Quantitative genetic parameter estimates for size and growth rate traits in Pacific white shrimp, Penaeus vannamei (Boone 1931) when reared indoors

Heritabilities of size traits and growth rate traits, as well as genetic, phenotypic and environmental correlations were estimated at three ages for a captive population of Pacific white shrimp (Penaeus vannamei) grown indoors. A covariate, mean size or mean growth rate during early growth in individual tanks before tagging and mixing, was introduced in the analyses to reduce the error caused by unique previous growth conditions on variance components. Heritabilities of size traits increased with age, with the h² for TL, AL, TWt and Wi1AS being 0.15, 0.20, 0.20 and 0.22, respectively, at 17 weeks, increasing to 0.28, 0.33, 0.34 and 0.35 at 29 weeks of age. Heritabilities of growth rate traits estimated between consecutive growth periods decreased from the first (h² for ΔTL 0.65, ΔAL 0.71, ΔTWt 0.63 and ΔWi1AS 0.84) to the second period (h² for ΔTL 0.34, ΔAL 0.50, ΔTWt 0.54 and ΔWi1AS 0.52). Phenotypic correlations were always larger than genetic correlations for both, size and growth rate traits. Genetic correlations between size traits within age were high (r_G >0.95), but those between the same size trait at different ages decreased as the age difference increased in spite of a consistently high environmental correlation (r_E 0.80–0.85) between the same trait at different ages. Phenotypic and genetic correlation's between the same growth rate trait at the two different growth periods evaluated were negative or zero (r_G TL –0.26, AL –0.24, Wi1AS 0.00) with the exception of total weight (r_G TW 0.35) and the environmental correlations between growth periods were also low (r_E 0.13–0.32).     

基于不同协变量Cokriging土壤养分空间预测精度研究——以陕西省蓝田县为例

以蓝田县西北部农耕区2012年1 114份土壤有机质、碱解氮、有效磷、速效钾4个指标为基础,利用地理信息系统和地统计学相结合的方法,在对协变量个数控制的前提下,通过交叉检验系数和精度提高系数,探索协同克里格插值法对各土壤养分空间分布预测精度的影响。结果表明:各土壤养分空间分布不均匀,土壤养分存在中等变异性;利用增加协同变量方法,依据协变量之间相关性强弱控制协变量进入模型的次序对各土壤养分指标进行协同克里格插值,能提高预测精度,当协变量个数达到3时,各养分指标精度提高分别为有机质0.353%,碱解氮1.114%,有效磷1.088%,速效钾0.646%。研究结果较为准确地预测了样区4个养分指标的空间分布特征,结合土壤类型及土壤施肥管理方法,探讨了土壤养分空间分布特征的原因。     

依据传统土壤数据应用数字土壤制图法对香港山地地区土壤建立土纲信息

Based on legacy soil data from a soil survey conducted recently in the traditional manner in Hong Kong of China, a digital soil mapping method was applied to produce soil order information for mountain areas of Hong Kong. Two modeling methods (decision tree analysis and linear discriminant analysis) were used, and their applications were compared. Much more eflort was put on selecting soil covariates for modeling. First, analysis of variance (ANOVA) was used to test the variance of terrain attributes between soil orders. Then, a stepwise procedure was used to select soil covariates for linear discriminant analysis, and a backward removing procedure was developed to select soil covariates for tree modeling. At the same time, ANOVA results, as well as our knowledge and experience on soil mapping, were also taken into account for selecting soil covariates for tree modeling. Two linear discriminant models and four tree models were established finally, and their prediction performances were validated using a multiple jackknifing approach. Results showed that the discriminant model built on ANOVA results performed best, followed by the discriminant model built by stepwise, the tree model built by the backward removing procedure, the tree model built according to knowledge and experience on soil mapping, and the tree model built automatically. The results highlighted the importance of selecting soil covariates in modeling for soil mapping, and suggested the usefulness of methods used in this study for selecting soil covariates. The best discriminant model was finally selected to map soil orders for this area, and validation results showed that thus produced soil order map had a high accuracy.     

Visual Scorings of Drought Stress Intensity as Covariates for Improved Variety Trial Analysis

Abiotic stress can enhance the heterogeneity of fields, which leads to imprecise estimates of genotypic effects in variety trials. Our study is based on multilocation field trials of triticale (×Triticosecale Wittm.). Two of the six locations were affected by drought stress and showed increased field heterogeneity. At Willstätt, drought stress intensity was visually scored twice, and at Issoudun, overall impression of a plot was scored once. We investigated if the visual scorings can be used as covariates in an analysis of covariance (ancova ) to eliminate the influence of different drought stress intensity on plot yields. For evaluation of the ancova models, we examined if the covariates were independent from the genotypic effects and linearly associated with grain yield. In addition, ancova models were compared with baseline and spatial models based on AIC and phenotypic correlation between genotype means estimated with the model under investigation in a drought stress location with genotype means calculated across the remaining locations. We found that both scorings in Willstätt fulfilled the requirements of an ancova and led to an increase in broad‐sense heritability (h²) and efficiency. ancova with the second scoring increased h² from 0.03 for the baseline model to 0.60, whereas the best spatial model increased h² only up to 0.50. The scoring at Issoudun was not independent from the genotypic effects and reduced phenotypic correlations. We concluded that environmental factors causing spatially differing yield potential can be scored or measured and used as covariates to obtain more precise genotypic estimates.     

