The goal of this body of work is to explore various aspects of data analytics (DA) and its applications in agriculture. In our research, we produce decisions with mathematical models, create models, evaluate existing models, and review how certain models are best applied. The increasing granularity in decisions being made on farm, like individualized feeding, sub-plot level crop management, and plant and animal disease prevention, creates complex systems requiring DA to identify variance and patterns in data collected. Precision agriculture requires DA to make decisions about how to feasibly improve efficiency or performance in the system. Our research demonstrates ways to provide recommendations and make decisions in such systems.
Our first research goal was to clarify research on the topic of endophyte-infected tall fescue by relating different infection-measuring techniques and quantifying the effect of infection-level on grazing cattle growth. Cattle graze endophyte-infected tall fescue in many parts of the U.S and this feedstuff is thought to limit growth performance in those cattle. Our results suggest ergovaline concentration makes up close to 80% of the effect of measured total ergot alkaloids and cattle average daily gain decreased 33 g/d for each 100ppb increase in ergovaline concentration. By comparing decreased weight gain to the costs of reseeding a pasture, producers can make decisions related to the management of infected pastures.
The next research goal was to evaluate experimental and feed factors that affect measurements associated with ruminant protein digestion. Measurements explored were 0-h washout, potentially degradable, and undegradable protein fractions, protein degradation rate and digestibility of rumen undegradable protein. Our research found that the aforementioned measurements were significantly affected by feedstuff characteristics like neutral detergent fiber content and crude protein content, and also measurement variables like bag pore size, incubation time, bag area, and sample size to bag area ratio. Our findings suggest that current methods to measure and predict protein digestion lack robustness and are therefore not reliable to make feeding decisions or build research models.
The first two research projects involved creating models to help researchers and farmers make better decisions. Next, we aimed to produce a summary of existing DA frameworks and propose future areas for model building in agriculture. Machine learning models were discussed along with potential applications in animal agriculture. Additionally, we discuss the importance of model evaluation when producing applicable models. We propose that the future of DA in agriculture comes with increasing decision making done without human input and better integration of DA insights into farmer decision-making.
After detailing how mathematical models and machine learning could be used to further research, models were used to predict cases of clinical mastitis (CM) in dairy cows. Machine learning models took daily inputs relating to activity and production to produce probabilities of CM. By considering the economic costs of treatment and non-treatment in CM cases, we provide insight into the lack of applicable models being produced, and why smarter data collection, representative datasets, and validation that reflects how the model will be used are needed.
The overall goal of this body of work was to advance our understanding of agriculture and the complex decisions involved through the use of DA. Each project sheds light on model building, model evaluation, or model applicability. By relating modeling techniques in other fields to agriculture, this research aims to improve translation of these techniques in future research. As data collection in agriculture becomes even more commonplace, the need for good modeling practices will increase. / Doctor of Philosophy / Data analytics (DA) has become more popular with the increasing data collection capabilities using technologies like sensors, improvement in data storage techniques, and expanding literature on algorithms that can be used in prediction and summarization. This body of work explores many aspects of agricultural DA and its applications on-farm. The field of precision agriculture has risen from an influx of data and new possibilities for using these data. Even small farms are now able to collect data using technologies like sensor-equipped tractors and drones which are relatively inexpensive. Our research shows how using mathematical models combined with these data can help researchers produce more applicable tools and, in turn, help producers make more targeted decisions. We examine cases where models improve the understanding of a system, specifically, the effect of endophyte infection in tall fescue pastures, the effect of measurement on protein digestibility for ration formulation, and methods to predict sparse diseases using big data. Although DA is widely applied, specific agricultural research on topics such as model types, model performance, and model utility needs to be done. This research presented herein expands on these topics in detail, using DA and mathematical models to make predictions and understand systems while utilizing applicable DA frameworks for future research.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/106592 |
| Date | 19 May 2020 |
| Creators | Liebe, Douglas Michael |
| Contributors | Animal and Poultry Sciences, White, Robin R., Easton, Zachary M., Persia, Michael E., House, Leanna L. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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