Slope Bias Research Articles

PSXI-23 Development of sub-models to estimate protein requirements and supply of lactating dairy cows using machine learning algorithms

Abstract The objectives of this study were to develop sub-models for predicting protein requirements and supply, encompassing 1) net protein for maintenance (NPm), 2) lactation (NPl), 3) rumen undegradable protein (RUP), and 4) duodenal microbial nitrogen (MicN) from the feed protein. The dataset used in this study was constructed by integrating in vivo experimental data collected from open databases (the National Animal Nutrition Program) and articles (Journal of Dairy Science), which includes a total of 1,779 observations from 436 publications. In the development of the model, animal information and feed chemical components were used as candidate variables, and two types of machine learning algorithms, Random Forest Regression (RFR) and Support Vector Regression (SVR) were employed. After testing, the following predictors were selected for predicting: 1) NPm: body weight (BW) and dry matter intake (DMI), 2) NPl: BW, DMI, days in milk (DIM), and dietary organic matter (OM) and crude protein (CP) contents, 3) RUP: DIM, DMI, dietary DM content, and CP fraction intake (B and C), and 4) MicN: DIM, DMI, DM, dietary neutral detergent fiber (NDF) content, CP fraction intake (A, B, and C). The selected models were assessed using a cross-validation method with the following statistical metrics including the coefficient of determination (R2), root-mean-square error of prediction (RMSEP), residual analyses, and concordance correlation coefficient (CCC). For the RUP and MicN models, they were compared with the NASEM (2021) model. In predicting NPm, both SVR and RFR algorithms demonstrated increased precision (R2 = 0.965 vs. 0.969) and accuracy (RMSEP = 9.7 vs. 9.2 g/d); however, during residual analysis, the RFR model showed a statistically significant slope bias (P < 0.05). In the NPl prediction, the RFR algorithm showed slightly greater performance compared with SVR (R2 = 0.864 vs. 0.814 and RMSEP = 86.7 vs. 98.5 g/d). However, similar to the NPm prediction, the RFR model displayed a statistically significant slope bias (P < 0.05). In the supply model, the RFR model exhibited the greatest precision and accuracy in predicting RUP (R2 = 0.60, RMSEP = 0.326 kg/d, and CCC = 0.71) without any biases. This model achieved a 2.37-fold increase in R2 and a decrease of 0.111 kg/d in RMSEP compared with the NASEM model. As for MicN prediction, the SVR model performed the best (R2 = 0.76, RMSEP = 42.4 g/d, and CCC = 0.86) without biases. This model attained a 19-times improvement in R2 and a reduction of 38.7 g/d in RMSEP when compared with the NASEM model. In conclusion, the models developed using machine learning algorithms can be helpful for accurately and precisely predicting protein requirements and supply based on animal information and the chemical composition of feed.

Analytical evaluation of the automated interleukin-6 assay on the Roche cobas e602 analyzer.

Interleukin-6 (IL-6) is a proinflammatory cytokine that is associated with many inflammatory diseases. This validation study evaluates the automated Roche Elecsys IL-6 electrochemiluminescent immunoassay that has been granted emergency use authorization by the US Food and Drug Administration. The Elecsys IL-6 assay was evaluated for precision, linearity, interference (by hemoglobin, bilirubin, triglycerides, and biotin) and clinical performance was compared to the V-PLEX Human IL-6 immunoassay (Meso Scale Discovery), performed by a reference laboratory. The Elecsys IL-6 assay is precise (intra-assay <3% coefficient of variation [CV], interassay <5% CV), exhibits an analytical measurable range of 1.5-4790 pg/mL, and is tolerant of significant interferences (H < 2522, I <62, L<2101, biotin <50ng/mL). Comparison with the V-PLEX assay revealed a 2.95 slope bias in patient samples evaluated for IL-6 concentration (n=43, range=1.5-1891 pg/mL, y=2.95x - 32.7, r2=0.84). Bland-Altman analysis revealed an absolute mean bias of 152 pg/mL (SD=254 pg/mL), or a mean percentage difference of 73%. The Roche IL-6 assay showed good analytical performance. The large systematic bias compared with another reference method precludes using multiple methods to monitor IL-6 response. The random-access nature of an automated IL-6 assay on the Roche platform makes the test available on demand.

