Data Augmentation Enhances Plant-Genomic-Enabled Predictions.

Genomic selection (GS) is revolutionizing plant breeding. However, its practical implementation is still challenging, since there are many factors that affect its accuracy. For this reason, this research explores data augmentation with the goal of improving its accuracy. Deep neural networks with data augmentation (DA) generate synthetic data from the original training set to increase the training set and to improve the prediction performance of any statistical or machine learning algorithm. There is much empirical evidence of their success in many computer vision applications. Due to this, DA was explored in the context of GS using 14 real datasets. We found empirical evidence that DA is a powerful tool to improve the prediction accuracy, since we improved the prediction accuracy of the top lines in the 14 datasets under study. On average, across datasets and traits, the gain in prediction performance of the DA approach regarding the Conventional method in the top 20% of lines in the testing set was 108.4% in terms of the NRMSE and 107.4% in terms of the MAAPE, but a worse performance was observed on the whole testing set. We encourage more empirical evaluations to support our findings.

Deep learning methods improve genomic prediction of wheat breeding.

In the field of plant breeding, various machine learning models have been developed and studied to evaluate the genomic prediction (GP) accuracy of unseen phenotypes. Deep learning has shown promise. However, most studies on deep learning in plant breeding have been limited to small datasets, and only a few have explored its application in moderate-sized datasets. In this study, we aimed to address this limitation by utilizing a moderately large dataset. We examined the performance of a deep learning (DL) model and compared it with the widely used and powerful best linear unbiased prediction (GBLUP) model. The goal was to assess the GP accuracy in the context of a five-fold cross-validation strategy and when predicting complete environments using the DL model. The results revealed the DL model outperformed the GBLUP model in terms of GP accuracy for two out of the five included traits in the five-fold cross-validation strategy, with similar results in the other traits. This indicates the superiority of the DL model in predicting these specific traits. Furthermore, when predicting complete environments using the leave-one-environment-out (LOEO) approach, the DL model demonstrated competitive performance. It is worth noting that the DL model employed in this study extends a previously proposed multi-modal DL model, which had been primarily applied to image data but with small datasets. By utilizing a moderately large dataset, we were able to evaluate the performance and potential of the DL model in a context with more information and challenging scenario in plant breeding.

Multivariate Genomic Hybrid Prediction with Kernels and Parental Information.

Genomic selection (GS) plays a pivotal role in hybrid prediction. It can enhance the selection of parental lines, accurately predict hybrid performance, and harness hybrid vigor. Likewise, it can optimize breeding strategies by reducing field trial requirements, expediting hybrid development, facilitating targeted trait improvement, and enhancing adaptability to diverse environments. Leveraging genomic information empowers breeders to make informed decisions and significantly improve the efficiency and success rate of hybrid breeding programs. In order to improve the genomic ability performance, we explored the incorporation of parental phenotypic information as covariates under a multi-trait framework. Approach 1, referred to as Pmean, directly utilized parental phenotypic information without any preprocessing. While approach 2, denoted as BV, replaced the direct use of phenotypic values of both parents with their respective breeding values. While an improvement in prediction performance was observed in both approaches, with a minimum 4.24% reduction in the normalized root mean square error (NRMSE), the direct incorporation of parental phenotypic information in the Pmean approach slightly outperformed the BV approach. We also compared these two approaches using linear and nonlinear kernels, but no relevant gain was observed. Finally, our results increase empirical evidence confirming that the integration of parental phenotypic information helps increase the prediction performance of hybrids.

Do feature selection methods for selecting environmental covariables enhance genomic prediction accuracy?

Genomic selection (GS) is transforming plant and animal breeding, but its practical implementation for complex traits and multi-environmental trials remains challenging. To address this issue, this study investigates the integration of environmental information with genotypic information in GS. The study proposes the use of two feature selection methods (Pearson's correlation and Boruta) for the integration of environmental information. Results indicate that the simple incorporation of environmental covariates may increase or decrease prediction accuracy depending on the case. However, optimal incorporation of environmental covariates using feature selection significantly improves prediction accuracy in four out of six datasets between 14.25% and 218.71% under a leave one environment out cross validation scenario in terms of Normalized Root Mean Squared Error, but not relevant gain was observed in terms of Pearson´s correlation. In two datasets where environmental covariates are unrelated to the response variable, feature selection is unable to enhance prediction accuracy. Therefore, the study provides empirical evidence supporting the use of feature selection to improve the prediction power of GS.

