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.

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.

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.

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.

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.

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.

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.

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.

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.

Multiscale computational models can guide experimentation and targeted measurements for crop improvement.

Computational models of plants have identified gaps in our understanding of biological systems, and have revealed ways to optimize cellular processes or organ-level architecture to increase productivity. Thus, computational models are learning tools that help direct experimentation and measurements. Models are simplifications of complex systems, and often simulate specific processes at single scales (e.g. temporal, spatial, organizational, etc.). Consequently, single-scale models are unable to capture the critical cross-scale interactions that result in emergent properties of the system. In this perspective article, we contend that to accurately predict how a plant will respond in an untested environment, it is necessary to integrate mathematical models across biological scales. Computationally mimicking the flow of biological information from the genome to the phenome is an important step in discovering new experimental strategies to improve crops. A key challenge is to connect models across biological, temporal and computational (e.g. CPU versus GPU) scales, and then to visualize and interpret integrated model outputs. We address this challenge by describing the efforts of the international Crops in silico consortium.

Interannual variability of ecosystem iso/anisohydry is regulated by environmental dryness.

●Plants are characterized by the iso/anisohydry continuum depending on how they regulate leaf water potential (ΨL ). However, how iso/anisohydry changes over time in response to year-to-year variations in environmental dryness and how such responses vary across different regions remains poorly characterized. ●We investigated how dryness, represented by aridity index, affects the interannual variability of ecosystem iso/anisohydry at the regional scale, estimated using satellite microwave vegetation optical depth (VOD) observations. This ecosystem-level analysis was further complemented with published field observations of species-level ΨL . ●We found different behaviors in the directionality and sensitivity of isohydricity (σ) with respect to the interannual variation of dryness in different ecosystems. These behaviors can largely be differentiated by the average dryness of the ecosystem itself: in mesic ecosystems, σ decreases in drier years with a higher sensitivity to dryness; in xeric ecosystems, σ increases in drier years with a lower sensitivity to dryness. These results were supported by the species-level synthesis. ●Our study suggests that how plants adjust their water use across years - as revealed by their interannual variability in isohydricity - depends on the dryness of plants' living environment. This finding advances our understanding of plant responses to drought at regional scales.

Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy.

The photosynthetic capacity or the CO2-saturated photosynthetic rate (Vmax), chlorophyll, and nitrogen are closely linked leaf traits that determine C4 crop photosynthesis and yield. Accurate, timely, rapid, and non-destructive approaches to predict leaf photosynthetic traits from hyperspectral reflectance are urgently needed for high-throughput crop monitoring to ensure food and bioenergy security. Therefore, this study thoroughly evaluated the state-of-the-art physically based radiative transfer models (RTMs), data-driven partial least squares regression (PLSR), and generalized PLSR (gPLSR) models to estimate leaf traits from leaf-clip hyperspectral reflectance, which was collected from maize (Zea mays L.) bioenergy plots with diverse genotypes, growth stages, treatments with nitrogen fertilizers, and ozone stresses in three growing seasons. The results show that leaf RTMs considering bidirectional effects can give accurate estimates of chlorophyll content (Pearson correlation r=0.95), while gPLSR enabled retrieval of leaf nitrogen concentration (r=0.85). Using PLSR with field measurements for training, the cross-validation indicates that Vmax can be well predicted from spectra (r=0.81). The integration of chlorophyll content (strongly related to visible spectra) and nitrogen concentration (linked to shortwave infrared signals) can provide better predictions of Vmax (r=0.71) than only using either chlorophyll or nitrogen individually. This study highlights that leaf chlorophyll content and nitrogen concentration have key and unique contributions to Vmax prediction.

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.

Ghrelin modulates dopaminergic neuron formation and attention deficit hyperactivity disorder-like behaviors: From animals to human models.

