Variation in digestible energy content of Australian sweet lupins (Lupinus angustifolius L.) and the development of prediction equations for its estimation1

Abstract

Sixty-three male pigs (Landrace × Large White) weighing 49.5 ± 0.40 kg were used to (1) examine the variation in DE content of Lupinus angustifolius L. in relation to variety and geographical growing region and (2) establish prediction equations for DE content from physical and chemical composition. The pigs were randomly allocated to a 4 × 2 factorial treatment design with respective factors being 4 varieties (cv. Belara, Coromup, Mandelup, and Tanjil) and 2 growing locations (northern and southern agricultural areas of Western Australia). In addition, a wheat control diet was fed as a reference for calculation of lupin DE content. The lupins were ground through a hammer mill fitted with a 4-mm screen to a mean particle size of 888 μm. Pigs were fed their respective experimental diets at 3 times maintenance energy level [3 × (0.458 × BW 0.75 )/diet DE] in the study. The DE content of lupins ranged from 13.3 to 15.7 MJ/kg with a mean value of 14.2 MJ/kg. Variety of lupins affected (P < 0.01) the DE content, and lupins grown in the northern agricultural region had a greater DE content than the same lupins grown in the southern agricultural area (P < 0.01). Although the variation in DE content of lupins was mostly caused by significantly greater DE content of cv. Coromup grown in the northern agricultural region, the results suggest that genetic and environmental conditions during the growth of lupins have a significant impact on the utilization of energy in grower pigs. Simple regression analysis showed that prediction of DE content was possible from the proportion of hulls [R 2 = 0.88, residual SD (RSD) = 1.116, P < 0.001], 1,000-seed weight (R 2 = 0.77, RSD = 1.092, P < 0.01), and soluble arabinoxylan content (R 2 = 0.64, RSD = 1.072, P < 0.05). Multiple regression analysis showed that adding total nonstarch polysaccharide (R 2 = 0.96, RSD = 1.187, P < 0.01) and soluble nonstarch polysaccharide (R 2 = 0.95, RSD = 1.200, P < 0.01) to the equation along with the proportion of hull and 1,000-seed weight significantly improved the accuracy of prediction. Results indicate that the DE content of lupins varies by up to 2.4 MJ/kg and that the DE content can be predicted with a good degree of accuracy using physical and chemical characteristics.

Effect of variety and drying method on the nutritive value of corn for growing pigs

“…The results are in agreement with those reported by Costa et al [23]. A brown discoloration and a parched corn odor were also observed by Coates et al [22] in corn dried at temperatures of 138 and 160°C. These characteristics suggest that a Maillard reaction has occurred in the grain kernel [24].…”

“…The reason for this observation was the ATTD of nutrients in the LS1 were greater than the other. It is likely that natural differences among varieties of corn exist, as has been demonstrated for corn and other cereal grains [14,20,21] and for sweet lupins [22]. …”

Evaluation of Energy Digestibility and Prediction of Digestible and Metabolizable Energy from Chemical Composition of Different Cottonseed Meal Sources Fed to Growing Pigs

“…A number of prediction equations for DE and ME contents of feed ingredients and diets have been successfully developed (Just et al, 1984; Noblet and Perez, 1993; Fairbairn et al, 1999; Kim et al, 2009; Olukosi and Adeola, 2009; Cozannet et al, 2010; Anderson et al, 2012). However, the models obtained from one hundred and fourteen diets (Noblet and Perez, 1993) are unsuitable when applied to a certain ingredient.…”

“…Given these challenges, however, prediction equations are a useful tool in estimating the energy value of feed ingredients utilized in the livestock industry. Although some models were established for complete diets (Noblet and Perez, 1993), barley (Fairbairn et al, 1999), sweet lupins (Kim et al, 2009), wheat-DDGS (Cozannet et al, 2010), and corn co-products (Anderson et al, 2012), to our knowledge, there have been no such equations generated for predicting digestible energy (DE) and metabolizable energy (ME) values of CSM fed to growing pigs. Therefore, the objectives of this study were to determine the energy digestibility and prediction of DE and ME value based on the chemical composition of CSM fed to growing pigs.…”

Evaluation of Energy Digestibility and Prediction of Digestible and Metabolisable Energy in Sunflower Seed Meal Fed to Growing Pigs

“…Some predicting models were established for DE and ME of complete diet (Noblet and Perez, 1993) and ingredients (Kim et al, 2009;Anderson et al, 2012). Therefore, it is important to estimate the variation of nutrient digestibility of SFSM in order to be better used in feed industry.…”