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1.
Dietary intervention to reduce methane emissions from lactating dairy cattle is both environmentally and nutritionally desirable due to the importance of methane as a causative agent in global warming and as a significant loss of feed energy. Reliable prediction systems for methane production over a range of dietary inputs could be used to develop novel dietary regimes for the limitation of feed energy loss to methane. This investigation builds on previous attempts at modeling methanogenesis and involves the development of a dynamic mechanistic model of wholerumen function. The model incorporates modifications to certain ruminal fermentation parameters and the addition of a postruminal digestive element. Regression analysis showed good agreement between observed and predicted results for experimental data taken from the literature (r2 = 0.76, root mean square prediction error = 15.4%). Evaluation of model predictions for experimental observations from five calorimetry studies (67 observations) with lactating dairy cows at the Centre for Dairy Research, in Reading, U.K., shows an underprediction (2.1 MJ/d) of methane production (r2 = 0.46, root mean square prediction error = 12.4%). Application of the model to develop diets for minimizing methanogenesis indicated a need to limit the ratio of lipogenic to glucogenic VFA in the rumen and hindgut. This may be achieved by replacing soluble sugars in the concentrate with starch or substituting corn silage for grass silage. On a herd basis, the model predicted that increasing dietary energy intake per cow can minimize the annual loss of feed energy through methane production. The mechanistic model is a valuable tool for predicting methane emissions from dairy cows.  相似文献   

2.
Methane production from enteric fermentation in cattle is one of the major sources of anthropogenic greenhouse gas emission in the United States and worldwide. National estimates of methane emissions rely on mathematical models such as the one recommended by the Intergovernmental Panel for Climate Change (IPCC). Models used for prediction of methane emissions from cattle range from empirical to mechanistic with varying input requirements. Two empirical and 2 mechanistic models (COWPOLL and MOLLY) were evaluated for their prediction ability using individual cattle measurements. Model selection was based on mean square prediction error (MSPE), concordance correlation coefficient, and residuals vs. predicted values analyses. In dairy cattle, COWPOLL had the lowest root MSPE and greatest accuracy and precision of predicting methane emissions (correlation coefficient estimate = 0.75). The model simulated differences in diet more accurately than the other models, and the residuals vs. predicted value analysis showed no mean bias (P = 0.71). In feedlot cattle, MOLLY had the lowest root MSPE with almost all errors from random sources (correlation coefficient estimate = 0.69). The IPCC model also had good agreement with observed values, and no significant mean (P = 0.74) or linear bias (P = 0.11) was detected when residuals were plotted against predicted values. A fixed methane conversion factor (Ym) might be an easier alternative to diet-dependent variable Ym. Based on the results, the 2 mechanistic models were used to simulate methane emissions from representative US diets and were compared with the IPCC model. The average Ym in dairy cows was 5.63% of GE (range 3.78 to 7.43%) compared with 6.5% +/- 1% recommended by IPCC. In feedlot cattle, the average Ym was 3.88% (range 3.36 to 4.56%) compared with 3% +/- 1% recommended by IPCC. Based on our simulations, using IPCC values can result in an overestimate of about 12.5% and underestimate of emissions by about 9.8% for dairy and feedlot cattle, respectively. In addition to providing improved estimates of emissions based on diets, mechanistic models can be used to assess mitigation options such as changing source of carbohydrate or addition of fat to decrease methane, which is not possible with empirical models. We recommend national inventories use diet-specific Ym values predicted by mechanistic models to estimate methane emissions from cattle.  相似文献   

