Comparison of repeatability and multiple trait threshold models for litter size in sheep using observed and simulated data in Bayesian analyses

Bayesian analyses were used to estimate genetic parameters on 5580 records of litter size in the first four parities from 1758 Mule ewes. To examine the appropriateness of fitting repeatability (RM) or multiple trait threshold models (MTM) to litter size of different parities, both models were used to estimate genetic parameters on the observed data and were thereafter compared in a simulation study. Posterior means of the heritabilities of litter size in different parities using a MTM ranged from 0.12 to 0.18 and were higher than the heritability based on the RM (0.08). Posterior means of the genetic correlations between litter sizes of different parities were positive and ranged from 0.24 to 0.71. Data sets were simulated based on the same pedigree structure and genetic parameters of the Mule ewe population obtained from both models. The simulation showed that the relative loss in accuracy and increase in mean squared error (MSE) was substantially higher when using the RM, given that the parameters estimated from the observed data using the opposite model are the true parameters. In contrast, Bayesian information criterion (BIC) selected the RM as most appropriate model given the data because of substantial penalty for the higher number of parameters to be estimated in the MTM model. In conclusion, when the relative change in accuracy and MSE is of main interest for estimation of breeding values of litter size of different parities, the MTM is recommended for the given population. When reduction in risk of using the wrong model is the main aim, the BIC suggest that the RM is the most appropriate model.     

Bayesian network modeling of early growth stages explains yam interplant yield variability and allows for agronomic improvements in West Africa

Yams (Dioscorea spp.) are important species, especially for resource-poor farmers of West Africa, where crop yields are affected by early plant size hierarchy linked with uneven emergence. Although the causes of this phenomenon are not fully known, yams, like other vegetatively propagated crops, have heavy planting material that is liable to induce such interplant variability. In addition, planting practices may mitigate this phenomenon via the selection of the seed-tuber size or state. To gain further insight into yam interplant variability, this study identified and quantified, for the first time, the direct and indirect dependency between planting practices, early growth variables and yield components of Dioscorea rotundata and Dioscorea alata, the two main food yam species. The experimental dataset came from six field trials carried out in Benin at two locations between 2007 and 2009. Additive Bayesian network modeling was used for structure discovery—its directed acyclic graph offers an ideal background for discussing complex systems when theoretical knowledge is lacking, e.g., for yams. Here we found that the emergence date was the only direct cause of plant yield variability common to both species. For D. rotundata, we observed a direct contribution of the cataphyll number to the plant tuber weight. These combined results suggest the existence of some uncontrolled latent variables (i.e., seed-tuber physiological age and reserves). For D. alata, the model did not reveal any effect of seed-tuber size, despite a strong effect noted for D. rotundata. We suggest that the transposition of traditional native D. rotundata planting practices may have led to oversized D. alata seed-tubers, resulting in wastage of planting material. This study demonstrated that traditional West African cropping systems have a serious drawback concerning the uncontrolled wide range of physiological ages and reserves in seed-tuber lots, which affect the plant size hierarchy and ultimately the marketable yield.     

我国农产品价格与CPI动态关联性分析

采用1997-03—2013-12的时间序列数据,构建贝叶斯DCC-GARCH模型,分析农产品价格与居民消费价格指数(CPI)之间的动态关联性。结果表明:1)农产品价格及CPI的波动具有非对称性,波动衰减速度缓慢,且衰减速度随着波动幅度的加大而减缓。2)畜产品价格与CPI关联更为密切,粮食价格与CPI关联相对较弱;畜产品价格中与CPI波动关联性最强的是猪肉价格,其次为鸡蛋和鸡肉价格,牛羊肉价格与CPI关联度相对较小,粮食价格中籼稻和粳稻价格与CPI的关联度更高。3)农产品价格与CPI的关联性具有时变性;畜产品价格与CPI波动关系大致表现出前期稳定,后期更为密切的特征。农产品市场重要程度、国家政策支持力度及随机事件冲击,致使关联性在品种间和时间上存在差异。     

