近红外光谱技术快速测定豆粕、玉米真可消化氨基酸含量的研究

本试验应用近红外光谱技术（ＮＩＲＳ）预测了豆粕、玉米中的真可消化氨基酸含量。氨基酸消化率用去盲肠公鸡作试验动物,用修正的Ｓｉｂｂａｌｄ强饲法进行测定。定标的结果表明,豆粕中除与胱氨酸有关的几个方程外,其它氨基酸的定标经检验证明具有良好的预测性能。玉米真可消化氨基酸的定标性能不如豆粕好,目前还不能进行实际的应用,但大部分氨基酸定标方程的相关系数经Ｆ检验达到极显著水平,说明用ＮＩＲＳ预测玉米真可消化氨基酸是可行的。近红外光谱技术提供了一种可用于日常测定鸡真可消化氨基酸的即时分析方法,它能够替代查书面值以及使用耗时而昂贵的体内法测定真可消化氨基酸含量。营养学家可根据真可消化氨基酸数据进行饲料配方,起到准确、及时、低投入、高产出、低污染、高效利用饲料资源的作用。     

评定蛋鸡豆粕代谢能和氨基酸可利用率的研究

本试验旨在评定罗曼蛋鸡对不同来源豆粕表观代谢能(AME)和氨基酸可利用率(AAA),并用傅里叶近红外光谱(NIRS)分析技术建立其预测模型。选择248只体重(1.60±0.10)kg、产蛋率85%的36周龄罗曼蛋鸡,按单因素完全随机设计,分为31组,每组8个重复,每个重复1只鸡。在训饲的基础上,采用全收粪法评定30种不同来源豆粕和1种基础饲粮的AME和AAA,然后用NIRS技术建立其生物效价的预测模型。结果如下:1)不同来源的30种豆粕AME在11.95~14.87 MJ/kg之间,平均值为(13.24±0.67)MJ/kg;总氨基酸可利用率(TAAA)在89.99%~94.96%之间,平均值为(93.73±1.23)%。2)豆粕AME的NIRS预测模型的校正决定系数(Rcal2)、交叉验证系数(Rcv2)、外部验证系数(Rval2)分别为99.24%、83.79%、80.73%,外部验证标准差(RMSEP)为0.22 MJ/kg;TAAA的NIRS预测模型的Rcal2、Rcv2、Rval2范围分别为94.20%~99.97%、76.38%~97.32%、61.80%~99.42%,RMSEP范围为0.06%~1.00%。结果表明:1)不同来源豆粕的AME、AAA在罗曼蛋鸡上有较大差异;2)利用NIRS分析技术可建立罗曼蛋鸡豆粕的AME、AAA预测模型,模型的Rcal2及预测的RMSEP较好。     

可消化氨基酸配方技术

一、可消化氨基酸配方的理论基础蛋白质原料是饲料原料中最贵重的物质之一,蛋白质的开销是饲料成本中最重要的部分。如何节约蛋白资源,利用可消化氨基酸平衡理论进行饲料日粮配方的设计是行之有效的方法。配合饲料中可消化氨基酸的含量及其平衡程度是影响饲料品质和效益的一个重要因素。准确地测定饲料中可消化氨基酸含量及动物可消化氨基酸的需要量是配制可消化氨基酸平衡日粮的技术关键。动物可消化氨基酸的需要量是源于理想蛋白质的概念。所谓理想蛋白质就是能够被特定动物完全     

利用可消化氨基酸配制家禽日粮技术

随着动物营养研究的不断深入,家禽蛋白质营养理论也不断得到丰富和完善。先后经历了粗蛋白质（ＣＰ）,可消化蛋白质（ＤＣＰ）、氨基酸（ＡＡ）、理想蛋白质（ＩＤ）、理想蛋白质可消化氨基酸模式（ＩＤ—ＤＡＡＰ）这一发展过程。因理想蛋白质氨基酸有效性定位于可消化必需氨基酸上,即考虑了动物对氨基酸的消化、代谢和作用,又与动物总体氨基酸需要相吻合。应当说此时动物对蛋白质的利用最佳、无效消耗最低、生产性能最好。国外如英国、法国、美国在这方面做了大量研究工作,许多研究成果已在生产中推广应用。国内近几年才陆续开展这方…     

