The impacts of rising vapour pressure deficit in natural and managed ecosystems.

An exponential rise in the atmospheric vapour pressure deficit (VPD) is among the most consequential impacts of climate change in terrestrial ecosystems. Rising VPD has negative and cascading effects on nearly all aspects of plant function including photosynthesis, water status, growth and survival. These responses are exacerbated by land-atmosphere interactions that couple VPD to soil water and govern the evolution of drought, affecting a range of ecosystem services including carbon uptake, biodiversity, the provisioning of water resources and crop yields. However, despite the global nature of this phenomenon, research on how to incorporate these impacts into resilient management regimes is largely in its infancy, due in part to the entanglement of VPD trends with those of other co-evolving climate drivers. Here, we review the mechanistic bases of VPD impacts at a range of spatial scales, paying particular attention to the independent and interactive influence of VPD in the context of other environmental changes. We then evaluate the consequences of these impacts within key management contexts, including water resources, croplands, wildfire risk mitigation and management of natural grasslands and forests. We conclude with recommendations describing how management regimes could be altered to mitigate the otherwise highly deleterious consequences of rising VPD.

Ground far-red sun-induced chlorophyll fluorescence and vegetation indices in the US Midwestern agroecosystems.

Sun-induced chlorophyll fluorescence (SIF) provides an opportunity to study terrestrial ecosystem photosynthesis dynamics. However, the current coarse spatiotemporal satellite SIF products are challenging for mechanistic interpretations of SIF signals. Long-term ground SIF and vegetation indices (VIs) are important for satellite SIF validation and mechanistic understanding of the relationship between SIF and photosynthesis when combined with leaf- and canopy-level auxiliary measurements. In this study, we present and analyze a total of 15 site-years of ground far-red SIF (SIF at 760 nm, SIF760) and VIs datasets from soybean, corn, and miscanthus grown in the U.S. Corn Belt from 2016 to 2021. We introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint. This long-term ground far-red SIF and VIs dataset provides important and first-hand data for far-red SIF interpretation and understanding the mechanistic relationship between far-red SIF and canopy photosynthesis across various crop species and environmental conditions.

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems.

Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

Solar-induced chlorophyll fluorescence captures the effects of elevated ozone on canopy structure and acceleration of senescence in soybean.

Solar-induced chlorophyll fluorescence (SIF) provides an opportunity to rapidly and non-destructively investigate how plants respond to stress. Here, we explored the potential of SIF to detect the effects of elevated O3 on soybean in the field where soybean was subjected to ambient and elevated O3 throughout the growing season in 2021. Exposure to elevated O3 resulted in a significant decrease in canopy SIF at 760 nm (SIF760), with a larger decrease in the late growing season (36%) compared with the middle growing season (13%). Elevated O3 significantly decreased the fraction of absorbed photosynthetically active radiation by 8-15% in the middle growing season and by 35% in the late growing stage. SIF760 escape ratio (fesc) was significantly increased under elevated O3 by 5-12% in the late growth stage due to a decrease of leaf chlorophyll content and leaf area index. Fluorescence yield of the canopy was reduced by 5-11% in the late growing season depending on the fesc estimation method, during which leaf maximum carboxylation rate and maximum electron transport were significantly reduced by 29% and 20% under elevated O3. These results demonstrated that SIF could capture the elevated O3 effect on canopy structure and acceleration of senescence in soybean and provide empirical support for using SIF for soybean stress detection and phenotyping.

Evaluation of developmental toxicity of safinamide in zebrafish larvae (Danio rerio).

