Untested Environments Research Articles

Modeling the impact of resource allocation decisions on genomic prediction using maize multi‐environment data

AbstractIn a hybrid maize (Zea mays L.) breeding program that utilizes genomic selection, resource allocation used in phenotypic data acquisition must be balanced between population size, number of environments, and the number of testers used for generating hybrids. Plant breeders evaluate newly developed inbred lines using multi‐environment trials to account for genotype‐by‐environment interaction effects. The replication of hybrids across environments in these trials impacts the training data accuracy for developing genomic prediction models. This study examined the impact of resource allocation scenarios on genomic prediction accuracy using a multi‐environment trial dataset generated using inbred lines crossed to multiple testers. A total of 369 Stiff Stalk double haploid lines from a synthetic mapping population were testcrossed to three non‐Stiff Stalk inbred lines as testers, PHZ51, PHK76, and PHP02, and evaluated across 34 environments by the Genomes to Fields Initiative in 2020 and 2021. Resource allocation scenarios significantly impacted site‐specific genomic prediction accuracy for unobserved hybrids in unobserved environments. A training set with three to five environments that had the highest quality data produced similar prediction accuracy as data from 10 random environments for both observed and unobserved hybrids, indicating that strong prediction models can be built with a limited set of environments for both grain yield and plant height. We found that resource‐efficient prediction models that use data from one tester and three to five environments can effectively conduct selection of untested hybrids and in untested environments. Public research programs are often limited in testing capacity, and this study provides support for genomic selection in resource‐limited breeding programs.

Statistical sampling of missing environmental variables improves biophysical genomic prediction in wheat.

The integration of genomic prediction with crop growth models enabled the estimation of missing environmental variables which improved the prediction accuracy of grain yield. Since the invention of whole-genome prediction (WGP) more than two decades ago, breeding programmes have established extensive reference populations that are cultivated under diverse environmental conditions. The introduction of the CGM-WGP model, which integrates crop growth models (CGM) with WGP, has expanded the applications of WGP to the prediction of unphenotyped traits in untested environments, including future climates. However, CGMs require multiple seasonal environmental records, unlike WGP, which makes CGM-WGP less accurate when applied to historical reference populations that lack crucial environmental inputs. Here, we investigated the ability of CGM-WGP to approximate missing environmental variables to improve prediction accuracy. Two environmental variables in a wheat CGM, initial soil water content (InitlSoilWCont) and initial nitrate profile, were sampled from different normal distributions separately or jointly in each iteration within the CGM-WGP algorithm. Our results showed that sampling InitlSoilWCont alone gave the best results and improved the prediction accuracy of grain number by 0.07, yield by 0.06 and protein content by 0.03. When using the sampled InitlSoilWCont values as an input for the traditional CGM, the average narrow-sense heritability of the genotype-specific parameters (GSPs) improved by 0.05, with GNSlope, PreAnthRes, and VernSen showing the greatest improvements. Moreover, the root mean square of errors for grain number and yield was reduced by about 7% for CGM and 31% for CGM-WGP when using the sampled InitlSoilWCont values. Our results demonstrate the advantage of sampling missing environmental variables in CGM-WGP to improve prediction accuracy and increase the size of the reference population by enabling the utilisation of historical data that are missing environmental records.

Large‐scale breeding applications of unoccupied aircraft systems enabled genomic prediction

