Rising food and feed demand, coupled with climate uncertainties, poses a growing threat to the sustainability and resilience of pasture and agriculture lands in the Great Plains. Smooth bromegrass (Bromus inermis, Lyess), a C3 cool-season grass, dominates cattle grazing pastures in eastern Nebraska and is valued for its productivity and nutritional quality. However, nitrogen fertilization of smooth brome pastures carries nitrate leaching risks, and potential water degradation. Dry Distiller Grains plus Solubles (DDGS), a by-product of corn ethanol production, has emerged as an alternative to mineral fertilization. This study investigated the interactive effects of long-term (2005-2023) pasture management (fertilization and rotational grazing) and climate variability on smooth bromegrass performance over four years (2020-2023). Treatments included mineral nitrogen fertilization, DDGS supplementation, and no fertilization, each under grazed and ungrazed conditions. Biomass (total and live), leaf area index, chlorophyll content, crude protein, acid detergent fiber, neutral detergent fiber, and soil moisture were measured seasonally and across years. Results showed that fertilization consistently produced higher biomass, leaf area index, and crude protein, relative to other treatments, reinforcing its role in sustaining forage productivity. DDGS, while offering a reduced environmental footprint, showed potential in enhancing forage production and quality under average or above average precipitation and soil moisture. However, its efficacy declined during dry years. While long-term fertilization treatment remained the most consistent strategy for sustaining productivity under both ungrazed and rotational grazing systems, DDGS supplementation emerged as a strategic option for regions with adequate growing season precipitation, reducing reliance on mineral fertilizers without compromising yields. These findings emphasize the value of adaptive practices that integrate fertilization and grazing to maintain smooth bromegrass productivity under increasing climate variability.

BackgroundAutofluorescence-based imaging has the potential to non-destructively characterize the biochemical and physiological properties of plants regulated by genotypes using optical properties of the tissue. A comparative study of stress tolerant and stress susceptible genotypes of Brassica rapa with respect to newly introduced stress-based phenotypes using machine learning techniques will contribute to the significant advancement of autofluorescence-based plant phenotyping research.MethodsAutofluorescence spectral images have been used to design a stress detection classifier with two classes, stressed and non-stressed, using machine learning algorithms. The benchmark dataset consisted of time-series image sequences from three Brassica rapa genotypes (CC, R500, and VT), extreme in their morphological and physiological traits captured at the high-throughput plant phenotyping facility at the University of Nebraska-Lincoln, USA. We developed a set of machine learning-based classification models to detect the percentage of stressed tissue derived from plant images and identified the best classifier. From the analysis of the autofluorescence images, two novel stress-based image phenotypes were computed to determine the temporal variation in stressed tissue under progressive drought across different genotypes, i.e., the average percentage stress and the moving average percentage stress.ResultsThe study demonstrated that both the computed phenotypes consistently discriminated against stressed versus non-stressed tissue, with oilseed type (R500) being less prone to drought stress relative to the other two Brassica rapa genotypes (CC and VT).ConclusionAutofluorescence signals from the 365/400 nm excitation/emission combination were able to segregate genotypic variation during a progressive drought treatment under a controlled greenhouse environment, allowing for the exploration of other meaningful phenotypes using autofluorescence image sequences with significance in the context of plant science.

Woody species encroachment is occurring within the semi-arid grasslands of the Nebraska Sandhills U.S., primarily driven by native Juniperus virginiana and Pinus ponderosa, altering ecosystems and the services they provide. Effective, low cost, and cross-scale monitoring of woody species growth and performance is necessary for integrated grassland and forest management in the face of climate variability and change. In this study, we sought to establish a relationship between remote sensing-derived vegetation indices (VIs), tree dendrochronological (raw and standardized tree ring width) measurements, and the abiotic environment [(precipitation, temperature, Palmer Drought Severity Index (PDSI), and soil water content (0-300 cm depth)], over a 30-year period (1984-2013), to assess the performance of encroaching woody J. virginiana and P. ponderosa within the Nebraska Sandhills. We also investigated whether VIs can be used as an effective alternative tool to replace or complement ground measurements. Our results indicate that precipitation, temperature, and PDSI were significant (p < 0.05) predictors of J. virginiana and P. ponderosa growth based on dendrochronological measurements and VIs, while soil water content from 40 to 300 cm depth was a significant predictor of J. virginiana performance. Out of the six VIs that were investigated, four were significant predictors of tree ring growth. R2 values between grassland VIs and growing season climate were greater than those of J. virginiana or P. ponderosa, while grassland performance was decoupled from soil water content. Additionally, climatic conditions in the previous year were significant determinants of current year growth of tree species but did not affect current year grassland performance. This study provides evidence for the efficacy of remote sensing-based VIs in monitoring interannual variation in the growth of woody species, while determining abiotic factors impacting the growth of grassland vegetation, J. virginiana, and P. ponderosa in the Nebraska Sandhills.

