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. Advantages of arm-based NMA are discussed.

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. In order to increase the precision of the analysis of individual trials laid out as alpha designs, we here make a proposal to create an objectively informed prior distribution for variance components for replicates, blocks and plots, based on the results of previous (historical) trials. We propose different modelling approaches for the prior distributions and evaluate the effectiveness of the hierarchical approach compared to the REML method, which is classically used for analysing individual trials in two-stage approaches for multi-environment trials.

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. We quantified stress treatment and host genotype effects on the microbiome. Plant genotype and source environment were predictive of microbiome abundance. Genome-wide association analysis identified host genetic variants linked to both rhizosphere microbiome abundance and source environment. We identified transposon insertions in a candidate gene linked to both the abundance of a keystone bacterium Massilia in our controlled experiments and total soil nitrogen in the source environment. Isolation and controlled inoculation of Massilia alone can contribute to root development, whole-plant biomass production and adaptation to low nitrogen availability. We conclude that locally adapted maize varieties exert patterns of genetic control on their root and rhizosphere microbiomes that follow variation in their home environments, consistent with a role in tolerance to prevailing stress.

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. These methods encompass different groups of supervised and unsupervised learning methods. Although several studies have compared the predictive performances of individual methods, studies comparing the predictive performance of different groups of methods are rare. However, such studies are crucial for identifying (i) groups of methods with superior genomic predictive performance and assessing (ii) the merits and demerits of such groups of methods relative to each other and to the established classical methods. Here, we comparatively evaluate the genomic predictive performance and informally assess the computational cost of several groups of supervised machine learning methods, specifically, regularized regression methods, deep, ensemble and instance-based learning algorithms, using one simulated animal breeding dataset and three empirical maize breeding datasets obtained from a commercial breeding program. RESULTS: Our results show that the relative predictive performance and computational expense of the groups of machine learning methods depend upon both the data and target traits and that for classical regularized methods, increasing model complexity can incur huge computational costs but does not necessarily always improve predictive accuracy. Thus, despite their greater complexity and computational burden, neither the adaptive nor the group regularized methods clearly improved upon the results of their simple regularized counterparts. This rules out selection of one procedure among machine learning methods for routine use in genomic prediction. The results also show that, because of their competitive predictive performance, computational efficiency, simplicity and therefore relatively few tuning parameters, the classical linear mixed model and regularized regression methods are likely to remain strong contenders for genomic prediction. CONCLUSIONS: The dependence of predictive performance and computational burden on target datasets and traits call for increasing investments in enhancing the computational efficiency of machine learning algorithms and computing resources.

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. We assess how richness and diversity of avian species, grouped into feeding guilds, responded to land use changes, primarily expansion of settlements and cultivation at three sites in the Lake Victoria Basin in western Kenya, following tsetse control interventions. Each site consisted of a matched pair of spatially adjacent natural/semi-natural and settled/cultivated landscapes. Significant changes occurred in bird species richness and diversity in the disturbed relative to the natural landscape. Disturbed areas had fewer guilds and all guilds in disturbed areas also occurred in natural areas. Guilds had significantly more species in natural than in disturbed areas. The insectivore/granivore and insectivore/wax feeder guilds occurred only in natural areas. Whilst species diversity was far lower, a few species of estrildid finches were more common in the disturbed landscapes and were often observed on the scrubby edges of modified habitats. In contrast, the natural and less disturbed wooded areas had relatively fewer estrildid species and were completely devoid of several other species. In aggregate, land use changes significantly reduced bird species richness and diversity on the disturbed landscapes regardless of their breeding range size or foraging style (migratory or non-migratory) and posed greater risks to non-migratory species. Accordingly, land use planning should integrate conservation principles that preserve salient habitat qualities required by different bird species, such as adequate patch size and habitat connectivity, conserve viable bird populations and restore degraded habitats to alleviate adverse impacts of land use change on avian species richness and diversity.

