RESUMEN
The issue of non-renewable energy scarcity has persisted over an extended period, primarily due to the depletion of fossil fuel reserves and the adverse effects of their utilization. This scarcity stems from the finite nature of fossil energy resources. The development of oil energy or biofuels aims to utilize oil-producing plants such as Jatropha curcas to develop alternative energy resources. However, metabolomic studies in Jatropha curcas are limited and need more investigations. Therefore, this research was essential to find biomarkers of metabolites among the fruit, leaf, and stem of Jatropha curcas using the GC-MS technique. We tested the metabolite profile with the R program, especially the metaboanalystR package, to determine fold change metabolite and pathway analysis. We found that 54 metabolites were detected in both fruit, leaf, and stem tissues of Jatropha curcas L, of which 19 metabolites were upregulated in the fruit, 20 metabolites in the leaf, and 15 up-regulated metabolites in the stem. The metabolites found formed three clusters based on correlation and networking metabolites analysis. The three clusters showed a relationship with the lipid biosynthesis pathway. In this study, provisional information was obtained that there was a different pattern of expression of metabolites between fruit, leaf, and stem tissues in Jatropha curcas, which was thought to be related to the critical metabolites of oleic acid and methylcyclohexane carboxylate in the biosynthetic pathway of fatty acids and unsaturated fatty acids. This information is essential as an initial reference for genetic engineering Jatropha curcas so that it can be used to transform plants, especially lipid-producing plants, as a source of oil.
RESUMEN
The genotype evaluation process requires analysis of GxE interactions to ascertain the responsiveness of a genotype to various environments, including the development of early maturing rice. However, the concept of interaction is relatively specific to grain yield. In contrast, grain yield is highly polygenic, so assessment should be carried out with multivariate approaches. Therefore, multivariate assessment in evaluating GxE interactions should be developed, especially for early maturing rice genotypes. The study aimed to develop a comprehensive multivariate approach to improve the comprehensiveness and responsiveness of GxE interaction analysis. The study was conducted in Bone and Soppeng districts, South Sulawesi, Indonesia, in two seasons. The study used a randomized complete block design, where replications were nested across two seasons and locations. Two check varieties and five early maturing varieties were replicated three times in each environment. Based on this study, a new approach to GxE interaction analysis based on multiple regression index analysis, BLUP analysis, factor analysis, and path analysis was considered adequate, especially for evaluating early maturing rice. This approach combined days to harvest, biological yield, and grain yield in multiple linear regression with weighting based on the combination of all analyses. The effectiveness of the GxE interaction assessment was reflected by high coefficient of determination (R2) and gradient (b) values above 0.8 and 0.9, respectively. Inpari 13 (R2 = 0.9; b=1.05), Cakrabuana (R2 = 0.98; b=0.99), and Padjajaran (R2 = 0.95; b=1.07) also have good grain yield with days to harvesting consideration, namely 7.83 ton ha-1, 98.12 days; 7.37 ton ha-1, 95.52 days; and 7.29 ton ha-1, 97.23 days, respectively. Therefore, this index approach can be recommended in GxE interaction analysis to evaluate early maturing rice genotypes. Furthermore, Inpari 13, Cakrabuana, and Padjajaran are recommended as adaptive early maturing varieties.
RESUMEN
Improving the tolerance of crop species to abiotic stresses that limit plant growth and productivity is essential for mitigating the emerging problems of global warming. In this context, imaged data analysis represents an effective method in the 4.0 technology era, where this method has the non-destructive and recursive characterization of plant phenotypic traits as selection criteria. So, the plant breeders are helped in the development of adapted and climate-resilient crop varieties. Although image-based phenotyping has recently resulted in remarkable improvements for identifying the crop status under a range of growing conditions, the topic of its application for assessing the plant behavioral responses to abiotic stressors has not yet been extensively reviewed. For such a purpose, bibliometric analysis is an ideal analytical concept to analyze the evolution and interplay of image-based phenotyping to abiotic stresses by objectively reviewing the literature in light of existing database. Bibliometricy, a bibliometric analysis was applied using a systematic methodology which involved data mining, mining data improvement and analysis, and manuscript construction. The obtained results indicate that there are 554 documents related to image-based phenotyping to abiotic stress until 5 January 2023. All document showed the future development trends of image-based phenotyping will be mainly centered in the United States, European continent and China. The keywords analysis major focus to the application of 4.0 technology and machine learning in plant breeding, especially to create the tolerant variety under abiotic stresses. Drought and saline become an abiotic stress often using image-based phenotyping. Besides that, the rice, wheat and maize as the main commodities in this topic. In conclusion, the present work provides information on resolutive interactions in developing image-based phenotyping to abiotic stress, especially optimizing high-throughput sensors in image-based phenotyping for the future development.