Accuracy of enteric methane emission models for cattle in sub-Saharan Africa: status quo and the way forward

Cattle emit over 65% of enteric methane (CH4) in sub-Saharan Africa (SSA), making them the focus of many mitigation strategies targeting livestock emissions. Since measured feed intake data are sparse, emission factors for enteric CH4 (EFCH4) are mainly estimated indirectly from gross energy intake (GEI) using the net energy (NE) requirements for different metabolic processes in cattle. However, all NE requirement systems commonly used for cattle in SSA were developed for cattle in temperate regions. Therefore, we assessed the suitability of different enteric CH4 models for estimating the GEI of cattle in SSA. The Intergovernmental Panel on Climate Change (IPCC) and South African models were identified as the main tier 2-based methods used to estimate enteric CH4 emissions from cattle in SSA. In the IPCC model, EFCH4 was estimated as (GEI * [Ym/100])/55.65, where Ym is the conversion factor (%) of gross energy in feed to CH4 and 55.65 the energy content of CH4 (MJ/kg). The GEI was estimated based on NE requirements for different metabolic processes in cattle as per the American National Research Council. In the South African model, EFCH4 was estimated as (Y/100 * GEI/55.22), where Y is the CH4 yield and 55.22 is the energy content of CH4; Y was calculated from the dry matter (DM) digestibility while GEI was calculated by predicting DM intake and multiplying it by 18.4 MJ (gross energy per kilogram DM). Also, the suitability of the British and German NE requirement systems was assessed as alternatives used for cattle nutrition in SSA. These NE systems were implemented in the IPCC model to yield the "AFRC" and "GfE" models, respectively. The four models were then evaluated using an evaluation dataset summarizing feed quality and DM intake results from 21 studies conducted in SSA, with 125 dietary treatments, and 822 cattle observations. The relative prediction error (RPE) and concordance correlation coefficient (CCC) were used to evaluate the models' accuracy. Only the South African model estimated the GEI in dairy cattle with an acceptable RPE (18.9%) and highest CCC (0.87), while the other three models yielded estimates with RPE > 20%. None of the four models we assessed estimated GEI for other cattle (i.e., nondairy) with an RPE < 20% or CCC > 0.30. The inaccuracy in GEI estimates suggests an error of the same magnitude in EFCH4 estimates. Therefore, a concerted effort is needed to improve the accuracy of enteric CH4 estimation models for cattle in SSA.

Intake, performance, and feeding behavior of Holstein and Holstein × Gyr heifers grazing intensively managed tropical grasses during the rainy season

Holstein × Gyr and Holstein are the primary dairy breeds used in tropical systems, but when rearing under pasture, feed intake, behavior, and performance might differ between them. This study aimed to evaluate the voluntary intake, nutrient digestibility, performance, and ingestive behavior of Holstein and Holstein × Gyr (½ Holstein × ½ Gyr) heifers managed in a rotational system of Guinea grass (Panicum maximum Jacq. cv. Mombaça). The experiment was conducted during the summer season throughout four periods of 21 d. Two 8-heifers (four Holstein and four Holstein × Gyr) groups, averaging 258.6 ± 24.79 kg and 157.1 ± 24.99 kg BW, were used. Each group grazed a separate set of 16 paddocks, and all heifers received a concentrate supplement daily. Heifers were weighed at the beginning and end of the experiment. Fecal, forage and concentrate samples were evaluated for their DM, CP, crude fat, ash, NDF, and indigestible NDF. Feeding behavior was evaluated through 24 h of live observation for 48 h of each experimental period. Grazing, ruminating, resting, and intake of concentrate times were recorded, and rumination criteria, bout criteria, mealtime, meal frequency, and meal duration were calculated. There was no difference in total dry matter intake (DMI), but forage DMI of Holstein × Gyr was 11.70 % greater than the Holstein heifers. The Holstein × Gyr heifers had greater NDF intake and feed efficiency tended to show greater CP and NDF digestibilities, consequently, they had greater average daily gain (ADG). Holstein grazed less than Holstein × Gyr heifers in the afternoon. Ruminating time was 18.43 % lower for Holstein than Holstein × Gyr heifers, and rumination criteria (i.e. longest non-feeding interval within a rumination event) were greater for Holstein heifers. Holstein heifers presented more prolonged rumination bouts and resting time than Holstein × Gyr heifers. Holstein × Gyr can ingest and ruminate greater amounts of fibrous material, and Holstein heifers needed to spend more time ruminating the cud. Overall, even though the behavior was not markedly different between breeds, rearing young Holstein heifers in tropical pasture conditions is less suitable than Holstein-Gyr because of their lower ADG. Therefore, this management condition seems appropriate for Holstein × Gyr but inappropriate for Holstein dairy heifers.