Monoamine oxidase-B (MAO-B), as a principal metabolizing enzyme, plays important roles in the metabolism of catecholamines and xenobiotics in the central nervous system and peripheral tissues. Safinamide, the third-generation reversible MAO-B inhibitor, has potential to alleviate many neurological diseases such as Parkinson's disease (PD) and depression. Exposure to clinical psychotropic drugs often has adverse effects on fetuses. Currently, a variety of studies of safinamide focus on its curative effect and pharmacological effect, while its side effect of embryonic development is barely studied. In this study, we used zebrafish as a model to evaluate the embryonic developmental toxicity of safinamide. Our results revealed that higher concentrations (30 µM) of safinamide treatment caused a decrease in hatching rate and an increase in malformation and mortality in zebrafish larvae. Meanwhile, we observed that lower safinamide exposure (10 µM) increased the body length of zebrafish larvae and resulted in hyperactivity-like behaviors. In addition, an increased trend in dopamine (DA) level was found in 3.3 µM and 10 µM safinamide-exposed groups. Transcriptome analysis identified that safinamide exposure may disturb a variety of physiological processes such as neuroactive ligand-receptor interaction signaling pathway. In summary, our study reveals that safinamide may cause developmental defects in zebrafish larvae and provides insights into its toxic reactions in early develoment.

Ghrelin regulates hyperactivity-like behaviors via growth hormone signaling pathway in zebrafish (Danio rerio).

Introduction: Ghrelin is originally identified as the endogenous ligand for the growth hormone secretagogue receptor (GHSR) and partially acts by stimulating growth hormone (GH) release. Our previous studies have identified GHRELIN as a novel susceptibility gene for human attention-deficit hyperactivity disorder (ADHD), and ghrelin-depleted zebrafish (Danio rerio) display ADHD-like behaviors. However, the underlying molecular mechanism how ghrelin regulates hyperactivity-like behaviors is not yet known. Results: Here, we performed RNA-sequencing analysis using adult ghrelin Δ/Δ zebrafish brains to investigate the underlying molecular mechanisms. We found that gh1 mRNA and genes related to the gh signaling pathway were significantly reduced at transcriptional expression levels. Quantitative polymerase chain reaction (qPCR) was performed and confirmed the downregulation of gh signaling pathway-related genes in ghrelin Δ/Δ zebrafish larvae and the brain of adult ghrelin Δ/Δ zebrafish. In addition, ghrelin Δ/Δ zebrafish displayed hyperactive and hyperreactive phenotypes, such as an increase in motor activity in swimming test and a hyperreactive phenotype under light/dark cycle stimulation, mimicking human ADHD symptoms. Intraperitoneal injection of recombinant human growth hormone (rhGH) partially rescued the hyperactivity and hyperreactive-like behaviors in ghrelin mutant zebrafish. Conclusion: Our results indicated that ghrelin may regulate hyperactivity-like behaviors by mediating gh signaling pathway in zebrafish. And the protective effect of rhGH on ghrelin Δ/Δ zebrafish hyperactivity behavior provides new therapeutic clues for ADHD patients.

Assessing long-term impacts of cover crops on soil organic carbon in the central US Midwestern agroecosystems.

Cover crops have been reported as one of the most effective practices to increase soil organic carbon (SOC) for agroecosystems. Impacts of cover crops on SOC change vary depending on soil properties, climate, and management practices, but it remains unclear how these control factors affect SOC benefits from cover crops, as well as which management practices can maximize SOC benefits. To address these questions, we used an advanced process-based agroecosystem model, ecosys, to assess the impacts of winter cover cropping on SOC accumulation under different environmental and management conditions. We aimed to answer the following questions: (1) To what extent do cover crops benefit SOC accumulation, and how do SOC benefits from cover crops vary with different factors (i.e., initial soil properties, cover crop types, climate during the cover crop growth period, and cover crop planting and terminating time)? (2) How can we enhance SOC benefits from cover crops under different cover crop management options? Specifically, we first calibrated and validated the ecosys model at two long-term field experiment sites with SOC measurements in Illinois. We then applied the ecosys model to six cover crop field experiment sites spanning across Illinois to assess the impacts of different factors on SOC accumulation. Our modeling results revealed the following findings: (1) Growing cover crops can bring SOC benefits by 0.33 ± 0.06 MgC ha-1  year-1 in six cover crop field experiment sites across Illinois, and the SOC benefits are species specific to legume and non-legume cover crops. (2) Initial SOC stocks and clay contents had overall small influences on SOC benefits from cover crops. During the cover crop growth period (i.e., winter and spring in the US Midwest), high temperature increased SOC benefits from cover crops, while the impacts from larger precipitation on SOC benefits varied field by field. (3) The SOC benefits from cover crops can be maximized by optimizing cover crop management practices (e.g., selecting cover crop types and controlling cover crop growth period) for the US Midwestern maize-soybean rotation system. Finally, we discussed the economic and policy implications of adopting cover crops in the US Midwest, including that current economic incentives to grow cover crops may not be sufficient to cover costs. This study systematically assessed cover crop impacts for SOC change in the US Midwest context, while also demonstrating that the ecosys model, with rigorous validation using field experiment data, can be an effective tool to guide the adaptive management of cover crops and quantify SOC benefits from cover crops. The study thus provides practical tools and insights for practitioners and policy-makers to design cover crop related government agricultural policies and incentive programs for farmers and agri-food related industries.

