In Western Canada an NIR network project was launched with the goal of improving livestock feeding programs for optimal cattle health and production, as well as increasing competitivity in the Canadian beef and and crop industries.
By Andy Harding, Graduate student at Feedlot Health Management Services
Feed cost of grain accounts for 65-80% of the total cost of feedlot cattle production. Characterizing the inherent variability in feedstuffs using wet chemistry takes a considerable amount of time and is expensive. With the use of NIR, real-time nutrient compositions can be measured. Data generated by the NIR network have shown that current physical measurements of grains entering feedyards such as weight, and appearance have little correlation with the nutrient profile as characterized by wet chemistry and NIR. With the information provided by NIR, a beef feedlot can access individual load samples of feedstuffs and estimate the nutrient profile and its potential for production. However, the difficult part to understand is if these potential changes in composition can have a positive or negative effect on cattle performance and ultimately affect the bottom-line in these operations.
The first step
Feedlot Health Management Services, Ltd, a feedlot consulting company in Okotoks, Alberta (FHMS), Canada, began a project funded by the Alberta Crop Industry Development Fund (ACIDF), with the goal of describing the variation in feedstuffs entering feedlots in western Canada. The network is comprised of ten instruments—nine at commercial feedlots and one at the FHMS headquarters. Shortly after installation, participating feedlots began scanning feedstuffs on arrival. Many of these samples are being retained for further NIR and wet chemistry analyses. In addition, this project has supported the training of highly qualified personnel within the Canadian feedlot industry by funding two post-graduate students and providing them with expertise in NIR and feedlot production.
The first step in the development of the network was to set up each of the nine NIR machines and to train feedlot personnel to properly scan samples. By having the ability to train the graduate students they have gained some “real world” experience, and also been instrumental in calibration validation and potential development of potentially important parameters to evaluate moving forward. Part of the experiences they were involved in were to organize and standardize the sampling procedures for each feedlot and help manage daily data collection. The students have also been responsible for describing the populations of feedstuffs received at feedlots in Alberta and disseminating that information through graduate student conferences, theses, and publications in peer-reviewed journals.
The power of the network
Some of the feedstuff characteristics which have been described include describing the proximate analysis of NIR selected samples via laboratory analysis relative to each samples NIR estimates, in vitro gas production kinetics of these selected grain types, and in vitro dry matter digestibility. Other outcomes of the project have included improvement of current prediction models and development of new prediction models for new commodities. Producers will ultimately be able to utilize this technology for management of localized “populations” of grains specific to their production systems.
The power of the network stems from the number of samples being scanned. With nine commercial feedlots scanning a large percentage of the loads of commodities they procure on a daily basis, thousands of scans can be generated in a relatively short amount of time. This tremendous bank of scans is then made available to each of those producers through the overlying network—a tremendous benefit of their membership in the network. FHMS is also currently developing an index that will give producers the ability to quantify variation in barley grain as expressed through animal performance. A feeding trial utilizing nearly 1,000 head is currently being conducted to test the efficacy of a proprietary FHMS-NIR cattle performance index.
Decreasing the variation in feedstuffs
The goal of this project was to find ways to make Canadian agriculture continue to be competitive by improving livestock feeding programs, through developing differential feeding programs based on feedstuff characteristics as estimated by NIR. The goal in time would be to decrease the variation in feedstuffs delivered to feedlots through feedback to commodity suppliers, and plant breeders by optimizing future grain-types for beef production. This project was designed to be beneficial to the Canadian beef industry, the crop industries, and potentially other agricultural stakeholders, while at the same time improving feedlot cattle health and production.
The team working on the NIR project at Feedlot Health Management Services consists of Dr. Matthew May, Feedlot Nutrition and Production Consultant, Dr. Luis O. Burciaga-Robles, managing partner at Feedlot Health Management Services since 2011, Clinton R. Krehbiel, Department of Animal Science at Oklahoma State University and Charlotte O’Neill and Andy Harding, both Master of Science candidates in Animal Science at Oklahoma State University, doing their graduate studies under the direction of Dr. Clint Krehbiel.