基于GLBM模型的中国大陆阿根廷滑柔鱼鱿钓渔业CPUE标准化

西南大西洋阿根廷滑柔鱼既是西南大西洋生态系统中的重要种类,也是鱿钓渔业的重要捕捞对像。单位捕捞努力量渔获量(CPUE)是表示渔业资源状况及其丰度的常用指标。根据2000-2010年中国大陆鱿钓船在西南大西洋的生产统计数据和海洋卫星遥感获得的海洋环境数据(表温,表温水平梯度,海面高度,叶绿素浓度),利用基于贝叶斯的广义线性模型(GLBM),分未加入固定交互选项、加入固定交互选项和加入随机交互选项3种情况对中国大陆西南大西洋阿根廷滑柔鱼鱿钓渔业的CPUE进行标准化。根据偏差信息准则 (DIC)值最小来确定最佳贝叶斯模型。结果表明,包含纬度、海表温度、表温水平梯度、海面高度、月×纬度、月×经度及年×纬度变量且加入随机交互项的GLBM模型为最适。标准化后的CPUE较名义CPUE小,年间变化平缓。与广义线性模型(GLM)和广义加性模型(GAM)标准化的CPUE比较,GLBM模型更能反映其资源丰度的真实水平。研究认为,2001-2010年间经GLBM模型标准化后的CPUE呈现逐年下降的趋势。     

The simulation of phenological development in dynamic crop model: The Bayesian comparison of different methods

The main objective of our study was to use Bayesian methods to quantify the uncertainties related to phenological development of maize (Zea mays L.) under various climate conditions. For this purpose, five different phenological methods were implemented in the dynamic crop growth model, which was subsequently optimized, using the data acquired at three different locations in Slovenia. The sensitivity analysis of the crop model was performed in order to find the set of most influential physiological parameters. Subsequent Bayesian model comparison was used in order to quantify the impact of phenological method selection on the final maize yield. The results revealed the importance of using an appropriate phenological method in order to correctly estimate the duration of the growing season and yield, when used within dynamic crop model. The limitations of the phenological methods used in this study are discussed. The selection of phenological method itself did not have a significant influence on the yield estimation, except in years with high temperatures and limiting water conditions. This raises the concern that inaccurate simulation of phenological development may increase the uncertainties of impact assessment on crop yield where crop models are fed with future climate projections.     

Vine Copula与贝叶斯模型平均结合的月径流预测及应用

准确可靠且预见期较长的月径流预测对水资源配置、防汛抗旱以及生态环境保护等具有重要意义。径流变化与降水、气温、潜在蒸散发以及前期径流等存在密切联系。鉴于Vine Copula可以灵活地将多个随机变量的边缘分布函数通过Copula对的形式联结起来构造多维联合分布函数以及贝叶斯模型平均(Bayesian Model Averaging,BMA)在处理多模型集合预报方面的优势,该研究基于BMA集合多个Vine Copula模型提出了一种BVC径流预测模型(简称BVC模型),应用于黄河流域上游4个水文站(唐乃亥站、民和站、红旗站和折桥站)的月径流预测,采用确定性系数(R²)、纳什效率系数(Nash-Sutcliffe Efficiency coefficient,NSE)和均方根误差(Root Mean Squared Error,RMSE)评价模型的预测性能。结果表明,验证期内预见期为1～3个月时,BVC模型在各水文站的R²均大于等于0.83、NSE均大于等于0.78且RMSE均维持在较低水平;与随机森林(Random Forest,RF)模型和长短...     

岷江上游干旱河谷区林-农-草地土壤水分特征与数值模拟

岷江上游干旱河谷土壤水分是农业生产和生态修复的关键因子。以干旱河谷典型区生态林、经果林、农地和荒草地4类土壤含水量为研究对象,通过对4类土壤含水量的变化分析和数值模拟,揭示土壤水分与土层深度的关系。结果表明:(1)土壤平均含水量依次为:农地(18.62%)生态林(17.47%)经果林(16.53%)荒草地(11.25%);(2)从干旱河谷(1500 m)到天然林下线(2500 m)的山地垂直方向上,林地土壤含水量呈浅U型分布;海拔2100~2500 m土壤含水量均值为19.77%,海拔1900~2100 m为13.72%,海拔1500~1900 m为16.00%;(3)土壤水分随土层深度的增加而减少,生态林土壤水分垂直变化最为显著,农地土壤水分垂直变化则相对稳定;(4)土壤水分多水平贝叶斯模型能够较好地说明4种土地利用类型下土壤含水量的变化和剖面分布特征;(5)林地与荒草地土壤含水量差异表明,土壤平均含水量14%是界定林-草地的分界值。研究结果可为岷江上游干旱河谷生态修复提供数据支撑。     

Ice thickness distribution and volume estimation of Burqin Glacier No. 18 in the Chinese Altay Mountains

