A coefficient of determination (R2 ) for generalized linear mixed models

Temporal prediction of suicidal ideation in an ecological momentary assessment study with recurrent neural networks

INTRODUCTION

Ecological Momentary Assessment (EMA) holds promise for providing insights into daily life experiences when studying mental health phenomena. However, commonly used mixed-effects linear statistical models do not fully utilize the richness of the ultidimensional time-varying data that EMA yields. Recurrent Neural Networks (RNNs) provide an alternative data analytic method to leverage more information and potentially improve prediction, particularly for non-normally distributed outcomes.

METHODS

As part of a broader research study of suicidal thoughts and behavior in people with borderline personality disorder (BPD), eighty-four participants engaged in EMA data collection over one week, answering questions multiple times each day about suicidal ideation (SI), stressful events, coping strategy use, and affect. RNNs and mixed-effects linear regression models (MEMs) were trained and used to predict SI. Root mean squared error (RMSE), mean absolute percent error (MAPE), and a pseudo-R2 accuracy metric were used to compare SI prediction accuracy between the two modeling methods.

RESULTS

RNNs had superior accuracy metrics (full model: RMSE = 3.41, MAPE = 42 %, pseudo-R2 = 26 %) compared with MEMs (full model: RMSE = 3.84, MAPE = 56 %, pseudo-R2 = 16 %). Importantly, RNNs showed significantly more accurate prediction at higher values of SI. Additionally, RNNs predicted, with significantly higher accuracy, the SI scores of participants with depression diagnoses and of participants with higher depression scores at baseline.

CONCLUSION

In this EMA study with a moderately sized sample, RNNs were better able to learn and predict daily SI compared with mixed-effects models. RNNs should be considered as an option for EMA analysis.

Bandgap prediction of non-metallic crystals through machine learning approach

The determination of bandgap is the heart of electronic structure of any material and is a crucial factor for thermoelectric performance of it. Due to large amount to data (features) that are related to bandgap are now a days available, it is possible to make use of machine learning (ML) approach to predict the bandgap of the material. The study commences by selecting the feature through Pearson correlation study between bandgap and various thermoelectric parameters in non-metallic crystals. Among the 42 parameters available in the dataset, the Seebeck coefficient and its corresponding temperatures show high correlation with the bandgap. With these three selected features we have used different ML models like multilinear regression, polynomial regression, random forest regression and support vector regression to predict the bandgap. Amongst the different ML models considered, random forest regression outperforms the other models to predict the bandgap withR2value of 97.55% between actual bandgap and predicted bandgap.

Crystal Phase Ionic Liquids for Energy Applications: Heat Capacity Prediction via a Hybrid Group Contribution Approach

In the selection and design of ionic liquids (ILs) for various applications, including heat transfer fluids, thermal energy storage materials, fuel cells, and solvents for chemical processes, heat capacity is a key thermodynamic property. While several attempts have been made to develop predictive models for the estimation of the heat capacity of ILs in their liquid phase, none so far have been reported for the ILs' solid crystal phase. This is particularly important for applications where ILs will be used for thermal energy storage in the solid phase. For the first time, a model has been developed and used for the prediction of crystal phase heat capacity based on extending and modifying a previously developed hybrid group contribution model (GCM) for liquid phase heat capacity. A comprehensive database of over 5000 data points with 71 unique crystal phase ILs, comprising 42 different cations and 23 different anions, was used for parameterization and testing. This hybrid model takes into account the effect of the anion core, cation core, and subgroups within cations and anions, in addition to the derived indirect parameters that reflect the effects of branching and distribution around the core of the IL. According to the results, the developed GCM can reliably predict the crystal phase heat capacity with a mean absolute percentage error of 6.78%. This study aims to fill this current gap in the literature and to enable the design of ILs for thermal energy storage and other solid phase applications.

GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting

KEY MESSAGE

We propose an "enviromics" prediction model for recommending cultivars based on thematic maps aimed at decision-makers. Parsimonious methods that capture genotype-by-environment interaction (GEI) in multi-environment trials (MET) are important in breeding programs. Understanding the causes and factors of GEI allows the utilization of genotype adaptations in the target population of environments through environmental features and factor-analytic (FA) models. Here, we present a novel predictive breeding approach called GIS-FA, which integrates geographic information systems (GIS) techniques, FA models, partial least squares (PLS) regression, and enviromics to predict phenotypic performance in untested environments. The GIS-FA approach enables: (i) the prediction of the phenotypic performance of tested genotypes in untested environments, (ii) the selection of the best-ranking genotypes based on their overall performance and stability using the FA selection tools, and (iii) the creation of thematic maps showing overall or pairwise performance and stability for decision-making. We exemplify the usage of the GIS-FA approach using two datasets of rice [Oryza sativa (L.)] and soybean [Glycine max (L.) Merr.] in MET spread over tropical areas. In summary, our novel predictive method allows the identification of new breeding scenarios by pinpointing groups of environments where genotypes demonstrate superior predicted performance. It also facilitates and optimizes cultivar recommendations by utilizing thematic maps.

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

KEY MESSAGE

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. Based on long-term variety trials of cereals, winter oilseed rape and grain peas tested across a wide range of environmental conditions in Germany, we quantified long-term breeding progress for NUE and related traits. We estimated the genotypic, environmental and genotype-by-environment interaction variation and correlation between traits and derived heritability coefficients. Nitrogen fertilizer application was considerably reduced between 1995 and 2021 in the range of 5.4% for winter wheat and 28.9% for spring wheat while for spring barley it was increased by 20.9%. Despite the apparent nitrogen reduction for most crops, grain yield (GYLD) and nitrogen accumulation in grain (NYLD) was increased or did not significantly decrease. NUE for GYLD increased significantly for all crops between 12.8% and 35.2% and for NYLD between 8% and 20.7%. We further showed that the genotypic rank of varieties for GYLD and NYLD was about equivalent to the genotypic rank of the corresponding traits of NUE, if all varieties in a trial were treated with the same nitrogen rate. Heritability of nitrogen yield was about the same as that of grain yield, suggesting that nitrogen yield should be considered as an additional criterion for variety testing to increase NUE and reduce negative environmental impact.

Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN

Soybean is an important grain and oil crop worldwide and is rich in nutritional value. Phenotypic morphology plays an important role in the selection and breeding of excellent soybean varieties to achieve high yield. Nowadays, the mainstream manual phenotypic measurement has some problems such as strong subjectivity, high labor intensity and slow speed. To address the problems, a three-dimensional (3D) reconstruction method for soybean plants based on structure from motion (SFM) was proposed. First, the 3D point cloud of a soybean plant was reconstructed from multi-view images obtained by a smartphone based on the SFM algorithm. Second, low-pass filtering, Gaussian filtering, Ordinary Least Square (OLS) plane fitting, and Laplacian smoothing were used in fusion to automatically segment point cloud data, such as individual plants, stems, and leaves. Finally, Eleven morphological traits, such as plant height, minimum bounding box volume per plant, leaf projection area, leaf projection length and width, and leaf tilt information, were accurately and nondestructively measured by the proposed an algorithm for leaf phenotype measurement (LPM). Moreover, Support Vector Machine (SVM), Back Propagation Neural Network (BP), and Back Propagation Neural Network (GRNN) prediction models were established to predict and identify soybean plant varieties. The results indicated that, compared with the manual measurement, the root mean square error (RMSE) of plant height, leaf length, and leaf width were 0.9997, 0.2357, and 0.2666 cm, and the mean absolute percentage error (MAPE) were 2.7013%, 1.4706%, and 1.8669%, and the coefficients of determination (R2) were 0.9775, 0.9785, and 0.9487, respectively. The accuracy of predicting plant species according to the six leaf parameters was highest when using GRNN, reaching 0.9211, and the RMSE was 18.3263. Based on the phenotypic traits of plants, the differences between C3, 47-6 and W82 soybeans were analyzed genetically, and because C3 was an insect-resistant line, the trait parametes (minimum box volume per plant, number of leaves, minimum size of single leaf box, leaf projection area).The results show that the proposed method can effectively extract the 3D phenotypic structure information of soybean plants and leaves without loss which has the potential using ability in other plants with dense leaves.

