Effect of nitrogen supply on stay-green sorghum in differing post-flowering water regimes.

MAIN CONCLUSION: The expression of stay-green (SG) characteristic in sorghum under water stress was related to N supply. SG genotype performed better than a non-stay-green (NSG) genotype at medium and high N levels. The differences in physiological parameters between SG and NSG genotypes were not significant at low N level and severe water stress. Grain sorghum [Sorghum bicolor (L.) Moench] with stay-green (SG) trait has the potential to produce more biomass and use soil water and nitrogen (N) more efficiently under post-flowering water stress. Previous studies were mostly conducted without N deficiency and more information is needed for interactions among soil N availability, SG genotype, and post-flowering water stress. In this study, the differences in leaf growth and senescence, shoot and root biomass, evapotranspiration (ET), water use efficiency (WUE), leaf photosynthetic responses, and nitrogen use efficiency (NUE) between a SG genotype (BTx642) and a non-stay-green (NSG) genotype (Tx7000) were examined. The two genotypes were grown at three N levels (Low, LN; Medium, MN; High, HN) and under three post-flowering water regimes (No water deficit, ND; Moderate water deficit, MD; Severe water deficit, SD). The genotypic difference was generally significant while it frequently interacted with N levels and water regimes. At medium and high N levels, SG genotype consistently had greater green leaf area, slower senescence rate, more shoot biomass and root biomass, and greater WUE and NUE than the NSG genotype under post-flowering drought. However, differences in several variables (e.g., leaf senescence, ET, WUE and NUE) between genotypes were not significant under SD at LN. At HN and MN, photosynthetic function of SG genotype was better maintained under drought. At LN, SG genotype maintained greater green leaf area but had lower photosynthetic activity than the NSG genotype. Nonetheless, adequate N supply is important for SG genotype under drought and greater root biomass may contribute to greater NUE in SG genotype.

Plant science decadal vision 2020-2030: Reimagining the potential of plants for a healthy and sustainable future.

Plants, and the biological systems around them, are key to the future health of the planet and its inhabitants. The Plant Science Decadal Vision 2020-2030 frames our ability to perform vital and far-reaching research in plant systems sciences, essential to how we value participants and apply emerging technologies. We outline a comprehensive vision for addressing some of our most pressing global problems through discovery, practical applications, and education. The Decadal Vision was developed by the participants at the Plant Summit 2019, a community event organized by the Plant Science Research Network. The Decadal Vision describes a holistic vision for the next decade of plant science that blends recommendations for research, people, and technology. Going beyond discoveries and applications, we, the plant science community, must implement bold, innovative changes to research cultures and training paradigms in this era of automation, virtualization, and the looming shadow of climate change. Our vision and hopes for the next decade are encapsulated in the phrase reimagining the potential of plants for a healthy and sustainable future. The Decadal Vision recognizes the vital intersection of human and scientific elements and demands an integrated implementation of strategies for research (Goals 1-4), people (Goals 5 and 6), and technology (Goals 7 and 8). This report is intended to help inspire and guide the research community, scientific societies, federal funding agencies, private philanthropies, corporations, educators, entrepreneurs, and early career researchers over the next 10 years. The research encompass experimental and computational approaches to understanding and predicting ecosystem behavior; novel production systems for food, feed, and fiber with greater crop diversity, efficiency, productivity, and resilience that improve ecosystem health; approaches to realize the potential for advances in nutrition, discovery and engineering of plant-based medicines, and "green infrastructure." Launching the Transparent Plant will use experimental and computational approaches to break down the phytobiome into a "parts store" that supports tinkering and supports query, prediction, and rapid-response problem solving. Equity, diversity, and inclusion are indispensable cornerstones of realizing our vision. We make recommendations around funding and systems that support customized professional development. Plant systems are frequently taken for granted therefore we make recommendations to improve plant awareness and community science programs to increase understanding of scientific research. We prioritize emerging technologies, focusing on non-invasive imaging, sensors, and plug-and-play portable lab technologies, coupled with enabling computational advances. Plant systems science will benefit from data management and future advances in automation, machine learning, natural language processing, and artificial intelligence-assisted data integration, pattern identification, and decision making. Implementation of this vision will transform plant systems science and ripple outwards through society and across the globe. Beyond deepening our biological understanding, we envision entirely new applications. We further anticipate a wave of diversification of plant systems practitioners while stimulating community engagement, underpinning increasing entrepreneurship. This surge of engagement and knowledge will help satisfy and stoke people's natural curiosity about the future, and their desire to prepare for it, as they seek fuller information about food, health, climate and ecological systems.

