Drought is widely recognized as one of the most significant agricultural constraints worldwide. A strategy to avoid the adverse effects of drought on crops is to cultivate high-yielding varieties by grafting them onto drought-tolerant rootstocks with a differentiated root system. Thus, the objective of this study was to evaluate fruit yield and quality, root system architecture, and water productivity of watermelon grafted onto Lagenaria siceraria rootstocks. To do so, a commercial watermelon cultivar “Santa Amelia” [Citrullus lanatus (Thunb.)] was grafted onto five L. siceraria rootstocks: ‘Illapel’, ‘Osorno’, ‘BG-48’, ‘GC’, and ‘Philippines’, which were grown under three irrigation treatments (100%, 75%, and 50% of evapotranspiration). The comparison of the L. siceraria rootstocks in the irrigation treatments demonstrated no significant effect on watermelon fruit quality parameters. The rootstocks ‘Illapel’, ‘Osorno’, and ‘GC’ significantly improved the fruit number and yield (total fruit weight) under water deficit. Similarly, ‘Illapel’, ‘Osorno’, and ‘GC’ consistently showed statistical differences for root system architecture traits compared to ‘BG-48’ and ‘Philippines’. Based on these results, we concluded that the used L. siceraria rootstocks did not affect the fruit yield and quality of grafted watermelon under water deficit. This study may help adjust the amount of applied water for watermelon production where L. siceraria rootstocks are utilized.

The main organic input for the elaboration of growing media is peat (Sphagnum spp.), due to its physical and chemical characteristics. However, the mining of this material creates a considerable impact in the local ecosystems from which this is obtained, along with a global impact because of the emission of greenhouse gasses. Thus, sustainable materials that can replace, or reduce the use of peat, while maintaining or improving attributes in the growing media and plant growth, are greatly needed. Therefore, this work aims to evaluate the effects of the use of different proportions of compost and biochar on the biological characteristics of growing media and (Lactuca sativa L.) seedling growth prior to transplanting. Out of the biological variables evaluated, the β-glucosidase activity showed the greatest results in growing media based on 80% peat and based on 70% peat, 5% compost and 5% biochar. Moreover, growing media based on the combination of compost, biochar and peat maintained most of the Lactuca sativa L. (Oak Leaf variety) seedling traits obtained in the growing media based on only peat. These findings emphasize the need to further investigate further biological conditions for alternative materials to peat, and the need to pay attention to feedstock initial characteristics and processing in order to obtain high quality organic inputs for optimum growing media.

The global concern about the gap between food production and consumption has intensified the research on the genetics, ecophysiology, and breeding of cereal crops. In this sense, several genetic studies have been conducted to assess the effectiveness and sustainability of collections of germplasm accessions of major crops. In this study, a spectral-based classification approach for the assignment of wheat cultivars to genetically differentiated subpopulations (genetic structure) was carried out using a panel of 316 spring bread cultivars grown in two environments with different water regimes (rainfed and fully irrigated). For that, different machine-learning models were trained with foliar spectral and genetic information to assign the wheat cultivars to subpopulations. The results revealed that, in general, the hyperparameters ReLU (as the activation function), adam (as the optimizer), and a size batch of 10 give neural network models better accuracy. Genetically differentiated groups showed smaller differences in mean wavelengths under rainfed than under full irrigation, which coincided with a reduction in clustering accuracy in neural network models. The comparison of models indicated that the Convolutional Neural Network (CNN) was significantly more accurate in classifying individuals into their respective subpopulations, with 92 and 93% of correct individual assignments in water-limited and fully irrigated environments, respectively, whereas 92% (full irrigation) and 78% (rainfed) of cultivars were correctly assigned to their respective classes by the multilayer perceptron method and partial least squares discriminant analysis, respectively. Notably, CNN did not show significant differences between both environments, which indicates stability in the prediction independent of the different water regimes. It is concluded that foliar spectral variation can be used to accurately infer the belonging of a cultivar to its respective genetically differentiated group, even considering radically different environments, which is highly desirable in the context of crop genetic resources management.