Factors affecting digital technology access in vocational education

If policies are not thoroughly designed, technology integration may fail. As a result, users' perceptions of technology, especially access to digital technology, are critical for technology integration in education. This study aimed to develop and validate a scale to model factors affecting digital technology access for instructional use in Indonesian vocational schools. The study also reports the structural model of the path analysis and tests of differences based on geographical areas. A scale adapted from prior studies was established, validated, and examined for its validity and reliability. A total of 1355 responses were measurable; partial least squares structural equation modeling (PLS-SEM) and t-test procedures were applied for the data analysis. The findings informed that the scale was valid and reliable. For the structural model, the strongest relationship emerged between motivational access and skills access, while the lowest existed between material access and skills access. However, motivational access has an insignificant effect on instructional use. The t-test results show that geographical areas were significantly different regarding all involved variables.

Improving the models for prognosis of aneurysmal subarachnoid hemorrhage with the neutrophil-to-albumin ratio

Objective

Many peripheral inflammatory markers were reported to be associated with the prognosis of aneurysmal subarachnoid hemorrhage (aSAH). We aimed to identify the most promising inflammatory factor that can improve existing predictive models.

Methods

The study was based on data from a 10 year retrospective cohort study at Sichuan University West China Hospital. We selected the well-known SAFIRE and Subarachnoid Hemorrhage International Trialists' (SAHIT) models as the basic models. We compared the performance of the models after including the inflammatory markers and that of the original models. The developed models were internally and temporally validated.

Results

A total of 3,173 patients were included in this study, divided into the derivation cohort (n = 2,525) and the validation cohort (n = 648). Most inflammatory markers could improve the SAH model for mortality prediction in patients with aSAH, and the neutrophil-to-albumin ratio (NAR) performed best among all the included inflammatory markers. By incorporating NAR, the modified SAFIRE and SAHIT models improved the area under the receiver operator characteristics curve (SAFIRE+NAR vs. SAFIRE: 0.794 vs. 0.778, p = 0.012; SAHIT+NAR vs. SAHIT: 0.831 vs. 0.819, p = 0.016) and categorical net reclassification improvement (SAFIRE+NAR: 0.0727, p = 0.002; SAHIT+NAR: 0.0810, p < 0.001).

Conclusion

This study illustrated that among the inflammatory markers associated with aSAH prognosis, NAR could improve the SAFIRE and SAHIT models for 3 month mortality of aSAH.

Air quality prediction models based on meteorological factors and real-time data of industrial waste gas

With the rapid economic growth, air quality continues to decline. High-intensity pollution emissions and unfavorable weather conditions are the key factors for the formation and development of air heavy pollution processes. Given that research into air quality prediction generally ignore pollutant emission information, in this paper, the random forest supervised learning algorithm is used to construct an air quality prediction model for Zhangdian District with industrial waste gas daily emissions and meteorological factors as variables. The training data include the air quality index (AQI) values, meteorological factors and industrial waste gas daily emission of Zhangdian District from 1st January 2017 to 30th November 2019. The data from 1st to 31th December 2019 is used as the test set to assess the model. The performance of the model is analysed and compared with the backpropagation (BP) neural network, decision tree, and least squares support vector machine (LSSVM) function, which has better overall prediction performance with an RMSE of 22.91 and an MAE of 15.80. Based on meteorological forecasts and expected air quality, a daily emission limit for industrial waste gas can be obtained using model inversion. From 1st to 31th December 2019, if the industrial waste gas daily emission in this area were decreased from 6048.5 million cubic meters of waste gas to 5687.5 million cubic meters, and the daily air quality would be maintained at a good level. This paper deeply explores the dynamic relationship between waste gas daily emissions of industrial enterprises, meteorological factors, and air quality. The meteorological conditions are fully utilized to dynamically adjust the exhaust gas emissions of key polluting enterprises. It not only ensures that the regional air quality is in good condition, but also promotes the in-depth optimization of the procedures of regional industrial enterprises, and reduces the conflict between environmental protection and economic development.

Expanding the Sediment Transport Tracking Possibilities in a River Basin through the Development of a Digital Platform—DNS/SWAT

Simulation of stochastic and variable sediment transport processes within models still poses a big challenge, especially in mountainous areas. Since sediment transport, including erosion and deposition, remains an unceasing problem in many areas, sediment modeling is perceived as a possible solution. This article combines a review of the selected sediment models with a presentation of the effects of several years of research using the DNS digital platform in the Western Carpathians. The review focuses on the main advantages and gaps in selected modeling tools with particular emphasis on one of the most popular: SWAT. The description of the digital platform—DNS is an example of how to answer these gaps by combining subsequent models, methods, and databases using their best features. To accentuate the benefits of such an approach, the effects of combining subsequent models (AdH/PTM) and methods (fingerprinting) on a common digital DNS space are presented, on the example of the Raba River (basin). In this way, both unique possibilities of estimating the amount of contamination carried with sediment particles and their sources, as well as sequencing of sedimentation in the reservoir, taking into account its subsequent zones, were obtained.

