共查询到19条相似文献，搜索用时 546 毫秒
1.
本研究通过对生产性能测定（DHI）数据的挖掘与分析建立预测宁夏地区奶牛体细胞数（SCC）的模型,为奶牛乳房炎的防制提供借鉴,使得DHI数据更加有效、及时地指导奶业的发展。对2011年9月~2016年2月宁夏地区奶牛平均SCC数据进行差分使其达到平稳化,采用季节ARIMA模型对数据进行分析、拟合和预测。利用R软件的auto.arima函数计算出合适的时间序列模型ARIMA（1,1,0）（1,1,0）[12],其AIC为-3.67。Acf检验说明残差没有明显的自相关性;Ljung-Box测试显示所有的P值>0.5,表明残差为白噪声,说明此模型可用来对未来的24个月进行预测。再利用R软件的forecast函数对2016年3月~2017年2月的数据进行预测,作出预测图。从预测的结果可以看出,宁夏地区奶牛SCC整体呈现下降趋势。2017年1月SCC最少,预测值约为25.31万个/mL;2016年3月SCC最大,预测值约为43.96万个/mL。从结果也可看出,宁夏地区奶牛SCC均大于隐性乳房炎的临界值（>20万个/mL）,说明宁夏地区还应该加大对奶牛乳房炎的防制。同时,若能及时添加新的SCC数据,就能对该数据模型进行更新,使其预测值更接近真实值,对实际生产的指导意义更大。 相似文献
2.
本试验旨在比较不同数据结构和单性状动物模型对高山美利奴羊（14月龄）重要数量性状遗传参数估计的影响，筛选出估计体重、产毛量、净毛率、净毛量、羊毛纤维直径、羊毛纤维直径变异系数和羊毛长度7个重要经济性状遗传参数的最适模型，准确估计遗传力并为下一步遗传评定奠定基础。使用R语言数据整理相关函数将涉及20 720只14月龄高山美利奴羊数据按系谱完整度和数据量分为数据集1和数据集2。采用R语言ANOVA分析检验鉴定年份、出生类型（单胎或双胎）、群别、性别4个非遗传因素在两个数据集中的显著性，将极显著效应（P < 0.01）放入动物模型中作为固定效应。将两个数据集和两个单性状动物模型组合得到4个模型，其中模型1、模型2分别使用数据集1、数据集2，随机效应为个体加性遗传效应、残差效应；模型3、模型4分别使用数据集1、数据集2，随机效应为个体加性遗传效应、个体永久环境效应和残差效应。用ASReml4软件实现方差组分估计。通过AIC准则、BIC准则评价各模型，用LRT检验比较各模型。最后，针对各性状选出最适模型进行遗传力估计。结果显示：①非遗传效应显著性检验得到鉴定年份和群别对数据集1和数据集2中所有性状均极显著（P < 0.01），出生类型对体重和数据集1中产毛量极显著（P < 0.01），性别仅对体重反应极显著（P < 0.01）。②各模型估计的体重遗传力为0.1614~0.2392；产毛量遗传力为0.1958~0.3254；净毛率遗传力为0.4395~0.5539；净毛量遗传力为0.2003~0.2393；羊毛纤维直径遗传力为0.4024~0.5897；羊毛纤维直径变异系数遗传力为0.3174~0.6077；毛长遗传力为0.2960~0.3669。③似然比检验结果表明，模型1和模型3对所有性状差异均不显著（P > 0.05）；模型1和模型4对体重和毛长差异极显著（P < 0.01）；模型2和模型3对净毛量差异极显著（P < 0.01），对其他性状差异不显著（P > 0.05）；模型2和模型4对所有性状差异不显著（P > 0.05）。最终得到对净毛量最适模型为模型1，体重、产毛量、净毛率、羊毛纤维直径、羊毛纤维直径变异系数、毛长的最适模型为模型2。所有性状受个体永久环境影响均不显著（P > 0.05）。基于最适模型估计高山美利奴羊（14月龄）体重、产毛量、净毛率、净毛量、羊毛纤维直径、羊毛纤维直径变异系数、毛长遗传力分别为0.2392、0.3254、0.4394、0.2893、0.4222、0.3175、0.3670。 相似文献
3.
