Genomic selection allowing for marker‐by‐environment interaction

Genomic selection has been routinely implemented in plant breeding in two stages. The first stage usually omits the marker information and estimates adjusted means of genotypes across environments. The second stage uses the adjusted means to predict genomic breeding values. However, if the effects of markers vary substantially between different environments, it may be important to account for this variation for varieties adapted to different environments. Using two maize data sets, we investigated whether modelling the marker‐by‐environment interaction can improve the predictive ability of genomic selection relative to modelling genotype‐by‐environment interaction alone. Modelling the marker‐by‐environment interaction did not substantially increase the predictive ability relative to modelling only the genotype‐by‐environment interaction for the two tested data sets. Thus, genomic selection, carried out in a stagewise fashion, such that the marker information is omitted until the last stage of the process, may suffice for most practical purposes. Moreover, predictive ability did not reduce substantially even when the number of markers with consistent effects across environments used for genomic prediction was reduced to about 50.     

What's normal anyway? Residual plots are more telling than significance tests when checking ANOVA assumptions

We consider two questions important for applying analysis of variance (ANOVA): Should normality be checked on the raw data or on the residuals (or is it immaterial which of the two approaches we take)? Should normality and homogeneity of variance be checked using significance tests or diagnostic plots (or both)? Based on two examples, we show that residuals should be used for model checking and that residual plots are better for checking ANOVA assumptions than statistical tests. We also discuss why one should be very cautious when using statistical tests to check the assumptions.     

Integrating a simple shading algorithm into CERES-wheat and CERES-maize with particular regard to a changing microclimate within a relay-intercropping system

Wheat/maize related multi-cropping systems are the dominant cropping systems in North China. To improve and adjust those systems, and to study competition effects within intercropping, extended field experiments are necessary. As field experiments are time consuming, laborious and expensive, a viable alternative is the use of crop growth models that can quantify the effects of management practices on crop growth and productivity. Field experiments showed that intercropped maize yielded as high as monocropped maize, and grain yield of intercropped wheat increased by up to 32%. Based on a process-oriented modeling approach, this study focuses on analyzing and modeling competitive relationships in a wheat/maize relay intercropping system with regard to yield, solar radiation and microclimate effects. A simple shading algorithm was applied and integrated into the CERES models, which are part of the DSSAT software shell vs. 4.5. The algorithm developed estimates the proportion of shading as affected by neighbouring plant height. The model was tested to investigate the applicability of this shading algorithm within the CERES models in the simulation of grain yield and dry matter yield of wheat and maize. Model error of grain and dry matter yield for both species was below 10%. There was a tendency for grain yield to be simulated adequately but for dry matter yield to be slightly underestimated. Increased top soil temperature in intercropped wheat increased the mineralization of nitrogen and improved N supply. The wheat/maize system was N efficient. Thus, N dynamics were taken into account for simulation as well as CO₂ dynamics based upon modified wind speed. Wheat border rows were exposed to a higher wind speed until mid-June and to reduced wind speed thereafter. As a result, solar radiation, soil temperature and wind speed differed between monocropping and intercropping and could provide a starting point for simulating intercropping. Microclimate effects are often small, subtle or non-existent, while spatial and climate variability and the heterogeneity of plant populations can be considerable. Quantifying microclimatic effects may prove difficult but should not be neglected when simulating intercropping systems.     

Appraising and selecting conservation measures to mitigate desertification and land degradation based on stakeholder participation and global best practices

Most desertification research focuses on degradation assessments without putting sufficient emphasis on prevention and mitigation strategies, although the concept of sustainable land management (SLM) is increasingly being acknowledged. A variety of already applied conservation measures exist at the local level, but they are not adequately recognised, evaluated and shared, either by land users, technicians, researchers, or policy makers. Likewise, collaboration between research and implementation is often insufficient. The aim of this paper is to present a new methodology for a participatory process of appraising and selecting desertification mitigation strategies, and to present first experiences from its application in the EU‐funded DESIRE project. The methodology combines a collective learning and decision approach with the use of evaluated global best practices. In three parts, it moves through a concise process, starting with identifying land degradation and locally applied solutions in a stakeholder workshop, leading to assessing local solutions with a standardised evaluation tool, and ending with jointly selecting promising strategies for implementation with the help of a decision support tool. The methodology is currently being applied in 16 study sites. Preliminary analysis from the application of the first part of the methodology shows that the initial stakeholder workshop results in a good basis for stakeholder cooperation, and in promising land conservation practices for further assessment. Study site research teams appreciated the valuable results, as burning issues and promising options emerged from joint reflection. The methodology is suitable to initiate mutual learning among different stakeholder groups and to integrate local and scientific knowledge. Copyright © 2009 John Wiley & Sons, Ltd.     

