Prediction Of Milk Yield Research Articles

Comparative Efficiency of Conventional and Connectionist Models in Prediction of First Lactation 305-day Milk Yield in Murrah Buffaloes

Comparative Efficiency of Conventional and Connectionist Models in Prediction of First Lactation 305-day Milk Yield in Murrah Buffaloes

Principal component regression analysis to predict lifetime milk yield of Jaffarabadi buffaloes

The study aims to devise most appropriate prediction model for lifetime milk production of Jaffarabadi Buffalo, based on principal components formulated on initially expressed lactation records as predictors. Lactation milk yield, lactation period and peak milk yield records of first, second and third lactations of animals under study were used of 24 years (1987 to 2010). Principal components (PCs) were derived from data set using principal component regression analysis (PCRA), the principal components were used as predictors for predicting lifetime milk yield (LTMY). Multiple linear regression models were fitted to identify the best fitted model for prediction of lifetime milk yield with the first principal component to all principal component as a predictor. The equation LTMY = 7825.8768+2.8118 (PC1) - 13.7098 (PC2) - 599.0908 (PC3) + 3.0266 (PC4) - 8.8196 (PC5) - 257.9315 (PC6) + 2.6042 (PC7) explained 98.9% variation in the estimated values with adjusted R2= 59.09% variation in the estimated values. The curve estimation analysis showing the appropriateness of first seven principal components as predictor was the most appropriate model for lifetime milk yield. These prediction equations may be helpful in selection at an early stage of Jaffarabadi Buffalo based on early part lactation records.

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.

Milk yield prediction in Friesian cows using linear and flexible discriminant analysis under assumptions violations

BackgroundThe application of novel technologies is now widely used to assist in making optimal decisions. This study aimed to evaluate the performance of linear discriminant analysis (LDA) and flexible discriminant analysis (FDA) in classifying and predicting Friesian cattle’s milk production into low (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:<$$\\end{document}4500 kg), medium (4500–7500 kg), and high (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:>$$\\end{document}7500 kg) categories. A total of 3793 lactation records from cows calved between 2009 and 2020 were collected to examine some predictors such as age at first calving (AFC), lactation order (LO), days open (DO), days in milk (DIM), dry period (DP), calving season (CFS), 305-day milk yield (305-MY), calving interval (CI), and total breeding per conception (TBRD).ResultsThe comparison between LDA and FDA models was based on the significance of coefficients, total accuracy, sensitivity, precision, and F1-score. The LDA results revealed that DIM and 305-MY were the significant (P < 0.001) contributors for data classification, while the FDA was a lactation order. Classification accuracy results showed that the FDA model performed better than the LDA model in expressing accuracies of correctly classified cases as well as overall classification accuracy of milk yield. The FDA model outperformed LDA in both accuracy and F1-score. It achieved an accuracy of 82% compared to LDA’s 71%. Similarly, the F1-score improved from a range of 0.667 to 0.79 for LDA to a higher range of 0.81 to 0.83 for FDA.ConclusionThe findings of this study demonstrated that FDA was more resistant than LDA in case of assumption violations. Furthermore, the current study showed the feasibility and efficacy of LDA and FDA in interpreting and predicting livestock datasets.

Early prediction of milk yield from Holstein-Friesian cow lactation in Querétaro, Mexico

Objective: To obtain a machine learning (ML) model to predict the milk yield adjusted to 305 d (MY305) from the same lactation period. Design/methodology/approach: A database of test days (TD) was used, made up by 11,892 records of daily milk production from cows with more than 150 days in milk (DIM), from 19 barns in Querétaro, Mexico. The milk production was standardized to specific DIMs (5, 10, 20, 30 and 40) and estimations of MY305 were obtained with these, using ML models. The following were incorporated as explicative variables of the herd: month of birth of the cow, month of start of lactation, number of lactation, number of days for three daily milking events, and the two first linear scores of somatic cells. Results: The best goodness of fit was achieved with ensemble models, obtaining a deviance of 1503584 in the training with 80% of data chosen randomly, while with 20% of the data reserved to evaluate the deviance model it was 1576776. The rate between data observed and predictions of MY305 of the ensemble models had a coefficient of determination of r2 = 0.79 and RMSE of 1256. In the best individual model (deviance of 2281420) of ‘deep learning’ type, the most important variables were daily milk production of 30, 10, 5 and 20 DIM (19.9, 16.6, 16.2 and 12.8%, respectively). Limitations on study/implications: The value of RMSE is high, although the TD databases are generated systematically and usually there are not many barns represented. Findings/conclusions: For the database examined, milk production in the early phase of lactation together with a set of automatic learning models resulted in an adequate prediction of MY305.

