A rapid and accurate method to determine or predict cattle diet quality is essential to
effectively manage free-ranging cattle production. One popular tool currently available
for predicting cattle diet quality is fecal Near Infrared Reflectance Spectroscopy (NIRS)
profiling, which requires considerable time and financial investment. Two approaches
were taken to develop a replacement of NIRS fecal analysis for predicting real-time
cattle diet quality. The first approach took advantage of a standing forage quantity
monitoring and prediction model, and its animal diet selection sub model to model
cattle diet quality. The second approach tested if a direct relationship is present between
cattle diet quality and a simple temperature driven variable.
The model used in the first approach is Phytomass Growth Model (PHYGROW). Using
the Growing Degree Days (GDD) concept, forage crude protein estimation equations
were developed. Coupled with PHYGROW diet selection sub model, cattle diet quality
values were modeled. The validation study revealed good correlation between predicted
diet quality and observed diet quality (r2=0.84). The Grazing Animal Nutrition lab (GAN lab) commercial fecal NIRS analyzing data
for Major Land Resource Area 42 (MLRA 42) was used to analyze the relationship
between GDD and cattle diet crude protein (CP). Repeatable high quality regressions
were found for CP and GDD. A simple temperature based model was then developed to
predict cattle diet quality for regional use. Another independent dataset for MLRA 116B
from the GAN lab fecal NIRS data and a controlled grazing study were used to validate
the relationship. The study showed that using GDD to predict cattle diet quality is a
dependable tool, but regional specific relationships need to be developed.
The two developed models set the foundation for remotely predicting cattle diet quality
for effectively managing cattle production. The approaches also set the framework for
developing broader applications for other animal species.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-3061 |
| Date | 15 May 2009 |
| Creators | Zhang, Yingjie |
| Contributors | Kreuter, Urs |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | electronic, application/pdf, born digital |
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