Weighted Estimation of AMMI and GGE Models

The AMMI/GGE model can be used to describe a two-way table of genotype–environment means. When the genotype–environment means are independent and homoscedastic, ordinary least squares (OLS) gives optimal estimates of the model. In plant breeding, the assumption of independence and homoscedasticity of the genotype–environment means is frequently violated, however, such that generalized least squares (GLS) estimation is more appropriate. This paper introduces three different GLS algorithms that use a weighting matrix to take the correlation between the genotype–environment means as well as heteroscedasticity into account. To investigate the effectiveness of the GLS estimation, the proposed algorithms were implemented using three different weighting matrices, including (i) an identity matrix (OLS estimation), (ii) an approximation of the complete inverse covariance matrix of the genotype–environment means, and (iii) the complete inverse covariance matrix of the genotype–environment means. Using simulated data modeled on real experiments, the different weighting methods were compared in terms of the mean-squared error of the genotype–environment means, interaction effects, and singular vectors. The results show that weighted estimation generally outperformed unweighted estimation in terms of the mean-squared error. Furthermore, the effectiveness of the weighted estimation increased when the heterogeneity of the variances of the genotype–environment means increased.     

Mycoplasma bovis infections in Swiss dairy cattle: a clinical investigation

Mycoplasma bovis causes mastitis in dairy cows and is associated with pneumonia and polyarthritis in cattle. The present investigation included a retrospective case–control study to identify potential herd-level risk factors for M. bovis associated disease, and a prospective cohort study to evaluate the course of clinical disease in M. bovis infected dairy cattle herds in Switzerland. Eighteen herds with confirmed M. bovis cases were visited twice within an average interval of 75 d. One control herd with no history of clinical mycoplasmosis, matched for herd size, was randomly selected within a 10 km range for each case herd. Animal health data, production data, information on milking and feeding-management, housing and presence of potential stress- factors were collected. Composite quarter milk samples were aseptically collected from all lactating cows and 5% of all animals within each herd were sampled by nasal swabs. Organ samples of culled diseased cows were collected when logistically possible. All samples were analyzed by real-time polymerase chain reaction (PCR). In case herds, incidence risk of pneumonia, arthritis and clinical mastitis prior to the first visit and incidence rates of clinical mastitis and clinical pneumonia between the two visits was estimated. Logistic regression was used to identify potential herd-level risk factors for M. bovis infection. In case herds, incidence risk of M. bovis mastitis prior to the first visit ranged from 2 to 15%, whereas 2 to 35% of the cows suffered from clinical pneumonia within the 12 months prior to the first herd visit. The incidence rates of mycoplasmal mastitis and clinical pneumonia between the two herd visits were low in case herds (0–0.1 per animal year at risk and 0.1-0.6 per animal year at risk, respectively). In the retrospective-case-control study high mean milk production, appropriate stimulation until milk-let-down, fore-stripping, animal movements (cattle shows and trade), presence of stress-factors, and use of a specific brand of milking equipment, were identified as potential herd-level risk factors. The prospective cohort study revealed a decreased incidence of clinical disease within three months and prolonged colonization of the nasal cavity by M. bovis in young stock.     

Time scales and heterogeneous structure in geodynamic earth models

Computer models of mantle convection constrained by the history of Cenozoic and Mesozoic plate motions explain some deep-mantle structural heterogeneity imaged by seismic tomography, especially those related to subduction. They also reveal a 150-million-year time scale for generating thermal heterogeneity in the mantle, comparable to the record of plate motion reconstructions, so that the problem of unknown initial conditions can be overcome. The pattern of lowermost mantle structure at the core-mantle boundary is controlled by subduction history, although seismic tomography reveals intense large-scale hot (low-velocity) upwelling features not explicitly predicted by the models.