现代肉鸡氨基酸和代谢能的需要

随着肉鸡遗传性状的改善,肉鸡氨基酸需要量不断增加。其结果是氨基酸推荐量也应该相应的提高。味之素(泰国)进行了罗氏308肉公鸡的相关试验,饲粮共分为4个氨基酸水平和2个代谢能水平。发现如果不考虑饲粮中代谢能水平,罗氏肉公鸡的氨基酸需要量至少要比最新的推荐量高10%。活体重、饲料转化率、胸肌率、腿肌率、瘦肉率以及饲料经济回报都随着饲粮中氨基酸水平的增高而呈线性提高。     

12个豆粕样品鸭代谢能值及氨基酸消化率的比较研究

本试验旨在通过研究鸭对12个豆粕样品的能量和氨基酸生物学利用率的差异,探讨豆粕原料中主要养分生物学效价的变异因素.试验采用单因素完全随机设计,以每个豆粕样品为1个处理,每个处理4个重复,每个重复3只鸭,以排空强饲法测定12个豆粕样品的代谢能值及氨基酸真消化率.结果表明,3#豆粕样品的鸭代谢能值最高,显著高于4#、8#、9#、10#、11#、12#豆粕样品的代谢能值(P<0.05).7#豆粕样品的鸭代谢能值次之,显著高于8#、11#、12#豆粕样品的代谢能值(P<0.05).在氨基酸消化率的差异上,7#、8#、9#豆粕样品的总氨基酸真消化率显著高于1#、2#、3#、4#、5#、12#豆粕样品(P<0.05).除6#、7#、8#、9#豆粕样品间、2#、3# 豆粕样品与1#、4#样品间在17种氨基酸的真消化率上无显著性差异外(P>0.05),其他样品两两间在某些氨基酸的真消化率上均存在显著性差异(P<0.05).从豆粕的化学成分与代谢能及氨基酸真消化率的相关关系看,不同来源豆粕代谢能值的差异可能与豆皮的含量有关,而氨基酸真消化率的差异可能与大豆的来源及生产工艺有关.     

鸡对活性肽蛋白代谢能值的研究

选用45只成年公鸡,应用套测法和强饲法相结合的研究手段,在日粮中添加不同水平的活性肽蛋白对鸡的表观代谢能值和真实代谢能值进行测定.研究表明:当活性肽蛋白的蛋白质含量为48.55%时,家禽日粮中添加10%活性肽蛋白,表观代谢能值为5.18MJ/kg,真实代谢能值为9.32 MJ/kg;添加15%活性肽蛋白,表观代谢能值为5.49MJ/kg,真实代谢能值为9.67 MJ/kg;添加20%活性肽蛋白,表观代谢能值为5.36MJ/kg,真实代谢能值为9.91MJ/kg.由此证明,48.55%粗蛋白质活性肽蛋白的表观代谢能值为5.35MJ/kg,真实代谢能值为9.63MJ/kg.     

净能体系和可消化氨基酸体系在养猪生产中的应用

<正>目前,畜牧业中能量饲料是成本最高的一种饲料,如何提高能量的利用率以及减少能量的浪费已成为畜牧行业中亟待解决的问题。净能体系最早应用于奶牛的日粮配制当中,在单胃动物中一直沿用着消化能和代谢能体系,但消化能和代谢能体系有高估蛋白质和纤维能值的趋势,低估了脂肪和淀粉的能值(Noblet,     

饲料原料的变异及用近红外光谱仪检测可消化氨基酸与代谢能

本文介绍了同种饲料原料不同批次间营养价值的差异及实测饲料代谢能和可消化氨基酸的程序、近红外(NIRS)数据库及标准曲线的建立、NIRS快速测定值与实际动物体内试验测定值的比较等内容。     

