Summary: Water deficit (drought stress) massively restricts plant growth and the yield of crops; reducing the deleterious effects of drought is therefore of high agricultural relevance. Drought triggers diverse cellular processes including the inhibition of photosynthesis, the accumulation of cell‐damaging reactive oxygen species and gene expression reprogramming, besides others. Transcription factors (TF) are central regulators of transcriptional reprogramming and expression of manyTFgenes is affected by drought, including members of theNACfamily. Here, we identify theNACfactorJUNGBRUNNEN1 (JUB1) as a regulator of drought tolerance in tomato (Solanum lycopersicum). Expression of tomatoJUB1(SlJUB1) is enhanced by various abiotic stresses, including drought. InhibitingSlJUB1by virus‐induced gene silencing drastically lowers drought tolerance concomitant with an increase in ion leakage, an elevation of hydrogen peroxide (H₂O₂) levels and a decrease in the expression of various drought‐responsive genes. In contrast, overexpression ofAtJUB1fromArabidopsis thalianaincreases drought tolerance in tomato, alongside with a higher relative leaf water content during drought and reduced H₂O₂levels. AtJUB1 was previously shown to stimulate expression ofDREB2A, aTFinvolved in drought responses, and of theDELLAgenesGAIandRGL1. We show here that SlJUB1 similarly controls the expression of the tomato orthologsSlDREB1,SlDREB2andSlDELLA. Furthermore, AtJUB1 directly binds to the promoters ofSlDREB1,SlDREB2andSlDELLAin tomato. Our study highlightsJUB1 as a transcriptional regulator of drought tolerance and suggests considerable conservation of the abiotic stress‐related gene regulatory networks controlled by thisNACfactor between Arabidopsis and tomato.

Virtually all examined plant species harbour fungal endophytes which asymptomatically infect or colonize living plant tissues, including leaves, branches, stems and roots. Endophyte-host interactions are complex and span the mutualist–pathogen continuum. Notably, mutualist endophytes can confer increased fitness to their host plants compared with uncolonized plants, which has attracted interest in their potential application in integrated plant health management strategies. In this review, we report on the many benefits that fungal endophytes provide to agricultural plants against common non-insect pests such as fungi, bacteria, nematodes, viruses, and mites. We report endophytic modes of action against the aforementioned pests and describe why this broad group of fungi is vitally important to current and future agricultural practices. We also list an extensive number of plant-friendly endophytes and detail where they are most commonly found or applied in different studies. This review acts as a general resource for understanding endophytes as they relate to potential large-scale agricultural applications.

The present study explored the eco-friendly approach of utilizing plant-growth-promoting rhizobacteria (PGPR) inoculation and foliar application of silicon (Si) to improve the physiology, growth, and yield of mung bean under saline conditions. We isolated 18 promising PGPR from natural saline soil in Saudi Arabia, and screened them for plant-growth-promoting activities. Two effective strains were selected from the screening trial, and were identified as Enterobacter cloacae and Bacillus drentensis using matrix-assisted laser desorption ionization-time-of-flight mass spectrometry and 16S rRNA gene sequencing techniques, respectively. Subsequently, in a 2-year mung bean field trial, using a randomized complete block design with a split-split plot arrangement, we evaluated the two PGPR strains and two Si levels (1 and 2 kg ha-1), in comparison with control treatments, under three different saline irrigation conditions (3.12, 5.46, and 7.81 dS m-1). The results indicated that salt stress substantially reduced stomatal conductance, transpiration rate, relative water content, total chlorophyll content, chlorophyll a, chlorophyll b, carotenoid content, plant height, leaf area, dry biomass, seed yield, and salt tolerance index. The PGPR strains and Si levels independently improved all the aforementioned parameters. Furthermore, the combined application of the B. drentensis strain with 2 kg Si ha-1 resulted in the greatest enhancement of mung bean physiology, growth, and yield. Overall, the results of this study provide important information for the benefit of the agricultural industry.

Ustilago hordei is a biotrophic parasite of barley (Hordeum vulgare). After seedling infection, the fungus persists in the plant until head emergence when fungal spores develop and are released from sori formed at kernel positions. The 26.1-Mb U. hordei genome contains 7113 protein encoding genes with high synteny to the smaller genomes of the related, maize-infecting smut fungi Ustilago maydis and Sporisorium reilianum but has a larger repeat content that affected genome evolution at important loci, including mating-type and effector loci. The U. hordei genome encodes components involved in RNA interference and heterochromatin formation, normally involved in genome defense, that are lacking in the U. maydis genome due to clean excision events. These excision events were possibly a result of former presence of repetitive DNA and of an efficient homologous recombination system in U. maydis. We found evidence of repeat-induced point mutations in the genome of U. hordei, indicating that smut fungi use different strategies to counteract the deleterious effects of repetitive DNA. The complement of U. hordei effector genes is comparable to the other two smuts but reveals differences in family expansion and clustering. The availability of the genome sequence will facilitate the identification of genes responsible for virulence and evolution of smut fungi on their respective hosts.

Developing rapid and non-destructive methods for chlorophyll estimation over large spatial areas is a topic of much interest, as it would provide an indirect measure of plant photosynthetic response, be useful in monitoring soil nitrogen content, and offer the capacity to assess vegetation structural and functional dynamics. Traditional methods of direct tissue analysis or the use of handheld meters, are not able to capture chlorophyll variability at anything beyond point scales, so are not particularly useful for informing decisions on plant health and status at the field scale. Examining the spectral response of plants via remote sensing has shown much promise as a means to capture variations in vegetation properties, while offering a non-destructive and scalable approach to monitoring. However, determining the optimum combination of spectra or spectral indices to inform plant response remains an active area of investigation. Here, we explore the use of a machine learning approach to enhance the estimation of leaf chlorophyll (Chl_t), defined as the sum of chlorophyll a and b, from spectral reflectance data. Using an ASD FieldSpec 4 Hi-Res spectroradiometer, 2700 individual leaf hyperspectral reflectance measurements were acquired from wheat plants grown across a gradient of soil salinity and nutrient levels in a greenhouse experiment. The extractable Chl_t was determined from laboratory analysis of 270 collocated samples, each composed of three leaf discs. A random forest regression algorithm was trained against these data, with input predictors based upon (1) reflectance values from 2102 bands across the 400–2500 nm spectral range; and (2) 45 established vegetation indices. As a benchmark, a standard univariate regression analysis was performed to model the relationship between measured Chl_t and the selected vegetation indices. Results show that the root mean square error (RMSE) was significantly reduced when using the machine learning approach compared to standard linear regression. When exploiting the entire spectral range of individual bands as input variables, the random forest estimated Chl_t with an RMSE of 5.49 µg·cm⁻² and an R² of 0.89. Model accuracy was improved when using vegetation indices as input variables, producing an RMSE ranging from 3.62 to 3.91 µg·cm⁻², depending on the particular combination of indices selected. In further analysis, input predictors were ranked according to their importance level, and a step-wise reduction in the number of input features (from 45 down to 7) was performed. Implementing this resulted in no significant effect on the RMSE, and showed that much the same prediction accuracy could be obtained by a smaller subset of indices. Importantly, the random forest regression approach identified many important variables that were not good predictors according to their linear regression statistics. Overall, the research illustrates the promise in using established vegetation indices as input variables in a machine learning approach for the enhanced estimation of Chl_t from hyperspectral data.