文章基于1978—2012年新疆羊肉产量的时间序列,先后通过平稳化检验及自相关与偏相关分析,建立模型为ARMA（1,1）;然后确定模型参数并对模型的残差序列进行白噪声检验,检验通过,满足预测的要求;运用该模型对新疆羊肉产量进行预测,结果显示平均相对误差率为2.19%,希尔不等系数5.81%,其中2012年相对误差率为2.50%,精度较高,预测良好;最后对2009—2012年进行短期预测,数据表明,羊肉产量将继续增长。 相似文献
4.
基于ARIMA的猪瘟发病率预测模型的建立 总被引：1，自引：0，他引：1
为探讨应用自回归-移动平均ARIMA(P,d,q)模型预测猪瘟发病率的可行性,本研究对1999年1月～2004年12月A、B、C 3个地区的逐月猪瘟发病率进行ARIMA模型的拟合,用2005年1月～2005年12月的猪瘟发病率进行模型预测效果的验证.结果显示ARIMA(0,1,2)模型计算出的预测值与实际值拟合较好,可用于对未来的猪瘟发病率进行预测,为猪瘟的防控工作提供可靠的参考依据. 相似文献
5.
氮素在苜蓿产量累积与品质提升中发挥着重要作用,但其不合理添加不仅会造成资源浪费,还会加剧环境污染,且其添加效应因试验区域、添加模式和种植管理等因素不同存在较大差异。本研究通过整合已发表的相关田间试验数据,利用Meta分析方法定量研究了氮素添加对苜蓿产量与品质（粗蛋白含量、酸性和中性洗涤纤维含量）的效应及主要影响因素。结果表明,与不添加氮素相比,添加氮素可显著提高苜蓿产量与品质,其中产量和粗蛋白含量分别平均提高12.6%（置信区间9.0%~16.2%）和7.3%（置信区间4.1%~10.6%）,酸性和中性洗涤纤维含量分别平均降低5.6%（置信区间3.5%~7.8%）和3.0%（置信区间1.0%~4.9%）。在甘肃,年平均气温<8 ℃和年平均降水量200~400 mm的地区,采用硝酸铵分次施肥及灌溉和播种量为26~30 kg·hm-2时,更有利于添加氮素对苜蓿产量和粗蛋白含量的提高效应及对酸性和中性洗涤纤维含量的降低效应;但适宜的施氮量及生长年限在产量与品质方面存在差异。该研究可为苜蓿生产力提升及氮营养高效利用提供参考。 相似文献
6.
《黑龙江畜牧兽医》2017,(21)
为了研究贵州小型猪生长发育规律,指导贵州小型猪的生产和管理,试验测定了0~8月龄不同性别贵州小型猪的体重,并运用Gompertz、Logistic、Von Bertalanffy三种生长模型拟合其生长曲线。结果表明:三种模型均能较好地拟合贵州小型猪体重的生长曲线,复相关指数(R~2)均高于0.99,结合残差平方和(E)和卡方(χ~2)适合性检验,Von Bertalanffy模型拟合精度更高,公、母猪的拟合方程分别为y=32.149(1-0.718e~(-0.161t))~3和y=42.568(1-0.736e~(-0.144t))~3。与母猪相比,公猪有较低的初始体重、极限体重、月增重和拐点体重。公猪拐点月龄为4.77月,母猪的拐点月龄为5.39月。 相似文献
7.
基于Landsat 8 OLI影像的渭-库绿洲植被地上生物量估算 总被引：1，自引：0，他引：1
干旱区绿洲植被地上生物量估算研究可为绿洲生态系统稳定性评价与区域碳储量估算提供重要依据。以渭干河-库车河三角洲绿洲为研究区，利用ENVI 5.3软件对Landsat 8 OLI 影像数据进行预处理，提取反映植被地上生物量信息的植被指数和波段因子，并结合样地实测数据，采用常规统计模型、多元逐步回归和偏最小二乘回归方法建立研究区植被地上生物量最优估测模型，从而揭示该绿洲植被地上生物量的空间分布特征。结果表明：1）所选的20个遥感因子与实测植被地上生物量呈极显著正相关关系，相关系数为0.5~0.7（P<0.01）。2）乔木与灌木地上生物量最优估测模型均为多元逐步回归模型，草本与农作物地上生物量的估测模型以偏最小二乘回归模型为最优，模型验证决定系数均在0.6以上，均方根误差和平均绝对误差均较小。3）研究区植被地上生物量主要在280~1450 g·m-2 分布，面积约为6973.82 km2，低水平地上生物量（ABG<65 g·m-2）分布区域约占研究区总面积的15.02%。地上生物量由高到低依次为：农作物>乔木>灌木>草本。根据不同的植被类型，基于地物光谱特征构建的遥感估测模型可准确估算干旱区绿洲植被地上生物量，并对其空间分布特征进行遥感定量反演。 相似文献
8.
