Effect of a Garlic and Citrus Extract Supplement on the Lactation Performance and Carbon Footprint of Dairy Cows under Grazing Conditions in Chile

Two trials were conducted to evaluate the effect of a garlic and citrus extract supplement (GCE) on the milk production performance and carbon footprint of grazing dairy cows in a Chilean commercial farm. A total of 36 early- to mid-lactation and 54 late-lactation Irish Holstein-Friesian cows were used in Trial 1 and Trial 2, respectively. In both trials, the cows were reared under grazing conditions and offered a supplementary concentrate without or with GCE (33 g/cow/d) for 12 weeks. The concentrate was fed in the afternoon when the cows visited the milking parlour. Consequently, the results of milk production performance in these trials were used to determine the effect of feeding with GCE on the carbon footprint (CFP) of milk using a life cycle assessment (LCA) model. In Trial 1 and Trial 2, feeding with GCE increased estimated dry matter intake (DMI, kg/d) by 8.15% (18.4 vs. 19.9) and 15.3% (15.0 vs. 17.3), energy-corrected milk (ECM, kg/d) by 11.4% (24.5 vs. 27.3) and 33.5% (15.5 vs. 20.7), and feed efficiency (ECM/DMI) by 3.03% (1.32 vs. 1.36) and 17.8% (1.01 vs. 1.19), respectively. The LCA revealed that feeding with GCE reduced the emission intensity of milk by 8.39% (1.55 vs. 1.42 kg CO2-eq/kg ECM). Overall, these results indicate that feeding with GCE improved the production performance and CFP of grazing cows under the conditions of the current trials.

A missense mutation of the WNK1 gene affects cold tolerance in Chinese domestic cattle

Inclement weather conditions, especially cold stress, have threatened the cattle industry. Cattle exposed to cold environments for a longer time suffer developmental delay, immunity decline, and eventually death. WNK1 is a member of With-no-lysine kinases (WNKs), widely expressed in animal organs and tissues. WNK1 and WNK4 are expressed in adipose tissue, and WNK4 promotes adipogenesis. WNK1 does not directly affect adipogenesis but has been shown to promote WNK4 expression in several tissues or organs. One missense mutation NC_037346.1:g.107692244, A > G, rs208265410 in the WNK1 gene was detected from the database of bovine genomic variation (BGVD). Here, we collected 328 individuals of 17 breeds representing four groups of Chinese cattle, northern group cattle, southern group cattle, central group cattle, and special group cattle (Tibetan cattle). We also collected the temperature and humidity data records from their relative locations. The frequencies of the G allele in Chinese breeds increased from northern China to southern China, and the frequencies of the A allele showed an opposite trend. Our results indicate that the WNK1 gene might be a candidate gene marker associated with cold tolerance.

Factors Affecting Enteric Emission Methane and Predictive Models for Dairy Cows

Enteric methane emission is the main source of greenhouse gas contribution from dairy cattle. Therefore, it is essential to evaluate drivers and develop more accurate predictive models for such emissions. In this study, we built a large and intercontinental experimental dataset to: (1) explain the effect of enteric methane emission yield (g methane/kg diet intake) and feed conversion (kg diet intake/kg milk yield) on enteric methane emission intensity (g methane/kg milk yield); (2) develop six models for predicting enteric methane emissions (g/cow/day) using animal, diet, and dry matter intake as inputs; and to (3) compare these 6 models with 43 models from the literature. Feed conversion contributed more to enteric methane emission (EME) intensity than EME yield. Increasing the milk yield reduced EME intensity, due more to feed conversion enhancement rather than EME yield. Our models predicted methane emissions better than most external models, with the exception of only two other models which had similar adequacy. Improved productivity of dairy cows reduces emission intensity by enhancing feed conversion. Improvement in feed conversion should be prioritized for reducing methane emissions in dairy cattle systems.