Raman-Based Differentiation of Hemp, Cannabidiol-Rich Hemp, and Cannabis.

Hemp (Cannabis sativa) has been used to treat pain as far back as 2900 B.C. Its pharmacological effects originate from a large variety of cannabinols. Although more than 100 different cannabinoids have been isolated from Cannabis plants, clear physiological effects of only a few of them have been determined, including delta-9 tetrahydrocannabinol (THC), cannabidiol (CBD), and cannabigerol (CBG). While THC is an illicit drug, CBD and CBG are legal substances that have a variety of unique pharmacological properties such as the reduction of chronic pain, inflammation, anxiety, and depression. Over the past decade, substantial efforts have been made to develop Cannabis varieties that would produce large amounts of CBD and CBG. Ideally, such plant varieties should produce very little (below 0.3%) if any THC to make their cultivation legal. The amount of cannabinoids in the plant material can be determined using high performance liquid chromatography (HPLC). This analysis, however, is nonportable, destructive, and time and labor consuming. Our group recently proposed to use Raman spectroscopy (RS) for confirmatory, noninvasive, and nondestructive differentiation between hemp and cannabis. The question to ask is whether RS can be used to detect CBD and CBG in hemp, as well as enable confirmatory differentiation between hemp, cannabis, and CBD-rich hemp. In this manuscript, we show that RS can be used to differentiate between cannabis, CBD-rich plants, and regular hemp. We also report spectroscopic signatures of CBG, cannabigerolic acid (CBGA), THC, delta-9-tetrahydrocannabinolic acid (THCA), CBD, and cannabidiolic acid (CBDA) that can be used for Raman-based quantitative diagnostics of these cannabinoids in plant material.

Confirmatory non-invasive and non-destructive differentiation between hemp and cannabis using a hand-held Raman spectrometer.

Cannabis is a generic term that is used to denote hemp plants (Cannabis sativa) that produce delta-9-tetrahydrocannabinolic acid (THCA) in amounts higher than industrial hemp. While THCA itself is not considered psychoactive, it is the source of the psychoactive delta-9 tetrahydrocannabinol (THC) that forms from its oxidation. About 147 million people, which is around 2.5% of the world population, consume cannabis. This makes cannabis by far the most widely cultivated and trafficked illicit drug in the world. Such enormous popularity of cannabis requires substantial effort by border control and law enforcement agencies to control illegal trafficking and distribution. Confirmatory diagnostics of cannabis is currently done by high pressure liquid chromatography (HPLC), which requires sample transportation to a certified laboratory, making THC diagnostics extremely time and labor consuming. This catalyzed a push towards development of a portable, confirmatory, non-invasive and non-destructive approach for cannabis diagnostics that could be performed by a police officer directly in the field to verify illicit drug possession or transport. Raman spectroscopy (RS) is a modern analytical technique that meets all these strict expectations. In this manuscript, we show that RS can be used to determine whether plant material is hemp or cannabis with 100% accuracy. We also demonstrate that RS can be used to probe the content of THCA in the analyzed samples. These findings suggest that a hand-held Raman spectrometer can be an ideal tool for police officers and hemp breeders to enable highly accurate diagnostics of THCA content in plants.

Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research.

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1-the summer 2015 and winter 2016 growing seasons-of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project's goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.

Host Suitability of Rapeseed for Heterodera schachtii.

Because rapeseed, especially canola, has the potential to be grown in rotation with sugarbeet in the north-central region of the United States, this study was initiated to assess its susceptibility to infection by Heterodera schachtii and to develop a screening method for Brassica germplasm. Existing methodology was adapted for growing Brassica juncea, B. napus, B. rapa, Brassica hybrids, and sugarbeet, Beta vulgaris, in H. schachtii-infested soil to count the females that developed on the roots. Cysts on sugarbeet contained a mean of 130 eggs compared with 240 for B. napus, lowest for the Brassica. Viability of eggs produced was assessed in soil planted with Brassica and sugarbeet and infested with with 0, 100, 1,000, 3,000, and 5,000 eggs to count resulting females and cysts. Number of females (y) was related linearly to infestation rate (x) by the regression equations y = 2.82 + 0.07(x) for the Brassica lines (R(2) = 0.79; P < 0.001) and y = 0.43 + 0.04(x) for sugarbeet (R(2) = 0.69; P < 0.007). These data indicated the potential for H. schachtii population increase if the two crops are used in rotation. All of the 111 germplasm lines tested were susceptible. The methodology developed during this research would benefit attempts to develop rapeseed cultivars resistant to H. schachtii.