Prediction Of Dry Matter Intake Research Articles

PSX-22 Boosting techniques for prediction of dry matter intake of cattle in confinement settings and SHAP analysis on the models’ output

Abstract Measurement of individual dry matter intake (DMI) is critical to improving the management of the beef herd and is necessary to determine the feed efficiency of individual cattle. Combining machine learning models and proxies for DMI (e.g., water intake, body weight, climatic variables) has the potential to replace the need for dedicated feed intake equipment. It would also open the possibility of measuring DMI in situations like grazing where quantifying DMI directly is not possible. In this study, we examined the relationship between DMI, animal variables and environmental variables. In this study, we developed and tested machine learning models based boosting techniques to develop approaches to predict DMI in cattle, using the same data we have previously published results from Random Forest techniques in the past. The dataset included both bulls and steers. The developed models include both regular boosting regression models and random effects boosting models. The regular boosting technique models such as gradient boosting regression, light gradient boosting regression, extreme gradient boosting regression. Similarly, random effects boosting machine learning model Gaussian process boosting (GPBoost) was developed to consider the random effects associated with the intake behavior of the animals. The extreme gradient boosting model outperformed other regular boosting regression models with a r2 of 0.81 and 0.48 on training and testing datasets respectively. However, it does not consider the random effects associated with the longitudinal nature of the animal intake and weather variables and prone to overfitting. GPBoost model variants were tested and developed to avoid overfitting and to incorporate a random effects model, which we propose as the overall best performing model. The GPBoost generalized well on the training and testing datasets with r2 scores of 0.55 and 0.54 respectively. To further understand the model’s performance on the unseen dataset, we used SHAP analysis on the GPBoost. The SHAP analysis revealed that water intake, full body weight and age of the animals contributed significantly to predicting unseen data.

194 Evaluation of a method to optimize diets of individual dairy cows to maximize income over feed cost

Abstract Recent optimization methods have demonstrated the potential to maximize marginal profits in beef (Marques et al., 2020) and dairy (Campos et al., 2023) by optimizing diet formulation. These methods present an opportunity to explore how diets could be optimized for individual dairy cows, such as in a closed-loop precision feeding system. Therefore, our objectives were to 1) compare individual income over feed costs (IOFC), calculated with observed dry matter intake (DMI), milk yield (MY), milk protein and milk fat, with a predicted IOFC for the same diet and cows using the NASEM (2021) model, and 2) optimize the inclusion rate of ingredients in the diet to maximize predicted IOFC. Previously, 20 multiparous (lactation 2 through 4) and 9 primiparous cows, ranging from 22 to 472 d in milk (DIM), were housed in a free-stall pen at the Ontario Dairy Research Centre (Canada) equipped with sensors to record DMI, body weight (BW; walk-over-weight) and body condition score (BCS; DeLavel 3D camera), in addition to daily MY, weekly milk component tests, and 4 X/d respiratory gas exchange measurements (GreenFeed, C-Lock Inc, USA) for estimation of daily energy flows (Kedzierski, 2020). Weekly means for each cow were used to evaluate IOFC in this study (n = 116). Each IOFC was calculated as milk revenue minus total feed cost. Milk revenue was calculated from milk fat, protein and other solids, consistent with pricing on Canadian dairy farms. The observed IOFC was calculated using the observed MY, milk components and DMI, and the same diet formulation and costs for all cows, whereas the predicted IOFC estimated these values using the NASEM (2021) model with all available observed animal inputs (e.g., BW, BCS, DIM). However, the observed DMI and MY had to be provided initially for the model, as the DMI prediction equation requires a target MY, but the prediction of MY requires DMI. Final predicted DMI and MY values were used in the calculation of predicted IOFC. The differential evolution algorithm from the ‘SciPy’ package (v 1.11.4) in python (v 3.12.0) was used to maximize IOFC by adjusting the inclusion of wheat straw, alfalfa silage, corn silage and high moisture corn grain, while fixing minerals to their original proportions. Overall, the concordance correlation coefficient (CCC) indicated moderate agreement (0.62) between the predicted IOFC and observed IOFC and good agreement (0.99) between the predicted IOFC and the optimized IOFC, suggesting the optimization method was able to select diets to meet a similar objective. Results indicate that a strategy is needed to minimize the residual error between predicted and observed values for individual cows, such as algebraic reparameterization of the NASEM model, before using an optimization method to automatically formulate diets.

