Recipient Organization
WASHINGTON STATE UNIVERSITY
240 FRENCH ADMINISTRATION BLDG
PULLMAN,WA 99164-0001
Performing Department
Veterinary Science
Non Technical Summary
This proposal will develop and implement a small scale field-based project to collect empirical data to establish a baseline framework to assess wellbeing in dairy cows in organic management systems. The approaches and data collected will be used to support a proposal to fund a regional assessment of wellbeing of organically managed dairy cows. We have 3 inter-related aims: We have 3 inter-related aims: 1. describe and quantify the underlying genetic background of cows (via traits) in a small sample of western organic dairy systems, 2. describe and quantify dairy cow phenotypes from a small sample of organic dairy farm systems including describing the range of homeorhetic responses for animals, and 3. match genetic traits to cow phenotype to identify the genetic and phenotypic influence on wellbeing within organic systems. The project is field based and we will work with 2 organic dairy herds located in Washington. On each farm, 50 cows will be enrolled. At enrollment, cows will be bled and whole blood used for genomic testing by a commercial service. Each cow will be described relative to traits for production and behavior. Enrolled cows will be followed for 10 months and production and health events recorded on a monthly basis. At each monthly visit, cows within 10-days of parturition and <120 days in milk will be bled and blood processed to quantitatively assess expression of select genes reflecting immune function and behavior. These data collected on a monthly interval will reflect cow phenotypes. The genetic trait data and phenome data will be combined with farm-based ecological data (management, weather, housing, etc) to inform a conceptual model of cow wellbeing. The model is based on SEM (structured equation modeling) with the goal to match genetic traits to cow phenotype and dairy system environment/ecology to identify and quantify the genetic, phenotypic, and environmental influences on successful animal phenotypes within organic systems.
Animal Health Component
80%
Research Effort Categories
Basic
20%
Applied
80%
Developmental
(N/A)
Goals / Objectives
Our study hypothesis is: organic dairy cow wellbeing (wellbeing is a latent variable of health, longevity, and productivity) is described by different genetic traits, phenotypes, and environment relative to conventionally managed dairy cows. This project will collect pilot data to understand the data to address the hypothesis with the following goals: 1. describe and quantify the underlying genetic background (via traits) of cows in a small sample of western organic dairy systems, 2. describe and quantify the range of dairy cow phenotypes in a small sample of organic dairy systems with a focus on the range of homeorhetic responses for animals, 3. Match genetic traits to cow phenotype and dairy system environment/ecology to identify the genetic, phenotypic, and environmental influence on successful animal wellbeing phenotypes within organic systems.
Project Methods
This proposal will develop and implement a small scale field-based project to collect empirical data and establish a baseline framework to assess wellbeing in dairy cows in organic management systems. Aim 1: We will enroll 2 organic dairy farms in Washington. We have secured agreement from veterinarians and an organic dairy cooperative to help us select the farms. We recognize that the principle drivers of management style are going to be conforming to Organic standards, but herd size, length of time as an organic dairy, and regional effects also are important elements but evaluating these elements is beyond the scope of this project. We will limit this study to herds that are USDA certified Organic, herd size > 200 milking cows and < 1000, and in business >5 years. Enrolling 2 herds will allow us to support the aims of the project within the proposed budget. However we will create a management inventory to describe these herds. The categories will relate to the requirements for being USDA organic certified[2] and based on the organizational structure described in farm-specific "Organic System Plans" (OSP) which each certified farm is required to complete and maintain.[13] The broad OSP categories are: outdoor access, crop production land, feed inventory (DMI), feeding system, wellbeing management, housing, pasture land and composition, and grazing season. Not specifically addressed in the OSP but to be included in our evaluation is a management inventory which includes: labor composition, management organization structure, consultant and advisors or information sources, continuing education, and motivation assessment. A specific set of questions for the management inventory will be similar to our previous on-farm surveys.[14-17] These surveys will be initially assessed at farm enrollment and then subsequently, on a quarterly basis, to capture changes in system management.On each of these two farms, 50 cows will be selected. Cows will be excluded from the study if they are to be removed from the herd or are non-pregnant at >200 days in milk. Pregnant heifers and cows are eligible to be enrolled. All study animals will be identified and enrolled during the first farm visit and randomly selected from the on-farm inventory. All selected cows will be genotyped and traits determined using a commercial system (Neogen, Ingenity© Prime, Lincoln NE). For each selected cow, a blood sample will be collected and handled per company recommendations and shipped to the company for genotyping and subsequent trait analyses. The system detects 42,000 genetic markers for Holstein and Jersey breeds. Traits identified by Canadian Organic producers as important to their systems are included in the Neogen system.