Understanding the dynamics of mastitis in milk yield: decoding onset and recovery patterns in response to mastitis occurrence

No recent study attempted to model daily milk losses before and after mastitis onset and the moment when it begins. Thus, we aimed to describe the impact of mastitis on milk production based on mastitis level and moment of occurrence. We used data from 11 dairy farms, and the data set consisted of 885,759 daily individual milk test records from 3,508 cows in different lactations, with an average milk yield from January 2017 to December 2022. We modeled the impact of mastitis severity [i.e., 1 (mild) and 2 (severe)] based on the drop and recovery of milk yield following 3 steps. First, we removed milk recorded on the day of diagnosis of mastitis from the data set and fitted a Wood's Curve for each cow and parity. Second, we returned the mastitis data to the data set and estimated the residual milk loss due to mastitis from 15 d before to 30 d after the mastitis event. Third, we used generalized additive mixed effect models to estimate the residual milk loss, including farm as a random effect. In addition to the random effect of the farm, we also included the predicted value of mastitis, the day effect before and after mastitis incidence, and the interaction between the predicted value of mastitis and days. On average, mastitis level 2 resulted in a more severe milk yield drop in all represented stages of lactation (80, 170, and 260 d in milk), suggesting a higher loss close to the lactation peak, approximately 130 kg more than mastitis level 1. Moreover, the occurrence of mastitis case level 1 during early phase of lactation (DIM 80) can cause an average milk loss of 158 L and a ML 2, an average loss of 288 L. The estimations suggest that milk drop occurs 14 to 4 d before mastitis onset and can last until 15 to 25 d from the diagnosis, which would be the necessary time for a cow to reestablish their predicted milk yield. Therefore, our study brings new perspectives to investigate milk yield drop and recovery due to mastitis infections and how much mastitis can deplete and impair milk production.

Prediction of First Lactation Milk Yield on The Basis of Test Day Yield Using Multiple Linear Regression in Gir Cows

The test-day model is a method of choice for the study of milk yield traits, and this method is very important in countries like India, where herd size is generally smaller and lacks a well-established milk recording system. The present investigation was carried out on 365 records of Gir cows maintained at a cattle breeding farm from 1986 to 2014 with the objective of predicting the first lactation milk yield using monthly test day milk records. MLR was used with the backward elimination method. The optimum equation had total five variables (test days) viz. TD1, TD2, TD3, TD4 and TD5. This equation gave an accuracy of prediction of 77.71%. Therefore, it is concluded that first lactation 305 day milk yield could be predicted as early as 5th month of lactation with higher degree of accuracy.

Genetic Diversity and Selection Signatures in Economically Important Traits of Ethiopian Indigenous Cattle Breed as a Base for Dairy Cattle Genetic Improvement: A Review

The aim of this paper was to review the genetic diversities and selection signatures in economical important traits of Ethiopian indigenous cattle. This allows obtaining rapid genetic gain, predicting genomic potential and developing conservation strategies. A genetic variation among Ethiopian breeds ranging from 0.1 to 4.55% and a within variation of 95.45 to 99.9% was reported by various scholars. About 3.51% differentiation between Boran and Fogera from Begait and 2.82% differentiation of Boran from Fogera and Begait was reported. Even though the observed and expected heterozygosity values among Ethiopian cattle population was lower, research reports shown considerably higher values ranging from 0.638±0.015 in Boran and 0.714±0.022 in Raya-Azebo to 0.700± 0.014 in Ambo and 0.735±0.017 in Sheko. Genes responsible for heat tolerance, control of metabolic disease, immunity genes, high altitude and low altitude adaptation were reported in Ethiopian cattle breeds. In African indicus and European taurian cattle different genes responsible for milk production, milk fat yield and synthesis and milk composition, were reported. Genes associated with milk traits; fertility and reproduction genes were also reported in Ethiopian cattle. The presence of high within breed variation in Ethiopian cattle creates favorable conditions for further improvement through selection. Beyond identifying candidate potential genes, a detailed study targeting genomic prediction of milk yield and genomic inbreeding had better be done, which allows getting information to optimize and speed up the breeding programs and genetic improvement.