302 Incremental Variables in Predicting Beef Cattle Dry Matter Intake

Abstract The great strain of increasing climate variability on animal agriculture necessitates improvement of current production processes, the development of methods to measure individual feed and water intakes, and the improvement of feed and water use efficiency in cattle. The use of machine learning (ML) approaches in predicting beef cattle dry matter intake (DMI) have provided groundwork for improvement of beef cattle production using big data and ML. However, despite wide variation between daily feed intakes, typical variables in animal intake-based ML approaches are not incremental, not allowing for daily prediction of intake. Our work introduces first-differenced intake, climate, and animal performance variables into a ML algorithm to predict beef cattle DMI and increase the immediacy of prediction applications. A total of 745 animals were evaluated in eight test groups from 11/25/2019 to 9/2/2021 in a dry lot equipped with In-Pen Weighing Positions and Feed and Water-Intake Nodes. Relationships among average daily gain (ADG), DMI, residual feed intake (RFI), water intake (WI), residual water intake (RWI), animal performance variables, and environmental variables at the individual animal level were investigated on a first test group of 125 Angus bulls and 53 crossbred steers. Out-Of-Bag root mean square error (OOB RMSE), an internal measure of Random Forest (RF) prediction accuracy, was used as a measure of error between model-predicted and observed DMI. Input variables were first-differenced by differencing a variable’s value between sequential test days for the entire test period. Two models were run using the RF package in R (ntree = 500, mtry = 11), with the first model (M1) using a standard test-train data split and the second model (M2) using all first-differenced and non-incremental observations to train the model. The model incorporating the test-train split performed marginally better than M2 (OOB RMSE = 1.39 and 1.44, respectively), indicating that first-differenced input variables can be used to predict daily individual DMI by within 1.39 kg by using M1. Using percent-increase in MSE (%IncMSE), first-differenced variables were found to be of less overall predictive value than non-incremental variables, with incremental daily gain and first-differenced water intake being ~55 and 45% less predictive than ADG and overall water intake in both M1 and M2. Though first-differenced variables were less predictive than non-incremental variables, inclusion of first-differenced variables allowed for prediction of individual DMI by within 1.39 kg. Our work demonstrates critical preliminary steps in the development of a deployable algorithm for the efficient prediction of DMI and the improvement of beef cattle intake efficiencies for the continued sustainability of animal agriculture.

Modeling the relationship between heat stress, feed intake, and day relative to calving in nonlactating dairy cows

Heat stress (HS) during the dry period can affect animal welfare, health, dry matter intake (DMI), and milk production in the subsequent lactation, which will negatively affect the profitability of dairy farms. In this study, the objective was to model the changes in DMI in pregnant nonlactating heat-stressed dairy cows with or without access to evaporative cooling systems. A database composed of individual DMI records from 244 pregnant nonlactating heat-stressed dairy cows from 7 experiments averaging from -35 (-42 to -21 d) to -1 d relative to calving (DRC) and housed in environmental conditions in which temperature-humidity index (THI) ranged from 58.4 to 83.3, with or without access to evaporative cooling systems was built. Generalized additive mixed-effects models were used to describe the relationships of DMI with HS and DRC. Changes in DMI with the increase in THI and the progression of pregnancy in cows with or without evaporative cooling systems were estimated using differential equations. On average, cows housed in barns without evaporative cooling systems had a reduction in DMI of 1.30 kg/d and increased rectal temperature in 0.22°C in relation to those housed in barns with evaporative cooling systems. Dry matter intake decreased as THI increased, but the reduction was greater for non-cooled cows as THI values increased. In addition, regardless of the THI, DMI started to decrease at DRC = -14 for cooled cows, while for non-cooled cows it already started at DRC = -30 relative to the previous days evaluated. The intensity of the reduction was lesser for cows that had access to evaporative cooling systems and/or were in the dry period in May-June as compared with those that were in the dry period in July-August or September-October. The models generated in this study, which include environmental variables, should lead to more accurate predictions of DMI during heat stress that can be used to formulate diets to meet the needs of the late pregnant cow because it is possible to predict changes in DMI as the heat load and DRC change. Such models are also expected to help dairy nutritionists to decide when and how to apply the dietary strategies available to attenuate the reductions in DMI with the intensity of heat stress and progression of pregnancy.