Assessing different cross-validation schemes for predicting novel traits using sensor data: An application to dry matter intake and residual feed intake using milk spectral data

Feed efficiency is important for economic profitability of dairy farms; however, recording daily dry matter intakes (DMI) is expensive. Our objective was to investigate the potential use of milk mid-infrared (MIR) spectral data to predict proxy phenotypes for DMI based on different cross-validation schemes. We were specifically interested in comparisons between a model that included only MIR data (Model M1), a model that incorporated different energy sink predictors, such as body weight, body weight change, and milk energy (Model M2), and an extended model that incorporated both energy sinks and MIR data (Model M3). Models M2 and M3 also included various cow level variables (stage of lactation, age at calving, parity) such that any improvement in model performance from M2 to M3, whether through a smaller root mean squared error (RMSE) or a greater squared predictive correlation (R2), could indicate a potential benefit of MIR to predict residual feed intake. The data used in our study originated from a multi-institutional project on the genetics of feed efficiency in US Holsteins. Analyses were conducted on 2 different trait definitions based on different period lengths: averaged across weeks vs. averaged across 28-d. Specifically, there were 19,942 weekly records on 1,812 cows across 46 experiments or cohorts and 3,724 28-d records on 1,700 cows across 43 different cohorts. The cross-validation analyses involved 3 different k-fold schemes. First, a 10-fold cow-independent cross-validation was conducted whereby all records from any one cow were kept together in either training or test sets. Similarly, a 10-fold experiment-independent cross-validation kept entire experiments together whereas a 4-fold herd-independent cross-validation kept entire herds together in either training or test sets. Based on cow-independent cross-validation for both weekly and 28-d DMI, adding MIR predictors to energy sinks (Models M3 vs M2) significantly (P < 10-10) reduced average RMSE to 1.59 kg and increased average R2 to 0.89. However, adding MIR to energy sinks (M3) to predict DMI either within an experiment-independent or herd-independent cross-validation scheme seemed to demonstrate no merit (P > 0.05) compared with an energy sink model (M2) for either R2 or RMSE (respectively, 0.68 and 2.55 kg for M2 in herd-independent scheme). We further noted that with broader cross-validation schemes, i.e., from cow-independent to experiment-independent to herd-independent schemes, the mean and slope bias increased. Given that proxy DMI phenotypes for cows would need to be almost entirely generated in herds having no DMI or training data of their own, herd-independent cross-validation assessments of predictive performance should be emphasized. Hence, more research on predictive algorithms suitable for broader cross-validation schemes and a more earnest effort on calibration of spectrophotometers against each other should be considered.

Exploring Potential Ethnic Bias Among MMPI-3 Scales in Assessing Personality Psychopathology.

This study examined statistical bias in the measurement of personality psychopathology in the Latinx population using the Minnesota Multiphasic Personality Inventory-3 (MMPI-3). Data were extracted from two studies that yielded a composite data set of 103 White individuals and 250 Latinx individuals. All participants were administered the MMPI-2-Restructured Form-Extended Battery (MMPI-2-RF-EX) or MMPI-3 and the Personality Inventory for the DSM-5 Short Form (PID-5-SF). First, we conducted correlation analyses between theoretically overlapping scales of the PID-5-SF and the MMPI-3 among White and Latinx individuals. The majority of theoretically associated scales were found to be at least moderately associated in the total sample. In addition, Steiger's z-tests indicated that correlations were similar in magnitude across the White and Latinx ethnic groups. Hierarchical regression subsequently determined the presence of slope and/or intercept bias. Only one analysis (the MMPI-3 Anger Proneness prediction of PID-5-SF Negative Affectivity) indicated statistically significant intercept bias. No evidence of slope bias was found. In other words, these analyses indicated that the vast majority of the relationships between MMPI-3 scales and associated personality psychopathology constructs (as measured by the PID-5-SF) remained consistent across both ethnic groups. Overall, the results supported the appropriate cross-cultural use of the MMPI-3 to assess personality psychopathology.

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 to develop different models to be used for different scenarios, depending on available data. An international dataset 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 dataset. 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 dataset: 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 dat sets with either 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 overprediction and underprediction of CO2 production when the models were evaluated on the GF and the RC test datasets, respectively. To address this bias, the 3 models were evaluated on a modified test dataset, 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 datasets. With 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.

