Search

Endophytic bacteria are known to influence vital activities of host plants. Endophytes can promote plant growth and provide a defense response against pathogens. The use of endophytes in crop production has the potential to reduce the application of fertilizer and pesticide input and thus improve the sustainability of crop production. In this study, we investigated the effects of seed inoculation with non-native endophytic bacteria, harvested from Brassica carinata, on oat (Avena sativa L.) growth with root vigor assays and greenhouse experiments. For root vigor assay experiments, seeds of two different oat cultivars were treated with 16 endophytic bacteria previously shown to promote growth benefits on multiple crop species. For the greenhouse experiments, the effect of seed inoculation with bacterial isolates was evaluated on ten oat cultivars at two fertilization levels. The root vigor assay showed that multiple isolates, including Bacillus licheniformis, Enterobacter kobei, B. halotolerans, B. cereus, B. aryabhattai, and Lysinibacillus fusiformis, had a positive effect on seedling root growth in one of the two oat cultivars. In the other cultivar, the bacterial isolates had either no effect or a negative effect on root growth. Greenhouse studies showed that the magnitude and direction of the effect of bacterial inoculation on oat growth varied with fertilization levels, bacterial strain, and oat cultivar. However, we identified two cultivars that were more responsive to bacterial inoculation than the others and for which bacterial inoculation of seed resulted in enhanced growth in several traits under both reduced and full nitrogen levels, and this response was observed for the two isolates tested. Our results show that inoculating oat seeds with non-native bacterial endophytes can promote root and shoot growth in oats. Developing biofertilizers that are effective across crop species, crop cultivars, and environmental conditions may be possible if cultivars are selected for their responsiveness across multiple bacterial isolates and in multiple growing environments. Overall, this study indicates that non-native endophytes could be considered for the development of biofertilizers with effectiveness across crop species.

The increase in intensity and frequency of drought due to global climate change has increased the urgency of developing crop cultivars suitable for dry environments. Drought tolerance is a complex trait that involves numerous physiological, biochemical, and morphological responses. A better understanding of those mechanisms is critical to develop drought tolerant cultivars. In this study, we aimed to understand the morphophysiological changes at the shoot and root levels in response to drought stress of ten oat genotypes with diverse root morphological characteristics. Twenty-one-day old plants were subjected to drought stress in a greenhouse by withholding water for two weeks. Several characteristics including chlorophyll content, relative water content (RWC), stomatal conductance, stomata number, shoot dry weight (SDW), root dry weight (RDW), root-to-shoot biomass ratio (RSR), root length, root area, and root volume were measured on well-watered, and drought-stressed plants. Grain yield was evaluated by continuing the drought treatment with a drying and rewatering cycle every 15 days until physiological maturity. The water regime had a significant impact on all traits evaluated. A significant interaction between genotype and water treatment was observed for RWC, chlorophyll content, stomatal conductance, stomata number, and grain yield but not for root traits, suggesting that the root system of all genotypes responded similarly to drought stress. Hayden, the cultivar with the lowest reduction in grain yield from the drought treatment, was among the genotypes with the lowest reduction in RWC and chlorophyll content but with a sharp decrease in stomata number, thus indicating that regulating the plant water status and maintaining the photosynthesis level are important for oat plants to maintain grain yield under drought stress. The size of the root system was not correlated with grain yield under drought, but the RWC and grain yield were significantly correlated under drought, thus suggesting that maintaining the RWC is an important characteristic for oat plants to maintain yield under drought stress.