Silver lining to a climate crisis in multiple prospects for alleviating crop waterlogging under future climates.

Extreme weather events threaten food security, yet global assessments of impacts caused by crop waterlogging are rare. Here we first develop a paradigm that distils common stress patterns across environments, genotypes and climate horizons. Second, we embed improved process-based understanding into a farming systems model to discern changes in global crop waterlogging under future climates. Third, we develop avenues for adapting cropping systems to waterlogging contextualised by environment. We find that yield penalties caused by waterlogging increase from 3-11% historically to 10-20% by 2080, with penalties reflecting a trade-off between the duration of waterlogging and the timing of waterlogging relative to crop stage. We document greater potential for waterlogging-tolerant genotypes in environments with longer temperate growing seasons (e.g., UK, France, Russia, China), compared with environments with higher annualised ratios of evapotranspiration to precipitation (e.g., Australia). Under future climates, altering sowing time and adoption of waterlogging-tolerant genotypes reduces yield penalties by 18%, while earlier sowing of winter genotypes alleviates waterlogging by 8%. We highlight the serendipitous outcome wherein waterlogging stress patterns under present conditions are likely to be similar to those in the future, suggesting that adaptations for future climates could be designed using stress patterns realised today.

Developmental and behavioral toxicity assessment of opicapone in zebrafish embryos.

The use of clinical psychoactive drugs often poses unpredictable threats to fetal development. Catechol-O-methyltransferase (COMT) is a key enzyme that regulates dopamine metabolism and a promising target for modulation of cognitive functions. Opicapone, a newly effective third-generation peripheral COMT inhibitor, is used for the treatment of Parkinson's disease (PD) and possibly to improve other dopamine-related disorders such as alcohol use disorder (AUD) and obsessive-compulsive disorder (OCD). The widespread use of opicapone will inevitably lead to biological exposure and damage to the human body, such as affecting fetal development. However, the effect of opicapone on embryonic development remains unknown. Here, zebrafish larvae were used as an animal model and demonstrated that a high concentration (30 µM) of opicapone exposure was teratogenic and lethal, while a low concentration also caused developmental delay such as a shortened body size, a smaller head, and reduced locomotor behaviors in zebrafish larvae. Meanwhile, opicapone treatment specifically increased the level of dopamine (DA) in zebrafish larvae. The depletion response of the total glutathione level (including oxidized and reduced forms of glutathione) and changed antioxidant enzymes activities in zebrafish larvae suggest oxidative damage caused by opicapone. In addition, enhanced glutathione metabolism and cytokine-cytokine receptor interaction were found in zebrafish larvae treated with opicapone, indicating that opicapone treatment caused an oxidation process and immune responses. Our results provide a new insight into the significant developmental toxicity of opicapone in zebrafish larvae.

Reductions in leaf area index, pod production, seed size, and harvest index drive yield loss to high temperatures in soybean.