AbstractBreeding for improved, reliable cultivars despite growing environmental irregularity can be challenging. Unoccupied aircraft systems (UAS) are a popular high‐throughput phenotyping technology that has been shown to help interpret the mechanisms associated with crop productivity and environmental response, creating potential for improved breeding strategies. Spectral reflectance indices (SRIs), encompassing both vegetation and water indices like normalized difference vegetation index (NDVI), normalized difference red‐edge index, and normalized water index, were employed to assess 4094 winter wheat genotypes across 11,593 breeding plots at Washington State University from 2019 through 2022. SRIs were then used with genomic data in univariate models as covariates and multivariate models as secondary response variables for predictions of grain yield. The prediction accuracy of models was evaluated using a leave‐one‐year‐out validation strategy against a base genomic prediction method. Including SRI data as fixed effects in univariate genomic prediction models can improve prediction accuracy over the control but is unreliable across years. When used in multivariate models, SRIs improve prediction performance across years but require high‐performance computational resources that could limit feasibility. In univariate models, when test year NDVI data were available and used to calculate breeding values, prediction performance was at least 16% better than the control, ranging in prediction accuracy from 0.54 in 2019 to 0.93 in 2020. This study highlights the limited reliability of SRI use in genomic prediction of untested environments and locations. However, a significant application for the technology can be found in early‐season UAS data collection to aid accurate predictions in late season, a helpful tool in tight turnaround times commonly experienced in winter crop breeding programs.

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

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.

Genotype by environment model predictive ability in Miscanthus

AbstractMiscanthus is a genus of perennial grasses native to East Asia that shows promise as a biofuel energy source. Breeding efforts for increasing biofuel capability in this genus have focused on two species, namely M. sinensis (Msi) and M. sacchariflorus (Msa). For these efforts to succeed, it is critical that both Msi and Msa, as well as their interspecific crosses, can be grown at a wide range of latitudes. Therefore, the purpose of this study was to investigate how well existing data from Msi and Msa trials grown at locations throughout the northern hemisphere can train state‐of‐the‐art genomic selection (GS) models to predict genomic estimated breeding values (GEBVs) of dry yield for untested Msi and Msa accessions in untested environments. We found that accounting for genotype by environment interaction in the GS model did not notably improve predictive ability. Additionally, we observed that locations at lower latitudes showed higher predictive ability relative to locations at higher latitudes. These results suggest that it is crucial to increase the number of trial locations at higher latitude locations to investigate the source of this correlation. This will make it possible to train GS models using data from environments that are similar to growing conditions at the locations targeted by Msi and Msa breeders. Such an increase of trial locations in target environments could pave the way toward advancing breeding efforts for overwintering ability in Msi and Msa, and ultimately support the potential of Miscanthus as a biofuel crop.

Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments

It is of paramount importance in plant breeding to have methods dealing with large numbers of predictor variables and few sample observations, as well as efficient methods for dealing with high correlation in predictors and measured traits. This paper explores in terms of prediction performance the partial least squares (PLS) method under single-trait (ST) and multi-trait (MT) prediction of potato traits. The first prediction was for tested lines in tested environments under a five-fold cross-validation (5FCV) strategy and the second prediction was for tested lines in untested environments (herein denoted as leave one environment out cross validation, LOEO). There was a good performance in terms of predictions (with accuracy mostly > 0.5 for Pearson’s correlation) the accuracy of 5FCV was better than LOEO. Hence, we have empirical evidence that the ST and MT PLS framework is a very valuable tool for prediction in the context of potato breeding data.

Genotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations

AbstractGenotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype‐specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega‐environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single‐step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank‐change) GEI with other sowing periods. Climatic variables related to temperature, radiation, wind, and precipitation affecting GY were identified and differed in each population. RRMs with selected climatic covariates improved the predictive ability in both tested and untested years and environments. Prediction using the complete dataset performed better than predicting within each ME.

