A general belief across the dairy community, both scientific and commercial, is that of an antagonistic association between milk production and reproductive performance of dairy cows. In this article, we critically review the evidence supporting this belief and discuss some of its limitations. Based on the fundamental principles of experimental design and inference, we consider relevant issues that, although critical to the very foundation of the perceived production-reproduction antagonism, seem to have been previously misrepresented or overlooked. In particular, we focus on issues of confounding, randomization, nature of inference, single- versus multiple-trait modeling, cow- versus herd-level modeling, and scope of inference, all within the context of dairy production systems. Taken together, these issues indicate that the production-reproduction antagonism may not be as pervasive as previously believed, suggesting the need for more rigorous methods of scientific investigation on this matter. We revisit the association between milk production and reproductive performance using a novel interdisciplinary approach based on cutting-edge statistical methods that accommodate some of the unique and previously ignored features of this problem. In fact, recent work supports a highly heterogeneous association between milk production and reproductive performance, whereby heterogeneity is partitioned across several scales and driven by many contributing factors, both physiological and managerial. We conclude that the relationship between milk production and reproductive performance is not necessarily that of a universal homogeneous antagonism and suggest better ways to study and even manage this association. A more comprehensive assessment that draws expertise from multiple scientific disciplines will be required to elicit management recommendations targeted to effectively optimize overall performance of dairy cows and commercial herds.

Sound design of experiments combined with proper implementation of appropriate statistical methods for data analysis are critical for producing meaningful scientific results that are both replicable and reproducible. This communication addresses specific aspects of design and analysis of experiments relevant to the dairy sciences and, in so doing, responds to recent concerns raised in a letter to the editor of the Journal of Dairy Science regarding journal policy for research publications on pen-based animal studies. We further elaborate on points raised, rectify interpretation of important concepts, and show how aspects of statistical inference and elicitation of research conclusions are affected.

Reproducible results define the very core of scientific integrity in modern research. Yet, legitimate concerns have been raised about the reproducibility of research findings, with important implications for the advancement of science and for public support. With statistical practice increasingly becoming an essential component of research efforts across the sciences, this review article highlights the compelling role of statistics in ensuring that research findings in the animal sciences are reproducible-in other words, able to withstand close interrogation and independent validation. Statistics set a formal framework and a practical toolbox that, when properly implemented, can recover signal from noisy data. Yet, misconceptions and misuse of statistics are recognized as top contributing factors to the reproducibility crisis. In this article, we revisit foundational statistical concepts relevant to reproducible research in the context of the animal sciences, raise awareness on common statistical misuse undermining it, and outline recommendations for statistical practice. Specifically, we emphasize a keen understanding of the data generation process throughout the research endeavor, from thoughtful experimental design and randomization, through rigorous data analysis and inference, to careful wording in communicating research results to peer scientists and society in general. We provide a detailed discussion of core concepts in experimental design, including data architecture, experimental replication, and subsampling, and elaborate on practical implications for proper elicitation of the scope of reach of research findings. For data analysis, we emphasize proper implementation of mixed models, in terms of both distributional assumptions and specification of fixed and random effects to explicitly recognize multilevel data architecture. This is critical to ensure that experimental error for treatments of interest is properly recognized and inference is correctly calibrated. Inferential misinterpretations associated with use of P-values, both significant and not, are clarified, and problems associated with error inflation due to multiple comparisons and selective reporting are illustrated. Overall, we advocate for a responsible practice of statistics in the animal sciences, with an emphasis on continuing quantitative education and interdisciplinary collaboration between animal scientists and statisticians to maximize reproducibility of research findings.

