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Over the past decades, various methods have been proposed to estimate daily milk yields from partial yields. Many of these methods divide milking interval time into varied classes, assuming that the yield correction factors are constant within classes but vary between classes. The DeLorenzo and Wiggans (D-W) method has been widely used in the United States, typically following a 2-step process. It calculates discrete yield factors for segmented milking interval classes and then refines them through a follow-up smoothing step. Such a 2-step approach is computationally inefficient, and discrete yield correction factors introduce biases. This study explored strategies to integrate continuous yield factors into established methods, exemplified by the D-W method. The renovated method, also called the polynomial-interaction regression model, postulates multiplicative yield correction factors as a linear or quadratic function of milking interval time, operating on interactions with partial yields. It uses all available data in a single step, exhibiting greater computability efficiency and higher estimation accuracy. A reparameterization leads to a linear model, making estimating the model parameters convenient. We evaluated the performance of the revised methods using a previous dataset of milking records from Holstein cows compared with some existing methods. The results showed that the refurbished model gave more accurate estimates of daily milk yields.

ABSTRACT: Most genotypes in the National Cooperator Database now originate from cows, but most previous studies validating genomic predictions have primarily focused on bulls. This study paired official within-breed genomic PTA (GPTA) and parent average (PA) for genotyped heifer calves born between 2019 and 2021 using the August 2021 database with their corresponding performance deviations (PDEV) for 17 different traits. The PDEV data became available when the heifers completed their first lactation and were extracted from the August 2023 database in which at least one PDEV value for those 17 traits existed for each genotyped heifer record. The separate breed analyses included records for 219 Ayrshires (AY), 2,715 Brown Swiss (BS), 1,055 Guernseys (GU), 949,904 Holsteins (HO), and 125,275 Jerseys (JE). These validation cows were heifer calves born between 2019 and 2021. However, due to timing or recording patterns, each trait had missing or incomplete PDEV data, leading to unbalanced distributions of records across traits. The squared accuracy of genomic prediction, or genomic reliability (r2), was divided by the corresponding heritability for each trait, as only the heritable portion of cow records could be predicted, and this reliability varied across different traits and breeds. For HO and JE, the predictive ability of GPTA outperformed PA in predicting cow PDEV for yield, productive life, SCS, fertility, and health traits. The improvement ranged from 33% to 142% compared with the predictive ability of the PA. However, the results for AY, BS, and GU breeds were less consistent due to the smaller number of genotyped heifers. The r2 gains in those breeds were smaller and aligned with the published reliabilities of GPTA. Weighted and unweighted regressions of PDEV on GPTA and PA traits mostly exceeded the expected value of 2.00 when predicting the future trait PDEV using GPTA or PA. The larger number of observations and lower SE of the weighted regression coefficient prediction in HO and JE breeds contributed to more stable and consistent regression coefficients for all traits except milk fever and heifer livability. Our study suggests that herd owners may experience greater benefits from genomics than originally forecast.

ABSTRACT: In the United States, lactation milk yields are not measured directly but are calculated from the test-day milk yields. Still, test-day milk yields are estimated from partial yields obtained from single milkings. Various methods have been proposed to estimate test-day milk yields, primarily to deal with unequal milking intervals dating back to the 1970s and 1980s. The Wiggans model is a de facto method for estimating test-day milk yields in the United States, which was initially proposed for cows milked 3 times daily, assuming a linear relationship between a proportional test-day milk yield and milking interval. However, the linearity assumption did not hold precisely in Holstein cows milked twice daily because of prolonged and uneven milking intervals. The present study reviewed and evaluated the nonlinear models that extended the Wiggans model for estimating daily or test-day milk yields. These nonlinear models, except step functions, demonstrated smaller errors and greater accuracies for estimated test-day milk yields compared with the conventional methods. The nonlinear models offered additional benefits. For example, the locally weighted regression model (e.g., locally estimated scatterplot smoothing) could utilize data information in scalable neighborhoods and weigh observations according to their distance in milking interval time. General additive models provide a flexible, unified framework to model nonlinear predictor variables additively. Another drawback of the conventional methods is a loss of accuracy caused by discretizing milking interval time into large bins while deriving multiplicative correction factors for estimating test-day milk yields. To overcome this problem, we proposed a general approach that allows milk yield correction factors to be derived for every possible milking interval time, resulting in more accurately estimated test-day milk yields. This approach can be applied to any model, including nonparametric models.

