First published in Milling and Grain, March 2015
Swine production has been facing substantial economic challenges in recent years, due to poor crop yields and increased competition for raw materials from the biofuel industry. As a consequence, feed prices have been variable and more industrial by-products have become available. At the same time, we have experienced increasing sustainability demands on animal production, for example to reduce nutrient release in effluent, while producing more and cheaper food for an increasing world population. All this has driven the swine industry to implement more professional, accurate and precise practices.
With feed costs accounting for 50 to 80 percent of total variable production costs, nutrition continues to be an area of major focus. The key target for nutritionists is to provide the animal with the correct amount of nutrients to support optimal performance. Both excess and a lack of nutrients are likely to result in economic losses, through higher costs and/or lower animal performance.
Thus, it is important for the nutritionist and raw material purchaser to have correct information on the composition and nutritional value of available ingredients. Accurate and regular analysis of feedstuffs and complete feed, to confirm diets are correctly formulated, is a key quality control measure.
To ensure consistency in diets, nutritionists traditionally used proximate analysis from approved laboratories where ingredients and feeds are analysed for their nutritional contents. Unfortunately, the majority of these analyses are time-consuming and expensive which restricts the number of samples that can be analysed and creates a delay between sampling and receiving results of the analyses. Alternatively, a Near Infra-red (1100-2500 nm wavelength) Reflectance spectrometer (NIR) can be used to predict composition, as this technology is cost effective and fast.
This allows nutritionists to get almost immediate feedback on in-coming ingredients and out-going feeds, and to analyse many more samples at a much-reduced cost. However, NIR has much greater potential uses in animal production. This article will discuss the use of NIR in feedstuff analysis and diet formulation, and opportunities to extend this technology beyond standard analysis to support greater efficiencies in swine production.
Predicting feed composition
NIR can predict chemical and physical properties by relating vibrational spectra obtained on a set of known samples to reference analytical methods performed on the same sample set. The resulting calibration can be used to predict the composition of unknown samples of the same type of materials. NIR offers important advantages over traditional methods, in that it is rapid, non-destructive, requires no chemicals and hence produces no waste. It is easy to operate, once calibrated, and requires minimal sample preparation.
It is common practice for nutritionists to formulate diets with average compositional data for ingredients, taking either a book value or actual analytical data, and often a safety margin based on the expected variability in the data. Safety margins can vary, depending on the formulator and the feedstuff, usually varying between zero (average data used) and one standard deviation from the average. Adjusting the nutritional value of the ingredient based on the standard deviation ensures that the majority of feeds produced will provide the expected level or higher of any nutrient.
NIR has been used in the feed industry for over 30 years, and is now approved by the AOAC to determine moisture, nitrogen (crude protein) and acid detergent fibre (ADF) in feed and forages. However, there is some scepticism across the industry regarding the accuracy of NIR to predict feed composition relative to wet chemistry. Some of this is due to the use of poor or inappropriate NIR calibrations, and some to poor sampling techniques; NIR can only predict the composition of samples similar to those used to develop the calibration, and the variation can never be less than that of the methods used to provide the data build the calibration.
It is common to assume that a wet chemistry result is always better than a NIR result; however, Undersander (2006) reported that when crude protein results differ, a re-run of the wet chemistry agreed with the NIR 80 percent of the time. This demonstrates that, as might be expected, there is less risk of making a mistake when taking a NIR spectrum than when running a laboratory analysis. However, the real advantage of NIR is that it is cheaper and quicker to analyse a number of samples for a range of analyses than to run one wet chemistry analysis, giving the formulator a much more complete real-time picture of the overall composition as well as variation within feed ingredients.
Predicting nutritive value
Feedstuffs are usually purchased on the basis of parameters such as test weight and crude protein content, both unrelated to a greater or lesser degree to their value in feed. Consequently, Rao (2012) indicated that approximately half of incidences of poor performance in a US commercial broiler company were related to the use of incorrect feedstuff nutritive values. The traditional method of predicting the energy value of feedstuffs or feeds is to use any of a number of published equations to calculate the productive energy from the analysed nutrient content.
These equations are usually developed from trials where a diet of known composition was fed to the target animals and the productive value, such as net or digestible energy, determined. The weaknesses of this approach are well known; for example, the assay methods used to develop the prediction equations may be different from those used to analyse the feedstuffs in question, and the feedstuffs or diets used in the animal trials may not represent those used commercially. Further, animal trials are prohibitively expensive and time-consuming. The production advantages of accurate feed formulations, based on NIR analyses rather than book values, in promoting extra broiler performance was recently demonstrated by Soto et al. (2013).
