Quantitative trait loci of frost tolerance and physiologically related trait in faba bean (Vicia faba L.)

In faba bean, field based winter-hardiness is a complex trait that is significantly correlated to frost tolerance. Frost tolerance could be used to indirectly select for faba bean winter-hardiness. The aim of this study was to identify putative QTL associated with frost tolerance and auxiliary traits and to quantify the efficiency of marker assisted selection. Thus, 101 recombinant inbred lines derived from the cross between two frost tolerant lines were tested for their hardened and unhardened frost tolerance and for their leaf fatty acid content in both treatments. Significant differences among the RIL were observed for all studied traits. For frost tolerance, five putative QTL were detected; three for unhardened frost tolerance that explained 40.7% (8.6% after cross-validation, CV) of its genotypic variance and two for hardened frost tolerance that explained 21.8% (1.0% after CV). For fatty acid content, three QTL were detected for oleic acid content in unhardened leaves that explained 62.9% (40.6% after CV) of its genotypic variance. This fatty acid was significantly correlated with unhardened frost tolerance. The unbiased genotypic variance explained enabled to draw realistic prospects of MAS for frost tolerance. In this study, combined MAS was more efficient than classical phenotypic selection and was expected to be higher on larger populations at early generations. Moreover, favourable alleles inherited from the exotic line BPL 4628 could be introgressed to European winter-hardy beans for further improvement.     

Testing the performance of spatial interpolation techniques for mapping soil properties

In this paper, we implement and compare the accuracy of ordinary kriging, lognormal ordinary kriging, inverse distance weighting (IDW) and splines for interpolating seasonally stable soil properties (pH, electric conductivity and organic matter) that have been demonstrated to affect yield production.The choice of the exponent value for IDW and splines as well as the number of the closest neighbours to include was decided from the root mean squared error (RMSE) statistic, obtained from a cross-validation procedure. Experimental variograms were fitted with the exponential, spherical, Gaussian and linear models using weighted least squares. The model with the smallest residual sum of squares (RSS) was further interrogated to find the number of neighbours that returned the best cross-validation result.Overall, all of the methods gave similar RMSE values. On this experimental field, ordinary kriging performed best for pH in the topsoil and lognormal ordinary kriging gave the best results when applied to electrical conductivity in the topsoil. IDW interpolated subsoil pH with the greatest accuracy and splines surpassed kriging and IDW for interpolating organic matter.In all uses of IDW, the power of one was the best choice, which may due to the low skewness of the soil properties interpolated. In all cases, a value of three was found to be the best power for splines. Lognormal kriging performed well when the dataset had a coefficient of skewness larger than one. No other summary statistics offered insight into the choice of the interpolation procedure or its parameters. We conclude that many parameters would be better identified from the RMSE statistic obtained from cross-validation after an exhaustive testing.     

Exploitation of spatial information in high resolution digital imagery to map leaf area index

Crop responses to management practices and the environment, as quantified by leaf area index (LAI), provide decision-making criteria for the delineation of crop management zones. The objective of this work was to investigate whether spatial correlations inferred from remotely sensed imagery can be used to interpolate and map LAI using a relatively small number of ground-based LAI measurements. Airborne imagery was recorded with the Airborne Imaging Spectrometer for Applications (AISA) radiometer over a 3.2 ha corn field. Spectral vegetation indexes (SVI) were derived from the image and aggregated to cells of 2 × 2 m², 4 × 4 m², and 8 × 8 m² resolution. The residual maximum likelihood method was used to estimate the LAI variogram parameters. A generalized least squares regression was used to relate ground truth LAI data and collocated image pixels. This regression result was then used to convert variograms from the imagery to LAI units as well as to interpolate and map LAI. The decrease in resolution by merging pixels led to an increase in the value of the r ² and to a decrease in root mean-squared error (RMSE) values. The accuracy of kriged estimates from the variogram of the measured LAI and that from the image derived variograms was estimated by cross-validation. There was no difference in the accuracy of the estimates using either variograms from measured LAI values or from those of converted SVIs. Maps of LAI from ground-based measurements made by kriging the data with image-derived variogram parameters were similar to those obtained by with kriging with the variogram of measured LAI. Similar coarse spatial trends of high, medium and low LAI were evident for both maps. Variogram parameters from ground-based measurements of LAI compared favorably with those derived from remotely sensed imagery and could be used to provide reasonable results for the interpolation of LAI measurements.     

采样点数量对长三角典型地区土壤肥力指标空间变异解析的影响

通过对江苏省如皋市888个采样点的不重复随机抽样,探讨了采样点数量对土壤肥力指标空间变异解析的影响。从半方差函数估计的可靠性角度考虑,在长江冲积物形成的土壤上,针对县级农业管理和生态环境规划的土壤肥力指标调查采样,采集250个样点较为合适。另外,仅使用交互验证和独立验证评价半方差函数估计的可靠性及确定采样点数量是不完善的,而平均克里格方差理论上随采样点数量的增加而单调递减,可以作为不同采样点数量条件下,综合评价半方差函数估计可靠性及确定采样点数量的补充指标。     

应用STAR模型研究海州湾小黄鱼春季资源量的时空分布

根据2011年、2013—2016年春季在海州湾进行的渔业资源调查数据,应用结构化加性回归（Structured Additive Regression , STAR）框架,结合delta方法,根据对空间数据的不同处理方式构建了5种物种分布模型,并通过AIC(akaike information criterion)和交叉验证比较了模型应对两种数据类型的拟合效果和预测性能。结果表明,加入空间因子后的模型残差空间自相关性显著降低,且positive模型和delta模型在加入空间因子后模型的拟合效果的提升较binomial模型更明显。空间加性模型（Geoadditive Models）的AIC值最低;变系数模型（Varying Coef?cient Models）的决定系数和AUC最高,模型拟合效果最佳。预测性能上,空间加性模型准确度最高。在最优模型的基础上,本研究根据FVCOM模拟环境数据,利用delta空间加性模型预测了海州湾小黄鱼春季资源的空间分布,结果表明小黄鱼资源分布主要集中于南部和西部近岸地区,随着水深的增加而逐渐减少,且年间变动明显。本研究旨在为海州湾小黄鱼渔业资源的开发和保护提供科学依据。