A meta-analysis of the relationship between milk protein production and absorbed amino acids and digested energy in dairy cattle

Milk protein production is the largest draw on AA supplies for lactating dairy cattle. Prior NRC predictions of milk protein production have been absorbed protein (MP)-based and used a first-limiting nutrient concept to integrate the effects of energy and protein, which yielded poor accuracy and precision (root mean squared error [RMSE] >21%). Using a meta-data set gathered, various alternative equation forms considering MP, absorbed total EAA, absorbed individual EAA, and digested energy (DE) supplies as additive drivers of production were evaluated, and all were found to be superior in statistical performance to the first limitation approach (RMSE = 14%-15%). Inclusion of DE intake and a quadratic term for MP or absorbed EAA supplies were found to be necessary to achieve intercept estimates (nonproductive protein use) that were similar to the factorial estimates of the National Academies of Sciences, Engineering, and Medicine (2021). The partial linear slope for MP was found to be 0.409, which is consistent with the observed slope bias of -0.34 g/g when a slope of 0.67 was used for MP efficiency in a first-limiting nutrient system. Replacement of MP with the supplies of individual absorbed EAA expressed in grams per day and a common quadratic across the EAA resulted in unbiased predictions with improved statistical performance as compared with MP-based models. Based on Akaike's information criterion and biological consistency, the best equations included absorbed His, Ile, Lys, Met, Thr, the NEAA, and individual DE intakes from fatty acids, NDF, residual OM, and starch. Several also contained a term for absorbed Leu. These equations generally had RMSE of 14.3% and a concordance correlation of 0.76. Based on the common quadratic and individual linear terms, milk protein response plateaus were predicted at approximately 320 g/d of absorbed His, Ile, and Lys; 395 g/d of absorbed Thr; 550 g/d of absorbed Met; and 70 g/d of absorbed Leu. Therefore, responses to each except Leu are almost linear throughout the normal in vivo range. De-aggregation of the quadratic term and parsing to individual absorbed EAA resulted in nonbiological estimates for several EAA indicating over-parameterization. Expression of the EAA as g/100 g total absorbed EAA or as ratios of DE intake and using linear and quadratic terms for each EAA resulted in similar statistical performance, but the solutions had identifiability problems and several nonbiological parameter estimates. The use of ratios also introduced nonlinearity in the independent variables which violates linear regression assumptions. Further screening of the global model using absorbed EAA expressed as grams per day with a common quadratic using an all-models approach, and exhaustive cross-evaluation indicated the parameter estimates for BW, all 4 DE terms, His, Ile, Lys, Met, and the common quadratic term were stable, whereas estimates for Leu and Thr were known with less certainty. Use of independent and additive terms and a quadratic expression in the equation results in variable efficiencies of conversion. The additivity also provides partial substitution among the nutrients. Both of these prevent establishment of fixed nutrient requirements in support of milk protein production.

Predicting orthophosphate in feces and manure from dairy cattle

Dairy cattle excreta are a valuable source of orthophosphate (Ortho-P), an inorganic form of phosphorus (P) that is readily available for microorganisms, plant growth, and development. There is, however, a growing environmental concern about the potential negative environmental impact of excessive amounts of Ortho-P excretion, which can lead to the eutrophication of water bodies. As a result, the development of mathematical equations to quantify and manage Ortho-P excretion on dairy farms could prove valuable for environmental sustainability. This study aimed to use literature data to develop empirical predictions for Ortho-P (g/kg dry matter [DM]) excretion using total P (TP [g/kg DM]) content of dairy cattle feces (Ortho-Pf) and manure (Ortho-Pm). Data sets from studies that evaluated and characterized the different forms of P in feces and manure from dairy cattle were compiled. After outlier exclusion, the final retained database for feces included 37 treatment means from 4 published papers while the manure comprised 23 treatment means from 7 published papers. A linear-mixed model was used to develop the predictive equations, incorporating the random effect of the study. A leave-one-out cross-validation procedure was used to evaluate the predictive ability of the developed models, whereby studies were regarded as folds. The fecal equation was determined as Ortho-Pf (g/kg DM) = −2.447 (0.572) + 0.966 (0.083) × TP (g/kg DM) (R2 = 0.79) and resulted in a root mean square prediction error as a percentage of mean observed value (RMSPE, %) of 32.8% and error due to random sources of 97.6%. Additionally, the manure equation was determined as Ortho-Pm (g/kg) = −0.204 (0.446) + 0.590 (0.065) × TP (g/kg) (R2 = 0.77) and had an RMSPE of 43.3% with a random error of 93.9%. Both models revealed minimal mean and slope biases on feces and manure data. Findings suggest that these sets of equations can be used to estimate excreted Ortho-P from total excreted P, helping nutritionists and farmers to understand the impact of dietary P changes on the environment. Further, these equations can be incorporated into extant models such as the Cornell Net Carbohydrate and Protein System (CNCPS) to aid in understanding and mitigating P and Ortho-P excretion from dairy cattle and to clarify the portion of P that migrates more rapidly into watersheds.