Effect of a native bacterial consortium on growth, yield, and grain quality of durum wheat (Triticum turgidum L. subsp. durum) under different nitrogen rates in the Yaqui Valley, Mexico.

A field experiment was carried out to quantify the effect of a native bacterial inoculant on the growth, yield, and quality of the wheat crop, under different nitrogen (N) fertilizer rates in two agricultural seasons. Wheat was sown under field conditions at the Experimental Technology Transfer Center (CETT-910), as a representative wheat crop area from the Yaqui Valley, Sonora México. The experiment was conducted using different doses of nitrogen (0, 130, and 250 kg N ha-1) and a bacterial consortium (BC) (Bacillus subtilis TSO9, B. cabrialesii subsp. tritici TSO2T, B. subtilis TSO22, B. paralicheniformis TRQ65, and Priestia megaterium TRQ8). Results showed that the agricultural season affected chlorophyll content, spike size, grains per spike, protein content, and whole meal yellowness. The highest chlorophyll and Normalized Difference Vegetation Index (NDVI) values, as well as lower canopy temperature values, were observed in treatments under the application of 130 and 250 kg N ha-1 (the conventional Nitrogen dose). Wheat quality parameters such as yellow berry, protein content, Sodium dodecyl sulfate (SDS)-Sedimentation, and whole meal yellowness were affected by the N dose. Moreover, the application of the native bacterial consortium, under 130 kg N ha-1, resulted in a higher spike length and grain number per spike, which led to a higher yield (+1.0 ton ha-1 vs. un-inoculated treatment), without compromising the quality of grains. In conclusion, the use of this bacterial consortium has the potential to significantly enhance wheat growth, yield, and quality while reducing the nitrogen fertilizer application, thereby offering a promising agro-biotechnological alternative for improving wheat production.

Efficacy of plant breeding using genomic information.

Genomic selection (GS) proposed by Meuwissen et al. more than 20 years ago, is revolutionizing plant and animal breeding. Although GS has been widely accepted and applied to plant and animal breeding, there are many factors affecting its efficacy. We studied 14 real datasets to respond to the practical question of whether the accuracy of genomic prediction increases when considering genomic as compared with not using genomic. We found across traits, environments, datasets, and metrics, that the average gain in prediction accuracy when genomic information is considered was 26.31%, while only in terms of Pearson's correlation the gain was of 46.1%, while only in terms of normalized root mean squared error the gain was of 6.6%. If the quality of the makers and relatedness of the individuals increase, major gains in prediction accuracy can be obtained, but if these two factors decrease, a lower increase is possible. Finally, our findings reinforce genomic is vital for improving the prediction accuracy and, therefore, the realized genetic gain in genomic assisted plant breeding programs.

Optimizing Sparse Testing for Genomic Prediction of Plant Breeding Crops.

While sparse testing methods have been proposed by researchers to improve the efficiency of genomic selection (GS) in breeding programs, there are several factors that can hinder this. In this research, we evaluated four methods (M1-M4) for sparse testing allocation of lines to environments under multi-environmental trails for genomic prediction of unobserved lines. The sparse testing methods described in this study are applied in a two-stage analysis to build the genomic training and testing sets in a strategy that allows each location or environment to evaluate only a subset of all genotypes rather than all of them. To ensure a valid implementation, the sparse testing methods presented here require BLUEs (or BLUPs) of the lines to be computed at the first stage using an appropriate experimental design and statistical analyses in each location (or environment). The evaluation of the four cultivar allocation methods to environments of the second stage was done with four data sets (two large and two small) under a multi-trait and uni-trait framework. We found that the multi-trait model produced better genomic prediction (GP) accuracy than the uni-trait model and that methods M3 and M4 were slightly better than methods M1 and M2 for the allocation of lines to environments. Some of the most important findings, however, were that even under a scenario where we used a training-testing relation of 15-85%, the prediction accuracy of the four methods barely decreased. This indicates that genomic sparse testing methods for data sets under these scenarios can save considerable operational and financial resources with only a small loss in precision, which can be shown in our cost-benefit analysis.

Integrating Parental Phenotypic Data Enhances Prediction Accuracy of Hybrids in Wheat Traits.