We investigated the synergic use of optical and biophysical traits to characterize Bromus inermis (smooth bromegrass) pasture lands and assess the combined effects of long-term (15-years) rotational grazing and management strategies of (i) no fertilization (C), (ii) mineral nitrogen (N) fertilization (HF), and (iii) supplemented fertilization through dry distiller grains plus soluble (DDGS; SF)- on forage growth, performance, and quality. We found that fertilization improved pasture's biomass, specific leaf area, leaf area index (LAI), as well as forage quality. The use of N fertilization did not offer an advantage (e.g., forage quality, yield) over DDGS under both grazed and ungrazed conditions. Optical, proximal sensing techniques allowed the characterization of pasture lands in a non-invasive and time-efficient manner. We tested established vegetation indices (VIs) for their accuracy in identifying and quantifying important physiological and morphological traits. Results showed that the Normalized Difference Vegetation Index (NDVI) and Vogelmann (VOG) were among the best performing indices. Results contribute to our understanding of the impact of long-term fertilization management on Bromus inermis pastures and validate the use of proximal sensing methods. Proximal sensing methods provide direct, non-invasive, and time efficient tool for assessment of the performance and health of vegetation, keys to successful integrative management strategies.

The paper introduces two novel algorithms for predicting and propagating drought stress in plants using image sequences captured by cameras in two modalities, i.e., visible light and hyperspectral. The first algorithm, VisStressPredict, computes a time series of holistic phenotypes, e.g., height, biomass, and size, by analyzing image sequences captured by a visible light camera at discrete time intervals and then adapts dynamic time warping (DTW), a technique for measuring similarity between temporal sequences for dynamic phenotypic analysis, to predict the onset of drought stress. The second algorithm, HyperStressPropagateNet, leverages a deep neural network for temporal stress propagation using hyperspectral imagery. It uses a convolutional neural network to classify the reflectance spectra at individual pixels as either stressed or unstressed to determine the temporal propagation of stress in the plant. A very high correlation between the soil water content, and the percentage of the plant under stress as computed by HyperStressPropagateNet on a given day demonstrates its efficacy. Although VisStressPredict and HyperStressPropagateNet fundamentally differ in their goals and hence in the input image sequences and underlying approaches, the onset of stress as predicted by stress factor curves computed by VisStressPredict correlates extremely well with the day of appearance of stress pixels in the plants as computed by HyperStressPropagateNet. The two algorithms are evaluated on a dataset of image sequences of cotton plants captured in a high throughput plant phenotyping platform. The algorithms may be generalized to any plant species to study the effect of abiotic stresses on sustainable agriculture practices.

The research introduces a novel algorithm called HyperStressPropagateNet that uses deep neural network based time series modeling to illustrate the qualitative and quantitative propagation of drought stress in a plant using hyperspectral imagery. The hyperspectral cameras typically capture a broad range of wavelengths at very narrow intervals of a few nanometers creating a hyperspectral cube. HyperStressPropagateNet uses spectral band difference-based segmentation method to create the binary mask of the plant which is then used to segment the plant in all bands of a hyperspectral cube to create the reflectance spectra at each plant pixel. The algorithm uses convolutional neural networks to classify the reflectance spectra generated at each pixel into either stressed or unstressed categories to determine the temporal propagation of stress. The limited water availability in the soil is confirmed by changes in the soil water content (SWC) measured using a hand-held device. The excellent correlation between the SWC and the corresponding temporal progression of percentage of stress pixels computed by HyperStressPropagateNet demonstrates the efficacy of the method. The algorithm is evaluated on a dataset of image sequences of cotton plants captured by the hyperspectral camera in the LemnaTec Scanalyzer 3D High Throughput Plant Phenotyping Platform in the University of Nebraska-Lincoln, USA. The excellent performance of the method is established based on evaluations using various metrics, e.g., confusion matrix, precision-recall curve, and F1-score. The method has the potential to be generalized to any plant species to study the effect of abiotic stresses on sustainable agriculture.