Methodology adjusting for least squares regression slope in the application of multiplicative scatter correction to near‐infrared spectra of forage feed samples

AbstractScatter corrections are commonly applied to refine near‐infrared (NIR) spectra. The aim of this study is to assess the impact of measurement errors when using ordinary least squares (OLS) for multiplicative scatter correction (MSC). Any measurement errors attached to the set‐mean spectrum may attenuate the OLS slope and that in turn will affect the estimate of the intercept and the adjustment of the spectra when using MSC methods to mitigate scattering. A corrected least squares slope may be used instead to prevent this problem, although the impact of this approach on the final outcome will depend on the relative size of the measurement errors in the individual spectra and the set‐mean spectrum. The errors‐in‐variables or type II regression model (also known as Deming regression) and its special cases, major axis (MA) and reduced major axis (RMA), are discussed and illustrated. The extent of OLS slope bias or attenuation is demonstrated as is the resulting MSC spectral distortion. Further modification to the MSC transformation method is also suggested. The influence of scattering correction (by MSC, standard normal variate (SNV) and detrending) and of using the maximum likelihood estimate of the slope for MSC on the prediction of chemical composition of Lucerne herbage from NIR spectra was assessed. The predictive performance was slightly improved by the use of scattering corrections with fairly minor differences among methods. Nonetheless, it seems well worth considering the use of type II regression models for assessing MSC application aiming at improving the goodness of prediction from NIR spectra.

Robust and Precise Quantitative Real-Time Polymerase Chain Reaction with an Amplification Efficiency-Aware Reaction Kinetics Model

Quantitative real-time PCR (qPCR) is a method extensively used in nucleic acid testing for plants and animals. During the coronavirus disease 2019 (COVID-19) pandemic, high-precision qPCR analysis was urgently needed since quantitative results obtained from conventional qPCR methods were not accurate and precise, causing misdiagnoses and high rates of false-negative. To achieve more accurate results, we propose a new qPCR data analysis method with an amplification efficiency-aware reaction kinetics model (AERKM). Our reaction kinetics model (RKM) mathematically describes the tendency of the amplification efficiency during the whole qPCR process inferred by biochemical reaction dynamics. Amplification efficiency (AE) was introduced to rectify the fitted data so as to match the real reaction process for individual tests, thus reducing errors. The 5-point 10-fold gradient qPCR tests of 63 genes have been verified. The results of a 0.9% slope bias and an 8.2% ratio bias using AERKM exceed 4.1 and 39.4%, respectively, of the best performance of existing models, which demonstrates higher precision, less fluctuation, and better robustness among different nucleic acids. AERKM also provides a better understanding of the real qPCR process and gives insights into the detection, treatment, and prevention of severe diseases.

Evaluation of Intergovernmental Panel on Climate Change (IPCC) equations to predict enteric methane emission from lactating cows fed Mediterranean diets

The study aimed to evaluate Intergovernmental Panel on Climate Change (IPCC) Tier 2 (2006 and 2019) to predict enteric CH4 emissions from lactating cows fed Mediterranean diets. The effects of the CH4 conversion factor (Ym; CH4 energy loss as a percentage of gross energy intake) and digestible energy (DE) of the diet were evaluated as model predictors. A data set was created using individual observations derived from 3 in vivo studies on lactating dairy cows housed in respiration chambers and fed diets typical of the Mediterranean region based on silages and hays. Five models using different Ym and DE were evaluated following a Tier 2 approach: (1) average values of Ym (6.5%) and DE (70%) from IPCC (2006); (2) average value of Ym (5.7%) and DE (70.0%) from IPCC (2019; 1YM); (3) Ym = 5.7% and DE measured in vivo (1YMIV); (4) Ym = 5.7 or 6.0%, depending on dietary NDF, and DE = 70% (2YM); and (5) Ym = 5.7 or 6.0%, depending on dietary NDF, and DE measured in vivo (2YMIV). Finally, a Tier 2 model for Mediterranean diets (MED) was derived from the Italian data set (Ym = 5.58%; DE = 69.9% for silage-based diets and 64.8% for hay-based diets) and validated on an independent data set of cows fed Mediterranean diets. The most accurate models tested were 2YMIV, 2YM, and 1YMIV with predictions of 384, 377, and 377 (g of CH4/d), respectively, versus the in vivo value of 381. The most precise model was 1YM (slope bias = 1.88%; r = 0.63). Overall, 1YM showed the highest concordance correlation coefficient value (0.579), followed by 1YMIV (0.569). Cross-validation on an independent data set of cows fed Mediterranean diets (corn silage and alfalfa hay) resulted in concordance correlation coefficient of 0.492 and 0.485 for 1YM and MED, respectively. The prediction of MED (397) was more accurate than 1YM (405) in comparison with the corresponding in vivo value of 396 g of CH4/d. The results of this study showed that the average values proposed by IPCC (2019) can adequately predict CH4 emissions from cows fed typical Mediterranean diets. However, the use of specific factors for the Mediterranean area, such as DE, improved the accuracy of the models.