3.
Improving N utilization in dairy cows and especially reducing N output in excreta is desirable due to global concerns of agricultural contribution of N to environmental pollution, particularly as ammonia. Data from five N balance experiments were used to develop a dynamic model that was evaluated with independent data. Model predictions of feces, urine, and milk outputs were close to observed values. Statistical analysis showed that 96% of mean square prediction error for feces and urine N output predictions was due to random variation. However, the model tends to overpredict milk N output, especially at higher N intake levels. Evaluation of model predictions for independent experimental observations from Agricultural Development Advisory Service at Bridgets (U.K.) showed good agreement between predicted and observed urine N output (95% due to random variation). However, there was a slight underprediction for fecal N output (14% mean square prediction error due to bias) and overprediction of milk N output (22% of mean square prediction error due to bias). The model predictions of N outputs in excreta were sensitive to changes in energy concentration of the diet. Dietary protein degradability had only a small influence on predicted fecal N output. However, the model was sensitive in its predictions of urine N when protein degradability was varied. Application of the model to assess reduction in ammonia emissions from dairy cows showed that increasing the energy concentration could potentially reduce ammonia emissions by up to 25% per cow. Similarly, reducing CP concentration in the diet to about 16% could reduce ammonia production by 20% and lower degradability of CP to match microbial requirement by 19% per cow. The model is a first step toward a mechanistic approach of nutrient modeling, and it is a valuable method for predicting N excretions and estimating N emissions from dairy systems.  相似文献   

4.
Abstract

The objective of this study was to evaluate the feed intake models in the Nordic feed evaluation system NorFor. Data from 196 feeding experiments with dairy cows, and 17 experiments of periodical data, and 135 experiments of complete data with growing cattle were used in the evaluation by mixed model regression. The feed intake by both dairy cows and growing cattle were overestimated by the models. A linear bias indicated that over prediction increased with level of intake both by dairy cows and growing cattle. Most animal and diet factors were significantly related to the residuals, which indicated that those factors did not act independently in the predictions of feed intake. This kind of errors can restrict future ration formulation and animal performance, since animal production parameters included in the prediction models will be a consequence of the diet fed at the time they were measured.  相似文献   

5.
An experiment was conducted to determine prediction equations that used readings for total body electrical conductivity (TOBEC) in the model for estimation of total fat-free lean and total fat weight in the pork carcass. Ultrasound measurements of live hogs were used to select 32 gilts that represented a range in weight, muscling, and fatness. The TOBEC readings were recorded on warm carcass sides, chilled carcass sides, and the untrimmed ham from the left carcass side. Physical dissection and chemical analyses determined fat-free lean and fat weight of the carcass. All of the ham tissues were analyzed separately from the remainder of the carcass tissues to incorporate ham measurements for prediction of total fat-free lean and total fat weight in the entire carcass. Prediction equations were developed using stepwise regression procedures. An equation that used a warm carcass TOBEC reading in the model was determined to be the best warm TOBEC equation (R2 = 0.91; root mean square error = 0.81). A three-variable equation that used chilled carcass TOBEC reading, chilled carcass temperature, and carcass length in the model was determined to be the best chilled TOBEC equation (R2 = 0.93; root mean square error = 0.73). A four-variable equation that included chilled carcass side weight, untrimmed ham TOBEC reading, ham temperature, and fat thickness beneath the butt face of the ham in the model was determined to be the best equation overall (R2 = 0.95; root mean square error = 0.65). The TOBEC and the fat-free lean weight of the ham are excellent predictors of total carcass fat-free lean weight.  相似文献   

6.
In a previous paper we have proposed a new concept of a model for the prediction of feed intake by Holstein Friesian dairy cows (Zom et al., 2011). This model predicts feed intake from feed composition and digestibility and the cow's lactation number, stage of lactation and pregnancy. Contrary to many other often used models, this does not include animal performance (milk yield, bodyweight) to predict feed intake. However, BW and MY are highly correlated with DMI. Therefore, the objective of present study was to evaluate the accuracy and robustness of the novel feed intake model and to compare its accuracy and robustness with four other commonly used models for the prediction of feed intake.An evaluation was performed using an independent dataset containing 8974 weekly means of DMI from 348 individual cows observed in 6 feeding experiments including a wide range of diets and management practices was used in this study. Sub-datasets were formed by combining the DMI data by experiment, lactation number, lactation week, and maize silage to grass silage ratios in order to compare the accuracy of the intake models for different feeding practices and groups of cows using mean square prediction error (MSPE) and relative prediction error (RPE) as criteria.The novel model was most accurate as indicated by the MSPEs and RPEs for the whole dataset and the most of the sub-datasets. The results prove that the model of Zom et al. (2011) is able to predict DMI without the use of milk yield or body weight as inputs. It was concluded that novel model was robust and can be applied to various diets and feeding management situations in lactating HF cows.  相似文献   