[18] Trait profiles will be used in subsequent analyses as epidemiological classes.Aim 2: All cows and heifers (100) identified at enrollment will be followed for 10 months across two data scales. The first data scale is information collected on the farm relative to milk production and composition, reproduction, health, environmental adaptation, and environmental footprint. Phenotypes will be based on an animal's own performance with standard summary data points reflecting monthly milk production, reproductive success relative to times bred, days to conception, length of lactation, and observation of health events and herd longevity. These data are collected on the farm by on-farm management. The second data scale will compare RNA expression in study cows through 120 days in milk. Across the 10 month period, herds will be visited monthly and study cows within 10 days of parturition and no more than 120 days in milk will have a whole blood sample taken from the tail vein. This sampling scheme allows us to assess each study animal at least 1 time across the study period and for some animals multiple times. Blood will be collected using Tempus Blood RNA tube (Life Technologies-Applied Biosystems, Foster City,CA) which stabilizes and prevents degradation of RNA.[19] These samples are stored on ice for transport back to the laboratory and stored at -80C for subsequent gene expression analysis (the sample in the Tempus Blood RNA tube is directly assayed for gene expression). We have tentatively identified a set of genes associated with homeorhetic mechanisms impacting two areas: 1. immune system response, and 2. behavior and well-being. The immune system response will be evaluated through an assessment of genes coding for: IL-6R, IL-4R, IL-12B, TLR4, CXCR2, IL-15, IL-2, TNF-alpha, IFN-gamma, and IL-1beta. The behavior and well-being homeorhesis will be assessed using genes coding HTR2A, IL-1beta, TPH2, HTR1A, additional target genes related to serotonin, tryptophan, cortisol production, and non-coding bovine regulatory miRNA.[20] We propose to use a commercially available hybridization assay to quantify gene expression, nanoString nCounter gene expression system. The assay is widely used to assay gene expression associated in human and mouse cancer studies, but custom systems can be designed for specific purposes (nanoString, Seattle WA).[21] The assay uses a proprietary system for detection and this will be outsourced to a service laboratory at the University of Washington. Data from the expression assay is downloaded and bioinformatics is accomplished with software provided by nanoString (nSolver Analysis Software). Quality control assessment, normalization, and measurements of gene expression are facilitated through the software. Assay outcome will be a comparative quantification of expression of the targeted genes.There are several steps to data analysis. In aims 1-2 we have a collection of data values: genetic trait profiles, farm-level attributes, and cow phenotypic data. For these data, the first analysis step is to establish the range of observed genetic and phenotypic traits (including gene expression) and establish the level of correlation between these traits. Given these data are multidimensional, we will first attempt dimension reduction using Principle Component Analysis or clustering approaches.[22] Though we will enroll only two farms, we will evaluate the prevalence of traits by farm, and multivariate evaluation of traits conditional on farm size, length of time as an organic dairy farm, and location. In addition, we will create summaries of ecology/system inventories as a pilot approach to categorize farm ecology. An additional outcome is to assess the status of the workforce and training needs for producers and employees which will be used to support future integrated research and extension projects. The data associated with expression will also be evaluated as life-event dependent outcomes in a longitudinal evaluation of phenotypes.The conceptual model of cow wellbeing is based on SEM (structured equation modeling) with the goal to match genetic traits to cow phenotype and dairy system environment/ecology to identify and quantify the genetic, phenotypic, and environmental influences on successful animal phenotypes within organic systems. The analysis conforms to the structure used in SEM.[23] For our proposed project, we have three latent variables: "wellbeing" as the outcome phenotype, "cow genetic base" and "dairy ecology". Each of these latent variables has empirical data that partially represent these latent variables. The first step in the analysis is to develop a conceptual meta-model for wellbeing in causal pathways associated with genetic traits, phenotypes, and dairy ecology described and collected in this project. An SEM will be developed based on the ideas embodied in a meta-model of ecological diverstiy.[24] Indicators for our conceptual construct will be chosen from the set of variables collected in Objectives 1-2 and computed quantities.