Multi-environment Genomic Selection in Rice Elite Breeding Lines

BackgroundAssessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic prediction models that integrate information from multi-environment trials and environmental covariates can be efficient tools in this context. The objective of this study was to assess the predictive ability of different genomic prediction models to optimize the use of multi-environment information. We used 111 elite breeding lines representing the diversity of the international rice research institute breeding program for irrigated ecosystems. The lines were evaluated for three traits (days to flowering, plant height, and grain yield) in 15 environments in Asia and Africa and genotyped with 882 SNP markers. We evaluated the efficiency of genomic prediction to predict untested environments using seven multi-environment models and three cross-validation scenarios.ResultsThe elite lines were found to belong to the indica group and more specifically the indica-1B subgroup which gathered improved material originating from the Green Revolution. Phenotypic correlations between environments were high for days to flowering and plant height (33% and 54% of pairwise correlation greater than 0.5) but low for grain yield (lower than 0.2 in most cases). Clustering analyses based on environmental covariates separated Asia’s and Africa's environments into different clusters or subclusters. The predictive abilities ranged from 0.06 to 0.79 for days to flowering, 0.25–0.88 for plant height, and − 0.29–0.62 for grain yield. We found that models integrating genotype-by-environment interaction effects did not perform significantly better than models integrating only main effects (genotypes and environment or environmental covariates). The different cross-validation scenarios showed that, in most cases, the use of all available environments gave better results than a subset.ConclusionMulti-environment genomic prediction models with main effects were sufficient for accurate phenotypic prediction of elite lines in targeted environments. These results will help refine the testing strategy to update the genomic prediction models to improve predictive ability.

Pedigree‐management‐flight interaction for temporal phenotype analysis and temporal phenomic prediction

AbstractUnoccupied aerial systems (UAS, aka drones) provide high dimensional temporal phenotype data for predictive plant breeding and genetic dissection. Methods to assess temporal phenotype data are an emerging need to predict temporal breeding values of genotypes. Here a novel interaction design was developed and evaluated to include drone flight dates as a component into the mixed model; allowing the temporal changes of drone image derived traits of maize hybrids across different flight dates as well as different management conditions to be monitored. Across 2017 and 2019 respectively, 228 and 100 maize hybrids were grown under two types of management (optimal and late plantings). Seven drone surveys were conducted over each management in 2017 while five drone surveys were conducted over each management in 2019. Temporal plant height (canopy height measurements, CHM) and normalized green‐red difference index (NGRDI) were extracted from each drone survey and used as phenotype data to evaluate the interaction design. Day of flight effects explained the highest amount of total variation for grain yield in the interaction model, meaning the majority of phenotypic variation of CHM and NGRDI occurred across growth with a unique temporal trajectory in each management system. Temporal repeatability values remained higher than 0.5 for CHM and NGRDI in each year. Temporal CHM and NGRDI breeding values of maize hybrids were combined in ridge and lasso regression prediction models. Yield prediction ability of untested genotypes in untested environments were predicted higher by using pedigree × management × flight (PMF) and pedigree× management (PM) interaction results (∼0.34 and 0.52 in 2017 and 2019). Combining environment specific phenomic data (PMF plus PM) gave a larger improvement in yield prediction when the tested and untested environments were less similar. Overall, combined temporal phenomic data could moderately predict grain yield under the most challenging predictive breeding scenario, untested and unrelated genotypes in untested environments.

Using genomic prediction with crop growth models enables the prediction of associated traits in wheat.

Crop growth models (CGM) can predict the performance of a cultivar in untested environments by sampling genotype-specific parameters. As they cannot predict the performance of new cultivars, it has been proposed to integrate CGMs with whole genome prediction (WGP) to combine the benefits of both models. Here, we used a CGM-WGP model to predict the performance of new wheat (Triticum aestivum) genotypes. The CGM was designed to predict phenology, nitrogen, and biomass traits. The CGM-WGP model simulated more heritable GSPs compared with the CGM and gave smaller errors for the observed phenotypes. The WGP model performed better when predicting yield, grain number, and grain protein content, but showed comparable performance to the CGM-WGP model for heading and physiological maturity dates. However, the CGM-WGP model was able to predict unobserved traits (for which there were no phenotypic records in the reference population). The CGM-WGP model also showed superior performance when predicting unrelated individuals that clustered separately from the reference population. Our results demonstrate new advantages for CGM-WGP modelling and suggest future efforts should focus on calibrating CGM-WGP models using high-throughput phenotypic measures that are cheaper and less laborious to collect.