7.
An automatic in vitro gas production technique was evaluated for predicting in vivo fiber (NDF) digestibility and effective first-order digestion rate of potentially digestible NDF (pdNDF) of 15 grass silages. Observed in vivo NDF digestibility of the silages harvested at different stages of maturity during 3 yr was determined by the total fecal collection in sheep fed at the maintenance level of intake. Isolated grass silage NDF was incubated for 72 h in the presence of rumen fluid and buffer to determine the pdNDF digestion kinetics based on cumulative gas production profiles. The digestion kinetic parameters were estimated by a 2-pool Gompertz function. The estimated parameter values were then used in a 2-compartment mechanistic rumen model to predict the in vivo digestibility of pdNDF. A total compartmental mean residence time of 50 h was used in the model, and a further assumption of the distribution of the residence time between the rumen nonescapable and escapable pools in a ratio of 0.4:0.6 was made. To make a distinction between potentially digestible and indigestible NDF, the potential extent of NDF digestion was determined by a 12-d ruminal in situ incubation. The model-predicted in vivo NDF digestibility accurately and precisely (root mean square error = 0.013 units, R(2) = 0.99). Effective first-order digestion rate was estimated from the predicted pdNDF digestibility, and the values were compared with those calculated from the in vivo pdNDF digestibility using the same passage kinetic parameters. The predicted effective first-order digestion rate was strongly correlated with digestion rate estimates derived from in vivo data (root mean square error = 0.006/h, R(2) = 0.86). It can be concluded that a simple first-order digestion rate can be estimated from a complicated gas production kinetic model including 6 parameters. This rate constant can be used in continuous steady-state dynamic mechanistic rumen models predicting the nutrient supply to the host animal.  相似文献   

8.
Soil intake may be the most prominent source of environmental contaminants for free range and organic hens, but there are no quantitative data concerning soil intake by domestic hens. Consumption of soil of 14–32 g a day can be estimated from literature, but such a dilution of nutrient intake seems incompatible with high productivity. In this study laying hens were fed pelleted diets with 0%, 10%, 20%, 25% and 30% of sand addition to determine its effect on productivity. Feed intake, feed and nutrient (feed minus sand) conversion ratio, egg production, egg weight and body weight gain were measured over a 4-week period. Acid insoluble ash concentration in the faeces was measured to determine the accuracy of estimating the soil ingestion by the soil-ingestion equation for wildlife as a way to determine soil ingestion of free range and organic hens under practical circumstances. The hens were able to compensate the dilution of the diet with 20%, 25% and 30% of sand by increasing their feed intake. Feed intake increased significantly and feed to egg conversion ratio decreased significantly with increasing sand levels in the diet. The nutrient to egg conversion ratio of the diet without sand tended to be worse than for the diets with sand, presumably due to the total absence of coarse material in the diet. There were no differences in egg production and egg weight between hens fed the different diets but body weight gain was significantly lower for the hens fed the diets with 20%, 25% and 30% of sand. Estimation of sand ingestion was done by the soil-ingestion equation for wildlife. Provided that the actual dry matter digestibility coefficient of the nutrient part of the diet is taken into account, estimating the soil ingestion according to the soil-ingestion equation for wildlife seems an appropriate way to determine soil ingestion for free range and organic hens under practical circumstances.  相似文献   