The Road Safety Education Program for Adolescents Using Social Media, Proving Increasing Knowledge, Beliefs, Attitudes, Intentions and Behavior

Deaths due to road traffic accidents (RTA) accounted for 2.46% of deaths out of the total deaths in Indonesia. Road safety education (RSE), as an effort to prevent RTA, focuses on increasing knowledge; however, variations of techniques, methods, and media are less used. This study aims to analyze the application of RSE innovations that have been compiled based on interests and needs of adolescents, which are expected to be able to increase knowledge, beliefs, attitudes, intentions, and safe driving behavior. This research used a quasi-experimental approach with a non-randomized pre-test–post-test control group design approach. The Zainafree Program intervention model was conducted for 6 weeks on 362 students who were selected using purposive sampling technique at two schools with the same characteristics. The bivariate analysis was conducted to observe the effect of the model on changes in knowledge, beliefs, attitudes, intentions, and behavior. We analyzed multivariately using GLM-RMA to determine the effectiveness of the model from various confounding factors. The Mann–Whitney test in the intervention and control group demonstrated a significant difference in the average post-test score of two on all dependent variables (p = 0.000). The results of the GLM-RMA test demonstrated the effect of the Zainafree Program on knowledge (p = 0.000; ETA Square = 35.1), beliefs (p = 0.000; ETA Square = 32.0), attitudes (p = 0.000; ETA Square = 50.9), intentions (p = 0.000, ETA Square = 20.7), and behavior (p = 0.000; ETA Square = 28.2), after adjusting for involvement between confounding variables (p = 0.000; ETA Square = 16.2), which demonstrated that the intervention was able to explain 16.2 changes that occur in the scores of five aspects together. The RSE program was proven to be successful in increasing students’ knowledge, beliefs, attitudes, intentions, and behavior compared to those who did not receive the program.

Natural Gas Consumption Forecasting Based on the Variability of External Meteorological Factors Using Machine Learning Algorithms

Natural gas consumption depends on many factors. Some of them, such as weather conditions or historical demand, can be accurately measured. The authors, based on the collected data, performed the modeling of temporary and future natural gas consumption by municipal consumers in one of the medium-sized cities in Poland. For this purpose, the machine learning algorithms, neural networks and two regression algorithms, MLR and Random Forest were used. Several variants of forecasting the demand for natural gas, with different lengths of the forecast horizon are presented and compared in this research. The results obtained using the MLR, Random Forest, and DNN algorithms show that for the tested input data, the best algorithm for predicting the demand for natural gas is RF. The differences in accuracy of prediction between algorithms were not significant. The research shows the differences in the impact of factors that create the demand for natural gas, as well as the accuracy of the prediction for each algorithm used, for each time horizon.

Can harbor seals (Phoca vitulina) discriminate familiar conspecific calls after long periods of separation?

The ability to discriminate between familiar and unfamiliar calls may play a key role in pinnipeds' communication and survival, as in the case of mother-pup interactions. Vocal discrimination abilities have been suggested to be more developed in pinniped species with the highest selective pressure such as the otariids; yet, in some group-living phocids, such as harbor seals (Phoca vitulina), mothers are also able to recognize their pup's voice. Conspecifics' vocal recognition in pups has never been investigated; however, the repeated interaction occurring between pups within the breeding season suggests that long-term vocal discrimination may occur. Here we explored this hypothesis by presenting three rehabilitated seal pups with playbacks of vocalizations from unfamiliar or familiar pups. It is uncommon for seals to come into rehabilitation for a second time in their lifespan, and this study took advantage of these rare cases. A simple visual inspection of the data plots seemed to show more reactions, and of longer duration, in response to familiar as compared to unfamiliar playbacks in two out of three pups. However, statistical analyses revealed no significant difference between the experimental conditions. We also found no significant asymmetry in orientation (left vs. right) towards familiar and unfamiliar sounds. While statistics do not support the hypothesis of an established ability to discriminate familiar vocalizations from unfamiliar ones in harbor seal pups, further investigations with a larger sample size are needed to confirm or refute this hypothesis.

