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Coffee Breeding programs have traditionally relied on observing plant characteristics over years, a slow and costly process. Genomic selection (GS) offers a DNA-based alternative for faster selection of superior cultivars. Stacking Ensemble Learning (SEL) combines multiple models for potentially even more accurate selection. This study explores SEL potential in coffee breeding, aiming to improve prediction accuracy for important traits [yield (YL), total number of the fruits (NF), leaf miner infestation (LM), and cercosporiosis incidence (Cer)] in Coffea Arabica. We analyzed data from 195 individuals genotyped for 21,211 single-nucleotide polymorphism (SNP) markers. To comprehensively assess model performance, we employed a cross-validation (CV) scheme. Genomic Best Linear Unbiased Prediction (GBLUP), multivariate adaptive regression splines (MARS), Quantile Random Forest (QRF), and Random Forest (RF) served as base learners. For the meta-learner within the SEL framework, various options were explored, including Ridge Regression, RF, GBLUP, and Single Average. The SEL method was able to predict the predictive ability (PA) of important traits in Coffea Arabica. SEL presented higher PA compared with those obtained for all base learner methods. The gains in PA in relation to GBLUP were 87.44% (the ratio between the PA obtained from best Stacking model and the GBLUP), 37.83%, 199.82%, and 14.59% for YL, NF, LM and Cer, respectively. Overall, SEL presents a promising approach for GS. By combining predictions from multiple models, SEL can potentially enhance the PA of GS for complex traits.

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The use of wild species as a source of genetic variability is a valued tool in the framework of crop breeding. Hordeum chilense Roem. et Schult is a wild barley species that can be a useful genetic donor for sustainable wheat breeding which carries genes conferring resistance to some diseases or increasing grain quality, among others. Septoria tritici blotch (STB), caused by the Zymoseptoria tritici fungus, is one of the most important wheat diseases worldwide, affecting both bread and durum wheat and having a high economic impact. Resistance to STB has been previously described in H. chilense chromosome 4Hch. In this study, we have developed introgression lines for H. chilense chromosome 4Hch in durum wheat using interspecific crosses, advanced backcrosses, and consecutive selfing strategies. Alien H. chilense chromosome segments have been reduced in size by genetic crosses between H. chilense disomic substitution lines in durum wheat and durum wheat lines carrying the Ph1 deletion. Hordeum chilense genetic introgressions were identified in the wheat background through several plant generations by fluorescence in situ hybridisation (FISH) and simple sequence repeat (SSR) markers. An STB infection analysis has also been developed to assess STB resistance to a specific H. chilense chromosome region. The development of these H. chilense introgression lines with moderate to high resistance to STB represents an important advance in the framework of durum breeding and can be a valuable tool for plant breeders.

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Introduction: Because Genomic selection (GS) is a predictive methodology, it needs to guarantee high-prediction accuracies for practical implementations. However, since many factors affect the prediction performance of this methodology, its practical implementation still needs to be improved in many breeding programs. For this reason, many strategies have been explored to improve the prediction performance of this methodology. Methods: When environmental covariates are incorporated as inputs in the genomic prediction models, this information only sometimes helps increase prediction performance. For this reason, this investigation explores the use of feature engineering on the environmental covariates to enhance the prediction performance of genomic prediction models. Results and discussion: We found that across data sets, feature engineering helps reduce prediction error regarding only the inclusion of the environmental covariates without feature engineering by 761.625% across predictors. These results are very promising regarding the potential of feature engineering to enhance prediction accuracy. However, since a significant gain in prediction accuracy was observed in only some data sets, further research is required to guarantee a robust feature engineering strategy to incorporate the environmental covariates.

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Most research on trinucleotide repeats (TRs) focuses on human diseases, with few on the impact of TR expansions on plant gene expression. This work investigates TRs' effect on global gene expression in Psidium guajava L., a plant species with widespread distribution and significant relevance in the food, pharmacology, and economics sectors. We analyzed TR-containing coding sequences in 1,107 transcripts from 2,256 genes across root, shoot, young leaf, old leaf, and flower bud tissues of the Brazilian guava cultivars Cortibel RM and Paluma. Structural analysis revealed TR sequences with small repeat numbers (5-9) starting with cytosine or guanine or containing these bases. Functional annotation indicated TR-containing genes' involvement in cellular structures and processes (especially cell membranes and signal recognition), stress response, and resistance. Gene expression analysis showed significant variation, with a subset of highly expressed genes in both cultivars. Differential expression highlighted numerous down-regulated genes in Cortibel RM tissues, but not in Paluma, suggesting interplay between tissues and cultivars. Among 72 differentially expressed genes with TRs, 24 form miRNAs, 13 encode transcription factors, and 11 are associated with transposable elements. In addition, a set of 20 SSR-annotated, transcribed, and differentially expressed genes with TRs was selected as phenotypic markers for Psidium guajava and, potentially for closely related species as well.