Bayesian approach for evaluation of lactation curve in cross bred cattle based on monthly and bimonthly test day milk yield.

In field progeny testing program milk recording at monthly or bimonthly intervals and prediction of first lactation 305-day milk yield (FL305DMY) from these test day yields have been adapted as an alternative to daily milk recording. Wood's incomplete gamma function is the one of the commonly used nonlinear lactation curve model. In recent years Bayesian approach of fitting nonlinear biological models is gaining attention among researchers. In this study Wood's incomplete gamma function was fitted using Bayesian approach using monthly (MTDY) and bimonthly test day (BTDY) yields. The lactation curve parameters thus obtained were used for prediction of FL305DMY. Efficiency of prediction based on monthly and bimonthly test day milk yield were compared using error of prediction. It was found to be 5.78% and 7.59% as root mean square error (RMSE) based on MTDY and BTDY respectively.The Breeding values of 97 Karan Fries sires were estimated using BLUP-AM based on actual and predicted FL305DMY thus obtained. The RMSE was calculated as the difference between estimated breeding values based on actual and predicted yield. It was found that RMSE calculated based on MTDY showed only a marginal superiority of 0.79% over BTDY and showed high degree of correlation with actual yield. Therefore, recording at bimonthly intervals could be an economical alternative without compromising the efficiency.

The Application of Ordinal Logistic Regression Model as a Robust Tool for Enhanced Prediction of Milk Yield in Dairy Cows.

The Application of Ordinal Logistic Regression Model as a Robust Tool for Enhanced Prediction of Milk Yield in Dairy Cows.

Udderly accurate: A deep learning based modelling for determination of dairyness of Sahiwal cow using computer vision

Dairy farming is a crucial agricultural practice for food and nutritional security. Selection of the best milching animals has always been a challenging task for optimising production efficiency. The efficacy of milk yield production is contingent upon a number of factors including inherent linear traits. Linear traits refer to the quantifiable physical characteristics that are associated with the production and reproduction capabilities of dairy animals. This article presents an innovative approach for the classification of Sahiwal cows into high, medium and low yielder categories based on images featuring linear traits through emerging deep learning and computer vision techniques. A large dataset of 4110 images highlighting important linear traits such as udder size, shape, and texture of different categories of Sahiwal cows has been created for training, validation and testing of the model. Images were collected from the herd of Sahiwal cows maintained at the National Dairy Research Institute, Karnal. The dataset was pre-processed using image augmentation techniques to enhance the model’s robustness. Different architectures of CNN models namely InceptionV3, ResNet50 and GoogleNet were trained and optimised. The Inception V3 model demonstrated the best result with 85.64% testing accuracy among all these models in classifying the cow. The developed model can be used under field conditions to determine the dairyness of a cow in real time mode in place of human experts. Additionally, the model’s interpretability is evaluated through feature visualisation, showcasing the importance of different udder features in milk yield prediction.

Milk Yield Prediction and Economic Analysis of Optimized Rearing Environment in a Cold Region Using Neural Network Model

The milk yield of dairy cows in a non-stressed state in the cold region in China is lower during cold seasons. In this study, the correlations between indoor environmental factors and milk production were analyzed. Temperature, relative humidity, and light intensity were found to be the main factors affecting milk yield. The warning values of these factors for lower milk production were 5 °C, 60%, and 300 lx, respectively. A neural network model predicting milk yield based on environmental factors was established, and the optimal model parameters were determined, resulting in a high accuracy of R2 = 0.802. This model was used to investigate the optimal measure for improving the indoor environment, which helps to increase milk production and economic benefits, including LED lights, heating radiators, and dehumidifiers. In conclusion, each type of device led to the growth of milk yield, reaching 2.341, 1.706, and 1.893 kg cow−1 Day−1. The combination of heating radiator and LED light resulted in the highest increased net benefit of 16.802 CNY cow−1 Day−1. This is the first time that a neural network model was successfully built to predict milk yield based on climatic features which was also applied to economic analysis of indoor environment improvement for dairy barns in extreme cold regions.

