Nutritional variability of corn in pig diets. Why must it be controlled?

Since its domestication, corn has proven to be a cereal with a great capacity to adapt to different environmental conditions. This has resulted in the development of almost 300 different biotypes capable of being cultivated in different places ranging from tropical regions to temperate zones of 3600 m.a.s.l.

However, this has also given way to differences in its nutritional content.

Corn is generally characterized by its high nutrient content, thus providing up to 65% metabolizable energy and 20% protein in monogastric diets.

Corn inclusion in diet formulation can vary between 50% to 70% of the total diet.

However, its nutritional value can be affected by certain characteristics such as:

• Genetics
• Growing conditions
• Geographical location
• And pre- and post-harvest processing

In this sense, genetic variation has been shown to be a factor of considerable variation within the nutritional composition, particularly in regards to its energy value.

NUTRITIONAL VARIABILITY IN CORN

Multiple trials have shown that variation factors such as geographical reference, growing conditions and drying techniques can contribute to establish differences in corn’s metabolizable energy values. Generating differences of up to 304 kcal/kg in birds for example(Melo et al., 2021).

Under commercial conditions, the factors that increase nutritional variation are conjugated within storage silos where grains from different batches and origins are mixed. This is true for cereals such as:

• Wheat
• Barley
• Soybeans
• And of course, corn!

Therefore, the implementation of this commercial practice, makes it even harder to control the nutritional variations of corn when formulating diets and rations.

In fact, the concentration of metabolizable energy corrected for nitrogen can vary by more than 400 kcal/kg between batches of corn in poultry diets (Cowieson, 2005).

A tool that can help control nutritional variability, and therefore contribute to adjust dietary nutrients, is the use ofNIRs to predict the proximal composition of raw materials.

However, the samples that are analyzed with this method may not be significantly representative of all the different variability factors. Which will lead to unbalanced diets in regards to nutritional composition. Having a negative impact on the farm’s productive parameters.

In fact, the concentration of components such as arabinoxylans (part of the NSPs) can modify the digestibility of nutrients such as protein and starch, affecting the productive performance of animals (Melo et al., 2021).

Although, traditionally, corn has been considered as a non-viscous cereal, due to:

• Its low concentration of soluble arabinoxylans
• The variation in quantity and relationship between soluble and insoluble arabinoxylans can reach up to 27 gr/kg between different genetic varieties

The variability in the concentration of soluble arabinoxylans (between 2.2 and 5.3 g/kg) can cause certain genetic varieties  to behave like wheat and lead to intestinal viscosity problems, thus complicating health management within the fam.

A trial conducted by Melo et al., (2021) showed that the genetic background of corn influenced the growth of broiler chickens fed with 8 iso protein diets, but formulated with 8 different genetic varieties of corn.

The authors concluded that the content and nature of NSPs are two of the main factors affecting the solubility and availability of nutrients within corn. Having potential negative effects on broilers’ growth performance.

In this sense, this article has evaluated the potential differences attributed to the genetic varieties of corn for metabolizable energy (DM) and NSPs concentration levels for pig diets.

In total, a sample of 10 cobs, for each variety, were obtained to be processed and then analyzed to determine their nutritional composition via NIRs.  Figure 2 shows the nutritional differences attributed to each genetic variety.

In general, the concentration of DM for pigs may differ by 344 kcal/kg, while the concentration of NSPs may vary by 27 g/kg.

Additionally, a negative correlation was observed between DM and part of the NSPs (soluble arabinoxylans), which would indicate that the availability of energy is a function of the content of certain antinutritional factors.

A transcendental objective in pig production is to achieve the maximum growth rate along with efficiency in the use of nutrients.

However, the variations that genetic variety can bring , as well as other uncontrolled factors, could be influencing the digestibility of nutrients such as:

• Protein
• Starch

That added to the low values of lysine and tryptophan of corn could significantly exacerbate the decrease in productive yield.

In animal feed the proportion of some nutrients is key, as in the case of lysine concentration and energy. On the other hand , lysine is the first limiting amino acid while energy influences the voluntary consumption of food.

Therefore, it is necessary to maintain an optimal ratio of lysine to energy when increasing the amino acid or energy content of the diet.

Extensive research in pigs has shown that weight gain and feed efficiency improves as the standardized ileal digestible lysine:EM ratio increases.

CONCLUSION

• In this context, and from the practical point of view, it is important to consider the control of the nutritional variation of maize, prior to the formulation of animal diets.
• Alterations in the true concentration of nutrients, particularly energy and amino acids, will impact the productive efficiency of animals.
• For this, it is recommended to incorporate additional mechanisms to the proximal analysis via wet or NIRs.
• Field tools such as controlled planting of genetic varieties, screening of grains of different sizes, can benefit the control of nutritional variability of cereals such as corn.
• Additionally, the determination of the concentration and chemical nature of components such as NSPs, and the use of enzymes such as carbohydrases could allow a better nutritional adjustment of diets.

Figure 2. Metabolizable energy values for pigs (A) and correlation between MS and soluble Arabinoxylans (B) analyzed from 16 different genetic varieties of corn.