Accurate predictions of barley phenotypes using genomewide markers and environmental covariates

AbstractPredicting the performance of new plant genotypes under new environmental conditions could accelerate the development of locally adapted and climate resilient cultivars. Enabling these predictions may rely on extending the genomewide prediction framework to include environmental covariates (EC), but such models have generally been tested under limited, less breeding‐realistic contexts. Using a barley (Hordeum vulgare L.) multi‐environment dataset, our objectives were to compare multi‐environment prediction models and scenarios that target genotypes from different breeding generations, use different levels and timescales of ECs, and are applied to different agronomic and quality traits. When predicting the phenotypes of previously tested genotypes in untested environments, models that included the interaction of genomewide markers and pre‐selected in‐season ECs resulted in more accurate predictions (rMG or rMP) within (rMG = 0.56–0.94) and across (rMP = 0.63–0.87) environments; similar accuracy was achieved within (rMP = 0.46–0.89) and across (rMP = 0.87–0.95) locations when using only ECs from realistically available historical climate data. Shifting the prediction target to a distinct, untested offspring population slightly reduced model performance within environments or locations, but rMP across environments (rMP = 0.60–0.86) or locations (rMP = 0.87–0.94) remained very high. Though we achieved moderately high rMP for most traits in the challenging scenario of predicting the offspring population in holdout environments, the similarity between training and target environments, like that between populations, will be a limiting factor for enabling accurate predictions of new genotypes under new growing conditions.

A field experiment characterizing variable detection rates during plant surveys.

Surveys aimed at finding threatened and invasive species can be challenging due to individual rarity and low and variable individual detection rates. Detection rate in plant surveys typically varies due to differences among observers, among the individual plants being surveyed (targets), and across background environments. Interactions among these 3 components may occur but are rarely estimated due to limited replication and control during data collection. We conducted an experiment to investigate sources of variation in detection of 2 Pilosella species that are invasive and sparsely distributed in the Alpine National Park, Australia. These species are superficially similar in appearance to other yellow‐flowered plants occurring in this landscape. We controlled the presence and color of flowers on target Pilosella plants and controlled their placement in plots, which were selected for their variation in cover of non‐target yellow flowers and dominant vegetation type. Observers mimicked Pilosella surveys in the plots and reported 1 categorical and 4 quantitative indicators of their survey experience level. We applied survival analysis to detection data to model the influence of both controlled and uncontrolled variables on detection rate. Orange‐ and yellow‐flowering Pilosella in grass‐ and heath‐dominated vegetation were detected at a higher rate than nonflowering Pilosella. However, this detection gain diminished as the cover of other co‐occurring yellow‐flowering species increased. Recent experience with Pilosella surveys improved detection rate. Detection experiments are a direct and accessible means of understanding detection processes and interpreting survey data for threatened and invasive species. Our detection findings have been used for survey planning and can inform progress toward eradication. Interaction of target and background characteristics determined detection rate, which enhanced predictions in the Pilosella eradication program and demonstrated the difficulty of transferring detection findings into untested environments.

Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data.

Technology advances have made possible the collection of a wealth of genomic, environmental, and phenotypic data for use in plant breeding. Incorporation of environmental data into environment-specific genomic prediction is hindered in part because of inherently high data dimensionality. Computationally efficient approaches to combining genomic and environmental information may facilitate extension of genomic prediction models to new environments and germplasm, and better understanding of genotype-by-environment (G×E) interactions. Using genomic, yield trial, and environmental data on 1,918 unique hybrids evaluated in 59 environments from the maize Genomes to Fields project, we determined that a set of 10,153 SNP dominance coefficients and a 5-day temporal window size for summarizing environmental variables were optimal for genomic prediction using only genetic and environmental main effects. Adding marker-by-environment variable interactions required dimension reduction, and we found that reducing dimensionality of the genetic data while keeping the full set of environmental covariates was best for environment-specific genomic prediction of grain yield, leading to an increase in prediction ability of 2.7% to achieve a prediction ability of 80% across environments when data were masked at random. We then measured how prediction ability within environments was affected under stratified training-testing sets to approximate scenarios commonly encountered by plant breeders, finding that incorporation of marker-by-environment effects improved prediction ability in cases where training and test sets shared environments, but did not improve prediction in new untested environments. The environmental similarity between training and testing sets had a greater impact on the efficacy of prediction than genetic similarity between training and test sets.

