Basic Role of Feed Formulation
Feed formulation is a process by which our knowledge of the nutrition of the animal can be translated into feeding programs which achieve our production objectives. These objectives typically include optimizing growth performance and producing a final carcass that can be converted into safe, healthy and appealing consumer meat products. This must be done in a profitable manner that concurrently contributes to environmental sustainability and animal welfare. Ideally, feeding programs developed in this manner will also result in predictable performance outcomes (Patience, 2017).
Historically, feed formulation has been a process in which the nutrients supplied by available ingredients are matched in required proportions to meet energy and nutrient levels required by the pig for maintenance or maintenance plus some productive purpose such as growth, lactation or pregnancy. In all instances, there is an implied level of performance which is assumed in establishing energy and nutrient requirements. For example, NRC (2012) provides specific growth rates and daily ME intakes levels attached to their requirement tables. For pigs over 25 kg, assumed protein deposition rates are also provided. Feed efficiency can be calculated from the available information. Notably, NRC (2012) does not provide energy requirements, but rather provides the energy levels (DE, ME and NE) that correspond to a typical corn soybean meal; these energy values can be used to determine lysine:energy ratios, phosphorus:energy ratios, etc. to allow adjustment when local conditions dictate that a higher or lower energy level will be more economical. The important point here is that the nutrient requirements are not defined for a pig of a certain body weight range, but rather for a pig of a certain body weight range performing at a specific level. If pigs are performing at different levels, such as in terms of protein deposition rate or daily energy intake, the requirements must be adjusted accordingly.
To illustrate this point, consider a 50 to 75 kg pig growing 975 g/d and with a protein deposition rate of 165 g/d (17% of gain as body protein) might have an SID lysine requirement of 0.90% while a similar pig growing only 800 g/d (PD = 120 g/d; 15% of gain as body protein) might have a lysine requirement of only 0.75%. The importance of economic analysis becomes immediately apparent. Feeding a diet containing 0.90% SID lysine when the pig is only going to grow 800 g/d is not only a waste of nutrients, but the unnecessarily high cost of the diet compounds the economic penalty suffered by the producer whose pigs are unable to grow at the higher level.
To be fully successful in feed formulation, the nutritionist requires at least a basic understanding of many of the biological sciences, including physiology, chemistry, biochemistry and increasingly microbiology and immunology. For example, when a nutritionist is presented with a novel fat source, a number of issues come into play. They must understand its chemistry, in order to determine its quality and composition. They must understand its biochemistry to be able to estimate a reasonable energy value as well as evaluate if the fat source will possibly impact the metabolic oxidative status of the animal. And by understanding both chemistry and physiology, the nutritionist will understand the impact of this particular fat source on de novo lipid synthesis (Shurson et al., 2015; Kellner et al., 2017; Kellner and Patience, 2017).
Looking at the topic more broadly, nutritionists rarely formulate an individual diet without context, and that context is widely known as the feeding program or feed budget – the sequence of diets that sees the growing pig from weaning through to market or the glit/sow from gilt development through mating and weaning. In the case of the newly weaned pig, the nutrient and ingredient composition of the phase 2 diet will be strongly influenced by the composition and feed budget for the phase 1 diet. And the nutrient and ingredient composition of the phase 3 diet will similarly be impacted by that of the phase 2 diet, and so on. Consequently, it is the sequence of diets and not individual diets, which defines the true value of the nutritionist to the feed company or to the pork producer. All of this has to be accomplished within the context of restrictions imposed by a myriad of external forces such as feed mill capability and capacity, restrictions on delivery volumes and supply chain irregularities which have become so common in the past 18 months, to name a few.
