained more C, total and organic N and water-available P with a slow release.

Two novel proteins expressed by the venom glands of Apis mellifera and Nasonia vitripennis share an ancient C1q-like domain.

An in-depth proteomic study of previously unidentified two-dimensional polyacrylamide gel electrophoresis spots of honey bee (Apis mellifera, Hymenoptera) venom revealed a new protein with a C1q conserved domain (C1q-VP). BlastP searching revealed a strong identity with only two proteins from other insect species: the jewel wasp, Nasonia vitripennis (Hymenoptera), and the green pea aphid, Acyrthosiphon pisum (Hemiptera). In higher organisms, C1q is the first subcomponent of the classical complement pathway and constitutes a major link between innate and acquired immunity. Expression of C1q-VP in a variety of tissues of honey bee workers and drones was demonstrated. In addition, a wide spatial and temporal pattern of expression was observed in N. vitripennis. We suggest that C1q-VP represents a new member of the emerging group of venom trace elements. Using degenerate primers the corresponding gene was found to be highly conserved in eight hymenopteran species, including species of the Aculeata and the Parasitica groups (suborder Apocrita) and even the suborder Symphyta. A preliminary test using recombinant proteins failed to demonstrate Am_C1q-VP-specific immunoglobulin E recognition by serum from patients with a documented severe bee venom allergy.

Predictions of methane emission levels and categories based on milk fatty acid profiles from dairy cows.

Milk fatty acid (MFA) have already been used to model methane (CH4) emissions from dairy cows. However, the data sets used to develop these models covered limited variation in dietary conditions, reducing the robustness of the predictions. In this study, a data set containing 140 observations from nine experiments (41 Holstein cows) was used to develop models predicting CH4 expressed as g/day, g/kg dry matter intake (DMI) and g/kg milk. The data set was divided into a training (n=112) and a test data set (n=28) for model development and validation, respectively. A generalized linear mixed model was fitted to the data using the marginal R 2 (m) and the Akaike information criterion to evaluate the models. The coefficient of determination of validation (R 2 (v)) for different models developed ranged between 0.18 and 0.41. Form the intake-related parameters, only inclusion of total DMI improved the prediction (R 2 (v)=0.58). In addition, in an attempt to further explore the relationships between MFA and CH4 emissions, the data set was split into three categories according to CH4 emissions: LOW (lowest 25% CH4 emissions); HIGH (highest 25% CH4 emissions); and MEDIUM (50% remaining observations). An ANOVA revealed that concentrations of several MFA differed for observations in HIGH compared with observations in LOW. Furthermore, the Gini coefficient was used to describe the MFA distribution for groups of MFA in each CH4 emission category. The relative distribution of the MFA, particularly of the odd- and branched-chain fatty acids and mono-unsaturated fatty acids of observations in category HIGH differed from those in the other categories. Finally, in an attempt to validate the potential of MFA to identify cases of high or low emissions, the observations were re-classified into HIGH, MEDIUM and LOW according to the proportion of each individual MFA. The proportion of observations correctly classified were recorded. This was done for each individual MFA and for the calculated Gini coefficients, finding that a maximum of 67% of observations were correctly classified as HIGH CH4 (trans-12 C18:1) and a maximum of 58% of observations correctly classified as LOW CH4 (cis-9 C17:1). Gini coefficients did not improve this classification. These results suggest that MFA are not yet reliable predictors of specific amounts of CH4 emitted by a cow, while holding a modest potential to differentiate cases of high or low emissions.

Genomic and transcriptional analysis of protein heterogeneity of the honeybee venom allergen Api m 6.

Several components of honeybee venom are known to cause allergenic responses in humans and other vertebrates. One such component, the minor allergen Api m 6, has been known to show amino acid variation but the genetic mechanism for this variation is unknown. Here we show that Api m 6 is derived from a single locus, and that substantial protein-level variation has a simple genome-level cause, without the need to invoke multiple loci or alternatively spliced exons. Api m 6 sits near a misassembled section of the honeybee genome sequence, and we propose that a substantial number of indels at and near Api m 6 might be the root cause of this misassembly. We suggest that genes such as Api m 6 with coding-region or untranslated region indels might have had a strong effect on the assembly of this draft of the honeybee genome.