Improvements in genetics, technology, and agricultural intensification have increased soybean yields; however, adverse climate conditions may prevent these gains from being fully realized in the future. Higher growing season temperatures reduce soybean yields in key production regions including the US Midwest, and better understanding of the developmental and physiological mechanisms that constrain soybean yield under high temperature conditions is needed. This study tested the response of two soybean cultivars to four elevated temperature treatments (+1.7, +2.6, +3.6, and +4.8 °C) in the field over three growing seasons and identified threshold temperatures for response and linear versus non-linear trait responses to temperature. Yield declined non-linearly to temperature, with decreases apparent when canopy temperature exceeded 20.9 °C for the locally adapted cultivar and 22.7°C for a cultivar adapted to more southern locations. While stem node number increased with increasing temperature, leaf area index decreased substantially. Pod production, seed size, and harvest index significantly decreased with increasing temperature. The seasonal average temperature of even the mildest treatment exceeded the threshold temperatures for yield loss, emphasizing the importance of improving temperature tolerance in soybean germplasm with intensifying climate change.

Baicalein exerts anxiolytic and antinociceptive effects in a mouse model of posttraumatic stress disorder: Involvement of the serotonergic system and spinal delta-opioid receptors.

Post-traumatic stress disorder (PTSD) is a serious mental disease featured by a stress dysfunction that occurs after an individual has faced intense mental stress, often accompanied by anxiety and chronic pain. Currently, the mainstream drug for PTSD is serotonin reuptake inhibitors (SSRIs), however, their pain management for patients is limited. Baicalein, a Chinese traditional herbal medicine, has shown promising results in treating anxiety, depression, and pain. In this study, we found that baicalein may alleviate single prolonged stress (SPS)-induced PTSD-like behaviors in mice without altering baseline nociceptive sensitivity or activity. Meanwhile, baicalein increased the noradrenaline (NE) and serotonin (5-HT) content and decreased the ratio of 5-hydroxyindoleacetic acid (5-HIAA)/5-HT by inhibiting the activity of monoamine oxidase A (MAO-A) in SPS-induce mice. The anxiolytic and antinociceptive effects induced by baicalein were totally abolished by 5-HT depleting agents. Moreover, the anxiolytic effects of baicalein could be abolished by the 5-HT1A receptor antagonist WAY-100635, and the analgesic effects could be abolished by delta-opioid receptor antagonists in the spinal. Taken together, our study provides compelling evidence that baicalein reversed anxiety-like behaviors and neuropathic pain in PTSD through serotonergic system and spinal delta-opioid receptors.

Recent cover crop adoption is associated with small maize and soybean yield losses in the United States.

Cover crops are gaining traction in many agricultural regions, partly driven by increased public subsidies and by private markets for ecosystem services. These payments are motivated by environmental benefits, including improved soil health, reduced erosion, and increased soil organic carbon. However, previous work based on experimental plots or crop modeling indicates cover crops may reduce crop yields. It remains unclear, though, how recent cover crop adoption has affected productivity in commercial agricultural systems. Here we perform the first large-scale, field-level analysis of observed yield impacts from cover cropping as implemented across the US Corn Belt. We use validated satellite data products at sub-field scales to analyze maize and soybean yield outcomes for over 90,000 fields in 2019-2020. Because we lack data on cover crop species or timing, we seek to quantify the yield impacts of cover cropping as currently practiced in aggregate. Using causal forests analysis, we estimate an average maize yield loss of 5.5% on fields where cover crops were used for 3 or more years, compared with fields that did not adopt cover cropping. Maize yield losses were larger on fields with better soil ratings, cooler mid-season temperatures, and lower spring rainfall. For soybeans, average yield losses were 3.5%, with larger impacts on fields with warmer June temperatures, lower spring and late-season rainfall, and, to a lesser extent, better soils. Estimated impacts are consistent with multiple mechanisms indicated by experimental and simulation-based studies, including the effects of cover crops on nitrogen dynamics, water consumption, and soil oxygen depletion. Our results suggest a need to improve cover crop management to reduce yield penalties, and a potential need to target subsidies based on likely yield impacts. Ultimately, avoiding substantial yield penalties is important for realizing widespread adoption and associated benefits for water quality, erosion, soil carbon, and greenhouse gas emissions.