9.
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.  相似文献   

10.
A computer model was developed to predict responses of lactating ewes to concentrate supplementation, whether on pasture or stall-fed, given concentrate once per day or in multiple feedings, and suckling multiple lambs. The model considers effects of concentrate supplementation on organic acid production, saliva flow, ruminal pH, and forage intake. The user defines ewe BW, feed composition, and concentrate feeding times and amounts. The reference ewe has free access to forage and water. Upon consumption, forages and concentrates enter into lag pools for 2.0 and 0.24 h, respectively. Carbohydrates then enter ruminal pools of degradable fiber, undegradable fiber, or nonstructural carbohydrate, from which they are degraded or pass to the lower gut. Rapid dissociation of organic acids from carbohydrate fermentation and buffers from rumination are simulated to determine ruminal pH according to the Henderson-Hasselbach equation. The pH, in turn, affects fiber degradation rates. Forage intake continues during daylight hours until ruminal NDF exceeds 1.0% of BW, or organic acid concentration exceeds 130 mM. A circadian pattern of organic acid concentrations and pH of rumen contents with multiple concentrate feedings was simulated by the model with root mean square prediction error of 7.7 and 3.0 to 4.0% of the observed mean, respectively. However, ignoring fermentation of dietary protein may have caused an underestimation of organic acid production rates. The model predicted the increase in total DMI and the substitution effect on forage intake of increasing levels of concentrate supplementation. Simulations suggested that a single concentrate meal daily was best fed in the evening to minimize the substitution effect, and that there was no benefit in forage intake to feeding 2 kg/d concentrate in more than two meals per day.  相似文献   

11.
Several mathematical or statistical and artificial intelligence models were developed to compare egg production forecasts in commercial layers. Initial data for these models were collected from a comparative layer trial on commercial strains conducted at the Poultry Research Farms, Auburn University. Simulated data were produced to represent new scenarios by using means and SD of egg production of the 22 commercial strains. From the simulated data, random examples were generated for neural network training and testing for the weekly egg production prediction from wk 22 to 36. Three neural network architectures-back-propagation-3, Ward-5, and the general regression neural network-were compared for their efficiency to forecast egg production, along with other traditional models. The general regression neural network gave the best-fitting line, which almost overlapped with the commercial egg production data, with an R(2) of 0.71. The general regression neural network-predicted curve was compared with original egg production data, the average curves of white-shelled and brown-shelled strains, linear regression predictions, and the Gompertz nonlinear model. The general regression neural network was superior in all these comparisons and may be the model of choice if the initial overprediction is managed efficiently. In general, neural network models are efficient, are easy to use, require fewer data, and are practical under farm management conditions to forecast egg production.  相似文献   

12.
Two different statistical models considering racetrack or individual race as fixed effect were compared, regarding genetic parameters and by using cross validation. Data for variance component estimation consisted of 48,942 performance observations from 4249 trotters. Variance components for the traits square root of rank at finish, racing time per km, and log of earnings per race were estimated by REML using two multiple trait animal models involving different racetracks or individual races. When including each individual race instead of racetracks in the statistical model, heritabilities increased from 0.05 to 0.07, 0.19 to 0.23, and 0.08 to 0.09 for square root of rank at finish, racing time per km, and log of earnings per race, respectively. Genetic and phenotypic correlations among traits increased also after consideration of individual races. Square root of rank at finish, as well as racing time per km and log of earnings per race, was highly genetically correlated with −0.99 and −0.88. The two statistical models were compared on the basis of their predictive ability by using cross validation. Data for these analyses consisted of 706,082 observations from 21,363 trotters. Randomly eliminated performance observations were predicted by cumulation of fixed and random effects obtained from estimation of breeding values for both models. Estimates for racing time showed lower bias and mean square error (MSE) when considering individual races instead of racetracks. Also, the correlation between predicted and true phenotypic value increased from 0.85 to 0.92. Estimates for square root of rank at finish were unbiased, but with a higher MSE when considering individual race effect. A similar high bias and MSE with both models were obtained for log of earnings. In order to avoid bias in estimation of genetic parameters and breeding values for racing time and square root of rank at finish, inclusion of each individual race in the statistical model was recommended.  相似文献   