Development of a dynamic model for prediction of energy in milk protein, lactose, fat, and enteric methane emissions in goats based on energy balance and indirect calorimetry studies.

Feed costs are overwhelmingly the largest expense for dairy producers. Thus, improving milk production efficiency (milk fat and protein are the main incomes for farmers) is of great economic importance in the dairy industry. The main objective of this study was to develop a dynamic energy partitioning model to describe and quantify how dietary energy from carbohydrate, protein, and fat is transferred to milk (protein, lactose, and fat) in dairy goats. In addition, due to increasing worldwide concerns regarding livestock contribution to global warming, methane (CH4) emission was quantified. For modeling purposes, 158 individual goat observations were used and randomly split into 2/3 for model development and 1/3 for internal evaluation. For external evaluation, 20 different energy balance studies from the literature (77 observations) were evaluated. The Root Mean Square Prediction Error (RMSPE) was 13.2% for loss of energy in CH4, 16.8% for energy in fat, 19.4% for energy in protein, and 22.3 energy in lactose. Mean bias was around zero for all variables and the slope bias was zero for milk energy in lactose, close to 1% for milk fat (1.01%), and around 3% and 10% for protein and CH4, respectively. Random bias was greater than 85% for energy in CH4 and milk energy components indicating non-systematic errors and that the equation in the model fitted the data properly. Analyses of residuals appeared to be randomly distributed around zero. Slopes of regression lines for residuals vs. predicted were positive for milk fat energy, zero for lactose, and negative for milk energy in protein and CH4. This model suggested for use with mixed diets and by-products to obtain balanced macronutrient supply, methane emissions, and milk performance during mid lactation could be an interesting tool to help farmers simulate scenarios that increase milk fat and protein, evaluate CH4 emissions, without the costs of running animal trials.

Use of a Vanadate Oxidation Conjugated Bilirubin Assay to Reduce Test Cancellations Resulting from Hemolyzed Specimens in Pediatric Patients.

We sought to replace the highly hemolysis-susceptible diazo conjugated bilirubin (Bc) assay with the more robust vanadate oxidation method and determine its impact on test cancellation in the pediatric population. Analytical validation of the Randox vanadate assay and comparison with the Roche diazo method were performed. The frequency of pediatric sample cancellation because of hemolysis was compared between the diazo and vanadate methods by retrospective analysis of clinical test data. The vanadate assay demonstrated no clinically significant interference from hemolysis up to a hemolysis index of 1,300 (approximately 13 g/L hemoglobin). There was a strong correlation with the diazo method (r2 = 0.97) but with a positive slope bias of 1.27. Implementing the vanadate method resulted in a significantly lower proportion of pediatric samples cancelled because of hemolysis compared with the diazo method (0.6% of 688 patients vs 30.6% of 10,464 patients, respectively; P < .001), with a 0.6% (n = 513) vs 43.2% (n = 6,464) reduction in test cancellations (P < .001) for children younger than 6 months of age. The vanadate method showed robust performance against hemolysis. Its implementation resulted in a significant decrease in pediatric tests cancelled because of hemolysis.

Prediction of methane per unit of dry matter intake in growing and finishing cattle from the ratio of dietary concentrations of starch to neutral detergent fiber alone or in combination with dietary concentration of ether extract.