Current Challenges in Feed Formulation
Diet formulation is fraught with many challenges, some of which are obvious and others which are quite subtle. For example, when developing a feeding program, should the focus be on the needs of the average pig, the superior pig, somewhere between the two, or even below the average pig? How is this decision affected by the fact that greater feed intake is at least in part supporting the greater performance of the superior pig compared to the average pig. For example, we have previously shown that up to half of these greater requirements are satisfied by above average feed intake (Patience, unpublished data). If formulating to the requirements of the superior pig, should this be the same in all circumstances, when feed prices are low and market prices are high as well as when feed prices are high and market prices are low? In other words, which is more important: maintaining maximal growth rate because barn throughput is so critical in most production systems or optimizing the return over feed cost, expressed on a per pig basis or barn turn basis? Is the decision different in a continuous flow barn compared with a barn operating on an all-in-all-out basis?
Another challenge is acquiring data on the pigs being fed. There is a clear lack of data on the nutrient requirements of animals with different genetic backgrounds. There is also a lack of understanding of how nutrient requirements may have changed due to genetic improvement over the past two or three decades. Do the more modern genotypes actually use nutrients with greater or less efficiency than their predecessors? Do modern genotypes have different maintenance requirements than their predecessors? The real question is whether differences in productivity (sows) and growth rate and feed efficiency (market hogs) explain higher nutrient requirements today compared with the past, or are they due to something more fundamental, such as efficiency of digestion, pace of basal metabolism or the efficiency of post-absorptive utilization. For such an important question, there is scant research upon which to base an opinion. Yet, nutritionists are dealing with this issue pretty much every day of the week. Another crucial bit of information which is frequently unavailable is the feed intake curve for specific batches of pigs being fed; if this information became available, nutritionists could be much more specific in their recommended feeding programs.
The source of ingredient data are anything but homogeneous. Some data have been generated by direct wet chemistry while others have been determined using NIR. Some of the data have been generated on only one or two samples of a particular ingredient, expecting it to be representative of all sources available in the marketplace. To illustrate this point, and using soybean hulls as an example, NRC (2012) has a value for its gross energy based on 1 sample. Dr. Hans Stein maintains an extensive database of nutrient profiles of some 200+ ingredients and has the results of testing only one sample for the DE, ME and NE on soy hulls. Moving closer to home in western Canada, NRC (2012) shows no values for the ME and NE of field peas and Stein shows an NE value based on 1 sample and an ME value based on 5 samples. For a more common ingredient, such as barley, NRC (2012) presents a DE value based on 8 samples and no values for ME or NE. Stein shows DE values based on 17 samples, ME based on 7 samples and NE based on 6 samples. Part of the reason for the low numbers in the NRC (2012) was their policy of using only values presented in refereed sources, which excluded many values presented on websites and in trade magazines; they did this, obviously, to maintain a certain level of quality control. However, the NRC and Stein databases provide the number of samples upon which their numbers are based, which is relatively unique in the work of ingredient evaluation.
In any event, such small numbers for such commonly used ingredients like barley is disappointing, especially when it is well known that its DE, for example, varies by about 15% (Fairbairn et al., 1999). Lopez et al. (2020) reported that, based on four or five samples from each nation, the ME value of soybean meal varies by 10% within Brazil and by 7% within the U.S. Soybean meal from Brazil, on average, had 2.6% greater ME than that sourced from the U.S. Li et al. (2014) evaluated 100 corn samples from the main corn growing regions of China and reported that the ME content varied by 8%, making it one of the most uniform basal ingredients available to the pig industry.
There is also the issue of combining data from different methodologies. For example, some NE values are based on indirect calorimetry and others are based on the use of prediction equations. Usually, the prediction equations are based on data derived from indirect calorimetry (Noblet et al., 1994). Some NE values have been derived from direct calorimetry, although this is rarely used today. Still other NE values have been determined using graded amounts of the test ingredient in growth studies based on feed efficiency (Boyd et al., 2010). Consequently, nutritionists must carefully vet the sources of their ingredient information to ensure they are not mixing methodologies. Evenso, within a methodology, there are differences in how the study was conducted, the nature of the animals used, the preparation of the diets and the interpretation of the results (Noblet et al., 2022).