External evaluation of the prediction equation for milk fat yield by the 2021 NASEM dairy model using data from eastern Canadian dairy herds

In 2021, the National Academies of Sciences, Engineering, and Medicine (NASEM) issued an equation to predict milk fat yield using dairy cow characteristics and diet composition as input variables. This model was evaluated externally using a data set composed of 541 feed and production records obtained from 23 eastern Canadian dairy herds. The use of the developed equation requires the prediction of dry matter intake. Cow intake used in the model assessment has been obtained by NASEM equations based on (1) animal factors, or (2) a combination of feed composition and animal factors. The prediction of milk fat yield was shown to be accurate. The best prediction was obtained using intake estimated based solely on animal factors (concordance correlation coefficient = 0.68).

Dry Matter Intake Prediction from Milk Spectra in Sarda Dairy Sheep

Individual dry matter intake (DMI) is a relevant factor for evaluating feed efficiency in livestock. However, the measurement of this trait on a large scale is difficult and expensive. DMI, as well as other phenotypes, can be predicted from milk spectra. The aim of this work was to predict DMI from the milk spectra of 24 lactating Sarda dairy sheep ewes. Three models (Principal Component Regression, Partial Least Squares Regression, and Stepwise Regression) were iteratively applied to three validation schemes: records, ewes, and days. DMI was moderately correlated with the wavenumbers of the milk spectra: the largest correlations (around ±0.30) were observed at ~1100-1330 cm-1 and ~2800-3000 cm-1. The average correlations between real and predicted DMI were 0.33 (validation on records), 0.32 (validation on ewes), and 0.23 (validation on days). The results of this preliminary study, even if based on a small number of animals, demonstrate that DMI can be routinely estimated from the milk spectra.

Evaluation of a Model (RUMINANT) for Prediction of DMI and CH4 from Tropical Beef Cattle.

Simulation models represent a low-cost approach to evaluating agricultural systems. In the current study, the precision and accuracy of the RUMINANT model to predict dry matter intake (DMI) and methane emissions from beef cattle fed tropical diets (characteristic of Colombia) was assessed. Feed intake (DMI) and methane emissions were measured in Brahman steers housed in polytunnels and fed six forage diets. In addition, DMI and methane emissions were predicted by the RUMINANT model. The model's predictive capability was measured on the basis of precision: coefficients of variation (CV%) and determination (R2, percentage of variance accounted for by the model), and model efficiency (ME) and accuracy: the simulated/observed ratio (S/O ratio) and slope and mean bias (MB%). In addition, combined measurements of accuracy and precision were carried out by means of mean square prediction error (MSPE) and correlation correspondence coefficient (CCC) and their components. The predictive capability of the RUMINANT model to simulate DMI resulted as valuable for mean S/O ratio (1.07), MB% (2.23%), CV% (17%), R2 (0.886), ME (0.809), CCC (0.869). However, for methane emission simulations, the model substantially underestimated methane emissions (mean S/O ratio = 0.697, MB% = -30.5%), and ME and CCC were -0.431 and 0.485, respectively. In addition, a subset of data corresponding to diets with Leucaena was not observed to have a linear relationship between the observed and simulated values. It is suggested that this may be related to anti-methanogenic factors characteristic of Leucaena, which were not accounted for by the model. This study contributes to improving national inventories of greenhouse gases from the livestock of tropical countries.

Understanding the relationship between weather variables and intake in beef steers.

The relationship between weather variables and dry matter intake (DMI) in beef steers was examined using daily intake data from 790 beef steers collected through a computer-controlled feeding system in nonsummer months. Daily data were condensed into weekly averages (N = 13,895 steer-weeks). The variables considered to predict DMI (2.50 to 23.60 kg/d) were body weight (197 to 796 kg), dietary net energy for maintenance (NEm; 0.79 to 2.97 Mcal/kg), ambient temperature (-23.73 °C to 21.40 °C), range of temperature (2.79 °C to 19.43 °C), dew point (-27.84 °C to 14.34 °C), wind speed (2.08 to 6.49 m/s), solar radiation (30.8 to 297.1 W/m2), and 2-wk lag (average of previous 2 wk's values) and monthly lag (average of previous 4 wk's values) of each weather variable. Toeplitz variance-covariance structure for repeated measures was used to determine the model to predict DMI, while accounting for the effects of body weight, dietary NEm, and other variables in the model. Two-week lag of ambient temperature interacted (P ≤ 0.005) with 2-wk lag of range of temperature, monthly lag of wind speed, 2-wk lag of solar radiation, and dew point to predict DMI. Interactions (P = 0.0001) between 2-wk lag of range of temperature vs. dew point and monthly lag of wind speed vs. 2-wk lag of solar radiation were also detected. This study reports important weather variables associated with differences in DMI of growing and finishing steers and will help improve the accuracy of DMI prediction equations for beef cattle. Improvements in the accuracy of predicting DMI should give producers better tools to plan and execute efficient feeding management programs. The R2 of the overall model was 0.8891.