Previous research demonstrated that a fixed value of 0.2433 (SE = 0.0134) Mcal of CH4/kg of dry matter intake (DMI) could be used to predict CH4 production with accuracy and precision on par with similar equations in the literature. Slope bias was substantially less for the fixed-coefficient equation than noted for the other DMI- or gross energy intake (GEI)-based equations, but mean bias was substantially greater, presumably reflecting the failure of the fixed-coefficient approach to account for dietary factors that affect CH4 production. In this article, we report on the use of the dietary ratio of concentrations of starch to neutral detergent fiber (NDF) and dietary ether extract (EE) concentration to improve the accuracy and precision of the fixed-coefficient equation. The same development data set used to create the fixed-coefficient equation was used in the present study, which included 134 treatment means from 34 respiration calorimetry studies. Based on stepwise regression with dietary NDF, starch, crude protein, EE, and the starch:NDF ratio as possible dependent variables, the starch:NDF ratio and EE were the only dietary variables selected (P ≤ 0.15). The study-adjusted relationship with the starch:NDF ratio (r2 = 0.673; root mean square error [RMSE] = 0.0327) was: Mcal of CH4/kg of DMI = 0.2883 − 0.03474 × starch:NDF; whereas the relationship with a model that included both starch:NDF ratio and dietary EE (r2 = 0.738; RMSE = 0.0315) was: Mcal of CH4/kg of DMI = 0.3227 − 0.0334 × starch:NDF − 0.00868 × % EE. A previously published independent data set with 129 treatment means from 30 respiration calorimetry studies was used to evaluate these two equations, along with two additional equations in which g/d of CH4 was predicted directly from DMI, starch:NDF ratio, and/or dietary EE. The two Mcal of CH4/kg of DMI equations had superior fit statistics to the previously published 0.2433 Mcal of CH4/kg of DMI equation, with a substantial decrease in mean bias and improved concordance correlation coefficients. Moreover, the Mcal of CH4/kg of DMI equations resulted in improved fit relative to direct prediction of g/d of CH4 from DMI, the starch:NDF ratio, and % EE. Based on these results, further evaluation of the dietary ratio of starch-to-NDF concentrations and EE concentration to predict methane production per unit DMI in beef cattle is warranted.

Odd-chain fatty acids as an alternative method to predict ruminal microbial nitrogen flow of feedlot Nellore steers fed grain-based diets supplemented with different nitrogen sources.

This study aimed to evaluate the use of total odd-chain fatty acids (OCFA) as a marker to estimate microbial nitrogen flow (MicN) and calculate the efficiency of microbial nitrogen synthesis (EMNS) in Nellore steers fed high-concentrate diets supplemented with different nitrogen supplements (NS). Ruminally and duodenally cannulated Nellore steers (n = 6; 354 ± 12 kg) were used in a 6 × 6 repeated switchback design balanced for residual effects. Treatments were arranged in a 3 × 3 factorial of three nitrogen (N) supplements (urea plus soybean meal; corn gluten meal; dried distillers' grains plus solubles) and three microbial markers (OCFA; double-labeled urea, 15N; microbial nucleic acid bases, MNAB). The total mixed ration was composed of fresh chopped sugarcane as the forage source in an 83:17 concentrate: forage ratio (dry matter basis). Linear regression was used to develop predictions of MicN from OCFA using 15N and MNAB as response variables. Microbial N flow was underestimated by the MNAB marker compared to 15N. Neither NS nor their respective interactions with the marker methods (MM) affected MicN or EMNS (P > 0.05). However, MicN was different for 15N and MNAB (P > 0.001 for both treatments). Marker methods affected EMNS in all energetic bases (total digestible carbohydrates P < 0.001; rumen-fermentable carbohydrates P < 0.001; organic matter truly degradable in the rumen P < 0.001). Equations that utilized OCFA as a regressor to predict MicN under different MM resulted in good fits of the data as observed by the coefficient of determination (R2; 15N = 0.78; MNAB = 0.69). Microbial N flow estimated from OCFA was overpredicted (15N by 7.46%; MNAB by 4.30%) compared with observed values. The OCFA model presented a small slope bias when methodological validation was applied (15N = 0.96%; MNAB = 3.90%), ensuring reliability of the proposed alternative method. Based on the conditions of this experiment, OCFA may be a suitable alternative to other methods that quantify MicN under different dietary conditions.

Evaluation of assumptions made by Hygroscopic Tandem Differential Mobility Analyzer inversion routines

The Hygroscopic Tandem Differential Mobility Analyzer (H-TDMA) measures the hygroscopicity of atmospheric particles, and many atmospheric processes that change this hygroscopicity also change the atmospheric size distribution. Two assumptions made during H-TDMA inversion create spurious hygroscopic trends as a function of the changing inlet size distribution. These two assumptions—that the particles exiting the first Differential Mobility Analyzer (DMA1) are singly charged and that the inlet size distribution has a slope of zero (flat)— generate Multi-Charge Dispersion (MCD) bias and Slope bias, respectively. First, we use a model, named TAO, to show that the inlet size distribution could theoretically change the measured ammonium sulfate hygroscopicity by 10%–20% as a function of diameter or experimental time with no change in relative humidity. Secondly, we show experimentally that aerosol emitted from the flaming combustion of grass creates MCD bias. In this experiment, we measure the CPC response of the first three charges and invert these responses using a new routine named Junior. Junior's inversion of each charge shows that one growth factor distribution describes all measured diameters (no growth dependence on diameter). As in the modeling study above, previous publications of this aerosol system, using traditional inversion assumptions, report a decrease in hygroscopicity as DMA1 diameter increases. Unlike traditional inversions, Junior's inversion does not assume the particles are singly charged nor does it make the flat inlet size distribution assumption. Instead, both the inlet size distribution and each charge's CPC response are measured quantities. Thus, the discrepancy between our inversion results and previous publications is likely due to the traditional inversion routine assumptions. This underscores the importance for accounting for Slope and MCD bias during inversions. Experimental results should be carefully analyzed when reporting hygroscopic trends with respect to diameter or experimental time when using the traditional inversion assumptions.

