从南美洲汇编的各种日粮中预测肉牛氮排泄量的模型。

IF 1.3 Q3 AGRICULTURE, DAIRY & ANIMAL SCIENCE Translational Animal Science Pub Date : 2024-05-09 eCollection Date: 2024-01-01 DOI:10.1093/tas/txae072
Vinícius C Souza, Guilhermo F S Congio, João P P Rodrigues, Sebastião C Valadares Filho, Flávia A S Silva, Luciana N Rennó, Ricardo A Reis, Abmael S Cardoso, Paulo H M Rodrigues, Telma T Berchielli, Juliana D Messana, Cecilia Cajarville, Yury T Granja-Salcedo, Ana L C C Borges, Gilberto V Kozloski, Jaime R Rosero-Noguera, Horacio Gonda, Alexander N Hristov, Ermias Kebreab
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引用次数: 0

摘要

这项荟萃分析的目的是开发和评估用于预测南美洲肉牛粪便、尿液和粪便中氮(N)排泄量的模型。该研究共纳入了 1,116 份粪便中氮排泄量的个体观测数据和 939 份粪便和尿液中氮排泄量(克/天)的个体观测数据,这些数据代表了南美洲不同的日粮、动物基因型和管理条件。数据集还包括干物质摄入量(DMI;kg/d)和氮摄入量(NI;g/d)、日粮成分浓度以及平均日增重(ADG;g/d)和平均体重(BW;kg)的数据。采用线性混合效应回归法得出模型,并为研究设置了随机截距。粪N排泄量与DMI、NI、非纤维碳水化合物、平均体重和ADG呈正相关,与日粮中EE和CP浓度呈负相关。以 DMI 为基础预测粪氮排泄量的单变量模型(模型 1)的表现略好于以 NI 为预测变量的单变量模型(模型 2),其均方根误差(RMSE)分别为 38.0% 和 39.2%,均方根误差-观测值标度比(RMSE-Observations SD ratio,RSR)分别为 0.81 和 0.84,一致性相关系数(conordance correlation coefficient,CCC)分别为 0.53 和 0.50。预测尿氮排泄量的模型不如预测粪氮排泄量的模型准确,其平均有效误差率分别为 43.7% 和 37.0%。尿氮和粪氮排泄量与DMI、NI、CP、平均体重和ADG呈正相关,而与日粮中的中性洗涤纤维浓度呈负相关。与粪氮排泄量相比,使用 NI 预测尿氮排泄量的单变量模型(模型 10)比使用 DMI 预测尿氮排泄量的单变量模型(模型 9)表现稍好,RMSE 分别为 36.0% 对 39.7%,RSR 分别为 0.85 对 0.93,CCC 分别为 0.43 对 0.29。本研究建立的模型适用于预测南美洲各种日粮组成和动物基因型的肉牛氮排泄量。建议使用以 DMI 为预测因子的单变量模型预测粪氮,而使用 NI 的单变量模型预测尿氮和粪氮的排泄,因为使用更复杂的模型几乎没有任何益处。不过,当氮排泄是一个需要考虑的因素时,考虑使用包含营养摄入量和日粮组成的更复杂模型进行决策可能会更有用。本研究中评估的三个现有方程有可能用于南美洲典型的热带条件,以良好的精度和准确性预测粪氮排泄。但是,由于这些方程的均方根误差较大,精度和准确度较低,因此不建议将它们用于预测尿液或粪便的氮排泄量。
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Models to predict nitrogen excretion from beef cattle fed a wide range of diets compiled from South America.

The objective of this meta-analysis was to develop and evaluate models for predicting nitrogen (N) excretion in feces, urine, and manure in beef cattle in South America. The study incorporated a total of 1,116 individual observations of N excretion in feces and 939 individual observations of N excretion in feces and in urine (g/d), representing a diverse range of diets, animal genotypes, and management conditions in South America. The dataset also included data on dry matter intake (DMI; kg/d) and nitrogen intake (NI; g/d), concentrations of dietary components, as well as average daily gain (ADG; g/d) and average body weight (BW; kg). Models were derived using linear mixed-effects regression with a random intercept for the study. Fecal N excretion was positively associated with DMI, NI, nonfibrous carbohydrates, average BW, and ADG and negatively associated with EE and CP concentration in the diet. The univariate model predicting fecal N excretion based on DMI (model 1) performed slightly better than the univariate model, which used NI as a predictor variable (model 2) with a root mean square error (RMSE) of 38.0 vs. 39.2%, the RMSE-observations SD ratio (RSR) of 0.81 vs. 0.84, and concordance correlation coefficient (CCC) of 0.53 vs. 0.50, respectively. Models predicting urinary N excretion were less accurate than those derived to predict fecal N excretion, with an average RMSE of 43.7% vs. 37.0%, respectively. Urinary and manure N excretion were positively associated with DMI, NI, CP, average BW, and ADG and negatively associated with neutral detergent fiber concentration in the diet. As opposed to fecal N excretion, the univariate model predicting urinary N excretion using NI (model 10) performed slightly better than the univariate model using DMI (model 9) as predictor variable with an RMSE of 36.0% vs. 39.7%, RSR 0.85 vs. 0.93, and CCC of 0.43 vs. 0.29, respectively. The models developed in this study are applicable for predicting N excretion in beef cattle across a broad spectrum of dietary compositions and animal genotypes in South America. The univariate model using DMI as a predictor is recommended for fecal N prediction, while the univariate model using NI is recommended for predicting urinary and manure N excretion because the use of more complex models resulted in little to no benefits. However, it may be more useful to consider more complex models that incorporate nutrient intakes and diet composition for decision-making when N excretion is a factor to be considered. Three extant equations evaluated in this study have the potential to be used in tropical conditions typical of South America to predict fecal N excretion with good precision and accuracy. However, none of the extant equations are recommended for predicting urine or manure N excretion because of their high RMSE, and low precision and accuracy.

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来源期刊
Translational Animal Science
Translational Animal Science Veterinary-Veterinary (all)
CiteScore
2.80
自引率
15.40%
发文量
149
审稿时长
8 weeks
期刊介绍: Translational Animal Science (TAS) is the first open access-open review animal science journal, encompassing a broad scope of research topics in animal science. TAS focuses on translating basic science to innovation, and validation of these innovations by various segments of the allied animal industry. Readers of TAS will typically represent education, industry, and government, including research, teaching, administration, extension, management, quality assurance, product development, and technical services. Those interested in TAS typically include animal breeders, economists, embryologists, engineers, food scientists, geneticists, microbiologists, nutritionists, veterinarians, physiologists, processors, public health professionals, and others with an interest in animal production and applied aspects of animal sciences.
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