Apparent ileal digestibility of amino acids and the digestible and metabolizable energy content of dry extruded-expelled soybean meal and its effects on growth performance of pigs

We conducted three experiments to determine the apparent ileal digestibility of amino acids (Exp. 1), metabolizable and digestible energy (Exp. 2), and feeding value (Exp. 3) of dry extruded-expelled soybean meal with (DEH) or without (DENH) hulls compared with solvent-extracted soybean meal with hulls removed (SBMNH). Soybeans used to produce DEH were unadulterated prior to extrusion, but those used for DENH were dehulled prior to extrusion. In Exp. 1, six nonlittermate barrows (initially 39 kg) were fitted with ileal T-cannulas and used in a replicated 3 x 3 Latin square design digestion trial. Experimental diets (0.80% total lysine) were cornstarch-based and contained soybean meal from one of the three different sources as the sole source of lysine. Apparent ileal digestibilies of nutrients were similar (P > 0.10) for DEH and DENH. Apparent ileal digestibilies of CP, Lys, Ile, Leu, Arg, Phe, and Val were greater (P < 0.05) for DEH and DENH than for SBMNH. In Exp. 2, six barrows (initially 41 kg) were fed three corn-based diets containing 25% of one of the three soybean meal sources. A fourth diet was fed at the end of the trial containing all ingredients except soybean meal, so that energy values of the soybean meal could be determined by difference. Digestible energy and ME contents were similar (P > 0.10) for DEH and DENH and both had greater (P < 0.05) DE and ME contents than SBMNH. In Exp. 3, pigs (n = 216, initially 10.6 +/- 1.3 kg and 35 +/- 3 d of age) were blocked by weight and allotted to six dietary treatments. Corn-soybean meal-based diets (0.95% digestible lysine and 3.44 kcal/g ME) containing DEH or DENH were compared with similar diets containing SBMNH or solvent-extracted soybean meal with hulls (SBMH). In addition, a diet containing a second expelled soybean meal with hulls (ESBM) was compared with a diet containing SBMH and soy oil. Growth performance of pigs fed diets containing DEH or DENH was not different (P > 0.10) than that of pigs fed corresponding diets containing SMBH or SBMNH. Pigs fed ESBM had lower (P < 0.05) ADG and G/F compared with its corresponding SBMH and soy oil diet. In conclusion, DEH and DENH are more digestible than conventional soybean meal and can be successfully used in swine diets.     

Digestible and metabolizable energy concentrations and amino acid digestibility of dried yeast and soybean meal for growing pigs

Energy values and amino acid (AA) digestibility of dried yeast (DY) and soybean meal (SBM) were determined in 2 experiments with growing pigs. Experiment 1 was conducted to determine the digestible energy (DE) and metabolizable energy (ME) in DY and SBM. Thirty barrows with a mean initial body weight (BW) of 20.6 kg (SD = 1.04) were assigned to 5 dietary treatments in a randomized complete block design with period and BW as blocking factors. A reference diet was prepared with corn, canola meal, and soybean oil as energy-contributing ingredients. Four additional diets were prepared by adding 5% and 10% DY or SBM at the expense of energy-contributing ingredients in the reference diet. The ratio of corn, canola meal, and soybean oil was kept consistent across the experimental diets. Each experimental period consisted of 5-d adaptation and 5-d quantitative collection of feces and urine. Test ingredient-associated DE or ME intake (kcal/d) was regressed against test ingredient intake [kg dry matter (DM)/d] to estimate the DE or ME in test ingredients as the slope of linear regression model. The DE in DY was estimated at 3,933 kcal/kg DM, which was not different from the estimated DE in SBM at 4,020 kcal/kg DM. Similarly, there was no difference between DY and SBM in the estimated ME (3,431 and 3,756 kcal/kg DM, respectively). Experiment 2 was conducted to determine the standardized ileal digestibility (SID) of AA in DY and SBM. Twenty-one barrows with a mean initial BW of 20.0 kg (SD = 1.31) were surgically fitted with T-cannulas at the distal ileum and assigned to 3 dietary treatments in a randomized complete block design with BW as a blocking factor. Two semi-purified diets containing DY or SBM as the sole nitrogen source and one nitrogen-free diet (NFD) were prepared. The NFD was used to estimate the basal ileal endogenous losses of CP and AA. Pigs were fed the 3 diets for 5 d as adaptation, followed by 2 d of feeding with ileal digesta collection. The SID of AA, except Gly and Pro, in DY was less (P < 0.05) than in SBM. The SID of indispensable AA in DY ranged from 64.1% for Thr to 85.2% for Arg, and those in SBM ranged from 83.9% for Thr to 91.8% for Arg. In conclusion, energy values of DY are not different from those of SBM, whereas AA in DY is less digestible than in SBM. The estimated DE and ME as well as the SID of AA in DY and SBM can be used in diet formulation for growing pigs using these ingredients.     