Comparison of Molly and Karoline models to predict methane production in growing and dairy cattle

Numerous empirical and mechanistic models predicting methane (CH4) production are available. The aim of this work was to evaluate the Molly cow model and the Nordic cow model Karoline in predicting CH4 production in cattle using a data set consisting of 267 treatment means from 55 respiration chamber studies. The dietary and animal characteristics used for the model evaluation represent the range of diets fed to dairy and growing cattle. Feedlot diets and diets containing additives mitigating CH4 production were not included in the data set. The relationships between observed and predicted CH4 (pCH4) were assessed by regression analysis using fixed and mixed model analysis. Residual analysis was conducted to evaluate which dietary factors were related to prediction errors. The fixed model analysis showed that the Molly predictions were related to the observed data (± standard error) as CH4 (g/d) = 0.94 (±0.022) × pCH4 (g/d) + 31 (±6.9) [root mean squared prediction error (RMSPE) = 45.0 g/d (14.9% of observed mean), concordance correlation coefficient (CCC) = 0.925]. The corresponding equation for the Karoline model was CH4 (g/d) = CH4 (g/d) = 0.98 (±0.019) × pCH4 (g/d) + 7.0 (±6.0) [RMSPE = 35.0 g/d (11.6%), CCC = 0.953]. Proportions of mean squared prediction error attributable to mean and linear bias and random error were 10.6, 2.2, and 87.2% for the Molly model, and 1.3, 0.3, and 98.6% for the Karoline model, respectively. Mean and linear bias were significant for the Molly model but not for the Karoline model. With the mixed model regression analysis RMSPE adjusted for random study effects were 10.9 and 7.9% for the Molly model and the Karoline model, respectively. The residuals of CH4 predictions were more strongly related to factors associated with CH4 production (feeding level, digestibility, fat concentrations) with the Molly model compared with the Karoline model. Especially large mean (underprediction) and linear bias (overprediction of low digestibility diets relative to high digestibility diets) contributed to the prediction error of CH4 yield with the Molly model. It was concluded that both models could be used for prediction of CH4 production in cattle, but Karoline was more accurate and precise based on smaller RMSPE, mean bias, and slope bias, and greater CCC. The importance of accurate input data of key variables affecting diet digestibility is emphasized.

Predicting metabolizable energy from digestible energy for growing and finishing beef cattle and relationships to the prediction of methane.

Reliable predictions of metabolizable energy (ME) from digestible energy (DE) are necessary to prescribe nutrient requirements of beef cattle accurately. A previously developed database that included 87 treatment means from 23 respiration calorimetry studies has been updated to evaluate the efficiency of converting DE to ME by adding 47 treatment means from 11 additional studies. Diets were fed to growing-finishing cattle under individual feeding conditions. A citation-adjusted linear regression equation was developed where dietary ME concentration (Mcal/kg of dry matter [DM]) was the dependent variable and dietary DE concentration (Mcal/kg) was the independent variable: ME = 1.0001 × DE – 0.3926; r2 = 0.99, root mean square prediction error [RMSPE] = 0.04, and P < 0.01 for the intercept and slope. The slope did not differ from unity (95% CI = 0.936 to 1.065); therefore, the intercept (95% CI = −0.567 to −0.218) defines the value of ME predicted from DE. For practical use, we recommend ME = DE – 0.39. Based on the relationship between DE and ME, we calculated the citation-adjusted loss of methane, which yielded a value of 0.2433 Mcal/kg of dry matter intake (DMI; SE = 0.0134). This value was also adjusted for the effects of DMI above maintenance, yielding a citation-adjusted relationship: CH4, Mcal/kg = 0.3344 – 0.05639 × multiple of maintenance; r2 = 0.536, RMSPE = 0.0245, and P < 0.01 for the intercept and slope. Both the 0.2433 value and the result of the intake-adjusted equation can be multiplied by DMI to yield an estimate of methane production. These two approaches were evaluated using a second, independent database comprising 129 data points from 29 published studies. Four equations in the literature that used DMI or intake energy to predict methane production also were evaluated with the second database. The mean bias was substantially greater for the two new equations, but slope bias was substantially less than noted for the other DMI-based equations. Our results suggest that ME for growing and finishing cattle can be predicted from DE across a wide range of diets, cattle types, and intake levels by simply subtracting a constant from DE. Mean bias associated with our two new methane emission equations suggests that further research is needed to determine whether coefficients to predict methane from DMI could be developed for specific diet types, levels of DMI relative to body weight, or other variables that affect the emission of methane.