Test-day and other milk recording options for prediction of lactation milk yield in Jaffarabadi (Bubalus bubalis) buffaloes

Generally, standard lactation milk yield is predicted based on test-day records collected at monthly intervals. Test-day milk production at different time intervals other than monthly intervals can be used to predict lactation milk yield of field bovines in field conditions. With the same possibility, this study was carried out to predict lactation milk yield in Jaffarabadi buffaloes from various test-day milk yield data retrieved for different time intervals. A total of 1,15,339 daily milk yield records in 176 lactations of 1st to 6th parity of 30 Jaffarabadi buffaloes lactating at the Cattle Breeding Farm, Kamdhenu University, Junagadh, Gujarat over a period of 28 years (1991 to 2018) were used for the study. Single monthly test-day milk yield recorded on 125th, 155th or 185th day i.e., 5th, 6th and 7th monthly test day yield alone provided only 50% reliability in determining the standard lactation milk yield. Daily peak yield alone was also found to be a poor predictor for lactation yield. Prediction equations using combination of consecutive two monthly test day yields from 4th to 10th monthly test day were found reliable source for prediction of lactation milk yield providing accuracy up to 82.19% whereas, daily peak yield in combination with single monthly test day yield at mid and late lactation was also predicted lactation milk yield with accuracies up to 72.23%. Milk production recorded at weekly interval could also be used to approximate milk production using the equation 15.35+6.91 × Sum of all weekly test-day yields, with precision of 98.93% or milk production recorded at fortnightly interval by the equation 18.04+14.65 × Sum of fortnightly test-day yields, with precision of 97.14%.&#x0D;

Prediction of some milk production traits using udder and teat measurements with a spotlight on their genetic background in Friesian cows

The aggregate udder shape (Bowl, Round, Cup), udder measurements (udder width, UW, udder front depth, UFD, udder rear depth, URD, udder levelness, ULV, udder heights, UH) and teat measurements (teat diameters, TD, front teat length, FTL, rear teat length, RTL, distance between front teats, DFT and distance between rear teats, DRT) were measured on 1300 Friesian cows located in a commercial farm under subtropical conditions (Egypt) to appraise udder and teats status and to evaluate the possible relationships with some milk production characteristics in conjunction with udder shape, age at first calving, sire and inbreeding effects on udder morphological traits and milk production ability. For such an available sample size, parity had affected (P < 0.01) UFD, TD, FTL and RTL. In addition, udder shape affected (P < 0.01) UW, UFD, URD, ULV and DFT. None of the other studied factors affected milk production traits. The bowl udder shape (P < 0.01) yielded a high total milk yield (3267.19 kg), adjusted milk yield (2443.01 kg) and lactation length (480.70 d) compared to other udder shapes. The genetic correlations of UW with total milk yield and persistency were strongly positive (0.86 and 0.93, respectively). However, strong negative genetic correlations were found between UW with peak milk yield and lactation length (− 0.92 and − 0.80, respectively), between RTL with peak milk yield (− 0.92) and DRT with persistency (− 0.79). As found from the stepwise multiple regression, UW and URD can be used as good indicators for predicting milk yield and lactation length. Additionally, this study spotlights the genetic background of udder characteristics based on reliable studies and the QTL database for cattle as a first step toward applying this knowledge side by side with phenotypic traits to improve the productivity of the Holstein breed under subtropical conditions.

A Gradient Boosting model to predict the milk production

Predicting the milk yield of cows is essential for efficient milk production in real-time operations. This is important for two reasons: firstly, to optimize the farm's income and secondly to anticipate the expected milk used by milk producers, to ensure reliability. In this paper, we focus on using the Gradient Boosting method to predict milk yield. We apply this method on the top three biggest farms obtained from an AgTech data platform - Farmtrace and compare the predicted total milk yield with the actual output. To measure performance, we use the percentage by which the prediction differs from the real output. Our study found that the maximum deviation was 2.2% on the validation set for farm II. The Gradient Boosting approach is considered the most suitable method for this case study, as it has proven from related literature to be highly accurate and reliable in predicting outcomes across various fields. Furthermore, it can identify and prioritize the key factors that affect the target variable, which is crucial for understanding and improving milk production.