Can we harness digital technologies and physiology to hasten genetic gain in US maize breeding?

Plant physiology can offer invaluable insights to accelerate genetic gain. However, translating physiological understanding into breeding decisions has been an ongoing and complex endeavor. Here we demonstrate an approach to leverage physiology and genomics to hasten crop improvement. A half-diallel maize (Zea mays) experiment resulting from crossing 9 elite inbreds was conducted at 17 locations in the USA corn belt and 6 locations at managed stress environments between 2017 and 2019 covering a range of water environments from 377 to 760 mm of evapotranspiration and family mean yields from 542 to 1,874 g m-2. Results from analyses of 35 families and 2,367 hybrids using crop growth models linked to whole-genome prediction (CGM-WGP) demonstrated that CGM-WGP offered a predictive accuracy advantage compared to BayesA for untested genotypes evaluated in untested environments (r = 0.43 versus r = 0.27). In contrast to WGP, CGMs can deal effectively with time-dependent interactions between a physiological process and the environment. To facilitate the selection/identification of traits for modeling yield, an algorithmic approach was introduced. The method was able to identify 4 out of 12 candidate traits known to explain yield variation in maize. The estimation of allelic and physiological values for each genotype using the CGM created in silico phenotypes (e.g. root elongation) and physiological hypotheses that could be tested within the breeding program in an iterative manner. Overall, the approach and results suggest a promising future to fully harness digital technologies, gap analysis, and physiological knowledge to hasten genetic gain by improving predictive skill and definition of breeding goals.

Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat (Triticum aestivum L.).

Genomic selection (GS) has the potential to improve the selection gain for complex traits in crop breeding programs from resource-poor countries. The GS model performance in multi-environment (ME) trials was assessed for 141 advanced breeding lines under four field environments via cross-predictions. We compared prediction accuracy (PA) of two GS models with or without accounting for the environmental variation on four quantitative traits of significant importance, i.e., grain yield (GRYLD), thousand-grain weight, days to heading, and days to maturity, under North and Central Indian conditions. For each trait, we generated PA using the following two different ME cross-validation (CV) schemes representing actual breeding scenarios: (1) predicting untested lines in tested environments through the ME model (ME_CV1) and (2) predicting tested lines in untested environments through the ME model (ME_CV2). The ME predictions were compared with the baseline single-environment (SE) GS model (SE_CV1) representing a breeding scenario, where relationships and interactions are not leveraged across environments. Our results suggested that the ME models provide a clear advantage over SE models in terms of robust trait predictions. Both ME models provided 2–3 times higher prediction accuracies for all four traits across the four tested environments, highlighting the importance of accounting environmental variance in GS models. While the improvement in PA from SE to ME models was significant, the CV1 and CV2 schemes did not show any clear differences within ME, indicating the ME model was able to predict the untested environments and lines equally well. Overall, our results provide an important insight into the impact of environmental variation on GS in smaller breeding programs where these programs can potentially increase the rate of genetic gain by leveraging the ME wheat breeding trials.

Multiple-trait, random regression, and compound symmetry models for analyzing multi-environment trials in maize breeding.

An efficient and informative statistical method to analyze genotype-by-environment interaction (GxE) is needed in maize breeding programs. Thus, the objective of this study was to compare the effectiveness of multiple-trait models (MTM), random regression models (RRM), and compound symmetry models (CSM) in the analysis of multi-environment trials (MET) in maize breeding. For this, a data set with 84 maize hybrids evaluated across four environments for the trait grain yield (GY) was used. Variance components were estimated by restricted maximum likelihood (REML), and genetic values were predicted by best linear unbiased prediction (BLUP). The best fit MTM, RRM, and CSM were identified by the Akaike information criterion (AIC), and the significance of the genetic effects were tested using the likelihood ratio test (LRT). Genetic gains were predicted considering four selection intensities (5, 10, 15, and 20 hybrids). The selected MTM, RRM, and CSM models fit heterogeneous residuals. Moreover, for RRM the genetic effects were modeled by Legendre polynomials of order two. Genetic variability between maize hybrids were assessed for GY. In general, estimates of broad-sense heritability, selective accuracy, and predicted selection gains were slightly higher when obtained using MTM and RRM. Thus, considering the criterion of parsimony and the possibility of predicting genetic values of hybrids for untested environments, RRM is a preferential approach for analyzing MET in maize breeding.