This study compared 3 correlational (best prediction, linear regression, and feed-forward neural networks) and 2 causal models (recursive structural equation model and recurrent neural networks) for estimating lactation milk yields. The correlational models assumed associations between test-day milk yields (health conditions), while the casual models postulated unidirectional recursive effects between these test-day variables. Wood lactation curves were used to simulate the data and served as a benchmark model. Individual Wood lactation curves provided an excellent parametric interpretation of lactation dynamics, with their prediction accuracies depending on the coverage of the lactation curve dynamics. Best prediction outperformed other models in the absence of mastitis but was suboptimal when mastitis was present and unaccounted for. Recurrent neural networks yielded the highest accuracy when mastitis was present. Although causal models facilitated the inference about the causality underlying lactation, precisely capturing the causal relationships was challenging because the underlying biology was complex. Misspecification of recursive effects in the recursive structural equation model resulted in a loss of accuracy. Hence, modeling causal relationships does not necessarily guarantee improved accuracies. In practice, a parsimonious model is preferred, balancing model complexity and accuracy. In addition to the choice of statistical models, the proper accounting for factors and covariates affecting milk yields is equally crucial.

A major concern for plant nurseries and land stewards is managing pathogens that induce soilborne diseases. Plant diseases cost nurseries millions of dollars in lost revenue and plant pathogens spread into the environment from nursery soil used for out-plantings. Recent research shows that steaming soil at high temperatures can effectively kill plant pathogens; however, few studies have investigated the influence of steaming on the soil microbiome, an essential component of plant health. We conducted a controlled experiment where nursery soil underwent a steaming event within a rectangular mesocosm (2.04 × 3.55 × 0.25 m) located at the National Ornamentals Research Site at Dominican University (NORS-DUC) (San Rafael, CA, U.S.A.). Soil samples were collected at four time points post-steaming. Thirty-two soil samples were used for DNA extraction and 18 for chemical analysis. The 16S rRNA gene and ITS genetic markers were used for amplicon-based sequencing to identify the bacterial and fungal soil communities. Results indicate that microbial diversity and composition shifted over time but that soil depth was not an important factor. Important bacterial taxa were the Firmicutes, especially 1 day post-steaming, and common fungal taxa included Aspergillus sp., Cladosporium sp., and Pseudallescheria sp. Plant nutrients such as B, C, Cl, K, Mg, SO4, and Zn, in addition to essential soil properties such as CEC, gypsum, moisture, and SAR, changed significantly with soil recovery. These results can help nursery practitioners make informed decisions for biotic and nutrient amendments post-steaming to promote soil and plant health. [Figure: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

ABSTRACT: Dairy producers have improved fertility of their herds by selecting bulls with higher conception rate evaluations. This research was motivated by the rapid increase in embryo transfer (ET) use to 11% of recent births and >1 million total births, with >5 times as many ET calves born in the United States in 2021 compared with just 5 yr earlier. Historical data used in genetic evaluations are stored in the National Cooperator Database. Recent records in the national pedigree database revealed that only 1% of ET calves have corresponding ET records in the breeding event database, 2% are incorrectly reported as artificial inseminations, and 97% have no associated breeding event. Embryo donation events are also rarely reported. Herd years reporting >10% of calves born by ET but less than half of the expected number of ET breeding events were removed to avoid potential biases. Heifer, cow, and sire conception rate evaluations were recalculated with this new data set according to the methods used for the official national evaluations. The edits removed about 1% of fertility records in the most recent 4 yr. Subsequent analysis showed that censoring herd years with inconsistent ET reporting had little effect on most bulls except for the highest ranking, younger bulls popular for ET use, and with largest effects on genomic selection. Improved ET reporting will be critical for providing accurate fertility evaluations, especially as the popularity of these advanced reproductive technologies continues to rise.