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

Simple SummaryOur study evaluated seven DMI models for dairy heifers grouped by their genotypes (Bos taurus or crossbred Bos taurus × Bos indicus) raised under tropical climatic conditions. The HHJ and OFNLin DMI models performed better for Bos taurus heifers, whereas the STA model performed better for crossbred heifers. NRC, HH, QUI, and OFLin DMI models had significant significant slope bias, mean bias, or both.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.

543 Late-Breaking: Development of a Model to Predict Dietary Metabolizable Energy from Digestible Energy in Beef Cattle

Abstract We aimed to assess whether predicting the metabolizable energy (ME) to digestible energy (DE) ratio (MDR), rather than a prediction of ME with DE, is feasible and to develop a model equation to predict MDR in beef cattle. For this, we constructed a literature database based on published data. A meta-analysis was conducted with 306 means from 69 studies containing both dietary DE and ME concentrations measured by calorimetry to test whether the exclusion of the y-intercept is adequate in the linear relationship between DE and ME. A random coefficient model with study as the random variable was used to develop equations to predict MDR in growing and finishing beef cattle. The developed equations were evaluated with other published equations. The no-intercept linear equation represented the relationship between DE and ME more appropriately than the equation with a y-intercept. Within our growing and finishing cattle data, the animal’s physiological stage was not a significant variable affecting MDR after accounting for the study effect (P = 0.213). The mean (± SE) of MDR was 0.849 (± 0.0063). Two linear equations with the dry matter intake and content of several dietary nutrients were developed to predict MDR. When using these equations, the observed ME was predicted with high precision (R2 = 0.92). The model accuracy was also high, as shown by the high concordance correlation coefficient (&amp;gt; 0.95) and small root mean square error of prediction (RMSEP), less than 5% of the observed mean. Moreover, a significant portion of the RMSEP was due to random bias (&amp;gt; 93%), without mean or slope bias (P &amp;gt; 0.05). We concluded that dietary ME in beef cattle could be accurately estimated from dietary DE and its conversion factor, MDR, using the two equations developed in this study.

284 Genetic Selection Through Ecofeed Index Reduces Carbon Footprint in Heifers

Abstract Efficient feed conversion by ruminants has a direct impact on the animal’s carbon footprint. The objective of this study was to determine the effect of using the EcoFeed® index by STgenetics® to reduce methane production. The EcoFeed® index is an integrated approach to genetic selection based on progeny testing of females for residual feed intake to identify animals who consume less feed while maintaining production compared to their herd mates to increase profitability and global sustainability. EcoFeed® is moderately heritable and uncorrelated with traits currently selected for in dairy cows. A dataset containing phenotypic and genomic information on 5,441 heifers was used for the analysis. Heifers were divided into low, medium and high EcoFeed® classes based on ±0.5 SD from the mean. To estimate methane emissions, 17 mathematical models were collated from the literature. All models were subjected to evaluation of performance using an independent dataset containing 458 individual-animal methane production measurements. The model recommended by Intergovernmental Panel for Climate Change had the lowest root mean square prediction error with 1% mean bias and 3.37% slope bias. This model was used to calculate emissions from low and high EcoFeed® heifers. In addition, an average of all model results was computed and used to compare different classes of heifers. Based on dietary information and feed intake, low and high EcoFeed® heifers were estimated to produce 194 and 164 g methane/d, respectively, using the selected model. Using model averages, the estimated methane production was 186 and 162 g methane/d, for low and high EcoFeed® heifers, respectively. The reduction in emission between low and high EcoFeed® classes was 12.6 to 15.4%. If savings from crop production were to be considered, there will be further reductions in the carbon footprint. Therefore, genomic selection is a powerful tool to reduce carbon footprint in ruminants.