Meta‐analysis of the energy and protein requirements of hair sheep raised in the tropical region of Brazil          下载免费PDF全文

A. P. Oliveira  E. S. Pereira  S. Biffani  A. N. Medeiros  A. M. A. Silva  R. L. Oliveira  M. I. Marcondes 《Journal of animal physiology and animal nutrition》2018,102(1):e52-e60

The objective of this study was to estimate, through mathematical models, energy and protein requirements for maintenance and gain of hair sheep raised in the tropical region of Brazil. To determine the equation parameters, a meta‐analysis of seven independent experiments of nutrient requirements was performed, comprising a total of 243 experimental units (animals), which were conducted under tropical conditions, using hair sheep in growing and finishing phases and endowed of the following quantitative data for each animal: body weight (BW ), empty body weight (EBW ), average daily gain (ADG ), empty body gain (EBG ), heat production (HP ), metabolizable energy intake (MEI ), retained energy (RE ), metabolizable protein intake (MPI ) and body protein content. The regression equations generated were as follows: for Net Energy for maintenance, (NE _m): ; for Net Energy for gain, (NE _g): ; for Metabolizable Protein for maintenance,(MP _m): MPI _(g/day) = 24.8470 (±7.3646) + 560.28 (±99.6582) × EBG _(kg/day); for Net Protein for gain, (NP _g): . The NE _m requirement was 0.246 MJ EBW^?0.75 day^?1. The metabolizable energy for maintenance requirement was 0.391 MJ EBW^?0.75 day^?1. Considering an ADG of 100 g, the NE _g requirement ranged from 0.496 to 1.701 MJ/day for animals with BW ranging from 10 to 40 kg respectively. The efficiencies of use of the metabolizable energy for maintenance and gain were 0.63 and 0.36 respectively. The MP _m requirement was 3.097 g EBW^?0.75 day^?1. Considering an ADG of 100 g, the NP _g requirement ranged from 12.4 to 10.5 g/day for animals with BW ranging from 10 to 40 kg respectively. The total metabolizable energy and protein requirements were lower than those reported by the NRC and AFRC systems. Thus, our results support the hypothesis that nutrient requirements of hair sheep raised in tropical regions differ from wool sheep raised in temperate regions. Therefore, the use of the equations designed in this study is recommended.  相似文献   

Geostatistical interpolation and aggregation of crop growth model outputs     

《European Journal of Agronomy》2016

Many crop growth models require daily meteorological data. Consequently, model simulations can be obtained only at a limited number of locations, i.e. at weather stations with long-term records of daily data. To estimate the potential crop production at country level, we present in this study a geostatistical approach for spatial interpolation and aggregation of crop growth model outputs. As case study, we interpolated, simulated and aggregated crop growth model outputs of sorghum and millet in West-Africa. We used crop growth model outputs to calibrate a linear regression model using environmental covariates as predictors. The spatial regression residuals were investigated for spatial correlation. The linear regression model and the spatial correlation of residuals together were used to predict theoretical crop yield at all locations using kriging with external drift. A spatial standard deviation comes along with this prediction, indicating the uncertainty of the prediction. In combination with land use data and country borders, we summed the crop yield predictions to determine an area total. With spatial stochastic simulation, we estimated the uncertainty of that total production potential as well as the spatial cumulative distribution function. We compared our results with the prevailing agro-ecological Climate Zones approach used for spatial aggregation. Linear regression could explain up to 70% of the spatial variation of the yield. In three out of four cases the regression residuals showed spatial correlation. The potential crop production per country according to the Climate Zones approach was in all countries and cases except one within the 95% prediction interval as obtained after yield aggregation. We concluded that the geostatistical approach can estimate a country’s crop production, including a quantification of uncertainty. In addition, we stress the importance of the use of geostatistics to create tools for crop modelling scientists to explore relationships between yields and spatial environmental variables and to assist policy makers with tangible results on yield gaps at multiple levels of spatial aggregation.  相似文献   

基于“3S”技术的辽宁省某尾矿库扩容工程生态影响预测与评价     

申彦科  张伟 《林业调查规划》2014,(1):5-8

以辽宁省某尾矿库为例,基于GIS技术,利用2010年的TM遥感影像及地形图,预测该尾矿库扩容后的生态影响。分析结果显示:尾矿库扩容工程对生态的影响主要体现在土地利用、植被、生态景观和地形地貌的变化4个方面。  相似文献   

Bioinformatics Analysis of Bone Morphogenetic Protein 4 (BMP4) of Sheep     

FAN Zhen-hua  ZHANG Qiu-yue  FAN Rui-wen  REN Yu-hong 《中国畜牧兽医》2015,42(6):1370-1376

The assay was aimed to explore the biological characteristics of bone morphogenetic protein 4 (BMP4) of sheep,NCBI,DNAMAN DNAStar,TMHMM Server v.2.0,PsortⅡ,SignalP various bioinformatical softwares were used to speculate the physical and chemical properties,hydrophobic property,phosphorylation site,conservative structure domain,protein secondary structure of BMP4 protein.Also,the three-dimensional structure was forecasted with the SWISS-MODEL Workspace software.The results indicated that the BMP4 of sheep had high homologies with the BMP4 of various species.The encoded protein was a hydrophilic protein which was unstable.There was no transmembrane regions and it was likely to be located in the nucleus.What was more,there was signal peptide and eighteen phosphorylation sites.Through the forecast of functional domains,the protein had two functional domains,including the transforming growth factor-beta (TGF-beta) superfamily and TGF-beta propeptide superfamily.The result was consistent with the function of BMP4 gene family,it also demonstrated that BMP4 was a growth factor and it had the function of signal transduction.The amino acid homology between the predicted 3D structure of protein and template 3bmp.1.A was 88.29%.The bioinformatics analysis of BMP4 gene could provide reference for the further study in practice.  相似文献   