Representation of Leaf-to-Canopy Radiative Transfer Processes Improves Simulation of Far-Red Solar-Induced Chlorophyll Fluorescence in the Community Land Model Version 5.

Recent advances in satellite observations of solar-induced chlorophyll fluorescence (SIF) provide a new opportunity to constrain the simulation of terrestrial gross primary productivity (GPP). Accurate representation of the processes driving SIF emission and its radiative transfer to remote sensing sensors is an essential prerequisite for data assimilation. Recently, SIF simulations have been incorporated into several land surface models, but the scaling of SIF from leaf-level to canopy-level is usually not well-represented. Here, we incorporate the simulation of far-red SIF observed at nadir into the Community Land Model version 5 (CLM5). Leaf-level fluorescence yield was simulated by a parametric simplification of the Soil Canopy-Observation of Photosynthesis and Energy fluxes model (SCOPE). And an efficient and accurate method based on escape probability is developed to scale SIF from leaf-level to top-of-canopy while taking clumping and the radiative transfer processes into account. SIF simulated by CLM5 and SCOPE agreed well at sites except one in needleleaf forest (R 2 > 0.91, root-mean-square error <0.19 W⋅m-2⋅sr-1⋅µm-1), and captured the day-to-day variation of tower-measured SIF at temperate forest sites (R 2 > 0.68). At the global scale, simulated SIF generally captured the spatial and seasonal patterns of satellite-observed SIF. Factors including the fluorescence emission model, clumping, bidirectional effect, and leaf optical properties had considerable impacts on SIF simulation, and the discrepancies between simulate d and observed SIF varied with plant functional type. By improving the representation of radiative transfer for SIF simulation, our model allows better comparisons between simulated and observed SIF toward constraining GPP simulations.

Informing Nature-based Climate Solutions for the United States with the best-available science.

Nature-based Climate Solutions (NbCS) are managed alterations to ecosystems designed to increase carbon sequestration or reduce greenhouse gas emissions. While they have growing public and private support, the realizable benefits and unintended consequences of NbCS are not well understood. At regional scales where policy decisions are often made, NbCS benefits are estimated from soil and tree survey data that can miss important carbon sources and sinks within an ecosystem, and do not reveal the biophysical impacts of NbCS for local water and energy cycles. The only direct observations of ecosystem-scale carbon fluxes, for example, by eddy covariance flux towers, have not yet been systematically assessed for what they can tell us about NbCS potentials, and state-of-the-art remote sensing products and land-surface models are not yet being widely used to inform NbCS policymaking or implementation. As a result, there is a critical mismatch between the point- and tree-scale data most often used to assess NbCS benefits and impacts, the ecosystem and landscape scales where NbCS projects are implemented, and the regional to continental scales most relevant to policymaking. Here, we propose a research agenda to confront these gaps using data and tools that have long been used to understand the mechanisms driving ecosystem carbon and energy cycling, but have not yet been widely applied to NbCS. We outline steps for creating robust NbCS assessments at both local to regional scales that are informed by ecosystem-scale observations, and which consider concurrent biophysical impacts, future climate feedbacks, and the need for equitable and inclusive NbCS implementation strategies. We contend that these research goals can largely be accomplished by shifting the scales at which pre-existing tools are applied and blended together, although we also highlight some opportunities for more radical shifts in approach.

Predicting biochemical acclimation of leaf photosynthesis in soybean under in-field canopy warming using hyperspectral reflectance.

Traditional gas exchange measurements are cumbersome, which makes it difficult to capture variation in biochemical parameters, namely the maximum rate of carboxylation measured at a reference temperature (Vcmax25 ) and the maximum electron transport at a reference temperature (Jmax25 ), in response to growth temperature over time from days to weeks. Hyperspectral reflectance provides reliable measures of Vcmax25 and Jmax25 ; however, the capability of this method to capture biochemical acclimations of the two parameters to high growth temperature over time has not been demonstrated. In this study, Vcmax25 and Jmax25 were measured over multiple growth stages during two growing seasons for field-grown soybeans using both gas exchange techniques and leaf spectral reflectance under ambient and four elevated canopy temperature treatments (ambient+1.5, +3, +4.5, and +6°C). Spectral vegetation indices and machine learning methods were used to build predictive models for Vcmax25 and Jmax25 , based on the leaf reflectance. Results showed that these models yielded an R2 of 0.57-0.65 and 0.48-0.58 for Vcmax25 and Jmax25 , respectively. Hyperspectral reflectance captured biochemical acclimation of leaf photosynthesis to high temperature in the field, improving spatial and temporal resolution in the ability to assess the impact of future warming on crop productivity.