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). WFBAS crop management followed the generated weather forecast-based advice whereas the control farmers (FP) did not receive any weather forecast-based advice, rather following their usual rice cultivation practices. The results of the experiments revealed that WFBAS farmers had a significant yield advantage over FP farmers. With the WFBAS technology, the farmers used inputs judiciously, utilized the benefit of favorable weather and minimized the risk resulting from extreme weather events. As a result, besides the yield enhancement, WFBAS provided a scope to protect the environment with the minimum residual effect of fertilizer and pesticides. It also reduced the pressure on groundwater by ensuring efficient water management. Finally, the farmers benefited from higher income through yield enhancement, reduction of the costs of production and reduction of risk. CONCLUSION: A successful and extensive implementation of WFBAS in the rice production system would assist Bangladesh in achieving Sustainable Development Goal 2.4, which focuses on rice productivity and profitability of farmers as well as long-term food security of the country. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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. Thus, full maximum likelihood (ML) has been proposed for estimating the parameters of this model. Maximum likelihood is known to yield biased variance component estimates in linear mixed models, and this problem is expected to also affect the ES model. The purpose of the present paper, therefore, is to propose a method based on residual (or restricted) maximum likelihood (REML). Our simulation shows that this new method is quite competitive to methods based on full ML in terms of bias and mean squared error. In addition, some limitations of the ES model are discussed. While this model splits direct and indirect evidence, it is not a plausible model for the cause of inconsistency.

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. We measured the increase in height of 352 European winter wheat varieties in 4 years to quantify phenology, and fitted an asymptotic temperature response model. The model used hourly fluctuations in temperature to parameterize the base temperature (Tmin), the temperature optimum (rmax), and the steepness (lrc) of growth responses. Our results show that higher Tmin and lrc relate to an earlier start and end of stem elongation. A higher rmax relates to an increased final height. Both final height and rmax decreased for varieties originating from the continental east of Europe towards the maritime west. A genome-wide association study (GWAS) indicated a quantitative inheritance and a large degree of independence among loci. Nevertheless, genomic prediction accuracies (GBLUPs) for Tmin and lrc were low (r≤0.32) compared with other traits (r≥0.59). As well as known, major genes related to vernalization, photoperiod, or dwarfing, the GWAS indicated additional, as yet unknown loci that dominate the temperature response.

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

KEY MESSAGE: 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. Stochastic simulations of soybean [Glycine max (L.) Merr.] breeding programs were performed to evaluate linear mixed models to estimate the realized genetic gain (RGG) from annual multi-environment trials (MET). True breeding values were simulated under an infinitesimal model to represent the genetic contributions to soybean seed yield under various MET conditions. Estimators were evaluated using objective criteria of bias and linearity. Covariance modeling and direct versus indirect estimation-based models resulted in a substantial range of estimated values, all of which were biased to some degree. Although no models produced unbiased estimates, the three best-performing models resulted in an average bias of [Formula: see text] kg/ha[Formula: see text]/yr[Formula: see text] ([Formula: see text] bu/ac[Formula: see text]/yr[Formula: see text]). Rather than relying on a single model to estimate RGG, we recommend the application of several models with minimal and directional bias. Further, based on the parameters used in the simulations, we do not think it is appropriate to use any single model to compare breeding programs or quantify the efficiency of proposed new breeding strategies. Lastly, for public soybean programs breeding for maturity groups II and III in North America, the estimated RGG values ranged from 18.16 to 39.68 kg/ha[Formula: see text]/yr[Formula: see text] (0.27-0.59 bu/ac[Formula: see text]/yr[Formula: see text]) from 1989 to 2019. These results provide strong evidence that public breeders have significantly improved soybean germplasm for seed yield in the primary production areas of North America.

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. In this paper, we first propose a new search algorithm that is combined with efficient update formulae, so that optimal designs with two blocking factors can be found within a reasonable time. Second, we compare augmented row-column designs generated with our new method to those obtained from CycDesigN, DiGGer, and the OPTEX procedure of SAS in terms of computing times as well as the quality of solutions. Third, we illustrate our proposed approach with four applications. We show an example where our efficient update formulae work while existing update formulae cannot be applied, and we use our search framework to generate augmented row-column, two-phase, and incomplete row-column designs. We end the paper with a conclusion along with suggestions for potential applications.

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. However, these two approaches are less often integrated to study complex traits with different genetic architectures. This simulation study demonstrates that the average semivariance can be applied to models incorporating Mendelian, oligogenic, and polygenic terms simultaneously and yields accurate estimates of the variance explained for all relevant variables. Our previous research focused on large-effect loci and polygenic variance separately. This work aims to synthesize and expand the average semivariance framework to various genetic architectures and the corresponding mixed models. This framework independently accounts for the effects of large-effect loci and the polygenic genetic background and is universally applicable to genetics studies in humans, plants, animals, and microbes.