Advanced methods for dose–response assessments are used to estimate the minimum concentrations of a nutrient that maximizes a given outcome of interest, thereby determining nutritional requirements for optimal performance. Contrary to standard modeling assumptions, experimental data often present a design structure that includes correlations between observations (i.e., blocking, nesting, etc.) as well as heterogeneity of error variances; either can mislead inference if disregarded. Our objective is to demonstrate practical implementation of linear and nonlinear mixed models for dose–response relationships accounting for correlated data structure and heterogeneous error variances. To illustrate, we modeled data from a randomized complete block design study to evaluate the standardized ileal digestible (SID) Trp:Lys ratio dose–response on G:F of nursery pigs. A base linear mixed model was fitted to explore the functional form of G:F relative to Trp:Lys ratios and assess model assumptions. Next, we fitted 3 competing dose–response mixed models to G:F, namely a quadratic polynomial (QP) model, a broken-line linear (BLL) ascending model, and a broken-line quadratic (BLQ) ascending model, all of which included heteroskedastic specifications, as dictated by the base model. The GLIMMIX procedure of SAS (version 9.4) was used to fit the base and QP models and the NLMIXED procedure was used to fit the BLL and BLQ models. We further illustrated the use of a grid search of initial parameter values to facilitate convergence and parameter estimation in nonlinear mixed models. Fit between competing dose–response models was compared using a maximum likelihood–based Bayesian information criterion (BIC). The QP, BLL, and BLQ models fitted on G:F of nursery pigs yielded BIC values of 353.7, 343.4, and 345.2, respectively, thus indicating a better fit of the BLL model. The BLL breakpoint estimate of the SID Trp:Lys ratio was 16.5% (95% confidence interval [16.1, 17.0]). Problems with the estimation process rendered results from the BLQ model questionable. Importantly, accounting for heterogeneous variance enhanced inferential precision as the breadth of the confidence interval for the mean breakpoint decreased by approximately 44%. In summary, the article illustrates the use of linear and nonlinear mixed models for dose–response relationships accounting for heterogeneous residual variances, discusses important diagnostics and their implications for inference, and provides practical recommendations for computational troubleshooting.

Understanding causal mechanisms among variables is critical to efficient management of complex biological systems such as animal agriculture production. The increasing availability of data from commercial livestock operations offers unique opportunities for attaining causal insight, despite the inherently observational nature of these data. Causal claims based on observational data are substantiated by recent theoretical and methodological developments in the rapidly evolving field of causal inference. Thus, the objectives of this review are as follows: 1) to introduce a unifying conceptual framework for investigating causal effects from observational data in livestock, 2) to illustrate its implementation in the context of the animal sciences, and 3) to discuss opportunities and challenges associated with this framework. Foundational to the proposed conceptual framework are graphical objects known as directed acyclic graphs (DAGs). As mathematical constructs and practical tools, DAGs encode putative structural mechanisms underlying causal models together with their probabilistic implications. The process of DAG elicitation and causal identification is central to any causal claims based on observational data. We further discuss necessary causal assumptions and associated limitations to causal inference. Last, we provide practical recommendations to facilitate implementation of causal inference from observational data in the context of the animal sciences.

Background: Insulinomas are the most common tumour of the endocrine pancreas in dogs. These malignant tumours have a high metastatic rate and limited chemotherapeutic options. The multi‐receptor tyrosine kinase inhibitor sunitinib malate has benefit in the treatment of metastatic insulinoma in people. Toceranib phosphate, an analogous veterinary agent, may provide benefit for dogs. Methods: A retrospective study describing the extent and duration of clinical outcomes and adverse events (AEs) in dogs diagnosed with insulinoma and receiving toceranib. Results: Records for 30 dogs diagnosed with insulinoma and having received toceranib were identified from a medical record search of five university and eight referral hospitals. The median progression‐free interval and overall survival time were 561 days (95% confidence interval (CI): [246, 727 days]) and 656 days (95% CI: [310, 1045 days]), respectively. Of the dogs for which the canine Response evaluation criteria for solid tumours tool could be applied, the majority (66.7%) showed either a complete response, partial response or stable disease. Time to clinical progression was associated with prior intervention and type of veterinary practice. Larger dogs were at increased risk for disease progression and death. No novel AEs were reported. Conclusions: Most dogs diagnosed with insulinoma and receiving toceranib appeared to have a clinical benefit. Randomised, prospective studies are needed to better elucidate and objectively quantify the potential effect and survival benefit of toceranib therapy for management of insulinoma in dogs.