High-throughput field phenotyping reveals genetic variation in photosynthetic traits in durum wheat under drought

Chlorophyll fluorescence (ChlF) is a powerful non-invasive technique for probing photosynthesis. Although proposed as a method for drought tolerance screening, ChlF has not yet been fully adopted in physiological breeding, mainly due to limitations in high-throughput field phenotyping capabilities. The light-induced fluorescence transient (LIFT) sensor has recently been shown to reliably provide active ChlF data for rapid and remote characterisation of plant photosynthetic performance. We used the LIFT sensor to quantify photosynthesis traits across time in a large panel of durum wheat genotypes subjected to a progressive drought in replicated field trials over two growing seasons. The photosynthetic performance was measured at the canopy level by means of the operating efficiency of Photosystem II ( Fq'/Fm' ) and the kinetics of electron transport measured by reoxidation rates ( Fr1' and Fr2' ). Short- and long-term changes in ChlF traits were found in response to soil water availability and due to interactions with weather fluctuations. In mild drought, Fq'/Fm' and Fr2' were little affected, while Fr1' was consistently accelerated in water-limited compared to well-watered plants, increasingly so with rising vapour pressure deficit. This high-throughput approach allowed assessment of the native genetic diversity in ChlF traits while considering the diurnal dynamics of photosynthesis.

Average semivariance yields accurate estimates of the fraction of marker-associated genetic variance and heritability in complex trait analyses

The development of genome-informed methods for identifying quantitative trait loci (QTL) and studying the genetic basis of quantitative variation in natural and experimental populations has been driven by advances in high-throughput genotyping. For many complex traits, the underlying genetic variation is caused by the segregation of one or more ‘large-effect’ loci, in addition to an unknown number of loci with effects below the threshold of statistical detection. The large-effect loci segregating in populations are often necessary but not sufficient for predicting quantitative phenotypes. They are, nevertheless, important enough to warrant deeper study and direct modelling in genomic prediction problems. We explored the accuracy of statistical methods for estimating the fraction of marker-associated genetic variance (p) and heritability (HM2) for large-effect loci underlying complex phenotypes. We found that commonly used statistical methods overestimate p and HM2. The source of the upward bias was traced to inequalities between the expected values of variance components in the numerators and denominators of these parameters. Algebraic solutions for bias-correcting estimates of p and HM2 were found that only depend on the degrees of freedom and are constant for a given study design. We discovered that average semivariance methods, which have heretofore not been used in complex trait analyses, yielded unbiased estimates of p and HM2, in addition to best linear unbiased predictors of the additive and dominance effects of the underlying loci. The cryptic bias problem described here is unrelated to selection bias, although both cause the overestimation of p and HM2. The solutions we described are predicted to more accurately describe the contributions of large-effect loci to the genetic variation underlying complex traits of medical, biological, and agricultural importance.

The contributions of individual genes to the phenotypic variation observed for genetically complex traits has been an ongoing and important challenge in biology, medicine, and agriculture. While many genes have statistically undetectable effects, those with large effects often warrant in-depth study and can be important predictors of complex phenotypes such as disease risk in humans or disease resistance in domesticated plants and animals. The genes identified through associations with genetic markers in complex trait analyses typically account for a fraction of the heritable variation, a genetic parameter we called ‘marker heritability’. We discovered that textbook statistical methods systematically overestimate marker heritability and thus overestimate the contributions of specific genes to the phenotypic variation observed for complex traits in natural and experimental populations. We describe the source of the upward bias, validate our findings through computer simulation, describe methods for bias-correcting estimates of marker heritability, and illustrate their application through empirical examples. The statistical methods we describe supply investigators with more accurate estimates of the contributions of specific genes or networks of interacting genes to the heritable variation observed in complex trait studies.