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Knowing how chromosome recombination works is essential for plant breeding. It enables the design of crosses between different varieties to combine desirable traits and create new ones. This is because the meiotic crossovers between homologous chromatids are not purely random, and various strategies have been developed to describe and predict such exchange events. Recent studies have used methylation data to predict chromosomal recombination in rice using machine learning models. This approach proved successful due to the presence of a positive correlation between the CHH context cytosine methylation and recombination rates in rice chromosomes. This paper assesses the question if methylation can be used to predict recombination in four plant species: Arabidopsis, maize, sorghum, and tomato. The results indicate a positive association between CHH context methylation and recombination rates in certain plant species, with varying degrees of strength in their relationships. The CG and CHG methylation contexts show negative correlation with recombination. Methylation data was key effectively in predicting recombination in sorghum and tomato, with a mean determination coefficient of 0.65 ± 0.11 and 0.76 ± 0.05, respectively. In addition, the mean correlation values between predicted and experimental recombination rates were 0.83 ± 0.06 for sorghum and 0.90 ± 0.05 for tomato, confirming the significance of methylomes in both monocotyledonous and dicotyledonous species. The predictions for Arabidopsis and maize were not as accurate, likely due to the comparatively weaker relationships between methylation contexts and recombination, in contrast to sorghum and tomato, where stronger associations were observed. To enhance the accuracy of predictions, further evaluations using data sets closely related to each other might prove beneficial. In general, this methylome-based method holds great potential as a reliable strategy for predicting recombination rates in various plant species, offering valuable insights to breeders in their quest to develop novel and improved varieties.

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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.

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The common bean (Phaseolus vulgaris L.) is an important leguminous crop providing low-cost protein in developing countries worldwide. Insect pests are the main threats to common bean production, and this article focuses on the soybean looper (SL) Chrysodeixis includens (Walker, 1858) (Lepidoptera: Noctuidae), which feeds on leaves and pods. The recurrent use of synthetic chemicals may lead to pest resistance. Genetically resistant plants may diminish their use. Thus, the objective was to study common bean genotypes' resistance toward SL. The plants were grown in greenhouse conditions. The biology, fertility life table, oviposition preference and free-choice feeding preference were analyzed. Phenol and flavonoid content were assessed in leaves using a biology assay. Uirapuru genotype negatively affected C. includens biology and reproduction. Tangará genotype favored these parameters. Genotypes Quero-Quero, Nhambu, Corujinha, Andorinha, ANFC 9, Siriri, BRS Radiante and Verdão were more attractive for third-instar larvae. Negative correlations between phenolic and flavonoid compounds with survival rate (from egg to adult) rate were found. Common bean genotypes with dark leaves are less preferred for oviposition.

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Compared to many countries, Cuba has made significant progress in advancing women's rights and gender equity; however, disparities remain. In the country's rural communities and agricultural sector, women continue to face barriers to equal participation and recognition for the value of their work. This case study shares the story of gender equity efforts that have been conducted within the framework of a broader development project-the Project to Strengthen a System of Innovation in Local Agricultural Development (PIAL, for its initials in Spanish). PIAL began in 2001 as a participatory plant-breeding initiative aimed at increasing the genetic diversity of key crops such as maize and beans. Over the course of two decades, the project's goals expanded to include an emphasis on increasing women's participation. In the beginning, those efforts focused on including women in the participatory plant-breeding activities, which enabled them to prioritize traits they cared about such as grain texture, cooking speed, and taste in the selection process. Over time, the participatory nature of the PIAL methodology empowered women to identify and pursue capacity-building in other areas of local agricultural innovation. While PPB remained central to PIAL, women also chose to pursue opportunities in seed bank management, leadership training, and small-scale farm-based entrepreneurship. The results of the PIAL work on gender have included not just more inclusive plant breeding, but also important economic improvements for rural women as they have been able to diversify their livelihoods, and social change as they have gained confidence and recognition as leaders in their households, communities, and beyond.