Feed Intake of Growing Dairy Heifers Raised under Tropical Conditions: A Model Evaluation Using Meta-Analysis

Several models for predicting dry matter intake (DMI) of replacement dairy heifers have been developed; however, only a few have been evaluated using data from heifers of different breeds raised under tropical conditions. Thus, the objective of this study was to evaluate the DMI equations for dairy heifers managed under tropical conditions. A total of 230 treatment means from 61 studies using dairy heifers (n = 1513 heifers, average body weight = 246 kg) were used. The animals were grouped into two groups based on their genetics: (1) Bos taurus (Holstein, Jersey, Brown Swiss, and Holstein × Jersey) and (2) crossbred (Bos taurus × Bos indicus). Seven previously published DMI equations (HH, HHJ, QUI, STA, 2001 NRC, OFLin, and OFNLin) for heifers were evaluated using mean bias, slope bias, mean squared prediction errors (MSPE) and its decomposition, and other model evaluation statistics. For Bos taurus heifers, our results indicated that OFNLin and HHJ had lower mean bias (0.13 and 0.16 kg/d, respectively) than other models. There was no significant slope or mean bias for HHJ and OFNLin (p > 0.05), indicating agreement between the observed and predicted DMI values. All other models had a significant mean bias (p < 0.05), whereas the QUI model also presented a significant slope bias (p < 0.02). For crossbred heifers, the STA equation was the only one that did not present mean and slope bias significance (p > 0.05). All other DMI models had significant mean bias when evaluated using crossbred data (p < 0.04), and QUI, OFLin, and OFNLin also presented significant slope bias (p < 0.01). Based on our results, predictions from OFNLin and HHJ best represented the observed DMI of Bos taurus heifers (MSPE ≤ 1.25 kg²/d², mean bias ≤ 0.16 kg/d), whereas STA was the best model for crossbred heifers (MSPE = 1.25 kg²/d², mean bias = 0.09 kg/d). These findings indicate that not all available models are adequate for estimating the DMI of dairy heifers managed under a tropical climate, with HHJ and OFNLin for Bos taurus and STA for crossbreds being the most suitable models for DMI prediction. There is evidence that models from Bos taurus heifers could be used to estimate the DMI of heifers under tropical conditions. For heifer ration formulation is necessary to consider that DMI is influenced by breed, diet, management, and climate. Future work should also include animal genetic and environmental variables for the prediction of DMI in dairy heifers.

Models to predict dry feed intake in Holstein calves to 4 months of age

Voluntary daily dry feed intake (DFI) in Holstein calves was predicted using 60,761 individual daily observations collected from 1,235 Holstein calves in 30 experiments from 4 research stations in the United States and Europe. Consumption of dry feed (calf starter and hay, kg/d or percent of body weight) was measured from 3 to 114 d of age. Linear models and 2- and 3-parameter nonlinear models were evaluated to predict DFI using age of calf, intake of milk replacer, ambient temperature, percent forage, and neutral detergent fiber concentration in ration dry matter (DM) as independent variables. The initial data set was randomly divided within study location into development (80% of all observations) and validation data sets, and initial screening was conducted using the development data set. Five nonlinear models and 3 linear models (candidate models) were identified and used in further model evaluation. Cross-validation studies (n = 20) with the validation data set were conducted by linear regression of DFI with predicted DFI as independent variable. Candidate models were subsequently evaluated with data from 12 published studies in 2 analyses. The exponential model that best predicted daily DFI in Holstein calves in original and external data sets was DFI (kg/d) = 1.3207 × e^{[(-5.3892 + 0.6376 × MEgap) × EXP(-0.0392 × Age)]} - 0.0013 × Temp + 0.0032 × NDFDM + 0.0026 × Age × MEgap - 0.3646 × PctForage [coefficient of determination (R²) = 0.92, concordance correlation coefficient (CCC) = 0.96, and mean square error of prediction (MSEP) = 0.10 kg]; where MEgap (Mcal/d) = difference of daily metabolizable energy (ME) requirement and ME intake from milk replacer; Age = age of calf (d) from 3 to 114, Temp = mean daily ambient temperature (°C), NDFDM = ration neutral detergent fiber (% DM); PctForage = percent forage in ration DM. The linear model that best predicted DFI was DFI (kg/d = -0.1349 + 0.0106 × Age + 0.1808 × MEgap + 0.0013 × Age × MEgap + 0.0001 × Temp + 0.00002 × Age × Temp (R² = 0.93, CCC = 0.96, and MSEP = 0.10 kg). When Temp and ration characteristics were not included, optimal models were 1.4362 × e^{[(-4.6646 + 0.5234 × MEgap) × EXP(-0.0361 × Age)]} + 0.0025 × Age × MEgap (R² = 0.92, CCC = 0.96, and MSEP = 0.11 kg) and -0.1344 + 0.0102 × Age + 0.1810 × MEgap + 0.0013 × Age × MEgap [R² = 0.93, CCC = 0.96, and MSEP = 0.10 kg]. Models of daily DFI may improve prediction of nutrient supply to young Holstein calves to approximately 4 mo of age, thereby increasing prediction of growth performance.