Evaluation of the Pampa Corte model for predicting dry matter intake and digestibility by sheep fed tropical forages

The aim of this study was to evaluate the adequacy of the Pampa Corte model associated to an in vitro gas technique as a tool for predicting dry matter (DM) intake and digestibility by sheep fed tropical forages. In addition, the adequacy of the Pampa Corte in predicting DM intake was also compared to NRC (2007). Individual data (n = 154) of observed DM intake and digestibility, as well as of body weight and chemical composition of consumed diet, were obtained from ten digestibility trials conducted with male growing wethers housed in metabolic cages and fed ad libitum tropical grasses associated or not with tropical legumes. Individual observations of each variable were averaged by treatment (n = 25) within trials, which were then compared to values predicted by the Pampa Corte or NRC models through concordance correlation coefficient (CCC) and regression analyses. The prediction of DM intake by Pampa Corte resulted in higher CCC (0.95 vs. 0.68) and R2 values (0.92 vs 0.74) than by NRC. Moreover, the slope of the linear regression between observed and predicted values was not different from 1 for Pampa Corte whereas it was lower than 1 for NRC (P < 0.05). The intercept of both linear relationships was not different from 0. Observed values of DM digestibility were linearly related to those predicted by Pampa Corte model (P < 0.05) with a low CCC (i.e. 0.54). In conclusion, the Pampa Corte model was adequate in predicting DM intake by sheep fed tropical forages, showing a better predictive performance than NRC (2007). However, DM digestibility was underpredicted by the Pampa Corte model.

Predicting dry matter intake in mid-lactation Holstein cows using point-in-time data streams available on dairy farms.

Quantifying dry matter intake (DMI) in lactating dairy cows is important for determining feed efficiency; however, there are no methods for economically quantifying individual cow DMI on dairy farms where cows are group-fed. Attempts have been made to model DMI using cow factors, milk production, milk infrared spectra, and behavioral sensors with reasonable success. Other data streams are available on the farm that may contribute to DMI predictions. In this study, our objective was to model DMI with multiple linear regression using data from a single point-in-time that can easily be accessed on-farm. Candidate predictor variables included cow descriptors, milk yield and composition, milk fatty acid profile, and production and efficiency predicting transmitting abilities (PTA). Observations of DMI were obtained from 350 cows across 6 cohorts using individual feed bunks. The cow to bunk ratio was 2:1, with an overall bunk occupation rate of 32% throughout the day. The following models were developed sequentially with milk data obtained from a single morning milking and other data from the same day: model B (production, metabolic body weight, body condition score, lactation category, and week of lactation), model BC [model B + fatty acid (FA) content], model BY (model B + FA yield), model BPE (model B + production and efficiency PTA), model BYP (model BY + production PTA), model BYE (model BY + efficiency PTA), and model BYPE (model BY + production and efficiency PTA). Outcome variables predicted in these models were the DMI on the previous day or current day relative to the morning milk sample. The predictions for DMI on the previous day outperformed current day DMI in every model for which they were both determined. Addition of milk FA and PTA as candidate predictor variable types to the models resulted in enhanced predictive ability, with incremental enhancements when combined. The most robust model (BYPE) included cow descriptors, protein and FA yields, and PTA for milk and residual feed intake. Model BYPE described 21 to 32% more of the variation in DMI (based on concordance correlation coefficient) than when other common DMI models were applied to the same data set. Overall, reasonable performance of models including single point-in-time cow descriptors, milk and FA production, and production and efficiency PTA commonly available to dairy farmers through dairy herd improvement programs offer an opportunity for on-farm prediction of DMI, yet further improvement may be possible.

