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.
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.