Information on the thickness distribution and volume of glacier ice is highly important for glaciological applications; however, detailed measurements of the ice thickness of many glaciers in the Chinese Altay Mountains remain lacking. Burqin Glacier No. 18 is a northeast-orientated cirque glacier located on the southern side of the Altay Mountains. This study used PulseEKKO^® PRO 100A enhancement ground-penetrating radar (GPR) to survey the ice thickness and volume of Burqin Glacier No. 18 in summer 2018. Together with GPR surveying, spatial distributed profiles of the GPR measurements were concurrently surveyed using the real-time kinematic (RTK) global navigation satellite system (GNSS, Unistrong E650). Besides, we used QuickBird, WorldView-2, and Landsat TM to delineate accurate boundary of the glacier for undertaking estimation of glacier ice volume. GPR measurements revealed that the basal topography of profile B1-B2 was flat, the basal topography of profile C1-C2 presented a V-type form, and the basal topography of profile D1-D2 had a typical U-type topographic feature because the bedrock near the central elevation of the glacier was relatively flat. The longitudinal profile A1-A2 showed a ladder-like distribution. Glacier ice was thin at the terminus and its thickness increased gradually from the elevation of approximately 2620 m a.s.l. along the main axis of the glacier tongue with an average value of 80 (±1) m. The average ice thickness of the glacier was determined as 27 (±2) m and its total ice volume was estimated at 0.031 (±0.002) km³. Interpretation of remote sensing images indicated that during 1989-2016, the glacier area reduced from 1.30 to 1.17 km² (reduction of 0.37%/a) and the glacier terminus retreated at the rate of 8.48 m/a. The mean ice thickness of Burqin Glacier No. 18 was less than that of the majority of other observed glaciers in China, especially those in the Qilian Mountains and Central Chinese Tianshan Mountains; this is probably attributable to differences in glacier type and climatic setting.     

Genetic associations of visual scores with subsequent rebreeding and days to first calving in Nellore cattle

Records of Nellore animals born from 1990 to 2006 were used to estimate genetic correlations of visual scores at yearling (conformation, C; finishing precocity, P; and muscling, M) with primiparous subsequent rebreeding (SR) and days to first calving (DC), because the magnitude of these associations is still unknown. Genetic parameters were estimated by multiple‐traits Bayesian analysis, using a nonlinear (threshold) animal models for visual scores and SR and a linear animal models for weaning weight (WW) and DC. WW was included in the analysis to account for the effects of sequential selection. The posterior means of heritabilities estimated for C, P, M, SR and DC were 0.24 ± 0.01, 0.31 ± 0.01, 0.30 ± 0.01, 0.18 ± 0.02 and 0.06 ± 0.02, respectively. The posterior means of genetic correlations estimated between SR and visual scores were low and positive, with values of 0.09 ± 0.02 (C), 0.19 ± 0.03 (P) and 0.18 ± 0.05 (M). On the other hand, negative genetic correlations were found between DC and C (?0.11 ± 0.09), P (?0.19 ± 0.09) and M (?0.16 ± 0.09). The primiparous rebreeding trait has genetic variability in Nellore cattle. The genetic correlations between visual scores, and SR and DC were low and favourable. The genetic changes in C, P and M were 0.02, 0.03 and 0.03/year, respectively. For SR and DC, genetic trends were 0.01/year and ?0.01 days/year, respectively, indicating that the increase in genetic merit for reproductive traits was small over time. Direct selection for visual scores together with female reproductive traits is recommended to increase the fertility of beef cows.     

Statistical and biological considerations in evaluating drug efficacy in equine strongyle parasites using fecal egg count data

Anthelmintic resistance (AR) is a serious problem for the control of equine gastrointestinal nematodes, particularly in the cyathostomins. The fecal egg count reduction test (FECRT) is the most common method for diagnosing AR and serves as the practical gold standard. However, accurate quantification of resistance and especially accurate diagnosis of emerging resistance to avermectin/milbemycin (A/M) drugs, is hampered by a lack of accepted standards for study design, data analysis, and data interpretation. In order to develop rational evidence-based standards for diagnosis of resistance, one must first take into account the numerous sources of variability, both biological and technical, that affect the measurement of fecal egg counts (FECs). Though usually ignored, these issues can greatly impact the observed efficacy. Thus, to accurately diagnose resistance on the basis of FECRT data, it is important to reduce levels of variability through improved study design, and then deal with inherent variability that cannot be removed, by performing thorough and proper statistical analysis. In this paper we discuss these issues in detail, and provide an explanation of the statistical models and methods that are most appropriate for analyzing these types of data. We also provide several examples using data from laboratory, field, and simulation experiments illustrating the benefits of these approaches.