Milk yield residuals and their link with the metabolic status of dairy cows in the transition period

The transition period is one of the most challenging periods in the lactation cycle of high-yielding dairy cows. It is commonly known to be associated with diminished animal welfare and economic performance of dairy farms. The development of data-driven health monitoring tools based on on-farm available milk yield development has shown potential in identifying health-perturbing events. As proof of principle, we explored the association of these milk yield residuals with the metabolic status of cows during the transition period. Over 2 yr, 117 transition periods from 99 multiparous Holstein-Friesian cows were monitored intensively. Pre- and postpartum dry matter intake was measured and blood samples were taken at regular intervals to determine β-hydroxybutyrate, nonesterified fatty acids (NEFA), insulin, glucose, fructosamine, and IGF1 concentrations. The expected milk yield in the current transition period was predicted with 2 previously developed models (nextMILK and SLMYP) using low-frequency test-day (TD) data and high-frequency milk meter (MM) data from the animal's previous lactation, respectively. The expected milk yield was subtracted from the actual production to calculate the milk yield residuals in the transition period (MRT) for both TD and MM data, yielding MRTTD and MRTMM. When the MRT is negative, the realized milk yield is lower than the predicted milk yield, in contrast, when positive, the realized milk yield exceeded the predicted milk yield. First, blood plasma analytes, dry matter intake, and MRT were compared between clinically diseased and nonclinically diseased transitions. MRTTD and MRTMM, postpartum dry matter intake and IGF1 were significantly lower for clinically diseased versus nonclinically diseased transitions, whereas β-hydroxybutyrate and NEFA concentrations were significantly higher. Next, linear models were used to link the MRTTD and MRTMM of the nonclinically diseased cows with the dry matter intake measurements and blood plasma analytes. After variable selection, a final model was constructed for MRTTD and MRTMM, resulting in an adjusted R2 of 0.47 and 0.73, respectively. While both final models were not identical the retained variables were similar and yielded comparable importance and direction. In summary, the most informative variables in these linear models were the dry matter intake postpartum and the lactation number. Moreover, in both models, lower and thus also more negative MRT were linked with lower dry matter intake and increasing lactation number. In the case of an increasing dry matter intake, MRTTD was positively associated with NEFA concentrations. Furthermore, IGF1, glucose, and insulin explained a significant part of the MRT. Results of the present study suggest that milk yield residuals at the start of a new lactation are indicative of the health and metabolic status of transitioning dairy cows in support of the development of a health monitoring tool. Future field studies including a higher number of cows from multiple herds are needed to validate these findings.

Division of Cow Production Groups Based on SOLOv2 and Improved CNN-LSTM

Udder conformation traits interact with cow milk yield, and it is essential to study the udder characteristics at different levels of production to predict milk yield for managing cows on farms. This study aims to develop an effective method based on instance segmentation and an improved neural network to divide cow production groups according to udders of high- and low-yielding cows. Firstly, the SOLOv2 (Segmenting Objects by LOcations) method was utilized to finely segment the cow udders. Secondly, feature extraction and data processing were conducted to define several cow udder features. Finally, the improved CNN-LSTM (Convolution Neural Network-Long Short-Term Memory) neural network was adopted to classify high- and low-yielding udders. The research compared the improved CNN-LSTM model and the other five classifiers, and the results show that CNN-LSTM achieved an overall accuracy of 96.44%. The proposed method indicates that the SOLOv2 and CNN-LSTM methods combined with analysis of udder traits have the potential for assigning cows to different production groups.

Can meteorological data improve the short-term prediction of individual milk yield in dairy cows?