Prediction of pasture intake by beef cattle in tropical conditions

Forage intake is the most important factor for beef cattle raised on pasture, as it is the basis of the diet. Thus, knowing the variables that affect this parameter can help supplementation programs. Thus, a meta-analytic study was conducted to develop and evaluate models for the prediction of pasture dry matter intake (DMI_pasture) by beef cattle in tropical conditions. Eight hundred four individual observations of DMI_pasture were used, taken from 23 studies through analysis of mixed models, including the study as a random effect. To evaluate the accuracy and precision of the new models proposed as well as for the models of Azevedo et al. (2016) and Minson and McDonald (1987), an independent databank with 87 means from treatments of 21 experiments (n = 888 animals) was used. Three prediction models were adjusted: model I (animal information), model II (animal information + supplement), and model III (animal information + supplement + pasture). The proposed models presented similarity for the average square root of the prediction error. The inclusion of the predictive variables for supplementation (supplement dry matter intake - DMI_supplement - % of the body weight and crude protein intake through supplement) with the variables for the animal (BW^0.75 and average daily gain) and of the pasture (% of crude protein) in model III improved accuracy and precision and provided higher determination and correlation coefficients, and agreement than the other proposed models. Similarly, it was found to be more accurate and precise than the equations of Azevêdo et al. (2016) and Minson and McDonald (1987), which presented lower precision. The DMI_pasture for beef cattle in tropical conditions is more accurate and precise when the information for the animal, supplement, and pasture is included.

Intake, digestibility, milk yield and composition, and ingestive behavior of cows supplemented with byproducts from biodiesel industry

This trial aimed to analyze the effects of including three byproducts from the biodiesel industry on the intake, digestibility, milk yield and composition, and feeding behavior of lactating cows. Eight crossbred Holstein-Zebu lactating cows with average body weight 525 ± 18.5 kg and average milk yield of 8 ± 1.45 kg day^-1 were assigned to four treatments (diets) in a double-Latin square design, as follows: a diet based on corn- and soybean meal-based concentrate and three diets with 20% inclusion of byproducts from the biodiesel industry (cottonseed cake, sunflower meal, and castor bean cake) on a total dry matter basis. The cows were housed in individual covered stalls with concrete floor equipped with individual concrete troughs for feeding and automatic drinkers, and fed diets containing 60% sugarcane and 40% concentrate. The inclusion of the byproducts in the diet changed the intake, digestibility of some nutritional components, milk yield and composition, and feeding behavior of lactating cows. The use of cottonseed cake and sunflower meal in the diet increased milk yield, and fat-corrected milk yield; while the use of castor bean cake reduced the intake, digestibility of dry matter and total digestible nutrients, milk yield, and fat-corrected milk yield. The inclusion of byproducts from the biodiesel industry in the diets did not change the fat, lactose, total solids, and solids-not-fat of milk. Therefore, the cottonseed cake and sunflower meal can be included at up to 20% of the total diet.

Prediction of dry matter intake by feedlot beef cattle under tropical conditions

Context Dry matter intake (DMI) is the variable that most affects beef cattle performance in feedlot conditions. Accurate prediction of DMI is essential because it is the basis for calculating nutritional requirements for maintenance and production. Aims A meta-analysis was conducted to develop DMI prediction models for feedlot beef cattle under tropical conditions, and to compare the models with those proposed by the National Research Council, USA, in 2000 and 2016, as well as those recommended by the Brazilian System of Nutritional Requirements (BR-Corte) and published by Azevêdo and colleagues in 2010 and 2016. Methods The dataset was created from 56 published studies conducted under tropical conditions. The dataset was randomly separated into two subsets for statistical analysis. The first subset was used to develop the models to predict DMI, and the second to evaluate the adequacy of the prediction models. The models were developed by using mixed linear and nonlinear analysis. Key results A nonlinear model and a linear model to predict DMI are proposed. These models were similar in terms of accuracy and were superior to the other evaluated models. The nonlinear and linear models explained, respectively, 59% and 62% of the DMI variation and had greater accuracy and precision than the other models. The 2016 model used by BR-Corte explained 55% of the DMI variation, and underestimated it at 0.20 kg/day. The remaining three models presented a systematic constant bias and were not adequate for predicting DMI. Conclusion The proposed nonlinear and linear prediction models of beef cattle in feedlot developed under tropical conditions are more precise and accurate than those recommended by the National Research Council and the 2010 model used by BR-Corte. They also present better prediction quality of DMI from beef cattle in feedlots under tropical conditions than the 2016 model used by BR-Corte. Implications The proposed models in the present study are the most suitable for use in predicting the DMI of beef cattle under tropical conditions.