Sustainable irrigation based on co-regulation of soil water supply and atmospheric evaporative demand.

Irrigation is an important adaptation to reduce crop yield loss due to water stress from both soil water deficit (low soil moisture) and atmospheric aridity (high vapor pressure deficit, VPD). Traditionally, irrigation has primarily focused on soil water deficit. Observational evidence demonstrates that stomatal conductance is co-regulated by soil moisture and VPD from water supply and demand aspects. Here we use a validated hydraulically-driven ecosystem model to reproduce the co-regulation pattern. Specifically, we propose a plant-centric irrigation scheme considering water supply-demand dynamics (SDD), and compare it with soil-moisture-based irrigation scheme (management allowable depletion, MAD) for continuous maize cropping systems in Nebraska, United States. We find that, under current climate conditions, the plant-centric SDD irrigation scheme combining soil moisture and VPD, could significantly reduce irrigation water use (-24.0%) while maintaining crop yields, and increase economic profits (+11.2%) and irrigation water productivity (+25.2%) compared with MAD, thus SDD could significantly improve water sustainability.

Assessing Marginal Land Availability Based on Land Use Change Information in the Contiguous United States.

Utilization of marginal land for growing dedicated bioenergy crops for second-generation biofuels is appealing to avoid conflicts with food production. This study develops a novel framework to quantify marginal land for the Contiguous United States (CONUS) based on a history of satellite-observed land use change (LUC) over the 2008-2015 period. Frequent LUC between crop and noncrop is assumed to be an indicator of economically marginal land; this land is also likely to have a lower opportunity cost of conversion from food crop to bioenergy crop production. We first present an approach to identify cropland in transition using the time series of Cropland Data Layer (CDL) land cover product and determine the amount of land that can be considered marginal with a high degree of confidence vs with uncertainty across the CONUS. We find that the biophysical characteristics of this land and its productivity and environmental vulnerability vary across the land and lie in between that of permanent cropland and permanent natural vegetation/bare areas; this land also has relatively low intrinsic value and agricultural profit but a high financial burden and economic risk. We find that the total area of marginal land with confidence vs with uncertainty is 10.2 and 58.4 million hectares, respectively, and mainly located along the 100th meridian. Only a portion of this marginal land (1.4-2.2 million hectares with confidence and 14.8-19.4 million hectares with uncertainty) is in the rainfed region and not in crop production and, thus, suitable for producing energy crops without diverting land from food crops in 2016. These estimates are much smaller than the estimates obtained by previous studies, which consider all biophysically low-quality land to be marginal without considering economical marginality. The estimate of marginal land for bioenergy crops obtained in this study is an indicator of the availability of economically marginal land that is suitable for bioenergy crop production; whether this land is actually converted to bioenergy crops will depend on the market conditions. We note the inability to conduct field-level validation of cropland in transition and leave it to future advances in technology to ground-truth land use change and its relationship to economically marginal land.

Machine Learning-Based Modeling of Spatio-Temporally Varying Responses of Rainfed Corn Yield to Climate, Soil, and Management in the U.S. Corn Belt.

