Don't BLUP Twice.

Large, complex data sets can be difficult to model in a single comprehensive genome-wide association study (GWAS). The best practice for two-stage analyses is to consider lines as fixed effects in the first stage statistical model. Best linear unbiased estimates of lines can then be used as input phenotypes to the second stage analysis.

Expanding the BonnMu sequence-indexed repository of transposon induced maize (Zea mays L.) mutations in dent and flint germplasm.

The BonnMu resource is a transposon tagged mutant collection designed for functional genomics studies in maize. To expand this resource, we crossed an active Mutator (Mu) stock with dent (B73, Co125) and flint (DK105, EP1, and F7) germplasm, resulting in the generation of 8064 mutagenized BonnMu F-families. Sequencing of these Mu-tagged families revealed 425 924 presumptive heritable Mu insertions affecting 36 612 (83%) of the 44 303 high-confidence gene models of maize (B73v5).

Factor-Analytic Variance-Covariance Structures for Prediction Into a Target Population of Environments.

Finlay-Wilkinson regression is a popular method for modeling genotype-environment interaction in plant breeding and crop variety testing. When environment is a random factor, this model may be cast as a factor-analytic variance-covariance structure, implying a regression on random latent environmental variables. This paper reviews such models with a focus on their use in the analysis of multi-environment trials for the purpose of making predictions in a target population of environments.

Accuracy of prediction from multi-environment trials for new locations using pedigree information and environmental covariates: the case of sorghum (Sorghum bicolor (L.) Moench) breeding.

We investigate a method of extracting and fitting synthetic environmental covariates and pedigree information in multilocation trial data analysis to predict genotype performances in untested locations. Plant breeding trials are usually conducted across multiple testing locations to predict genotype performances in the targeted population of environments. The predictive accuracy can be increased by the use of adequate statistical models.

Hierarchical modelling of variance components makes analysis of resolvable incomplete block designs more efficient.

The standard approach to variance component estimation in linear mixed models for alpha designs is the residual maximum likelihood (REML) method. One drawback of the REML method in the context of incomplete block designs is that the block variance may be estimated as zero, which can compromise the recovery of inter-block information and hence reduce the accuracy of treatment effects estimation. Due to the development of statistical and computational methods, there is an increasing interest in adopting hierarchical approaches to analysis.

The use of fixed study main effects in arm-based network meta-analysis.

Methods of network meta-analysis (NMA) can be classified as arm-based and contrast-based approaches. There are several arm-based approaches, and some of these have been criticized because they recover inter-study information and hence do not obey the principle of concurrent control. Here, we point out that recovery of inter-study information in arm-based NMA can be prevented by fitting a fixed main effect for studies.

Heritable microbiome variation is correlated with source environment in locally adapted maize varieties.

Beneficial interactions with microorganisms are pivotal for crop performance and resilience. However, it remains unclear how heritable the microbiome is with respect to the host plant genotype and to what extent host genetic mechanisms can modulate plant-microbiota interactions in the face of environmental stresses. Here we surveyed 3,168 root and rhizosphere microbiome samples from 129 accessions of locally adapted Zea, sourced from diverse habitats and grown under control and different stress conditions.

Breeding progress of nitrogen use efficiency of cereal crops, winter oilseed rape and peas in long-term variety trials.

Grain yield and NUE increased over time while nitrogen yield did not drop significantly despite reduced nitrogen input. Selection for grain and nitrogen yield is equivalent to selection for NUE. Breeding and registration of improved varieties with high yield, processing quality, disease resistance and nitrogen use efficiency (NUE) are of utmost importance for sustainable crop production to minimize adverse environmental impact and contribute to food security.

Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data.

Background: The accurate prediction of genomic breeding values is central to genomic selection in both plant and animal breeding studies. Genomic prediction involves the use of thousands of molecular markers spanning the entire genome and therefore requires methods able to efficiently handle high dimensional data. Not surprisingly, machine learning methods are becoming widely advocated for and used in genomic prediction studies.

Bird species richness and diversity responses to land use change in the Lake Victoria Basin, Kenya.

The increasing demand for cultivated lands driven by human population growth, escalating consumption and activities, combined with the vast area of uncultivated land, highlight the pressing need to better understand the biodiversity conservation implications of land use change in Sub-Saharan Africa. Land use change alters natural wildlife habitats with fundamental consequences for biodiversity. Consequently, species richness and diversity typically decline as land use changes from natural to disturbed.

High-throughput field phenotyping reveals that selection in breeding has affected the phenology and temperature response of wheat in the stem elongation phase.

Crop growth and phenology are driven by seasonal changes in environmental variables, with temperature as one important factor. However, knowledge about genotype-specific temperature response and its influence on phenology is limited. Such information is fundamental to improve crop models and adapt selection strategies.

A REML method for the evidence-splitting model in network meta-analysis.