空间回归分析在土壤属性预测制图中的应用

以河南省孟津县为研究区,选取坡度、高程、地面曲率和复合地形指数(CTI)作为表层土壤缓效钾含量空间预测的环境协变量,系统探讨了空间回归分析技术在土壤属性预测制图中的应用.结果表明:土壤缓效钾的空间自相关距离阈值约为10 000 m,与坡度、高程和地面曲率存在显著相关性;尽管空间回归模型的预测精度和普通回归模型相近,但前者可以更加准确地表征土壤缓效钾的空间分布格局及空间分异细节特征.     

空间回归模型在区域数字化土壤制图中的应用——以河南封丘县为例

以河南省封丘县为研究区,以环境协变量信息和先期获得的土壤数值化分类结果为基础数据源,在土壤分类距离空间自相关性分析的基础上,构建土壤分类距离—环境协变量空间回归模型,实施土壤分类距离空间预测,并最终实现研究区25 m分辨率数字化土壤制图.输出结果表明,研究区5种主要土壤类型中,普通底锈干润雏形土分布面积最大、弱盐灌於干润雏形土次之,分布比例分别为36％和24％.结合确定性趋势距离和非确定性残差的空间变异特征,阐释了研究区土壤空间分布格局的发生学背景和随机性因素的影响.与基于随机模型的土壤预测制图相比,基于环境协变量空间回归模型的数字化土壤制图输出结果展示了相似的研究区土壤空间分布整体格局,且具有细节清晰、图斑边界自然的特点.一方面能更好地诠释土壤空间分布的连续性和渐变性特征;另一方面能较好地反映微域成土环境对土壤发生学特性空间变异特征的影响.     

An integrated method of selecting environmental covariates for predictive soil depth mapping

Environmental covariates are the basis of predictive soil mapping. Their selection determines the performance of soil mapping to a great extent, especially in cases where the number of soil samples is limited but soil spatial heterogeneity is high. In this study, we proposed an integrated method to select environmental covariates for predictive soil depth mapping. First, candidate variables that may influence the development of soil depth were selected based on pedogenetic knowledge. Second, three conventional methods (Pearson correlation analysis (PsCA), generalized additive models (GAMs), and Random Forest (RF)) were used to generate optimal combinations of environmental covariates. Finally, three optimal combinations were integrated to produce a final combination based on the importance and occurrence frequency of each environmental covariate. We tested this method for soil depth mapping in the upper reaches of the Heihe River Basin in Northwest China. A total of 129 soil sampling sites were collected using a representative sampling strategy, and RF and support vector machine (SVM) models were used to map soil depth. The results showed that compared to the set of environmental covariates selected by the three conventional selection methods, the set of environmental covariates selected by the proposed method achieved higher mapping accuracy. The combination from the proposed method obtained a root mean square error (RMSE) of 11.88 cm, which was 2.25–7.64 cm lower than the other methods, and an R² value of 0.76, which was 0.08–0.26 higher than the other methods. The results suggest that our method can be used as an alternative to the conventional methods for soil depth mapping and may also be effective for mapping other soil properties.     

利用协变量改进作物新品种稳定性参数估计

据线性模型原理,提出了利用协变量改进作物新品种性状表现方差估计的方法,指出了方差估计效果评价的指标,在此基础上,分析讨论了利用协变量改进作物新品种性状表现方差估计的可能性及其条件。最后分析了陕西省关中灌区小麦区域试验产量资料。     

Extending Finlay–Wilkinson regression with environmental covariates

Finlay–Wilkinson regression is a popular method for analysing genotype–environment interaction in series of plant breeding and variety trials. It involves a regression on the environmental mean, indexing the productivity of an environment, which is driven by a wide array of environmental factors. Increasingly, it is becoming feasible to characterize environments explicitly using observable environmental covariates. Hence, there is mounting interest to replace the environmental index with an explicit regression on such observable environmental covariates. This paper reviews the development of such methods. The focus is on parsimonious models that allow replacing the environmental index by regression on synthetic environmental covariates formed as linear combinations of a larger number of observable environmental covariates. Two new methods are proposed for obtaining such synthetic covariates, which may be integrated into genotype-specific regression models, that is, criss-cross regression and a factor-analytic approach. The main advantage of such explicit modelling is that predictions can be made also for new environments where trials have not been conducted. A published dataset is employed to illustrate the proposed methods.