Phenotypic characterization of the variability present within bottle gourd has been limited to morpho-agronomic traits, and this evaluation is a prerequisite for a bottle gourd breeding program. Despite playing an important role in the phenotypic variation in plants, the root system has limited use in studies of morphological diversity. Thus, the objective of this study was to characterize the morphological diversity present in bottle gourd accessions of different countries based on roots and morpho-agronomic traits. The magnitude of morphological diversity and divergence among nineteen bottle gourd accessions that represent nine different countries of origin were evaluated with analysis of variance, principal component analysis, and an agglomerative hierarchical clustering (AHC) analysis. ANOVA for morpho-agronomic and root traits revealed significant morphological effects among the accessions, suggesting substantial differences among the bottle gourd accessions. The nineteen accessions were grouped into three clusters, and while these were not grouped according to the country of origin, clear differences among the roots and flowering traits were observed between the L. siceraria var. hispida with L. siceraria var. siceraria accessions, which were confirmed with the AHC analysis, revealing the divergence between these varieties and the opportunities for rootstock breeding programs.

Eucalyptus species constitute a rich reservoir of bioactive phenolic compounds. In contrast to the extensively characterized essential oils, the non-volatile phenolic fraction remains fragmented across the literature and insufficiently integrated from biochemical, ecological, and applied perspectives. This review synthesizes current evidence on the structural diversity, tissue distribution, and biosynthetic regulation of Eucalyptus phenolics, and uniquely bridges their ecological functions with their translational relevance in pharmaceutical, agricultural (biopesticides), and environmental applications. Quantitative analyses indicate that lignified organs such as bark and stump wood consistently contain the highest phenolic concentrations, reaching 474.9 mg/g in E. camaldulensis bark, whereas foliar tissues show pronounced intra- and interspecific variation driven by extraction polarity. We identify a critical gap in the field: although over 103 phenolic compounds have been documented, many reports lack methodological standardization, quantitative reproducibility, and chemometric alignment, limiting their comparability and downstream industrial translation. Across organs, several dominant phenolics are recurrently identified, including ellagic acid, epicatechin, gallic acid, quercetin-glucuronide, and sinapic acid. Bark of E. urograndis , E. grandis , and E. camaldulensis is especially enriched in ellagitannins and flavan-3-ols, while leaf extracts of E. globulus accumulate high levels of glucuronidated flavonols. Distinctive metabolites, including rosmarinic acid, occur only in E. marginata . Beyond compositional surveys, this review examines biosynthetic regulation, ecological roles, and emerging technological applications. We highlight that persistent challenges include extraction standardization, metabolic engineering, and the design of biodelivery systems. Overall, this review positions Eucalyptus phenolics as strong candidates for innovation, while providing a clearer roadmap to overcome persistent limitations, particularly in compound-specific bioactivities, transcriptional and metabolic regulatory pathways, and genotype–environment–management interactions.

Genomic and phenomic selection have transformed modern breeding by enabling data-driven prediction of complex traits. Deep learning (DL) can further enhance predictive ability by capturing nonlinear patterns that classical and Bayesian approaches often fail to represent. However, despite its potential, the adoption of DL in breeding programs remains limited due to its computational demands and the lack of accessible tools for users without extensive programming experience. This study introduces the MTMEGPS (Multi-Trait and Multi-Environment Genomic and Phenomic Selection), an R package that provides a streamlined end-to-end workflow for Uni- and Multi-Trait (UT and MT, respectively) and Uni- and Multi-Environment (UE and ME, respectively) genomic and phenomic prediction. The package supports data preparation, hyperparameter optimization, model training, and DL-based evaluation. To assess its performance, MTMEGPS was applied to the two default datasets included in the package: Maize (genomic data) and Eucalyptus (near-infrared spectroscopy, NIR, data), as well as to an independent publicly available multi-environment validation dataset. Across most scenarios, MTMEGPS showed superior predictive ability compared with all benchmark models, particularly under UT for the internal datasets and MT for the independent multi-environment dataset. Mean squared error (MSE) values were similar across models, all falling within a moderate range. Overall, these results demonstrate the efficiency and practical utility of MTMEGPS for genomic and phenomic selection, even in scenarios where prediction errors remain moderate.