13.
The overall objective of this work was to develop empirical equations from a meta-analysis study to be used to implement initial values in a mechanistic heat balance model. The meta-analysis was conducted to 1) develop prediction equations for sweating and respiration rate (SR, g·m(-2)·h(-1) and RR, breaths·min(-1), respectively) based on skin and body temperature (T(s) and T(b), °C, respectively) for different breed types: Bos indicus, Bos taurus, and their crossbreds, and 2) evaluate the fit of existing SR equations and the SR and RR equations (from objective 1) against independent data sets. Fourteen studies were collected for the SR analysis, 12 for fitting and 2 for evaluation. The fitted SR equations (Thompson model) for the 3 breeds types were B. indicus, SR = 0.085e(0.22·T(s)); B. taurus, SR = 0.75e(0.15·T(s)); and crossbreds, SR = 0.015e(0.25·T(s)). Twenty-three studies were collected for the RR analysis, 20 for fitting and 3 for evaluation. The fitted RR equations for the 3 breed types were B. indicus, RR = -1,660 + 43.8·T(b); B. taurus, RR = -1,385 + 37·T(b); and crossbreds, RR = -2,226 + 59·T(b). Three SR equations (Maia, McArthur, and Gatenby models) from the literature were evaluated against the Thompson model using the 14 studies. The McArthur model predicted SR within the correct range, but with an increased slope bias because the equation was linear and not the correct shape. The Maia model overpredicted SR for all breed types with the greatest overprediction being for crossbreds. The Gatenby model overpredicted SR for B. taurus (root mean square error of prediction = 506 g·m(-2)·h(-1)), but was the best predictor for B. indicus. The Thompson model overpredicted SR for B. indicus (root mean square error of prediction ranged from 134 to 265 g·m(-2)·h(-1)), but was the best predictor for B. taurus and crossbreds. The Thompson model was a good predictor for RR across all breed types. The meta-analysis showed that the Thompson model outperformed previous models for both RR and SR with the exception of the SR of B. indicus, which was best predicted by the Gatenby model.  相似文献   

14.
金莲花产量抽样调查的样地最小面积与形状研究   总被引：1，自引：0，他引：1  
抽样调查是植物资源调查的一种重要方法,样地的面积与形状是抽样调查的基础,影响着调查的效率与精度。为确定野生金莲花产量抽样调查的最适样地面积与形状,设计了边长或半径逐步增大的正方形、长方形、圆形3种样地,选择了Generalized Mitscherlich方程、Richard方程与Logistic方程拟合变异系数－面积的变化趋势。提出了以变异系数的变化率与调查费用最小为判据,确定样地最小面积的2种方法,并对不同形状的样地进行比较以确定适合的样地形状。结果表明,3种变异系数－面积曲线的拟合效果均较好,相关指数均达到0.94以上,但以Logistic方程最为稳定。以变化率确定的最小面积,同一样地形状表现为Richard方程＞Logistic方程＞Generalized Mitscherlich方程;采用同一个回归方程,样地最小面积表现为圆形＞正方形＞长方形,正方形与长方形较为接近。基于最小费用研究表明,同一样地形状采用不同回归方程所得最小费用相近,但最小面积各不相同;对于同一回归方程,样地最小面积同样表现为圆形＞正方形＞长方形,正方形与长方形相近的趋势。最后,综合确定正方形样地的最小样地面积为36 m2(6 m×6 m),长方形为32 m2(8 m×4 m),圆形为78.5 m2(半径5 m)。不同形状的样地,从所能达到的最小变异系数、相同面积与精度时的调查费用与不同回归模型反映的稳定性来说,长方形样地最好,正方形次之,但二者相差不大,圆形最差。  相似文献   