Estimation of between-Cow Variability in Nutrient Digestion of Lactating Dairy Cows Fed Corn-Based Diets

Simple Summary

Cow variability present in nutrient digestibility studies differs for different diets and nutrients. It is a major factor determining adequate sample size so that studies are not under-powered or over-powered. The objective of the current study was to develop cow variability estimates that can be used to determine the optimal sample size for digestibility trials having randomized block designs using mid-lactation dairy cows when fed corn-based diets having different neutral detergent fiber:starch ratio (0.7, 1.0, and 1.3). Cow variability is greater for digestibility of fiber and dry matter and less for starch. Estimated cow variability as standard deviations for digestibility of dry matter, neutral detergent fiber and starch were 3.8 g/kg, 5.1 g/kg and 3.3 g/kg, respectively. A major implication of this study is that cow variability is greatest for fiber digestibility and the use of a minimum of 12 cows per dietary treatment is adequate to reliably detect treatment effects on the digestibility of fiber, starch and dry matter using lactating dairy cows fed in groups with randomized block design under current experimental conditions.

Abstract

The objective of this study was to estimate cow variability that can be used to determine the optimal sample size for digestibility trials using lactating dairy cows. Experimental design was randomized complete block design having three blocks and three dietary treatments. Three similarly managed nearby intensive farms were considered as blocks, and three diets were formulated to have 0.7, 1.0, and 1.3 neutral detergent fiber (NDF): starch ratio. In each farm, 18 cows were assigned for each dietary treatment and five sample sizes per each treatment group were simulated by simple random sampling of data from 18, 15, 12, 9 and 6 cows respectively. Intake was not affected by diet or sample size (p > 0.05). Estimated cow variability (as standard deviation) for digestibility of dry matter, NDF and starch were 3.8 g/kg, 5.1 g/kg and 3.3 g/kg, respectively. A major implication of this study is that cow variability is greatest for NDF digestibility and the use of a minimum of 12 cows per dietary treatment is adequate to reliably detect treatment effects on the digestibility of NDF, starch and dry matter using cows fed in groups with randomized block design under these experimental conditions.

Predicting dry matter intake using conceptual models for cattle kept under tropical and subtropical conditions1

Using empirical models to predict voluntary dry matter intake (VDMI) of cattle across production systems in the (Sub-)Tropics often yields VDMI estimates with low adequacy (i.e., accuracy and precision). Thus, we investigated whether semimechanistic conceptual mathematical models (CMM) developed for cattle in temperate areas could be adopted and adjusted to adequately predict VDMI of stall-fed cattle in the (Sub-)Tropics. The CMM of Conrad et al. (1964) (C1) and Mertens (1987) (M1) were identified and adopted for their simplicity in reflecting physicophysiological VDMI regulation. Both CMM use 2 equations that estimate the physiologically and physically regulated VDMI and retain the lower VDMI prediction as actual VDMI. Furthermore, C1 was modified by increasing the daily average fecal dry matter output from 0.0107 to 0.0116 kg/kg body weight, yielding the modified model C2. For M1, the daily neutral detergent fiber intake capacity was increased from 0.012 to 0.0135 kg/kg body weight and the daily metabolizable energy requirements for maintenance from 0.419 to 0.631 MJ/kg0.75 body weight, whereas the metabolizable energy requirements for gain was reduced from 32.5 to 24.3 MJ/kg body weight gain, yielding the modified model M2. Last, also the mean of the physically and physiologically regulated VDMI rather than the lower of both estimates was retained as actual VDMI to generate the models C3 (from C1), C4 (from C2), M3 (from M1), and M4 (from M2). The 8 CMM were then evaluated using a data set summarizing results from 52 studies conducted under (sub)tropical conditions. The mean bias, root mean square error of prediction (RMSEP) and concordance correlation coefficient (CCC) were used to evaluate adequacy and robustness of all CMM. The M4, C2, and C1 were the most adequate CMM [i.e., lowest mean biases (0.07, -0.22, and 0.14 kg/animal and day, respectively), RMSEP (1.62, 1.93, and 2.0 kg/animal and day, respectively), and CCC (0.91, 0.86, and 0.85, respectively)] and robust of the 8 CMM. Hence, CMM can adequately predict VDMI across diverse stall-fed cattle systems in the (Sub-)Tropics. Adjusting CMM to reflect the differences in feed quality and animal physiology under typical husbandry conditions in the (Sub-)Tropics and those in temperate areas improves the adequacy of their VDMI predictions.

