Recipient Organization
WASHINGTON STATE UNIVERSITY
240 FRENCH ADMINISTRATION BLDG
PULLMAN,WA 99164-0001
Performing Department
Veterinary Science
Non Technical Summary
A critical gap in our understanding of organic dairy systems is defining the inter-relationships between pastures and animals that optimize system resilience and wellbeing. This field-based proposal has two objectives: 1) Develop a framework characterizing the nature of this interactive system of pastures and animals that defines a resilient organic dairy. 2) Develop an extension program that utilizes this knowledge gained to improve the resilience of commercial organic dairies. We will describe the qualities of these central elements and investigate their interactions as indicators of system resilience/wellbeing. Pasture qualities include diversity, density, dry matter, nutrient quality, and root structure. Cow "qualities" include the fecal microbiome, health indices (e.g., lameness, mastitis, postpartum disease), and production levels. A co-occurrence network analysis approach will be used to determine which specific pasture factors from different sampling periods can define specific microbial community structures associated with animal health and production. This analysis will enable us to classify fecal samples into groups according to similarities and differences in the microbial structure across different pasture compositions influencing health indices and wellbeing. The metrics for wellbeing are resilience of a system (pasture, animal) to both recover from short term perturbations and to express its potential across a range of environments. Ultimately, our project is focused on advancing on-farm research, helping producers improve pasture health, demonstrating tools for Extension personnel who advise organic pasture-based producers, and improving organic systems-based animal production and health performance dependent upon pasture management. Our extension will deliver and interpret research results and develop tools to implement assessments of pasture and animal resilience and wellbeing.
Animal Health Component
25%
Research Effort Categories
Basic
50%
Applied
25%
Developmental
25%
Goals / Objectives
The food systems in the U.S. (and around the globe) are changing. While the need for providing a sufficient quantity of nutritious food is still paramount, the nature of how food is produced is becoming more important to consumers. For food animal production systems, organic systems are rapidly becoming a key component of the U.S. food system and a model for sustainability. One of the important gaps in our understanding of organic dairy systems is the attributes and inputs to sustain a fully integrated soil, pasture, and animal system able to sustain high levels of resilience and productivity, i.e., system wellbeing. The methods and approaches described in our project support the development and maintenance of an Organic System Plan by demonstrating better documentation of health events, accurate assessments of soil and pasture health and utilization, and access to information to support the adoption of animal care processes. The novel aspects of our proposal are defining pasture and animal wellbeing and resilience and developing an integrated conceptual model that optimizes organic dairy systems.Our long-term research goal is to work with producers, veterinarians, and support consultants engaged in organic dairy production and focused on critical health, economic, and ecosystem issues on commercial dairy farms in the Pacific Northwest (PNW) of the U.S. Our long-term extension goal is to effectively disseminate and implement research findings to encourage and support a thriving organic livestock industry. This proposal is addressing the critical need to understand and manage the inherent variability within organic dairy pasture-based systems. We currently lack research outcomes and extension programs that can explain the range of pasture and animal variability, and define successful strategies for organic system resilience and wellbeing.This field-based proposal has two objectives: 1) Develop a framework characterizing the nature of this interactive system of pastures and animals that defines a resilient organic dairy and healthy cows. 2) Develop an extension program that utilizes this knowledge to improve the resilience of commercial organic dairies. Objective 1 contains three aims: 1) Develop and implement tools to objectively define the time-dependent quality of organic pastures. 2)Describe the temporal, management, and environmental impacts on the fecal microbiome of cows on participant herd. 3) Describe animals, demographics, productivity, health, reproduction, and herd "survival" as the phenotypes that underpin resilience and wellbeing for all cows enrolled in the study. A critical gap in our understanding of organic dairy systems is defining the interrelationships between pasture and animal qualities that optimize system resilience and wellbeing. Pasture qualities include diversity, density, dry matter, nutrient quality, and root structure. Cow qualities include fecal microbiomes, health (e.g., postpartum disease, lameness, mastitis), and production metrics. The metrics for wellbeing are resilience of a system (pasture, animal) to both recover from short term perturbations and to express its potential across a range of environments.