15.
本试验研究了日粮中不同中性洗涤纤维/非纤维性碳水化合物（NDF/NFC）水平对周岁后荷斯坦奶牛生产性能、营养物质消化率、瘤胃发酵特征及甲烷产量的影响，并在此基础上建立了甲烷排放预测模型，旨在获得我国生产模型下的甲烷排放规律和甲烷转化因子，为提高奶牛能量利用效率、建立国家或区域性温室气体排放清单和探索减排策略提供科学依据和支撑。将45头体况良好，平均为15月龄的荷斯坦后备奶牛随机分为3组，每组15头牛：低日粮NDF/NFC组（NDF/NFC=0.60）、中日粮NDF/NFC组（NDF/NFC=0.75）和高日粮NDF/NFC组（NDF/NFC=0.90），试验期为70 d，包括14 d的预饲期和56 d的正试期。结果表明：1）提高日粮NDF/NFC水平显著降低了奶牛的干物质采食量、有机物采食量、平均日增重、干物质和粗蛋白的表观消化率（P<0.05）；2）提高日粮NDF/NFC水平显著增加了瘤胃内总挥发性脂肪酸产量、乙酸的相对含量和乙酸/丙酸比例（P<0.05），显著降低了丙酸的相对含量（P<0.05）；3）随着日粮NDF/NFC水平的提高，瘤胃甲烷和甲烷能产量、甲烷/代谢体重、甲烷/干物质采食量、甲烷/有机物采食量、甲烷/中性洗涤纤维采食量显著提高（P<0.05）。甲烷转化因子也随着日粮NDF/NFC水平的增加而显著提高（P<0.05）；4）基于体重、采食量、营养物质含量和NDF/NFC分别建立了甲烷预测模型，其中基于干物质采食量和中性洗涤纤维采食量建立的预测模型的决定系数最高（R2=0.77）。因此，提高日粮中NDF/NFC水平可显著降低周岁后荷斯坦奶牛的生产性能、营养物质消化率和瘤胃内丙酸的相对含量，可显著提高瘤胃甲烷产量和甲烷转化因子。  相似文献   

16.
Methane production in goats given diets based on lucerne hay and barley   总被引：1，自引：0，他引：1  
An analysis of data from 102 energy balance trials carried out with Granadina goats, 32 with castrated males and 70 with lactating females, was made with the aim of establishing relationships between methane production and some nutritive attributes of the diet. The diets were based on pelleted lucerne (Medicago sativa) hay and barley and differed widely in the amounts and proportions of their ingredients. Methane production was measured by open-circuit respirometry. The between-animal variation in CH4 production was found to be +/- 8%. In absolute terms, CH4 production increased on increasing the intake of energy. The CH4 loss was 6.56 or 9.75 kJ/100 kJ of gross energy or digestible energy intake, respectively. The prediction of the CH4 production was best described from knowledge of the amounts of the "in vitro" digestible fractions (g/d) of both the neutral-detergent solubles (DNDS) and the neutral-detergent fibre (DNDF), according to the equation CH4 = 2.24 + 0.0299 DNDS + 0.0889 DNDF. The regression was highly significant and the residual standard deviation +/- 6.63 or +/- 20% of the mean. It was also found that on increasing the feeding level by one multiple of maintenance there was a reduction in CH4 losses of 1.51 kJ or 2.19 kJ/100 kJ of gross energy or digestible energy intake, respectively. The CH4 production (kJ/100 kJ of gross energy intake) was also related to the apparent digestibility of energy (D, %) determined at the level of nutrition close to maintenance. The equation was CH4 = -2.58 + 0.151 D. The regression was highly significant statistically.  相似文献   

17.
The sulphur hexafluoride (SF(6)) gas tracer method was used to measure methane (CH(4)) production of crossbred (3/4 Holstein x Zebu) dairy heifers fed two types of sugarcane (Saccharum officinarum L.; cultivar IAC-862480 (CC1) or cultivar IAC-873184 (CC2)) and supplemented with urea or concentrate. The study was performed at Embrapa Southeast Cattle, S?o Carlos, SP, Brazil, using a completely randomised design. Differences between treatments were significant for digestibility of dry matter, organic matter and energy. When animals were supplemented with urea differences between sugarcane cultivars did occur for NDF consumption, but not for daily methane production. This suggest that variation in chemical composition of sugarcane did not affect bovine ruminal CH(4) emissions. Concentrate inclusion in animal diet increased digestible organic matter intake, improving the nutrient intake by animals, but did not reduce CH(4) production expressed as a percentage of gross energy intake.  相似文献   