粉碎粒度对近红外分析仪测定鱼粉和豆粕氨基酸含量的影响

用近红外方法分析测定一个饲料原料样品的氨基酸含量只需几分钟 ,其测定误差一般在 5 %以下。我国从 2 0世纪 80年代初在农产品质量分析技术上开展近红外光谱分析研究。但定标粒度对测定样品氨基酸影响的报道不多 ,本试验围绕近红外光谱技术测试条件的研究 ,通过近红外光谱分析技术对两种粉碎粒度的 1 4个鱼粉和 2 7个豆粕样品中 1 0种氨基酸含量的测定 ,探讨粒度对测定效果的影响 ,为应用近红外光谱分析技术检测样品氨基酸含量提供技术参考。1　材料和方法1 1　样品制备　 1 4个鱼粉样品、2 7个豆粕样品。样品均分别通过 0 75ｍｍ和 0 5…     

Predicting metabolizable energy from digestible energy for growing and finishing beef cattle and relationships to the prediction of methane

Reliable predictions of metabolizable energy (ME) from digestible energy (DE) are necessary to prescribe nutrient requirements of beef cattle accurately. A previously developed database that included 87 treatment means from 23 respiration calorimetry studies has been updated to evaluate the efficiency of converting DE to ME by adding 47 treatment means from 11 additional studies. Diets were fed to growing-finishing cattle under individual feeding conditions. A citation-adjusted linear regression equation was developed where dietary ME concentration (Mcal/kg of dry matter [DM]) was the dependent variable and dietary DE concentration (Mcal/kg) was the independent variable: ME = 1.0001 × DE – 0.3926; r² = 0.99, root mean square prediction error [RMSPE] = 0.04, and P < 0.01 for the intercept and slope. The slope did not differ from unity (95% CI = 0.936 to 1.065); therefore, the intercept (95% CI = −0.567 to −0.218) defines the value of ME predicted from DE. For practical use, we recommend ME = DE – 0.39. Based on the relationship between DE and ME, we calculated the citation-adjusted loss of methane, which yielded a value of 0.2433 Mcal/kg of dry matter intake (DMI; SE = 0.0134). This value was also adjusted for the effects of DMI above maintenance, yielding a citation-adjusted relationship: CH₄, Mcal/kg = 0.3344 – 0.05639 × multiple of maintenance; r² = 0.536, RMSPE = 0.0245, and P < 0.01 for the intercept and slope. Both the 0.2433 value and the result of the intake-adjusted equation can be multiplied by DMI to yield an estimate of methane production. These two approaches were evaluated using a second, independent database comprising 129 data points from 29 published studies. Four equations in the literature that used DMI or intake energy to predict methane production also were evaluated with the second database. The mean bias was substantially greater for the two new equations, but slope bias was substantially less than noted for the other DMI-based equations. Our results suggest that ME for growing and finishing cattle can be predicted from DE across a wide range of diets, cattle types, and intake levels by simply subtracting a constant from DE. Mean bias associated with our two new methane emission equations suggests that further research is needed to determine whether coefficients to predict methane from DMI could be developed for specific diet types, levels of DMI relative to body weight, or other variables that affect the emission of methane.     