Project Methods
Experimental Design: This project will be conducted on an organic dairy farm located in Bow, Washington. The herd has been USDA-Organic certified since 2006 and houses approximately 700 lactating Holstein cows. Average milk production is 19 kg per day (4% fat, 3% protein). Animals receive conserved forage during the non-pasture months extending from approximately October through March. Throughout the rest of the year cows have access to mixed grass and legume pasture. The grazing management used in this farm is seasonal rotation and strip with irrigation as needed. Samples will be collected at four different time points to represent changes in pasture composition across seasons. At each sampling point, 54 cows will be enrolled in the study and allocated to one of the three groups (n=18 per group) according to their lactation stage (early lactation: 20-60 days in milk (DIM); mid-lactation 100-150 DIM, and late-lactation 200+ DIM). Within each group, cows will have the same pregnancy status, breed and production level (+/- 5kg). Therefore, we propose to collect and analyze 216 fecal samples from 216 cows. Fecal samples: Approximately 5 g of feces will be collected manually per rectum at enrollment. Samples will be transported to Pullman, on dry ice, for further processing within the Field Disease Investigation Unit laboratory. In the laboratory, samples will be frozen at - 80 °C until they are processed. At the time of processing, samples will be well mixed and 0.2 g sample removed and placed into a bead beating tube. The sample will be homogenized (Bead Mill 24, Fisher Scientific) following manufacturer's instructions. From the homogenized sample, DNA will be extracted using ZYMObiomics miniprep kit (Zymo Research, Irvine CA). Eluted DNA will be sent to Zymo Research for 16S-rRNA V3-V4 region amplification and subsequent sequencing. The resulting fastq files generated by 2.250 Illumina Miseq amplicon reads will be analyzed using the R (R Project for Statistical Computing) package DADA2. Taxonomy will be assigned to the ASV table using SPINGO according to the SILVA 123 database. Composition visualization, alpha-diversity, and beta-diversity analyses will be performed with Qiime v.1.9.1. Taxonomies that have significant abundance among different groups will be identified by linear discriminant analysis effect size (LEfSe). A co-occurrence network analysis approach will be used to determine which specific pasture factors (e.g., species diversity, density, dry matter, nutrients, and/or root structure) from different sampling periods can define specific microbial community structures. In addition, in order to associate specific fecal microbiome profiles with animal health and production we will use a cluster analysis. This analysis will enable us to classify fecal samples into groups according to similarities and differences in the microbial structure across different pasture compositions. These results will be compared to established literature regarding Archaea and bacterial species that have been reported as positively or negatively associated with low and high producing cows, health indices, and reproductive success. The TMR and silage samples will be used as a baseline for these comparisons. A co-occurrence network analysis will provide graphical representation of the relationship between variables and insight into bacterial and archaeal community structure underlying animal productivity (e.g., health, milk production, reproductive success). Importantly, this co-occurrence network analysis will be used to assess the Archaea community results alongside associated microbial communities. We expect to find that bacterial communities that produce less H2 during pasture fermentation will be associated with Archaea communities supportive of low CH4 emissions. Understanding the links between pasture composition and Archaeal and bacterial communities will transform our knowledge of how microbes interact with each other under different pasture compositions, and how pasture management may impact greenhouse gas emissions from organic dairy cows.There are two elements for perennial pasture quality assessment: attributes that affect pasture quality as a feed source and attributes that affect pasture ecosystem resilience and sustainability. Attributes supportive of ecosystem resilience and sustainability will include measures of species diversity that can complement the pasture to support animal wellbeing. Farm pasture assessments will include plant species, soil fertility management, pH, lime requirement test, cation exchange capacity, organic matter, lime, nitrogen, phosphorus, potassium, sulfur, calcium, magnesium, selected micronutrients, irrigation profile, liquid and solid manure applications, supplementary feedstuffs, seeding, use of confinement areas, and pasture productivity. Pasture samples: One day prior to the collection of fecal samples, we will collect and analyze 5 pasture samples per grazing paddock. Destructive quadrant hand clipped samples and clipped samples under the rising plate meter will be collected separately at a height of 7.5 cm (reflecting the grazing height), weighed, and stored in paper bags until they are delivered to Ag Health Laboratories, Sunnyside, WA. Samples will be ground using a 1 mm Wiley mill followed by 1 mm cyclone grinding for NIRS analysis. Predicted results from NIRS include but not limited to: crude protein, ash, ash free neutral detergent fiber, acid detergent fiber, lignin, water soluble carbohydrates, ethanol soluble carbohydrates, 30-hour digestible neutral detergent fiber, 48-hour digestible neutral detergent fiber, 48-hour in vitro digestible dry matter, Ca, K, P, and Mg. Outlier samples will be validated using wet laboratory procedures at the same commercial lab as NIRS. In addition, we propose to analyze 2 samples each of the total mixed ration (TMR) and silage offered to the cows during the grazing season and the winter to use as a baseline to compare against the nutritive values of the pasture composition during the grazing season.Ultimately the biological and economic outcome of an organic pasture-based dairy farm is reliant on having resilient cows that prosper under a dynamic but well managed farm system. Our hypothesis is: cow wellbeing as measured by health and performance is resilient to the system dynamics of soil, pasture, season, and management and conditional on cow genetics and time in the herd. The outcome is cow-level resilience as measured by deviations from overall herd performance with regard to lactation milk production, reproduction, and health maintenance. The principle analytic approach for evaluating the aim's hypothesis is evaluating resilience, i.e. stability of health, production, and reproduction relative to herd-mates. The focus of this aim is to define the impact of pasture quality on cow resilience. Using milk yield as an example, cow based milk yield (daily or monthly) will be used to construct a farm based lactation curve. The curve will be calculated using a similar approach described by Poppe et. al., using a fourth order polynomial stratified by primiparous and multiparous cows for yield by DIM. For a cow, variance between actual production and predicted production for DIM is determined and resilience is defined as relative deviation compared to herd-mates and trend for the animal. The general model form is:milk yield variance (DIM) = f(pasture quality category(DIM) + fecal microbiome category(DIM) + calving date+ErrorThis approach will be generalized to the individual and categorized cow phenotypes and their variance will be created to determine similarity clusters and develop an animal based resilience category. Categories will also be regressed as multinomial outcomes conditional on pasture and fecal microbiome index/category.