Another key challenge in diet formulation is the assumption that nutrient utilization changes in a linear fashion, when there are now data showing that this is not completely true. For example, as fiber increases in the diet, the digestibility of other nutrients decline, but not in a linear fashion. Consequently, nutrient loading values employed in diet formulation may be correct at one level of fiber content, but not at another.
It is apparent that what outwardly appears to be a fairly straight forward mathematical process is, in fact, very complex due to the volume of information required to achieve success (Black, 2000). Despite these challenges, feed formulation has helped to move the feeding of animals forward in a very impressive way. For example, it has been estimated that between 1974 and 2020, average market weight increased 22% to 128 kg, average daily gain increased by 33% to 865 g/d and feed conversion has improved 18% to 2.80. In fact, market weight has increased by 23 kg but time to market has actually decreased by 5 days. While genetics, housing and other factors have certainly contributed to this success, nutrition and diet formulation have obviously played a critical role as well.
While feed formulation technology has served the industry well in the past, many challenges remain. Changes will occur in feed formulation as these, and other problems, get corrected. It will also evolve because the industry, and the world it operates in, is changing. The rest of this presentation will identify what I suggest, with all due humility, are some of the changes that we might expect in feed formulation in the coming decades. Please remember this is only one person’s opinion, although I discussed the topic with a number of colleagues that I hold in very high regard. I will not attempt to offer timelines for any of the proposed changes; some are already underway and will accelerate while others will transpire over many decades, but most will become part of our lives in the next decade or two.
Energy Systems vs Energy Modelling
Dietary energy represents perhaps the greatest challenge to nutritionists. It is by far the most expensive component of the diet, and it drives performance, including rate and efficiency of gain and carcass composition. The energy content of most ingredients is highly variable, as previously described. Compounding the challenge of energy is the fact that it is utilized with different efficiencies depending on the source of energy (non-starch polysaccharides vs starch vs protein vs fat); independent of source, its efficiency of utilization is also dependent on its fate in the body – maintenance vs protein accretion vs lipid accretion in the carcass (Black, 1995; Birkett and de Lange, 2001).
While measuring energy may be the most repeatable and accurate in the modern nutrition laboratory, such as by isoperibol bomb calorimetry, the most common method in use today, this type of gross measurement fails to provide the detail required to predict performance with a high degree of accuracy. For example, considering a diet containing 79% corn, 16% soybean meal and 2% choice white grease, approximately 71% of its net energy will be derived from starch, 14% from amino acids, 14% from fat, etc. If this diet is reformulated to include higher fiber ingredients such as 20% corn DDGS, 10% wheat middlings and 10% corn (corn declines to 41%, soybean meal drops to 11% and choice white grease increases to 5% to maintain constant net energy), only 54% of the net energy now comes from starch, 26% comes from fat and 18% from amino acids; it is difficult to estimate the energy derived from fibre, but it is quite low. Given our knowledge of how different energy sources are used with differing efficiencies, no classical energy system, not even one as refined as the net energy system, has the capability to provide the information needed to fully understand energy supply and energy utilization.
One possible solution could be an energy model which would be more dynamic than an energy system and thus provide the insight necessary to predict animal performance over a wide array of energy sources and levels, and pig genotypes. The value of such a system is apparent, because it will address the utilization of the most expensive component of the pig’s diet. As such, it has the greatest capacity to manage the cost of energy to deliver the most economical feeding program to the pig. Some of this is already accomplished to a greater or lesser extent by growth simulation models. This may be the most advantageous path to follow, because such models bring amino acids and phosphorus into the calculation along with energy (Birkett and de Lange, 2001; Van Milgen et al., 2008). Regrettably there are less than a handful of researchers operating in the public domain worldwide whose focus is on growth simulation modeling; fortunately, there is considerable activity in the private sector at the present time. This should be a matter of priority, because feed formulation needs this elevated level of sophistication to fulfil its commitment to optimum performance, maximum net income, minimal impact on the environment and accurate prediction of animal outcomes.
