Aluminum (Al) toxicity induces changes in the expression of several genes, some of which are involved in plant responses to oxidative stress. Using mRNA differential display, we identified a maize Al-inducible cDNA encoding a glutathione S-transferase (GST). The gene was named GST27.2 owing to its homology to the maize gene GST27, which is known to be induced by xenobiotics. GST27.2 is present in the maize genome as a single copy and analysis of its expression pattern revealed that the gene is expressed mainly in the root tip. Expression was up-regulated in response to various Al and Cd concentrations in both Al-tolerant and Al-sensitive maize lines. Consistent with its role in plants, phylogenetic analysis of theta-type GSTs revealed that GST27.2 belongs to a group of proteins that respond to different stresses. Finally, structural analysis of the polypeptide chain indicates that the two amino acids that differ between GST27.2 and GST27 (E102K and P123L) could be responsible for alterations in activity and / or specificity. Together, these results suggest that GST27.2 may play an important part in plant defenses against Al toxicity.

Several extraction methods of genomic DNA for identification and characterization of genetic diversity in different plant species are routinely applied during molecular analysis. However, the presence of undesirable compounds such as polyphenols and polysaccharides is one of the biggest problems faced during the isolation and purification of high quality DNA in plants. Therefore, achievement of fast and accurate methods for DNA extraction is crucial in order to produce pure samples. Leaves of strawberry genotypes (Fragaria ananassa) have high contents of polysaccharides and polyphenols which increase the sample viscosity and decrease the DNA quality, interfering with the PCR performance. Thereby, in this study we evaluated the quality and amount of genomic DNA extracted from young leaves of strawberry after tissue lyophilization and maceration in presence of polivinilpirrolidone (PVP). The CTAB method was used as reference procedure and it was modified to improve the DNA extraction. The modifications consisted of tissue lyophilization overnight until it was completely freeze-dried and addition of PVP during the tissue maceration in liquid nitrogen. The results showed the efficiency and reliability of the modified method compared to the unmodified method, indicating that combination of lyophilization and PVP improve the quality and amount of the DNA extracted from strawberry leaves.

Currently, Brazil is the leading producer of sugarcane in the world, with self-sufficiency in the use of ethanol as a biofuel, as well as being one of the largest suppliers of sugar to the world. This study aimed to develop a predictive model for sugarcane production based on data extracted from aerial imagery obtained from drones or satellites, allowing the precise tracking of plant development in the field. A model based on a semiparametric approach associated with the inverse Gaussian distribution applied to vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI) and Visible Atmospherically Resistant Index (VARI), was developed with data from drone images obtained from two field experiments with randomized replications and four sugarcane varieties. These experiments were performed under conditions identical to those applied by sugarcane farmers. Further, the model validation was carried out by scaling up the analyses with data extracted from Sentinel-2 images of several commercial sugarcane fields. Very often, in countries such as Brazil, sugarcane crops occupy extensive areas. Consequently, the development of tools capable of being operated remotely automatically benefits the management of this crop in the field by avoiding laborious and time-consuming sampling and by promoting the reduction of operation costs. The results of the model application in both sources of data, i.e., data from field experiments as well as the data from commercial fields, showed a suitable level of overlap between the data of predicted yield using VIs generated from drone and satellite images with the data of verified yield obtained by measuring the production of experiments and commercial fields, indicating that the model is reliable for forecasting productivity months before the harvest time.

Climate change exacerbates drought stress, posing challenges to global soybean grain yield. This study assesses the effectiveness of microbial inoculants derived from two Bacillus velezensis (strains 5D5, 6E9) and one Bacillus subtilis (strain 1A11), which were previously selected in vitro to promote growth and enhance drought resilience in soybeans (Glycine max [L.] Merr.), and evaluated through agronomic metrics and remote sensing. A greenhouse experiment was conducted to evaluate the performance of these inoculants under both irrigated and drought conditions. The inoculants were applied at the dose-range of 1, 2, 3 and 4 mL Kg-1 of seed to identify the optimal dose. The greenhouse results indicated that for many tested doses, the three bacterial strains significantly increased shoot fresh weight, shoot dry weight, and root dry weight compared to control treatments. Multi-location field-trials in Brazil (Birigui, Itapira and Piracicaba) were conducted during the growing seasons of 2022–2023 and 2023–2024, using 3 mL Kg-1 of seed as a reference dose. These field-trials revealed yield improvements of 11.3 to 18\% for inoculated treatments, with B. subtilis 1A11 achieving the highest grain yield of 620 Kg ha-1 over the control. However, all three microbial inoculants significantly enhanced soybean development and grain yield relative to non-inoculated controls. Vegetation indices, particularly the Enhanced Vegetation Index 2 (EVI2), derived from PlanetScope satellite and Unmanned Aerial Vehicle (UAV) imagery, demonstrated a high overlap between field data and model predictions, confirming the value of remote sensing as a predictive tool. Climatic variability significantly impacted the yield in field-trials, with 2022–2023 (4.28 t ha-1 outperforming 2023–2024 (3.34 t ha-1) due to higher temperatures (>40 °C) and lower rainfall in the last season. Meanwhile, locations with balanced precipitation, like Itapira, showed superior grain yield. Statistical modeling confirmed inoculant efficacy and EVI2's utility in production measurement. This study emphasizes that microbial inoculants can serve as sustainable strategies to mitigate the impacts of drought. By integrating Bacillus-based bioinoculants into soybean cultivation and utilizing both agronomic and remote sensing metrics for validation, we can enhance resilience and ultimately support food security amid climate variability.