Accurate and timely monitoring of biomass in breeding nurseries is essential for evaluating plant performance and selecting superior genotypes. Traditional methods for phenotyping above-ground biomass in field conditions requires significant time, cost, and labor. Unmanned Aerial Vehicles (UAVs) offer a rapid and non-destructive approach for phenotyping multiple field plots at a low cost. While Vegetation Indices (VIs) extracted from remote sensing imagery have been widely employed for biomass estimation, they mainly capture spectral information and disregard the 3D canopy structure and spatial pixel relationships. Addressing these limitations, this study, conducted in 2020 and 2021, aimed to explore the potential of integrating UAV multispectral imagery-derived canopy spectral, structural, and textural features with machine learning algorithms for accurate oat biomass estimation. Six oat genotypes planted at two seeding rates were evaluated in two South Dakota locations at multiple growth stages. Plot-level canopy spectral, structural, and textural features were extracted from the multispectral imagery and used as input variables for three machine learning models: Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR). The results showed that (1) in addition to canopy spectral features, canopy structural and textural features are also important indicators for oat biomass estimation; (2) combining spectral, structural, and textural features significantly improved biomass estimation accuracy over using a single feature type; (3) machine learning algorithms showed good predictive ability with slightly better estimation accuracy shown by RFR (R2 = 0.926 and relative root mean square error (RMSE%) = 15.97%). This study demonstrated the benefits of UAV imagery-based multi-feature fusion using machine learning for above-ground biomass estimation in oat breeding nurseries, holding promise for enhancing the efficiency of oat breeding through UAV-based phenotyping and crop management practices.

Oat is among the food crops and ancient grains cultivated and consumed worldwide. It is gaining in popularity owing to its nutritional composition and multifunctional benefits of select bioactive compounds. Beta-glucan is an important component of dietary fiber found in oat grains. It is the major active compound in oats with proven cholesterol-lowering and antidiabetic effects. Oats also provide substantial levels of other bioactive compounds such as phenolic acids, tocols, sterols, avenacosides, and avenanthramides. The consumption of oats has been determined to be beneficial for human health by promoting immunomodulation and improving gut microbiota. In addition, oat consumption assists in preventing diseases such as atherosclerosis, dermatitis, and some forms of cancer. While much has been published in relation to oat nutrients and oat fibers and their impact on major diseases, the oat industries and consumers may benefit from greater knowledge and understanding of clinical effects, range of occurrence, distribution, therapeutic doses and food functional attributes of other oat bioactives such as avenanthramides and saponins as well as other anti-inflammatory agents found in the cereal. This review focuses on the various studies relevant to the contribution of the consumption of oats and oat-based products in preventing human diseases and promoting human health.

Current strategies for phenotyping above-ground biomass in field breeding nurseries demand significant investment in both time and labor. Unmanned aerial vehicles (UAV) can be used to derive vegetation indices (VIs) with high throughput and could provide an efficient way to predict forage yield with high accuracy. The main objective of the study is to investigate the potential of UAV-based multispectral data and machine learning approaches in the estimation of oat biomass. UAV equipped with a multispectral sensor was flown over three experimental oat fields in Volga, South Shore, and Beresford, South Dakota, USA, throughout the pre- and post-heading growth phases of oats in 2019. A variety of vegetation indices (VIs) derived from UAV-based multispectral imagery were employed to build oat biomass estimation models using four machine-learning algorithms: partial least squares (PLS), support vector machine (SVM), Artificial neural network (ANN), and random forest (RF). The results showed that several VIs derived from the UAV collected images were significantly positively correlated with dry biomass for Volga and Beresford (r = 0.2–0.65), however, in South Shore, VIs were either not significantly or weakly correlated with biomass. For Beresford, approximately 70% of the variance was explained by PLS, RF, and SVM validation models using data collected during the post-heading phase. Likewise for Volga, validation models had lower coefficient of determination (R2 = 0.20–0.25) and higher error (RMSE = 700–800 kg/ha) than training models (R2 = 0.50–0.60; RMSE = 500–690 kg/ha). In South Shore, validation models were only able to explain approx. 15–20% of the variation in biomass, which is possibly due to the insignificant correlation values between VIs and biomass. Overall, this study indicates that airborne remote sensing with machine learning has potential for above-ground biomass estimation in oat breeding nurseries. The main limitation was inconsistent accuracy in model prediction across locations. Multiple-year spectral data, along with the inclusion of textural features like crop surface model (CSM) derived height and volumetric indicators, should be considered in future studies while estimating biophysical parameters like biomass.