Expectation for the Future
The net energy system, or its relatives, will continue to expand in market share in North America, although DE and ME are unlikely to completely disappear. In the longer term, energy models which quantify how energy is supplied to the pig and used by the pig will become more sophisticated and more commonly utilized by the industry to most effectively achieve predictable outcomes. Progress and adoption will be impaired by the small number of people working on this topic and the need for detailed training to achieve effective implementation. Energy models could be incorporated into growth models as a means of adoption as well.
Modelling Outcomes Against Input Costs
There are many, many variables affecting pig performance. As one example, surveys have shown that feed intake varies by at least 30% among farms. It is also clear that the utilization of energy and nutrients within feedstuffs is a dynamic phenomenon, as is the energy requirement of pigs for production and maintenance (van Milgen et al., 2008). Traditional diet formulation assumes the opposite – that the available energy and nutrient content of ingredients can be listed as unique and discrete values in tables of feed composition and that amino acid requirements, for example, can be list as a single value for a given weight of pig (PIC, 2016). This approach has achieved near universal application within the pork industry for a variety of reasons, chief among them: 1) insufficient information is available on how to adjustment nutrient utilization and nutrient requirements, 2) difficulty in acquiring information from farms upon which to make adjustments, 3) an industry structure that favours high volume, low cost throughput, 4) absence of tools to handle data needed to make necessary adjustments, 5) lack of training on how to develop and implement feeding programs that are tailored to specific genetics, environment, management, and financial circumstances, and 6) limited quantity of data, especially that collected under commercial conditions, which clearly show the advantages of tailored feeding programs. The situation is perhaps illustrated by the implementation of the Nutrient Requirements of Swine (NRC, 2012) which provides both discrete requirement values based on body weight and values based on three levels of performance (and a model to fully tailor requirements); in practice, the discrete values tend to be the ones most frequently used.
As mentioned above, pressure will continue on nutritionists to find ways to further reduce the cost of production, optimize net income and minimize environmental impacts of pork production. Growth simulation models represent one means by which to achieve these objective (de Lange et al., 2001). The biggest hurdle will be demonstration of the effectiveness of models under practical conditions.
Expectation for the Future
Growth simulation and economic models will increase in use, but progress will be slow, due to the many barriers to their immediate application. As they evolve, they will include not only growth performance and financial outcomes, but also project environmental impact.
Beyond Nutrients - Functional Properties of Feed Ingredients
The choice of dietary ingredients increasingly goes well beyond the need to provide energy and nutrients to the pig; there is growing recognition that the functional properties of ingredients is also important (Shurson et al., 2021). For example, ingredient selection can impact resistance to disease (or exacerbate illness), viscosity of the digesta, oxidative status, rate of passage, the microbial profile, the immune system and bulkiness of the digesta and feces, to name a few. The list is rather long and will become even more critical in antibiotic free production systems. One example of an ingredient possessing unique functional properties is limestone, which has the ability to buffer the pH of the gastrointestinal tract, notably in the stomach. Another example would be the report by Wilberts et al. (2014) that corn DDGS may increase a growing pig’s susceptibility to swine dysentery, while replacement of this insoluble fiber source to one which is more readily fermented appears to provide protection (Helm et al., 2021).
Fiber is a well-known functional ingredient, or a critical component of functional ingredients. While fibre has traditionally been viewed as a nuisance in pig diets, it is now viewed as having functional properties that can provide benefit to the pig, especially one experiencing gastrointestinal pathologies. As one example, Li et al. (2019) found that sugar beet pulp, a highly fermentable fibre, when fed along with a carbohydrase enzyme, improves performance of pigs challenged with Escherichia coli; the authors also reported improvements in markers of gut barrier integrity and lowered markers of inflammation.
Expectation for the Future
Nutritionists have already been selecting specialty ingredients for the non-nutritive benefit they provided to pigs; this occurred most frequently in phase 1 and 2 starter diets (eg. whey powder, plasma proteins, etc). The practice will become more widespread – and beyond the nursery phase - and will provide more predictable benefits in the future. This will be particularly true in antibiotic free pork production.