Checking for possible inconsistency between direct and indirect evidence is an important task in network meta-analysis. Recently, an evidence-splitting (ES) model has been proposed, that allows separating direct and indirect evidence in a network and hence assessing inconsistency. A salient feature of this model is that the variance for heterogeneity appears in both the mean and the variance structure.

Models to estimate genetic gain of soybean seed yield from annual multi-environment field trials.

Simulations demonstrated that estimates of realized genetic gain from linear mixed models using regional trials are biased to some degree. Thus, we recommend multiple selected models to obtain a range of reasonable estimates. Genetic improvements of discrete characteristics are obvious and easy to demonstrate, while quantitative traits require reliable and accurate methods to disentangle the confounding genetic and non-genetic components.

Evidence for tropospheric ozone effects on rice production in Bangladesh.

Although Bangladesh is known to be burdened with elevated tropospheric ozone levels, little is known about its effects on food security. We conducted field experiments in four highly polluted rice growing environments of Bangladesh in three cropping seasons (2020-2022), in which we grew 20 different rice varieties with or without application of the ozone protectant ethylene diurea (EDU). The average daytime ozone concentrations at the study sites during the rice growing seasons ranged from 53 ppb to 84 ppb, with the lowest concentrations occurring in the year 2020.

Sustaining rice productivity through weather-resilient agricultural practices.

Background: Enhancing productivity and profitability and reducing climatic risk are the major challenges for sustaining rice production. Extreme weather can have significant and varied effects on crops, influencing agricultural productivity, crop yields and food security.

Results: In this study, a comparative evaluation of two crop management systems was performed involving farmers adopting a weather forecast-based advisory service (WFBAS) and usual farmers' practice (FP).

Generating designs for comparative experiments with two blocking factors.

Often, comparative experiments involve a single treatment factor and two blocking factors, for example, augmented row-column, two-phase, and incomplete row-column experiments. These experiments are widely used in agriculture. Finding good designs for these experiments is a major challenge when the number of treatments is large and the blocking structure is complex.

Timing and intensity of heat and drought stress determine wheat yield losses in Germany.

Crop yields are increasingly affected by climate change-induced weather extremes in Germany. However, there is still little knowledge of the specific crop-climate relations and respective heat and drought stress-induced yield losses. Therefore, we configure weather indices (WIs) that differ in the timing and intensity of heat and drought stress in wheat (Triticum aestivum L.

Complex traits and candidate genes: estimation of genetic variance components across multiple genetic architectures.

Large-effect loci-those statistically significant loci discovered by genome-wide association studies or linkage mapping-associated with key traits segregate amidst a background of minor, often undetectable, genetic effects in wild and domesticated plants and animals. Accurately attributing mean differences and variance explained to the correct components in the linear mixed model analysis is vital for selecting superior progeny and parents in plant and animal breeding, gene therapy, and medical genetics in humans. Marker-assisted prediction and its successor, genomic prediction, have many advantages for selecting superior individuals and understanding disease risk.

An adjusted coefficient of determination (R ) for generalized linear mixed models in one go.

The coefficient of determination (R ) is a common measure of goodness of fit for linear models. Various proposals have been made for extension of this measure to generalized linear and mixed models. When the model has random effects or correlated residual effects, the observed responses are correlated.

Long-term trends in yield variance of temperate managed grassland.

Unlabelled: The management of climate-resilient grassland systems is important for stable livestock fodder production. In the face of climate change, maintaining productivity while minimizing yield variance of grassland systems is increasingly challenging. To achieve climate-resilient and stable productivity of grasslands, a better understanding of the climatic drivers of long-term trends in yield variance and its dependence on agronomic inputs is required.

Disturbed circadian rhythm of locomotor activity of pullets is related to feather pecking in laying hens.

Various aspects of activity, such as spontaneous activity, explorative activity, activity in open-field tests, and hyperactivity syndrome have been explored as causal factors of feather pecking in laying hens, with no clear results. In all previous studies, mean values of activity over different time intervals were used as criteria. Incidental observation of alternated oviposition time in lines selected for high (HFP) and low feather pecking (LFP), supported by a recent study which showed differentially expressed genes related to the circadian clock in the same lines, led to the hypothesis that feather pecking may be related to a disturbed diurnal activity rhythm.

Fibrous food and particle size influence electromyography and the kinematics of oral processing.

Structure-sensory relationships are essential for understanding food perception. Food microstructure impacts how a food is comminuted and processed by the human masticatory system. This study investigated the impact of anisotropic structures, explicitly the structure of meat fibers, on the dynamic process of mastication.

Effects of systematic data reduction on trend estimation from German registration trials.

VCU trials can provide unbiased estimates of post-breeding trends given that all data is used. Dropping data of genotypes tested for up to two years may result in biased post-breeding trend estimates. Increasing yield trends are seen on-farm in Germany.