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.

Effects of Paper Mulberry Silage on the Milk Production, Apparent Digestibility, Antioxidant Capacity, and Fecal Bacteria Composition in Holstein Dairy Cows

Simple Summary

Paper mulberry (Broussonetia papyrifera; PM) is a type of roughage rich in bioactive substances, such as phenolics and flavonoids, which are beneficial for animal health. This study evaluated the apparent digestibility of PM silage in Holstein dairy cows and its effect on the milk production, antioxidant capacity, and fecal bacteria composition of the animals. The results showed that the PM silage had no significant influence on the milk yield, apparent digestibility, and fecal bacteria composition of dairy cows. However, diets with PM silage can enhance the antioxidant and immune capacity of dairy cows, mainly due to the bioactive substance in PM. Today, faced with a shortage of feedstuff resources in ruminants, PM can be a useful feed resource for ruminants. Simultaneously, with the ban on antibiotics, PM may become an important functional feed for protecting animal health.

Abstract

Paper mulberry (Broussonetia papyrifera; PM) is an excellent and extensive type of roughage in Asia. This study aimed to evaluate the effects of PM silage on the milk production, apparent digestibility, antioxidant capacity, and fecal bacteria composition in Holstein dairy cows. Forty-five lactating Holstein dairy cows with a similar milk yield and parity were selected and randomly assigned to three groups. The control group was fed a non-PM silage diet, and the PM-treated groups were fed 4.5 and 9.0% PM silage supplementary diets for 28 days. Then, treatment groups were fed diets containing 13.5 and 18.0% PM silage for the next 28 days, respectively. PM silage increased the milk urea nitrogen and decreased the somatic cell count (p < 0.05), but did not affect the dry matter intake, milk yield, apparent digestibility, and energy balance of dairy cows. PM silage can enhance the blood total antioxidant capacity, superoxide dismutase, and immune globulin content (p < 0.05). The PM silage significantly decreased the relative abundance of the genera Ruminococcaceae UCG-013 and Tyzzerella-4 (p < 0.05). In conclusion, PM silage enhanced the antioxidant capacity and immunity of dairy cows, but did not influence the milk yield, dry matter digestibility, and fecal bacteria composition.

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 &lt;5 l/day, separate prediction equations are recommended.

Recombinant bovine somatotropin on heifer’s biometric measures, bodyweight, blood metabolites, and dry matter intake predictions

This study aimed to determine the influence of sustained-release recombinant bovine somatotropin (rbST) injections on biometrics measures, bodyweight (BW), average BW gain, observed and predicted DM intake, accuracy of recent methods to estimate DM intake, blood metabolites, haematological profile and rectal temperature in dairy heifers. Thirty Holstein heifers (132 ± 27 kg BW and 6.2 ± 0.35 months of age) were used in a complete randomised design experiment. Heifers were assigned to treatments: (1) Control (CON), 250 mL of saline solution, or (2) rbST, 250 mg of sustained-release rbST every 15 days. Treatments were injected in the subcutaneous of ischiorectal fossa or subscapular region in a regular alternating manner (right and left side) every 15 days throughout a period of 90 days. Prediction of DM intake was calculated using either non-linear or linear models for heifers in tropical conditions. rbST injections increased the average values of thoracic perimeter, length, and rump width in heifers. rbST-treated heifers had higher average BW and BW gain than CON. Regardless of the model applied, both observed and predicted DM intake were higher for heifers rbST-treated in relation to CON. Non-linear model was accurate without significant bias. rbST injections elevated blood glucose and high-density lipoprotein cholesterol concentration in heifers. No differences were detected on haematological profile and rectal temperature of heifers. rbST injections every 15 days to growing heifers promoted animal performance by increasing biometrics measures and BW gain. In addition, non-linear model was accurate to predict DM intake of heifers.