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RESUMEN El maíz se utiliza masivamente para producir alimentos para el hombre y los animales domésticos con granos de varias entidades taxonómicas o razas. Para los rumiantes domésticos también se utiliza la planta entera como forraje. En Argentina se utiliza el grano forrajero y el silaje de planta entera para el ganado vacuno de carne y leche. El objetivo de este trabajo fue desarrollar la historia y las perspectivas del maíz para grano y ensilaje, centrados en la selección y el mejoramiento genético. El maíz forrajero en sus dos variantes (grano forrajero y planta entera como silaje) tuvo distinta importancia a través del tiempo. Remarcamos la importancia del mejoramiento genético específico del maíz para silaje de planta entera para alcanzar un potencial de rendimiento y valor nutritivo superior a la de los híbridos graníferos. Se analizaron las cuatro estructuras genéticas poblacionales utilizadas en el tiempo como cultivares, que determinaron la evolución del proceso tecnológico de selección y mejoramiento genético. Con las investigaciones efectuadas, las empresas semilleras incorporaron nuestros protocolos a sus programas de desarrollo y de mejoramiento genético. La contribución de la selección y del mejoramiento genético en Argentina fue efectiva para transformar la planta de maíz en alimento para rumiantes y esto se incrementará con la obtención de híbridos específicos para silaje.

ABSTRACT Corn is used to massively produce food for humans and domestic animals with grains of various taxonomic entities or races. For domestic ruminants, the whole plant is also used as forage. In Argentina, both corn grain and whole-plant silage are used for beef and dairy cattle production. This paper aimed to develop the history and perspectives of corn grown for grain and silage, focusing on plant breeding. The importance of corn fodder in its two variants (grain and whole-plant silage) has varied over time. We emphasize herein the importance of the specific genetic breeding of corn used for whole-plant silage to achieve higher yield potential and nutritional value than grain hybrids. The four population genetic structures used over time as cultivars, which determined the evolution of the technological process of selection and breeding, were analyzed. Based on the research carried out, seed companies have incorporated our protocols into their development and breeding programs. The contribution of selection and breeding in Argentina was effective in transforming the corn plant into ruminant feed, and this will increase with the development of specific silage hybrids.

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RESUMEN La cebadilla criolla (Bromus catharticus Vahl) es la gramínea forrajera nativa anual/bianual de clima templado de mayor importancia y difusión en la Argentina. Varios autores han estudiado la variabilidad fenotípica en caracteres morfofisiológicos en cultivares y poblaciones nativas de cebadilla criolla. Las primeras variedades cultivadas comerciales de cebadilla criolla, eran poblaciones heterogéneas a las que se las consideraba de mejor adaptación a toda la región de cultivo. Con los estudios de caracterización genética, agronómica y molecular de las poblaciones base de selección y del germoplasma conservado en el banco activo de INTA Pergamino, se contribuyó a conocer y conservar la diversidad genética disponible y al mismo tiempo, disponer de variabilidad genética utilizable en programas de mejoramiento. Los cultivares públicos más representativos considerados en este trabajo fueron: Pergamino Martín Fierro MAG, Fierro Plus INTA, Bar INTA 200, Rosalía INTA e INTA Calvu, en los que se desarrollaron paquetes tecnológicos asociados a la utilización en producción animal.

ABSTRACT The prairie grass (Bromus catharticus Vahl) is the annual/biennial native forage grass of temperate climates of most importance and diffusion in Argentina. Several authors have studied the phenotypic variability in morphophysiological characters in cultivars and native populations of prairie grass. The first commercial cultivars were heterogeneous populations that were considered the best adapted in the temperate region of Argentina. Genetic, agronomic and molecular characterization of the selection base populations and of the germplasm conserved in the active bank of INTA Pergamino, contributed to knowing and conserving the available genetic diversity and, at the same time, provided usable genetic variability in breeding. The most representative cultivars considered in this work were Pergamino Martín Fierro MAG; Fierro Plus INTA, Bar INTA 200, Rosalía INTA and INTA Calvu, in which technological packages associated with the utilization in animal production were developed.