4 Developing a Dry Matter Intake Prediction Equation for Grazing Animals based on Real-Time Enteric Emissions Measurements

Abstract Cattle dry matter intake (DMI) is an essential component of calculating cattle stocking rates, determining nutrient requirements, and evaluating grazing efficiency. Cattle DMI and digestion of forages impact enteric greenhouse gas (CO2e) emissions. Enteric emissions include methane (CH4) and carbon dioxide (CO2), that are eructated by ruminants. The amount of methane produced is affected by consumption, quality, and type of feedstuffs. Intake of grazing animals varies on environmental factors and physiological stage. Additionally, increased GHG levels indicate energy loss during the rumen fermentation process. However, there may be a silver lining to enteric GHG emissions to predict DMI of grazing animals since they are highly correlated with DMI and forage composition. There is limited data on the relationship of DMI and GHG on extensive rangeland systems. Obtaining data for beef cattle DMI and enteric emissions on forage-based diets similar to extensive rangelands is needed to develop an equation capable of predicting DMI for grazing cattle. Therefore, our objective was to determine the relationship between CH4, CO2, oxygen (O2), and hydrogen (H2) emissions and DMI of dry beef cows to develop a mathematical model that predicts grazing DMI from enteric emissions. The predictive equation or precision system model (PSM; Menendez et al., 2022) was developed using data from two feeding trials that were conducted using GreenFeed, SmartFeed Pro, and SmartScale (C-Lock Inc. Rapid City, SD). This study was conducted at the SDSU Cottonwood Field Station (Cottonwood, SD). The two feeding trials consisted of dry beef cows (n=10) receiving low (8% CP) or high (15% CP) quality grass hay using a 14-day adaptation period and a 14-day period of data collection. Regression, artificial neural network, and dynamic-mechanistic models were developed using these data and assessed to identify a model that accurately and precisely predicts forage DMI for dry beef cows on pasture. Model evaluation of the machine learning algorithms used a training, testing, and cross-validation scheme to determine model accuracy. Evaluation of mechanistic models used the Model Evaluation System (MES; Tedeschi, 2006) to measure accuracy (mean bias, Cb, RMSEP), precision (R2, MEF, CCC), and screening for systematic errors. This study successfully integrated three precision technologies which improve research capabilities on extensive rangeland systems through precision enhanced data collection. Deploying a precision-based DMI algorithm enhances research-based capabilities to manage range beef cattle on an individual level by more precisely setting stocking rates, providing supplementation, and evaluating individual animal efficiency; ultimately leading to lower cost, optimized resources, and enhanced environmental sustainability. Further, the enteric emissions data collected fills a gap for missing GHG data of dry beef cows in maintenance phase in semi-arid western South Dakota rangelands.

Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks.

Dry matter intake (DMI) is a fundamental component of the animal's feed efficiency, but measuring DMI of individual cows is expensive. Mid-infrared reflectance spectroscopy (MIRS) on milk samples could be an inexpensive alternative to predict DMI. The objectives of this study were (1) to assess if milk MIRS data could improve DMI predictions of Canadian Holstein cows using artificial neural networks (ANN); (2) to investigate the ability of different ANN architectures to predict unobserved DMI; and (3) to validate the robustness of developed prediction models. A total of 7,398 milk samples from 509 dairy cows distributed over Canada, Denmark, and the United States were analyzed. Data from Denmark and the United States were used to increase the training data size and variability to improve the generalization of the prediction models over the lactation. For each milk spectra record, the corresponding weekly average DMI (kg/d), test-day milk yield (MY, kg/d), fat yield (FY, g/d), and protein yield (PY, g/d), metabolic body weight (MBW), age at calving, year of calving, season of calving, days in milk, lactation number, country, and herd were available. The weekly average DMI was predicted with various ANN architectures using 7 predictor sets, which were created by different combinations MY, FY, PY, MBW, and MIRS data. All predictor sets also included age of calving and days in milk. In addition, the classification effects of season of calving, country, and lactation number were included in all models. The explored ANN architectures consisted of 3 training algorithms (Bayesian regularization, Levenberg-Marquardt, and scaled conjugate gradient), 2 types of activation functions (hyperbolic tangent and linear), and from 1 to 10 neurons in hidden layers). In addition, partial least squares regression was also applied to predict the DMI. Models were compared using cross-validation based on leaving out 10% of records (validation A) and leaving out 10% of cows (validation B). Superior fitting statistics of models comprising MIRS information compared with the models fitting milk, fat and protein yields suggest that other unknown milk components may help explain variation in weekly average DMI. For instance, using MY, FY, PY, and MBW as predictor variables produced a predictive accuracy (r) ranging from 0.510 to 0.652 across ANN models and validation sets. Using MIRS together with MY, FY, PY, and MBW as predictors resulted in improved fitting (r = 0.679-0.777). Including MIRS data improved the weekly average DMI prediction of Canadian Holstein cows, but it seems that MIRS predicts DMI mostly through its association with milk production traits and its utility to estimate a measure of feed efficiency that accounts for the level of production, such as residual feed intake, might be limited and needs further investigation. The better predictive ability of nonlinear ANN compared with linear ANN and partial least squares regression indicated possible nonlinear relationships between weekly average DMI and the predictor variables. In general, ANN using Bayesian regularization and scaled conjugate gradient training algorithms yielded slightly better weekly average DMI predictions compared with ANN using the Levenberg-Marquardt training algorithm.