Better understanding the variabilities in crop yield and production is critical to assessing the vulnerability and resilience of food production systems. Both environmental (climatic and edaphic) conditions and management factors affect the variabilities of crop yield. In this study, we conducted a comprehensive data-driven analysis in the U.S. Corn Belt to understand and model how rainfed corn yield is affected by climate variability and extremes, soil properties (soil available water capacity, soil organic matter), and management practices (planting date and fertilizer applications). Exploratory data analyses revealed that corn yield responds non-linearly to temperature, while the negative vapor pressure deficit (VPD) effect on corn yield is monotonic and more prominent. Higher mean yield and inter-annual yield variability are found associated with high soil available water capacity, while lower inter-annual yield variability is associated with high soil organic matter (SOM). We also identified region-dependent relationships between planting date and yield and a strong correlation between planting date and the April weather condition (temperature and rainfall). Next, we built machine learning models using the random forest and LASSO algorithms, respectively, to predict corn yield with all climatic, soil properties, and management factors. The random forest model achieved a high prediction accuracy for annual yield at county level as early as in July (R 2 = 0.781) and outperformed LASSO. The gained insights from this study lead to improved understanding of how corn yield responds to climate variability and projected change in the U.S. Corn Belt and globally.

Quantifying high-temperature stress on soybean canopy photosynthesis: The unique role of sun-induced chlorophyll fluorescence.

High temperature and accompanying high vapor pressure deficit often stress plants without causing distinctive changes in plant canopy structure and consequential spectral signatures. Sun-induced chlorophyll fluorescence (SIF), because of its mechanistic link with photosynthesis, may better detect such stress than remote sensing techniques relying on spectral reflectance signatures of canopy structural changes. However, our understanding about physiological mechanisms of SIF and its unique potential for physiological stress detection remains less clear. In this study, we measured SIF at a high-temperature experiment, Temperature Free-Air Controlled Enhancement, to explore the potential of SIF for physiological investigations. The experiment provided a gradient of soybean canopy temperature with 1.5, 3.0, 4.5, and 6.0°C above the ambient canopy temperature in the open field environments. SIF yield, which is normalized by incident radiation and the fraction of absorbed photosynthetically active radiation, showed a high correlation with photosynthetic light use efficiency (r = 0.89) and captured dynamic plant responses to high-temperature conditions. SIF yield was affected by canopy structural and plant physiological changes associated with high-temperature stress (partial correlation r = 0.60 and -0.23). Near-infrared reflectance of vegetation, only affected by canopy structural changes, was used to minimize the canopy structural impact on SIF yield and to retrieve physiological SIF yield (ΦF ) signals. ΦF further excludes the canopy structural impact than SIF yield and indicates plant physiological variability, and we found that ΦF outperformed SIF yield in responding to physiological stress (r = -0.37). Our findings highlight that ΦF sensitively responded to the physiological downregulation of soybean gross primary productivity under high temperature. ΦF , if reliably derived from satellite SIF, can support monitoring regional crop growth and different ecosystems' vegetation productivity under environmental stress and climate change.

Potential of hotspot solar-induced chlorophyll fluorescence for better tracking terrestrial photosynthesis.

Remote sensing of solar-induced fluorescence (SIF) opens a new window for quantifying a key ecological variable, the terrestrial ecosystem gross primary production (GPP), because of the revealed strong SIF-GPP correlation. However, similar to many other remotely sensed metrics, SIF observations suffer from the sun-sensor geometry effects, which may have important impacts on the SIF-GPP relationship but remain poorly understood. Here we used remotely sensed SIF, globally distributed tower GPP data, and a mechanistic model to provide a systematic analysis. Our results reveal that leaf physiology, canopy structure, and sun-sensor geometries all affect the SIF-GPP relationship. In particular, we found that SIF observations in the sun-tracking hotspot direction can be a better proxy of GPP due to the similar responses of light use efficiency and SIF escaping probability in the hotspot direction to the increasing incoming solar radiation. Such conclusions are supported by a variety of modeling simulations and satellite observations over various plant function types, at different time scales and with satellite observational modes. This study demonstrates the potential and advantage of normalizing SIF observations to the hotspot direction for better global GPP estimations. This study also demonstrates the great potentials of current and future spaceborne sun-tracking satellite missions for a significant improvement in measuring and monitoring, at a wide range of spatial and temporal scales, the changes in terrestrial ecosystem GPP in response to anticipated changes in the Earth's environmental conditions.