Genomic selection (GS) is a methodology that is revolutionizing plant breeding because it can select candidate genotypes without phenotypic evaluation in the field. However, its practical implementation in hybrid prediction remains challenging since many factors affect its accuracy. The main objective of this study was to research the genomic prediction accuracy of wheat hybrids by adding covariates with the hybrid parental phenotypic information to the model. Four types of different models (MA, MB, MC, and MD) with one covariate (same trait to be predicted) (MA_C, MB_C, MC_C, and MD_C) or several covariates (of the same trait and other correlated traits) (MA_AC, MB_AC, MC_AC, and MD_AC) were studied. We found that the four models with parental information outperformed models without parental information in terms of mean square error by at least 14.1% (MA vs. MA_C), 5.5% (MB vs. MB_C), 51.4% (MC vs. MC_C), and 6.4% (MD vs. MD_C) when parental information of the same trait was used and by at least 13.7% (MA vs. MA_AC), 5.3% (MB vs. MB_AC), 55.1% (MC vs. MC_AC), and 6.0% (MD vs. MD_AC) when parental information of the same trait and other correlated traits were used. Our results also show a large gain in prediction accuracy when covariates were considered using the parental phenotypic information, as opposed to marker information. Finally, our results empirically demonstrate that a significant improvement in prediction accuracy was gained by adding parental phenotypic information as covariates; however, this is expensive since, in many breeding programs, the parental phenotypic information is unavailable.

Phenotyping Mediterranean Durum Wheat Landraces for Resistance to Zymoseptoria tritici in Tunisia.

Durum wheat landraces have huge potential for the identification of genetic factors valuable for improving resistance to biotic stresses. Tunisia is known as a hot spot for Septoria tritici blotch disease (STB), caused by the fungus Zymoseptoria tritici (Z. tritici). In this context, a collection of 3166 Mediterranean durum wheat landraces were evaluated at the seedling and adult stages for STB resistance in the 2016-2017 cropping season under field conditions in Kodia (Tunisia). Unadapted/susceptible accessions were eliminated to reach the final set of 1059 accessions; this was termed the Med-collection, which comprised accessions from 13 countries and was also screened in the 2018-2019 cropping season. The Med-collection showed high frequency of resistance reactions, among which over 50% showed an immune reaction (HR) at both seedling and adult growth stages. Interestingly, 92% of HR and R accessions maintained their resistance levels across the two years, confirming the highly significant correlation found between seedling- and adult-stage reactions. Plant Height was found to have a negative significant effect on adult-stage resistance, suggesting that either this trait can influence disease severity, or that it can be due to environmental/epidemiological factors. Accessions from Italy showed the highest variability, while those from Portugal, Spain and Tunisia showed the highest levels of resistance at both growth stages, suggesting that the latter accessions may harbor novel QTLs effective for STB resistance.

Breeder friendly phenotyping.

The word phenotyping can nowadays invoke visions of a drone or phenocart moving swiftly across research plots collecting high-resolution data sets on a wide array of traits. This has been made possible by recent advances in sensor technology and data processing. Nonetheless, more comprehensive often destructive phenotyping still has much to offer in breeding as well as research. This review considers the 'breeder friendliness' of phenotyping within three main domains: (i) the 'minimum data set', where being 'handy' or accessible and easy to collect and use is paramount, visual assessment often being preferred; (ii) the high throughput phenotyping (HTP), relatively new for most breeders, and requiring significantly greater investment with technical hurdles for implementation and a steeper learning curve than the minimum data set; (iii) detailed characterization or 'precision' phenotyping, typically customized for a set of traits associated with a target environment and requiring significant time and resources. While having been the subject of debate in the past, extra investment for phenotyping is becoming more accepted to capitalize on recent developments in crop genomics and prediction models, that can be built from the high-throughput and detailed precision phenotypes. This review considers different contexts for phenotyping, including breeding, exploration of genetic resources, parent building and translational research to deliver other new breeding resources, and how the different categories of phenotyping listed above apply to each. Some of the same tools and rules of thumb apply equally well to phenotyping for genetic analysis of complex traits and gene discovery.

Genomic Prediction of Gene Bank Wheat Landraces.

This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH), and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% testing (TST) (TRN20-TST80) sets, and (2) two types of core sets, "diversity" and "prediction", including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15-20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm into elite materials.

Unlocking the genetic diversity of Creole wheats.

Climate change and slow yield gains pose a major threat to global wheat production. Underutilized genetic resources including landraces and wild relatives are key elements for developing high-yielding and climate-resilient wheat varieties. Landraces introduced into Mexico from Europe, also known as Creole wheats, are adapted to a wide range of climatic regimes and represent a unique genetic resource. Eight thousand four hundred and sixteen wheat landraces representing all dimensions of Mexico were characterized through genotyping-by-sequencing technology. Results revealed sub-groups adapted to specific environments of Mexico. Broadly, accessions from north and south of Mexico showed considerable genetic differentiation. However, a large percentage of landrace accessions were genetically very close, although belonged to different regions most likely due to the recent (nearly five centuries before) introduction of wheat in Mexico. Some of the groups adapted to extreme environments and accumulated high number of rare alleles. Core reference sets were assembled simultaneously using multiple variables, capturing 89% of the rare alleles present in the complete set. Genetic information about Mexican wheat landraces and core reference set can be effectively utilized in next generation wheat varietal improvement.