Enhanced Understanding of Dietary Fibre and Fibre Assays
From a physiological perspective, fibre is the carbohydrate and lignin fractions of feeds and ingredients that are indigestible by endogenous enzymes; however, the fibre may be fermented in the lower small intestine and large intestine of mammalian species (Fahey et al., 2019). Fiber can also be defined chemically as the sum of all non-starch polysaccharides, oligosaccharides and lignin. This will include celluloses, hemicelluloses, lignin, gums, modified cellulloses, mucilages, oligosaccharides, pectins, β-glucans, waxes, cutin and suberin (DeVries et al., 1999).
Chemical assays to define the quantity of fibre in the diet, or specific fractions of the total fibre, have evolved tremendously over the past 50 years, and include crude fibre (CF), neutral detergent fibre (NDF), amylase-treated NDF (aNDF), acid detergent fibre (ADF), total dietary fibre (TDF),insoluble dietary fibre (IDF), soluble dietary fibre (SDF) and non-starch polysaccharides (NSP; Fahey et al., 2019).
Based on the above assays, arithmetic can be used to provide greater detail on fiber composition. For example, NDF - ADF = hemicellulose and ADF – ADL = cellulose (where ADL is acid detergent lignin). Total NSP can be calculated, rather than doing the wet chemistry, from TDF – ADL and non-cellulosic NSP can be calculated from TDF – (ADF - ADL).
One of the great challenges of fibre analyses is the difficulty of relating in chemical terms, what is happening physiologically. Part of the problem is that cellulose is not a single entity, but rather a structure that varies in composition and thus in its role in the gut. In the same way, hemicellulose is also not a single entity but also varies in its detailed chemical structure based on its origin within the plant kingdom. Given this reality, it is not surprising to learn that the impact of insoluble dietary fibre in the gastrointestinal tract varies because the cellulose and hemicellulose, which are its main constituents, differ. In the same way, soluble dietary fibre is quite diverse in its composition, resulting in quite different impacts in the gastrointestinal tract. As one example, some soluble dietary fibres are quite fermentable while others are very poorly fermented; β-glucan, guar gum and pectins are soluble and fermentable, while psyllium is soluble but not fermentable. Even within soluble dietary fibres, there is quite a range in the rate of fermentability.
It is inevitable that fibre will play an increasing role in swine diets of the future; cereal grains will be used in greater quantities for industrial purposes, leaving co-products to be used where wheat, barley and corn used to be fed. What will be the best methods for quantifying fibre to most effectively predict its function in the gut?
Crude Fibre Method
Crude fibre was developed in the 1860s at the Weende Agricultural Experiment Station in Germany (Henneberg and Stohmann, 1864). It includes most cellulose, but only captures a portion of lignin and hemicellulose and therefore is not a candidate for future quantification of the fibre content of feeds and ingredients. This inability to quantify meaningful portions of dietary fibre means that it should no longer be used in animal nutrition. Unfortunately, it is currently a required method for fibre guarantees according to many regulatory agencies.
Detergent Method
The detergent system of fibre analysis (ADF, NDF, ADL) was developed more than 50 years ago by Dr. Pete Van Soest at Cornell University (Van Soest, 1963). This system has achieved considerable acceptance by swine nutritionists even though it was originally developed for use with forages and for ruminant species.
Perhaps the greatest limitation of the detergent system, as it relates to swine nutrition, is the fact that it does not include soluble dietary fibre. However, the soluble dietary fibre concentration of corn and corn co-products is less than about 1.5% (Navarro et al., 2018; Abelilla and Stein, 2019b). Therefore, the detergent system is a suitable method to quantify the fiber concentration of corn and its co-products. Another limitation of the detergent system is the fact that it is not corrected for the ash and protein remining in the assay residues. This may explain why NDF is frequently found to be higher in corn and its co-products, as compared to TDF.