Determination and prediction of digestible and metabolizable energy from chemical analysis of corn coproducts fed to finishing pigs

Twenty corn coproducts from various wet- and dry-grind ethanol plants were fed to finishing pigs to determine DE and ME and to generate equations predicting DE and ME based on chemical analysis. A basal diet comprised corn (97.05%), limestone, dicalcium phosphate, salt, vitamins, and trace minerals. Twenty test diets were formulated by mixing the basal diet with 30% of a coproduct, except for dried corn solubles and corn oil, which were included at 20 and 10%, respectively. There were 8 groups of 24 finishing gilts (n = 192; BW = 112.7 ± 7.9 kg). Within each group, gilts were randomly assigned to 1 of 5 test diets or the basal diet for a total of 4 replications per diet per group. Two groups of gilts were used for each set of coproducts, resulting in 8 replications per coproduct and 32 replications of the basal diet. The experiment was conducted as a completely randomized design. Gilts were placed in metabolism crates and offered 3 kg daily of their assigned test diet for 13 d, with total collection of feces and urine during the last 4 d. Ingredients were analyzed for DM, GE, CP, ether extract, crude fiber, NDF, ADF, total dietary fiber (TDF), ash, AA, and minerals, and in vitro OM digestibility was calculated for each ingredient. The GE was determined in the diets, feces, and urine to calculate DE and ME for each ingredient. The DE and ME of the basal diet were used as covariates among groups of pigs. The DE of the coproducts ranged from 2,517 kcal/kg of DM (corn gluten feed) to 8,988 kcal/kg of DM (corn oil), and ME ranged from 2,334 kcal/kg of DM (corn gluten feed) to 8,755 kcal/kg of DM (corn oil). By excluding corn oil and corn starch from the stepwise regression analysis, a series of DE and ME prediction equations were generated. The best fit equations were as follows: DE, kcal/kg of DM = -7,471 + (1.94 × GE) - (50.91 × ether extract) + (15.20 × total starch) + (18.04 × OM digestibility), with R(2) = 0.90, SE = 227, and P < 0.01; ME, kcal/kg of DM = (0.90 × GE) - (29.95 × TDF), with R(2) = 0.72, SE = 323, and P < 0.01. Additional equations for DE and ME included NDF in the instance that TDF data were not available. These results indicate that DE and ME varied substantially among corn coproducts and that various nutritional components can be used to accurately predict DE and ME in corn coproducts for finishing pigs.     

Comparison of digestible and metabolizable energy and digestible phosphorus and amino acid content of corn ethanol coproducts from Brazil and the United States produced using fiber separation technology for swine

