Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
A meta-analysis was conducted to develop a model for predicting dry matter intake (DMI) in dairy cows under the tropical conditions of Brazil and to assess its adequacy compared with 5 currently available DMI prediction models: Agricultural and Food Research Council (AFRC); National Research Council (NRC); Cornell Net Carbohydrate and Protein System (CNCPS; version 6); and 2 other Brazilian models. The data set was created using 457 observations (n=1,655 cows) from 100 studies, and it was randomly divided into 2 subsets for statistical analysis. The first subset was used to develop a DMI prediction equation (60 studies; 309 treatment means) and the second subset was used to assess the adequacy of DMI predictive models (40 studies; 148 treatment means). The DMI prediction model proposed in the current study was developed using a nonlinear mixed model analysis after reparameterizing the NRC equation but including study as a random effect in the model. Body weight (mean = 540 ± 57.6 kg), 4% fat-corrected milk (mean = 21.3 ± 7.7 kg/d), and days in milk (mean = 110 ± 62 d) were used as independent variables in the model. The adequacy of the DMI prediction models was evaluated based on coefficient of determination, mean square prediction error (MSPE), root MSPE (RMSPE), and concordance correlation coefficient (CCC). The observed DMI obtained from the data set used to evaluate the prediction models averaged 17.6 ± 3.2 kg/d. The following model was proposed: DMI (kg/d) = [0.4762 (± 0.0358) × 4% fat-corrected milk + 0.07219 (± 0.00605) × body weight(0.75)] × (1 - e(-0.03202 (± 0.00615) × [days in milk + 24.9576 (± 5.909)])). This model explained 93.0% of the variation in DMI, predicting it with the lowest mean bias (0.11 kg/d) and RMSPE (4.9% of the observed DMI) and the highest precision [correlation coefficient estimate (ρ) = 0.97] and accuracy [bias correction factor (Cb)=0.99]. The NRC model prediction equation explained 92.0% of the variation in DMI and had the second lowest mean bias (0.42 kg/d) and RMSPE (5.8% of the observed DMI), as well as the second highest precision (ρ = 0.94) and accuracy (Cb = 0.98). The CNCPS and AFRC DMI prediction models explained 93.0 and 85.0% of the variation in DMI but underpredicted DMI by 1.8 and 1.4 kg/d, respectively. These 2 models (CNCPS and AFRC) resulted, respectively, in RMSPE of 11.3 and 10.7% of the observed DMI, with moderate to high precision (ρ = 0.81 and 0.82) and accuracy (Cb = 0.84 and 0.89). The remaining 2 models resulted in the poorest results, underpredicting DMI by 2.3 and 1.9 kg/d, with RMSPE of 22.8 and 14.9% of the observed DMI and moderate to low precision (ρ = 0.49 and 0.76) and accuracy (Cb = 0.81 and 0.86). The new model derived from the current meta-analytical approach provided the best accuracy and precision for predicting DMI in lactating dairy cows under Brazilian conditions.
A meta-analysis was conducted to develop a model for predicting dry matter intake (DMI) in dairy cows under the tropical conditions of Brazil and to assess its adequacy compared with 5 currently available DMI prediction models: Agricultural and Food Research Council (AFRC); National Research Council (NRC); Cornell Net Carbohydrate and Protein System (CNCPS; version 6); and 2 other Brazilian models. The data set was created using 457 observations (n=1,655 cows) from 100 studies, and it was randomly divided into 2 subsets for statistical analysis. The first subset was used to develop a DMI prediction equation (60 studies; 309 treatment means) and the second subset was used to assess the adequacy of DMI predictive models (40 studies; 148 treatment means). The DMI prediction model proposed in the current study was developed using a nonlinear mixed model analysis after reparameterizing the NRC equation but including study as a random effect in the model. Body weight (mean = 540 ± 57.6 kg), 4% fat-corrected milk (mean = 21.3 ± 7.7 kg/d), and days in milk (mean = 110 ± 62 d) were used as independent variables in the model. The adequacy of the DMI prediction models was evaluated based on coefficient of determination, mean square prediction error (MSPE), root MSPE (RMSPE), and concordance correlation coefficient (CCC). The observed DMI obtained from the data set used to evaluate the prediction models averaged 17.6 ± 3.2 kg/d. The following model was proposed: DMI (kg/d) = [0.4762 (± 0.0358) × 4% fat-corrected milk + 0.07219 (± 0.00605) × body weight(0.75)] × (1 - e(-0.03202 (± 0.00615) × [days in milk + 24.9576 (± 5.909)])). This model explained 93.0% of the variation in DMI, predicting it with the lowest mean bias (0.11 kg/d) and RMSPE (4.9% of the observed DMI) and the highest precision [correlation coefficient estimate (ρ) = 0.97] and accuracy [bias correction factor (Cb)=0.99]. The NRC model prediction equation explained 92.0% of the variation in DMI and had the second lowest mean bias (0.42 kg/d) and RMSPE (5.8% of the observed DMI), as well as the second highest precision (ρ = 0.94) and accuracy (Cb = 0.98). The CNCPS and AFRC DMI prediction models explained 93.0 and 85.0% of the variation in DMI but underpredicted DMI by 1.8 and 1.4 kg/d, respectively. These 2 models (CNCPS and AFRC) resulted, respectively, in RMSPE of 11.3 and 10.7% of the observed DMI, with moderate to high precision (ρ = 0.81 and 0.82) and accuracy (Cb = 0.84 and 0.89). The remaining 2 models resulted in the poorest results, underpredicting DMI by 2.3 and 1.9 kg/d, with RMSPE of 22.8 and 14.9% of the observed DMI and moderate to low precision (ρ = 0.49 and 0.76) and accuracy (Cb = 0.81 and 0.86). The new model derived from the current meta-analytical approach provided the best accuracy and precision for predicting DMI in lactating dairy cows under Brazilian conditions.
Corn and sorghum are the main energy sources used in the diets of ruminants in our country. These two cereals have two characteristics that can hinder the absorption and use of starch by animals. They have two striking characteristics that hinder the absorption and use of starch by animals. The first is related to the consistency of the endosperm, classified as floury or hard, preventing the maximum use of the material, and the second is the protein matrix that covers the starch granules, functioning as an excellent physical-chemical barrier in the protection against weather conditions, problems harvesting and attacking insects and / or rodents. Processing over grains can be used as a way to improve the factors that limit their absorption, breaking the protein matrix that covers the starch granules, favoring the performance of digestive enzymes. The objective is to bring arguments and evidence on the efficiency of corn processing in the form of rehydration and silage, on aspects of ruminal degradability, improvement in feed conversion and increase in production. To this end, a review was carried out through scientific articles, analyzing the corn processing techniques and their performance in the productive characteristics of ruminants. Being able to conclude that the processing on the grain, can increase the nutritional value of corn with vitreous endosperm, improving the zootechnical aspects in the nutrition of ruminants.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.