马铃薯重要农艺性状的相关性、主成分与聚类分析

为明确国内外马铃薯种质资源在云南大春季的表现,本研究对68份马铃薯品种(系)的出苗率、长势、主茎数、茎粗、株高、开花性、病情发展曲线下面积(area under disease progress curve, AUDPC)、成熟期、商品率、单株块茎数、单株块茎重、平均块茎重、匍匐茎数量、匍匐茎长度共14个农艺性状进行相关性、主成分与聚类分析。结果表明:单株块茎数、AUDPC、单株块茎重、开花性的变异程度较明显,变异系数分别为50.42%、43.91%、39.81%和39.67%;单株块茎重与长势、茎粗、株高、开花性、商品率、平均块茎重、匍匐茎数量极显著正相关,与单株块茎数显著正相关,与成熟期极显著负相关。AUDPC与成熟期极显著正相关且相关系数达到0.802。在主成分分析中,前6个主成分方差累积贡献率为83.290%,可用来代替14个农艺性状指标。聚类分析将68份马铃薯品种(系)分为3个类群,Ⅰ类群表现为晚熟、高抗晚疫病病,Ⅱ类群表现为中熟、中抗晚疫病,第Ⅲ类群表现为早熟感晚疫病。本研究结果为云南大春马铃薯种质资源的合理利用提供了科学依据。     

Artificial neural network approach for prediction of ammonia emission from field-applied manure and relative significance assessment of ammonia emission factors

This article presents a systematic method for enhancing the estimation accuracy of ammonia emission from field-applied manure and for assessing the relative significance of ammonia emission factors, using the feedforward-backpropagation artificial neural network (ANN) approach.

The multivariate linear regression (MLR) method well describes the ammonia emission tendency with the emission factor variation. However, ammonia emission from manure slurry is too complex to be captured in a linear regression model. This necessitates a model which can describe complex nonlinear effects between the ammonia emission variables such as soil and manure states, climate and agronomic factors. In the present study, a principle component analysis (PCA) based preprocessing and weight partitioning method (WPM) based postprocessing ANN approach (called the PWA approach) is proposed to account for the complex nonlinear effects.

The ammonia emission is predicted with precision by the 11 emission factors, using the nonlinear ANN approach. The relative importance among the 11 emission factors is identified using the elasticity analysis in the MLR method and using the WPM in the ANN approach. The relative significance obtained quantitatively by the PWA approach in the present study gives an excellent explanation of the most important processes controlling NH₃ emission.     

Use of crop models to understand genotype by environment interactions for drought in real-world and simulated plant breeding trials

Crop simulation models of plant processes capture the biological interactions between the sensing of signals at an organ level (e.g. drought affecting roots), the response of the plant at a biochemical level (e.g. change in development rate) and the result at the organ (or crop) level (e.g. reduced growth). In dissecting the complex control of phenotypes like yield, simulation models have several roles. Models have been used to generate an index of the climatic environment (e.g. of drought stress) for breeding programme trials. In wheat and sorghum grown in northern Australia, this has shown that mid-season drought generates large genotype by environment interaction. By defining gene action to calculate the value of input trait parameters to crop models, simulated multi-environment trials estimate the yield of ‘synthetic’ sorghum cultivars grown in historical or artificial climates with current or potential management regimes. In this way, the biological interactions among traits constrain the crop yields to only those that are biologically possible in the given set of environments. This allows the construction of datasets that are more ‘realistic’ representations of gene by trait by environment interaction than is possible using only the statistical attributes (e.g. means, variances and correlations) of real-world trait datasets. This approach has an additional advantage in that ‘biological and experimental noise’ can be manipulated separately. These ‘testbeds’ for statistical techniques can be extended to the interpretation of a crossing and selection programme where the processes of chromosomal recombination are simulated using a quantitative genetics model and applied to the trait parameters. Statisticians are challenged to develop improved methods for the resulting simulated phenotype datasets, with the objective of revealing the (known) underlying genetic and environment structure that was input to the simulations. These improved methods can then be applied to existing plant breeding programmes.     

A simple procedure for yield component analysis

Summary This paper introduces a simple method of analysing yield components. It allows quantification of the contribution of each component to the variability of final yield. The method is based on log-transforms of measurements of the component traits. Applications in heterosis breeding and stability analysis are discussed. The method is compared to other procedures of yield component analysis.     

A mathematical model on the pulverized coal blending optimization for blast furnace injection based on principal component analysis

A mathematical model of coal blending optimization for blast furnace injection is proposed. Because Chinese current blast furnaces blend pulverized coal by simple mix and traditional lab blending optimization needs plenty of work for the larger number of characteristics of pulverized coal. The new model is based on the principal component analysis indexes P1, P2 and P3 combined with the proximate analysis and elementary analysis of pulverized coal. P1, P2 and P3 are obtained from principal component analysis of the injection characteristic of pulverized coal determined by lab experiment. This model is proved to be effective on both lab experiment coal blending and actual production of BF.     

A method of production data analysis for multi-fractured horizontal well in shale gas reservoir

Production data analysis is an effective method of evaluating gas in place, parameters of reservoir and hydraulic fractures. Current model of production data analysis fails to make sense because of the existence of absorption, multi-fracturing, and stimulated reservoir volume (SRV). Considering the effect of desorption, fracture interacting and closed boundary, the authors calculate the productivity of multi-fractured horizontal well in shale gas by Laplace transform and pressure superposition principle. Then theoretical charts of Agarwal-Gardner production decline curves are presented by employing new definition of dimensionless material balance time and dimensionless production, which is based on asymptotic approximations. The result shows that Agarwal-Gardner curves can be divided into two parts where they are scattered in early transient period, and where their shape was influenced by parameters relevant to adsorption, position of each fracture and reservoir geometry, and then drawn to Arps harmonious decline in pseudo steady state period. Reservoir parameters, hydraulic fracture characteristics and gas in place are evaluated by using the type curve matching with actual production data.     

A stability analysis of time to flowering as a screen for responsiveness to temperature and photoperiod in cowpea (Vigna unguiculata)

Summary Twenty-one genotypes of cowpea (Vigna unguiculata), comprising landraces and varieties, were grown in 22 photothermal environments in Nigeria and Niger, West Africa, and a stability analysis of days from sowing to flowering (f) was carried out. Cowpeas are rarely insensitive to photoperiod; they are typically quantitative shortday plants wherein f is delayed when photoperiod (P) is longer than the critical photoperiod (P _c ). Therefore, in order to quantify genotypic variation in temperature sensitivity, genotype f was regressed against the mean trial f in circumstances where P c (i.e. approximately 13 hd^-1) and mean temperature (T) was between 19° and 28° C. Correspondingly, in order to assess genotypic variation in photoperiod sensitivity, trials where T was near optimal (25°–28° C) but where P ranged from 10–14.5 hd^-1 were used. These stability analyses detected no significant differences (P>0.05) between genotypes 9n temperature sensitivity but revealed significant differences (P<0.001) in photoperiod sensitivity. Regression coefficients from the stability analysis were strongly correlated (r=0.94, 19df) with a photoperiod sensitivity constant, c, determined from a photothermal flowering model. A stability analysis of f from field trials can therefore identify and quantify genotypic variation in response to temperature and photoperiod in cowpea.Abbreviations f days from sowing to flowering - P mean photoperiod - P _c critical photoperiod - P _ce ceiling photoperiod - T mean temperature - T _b base temperature - T _o optimum temperature - SDP short-day plant