Build up of patches caused by Rhizoctonia solani

Rhizoctonia solani is a complex species that is composed of different anastomosis groups (AG). Although these different AGs show differences in their host ranges, generally R. solani is a phytopathogenic species with a wide spectrum of hosts. It has the ability to grow as a saprotroph, which further complicates its behaviour as a parasite. The losses caused by R. solani are very important and need a sustainable management strategy. The patchy appearance of the disease caused by this pathogen is well-known. The patches show within and between season dynamics. The factors which affect the spread of the disease can be grouped into three main categories: host plant, pathogen and environment. However, each of the categories in its detail may depend on or react with the other categories. There are a number of factors that may be involved in dynamics of patches. These potential mechanisms are discussed. It is essential to know about the mechanisms involved to develop an effective control strategy. Although more work is needed to investigate different mechanisms of parasitism deployed by different AGs in different hosts, it seems that many mechanisms external to the host are operating at the same time which necessitates an integrative research approach to study and control the diseases caused by R. solani.     

Assessing uncertainty and complexity in regional-scale crop model simulations

Crop models are imperfect approximations to real world interactions between biotic and abiotic factors. In some situations, the uncertainties associated with choices in model structure, model inputs and parameters can exceed the spatiotemporal variability of simulated yields, thus limiting predictability. For Indian groundnut, we used the General Large Area Model for annual crops (GLAM) with an existing framework to decompose uncertainty, to first understand how skill changes with added model complexity, and then to determine the relevant uncertainty sources in yield and other prognostic variables (total biomass, leaf area index and harvest index). We developed an ensemble of simulations by perturbing GLAM parameters using two different input meteorology datasets, and two model versions that differ in the complexity with which they account for assimilation. We found that added complexity improved model skill, as measured by changes in the root mean squared error (RMSE), by 5–10% in specific pockets of western, central and southern India, but that 85% of the groundnut growing area either did not show improved skill or showed decreased skill from such added complexity. Thus, adding complexity or using overly complex models at regional or global scales should be exercised with caution. Uncertainty analysis indicated that, in situations where soil and air moisture dynamics are the major determinants of productivity, predictability in yield is high. Where uncertainty for yield is high, the choice of weather input data was found critical for reducing uncertainty. However, for other prognostic variables (including leaf area index, total biomass and the harvest index) parametric uncertainty was generally the most important source, with a contribution of up to 90% in some cases, suggesting that regional-scale data additional to yield to constrain model parameters is needed. Our study provides further evidence that regional-scale studies should explicitly quantify multiple uncertainty sources.     

24个甘蔗组合早代重演力和预测力分析

为研究甘蔗遗传特点和育种潜力,对常用的29个甘蔗亲本24个组合的7个性状进行重演力测试并进行相关分析和回归分析。结果表明,相关系数(r)和回归系数(b)都达到显著水平的性状有茎径、有效茎、锤度和单茎重,说明这些性状受环境影响较小,在甘蔗育种早期世代根据这一结果制订相应的选择标准,增大选择压力,可提高甘蔗育种效率。29个亲本中表现较好的亲本是ROC24、粤糖91-976、ROC1、ROC22,24个组合中综合性状较好的是ROC24×粤糖91-976、ROC1×ROC22、粤糖89-113×ROC22,在今后育种计划中可广泛利用。     

宁夏各月降水特征和可预测性分析

针对宁夏全境19个气象观测站的月降水资料和全球74类大气环指数资料,利用方差分析、EOF分解、最优子集回归回报检验等方法分析了时空分布特征和可预测性。结果表明,冬季各月降水偏离平均值的程度较大;各月降水EOF1的方差贡献率1-12月的整体趋势为下凹型分布,EOF2呈相反趋势变化;月降水的EOF1空间分布从南到北大致分为3个带状区域;利用OSR方法回报检验各站的历史拟合率很不均衡,1-4月维持一个较高的拟合率,然后逐渐降低,5月达到一个最低值。