مطالعه بازده خوراک براساس شاخص مازاد مصرف خوراک و بازدهی خوراکی براساس شیر تصحیح‌شده برای 4 درصد چربی در گاوهای شیرده نژاد هلشتاین

زمینه مطالعاتی: بررسی بازده خوراک در گاوهای شیرده از اهمیت ویژه‌ای برخوردار است. هدف: مطالعه دینامیک بازدهی خوراک براساس شاخص مازاد مصرف خوراک (rfi) و شیر تصحیح‌شده برای 4 درصد چربی (fcm4/dmi) در گاوهای شیرده نژاد هلشتاین بود. روش کار: تعداد 30 راس گاو شیرده در اواسط شیردهی به مدت 60 روز تغذیه شد‌ند. ماده خشک مصرفی (dmi)، تولید شیر، وزن زنده بدن، نمره وضعیت بدنی، افزایش وزن روزانه و ترکیبات شیر رکورد‌برداری شد. همچنین انرژی خروجی شیر، انرژی تغییرات بدنی و وزن متابولیکی براساس داده‌های اولیه و معادلات انرژی (nrc, 2001) محاسبه گردید. rfi به‌وسیله 4 مدل مختلف رگرسیونی خطی چند متغیره برای دو فاصله 30 و 60 روزه مدل‌سازی شد. نتایج: در جامعه مورد مطالعه، rfi قابل اندازه‌گیری است. ضریب تبیین مدل‌ها برای دوره 60 روزه برای مدل 1، 2، 3 و 4 به ترتیب توانست 88.51، 78.82، 80.05 و 64.41 درصد از تغییرات متغیر وابسته (dmi) توسط متغیرهای مستقل را تبیین نماید. میانگین و انحراف معیار rfi برای دوره 60 روزه برای مدل 1، 2، 3 و 4 به ترتیب 0.86 ± 0، 1.25 ± 0، 1.18 ± 0 و 1.68 ± 0 کیلوگرم ماده خشک در روز بود. همین روند برای دوره 30 روزه نیز مشاهده شد. ضریب همبستگی پیرسون برای dmi بین دوره 30 و 60 روزه 0.994 و rfi براساس مدل 1 بین دوره 30 و 60 روزه 0.882 بود. همبستگی بین dmi، fcm4/dmi و rfi با دیگر صفات عملکردی و تولیدی، روابط منطقی بین متغیرها مشاهده شد. رابطه منفی معنی‌دار بین fcm4/dmi با درصد پروتئین خام شیر (p<0.001) و روند معنی‌داری بین rfi با درصد چربی و پروتئین شیر (p<0.1) بسیار مهم بود. نتیجه‌گیری نهایی: اندازه‌گیری بازده خوراک براساس rfi حداقل در یک دوره 30 روزه قابل اندازه‌گیری است. همچنین اگر چه مدل‌ 1 و متغیرهای آن به خوبی متغیر وابسته (dmi) را تبیین نموده‌اند، اما تلاش علمی براساس رویکرد کل‌نگرانه برای یافتن دیگر متغیرها موثر برای بهبود ضریب تبیین مدل‌ها پیشنهاد می‌شود.

Study of feed efficiency based on residual feed intake and fat corrected milk in Iranian Holstein dairy cows