9.
[目的]了解安徽省中等规模种猪场猪伪狂犬病病毒（PRV）的感染情况。[方法]2013年8月至11月,开展了中等规模种猪场猪伪狂犬病群流行率和风险因素的横断面研究。通过简单随机抽样方法,选取了380个中等规模种猪场;用发现疫病的抽样策略,在每个种猪场采集了5份母猪血清,使用IDEXX公司猪伪狂犬病病毒gE-ELISA试剂盒进行了抗体检测。同时开展了问卷调查,并对种猪场之间PR传播潜在的29个风险因素进行了统计学分析。[结果]2013年安徽省中等规模种猪场猪伪狂犬病的群流行率为34.1%（95%置信区间29.3%~38.9%）;没有灭鼠措施（OR=2.5,95%置信区间：1.4~3.7,P<0.001）、购入种猪未经检测（OR=2.2,95%置信区间：1.1~4.2,P=0.024）、未免疫含PR 6种以上猪病（OR=2.0,95%置信区间：1.2~3.1, P=0.004）是2013年安徽省中小规模种猪场感染PRV的风险因素。Logistic 回归拟合度较好（P>0.05）,ROC曲线下面积为0.68（95%置信区间：0.62-0.73）。[结论]安徽省中等规模种猪场场间PR流行率较高,建议购入种猪前需进行检测,免疫含PR等6种以上猪病疫苗并定期灭鼠。 相似文献
10.
《黑龙江畜牧兽医》2017,(3)
为了比较康奈尔净碳水化合物和蛋白系统(CNCPS)模型预测的安格斯生长牛干物质采食量和平均日增重与实测值的相近程度,试验采用随机分组(1组为对照组,2组为添加蛋氨酸组)的方法,测定了安格斯生长牛的实际采食量和平均日增重。结果表明:1)两组牛的干物质采食量分别有86.7%和86.7%的点落在95%的置信区间内;两组牛的平均日增重分别有80.0%和73.3%的点落在95%的置信区间内;两组牛干物质采食量和平均日增重的预测值与实测值均差异不显著(P0.05)。2)经线性回归分析,两组牛的干物质采食量实测值和预测值有较高的相关系数(0.892 9和0.812 1),两组牛的平均日增重实测值和预测值有较高的相关系数(0.862 2和0.765 0),均具有较高的相似度。说明CNCPS模型对安格斯生长牛干物质采食量和平均日增重具有较好的预测能力。 相似文献
11.
Through dairy herd improvement (DHI) data analysis to predict somatic cell count (SCC) of Ningxia area in time, which providing a reference for the prevention and treatment of mastitis in dairy cows, making the DHI data more effective and timely in guiding the dairy industry production. Using the difference method to make the average cow somatic cell data form September 2011 to February 2016 stabled, then used the seasonal ARIMA model to analysis, fitting and forecasting data. The auto.arima function of R software had been used to calculate the optimal time series that finally confirmed the model was ARIMA (1,1,0)(1,1,0)[12], AIC was -3.67. The Acf test showed that the residual had no significant autocorrelation; Ljung-Box test showed that all P-value>0.5, indicating that the residual was white noise, and this model could be used to make predication for the next 24 months. The forecast function of the R software was used to predict the dairy cows SCC from March 2016 to February 2017, and the forecast map was drawn. The predicted results showed that the SCC of the entire Ningxia area were showing a downward trend. The SCC would be the least in January 2017,and the predictive value was about 253 100 per mL. It would be the largest in March 2016, was about 439 600 per mL. The results also showed that the dairy cows SCC in Ningxia was higher than the critical value of 20 million of subclinical mastitis. It suggested that the prevention and treatment of dairy cow mastitis need to be strengthened in Ningxia area. At the same time, if the data of the dairy cows SCC was added in timely, the data model should be updated to make it more close to the true value, which would be more meaningful to the actual instruction. 相似文献
12.