Various methods have been proposed to estimate daily yield from partial yields, primarily to deal with unequal milking intervals. This paper offers an exhaustive review of daily milk yields, the foundation of lactation records. Seminal advancements in the late 20th century concentrated on two main adjustment metrics: additive additive correction factors (ACF) and multiplicative correction factors (MCF). An ACF model provides additive adjustments to two times AM or PM milk yield, which then becomes the estimated daily yields, whereas an MCF is a ratio of daily yield to the yield from a single milking. Recent studies highlight the potential of alternative approaches, such as exponential regression and other nonlinear models. Biologically, milk secretion rates are not linear throughout the entire milking interval, influenced by the internal mammary gland pressure. Consequently, nonlinear models are appealing for estimating daily milk yields as well. MCFs and ACFs are typically determined for discrete milking interval classes. Nonetheless, large discrete intervals can introduce systematic biases. A universal solution for deriving continuous correction factors has been proposed, ensuring reduced bias and enhanced daily milk yield estimation accuracy. When leveraging test-day milk yields for genetic evaluations in dairy cattle, two predominant statistical models are employed: lactation and test-day yield models. A lactation model capitalizes on the high heritability of total lactation yields, aligning closely with dairy producers’ needs because the total amount of milk production in a lactation directly determines farm revenue. However, a lactation yield model without harnessing all test-day records may ignore vital data about the shapes of lactation curves needed for informed breeding decisions. In contrast, a test-day model emphasizes individual test-day data, accommodating various intervals and recording plans and allowing the estimation of environmental effects on specific test days. In the United States, the patenting of test-day models in 1993 used to restrict the use of test-day models to regional and unofficial evaluations by the patent holders. Estimated test-day milk yields have been used as if they were accurate depictions of actual milk yields, neglecting possible estimation errors. Its potential consequences on subsequent genetic evaluations have not been sufficiently addressed. Moving forward, there are still numerous questions and challenges in this domain.

Cows are typically milked 2 or more times on a test-day, but not all these milkings are sampled and weighed. The initial approach estimated a test-day yield with doubled morning (AM) or evening (PM) yield in the AM-PM milking plans, assuming equal AM and PM milking intervals. However, AM and PM milking intervals can vary, and milk secretion rates may be different between day and night. Statistical methods have been proposed to estimate daily yields in dairy cows, focusing on various yield correction factors in 2 broad categories: additive correction factors (ACF) and multiplicative correction factors (MCF). The ACF are evaluated by the average differences between AM and PM milk yield for various milking interval classes, coupled with other categorical variables. We show that an ACF model is equivalent to a regression model of daily yield on categorical regressor variables, and a continuous variable for AM or PM yield with a fixed regression coefficient of 2.0. Similarly, a linear regression model can be implemented as an ACF model with the regression coefficient for AM or PM yield estimated from the data. The linear regression models improved the accuracy of the estimates compared with the ACF models. The MCF are ratios of daily yield to yield from single milkings, but their statistical interpretations vary. Overall, MCF were more accurate for estimating daily milk yield than ACF. The MCF have biological and statistical challenges. Systematic biases occurred when ACF or MCF were computed on discretized milking interval classes, leading to accuracy loss. An exponential regression model was proposed as an alternative model for estimating daily milk yields, which improved the accuracy. Characterization of ACF and MCF showed how they improved the accuracy compared with doubling AM or PM yield as the daily milk yield. All the methods performed similarly with equal AM and PM milkings. The methods were explicitly described to estimate daily milk yield in AM and PM milking plans. Still, the principles generally apply to cows milked more than 2 times a day and apply similarly to the estimation of daily fat and protein yields with some necessary modifications.

Abstract Background Many dairy cows experience a state of energy deficit as they transition from late gestation to early lactation. The aims of this study were to 1) determine if the development of hyperketonemia in early lactation dairy cows is indicated by their gut microbiome, and 2) to identify microbial features which may inform health status. We conducted a prospective nested case-control study in which cows were enrolled 14 to 7 days before calving and followed through their first 14 days in milk (DIM). Hyperketonemic cows (HYK, n = 10) were classified based on a blood β-hydroxybutyrate (BHB) concentration 1.2 mmol/L within their first 14 DIM. For each HYK cow, two non-HYK (CON, n = 20) cows were matched by parity and 3 DIM, with BHB