Three experiments (exp.) were conducted to determine and compare the digestibility of nutrients and energy of corn distillers dried grains with solubles (DDGS) from the United States (USDDGS), a dried mixture of corn bran with solubles (CBS) from Brazil (BRCBS), and high protein corn distillers dried grains (HP-DDG) from the United States (USHPDG) and Brazil (BRHPDG) in growing pigs. The feed ingredients were evaluated for apparent total tract digestibility (ATTD) of gross energy (GE), dry matter (DM), crude protein (CP), ether extract, neutral and acid detergent fiber (NDF and ADF, respectively), and digestible and metabolizable energy (DE and ME, respectively) using the total collection and index methods in exp. 1; ATTD and standardized total tract digestibility (STTD) of phosphorus (P) in exp. 2; and apparent (AID) and standardized (SID) ileal digestibilities of CP and amino acids (AA) in exp. 3. Fifty crossbred barrows (32.4 ± 6.9, 38.3 ± 5.2, and 46.2 ± 5.3 kg body weight [BW], in exp. 1, 2, and 3, respectively) were fed a corn basal diet in exp. 1, a P-free diet in exp. 2, and an N-free diet in exp. 3 or diets with 40% inclusion of test ingredients to provide 10 replications per treatment. Pigs were housed individually in metabolism cages (exp. 1) or in pens (exp. 2 and 3) and fed at 2.8 times the maintenance DE requirement (110 kcal/kg BW^0.75) based on their BW at the beginning of each experiment. Except for ATTD of NDF, which tended (P = 0.058) to be greater by the index method compared with the total collection method, no difference between the total collection and index methods was observed for ATTD of remaining nutrients and DE. The ATTD of DM, GE, NDF, and DE content of BRHPDG were greater (P < 0.001) than USHPDG, BRCBS, and USDDGS. The AID of CP, Arg, His, Ile, Leu, Lys, Thr, and Val and the SID of His, Leu, Lys, and Val of BRHPDG were 8% to 36% greater (P < 0.05) than those from USHPDG. Except for Trp, all AID and SID AA values were greater (P < 0.05) in BRHPDG than in USHPDG. The ATTD of DM, GE, NDF, and ADF; DE and ME content; AID of CP, Arg, Ile, Leu, Phe, Thr, and Trp; and SID of CP, Arg, Phe, and Thr of USDDGS were 9% to 45% greater (P < 0.05) than those in BRCBS. The ATTD and the STTD of P in USHPDG and USDDGS were 26% to 42% greater (P < 0.05) compared with BRHPDG and BRCBS. In conclusion, BRHPDG had a greater digestibility of energy and most of the AA than USHPDG, while the BRCBS evaluated had lower nutritional value than the USDDGS source.     

Additivity and associative effects of metabolizable energy and amino acid digestibility of corn,soybean meal,and wheat red dog for White Pekin ducks

The additivity of true amino acid digestibility (TAAD) and true metabolizable energy (TME) values in corn, soybean meal, and wheat red dog for White Pekin ducks was investigated. Differences between observed values for the complete diets and values predicted from measurements of individual ingredients were used to test additivity. Eight ducks were each assigned to the following dietary treatments: corn, soybean meal (44% CP), wheat red dog (wheat by-product with less than 4% fiber), complete Diet 1 (corn-soybean meal), complete Diet 2 (corn-red dog-soybean meal), and dextrose. Dextrose-fed ducks were used to estimate endogenous losses. The nitrogen-corrected TME (TMEn) in corn, soybean meal, wheat red dog, and two complete diets were 3.411, 2.919, 2.502, 3.148, and 3.111 kcal/g, respectively. In general, the TME and TMEn values observed in the two complete diets were not different (P > 0.05) from predicted values and indicated that the TME and TMEn in corn, soybean meal, and wheat red dog were all additive. The mean TAAD of corn, soybean meal, wheat red dog, and the two complete diets were not different, and were 87.03, 88.15, 90.58, 85.83, and 87.02%, respectively. The differences in TAAD between observed and predicted values were significant (P < 0.05) only for arginine, lysine, and aspartate in complete Diet 1, and for arginine, histidine, lysine, and aspartate in complete Diet 2. These results indicated that TME and TMEn values for corn,soybean meal, and wheat red dog were all additive in the two complete diets, but digestibilities of some amino acids were not additive and demonstrated some associative effects.     