Nonetheless, the detergent method has proven itself to be effective in explaining the digestibility of energy in numerous ingredients, even those containing significant amounts of soluble dietary fibre. As one example, Fairbairn et al. (1999) found that ADF explained the variation in the apparent total tract digestibility of gross energy in 20 diverse barley samples with R2 of 0.85. As a second example, Zijlstra et al. (1999) found that a combination of NDF and crude protein explained the variation in the apparent total tract digestibility of gross energy in wheat with R2 of 0.75.
Going forward, the detergent system has served swine nutritionists well in the past. It is a relatively inexpensive assay; however, there appear to be large differences in assay results among labs, placing suspicion on the veracity of assay results. Knowing that NDF ignores the soluble portion of dietary fibre, it is clear that NDF works reasonably well for diets containing corn and corn co-products, because soluble dietary fibre is present in low amounts. However, when using ingredients containing significant amounts of soluble dietary fibre, NDF has limitations which cannot be ignored.
Total Dietary Fibre Method
Unlike the previous methods, the total dietary fibre method came to animal nutrition from human nutrition, following work that started in the late 1970s and early 1980s. Two methods evolved from this research, one being based on an enzymatic-gravimetric approach. This early approach, referred to as AOAC methods 985.29 and 991.43, could be used to determine total dietary fibre and to separate the result into insoluble and soluble components. However, these methods miss or underestimate a number of important fibre components from the perspective of pig nutrition, namely resistant starch, inulin, fructooligosaccharides and galactooligosaccharides, the latter of which includes raffinose and stachyose which represent between 3 and 5% of soybean meal. The problem was resolved with the development of AOAC 2009.01 and 2011.25, both of which are enzymatic-gravimetric-liquid chromatographic methods. The TDF methods have the obvious advantage over other fibre assay methods, and that is their inclusion of both soluble and insoluble fibre components. However, simply defining the soluble and insoluble content of a fibre source does not necessarily provide an accurate estimation of fermentability or other function in the gut. The biggest impediment to wider adoption is cost, which is much, much greater than that of ADF and NDF, as well as the limited number of laboratories offering this assay. Nonetheless, in order to make progress in the use of dietary fibre, both as a source of nutrients and as a functional ingredient, there is little option than to transition from the detergent methods to the total dietary fibre method.
Non-Starch Polysaccharide Method
One option to the total dietary fibre method is the measurement of non-starch polysaccharides, which provide more detailed information and can still separate assay outcomes into soluble and insoluble components. Knowing the level of individual sugars – and their solubility and insolubility - in the polysaccharide chain will help to distinguish certain functional aspects of fibre beyond that provided by total soluble and total insoluble fibre content. However, the NSP assay is complex and requires sophisticated equipment that many labs lack. It also does not yet have the endorsement by AOAC International, because a standard procedure which consistently demonstrates full recovery of all NSPs has not yet been approved.
Physicochemical Properties of Fibre
Another approach to predicting the physiological impact of fibre in the diet is to quantify its physicochemical properties. Water holding capacity provides information on stool bulk as well as intestinal transit time. Viscosity will slow gastric emptying and possibly reduce nutrient digestion. Monosaccharide composition and chain conformation can help to estimate the rate and degree of fermentation. There is a lengthy list of physicochemical properties which can be measured; the question remains which ones are relevant swine nutrition. An important but as of yet outstanding issue is the absence of any approved methods for water holding capacity, water binding capacity and viscosity. Without some form of standardized methods, including physicochemical properties in the evaluation of fibre products for use in pig diets will be extremely problematic – although the concepts have the potential to be valuable.
Other Considerations
One of the most common complaints about all fibre assays is inconsistency of results within and among labs. For this reason, and to achieve cost savings, many academic labs undertake their own fiber analyses. Because even minor deviations from standard protocols can compromise fiber assay outcomes, it is imperative that all procedures are followed with absolute care and dedication.