基于贡献度随机森林模型的公司债信用风险实证分析     

汪政元  伍业锋 《湖南农业大学学报(自然科学版)》2016,(3):33-40

运用贡献度随机森林方法（CRF）方法探讨公司债财务指标比率与其违约率的关系.运用连续属性离散化方法（OB）进行财务指标最优降维；运用WOE变换进行模型变量约简.研究表明，CRF模型的分类性能显著优于其他模型，测试集评估总体正确率达90.47%，AUC统计量、AR比率及K-S值分别提升了2.6%、7.6%、4.38%，变量贡献度量化了各财务指标对违约率影响，为诠释随机森林预测机制提供了依据.  相似文献   

基于Lasso和BP神经网络的组合预测及其应用——以居民消费支出预测为例     

喻胜华  张静 《湖南农业大学学报(自然科学版)》2016,(1):123-128

在变量选择的基础上，构建基于Lasso方法和BP神经网络的预测模型，并对我国城乡居民的消费支出进行预测，结果显示：基于Lasso方法和BP神经网络的组合预测精度要明显高于BP神经网络、Lasso方法的预测精度；在2014~2020年，我国农村居民消费增长率有所提升，城镇居民消费增长率减缓，城乡居民消费增长率之间的差距呈下降趋势，但短期内城乡居民消费差距依然难以缓和。  相似文献   

花生黄曲霉生长预测模型的研究   总被引：1，自引：1，他引：0  

房保海  王波  刘会梅  雷质文  张艺兵  门爱军  祝素珍  李正义  唐静 《山东农业科学》2011,(5):29-32

通过对分离自花生的黄曲霉LYYSS03-3在不同温度(15-35℃)、不同相对湿度(水活度分别为0.5,0.7,0.9)下的菌落生长特性进行检测,采用Boltzmann和Logistic 模型对黄曲霉的生长曲线进行拟合,结果表明,在适宜温湿度阶段,花生黄曲霉的生长曲线更适合用Boltzmann模型拟合.  相似文献   

土耳其斯坦裂体吸虫23kDa蛋白基因的序列分析及抗原表位预测     

宿文文  张莹  那璐  王文涛  常巧呈  张艳  王春仁 《黑龙江八一农垦大学学报》2014,26(4):26-29

为研究土耳其斯坦裂体吸虫23 kDa蛋白的生物学特性,以土耳其斯坦裂体吸虫cDNA为模版,利用PCR对23 kDa基因进行扩增,并将其克隆到T-easy载体后进行序列测定。利用生物信息学对其结构、抗原指数进行分析,并对其抗原表位进行预测。序列分析结果表明,该虫体的23 kDa基因长度为657 bp,A+T含量为57.38%,与曼氏血吸虫、埃及血吸虫和日本血吸虫的23 kDa基因的相似性分别为85.24%、83.71%和81.89%。蛋白二级结构分析表明,23 kDa蛋白经过4次跨膜,主要由6个α-螺旋、3个β-折叠、7个β转角和若干个无规则卷曲构成。抗原表位预测结果表明,该蛋白有3个B细胞抗原表位。综合分析土耳其斯坦裂体吸虫23 kDa蛋白是一种较好的抗原分子,是土耳其斯坦裂体吸虫疫苗的重要候选分子。  相似文献   

湖南省粮食产量波动的影响因素分析及预测     

杨凡雨  刘黎明  袁承程 《中国农学通报》2020,36(29):153-160

旨在维护国家稳定,为预判粮食生产前景、提高粮食生产效率、保障粮食安全提供理论依据。利用湖南省统计数据,运用灰色关联分析法筛选关联性较强的影响因素,并建立GM(1,N)预测模型预测粮食产量。2008—2017年与湖南省粮食产量关联度最大的影响因素是粮食作物播种面积和农业机械总动力;科技因素是影响2008—2017年湖南省粮食产量的主要因素,其次是自然因素,社会因素;2018—2027年湖南省粮食产量有较小波动,且农业机械总动力和财政农业支出影响较大;农业机械总动力在前后十年对粮食产量都有较重要的影响,越来越占据主导地位。粮食产量受国家政策的影响,受农业机械总动力影响最大,维持产量水平需高度重视农业机械化水平,稳步提高粮食作物播种面积。  相似文献   

基于改进GM(1,N)模型的我国大豆价格影响因素分析及预测研究   总被引：2，自引：0，他引：2  

范震  马开平  姜顺婕  石波 《大豆科学》2016,(5):847-852

大豆是我国重要的粮食作物和油料作物,其价格对于国民经济尤其是农业经济的影响意义深远。大豆价格的稳定对于我国大豆市场的健康发展有着重要的现实意义。在灰色理论的基础上,提出了一种改进GM(1,N)大豆价格预测模型,首先运用灰色关联分析法对我国大豆价格的影响因素进行分析,选择主要的影响因素;再将这些影响因素作为模型的相关因素变量,构建GM(1,N)大豆价格预测模型。采用2010-2015年的大豆数据进行实证研究,模型选取国内大豆自给量、世界大豆产量、国民消费价格指数、消费者信心指数4个变量作为相关因素变量;模型预测误差为2.10%,预测精度较高,能够较好地掌握大豆价格的变化规律,可以为大豆价格市场预测及国家宏观政策的制定提供理论指导。  相似文献