Introduction: Understanding feed efficiency (FE) in dairy cows and its improvement is essential. Dry matter intake (DMI) is fundamentally important in nutrition because it establishes the amount of nutrients available to an animal for health and production. Residual feed intake (RFI) is calculated as the residual in the linear model to predict feed intake of individual animals and thus is essentially the difference between an individual’s observed feed consumption and its predicted feed consumption. An animal with a negative RFI consumes less than expected for its level of production and thus is more efficient when RFI is used to define feed efficiency. Because RFI is independent of production level, recent attention has been given to using RFI as a tool to assess feed efficiency in dairy cattle for purposes of genetic selection. Also, feed efficiency based fatcorrected milk (FCM4) dived DMI (FCM4/DMI) is another factor to depict feed efficiency. Therefore, this research aimed to investigate the dynamics of RFI and FCM4/DMI in Iranian Holstein dairy cows.Material and methods: Lactating Iranian Holstein cows (n = 30; 10 primiparous and 20 multiparous), averaging (mean ± standard deviation, SD) 594 ± 62.6 kg of body weight, 38.81 ± 6.22 kg of milk/d, and 94.5 ± 21.5 day postpartum, were fed a diet balanced with CPM Dairy V3 ration software. Diet consisted of 40 % forage and 60 % concentrate and fed as total mixed ration (TMR). Cows were fed once per day a fresh diet and orts were removed and weighed daily before feeding. Milk yield was recorded electronically at each milking, and milk samples were obtained from 3 consecutive milkings per week. Milk samples were analyzed for fat, true protein, and lactose with infrared spectroscopy. Body weight (BW) for each cow was recorded 2 consecutive days per month immediately after the morning milking. Daily weight gain was calculated based on body weight for each cow at the beginning and end of each period (30 and 60days period). Body condition score (BCS) was determined on a 5point scale in 0.25 increments by a trained investigator and recorded for each cow at the beginning and end of each period (30 and 60days period). Also, milk energy output (MilkE; Mcal/d), metabolic body weight for a cow (MBW), and energy expended for body tissue gain (ΔBodyE; Mcal/d) were estimated based on NRC 2001 equations. Dry matter intake for an individual cow during each 30 and 60days period was regressed as a function of major energy sinks through the four different models (Model 1, Model 2, Model, and Model 4) using Mintab software (version 19). To define RFI, DMI was modeled as follows:〖Model 1: DMI〗_i= β_0+ β_1 ×MILKE_i+ β_2 × MBW_i+ β_3 × ∆BodyE_i+〖β_4 × 〖Weight G〗_i +β〗_5 × Parity_i+ β_6 × 〖Lactation W〗_i+ ε_i〖Model 2: DMI〗_i= β_0+ β_1 ×〖FCM4〗_i+ β_2 × MBW_i+ β_3 × 〖Weight G〗_i+ ε_i〖Model 3: DMI〗_i= β_0+ β_1 ×MILKE_i+ β_2 × MBW_i+ β_3 × ∆BodyE_i+β_4 × Parity_i+ ε_i〖Model 4: DMI〗_i= β_0+ β_1 ×MILKE_i+ ε_iWhere DMIi was the observed DMI, MilkEi was the observed milk energy output, MBWi was the average BW0.75, ΔBodyEi was the estimated change in body energy, based on measured BW and BCS, Weight Gi was daily weight gain, Parityi was the parity, Lactation Wi was week of lactation, and FCM4i was fatcorrected milk for ith cow. RFI was defined as the error term (ε_i) in the model. Also, we reported Pearson correlation coefficient between DMI, FCM4/DMI, and RFI with measured and estimated traits for a 30days and 60days period.Results and discussion: The results showed that RFI was observed and measured in our study population and it is possible to classify animals based on RFI. The model adjusted R2 for models 1, 2, 3 and 4 were 88.51, 78.82, 80.05, and 64.41 respectively, in a 60days period. The mean ± SD for models 1, 2, 3 and 4 were 0 ± 0.86, 0 ± 1.25, 0 ± 1.18, and 0 ± 1.68 (kg DM per a day), respectively. For model 1 (in 60days period, full model), milk energy output (MilkE), metabolic body weight for a cow (MBW), energy expended for body tissue gain (ΔBodyE), daily weight gain, and week of lactation were significant (P<0.05) except for parity which showed a significant trend (P<0.1). Partial regression coefficients for MilkE, MBW, ΔBodyE, daily weight gain, parity, and week of lactation for the model 1 used to predict DMI were 0.566, 0.1, 3.46, 9.51, 4.13, and 0.1771, respectively. For model 3 (in 60days period), milk energy output (MilkE), metabolic body weight for a cow (MBW), and energy expended for body tissue gain (ΔBodyE) were significant (P<0.05) except for parity. Partial regression coefficients for MilkE, MBW, ΔBodyE, and parity in the model 3, were 0.429, 0.1381, 0.791, and 0.229, respectively. Pearson correlation coefficient for RFI between model 1 with 2, 3 and 4 was 0.817, 0.728, and 0.515, respectively (P<0.01). The same trend was observed for a 30days period. Pearson correlation coefficient for DMI between 60days and 30days period was 0.994 and for RFI between 60days and 30days period was 0.882. Also, based on Pearson correlation coefficient for DMI, FCM4/DMI, and RFI with other biological parameters, we observed that there were the reasonable correlations, significant at the P=0.01. Surprisingly, there was a negative correlation between FCM4/DMI and milk protein percentage (P<0.0001). Also, negatively significant trend between RFI with milk fat and protein percentage (P<0.1) was observed.Conclusion: Measuring feed efficiency through RFI in a 30days period is predictable. Although the model 1 used in this study and its parameters explained DMI in accurate manner, scientific exploration for finding other parameters for improvement of model R2 is suggested. Therefore, finding the effective models would result in an accurate estimation of RFI for individual dairy cows, classifying efficient and inefficient dairy cows correctly and clarifying the reasons for these differences through a holistic approach. Also, the study of the reasons for the significant negative correlation between FE and RFI with milk protein and fat percentage as a novel observation in our study is recommended in the future.