Prediction of dry matter intake and gross feed efficiency using milk production and live weight in first-parity Holstein cows.

Direct measurement of dry matter intake (DMI) presents a major challenge in estimating gross feed efficiency (GFE) in dairy cattle. This challenge can, however, be resolved through the prediction of DMI and GFE from easy-to-measure traits such as milk production (i.e. milk yield, energy-corrected milk (ECM), butterfat, protein, lactose) and live weight (LW). The main objective of this study was, therefore, to investigate the feasibility of predicting dry matter intake and gross feed efficiency for first-parity Holstein cows using milk production traits and LW. Data comprised of 30 daily measurements of DMI and milk production traits, and 25 daily LW records of a group of 100 first-parity Holstein cows, fed a total mixed ration. Gross feed efficiency was calculated as kg ECM divided by kg DMI. The initial step was to estimate correlations of milk production traits and LW with DMI and GFE, to identify the best potential predictors of DMI and GFE. Subsequently, a forward stepwise regression analysis was used to develop models to predict DMI and GFE from LW and milk production traits, followed by within-herd validations. Means for DMI, butterfat yield (BFY) and LW were 21.91 ± 2.77kg/day, 0.95 ± 0.14kg/day and 572 ± 15.58kg/day, respectively. Mean GFE was 1.32 ± 0.22. Dry matter intake had positive correlations with milk yield (MY) (r = 0.32, p < 0.001) and LW (r = 0.76, p < 0.0001) and an antagonistic association with butterfat percent (BFP) (r = - 0.55, p < 0.001). On the other hand, GFE was positively associated with MY (r = 0.36, p < 0.001), BFP (r = 0.53, p < 0.001) and BFY (r = 0.83, p < 0.0001), and negatively correlated with LW (r = - 0.23, p > 0.05). Dry matter intake was predicted reliably by a model comprising of only LW and MY (R2 = 0.79; root mean squared error (RMSE) = 1.05kg/day). A model that included BFY, MY and LW had the highest ability to predict GFE (R2 = 0.98; RMSE = 0.05). Live weight and BFY were the main predictor traits for DMI and GFE, respectively. The best models for predicting DMI and GFE were as follows: DMI (kg/day) = - 54.21 - 0.192 × MY (kg/day) + 0.146 × LW (kg/day) and GFE (kg/day) = 4.120 + 0.024 × MY (kg/day) + 1.000 × BFY (kg/day) - 0.008 × LW (kg/day). Thus, daily DMI (kg/day) and GFE can be reliably predicted from LW and milk production traits using these developed models in first-parity Holstein cows. This presents a big promise to generate large quantities of data of individual cow DMI and GFE, which can be used to implement genetic improvement of feed efficiency.

Solar radiation and temperature as predictor variables for dry matter intake in beef steers

Solar radiation may be an important weather variable that has not been included in previous dry matter intake (DMI) prediction models. Solar radiation affects the overall effective ambient temperature, which in turn contributes to the net gain of heat in an animal’s body. This experiment examined ambient temperature and solar radiation with DMI in beef steers. Data from 790 beef steers collected between 2011 and 2018 using an Insentec feeding system was used. Daily data was condensed into weekly averages (n = 13,895 steer-weeks). The variables considered for this study were DMI (2.50 to 23.60 kg/d), body weight (197 to 796 kg), calculated dietary energy density (NEm; 0.79 to 2.97 Mcal/kg), ambient temperature (-23.73 to 21.40°C), two-week lag of ambient temperature (-20.73 to 23.56°C), monthly lag of ambient temperature (-17.95 to 22.74°C), solar radiation (30.8 to 297.1 W/m2), two-week lag of solar radiation (34.6 to 272 W/m2) and monthly lag of solar radiation (43.7 to 256.6 W/m2). Residuals of DMI fitting week of the year (fixed) and experiment (random) were used to generate scatter plots with other explanatory variables to identify if non-linear relationships existed. Body weight and NEm had both linear and quadratic relationships with DMI, while the relationship with DMI for other variables was linear. The MIXED procedure of SAS with Toeplitz variance-covariance structure was used to determine the final model of DMI. After accounting for body weight and NEm in the model, two-week lag of ambient temperature and monthly lag of solar radiation interacted together (P = 0.0001), and this accounted for 0.7790 (R2) variation in DMI and improved the model fit. Therefore, these two variables and their interactions should be considered in DMI prediction equations of beef steers.