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.). We construct these WIs using gridded weather and phenology time series data from 1995 to 2019 and aggregate them with Germany-wide municipality level on-farm wheat yield data. We statistically analyze the WI's explanatory power and region-specific effect size for wheat yield using linear mixed models. We found the highest explanatory power during the stem elongation and booting phase under moderate drought stress and during the reproductive phase under moderate heat stress. Furthermore, we observed the highest average yield losses due to moderate and extreme heat stress during the reproductive phase. The highest heat and drought stress-induced yield losses were observed in Brandenburg, Saxony-Anhalt, and northern Bavaria, while similar heat and drought stresses cause much lower yield losses in other regions of Germany.

An adjusted coefficient of determination (R2 ) for generalized linear mixed models in one go.

The coefficient of determination (R2 ) 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. This paper proposes a new coefficient of determination for this setting that accounts for any such correlation. A key advantage of the proposed method is that it only requires the fit of the model under consideration, with no need to also fit a null model. Also, the approach entails a bias correction in the estimator assessing the variance explained by fixed effects. Three examples are used to illustrate new measure. A simulation shows that the proposed estimator of the new coefficient of determination has only minimal bias.

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

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. Based on the Park Grass Experiment at Rothamsted (UK), we report for the first time the long-term trends in yield variance of grassland (1965-2018) in plots given different fertilizer and lime applications, with contrasting productivity and plant species diversity. We implemented a statistical model that allowed yield variance to be determined independently of yield level. Environmental abiotic covariates were included in a novel criss-cross regression approach to determine climatic drivers of yield variance and its dependence on agronomic management. Our findings highlight that sufficient liming and moderate fertilization can reduce yield variance while maintaining productivity and limiting loss of plant species diversity. Plots receiving the highest rate of nitrogen fertilizer or farmyard manure had the highest yield but were also more responsive to environmental variability and had less plant species diversity. We identified the days of water stress from March to October and temperature from July to August as the two main climatic drivers, explaining approximately one-third of the observed yield variance. These drivers helped explain consistent unimodal trends in yield variance-with a peak in approximately 1995, after which variance declined. Here, for the first time, we provide a novel statistical framework and a unique long-term dataset for understanding the trends in yield variance of managed grassland. The application of the criss-cross regression approach in other long-term agro-ecological trials could help identify climatic drivers of production risk and to derive agronomic strategies for improving the climate resilience of cropping systems. Supplementary Information: The online version contains supplementary material available at 10.1007/s13593-023-00885-w.

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. For a general understanding of texture-structure relationships, the three typically used deformation-tests: Kramer shear cell-, Guillotine cutting- and texture-profile-analyses were conducted. 3D jaw movements and muscle activities of the masseter muscle were additionally tracked and visualized using a mathematical model. Particle size had a significant effect on jaw movements and muscle activities for both the homogeneous (isotropic) and fibrous (anisotropic) meat-based samples with the same composition. Mastication was described using jaw movement and muscle activity parameters determined for each individual chew. The adjusted effect of fiber length was extracted from the data, suggesting that longer fibers induce a more strenuous chewing in which the jaw undergoes faster and wider movements requiring more muscle activity. To the authors' knowledge, this paper presents a novel data analysis approach for identifying oral processing behavior differences. This is an advancement on previous studies because a holistic overview of the entire mastication process can be visualized.

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. Hence activity recordings of a previous generation of these lines have been reanalyzed. Data sets of a total of 682 pullets of 3 subsequent hatches of HFP, LFP, and an unselected control line (CONTR) were used. Locomotor activity was recorded in pullets housed in groups of mixed lines in a deep litter pen on 7 consecutive 13-h light phases, using a radio-frequency identification antenna system. The number of approaches to the antenna system was recorded as a measure of locomotor activity and analyzed using a generalized linear mixed model including hatch, line, time of day and the interactions of hatch × time of day and line × time of day as fixed effects. Significant effects were found for time and the interaction line × time of day but not for line. All lines showed a bimodal pattern of diurnal activity. The peak activity of the HFP in the morning was lower than that of the LFP and CONTR. In the afternoon peak all lines differed with the highest mean in the LFP followed by CONTR and HFP. The present results provide support for the hypothesis that a disturbed circadian clock plays a role in the development of feather pecking.