Development of equations, based on milk intake, to predict starter feed intake of preweaned dairy calves

There is a lack of studies that provide models or equations capable of predicting starter feed intake (SFI) for milk-fed dairy calves. Therefore, a multi-study analysis was conducted to identify variables that influence SFI, and to develop equations to predict SFI in milk-fed dairy calves up to 64 days of age. The database was composed of individual data of 176 calves from eight experiments, totaling 6426 daily observations of intake. The information collected from the studies were: birth BW (kg), SFI (kg/day), fluid milk or milk replacer intake (MI; l/day), sex (male or female), breed (Holstein or Holstein×Gyr crossbred) and age (days). Correlations between SFI and the quantitative variables MI, birth BW, metabolic birth BW, fat intake, CP intake, metabolizable energy intake, and age were calculated. Subsequently, data were graphed, and based on a visual appraisal of the pattern of the data, an exponential function was chosen. Data were evaluated using a meta-analysis approach to estimate fixed and random effects of the experiments using nonlinear mixed coefficient statistical models. A negative correlation between SFI and MI was observed (r=-0.39), but age was positively correlated with SFI (r=0.66). No effect of liquid feed source (milk or milk replacer) was observed in developing the equation. Two equations, significantly different for all parameters, were fit to predict SFI for calves that consume less than 5 (SFI5) l/day of milk or milk replacer: ${\rm SFI}_{{\,\lt\,5}} {\equals}0.1839_{{\,\pm\,0.0581}} {\times}{\rm MI}{\times}{\rm exp}^{{\left( {\left( {0.0333_{{\,\pm\,0.0021 }} {\minus}0.0040_{{\,\pm\,0.0011}} {\times}{\rm MI}} \right){\times}\left( {{\rm A}{\minus}{\rm }\left( {0.8302_{{\,\pm\,0.5092}} {\plus}6.0332_{{\,\pm\,0.3583}} {\times}{\rm MI}} \right)} \right)} \right)}} {\minus}\left( {0.12{\times}{\rm MI}} \right)$ ; ${\rm SFI}_{{\,\gt\,5}} {\equals}0.1225_{{\,\pm\,0.0005 }} {\times}{\rm MI}{\times}{\rm exp}^{{\left( {\left( {0.0217_{{\,\pm\,0.0006 }} {\minus}0.0015_{{\,\pm\,0.0001}} {\times}{\rm MI}} \right){\times}\left( {{\rm A}{\minus}\left( {3.5382_{{\,\pm\,1.3140 }} {\plus}1.9508_{{\,\pm\,0.1710}} {\times}{\rm MI}} \right)} \right)} \right)}} {\minus}\left( {0.12{\times}{\rm MI}} \right)$ where MI is the milk or milk replacer intake (l/day) and A the age (days). Cross-validation and bootstrap analyses demonstrated that these equations had high accuracy and moderate precision. In conclusion, the use of milk or milk replacer as liquid feed did not affect SFI, or development of SFI over time, which increased exponentially with calf age. Because SFI of calves receiving more than 5 l/day of milk/milk replacer had a different pattern over time than those receiving <5 l/day, separate prediction equations are recommended.

Avaliação de equações para estimar o consumo de vacas leiteiras

RESUMO Objetivou-se com este estudo realizar uma comparação do poder de predição de diferentes equações do consumo de matéria seca de vacas leiteiras. O trabalho foi conduzido por meio de pesquisas na literatura coletando informações sobre massa corporal, produção de leite, dias de lactação, consumo de matéria seca e teor de gordura no leite de vacas leiteiras criadas no Brasil. Todas essas informações, menos o consumo de matéria seca observado, foram utilizadas para calcular o consumo de matéria seca predito com as equações dos modelos: National Research Council (NRC, 2001), Cornell Net Carbohydrate and Protein System (CNCPS, 2004), Agricultural and Food Research Council (AFRC, 1993) e De Freitas et al. (2006). Posteriormente, as estimativas das equações foram usadas para avaliar o poder de predição dos modelos por meio da comparação gráfica dos seus resíduos padronizados conforme Draper & Smith (1966) e Montgomery (2005) e do critério de Akaike (AKAIKE, 1974). Para os dados analisados neste estudo, o NRC (2001) foi considerado como melhor escolha por apresentar o ERr = 1. Os demais, apresentaram ERr maior do que 20 e, portanto, não foram adequados para a predição do CMS. O modelo AFRC (1993) apresentou tendência a subestimar os valores preditos com 76% dos pontos acima da linha de nulidade.