3. Evaluation of NASEM 2021 model predictions of dry matter intake and performance of dairy cows consuming fresh ryegrass-based diets

3. Evaluation of NASEM 2021 model predictions of dry matter intake and performance of dairy cows consuming fresh ryegrass-based diets

Correlations of feed intake predicted with milk infrared spectra and breeding values in the Dutch Holstein population

Feed is a major cost in dairy production, and substantial genetic variation in feed efficiency exists between cows. Therefore, breeders aim to improve feed efficiency of dairy cattle. However, phenotypic data on individual feed intake on commercial farms is scarce, and accurate measurements are very costly. Several studies have shown that information from Fourier-transformed infrared spectra of milk samples (milk infrared, milk IR) can be used to predict phenotypes such as energy balance and energy intake, but this is usually based on small data sets obtained under experimental circumstances. The added value of information from milk IR spectra for estimation of breeding values is unknown. The objectives of this study were (1) to develop prediction equations for dry matter intake (DMI) and residual DMI (rDMI) from milk IR spectra; (2) to apply these for a data set of milk IR spectra from commercial Dutch dairy farms; (3) to estimate genetic parameters for these traits; and (4) to estimate correlations between these predictions and other traits in the breeding goal. We used data from feeding trials where individual feed intake was recorded daily and for which milk IR spectra were determined weekly to develop prediction equations for DMI and rDMI with partial least squares regression. This data set contained over 7,600 weekly averaged DMI records linked with milk IR spectra from 271 cows. The equations were applied for a data set with test day information from 676 Dutch dairy herds with 621,567 records of 78,488 cows. Both milk IR-predicted DMI and rDMI were analyzed with an animal model to obtain genetic parameters and sire effect estimates that could be correlated with breeding values. A partial least squares regression model with 10 components from the milk IR spectra explained around 25% of DMI variation and less than 10% of rDMI variation in the validation set. Nearly all variation in the milk IR spectra was captured by 7 components; additional components contributed marginally to the spectral variation but decreased prediction errors for both traits. Accuracies of predictions of DMI and rDMI from milk IR spectra for a large feeding experiment were 0.47 and 0.26 on average, respectively, with small differences between ration treatments (ranging from 0.43 to 0.55 and from 0.21 to 0.34, respectively) and among lactation stages (ranging from 0.24 to 0.59 and from 0.13 to 0.36, respectively), with the highest prediction accuracies in early lactation. The estimated heritabilities for predicted DMI and rDMI were 0.3 and 0.4, respectively, which suggests genetic potential for both predicted traits. The correlations of sire estimates for milk IR-predicted DMI with official Dutch breeding values were strongest with milk production (0.33), longevity (0.26), and fertility (-0.27), indicating that cows that eat more produce more, live longer, and have poorer fertility. The correlations of sire estimates for predicted DMI and rDMI with the official breeding values for DMI were low (0.14 and 0.03, respectively). This implies that the added value of including milk IR-predicted DMI information in the estimation procedure of breeding values for DMI would be considered insufficient for practical application.

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.

Prediction of dry matter intake by meat sheep on tropical pastures.