Offshore Cone Penetration Test and Its Application in Full Water-Depth Geological Surveys

Characterizing seabed sediments is one of the most important processes in marine engineering surveys. Marine sediments, which are characterized by low strength, typically consist of soft fine-grained sediments (either clay, or in some regions, carbonate muds and silts) that have been deposited relatively slowly. Sampling these soft sediments is extremely difficult, and in situ testing is preferable. Cone penetration test (CPT) is widely used due to its prominent advantages in accurately determining the physical-mechanical properties and types of seabed sediments. Due to the world’s escalating demand for energy, combined with the continued depletion of oil and gas reserves in shallow waters, offshore engineering has moved beyond the immediate continental shelf into deeper waters and untested environments, thus posing a challenge to the operating water depth of CPT. The paper presents an overview of recent developments and applications in CPT technology associated with different water depths, including the cone penetration equipment suitable for the beach/shallow sea, the areas with normal water depth, and the extremely deep sea. Furthermore, the nature of CPT design has changed radically partly due to the types of engineering and partly because of the specific nature of the seabed sediments. Thus, the CPTs with multiple functions are also reviewed.

Genome-based trait prediction in multi-\u2009environment breeding trials in groundnut

Key messageComparative assessment identified naïve interaction model, and naïve and informed interaction GS models suitable for achieving higher prediction accuracy in groundnut keeping in mind the high genotype × environment interaction for complex traits.Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut.A training population was constituted with 340 elite lines followed by genotyping with 58 K ‘Axiom_Arachis’ SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400–0.600) were obtained for pods/plant, shelling %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut.

Cyber Threat Actors for the Factory of the Future

The increasing degree of connectivity in factory of the future (FoF) environments, with systems that were never designed for a networked environment in terms of their technical security nature, is accompanied by a number of security risks that must be considered. This leads to the necessity of relying on risk assessment-based approaches to reach a sufficiently mature cyber security management level. However, the lack of common definitions of cyber threat actors (CTA) poses challenges in untested environments such as the FoF. This paper analyses policy papers and reports from expert organizations to identify common definitions of CTAs. A significant consensus exists only on two common CTAs, while other CTAs are often either ignored or overestimated in their importance. The identified motivations of CTAs are contrasted with the specific characteristics of FoF environments to determine the most likely CTAs targeting FoF environments. Special emphasis is given to corporate competitors, as FoF environments probably provide better opportunities than ever for industrial espionage if they are not sufficiently secured. In this context, the study aims to draw attention to the research gaps in this area.

Genotype-by-environment interaction in coast redwood outside natural distribution - search for environmental cues

BackgroundEffective matching of genotypes and environments is required for the species to reach optimal productivity and act effectively for carbon sequestration. A common garden experiment across five different environments was undertaken to assess genotype x environment interaction (GxE) of coast redwood in order to understand the performance of genotypes across environments.ResultsThe quantitative genetic analysis discovered no GxE between investigated environments for diameter at breast height (DBH). However, no genetic component was detected at one environment possibly due to stressful conditions. The implementation of universal response function allowed for the identification of important environmental factors affecting species productivity. Additionally, this approach enabled us to predict the performance of species across the New Zealand environmental conditions.ConclusionsIn combination with quantitative genetic analysis which identified genetically superior material, the URF model can directly identify the optimal geographical regions to maximize productivity. However, the finding of ideally uncorrelated climatic variables for species with narrow ecological amplitude is rather challenging, which complicates construction of informative URF model. This, along with a small number of tested environments, tended to overfit a prediction model which resulted in extreme predictions in untested environments.