Regression models for order-of-addition experiments

The purpose of order-of-addition (OofA) experiments is to identify the best order in a sequence of m components in a system. Such experiments may be analyzed by various regression models, the most popular ones being based on pairwise ordering (PWO) factors or on component-position (CP) factors. This paper reviews these models and extensions and proposes a new class of models based on response surface (RS) regression using component position numbers as predictor variables. Using two published examples, it is shown that RS models can be quite competitive. In case of model uncertainty, we advocate the use of model averaging for analysis. The averaging idea leads naturally to a design approach based on a compound optimality criterion assigning weights to each candidate model.

Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review

This paper provides a comprehensive literature review on forest aboveground biomass (AGB) estimation and mapping through high-resolution optical satellite imagery (≤5 m spatial resolution). Based on the literature review, 44 peer-reviewed journal articles were published in 15 years (2004–2019). Twenty-one studies were conducted in Asia, eight in North America and Africa, five in South America, and four in Europe. This review article gives a glance at the published methodologies for AGB prediction modeling and validation. The literature review suggested that, along with the integration of other sensors, QuickBird, WorldView-2, and IKONOS satellite images were most widely used for AGB estimations, with higher estimation accuracies. All studies were grouped into six satellite-derived independent variables, including tree crown, image textures, tree shadow fraction, canopy height, vegetation indices, and multiple variables. Using these satellite-derived independent variables, most of the studies used linear regression (41%), while 30% used linear multiple regression and 18% used non-linear (machine learning) regression, while very few (11%) studies used non-linear (multiple and exponential) regression for estimating AGB. In the context of global forest AGB estimations and monitoring, the advantages, strengths, and limitations were discussed to achieve better accuracy and transparency towards the performance-based payment mechanism of the REDD+ program. Apart from technical limitations, we realized that very few studies talked about real-time monitoring of AGB or quantifying AGB change, a dimension that needs exploration.

partR2: partitioning R2 in generalized linear mixed models

The coefficient of determination R² quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatability R (intra-class correlation) for the variance explained by random effects and thus as a tool for variance decomposition. The R² of a model can be further partitioned into the variance explained by a particular predictor or a combination of predictors using semi-partial (part) R² and structure coefficients, but this is rarely done due to a lack of software implementing these statistics. Here, we introduce partR2, an R package that quantifies part R² for fixed effect predictors based on (generalized) linear mixed-effect model fits. The package iteratively removes predictors of interest from the model and monitors the change in the variance of the linear predictor. The difference to the full model gives a measure of the amount of variance explained uniquely by a particular predictor or a set of predictors. partR2 also estimates structure coefficients as the correlation between a predictor and fitted values, which provide an estimate of the total contribution of a fixed effect to the overall prediction, independent of other predictors. Structure coefficients can be converted to the total variance explained by a predictor, here called ‘inclusive’ R², as the square of the structure coefficients times total R². Furthermore, the package reports beta weights (standardized regression coefficients). Finally, partR2 implements parametric bootstrapping to quantify confidence intervals for each estimate. We illustrate the use of partR2 with real example datasets for Gaussian and binomial GLMMs and discuss interactions, which pose a specific challenge for partitioning the explained variance among predictors.

Projecting results of zoned multi-environment trials to new locations using environmental covariates with random coefficient models: accuracy and precision

We propose the utilisation of environmental covariates in random coefficient models to predict the genotype performances in new locations. Multi-environment trials (MET) are conducted to assess the performance of a set of genotypes in a target population of environments. From a grower's perspective, MET results must provide high accuracy and precision for predictions of genotype performance in new locations, i.e. the grower's locations, which hardly ever coincide with the locations at which the trials were conducted. Linear mixed modelling can provide predictions for new locations. Moreover, the precision of the predictions is of primary concern and should be assessed. Besides, the precision can be improved when auxiliary information is available to characterize the targeted locations. Thus, in this study, we demonstrate the benefit of using environmental information (covariates) for predicting genotype performance in some new locations for Swedish winter wheat official trials. Swedish MET locations can be stratified into zones, allowing borrowing information between zones when best linear unbiased prediction (BLUP) is used. To account for correlations between zones, as well as for intercepts and slopes for the regression on covariates, we fitted random coefficient (RC) models. The results showed that the RC model with appropriate covariate scaling and model for covariate terms improved the precision of predictions of genotypic performance for new locations. The prediction accuracy of the RC model was competitive compared to the model without covariates. The RC model reduced the standard errors of predictions for individual genotypes and standard errors of predictions of genotype differences in new locations by 30-38% and 12-40%, respectively.

Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020

The coronavirus COVID-19 has recently started to spread rapidly in Malaysia. The number of total infected cases has increased to 3662 on 05 April 2020, leading to the country being placed under lockdown. As the main public concern is whether the current situation will continue for the next few months, this study aims to predict the epidemic peak using the Susceptible-Exposed-Infectious-Recovered (SEIR) model, with incorporation of the mortality cases. The infection rate was estimated using the Genetic Algorithm (GA), while the Adaptive Neuro-Fuzzy Inference System (ANFIS) model was used to provide short-time forecasting of the number of infected cases. The results show that the estimated infection rate is 0.228 ± 0.013, while the basic reproductive number is 2.28 ± 0.13. The epidemic peak of COVID-19 in Malaysia could be reached on 26 July 2020, with an uncertain period of 30 days (12 July-11 August). Possible interventions by the government to reduce the infection rate by 25% over two or three months would delay the epidemic peak by 30 and 46 days, respectively. The forecasting results using the ANFIS model show a low Normalized Root Mean Square Error (NRMSE) of 0.041; a low Mean Absolute Percentage Error (MAPE) of 2.45%; and a high coefficient of determination (R2) of 0.9964. The results also show that an intervention has a great effect on delaying the epidemic peak and a longer intervention period would reduce the epidemic size at the peak. The study provides important information for public health providers and the government to control the COVID-19 epidemic.

Decoupling of impact factors reveals the response of German winter wheat yields to climatic changes

Yield development of agricultural crops over time is not merely the result of genetic and agronomic factors, but also the outcome of a complex interaction between climatic and site-specific soil conditions. However, the influence of past climatic changes on yield trends remains unclear, particularly under consideration of different soil conditions. In this study, we determine the effects of single agrometeorological factors on the evolution of German winter wheat yields between 1958 and 2015 from 298 published nitrogen (N)-fertilization experiments. For this purpose, we separate climatic from genetic and agronomic yield effects using linear mixed effect models and estimate the climatic influence based on a coefficient of determination for these models. We found earlier occurrence of wheat growth stages, and shortened development phases except for the phase of stem elongation. Agrometeorological factors are defined as climate covariates related to the growth of winter wheat. Our results indicate a general and strong effect of agroclimatic changes on yield development, in particular due to increasing mean temperatures and heat stress events during the grain-filling period. Except for heat stress days with more than 31°C, yields at sites with higher yield potential were less prone to adverse weather effects than at sites with lower yield potential. Our data furthermore reveal that a potential yield levelling, as found for many West-European countries, predominantly occurred at sites with relatively low yield potential and about one decade earlier (mid-1980s) compared to averaged yield data for the whole of Germany. Interestingly, effects related to high precipitation events were less relevant than temperature-related effects and became relevant particularly during the vegetative growth phase. Overall, this study emphasizes the sensitivity of yield productivity to past climatic conditions, under consideration of regional differences, and underlines the necessity of finding adaptation strategies for food production under ongoing and expected climate change.

Heritability in Plant Breeding on a Genotype-Difference Basis

In plant breeding, heritability is often calculated (i) as a measure of precision of trials and/or (ii) to compute the response to selection. It is usually estimated on an entry-mean basis, since the phenotype is usually an aggregated value, as genotypes are replicated in trials, which stands in contrast with animal breeding and human genetics. When this was first proposed, assumptions such as balanced data and independent genotypic effects were made that are often violated in modern plant breeding trials/analyses. Due to this, multiple alternative methods have been proposed, aiming to generalize heritability on an entry-mean basis. In this study, we propose an extension of the concept for heritability on an entry-mean to an entry-difference basis, which allows for more detailed insight and is more meaningful in the context of selection in plant breeding, because the correlation among entry means can be accounted for. We show that under certain circumstances our method reduces to other popular generalized methods for heritability estimation on an entry-mean basis. The approach is exemplified via four examples that show different levels of complexity, where we compare six methods for heritability estimation on an entry-mean basis to our approach (example codes: https://github.com/PaulSchmidtGit/Heritability). Results suggest that heritability on an entry-difference basis is a well-suited alternative for obtaining an overall heritability estimate, and in addition provides one heritability per genotype as well as one per difference between genotypes.