This study was undertaken to determine whether equations for prediction of dry matter intake (DMI) by meat sheep are valid for animals raised solely on tropical pastures and to propose a new equation to predict the DMI of sheep raised on tropical pastures. The DMI prediction from published equations was evaluated by regressing the predicted and observed values, using the F test, for the identity of the parameters (β0 = 0 and β1 = 1) of the regression of predicted on observed data. If the null hypothesis is not rejected, the tested equation accurately estimates DMI. The proposed equation was evaluated in the same way as the published equations. The animal performance and pasture structure and chemical composition data used originated from an experiment conducted with 32 Santa Inês sheep raised on tropical pastures. In the analysis of model adequacy, the null hypothesis was rejected (P < 0.001) and the equations generated predictions that differ (β0 = 0 and β1 = 1) from the DMI observed under practical feeding conditions for grazing sheep. The proposed equation, DMI (%LW) = 7.16545 (± 0.76522) - 0.21799 (± 0.01812) * LW + 0.00273 (± 0.00034) * LW2-0.00688 (± 0.00299) * GT + 0.000007 (± 0.000002) * GT2 + 0.00271 (± 0.00108) * GHA, where LW is live weight (kg), GT is grazing time (min/day), and GHA is green herbage allowance (kg DM/100kg LW), should be used to more accurately predict DMI by grazing sheep.

Exploring the potential of ingestive behaviour, body measurements, thermal imaging, heart rate and blood pressure to predict dry matter intake in grazing dairy cows

The objective of this study was to develop and validate models to predict dry matter intake (DMI) of grazing dairy cows using animal energy sinks and status traits in combination with traits related to grazing behaviour, body measurements, thermal imaging, heart rate and blood pressure. The dataset used to develop the models comprised 33 measurements from 113 Holstein-Friesian dairy cows. Multivariable regression models were constructed incorporating each independent variable into a benchmark model incorporating the energy sinks (milk yield [MY], fat %, protein % and body weight [BW]) and status traits (feeding treatment, parity and calving day of year). Of the 33 variables tested, 10 showed an association with DMI (P &lt; 0.25). These variables were incorporated into a backward linear regression model. Variables were retained in this model if P &lt; 0.05. Grazing bout duration and rumination mastication rate were retained in the final model. The inclusion of these variables in the model increased DMI prediction by 0.01 (coefficient of determination [R2] = 0.85) compared to the benchmark model alone (R2 = 0.84). The models were applied to data recorded on an independent herd of 51 dairy cows. The R2 upon validation was 0.80 for the benchmark model and 0.79 for the model incorporating rumination mastication rate and grazing bout duration in combination with the benchmark variables. The separation of grazing bout duration and rumination mastication rate to predict DMI revealed rumination mastication rate slightly increases predictive accuracy upon external validation (R2 = 0.81), whereas grazing bout duration did not (R2 = 0.78). This suggests that grazing bout duration is not a robust trait for DMI prediction. Results from this study suggest that rumination mastication rate can slightly increase the accuracy of DMI prediction surpassing known energy sinks and status traits.

Multiple Country Approach to Improve the Test-Day Prediction of Dairy Cows' Dry Matter Intake.

Simple SummaryDry matter intake, related to the number of nutrients available to an animal to meet its production and health needs, is crucial for the economic, environmental, and welfare management of dairy herds. Because the equipment required to weigh the ingested food at an individual level is not broadly available, we propose some new ways to approach the actual dry matter consumed by a dairy cow for a given day. To do so, we used regression models using parity (number of lactations), week of lactation, milk yield, milk mid-infrared spectrum, and prediction of bodyweight, fat, protein, lactose, and fatty acids content in milk. We chose these elements to predict individual dry matter intake because they are either easily accessible or routinely provided by regional dairy organizations (often called “dairy herd improvement” associations). We succeeded in producing a model whose dry matter intake predictions were moderately related to the actual values.We predicted dry matter intake of dairy cows using parity, week of lactation, milk yield, milk mid-infrared (MIR) spectrum, and MIR-based predictions of bodyweight, fat, protein, lactose, and fatty acids content in milk. The dataset comprised 10,711 samples of 534 dairy cows with a geographical diversity (Australia, Canada, Denmark, and Ireland). We set up partial least square (PLS) regressions with different constructs and a one-hidden-layer artificial neural network (ANN) using the highest contribution variables. In the ANN, we replaced the spectra with their projections to the 25 first PLS factors explaining 99% of the spectral variability to reduce the model complexity. Cow-independent 10 × 10-fold cross-validation (CV) achieved the best performance with root mean square errors (RMSECV) of 3.27 ± 0.08 kg for the PLS regression and 3.25 ± 0.13 kg for ANN. Although the available data were significantly different, we also performed a country-independent validation (CIV) to measure the models’ performance fairly. We found RMSECIV varying from 3.73 to 6.03 kg for PLS and 3.69 to 5.08 kg for ANN. Ultimately, based on the country-independent validation, we discussed the developed models’ performance with those achieved by the National Research Council’s equation.