Cost-effective milking plans have been adapted to supplement the standard supervised twice-daily monthly testing scheme since the 1960s. Various methods have been proposed to estimate daily milk yields (DMY), focusing on yield correction factors. The present study evaluated the performance of existing statistical methods, including a recently proposed exponential regression model, for estimating DMY using 10-fold cross-validation in Holstein and Jersey cows. The initial approach doubled the morning (AM) or evening (PM) yield as estimated DMY in AM-PM plans, assuming equal 12-h AM and PM milking intervals. However, in reality, AM milking intervals tended to be longer than PM milking intervals. Additive correction factors (ACF) provided additive adjustments beyond twice AM or PM yields. Hence, an ACF model equivalently assumed a fixed regression coefficient or a multiplier of “2.0” for AM or PM yields. Similarly, a linear regression model was viewed as an ACF model, yet it estimated the regression coefficient for a single milk yield from the data. Multiplicative correction factors (MCF) represented daily to partial milk yield ratios. Hence, multiplying a yield from single milking by an appropriate MCF gave a DMY estimate. The exponential regression model was analogous to an exponential growth function with the yield from single milking as the initial state and the rate of change tuned by a linear function of milking interval. In the present study, all the methods had high precision in the estimates, but they differed considerably in biases. Overall, the MCF and linear regression models had smaller squared biases and greater accuracies for estimating DMY than the ACF models. The exponential regression model had the greatest accuracies and smallest squared biases. Model parameters were compared. Discretized milking interval categories led to a loss of accuracy of the estimates. Characterization of ACF and MCF revealed their similarities and dissimilarities and biases aroused by unequal milking intervals. The present study focused on estimating DMY in AM-PM milking plans. Yet, the methods and relevant principles are generally applicable to cows milked more than two times a day.

Classroom observation tools are used to evaluate teaching and learning activities, and to provide constructive feedback to instructors. To help instructors with selecting a suitable tool based on their needs and available resources, in this study, a group of observers assessed lectures of an introductory biology course using three, broadly cited classroom assessment tools in the STEM field: the Classroom Observation Protocol for Undergraduate STEM (COPUS); the Practical Observation Rubric to Assess Active Learning (PORTAAL); and the Decibel Analysis for Research in Teaching (DART). From a user’s perspective, we evaluated 1) the type and extent of information each tool provides, and 2) the time investment and difficulty of working with each tool. The assessment result of each tool was compared, with a list of expected outcomes generated by surveying a group of college instructors and with the result of a self-teaching assessment tool, Teaching Practices Inventory (TPI). Our findings conclude that each tool provided valuable assessment with a broad range of outcomes and time investment: PORTAAL offered the most detailed information on the quality of teaching practices and students’ engagement, but it demanded the greatest time investment. DART provided a basic estimation of active learning proportion with the least effort. The level of assessment outcome and the time investment when using COPUS was found to be less than PORTAAL, and more than DART. The TPI self-assessment outcome was found to be slightly optimistic regarding the proportion of active learning practices used in the studied course. This comparative study can help instructors in selecting a tool that suits their needs and available resources for a better assessment of their classroom teaching and learning.

Our objectives were to robustly characterize a cohort of Holstein cows for udder and teat type traits and perform high-density genome-wide association studies for those traits within the same group of animals, thereby improving the accuracy of the phenotypic measurements and genomic association study. Additionally, we sought to identify a novel udder and teat trait composite risk index to determine loci with potential pleiotropic effects related to mastitis. This approach was aimed at improving the biological understanding of the genetic factors influencing mastitis. Cows (N = 471) were genotyped on the Illumina BovineHD777k beadchip and scored for front and rear teat length, width, end shape, and placement; fore udder attachment; udder cleft; udder depth; rear udder height; and rear udder width. We used principal component analysis to create a single composite measure describing type traits previously linked to high odds of developing mastitis within our cohort of cows. Genome-wide associations were performed, and 28 genomic regions were significantly associated (Bonferroni-corrected p < 0.05). Interrogation of these genomic regions revealed a number of biologically plausible genes whicht may contribute to the development of mastitis and whose functions range from regulating cell proliferation to immune system signaling, including ZNF683, DHX9, CUX1, TNNT1, and SPRY1. Genetic investigation of the risk composite trait implicated a novel locus and candidate genes that have potentially pleiotropic effects related to mastitis.