Linear variance models for plant breeding trials

Most plant breeding trials involve a layout of plots in rows and columns. Resolvable row–column designs have proven effective in obtaining efficient estimates of treatment effects. Further improvement may be possible by postblocking or by inclusion of spatial model components. This study reviews options for augmenting a baseline row–column model by the addition of spatial components. The models considered are different variants of the linear variance model in one and two dimensions. Usefulness of these options is assessed by analysing a number of field trials from plant breeding and variety testing.     

Influence of leaching on the chemical composition of grassland biomass for combustion

Bioenergy production from grassland biomass is an alternative use for semi‐natural grasslands, but chemical composition of grassland herbage can limit its suitability for combustion. Leaching by precipitation during the field period is currently used to remove unwanted elements from other herbaceous biofuels. The aims of this study were to assess the influence of leaching on chemical composition of grassland biomass, to establish the influence of leaching intensity and to evaluate the feasibility of a simplified laboratory method for leaching studies. Freshly cut and dried herbage from two contrasting grasslands were leached using 12 and 36 mm of simulated rain. A second experiment compared three methods of different leaching intensity. Leaching resulted in strong reduction in Cl and K concentrations; ash was moderately reduced and Mg slightly reduced. Concentrations of N, S and Ca were not affected. Leaching occurred in dried, but not in fresh herbage, and Cl leaching was affected by herbage type. Cl, K and ash responded differently to increased leaching intensity. Slagging, fouling and corrosion problems during combustion, which are closely linked to Cl and K concentrations, could be reduced by natural leaching under suitable weather conditions. A standardized laboratory leaching method is suggested for further investigating the influence of herbage characteristics on the leachability of K and Cl.     

Stability analysis of farmer participatory trials for conservation agriculture using mixed models

Normally, the data generated from farmer participatory trials (FPT) are highly unbalanced due to variation in the number of replicates of different treatments, the use of different varieties, farmers’ management of the trials, and their preferences for testing different treatments. The incomplete nature of the data makes mixed models the preferred class of models for the analysis. When assessing the relative performances of technologies, stability over a range of environments is an important attribute to consider. Most of the common models for stability may be fitted in a mixed-model framework where environments are a random factor and treatments are fixed. Data from on-farm trials conducted in the Indo-Gangetic Plain (IGP) of South Asia under the umbrella of Rice-Wheat Consortium (RWC) were analyzed for grain yield stability using different stability models. The objective was to compare improved resource management technologies with farmers’ practice. The variance components of an appropriate mixed model serve as measures of stability. Stability models were compared allowing for (i) heterogeneity of error variances and (ii) heterogeneity of variances between environments for farmers-within-environment effects. Mean comparisons of the treatments were made on the basis of the best fitting stability model. Reduced-till (non-puddled) transplanted rice (RT-TPR) and reduced-till drill-seeded wheat using a power tiller - operated seeder with integrated crop and resource management RTDSW(PTOS)ICRM ranked first in terms of both adjusted mean yield and stability.     

An on-farm approach to quantify yield variation and to derive decision rules for site-specific weed management

Grain yield often varies within agricultural fields as a result of the variation in soil characteristics, competition from weeds, management practices and their causal interactions. To implement appropriate management decisions, yield variability needs to be explained and quantified. A new experimental design was established and tested in a field experiment to detect yield variation in relation to the variation in soil quality, the heterogeneity of weed distribution and weed control within a field. Weed seedling distribution and density, apparent soil electrical conductivity (EC_a) and grain yield were recorded and mapped in a 3.5 ha winter wheat field during 2005 and 2006. A linear mixed model with an anisotropic spatial correlation structure was used to estimate the effect of soil characteristics, weed competition and herbicide treatment on crop yield. The results showed that all properties had a strong effect on grain yield. By adding herbicide costs and current grain price into the model, thresholds of weed density were derived for site-specific weed control. This experimental approach enables the variation of yield within agricultural fields to be explained, and an understanding of the effects on yield of the factors that affect it and their causal interactions to be gained. The approach can be applied to improve decision algorithms for the patch spraying of weeds.     