18.
This study was undertaken to develop models which could be used in conjunction with the near infrared reflectance spectroscopy (NIRS) analysis of grass silage to accurately predict the intake potential of grass silage when offered to lactating dairy cows as part of a mixed diet. Empirical models were developed with data collected from two large-scale studies carried out at the Institute. The models comprised of (1) a linear equation for converting the NIRS-based predicted intake of a given silage for beef cattle to dairy cows and (2) a model which corrected the intake potential of the grass silage for supplementary concentrates. Furthermore, a milk yield adjustment factor of 0.14 kg DM/kg milk was utilised to standardise milk yields. Both linear and exponential models were developed to describe the decrease in silage intake as concentrate intake increased, with y-axis intercepts corresponding to unsupplemented silage intakes (NIRS-based predictions for beef cattle adjusted for dairy cows) and common x-axis intercept of 168.0 (SE=20.50) and 203.8 (SE=5.64) g/kg W0.75, respectively, corresponding to concentrate intake when offered as a sole feed. A common r parameter (model curvature) of 1.0047 (SE=0.00397) was assumed for the exponential model. When the models were validated against the data from an independent study, the predictions from the two models were not significantly different, giving R2 values of 0.70. The intercept and slope from the linear model were 5.39 and 1.01, respectively, and the intercept and slope from the exponential model were 6.10 and 0.98, respectively. Both intercepts and slopes were not significantly different from 0 and 1, respectively. Ninety-three percent of predictions were within 10% of observed intakes in the validation data.  相似文献   

19.
Tropical Animal Health and Production - The aim of this study was to evaluate the effect of diet and animal shearing on the feed and nutrient intakes, water intake, in vitro ruminal methane...  相似文献   

20.
土壤退化是草地退化的更深层次指示,运用遥感手段大面积测定土壤有机碳进而评估草地土壤状况有助于对草地退化状态的正确认识。以甘南州高寒草地土壤为研究对象,使用ASD地物光谱仪,在室内条件下对土壤样品进行可见光/近红外光谱测量,分析8种光谱变换形式与土壤有机碳含量的相关性并选取特征波段,利用3种多元回归方法(逐步多元线性回归、主成分回归、偏最小二乘回归),通过验证样本的决定系数(Rv2)、均方根误差(RMSE)和剩余估计偏差(RPD)来评价模型,进而确定高寒草地土壤有机碳的最佳估测模型。结果表明,微分变换方法可以显著提高光谱特征与土壤有机碳含量的相关性,在所有变换形式中以光谱反射率的一阶微分与土壤有机碳含量相关性最好,最大相关系数绝对值为0.865;基于光谱反射率一阶微分变换形式的3种多元回归方法对土壤有机碳均有极好的预测能力,表明对于土壤有机碳的稳定监测来说光谱反射率的一阶微分是非常有效的变换形式;综合考虑基于所有光谱变换形式的3种多元回归方法的预测结果,偏最小二乘回归法具有高的Rv2和RPD,同时具有低的RMSE值,是研究区土壤有机碳估测的最优回归方法;基于光谱反射率对数的一阶微分变换形式所建立的偏最小二乘回归模型具有相对较高的预测集决定系数(Rv2=0.878)、最大剩余估计偏差(RPD=2.946)和最小均方根误差(RMSE=7.520),因此该模型为甘南高寒草地土壤有机碳的最优估测模型,最优模型的RPD大于2.5说明该模型有足够的稳定性可以应用于其他地区土壤有机碳的估测。  相似文献   

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