Expectation for the Future
Expect, or maybe hope, that crude fibre disappears completely. The detergent system will be gradually replaced by the TDF system, with diets formulated on a total dietary fibre and soluble:insoluble ratio basis, with the ratio depending on specific circumstances. The latter will evolve to fermentable:unfermentable ratio. Consideration of physicochemical properties of fibre sources will come into play as well. NSP methods could also become more common as standardized procedures achieve approval.
Enhanced tables of nutrient composition
It has long been known that growing pigs and adult pigs digest energy and nutrients with differing efficiency (Noblet and Shi, 1993). Most comparisons reported in the literature use ad libitum-fed growing pigs and limit-fed gestating sows, due to the challenges of undertaking such studies with lactating sows; this leaves the reader wondering if the difference is due to age, or due to the differences in feed intake. Stein et al. (2001) compared ad libitum-fed growing pigs and lactating sows with limit-fed gestating sows and reported that the biggest differences in amino acid digestibility existed between the growing pigs and gestating sows, with smaller differences with lactating sows. To achieve maximal precision in diet formulation, digestible nutrients should be defined specifically for growing pigs and for sows. The differences are not large and the impact on performance outcomes remains unclear. However, with greater differences in fibrous ingredients, developing separate matrices makes sense.
Expectation for the Future
Over time, separate ingredient matrices will be adopted for growing pigs and for sows; some nutritionists may go so far as to include young pigs as distinct from growing pigs. While some nutritionists already make this distinction, the pace of adoption across the industry will be small, impeded by limited data on the topic, and uncertain financial and performance advantage.
Limited water resources
Water is the “forgotten nutrient” in many ways, one of which is in the assumption that drinking water supplies are unlimited. In reality, increasing pressure is being placed on available potable water sources by urban dwellers and industrial users, placing agriculture on a collision course with other consumers in parts of the U.S. and around the world (Patience, 2012). In the future, water will become increasingly expensive and regulated, something which is already observed in the west coast of the US. Nutritionists have limited control over water utilization by the pig, although some aspects of diet composition influences ad libitum intake (Schiavon and Emmans, 2000; Shaw et al., 2006).
Expectation for the Future
As a limited natural resource, water will become a topic of increasing concern, and nutritionists will be expected to play whatever role they can to minimum water utilization by the pig industry.
Other Anticipated Changes
The following lists other changes which may occur in feed formulation but were not discussed in detail due to space limitations:
1. Big data is coming and whoever said that knowledge is power were right on the money. For example, imagine a situation where data from the genetic nucleus through multiplication, production, harvest, processing and consumer sales could be integrated into an all-encompassing data management/analysis system. The information would transform production by focusing attention – and investment – on processes that really matter to profitability, to environmental sustainability and to consumer preference for the final pork products.
2. We have been trained to develop feeding and management systems that maximize growth rate. Yet, situations will most certainly occur when producers must slow or stop growth, due to interruptions in harvest or animal movement. Research has shown that growth rate can be dramatically slowed or virtually stopped by lowering amino acid intake or feeding an acidogenic diet, respectively (Helm et al., 2021a, b). Slowing or stopping growth is far superior to mass euthanasia which has been shown to be widely criticized by animal rights organizations and consumers.
3. Attention to the whole subject of climate change and environmental sustainability is growing. Nutritionists would be well advised to work as a national entity to quantify the positive impact they have on the environmental sustainability of pork production. Topics could include conservation of non-renewable resources such as phosphorus and reduction of greenhouse gases.
4. Antibiotic resistance is a critical issue in human medicine and is also involved in veterinary medicine as well. The high profile of this topic has led to consumers requesting pork from animals that have not received antibiotics – in the feed or otherwise. We know that it is impossible to raise all animals without antibiotics; it is not practical and certainly not friendly to the welfare of pigs. Through changes in diet formulation and the careful selection of non-antibiotic feed additives, nutritionists can play a central role in contributing to systems that are compatible with at least the majority of pigs reaching market without antibiotics.
Presented at the 2022 Animal Nutrition Conference of Canada. For information on the next edition, click here. 