依据粮农组织数据库中给出的1961—2005年的中国牛肉产量数据为基础,分别通过差分把非平稳时间序列转化为平稳时间序列。建立了ARIMA(1,2,1)模型对中国牛肉产量进行预测。根据新华社对2006年中国牛肉产量的报道和德国莱茵农业协会对2007年中国牛肉产量的估计结果来看,ARIMA模型的预测误差只有1%左右,模型预测良好。 相似文献
13.
时间序列分析在蚕种销售中的应用研究 总被引：2，自引：0，他引：2
基于探讨广西蚕区家蚕普通种销售市场发展规律,为广西蚕业生产的宏观管理提供决策依据的目的,采用时间序列季节性乘积模型对家蚕普通种销售市场的发展变化规律进行了研究。依据广西蚕区2004-2006年36个月份的每月蚕种销售数量,运用社会科学统计软件包(SPSS)软件进行时间序列分析,建立了广西蚕区家蚕普通种市场销售规律的统计预测模型,即季节性乘积求和自回归滑动平均模型:AR IMA(0,1,1)×(0,1,1)12,并通过白噪声检验。利用该模型对广西蚕区2006-2007年家蚕普通种每月销售量进行了预测,得到较为准确的预测结果。 相似文献
14.
基于ARIMA的猪丹毒预测模型研究 总被引：1，自引：0，他引：1
为研究ARIMA时间序列模型预测猪丹毒(SE)的可行性,本实验选取兽医公报发布的我国某省SE的发病数据,对2005年1月～2009年6月该地区的SE发病资料进行模型构建与拟合,采用构建的模型对2009年7月～2009年12月的SE发病率进行预测并验证预测效果.结果显示ARIMA(2,1,0)模型可以拟合既往时间段上的发病率序列,预测结果显示MSE为0.203×10-10、MAPE为0.293,该模型可以预测未来SE的发生. 相似文献
15.
L. Yang Z.‐W. Bi Z.‐Q. Kou X.‐J. Li M. Zhang M. Wang L.‐Y. Zhang Z.‐T. Zhao 《Zoonoses and public health》2015,62(3):228-235
Human brucellosis is a re‐emerging bacterial anthropozoonotic disease, which remains a public health concern in China with the growing number of cases and more widespread natural foci. The purpose of this study was to short‐term forecast the incidence of human brucellosis with a prediction model. We collected the annual and monthly laboratory data of confirmed cases from January 2004 to December 2013 in Shandong Diseases Reporting Information System (SDRIS). Autoregressive integrated moving average (ARIMA) model was fitted based on the monthly human brucellosis incidence from 2004 to 2013. Finally, monthly brucellosis incidences in 2014 were short‐term forecasted by the obtained model. The incidence of brucellosis was increasing from 2004 to 2013. For the ARIMA (0, 2, 1) model, the white noise diagnostic check (x2 = 5.58 P = 0.35) for residuals obtained was revealed by the optimum goodness‐of‐fit test. The monthly incidences that fitted by ARIMA (0, 2, 1) model were closely consistent with the real incidence from 2004 to 2013. And forecasting incidences from January 2014 to December 2014 were, respectively, 0.101, 0.118, 0.143, 0.166, 0.160, 0.172, 0.169, 0.133, 0.122, 0.105, 0.103 and 0.079 per100 000 population, with standard error 0.011–0.019 and mean absolute percentage error (MAPE) of 58.79%. 相似文献
16.