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

KEY MESSAGE: 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. The increase is based on genetic trend in registered genotypes and changes in agronomic practices and climate. To estimate both genetic and non-genetic trends, historical wheat data from variety trials evaluating a varieties' value for cultivation und use (VCU) were analyzed. VCU datasets include information on varieties as well as on genotypes that were submitted by breeders and tested in trials but could not make it to registration. Therefore, the population of registered varieties (post-registration population) is a subset of the population of genotypes tested in VCU trials (post-breeding population). To assess post-registration genetic trend, historical VCU trial datasets are often reduced, e.g. to registered varieties only. This kind of drop-out mechanism is statistically informative which affects variance component estimates and which can affect trend estimates. To investigate the effect of this informative drop-out on trend estimates, a simulation study was conducted mimicking the structure of German winter wheat VCU trials. Zero post-breeding trends were simulated. Results showed unbiased estimates of post-breeding trends when using all data. When restricting data to genotypes tested for at least three years, a positive genetic trend of 0.11 dt ha-1 year-1 and a negative non-genetic trend (- 0.11 dt ha-1 year-1) were observed. Bias increased with increasing genotype-by-year variance and disappeared with random selection. We simulated single-trait selection, whereas decisions in VCU trials consider multiple traits, so selection intensity per trait is considerably lower. Hence, our results provide an upper bound for the bias expected in practice.

50 years of rice breeding in Bangladesh: genetic yield trends.

To assess the efficiency of genetic improvement programs, it is essential to assess the genetic trend in long-term data. The present study estimates the genetic trends for grain yield of rice varieties released between 1970 and 2020 by the Bangladesh Rice Research Institute. The yield of the varieties was assessed from 2001-2002 to 2020-2021 in multi-locations trials. In such a series of trials, yield may increase over time due to (i) genetic improvement (genetic trend) and (ii) improved management or favorable climate change (agronomic/non-genetic trend). In both the winter and monsoon seasons, we observed positive genetic and non-genetic trends. The annual genetic trend for grain yield in both winter and monsoon rice varieties was 0.01 t ha-1, while the non-genetic trend for both seasons was 0.02 t ha-1, corresponding to yearly genetic gains of 0.28% and 0.18% in winter and monsoon seasons, respectively. The overall percentage yield change from 1970 until 2020 for winter rice was 40.96%, of which 13.91% was genetic trend and 27.05% was non-genetic. For the monsoon season, the overall percentage change from 1973 until 2020 was 38.39%, of which genetic and non-genetic increases were 8.36% and 30.03%, respectively. Overall, the contribution of non-genetic trend is larger than genetic trend both for winter and monsoon seasons. These results suggest that limited progress has been made in improving yield in Bangladeshi rice breeding programs over the last 50 years. Breeding programs need to be modernized to deliver sufficient genetic gains in the future to sustain Bangladeshi food security.

Studying Stem Rust and Leaf Rust Resistances of Self-Fertile Rye Breeding Populations.

Stem rust (SR) and leaf rust (LR) are currently the two most important rust diseases of cultivated rye in Central Europe and resistant cultivars promise to prevent yield losses caused by those pathogens. To secure long-lasting resistance, ideally pyramided monogenic resistances and race-nonspecific resistances are applied. To find respective genes, we screened six breeding populations and one testcross population for resistance to artificially inoculated SR and naturally occurring LR in multi-environmental field trials. Five populations were genotyped with a 10K SNP marker chip and one with DArTseqTM. In total, ten SR-QTLs were found that caused a reduction of 5-17 percentage points in stem coverage with urediniospores. Four QTLs thereof were mapped to positions of already known SR QTLs. An additional gene at the distal end of chromosome 2R, Pgs3.1, that caused a reduction of 40 percentage points SR infection, was validated. One SR-QTL on chromosome 3R, QTL-SR4, was found in three populations linked with the same marker. Further QTLs at similar positions, but from different populations, were also found on chromosomes 1R, 4R, and 6R. For SR, additionally seedling tests were used to separate between adult-plant and all-stage resistances and a statistical method accounting for the ordinal-scaled seedling test data was used to map seedling resistances. However, only Pgs3.1 could be detected based on seedling test data, even though genetic variance was observed in another population, too. For LR, in three of the populations, two new large-effect loci (Pr7 and Pr8) on chromosomes 1R and 2R were mapped that caused 34 and 21 percentage points reduction in leaf area covered with urediniospores and one new QTL on chromosome 1R causing 9 percentage points reduction.