Development of a model to predict dietary metabolizable energy from digestible energy in beef cattle

Understanding the utilization of feed energy is essential for precision feeding in beef cattle production. We aimed to assess whether predicting the metabolizable energy (ME) to digestible energy (DE) ratio (MDR), rather than a prediction of ME with DE, is feasible and to develop a model equation to predict MDR in beef cattle. We constructed a literature database based on published data. A meta-analysis was conducted with 306 means from 69 studies containing both dietary DE and ME concentrations measured by calorimetry to test whether exclusion of the y-intercept is adequate in the linear relationship between DE and ME. A random coefficient model with study as the random variable was used to develop equations to predict MDR in growing and finishing beef cattle. Routinely measured or calculated variables in the field (body weight, age, daily gain, intake, and dietary nutrient components) were chosen as explanatory variables. The developed equations were evaluated with other published equations. The no-intercept linear equation was found to represent the relationship between DE and ME more appropriately than the equation with a y-intercept. The y-intercept (−0.025 ± 0.0525) was not different from 0 (P = 0.638), and Akaike and Bayesian information criteria of the no-intercept model were smaller than those with the y-intercept. Within our growing and finishing cattle data, the animal’s physiological stage was not a significant variable affecting MDR after accounting for the study effect (P = 0.213). The mean (±SE) of MDR was 0.849 (±0.0063). The best equation for predicting MDR (n = 106 from 28 studies) was 0.9410 ( ± 0.02160) +0.0042 ( ± 0.00186) × DMI (kg) – 0.0017 ( ± 0.00024) × NDF(% DM) – 0.0022 ( ± 0.00084) × CP(% DM). We also presented a model with a positive coefficient for the ether extract (n = 80 from 22 studies). When using these equations, the observed ME was predicted with high precision (R² = 0.92). The model accuracy was also high, as shown by the high concordance correlation coefficient (>0.95) and small root mean square error of prediction (RMSEP), <5% of the observed mean. Moreover, a significant portion of the RMSEP was due to random bias (> 93%), without mean or slope bias (P > 0.05). We concluded that dietary ME in beef cattle could be accurately estimated from dietary DE and its conversion factor, MDR, predicted by the dry matter intake and concentration of several dietary nutrients, using the 2 equations developed in this study.     

Effect of soybean meal particle size on amino acid and energy digestibility in grower-finisher swine

A study was conducted using the ileal digestibility technique with grower-finisher pigs to evaluate the effects of particle size reduction of soybean meal (SBM) on amino acid and energy digestibility. Soybean meal was processed through a hammer mill to achieve average particle sizes of 900, 600, 300, and 150 microm. The treatments included the use of two soybean meal sources and soy protein concentrate. One source of SBM was ground to four different particle sizes (i.e., 949, 600, 389, 185); a second source was a common SBM source used in other trials (i.e., 800 microm). The soy protein concentrate had an average particle size of 385 microm. A low-protein (5% casein) diet was fed to determine endogenous amino acid losses. This experiment was conducted in a 7 x 7 Latin Square design in two replicates using 14 crossbred barrows ([Landrace x Yorkshire] x Duroc) that averaged 28 kg BW and 60 d of age. Animals were surgically fitted with a T-cannula at the distal ileum. Treatment diets were fed in meal form, initially at 0.09 kg BW(0.75) and at graded increases at each subsequent period. Pigs within replicate were fed a constant quantity of their treatment diet for a 5-d adjustment period followed by a 2-d collection of ileal digesta samples. Apparent and true digestibility of amino acids was calculated by use of chromic oxide (0.5%) as an indigestible marker. Apparent digestibility of isoleucine, methionine, phenylalanine, and valine increased linearly (P < 0.05) as particle size decreased. True digestibility of isoleucine, methionine, phenylalanine, and valine increased linearly (P < 0.05) as particle size decreased. When the essential amino acids were averaged, apparent digestibility increased (P < 0.10) from 83.5% to 84.9% as particle size decreased, whereas, nonessential amino acid digestibility increased only slightly (P > 0.15). Essential amino acid true digestibility increased numerically from 91.0% to 92.4% as particle size decreased. Energy digestibility was not affected by particle size (P > 0.15). These results suggest that a reduction in particle size of soybean meal resulted in a small increase in the digestibility of its amino acids with the essential amino acids being affected more than the nonessential amino acids. The largest improvement in digestibility, however, was obtained when the particle size was reduced to 600 microm.