Predicting campylobacteriosis cases is a matter of considerable concern in New Zealand, after the number of the notified cases was the highest among the developed countries in 2006. Thus, there is a need to develop a model or a tool to predict accurately the number of campylobacteriosis cases as the Microbial Risk Assessment Model used to predict the number of campylobacteriosis cases failed to predict accurately the number of actual cases. We explore the appropriateness of classical time series modelling approaches for predicting campylobacteriosis. Finding the most appropriate time series model for New Zealand data has additional practical considerations given a possible structural change, that is, a specific and sudden change in response to the implemented interventions. A univariate methodological approach was used to predict monthly disease cases using New Zealand surveillance data of campylobacteriosis incidence from 1998 to 2009. The data from the years 1998 to 2008 were used to model the time series with the year 2009 held out of the data set for model validation. The best two models were then fitted to the full 1998–2009 data and used to predict for each month of 2010. The Holt‐Winters (multiplicative) and ARIMA (additive) intervention models were considered the best models for predicting campylobacteriosis in New Zealand. It was noticed that the prediction by an additive ARIMA with intervention was slightly better than the prediction by a Holt‐Winter multiplicative method for the annual total in year 2010, the former predicting only 23 cases less than the actual reported cases. It is confirmed that classical time series techniques such as ARIMA with intervention and Holt‐Winters can provide a good prediction performance for campylobacteriosis risk in New Zealand. The results reported by this study are useful to the New Zealand Health and Safety Authority's efforts in addressing the problem of the campylobacteriosis epidemic. 相似文献
17.
Yasumoto ADACHI Kohei MAKITA 《The Journal of veterinary medical science / the Japanese Society of Veterinary Science》2015,77(9):1129-1136
Mycobacteriosis in swine is a common zoonosis found in abattoirs during meat inspections,
and the veterinary authority is expected to inform the producer for corrective actions
when an outbreak is detected. The expected value of the number of condemned carcasses due
to mycobacteriosis therefore would be a useful threshold to detect an outbreak, and the
present study aims to develop such an expected value through time series modeling. The
model was developed using eight years of inspection data (2003 to 2010) obtained at 2
abattoirs of the Higashi-Mokoto Meat Inspection Center, Japan. The resulting model was
validated by comparing the predicted time-dependent values for the subsequent 2 years with
the actual data for 2 years between 2011 and 2012. For the modeling, at first,
periodicities were checked using Fast Fourier Transformation, and the ensemble average
profiles for weekly periodicities were calculated. An Auto-Regressive Integrated Moving
Average (ARIMA) model was fitted to the residual of the ensemble average on the basis of
minimum Akaike’s information criterion (AIC). The sum of the ARIMA model and the weekly
ensemble average was regarded as the time-dependent expected value. During 2011 and 2012,
the number of whole or partial condemned carcasses exceeded the 95% confidence interval of
the predicted values 20 times. All of these events were associated with the slaughtering
of pigs from three producers with the highest rate of condemnation due to
mycobacteriosis. 相似文献
18.
内蒙古肉羊产业发展预测及研究 总被引：1，自引：0，他引：1
综述了内蒙古肉羊产业发展现状及特点,并以计量模型的方式分析了肉羊存栏数与畜牧业产值的相关性。采用ARIMA模型,对2010—2014年肉羊存栏量进行趋势预测,得出肉羊存栏下降会对内蒙古畜牧业产值增长带来负面效应的结论。提出了解决畜牧业产值负增长的对应措施。 相似文献
19.
The present study constructed a spatial-temporal statistical model to identify the risk and protective factors for haemorrhagic disease (HD) in white-tailed deer in the five states of Alabama, Georgia, South Carolina, North Carolina and Tennessee. The response variable was binary, indicating the presence or absence of HD in an individual county, measured annually from 1983 to 2000. Predictor variables included climatic factors of temperature, rainfall, wind speed and dew point, remotely sensed data of normalised difference vegetation index (NDVI) and land surface temperature derived from archived remotely sensed advanced very-high-resolution radiometer (AVHRR) satellite data, elevation, a spatial autocorrelation (SA) term and a temporal autocorrelation term. This study first applied principal component factor analysis to reduce the volume of climatic data and remotely sensed data. Then, a generalised linear mixed model framework (GLMM) was used to develop a spatial-temporal statistical model. The results showed that the area under receiver operating characteristic curve (ROC) was 0.728, indicating a good overall fit of the model. The total prediction accuracy over the 18 year period with optimal cut-off probability was 67 per cent. The prediction accuracy for individual years ranged from 48 to 75 per cent. 相似文献