Many farms document daily milk yields of individual cows because these are a good indicator of cow well-being. It is established that extreme meteorological conditions influence the milk yields by causing heat and cold stress, whereas less is known about the effects of moderate changes in meteorological conditions. Thus, the aim of the present study was to evaluate whether individual daily milk yield predictions can be improved by considering such changes. We evaluated 8 years of milking and meteorological data from Eastern Switzerland with a total of 33,938 daily milkings from 145 Brown Swiss and 64 Swiss Fleckvieh cows. The cows were aged between 1.9 and 13.5 years at parturition. The data set was split into 7 periods according to the days in milk (DIM) and subsequently filtered into subsets by breed and parity. We applied Gaussian process regression to predict individual daily milk yield. We compared different models including DIM, lagged milk yield, and meteorological variables as features and found that models including the lagged milk yield performed best. Within the period of 5 to 90 DIM, we were able to predict individual next-day milk yield from the cow's last milkings with a root mean squared error (RMSE) of 2.1 kg. In contrast, without information on the previous milk yield, accuracy of milk yield prediction was lower, with an RMSE close to 8 kg. The models holding information about previous milk yields showed a substantial increase in performance. Within a more homogeneous data subset filtered by breed or parity or both, predictions were even better, with a relative RMSE of 4.3% for first-parity Fleckvieh cows. However, we found that including meteorological features, such as temperature, rainfall, wind speed, temperature humidity index, cooling degree, and barometric pressure, did not improve the predictions in any of the evaluated periods. This finding indicates that considering meteorological features in daily milk yield prediction models is not useful in moderate climates; considering lagged milk yield is sufficient. We hypothesize that this meteorological information, among other influences, is indirectly contained in the lagged milk yield.

Effects of cut height and inoculant application on yield, nutrient composition, fermentative profile, and in vitro degradability of brown midrib whole-plant corn silage

Our objective was to determine the effects of cut height and inoculant application on brown midrib (BMR) whole-plant corn silage (WPCS) quality. Corn was harvested at 2 cut heights: low-cut height (LC, 30.5 cm) and high-cut height (HC, 56 cm). Three commercially available inoc- ulants were used as treatments: Lactobacillus plantarum and Pediococcus pentosaceus (SA), Propionibacteria freud- enreichii and Pediococcus pentosaceus (P2), and Lactoba- cillus buchneri and Pediococcus pentosaceus (B500). Mini silos were opened at 5 fermentation time points: 0, 2, 5, 30, and 90 d. Gross yield and DM yield of BMR WPCS decreased for HC compared with LC. The HC increased the concentrations of BMR WPCS DM, CP, and starch and predicted milk yield; decreased the concentration of NDF and ADF; and tended to increase NDF digestibility in 30-h in vitro digestion. The greater nutritive value of the HC possibly allows for an increased quantity of forage to be included in the TMR and de- creases the amount of ground corn needed to be added to the diet, potentially providing an economic benefit. The HC had lower acetic acid concentration compared with LC for CON and B500. The HC tended to have lower total acids and lactic acid compared with LC for all treatments. There was an interaction of height × length of storage for the fermentative profile. The increased acid concentra- tions during ensiling were expected and the greater acids concentration for LC compared with HC was probably due to more water-soluble carbohydrates for LC, which should have improved fermentation. The HC yielded a WPCS with better nutritional value; however, the fermen- tative profile was reduced compared with LC. Our results indicate that in the present conditions, the use of these particular inoculants did not improve WPCS fermentation profile.

Changes in milk production and estimated income over feed cost of group-housed dairy cows when moved between pens

Several studies have shown advantages of feeding cows in groups according to their nutrient requirements. However, there are concerns about losing milk when cows change from a high- to a low-nutrient diet. Data from 1,960 cows from 3 herds were analyzed to assess the impact on milk yield and estimated income over feed cost (IOFC) when moving cows between pens and rations. Pen feed intake and individual body weight and milk yield were recorded daily for 21 d before and 21 d after every pen movement. The data included 2,142 pen movements, and for each movement, milk production was recorded for 21 d before and 21 d after the change. Within farm, group, and cow, milk yield during the 21 d preceding a pen movement was used to predict milk yield for the subsequent 21 d using linear regression. Individual dry matter intake was estimated based on cow performance and diet composition, and then individual IOFC was calculated. Differences between actual and predicted milk yield and IOFC during the 21 d following a movement were determined and contrasted against zero using t-tests. In all 3 farms, every pen movement involved a change from a more expensive ration with more energy and protein than the one fed after the movement. On average, in all cases but one, observed milk yield was lower than the forecasted milk yield had cows not been moved. However, IOFC, on average, was positive in all cases except for 2 pen movements on 2 farms. Reasons for the improved IOFC with lower milk yield were due to a nutrient cost advantage, whereas, when IOFC decreased, differences in nutrient supply and dietary cost were not able to offset differences in milk yield. Moving cows according to milk production level does not result in economic advantages when the unit cost of nutrients is greater in the original than in the receiving ration. However, when the cost and nutrient difference between the diets offset potential losses in milk, forming groups according to production is advantageous.