Despite conservation efforts in the U.S. Great Plains, woody species have continued to expand at an unprecedented rate, threatening key ecosystem services and resilience. Cross-scale monitoring of these grasslands is key to successful integrative management strategies. In this study we measured plant optical traits derived from hyperspectral proximal sensing techniques with a field spectrometer, coupled with field-based measurements, including fluorescence and chlorophyll content, to determine the impacts of Juniperus virginiana and Pinus ponderosa expansion on grasslands health in Nebraska Sandhills, and investigated the use of optical-based approaches as indicators of successful monitoring of grasslands. Our results showed that higher woody species cover in grasslands was associated with lower soil moisture, decline in forbs, shrubs, and grasses cover and productivity, as well as herbaceous chlorophyll content and fluorescence, compared to non-invaded grasslands. We derived 13 vegetation indices (VIs) from optical-based methods and validated them against traditional handheld measurements of plant ecophysiological traits and vegetation biomass and composition. VIs, including Normalized Difference Vegetation Index (NDVI), Water Index (WI) and Chlorophyll Index at red edge (CIred edge) performed best when tested against biomass, and chlorophyll content and fluorescence (Fv/Fm), suggesting their potential use for assessing grasslands vegetation health. We demonstrate that optical-based approaches can serve as efficient non-invasive tools that can be part of multi-scale successful integrative management strategies.

Early plant selection for desirable traits is important in tree improvement programs and sustainable forest management. In this study, we demonstrate the use of image based high-throughput plant phenotyping (HTPP, LemnaTec 3D Scanalyzer, Germany), with Red, Green, Blue (RGB), and hyperspectral cameras, to quantify Quercus bicolor and Quercus prinoides seedlings growth and development [plant height, projected leaf area (LA), plant/canopy width, ConvexHull, and plant aspect ratio], and assess their response to a dry-down period, under controlled environment. HTPP images were validated against low throughput measurements, including gas exchange, leaf spectral properties, and morphological traits. Using HTPP, we recorded significant differences in growth dynamic in examined species, with faster initial growth rate early in the growing season, higher photosynthetic rates, larger LA, and seedling dimension, in Q. bicolor, compared to Q. prinoides. This has ecological implications on species responses to shading and timing of drought stress, as well as their competitive relationship with each other and with other species under changing climate. HTTP showed that both growth and leaf expansion ceased under dry-down treatment. Image derived and measured morphological traits were highly and significantly correlated under both well-watered and dry-down conditions for both species. To obtain meaningful physiological information using spectrometry, we calculated 12 vegetation indices (VIs) from both HTPP and handheld spectrometers. Vogelmann and Maccioni indices had the highest correlations across methods, suggesting their potential use for assessing oak seedlings performance and health. Our results emphasized the importance of VIs ground truthing, since VIs performance can vary significantly between species and treatments. HTPP tools can successfully be used to effectively assess forest seedlings of the two Quercus species, important for early plant selection for forest management purposes and tree improvement programs.

Remnant populations of Betula papyrifera Marshall have persisted in the Great Plains after the Wisconsin Glaciation along the Niobrara River Valley, Nebraska. Population health has declined in recent years, which has been hypothesized to be due to climate change. We used dendrochronological techniques to assess the response of B. papyrifera to microclimate (1950–2014) and the normalized difference vegetation index (NDVI) derived from satellite imagery (Landsat 5 TM (1985–2011) and MODIS (2000–2014)) as a proxy for population health. Growing-season streamflow and precipitation were positively correlated with raw and standardized tree-ring widths and basal area increment increase. Increasing winter and spring temperatures were unfavorable for tree growth, while increasing summer temperatures were favorable in the absence of drought. The strongest predictor for standardized tree rings was the Palmer Drought Severity Index, suggesting that B. papyrifera is highly responsive to a combination of temperature and water availability. The NDVI from the vegetation community was positively correlated with standardized tree-ring growth, indicating the potential of these techniques to be used as a proxy for ex situ monitoring of B. papyrifera. These results aid in forecasting the dynamics of the species in the face of climate variability and change in both remnant populations and across its current distribution in northern latitudes of North America.