Phenotyping transgenic wheat for drought resistance.

Realistic experimental protocols to screen for drought adaptation in controlled conditions are crucial if high throughput phenotyping is to be used for the identification of high performance lines, and is especially important in the evaluation of transgenes where stringent biosecurity measures restrict the frequency of open field trials. Transgenic DREB1A-wheat events were selected under greenhouse conditions by evaluating survival and recovery under severe drought (SURV) as well as for water use efficiency (WUE). Greenhouse experiments confirmed the advantages of transgenic events in recovery after severe water stress. Under field conditions, the group of transgenic lines did not generally outperform the controls in terms of grain yield under water deficit. However, the events selected for WUE were identified as lines that combine an acceptable yield-even higher yield (WUE-11) under well irrigated conditions-and stable performance across the different environments generated by the experimental treatments.

Gene action of canopy temperature in bread wheat under diverse environments.

While canopy temperature (CT) shows a strong and reliable association with yield under drought and heat stress and is used in wheat breeding to select for yield, little is known of its genetic control. The objective of this study was to determine the gene action controlling CT in five wheat populations grown in diverse environments (heat, drought, and well-irrigated conditions). CT showed negative phenotypic correlations with grain yield under drought and well-irrigated environments. Additive x additive effects were most prevalent and significant for all crosses and environments. Dominance and dominance x dominance gene actions were also found, though the significance and direction was specific for each environment and genotypic cross. The use of CT as a selection criterion to improve tolerance to drought was supported by its significant association with grain yield and the genotype differences observed between cultivars. Our results indicated that genetic gains for CT in wheat could be achieved through conventional breeding. However, given some dominance and epistatic effects, it would be necessary to delay the selection process until the frequency of heterozygous loci within families is reduced.

Soil resource acquisition mechanisms: nutrient concentrations and growth in perennial grasses

Se estudiaron las relaciones entre los mecanismos de adquisición de recursos del suelo y los componentes de producción de tejido foliar en la especie de etapas serales tardías, tolerante al pastoreo y competitiva Stipa clarazii Ball y en las especies de etapas serales mas tempranas, menos tolerantes al pastoreo y competitivas S tenius Phil y S. ambigua Speg. La hostoria del pastoreo y/o fuego determina la abundancia de estas gramíneas perennes cespitosas C3 en los pastizales templados semiáridos de Argentina. La hipótesis de trabajo fue que las plantas defoliadas tendrían menor densidad de longitud de raíces (DLR) y porcentaje y porcentaje de colonozación por micorrizas vesiculares-arbusculares (por ciento MAV) que aquellas no defoliadas y no defoliadas de S. clarazii tendrían mayores valores de DRL y por cientoMAV que aquellas de las otras especies, por su mayor capacidad competitiva con tolerancias a la defoliación. Se condujo un estudio bajo condiciones de campo en un área excluída añ pastoero por herbívoros domésticos durante 2 años. Un grupo de plantas fue defoliado una vez a 5cm del suelo a principios de primavera, mientras que otro grupo fue defoliado dos veces, a principios y mediados de primavera. Un tercer grupo permaneció no defoliado. Las mediciones se condujeron 6-10 días después de cada defoliación, y al final de la estación de crecimiento. DLR y por cientoMAV fueron similares en plantas defoliadas en las tres especies. La mayor producción de rebrote en S. clarazii no estuvo asociado con mayores DLR y por cientoMAV en la especie más competitiva. La relación positiva entre DLR y producción de peso seco, o la concentración de N y P en el tejido de estas especies, sugiere que su actividad radical debe mantenerse tras la defoliación para establecer una superficie fotosintética y mantener la distribución de recursos en equilibrio. Todas las especies mostraron valores consistentemente altos de por cientoMAV, sugeriendo que pueden tener una asociación mutualística obligada con las micorrizas. La correlación negativa entre por cientoMAV y DRL o P disponible del suelo para S. clarazii podría representar un mecanismo para evitar cambios de asociación mutualística a parasística en la relación planta-micorriza