Starting in 1996, a major research program has been undertaken in Australia to develop NIR calibrations to predict the nutritive value of commonly used feedstuffs across several animal species, including ruminants, pigs and poultry. Close to 4000 cereal grain and protein feedstuffs were surveyed, and over 350 of these were fed to animals (>100 for swine) to determine available energy and intake index as well as composition, reactive lysine and standardised ileal amino acid digestibility (Black and Spragg, 2010, Black et al., 2014).
The energy value (faecal DE) of cereals for pigs varied within and between cereal types (Table 1), ranging to as high as 4 MJ/kg for barley. It was estimated that, taking cereals at US$250/t as an example, a 1 MJ/kg difference would be worth between US$15-20/t in swine feed. With well over 100 million tons of cereals used in swine feeds per annum, this equates to potential savings of several billion dollars to the swine feed sector worldwide for energy alone!
Analysing all incoming feed raw materials, even by wet chemistry, would be both time consuming and expensive and the delay in receiving results would make this practically ineffective. However, this Australian project has used animal data to develop NIR calibrations to predict energy content and intake index (from 0 - 100) as well as composition, allowing incoming raw materials to be quickly analysed and segregated on arrival at the mill.
The value of using NIR to determine the composition of in-coming feedstuffs has recently been demonstrated by an integrated UK company. By simply segregating in-coming wheat and soybean meal into either high- and low-protein bins for each, this company was able to save over US$3 per ton in feed formulations as well as close to US$20000 per annum on wet chemistry costs. As indicated above, extending this to the more variable energy value would save much greater sums.
High phosphate prices, increasing environmental pressures and more effective enzyme products have encouraged feed manufacturers to increasingly replace inorganic phosphates with phytases. However, the extent of phosphorus release by phytases depends to a large extent on the phytate content of the diet. As phytate levels can vary between and within feedstuffs, it is difficult to accurately predict the phytate content of a final feed. While several laboratory methods are available to determine phytate levels in feeds, these are all relatively expensive and time consuming. Recently, NIR calibrations based on an enzymatic laboratory method were developed to give the real-time prediction of the phytate content of feedstuffs and diets, allowing feed manufacturers to maximise phytase inclusion and thus feed cost savings (Santos and Bedford, 2012).
Delivering NIR services
Today NIR equipment is usually laboratory based and loaded with appropriate calibrations. This presents some challenges; for example, sample delivery to the laboratory can result in delays that eradicate the advantages of speed of analysis. Further, calibrations quickly become outdated; this requires updated calibrations to be updated on a frequent and on-going basis.
Recent developments in NIR hardware have allowed the production of robust, portable, battery-operated units. This allows the analysis to be carried out at the point of interest, for example at the grain silo or feed mill intake. Further, in-line NIR equipment is currently available that allows feedstuffs to be monitored during harvesting or feeds to be continually analysed during production in the feed mill.
Software and communications developments have allowed web-enabled NIR services, where spectra are downloaded to a master machine containing all appropriate calibrations, with instantaneous feedback. This has several advantages; for example the analyst can pay on an “as-used” basis rather than paying a fixed up-front fee for a calibration, independent of sample numbers. This can also give the analyst access to a wide range of calibrations, and the calibrations can be updated regularly as they essentially sit on one computer.
Novel uses of NIR
Beyond the standard prediction of dietary composition, NIR use has recently been extended into, for example, sample identification. Work in other areas suggests that, providing suitable standards are available, NIR can be used to confirm the growing condition of feedstuffs. Another example of an extension of NIR technology is the determination of mixer profiles in feed manufacture. Mixer profiles are usually determined by analysing the variation (percent CV) in components such as salt/sodium or protein/nitrogen in 5-10 feed samples.
However, this approach will include the variation in the assay procedure used to analyse the component chosen, and the result could thus be considered to only apply to the specific component analysed. Thus, if sodium is chosen, the mixer profile will reflect primarily the variability in the dispersion of added salt. This can be overcome by looking at the CV across the NIR spectra of a series of samples. The CV as estimated from ten samples of feed taken from a mixer run for 1-5 minutes clearly shows an optimal mixing time of 3-4 minutes, and that the NIR gives the same result as the analysis of specific feed components, but with lower variability (Table 2).
NIR is currently used to analyse feedstuff and feed compositions for quality control within the swine feed industry. However, developments in hardware and software present the possibility of using this technology to determine the value of incoming raw materials as well as to control in-line and in real-time the accuracy of feed formulations. This is potentially worth several billion dollars in terms of feed cost savings and more predictable animal performance for the worldwide swine industry. In the future we can expect to see laboratory, hand-held and in-line NIR equipment used widely in the purchase feedstuffs and in feed manufacture.
References available on request.
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