戊型肝炎病毒研究新进展

戊型肝炎病毒(hepatitis Evirus,HEV)是非甲非乙型急性肝炎的病原体,主要经粪-口途径传播,也有报道可以通过血液传播。戊型肝炎在亚洲、非洲及美洲的墨西哥等发展中国家常呈爆发流行,我国1986-1988年在新疆曾经发生戊型肝炎暴发流行[1],而在包括发达国家在内的世界各地呈散在传     

The use of multilocation trials to select cultivars that are better than a control

Multilocation trials are a common means for evaluating new cultivars or strains. Their statistical analysis is often based on multiple comparisons with one or more controls. The purpose of this paper is to demonstrate the use of the Chen-Picket procedure for selecting cultivars that are better than a control, which does not require stringent statistical assumptions and allows conclusions about the probability that the selected subset contains all cultivars that are better than the control. This procedure appears preferable to the multiple t-test, which is often used in the evaluation of yield trials.     

Ermittlung des anteils verschiedener ertragskomponenten an einer ertragsdifferenz

In pflanzenbaulichen Untersuchungen ist es häufig von Interesse, den Einfluß verschiedener Ertragskomponenten auf den Ertrag zu quantifizieren. Beim Vergleich zweier Behandlungen stellt sich die Frage, welche Komponenten maßgeblich für einen möglichen Ertragsunterschied verantwortlich sind. Dieser Artikel beschäftigt sich mit einem Ansatz, der die relative Bedeutung der Ertragskomponenten prozentual quantifiziert. Es wird eine Modifikation vorgeschlagen, die zu einer eindeutigen Charakterisierung der Komponenten führt.     

Are ordinal rating scales better than percent ratings? a statistical and “psychological” view

Cytoplasmic male sterility (CMS) is considered an efficient genetic tool in pearl millet hybrid breeding. Of the several CMS sources available in pearl millet, A₁ is the only CMS widely exploited to produce commercial hybrids in India. To explore the possibility of using alternate CMS sources, we studied the cytoplasmic effects of different CMS sources on agronomic characters in pearl millet. Five CMS (A) lines representing A₁, A₂, A₃, A₄ and A₅ cytoplasms, their respective maintainer (B) lines and eight restorer (R) lines were used to generate 40 A × R and B × R experimental crosses. The experimental material was evaluated at two different locations in India. Analysis of combining ability and heterosis revealed that A₄ and A₅ cytoplasms had desirable effects for earliness. The A₅ CMS was found to be particularly promising, as compared to other CMS sources for improving grain yield. The study also indicated that the cytoplasmic effects on general combining ability (GCA) for various agronomic characters were largely non-significant. However, cytoplasmic effects on specific combining ability and heterosis were found to be modulated by cytoplasmic-nuclear interactions and influenced by the environmental conditions. The study also demonstrated the advantage of utilizing diverse male-sterile and restorer combinations in maximizing the productivity as well as for genetic and cytoplasmic diversification of hybrids in pearl millet.     

Transformations in mixed models: Application to risk analysis for a multienvironment trial

An important trait in crop cultivar evaluation is stability of performance across environments. There are many different measures of stability, most of which are related to variance components of a mixed model. We believe that stability measures assessing yield risk are of particular relevance, because they integrate location and scale parameters in a meaningful way. A prerequisite for obtaining valid risk estimates is an appropriate model for the distribution of yield across environments. Multienvironment trials (MET) are often analyzed by mixed linear models, assuming that environments are a random sample from a target population, and that random terms in the model are normally distributed. The normality assumption may not always be tenable, and consequently, risk estimates may be biased. In this article, we suggest a transformation approach based on the Johnson system to cope with nonnormality in mixed models. The methods are exemplified using an international wheat yield trial. The importance of accounting for nonnormality in risk analyses based on MET is emphasized. We suggest that transformations should be routinely considered in analyses to assess risk.