Prediction of apparent metabolizable energy and metabolizable energy corrected for nitrogen of corn according to physical classification of the grain

Abstract

The objective of this study was to develop an equation to determine the apparent metabolizable energy (AME) and metabolizable energy corrected for nitrogen balance (AMEn) using a physical-based classification of corn. A total of 5,055 samples were taken from bulk cargo trucks, over a five-year period. The parameters studied were the variables related to the physical characteristics of grains. The density of maize was evaluated, and AME and AMEn were calculated. The average value for AME was 3,375 kcal/kg, and two groups were formed of high quality and low quality for all samples. Stepwise regression analysis was then carried out using grain quality to estimate AME and AMEn, and the validation of the equations was carried out with 6,490 independent samples. The average value for density was 767.7 kg/m 3. The multiple regressions used to estimate AME and AMEn as a function of humidity, density, and physical composition of corn kernels showed that moisture was included for AME, but not for AMEn. The equations presented high coefficients of determination (R 2) for AME (0.994) and AMEn (0.987). The discriminant analyses correctly classified 98% of the high-quality samples and 96.69% of low-quality samples, so the error was smaller than the expected. The calculated equations were shown to be good at discriminating between samples of high and low quality of corn according to its physical composition, and the most important variables for separation between groups were damaged grain fraction, impurities, burnt, and soft. The correlation between calculated (independent samples) and estimated metabolizable energy and AMEn were, respectively, 0.9942 and 0.9859. The corn energy values can be estimated based on physical evaluation of the grain.

Energy values and metabolizability coefficients of maize kernels with different specific gravities

“…Regarding AMEn FM , the same behavior displayed 2), occurred due to the decreasing DM content of the kernels as their specific gravity increased. The AMEn FM values found in the present study were lower than those cited in the literature (4,8,14) (3247 to 3562 kcal/kg), but higher than the 2937 kcal/kg found by Silva et al (14) in maize fractions of low specific gravity.…”

“…The MC CP values found did not differ statistically between the maize specific gravity fractions (P>0.05) (Table 5). The MC CP values and CP levels (Table 3) were lower than those recommended in the Brazilian food composition table 7 (87% MC CP ), but higher than the mean value of 57.3% found by Rodrigues et al (8) in different maize fractions. These results suggest that, in the present study, the higher CP level found at the lowest specific gravity fraction, resulting from the lower starch concentration in these kernels, did not influence the levels of digestible amino acids or low-nutritional-value zein proteins (10) .…”

“…Although the correlations between specific gravity and chemical parameters were shown to have little or no significance (Table 3), different authors have reported differences in the chemical composition of maize kernels with different densities (8,13,14) . Thus, to evaluate the impact of maize density and kernel quality on nutritional and energy values, we conducted a metabolism trial.…”

“…However, despite the updates, the chemical and energy composition of an ingredient is known to vary according to several factors related to its cultivation and especially with the selection and development of new varieties, in the case of maize. Another highlighted indirect method are prediction equations for the nutritional and energy values of ingredients (7,8) , whose estimates help to increase the accuracy of feed formulations. These equations mainly consider physical and chemical quality characteristics of the kernels, constituting an important practical tool for correcting the nutrient matrix of ingredients used locally in feed factories (9) .…”

“…Various factors can influence density, such as time of planting, incidence of sunlight or excessive shading, temperature, planting density, harvest time, transport, drying, and storage (11,12) . Several authors have attempted to better understand the influence of kernel density on its energy and nutritional content (8,10,13,14) . Variations in the nutritional composition of kernels can impact the production potential of chickens, e.g.…”