Attention deficit hyperactivity disorder (ADHD) is one of the most prevalent psychiatric disorders in children. The orexigenic hormone ghrelin is important in neuroprotection and neurodevelopment, which may play an important role in psychopathogenesis of ADHD. This study aimed to systematically investigate the genomic and pharmacological manipulations of ghrelin functioning in ADHD-like symptoms in zebrafish models and validated the effects of ghrelin polymorphisms in human subjects with ADHD. We firstly generated ghrelinΔ/Δ zebrafish mutant, which displayed hyperactive, attention deficit-like and impulsive-like behaviors, as well as endophenotypes, mimicking human ADHD. GhrelinΔ/Δ zebrafish exhibited downregulated expression levels of wnt1, wnt3a, wnt5a that are critical for dopaminergic neuron development to possibly regulate their number and spatial organization. Pharmacological blockade of wnt signaling with XAV939 induced a reduced moving activity and less dopaminergic neurons; whereas, wnt agonist SB415286 rescued hyperactivity and dopaminergic neuron loss in ghrelinΔ/Δ zebrafish. In addition, we further identified and validated a SNP, rs696217, on orexigenic hormone preproghrelin/ghrelin (T408T, Met72Met) to be associated with a higher risk of ADHD in a case-controlled association study with 248 subjects with ADHD and 208 subjects of healthy controls. Together, our results reveal a novel endogenous role for orexigenic hormone ghrelin in ADHD, which provides insights into genetic regulation and drug screens for the identification of novel treatments of ADHD.

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.

Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression.

The lack of efficient means to accurately infer photosynthetic traits constrains understanding global land carbon fluxes and improving photosynthetic pathways to increase crop yield. Here, we investigated whether a hyperspectral imaging camera mounted on a mobile platform could provide the capability to help resolve these challenges, focusing on three main approaches, that is, reflectance spectra-, spectral indices-, and numerical model inversions-based partial least square regression (PLSR) to estimate photosynthetic traits from canopy hyperspectral reflectance for 11 tobacco cultivars. Results showed that PLSR with inputs of reflectance spectra or spectral indices yielded an R2 of ~0.8 for predicting V cmax and J max , higher than an R2 of ~0.6 provided by PLSR of numerical inversions. Compared with PLSR of reflectance spectra, PLSR with spectral indices exhibited a better performance for predicting V cmax (R2 = 0.84 ± 0.02, RMSE = 33.8 ± 2.2 µmol m-2 s-1 ) while a similar performance for J max (R2 = 0.80 ± 0.03, RMSE = 22.6 ± 1.6 µmol m-2 s-1 ). Further analysis on spectral resampling revealed that V cmax and J max could be predicted with ~10 spectral bands at a spectral resolution of less than 14.7 nm. These results have important implications for improving photosynthetic pathways and mapping of photosynthesis across scales.

Plot-level rapid screening for photosynthetic parameters using proximal hyperspectral imaging.

Photosynthesis is currently measured using time-laborious and/or destructive methods which slows research and breeding efforts to identify crop germplasm with higher photosynthetic capacities. We present a plot-level screening tool for quantification of photosynthetic parameters and pigment contents that utilizes hyperspectral reflectance from sunlit leaf pixels collected from a plot (~2 m×2 m) in <1 min. Using field-grown Nicotiana tabacum with genetically altered photosynthetic pathways over two growing seasons (2017 and 2018), we built predictive models for eight photosynthetic parameters and pigment traits. Using partial least squares regression (PLSR) analysis of plot-level sunlit vegetative reflectance pixels from a single visible near infra-red (VNIR) (400-900 nm) hyperspectral camera, we predict maximum carboxylation rate of Rubisco (Vc,max, R2=0.79) maximum electron transport rate in given conditions (J1800, R2=0.59), maximal light-saturated photosynthesis (Pmax, R2=0.54), chlorophyll content (R2=0.87), the Chl a/b ratio (R2=0.63), carbon content (R2=0.47), and nitrogen content (R2=0.49). Model predictions did not improve when using two cameras spanning 400-1800 nm, suggesting a robust, widely applicable and more 'cost-effective' pipeline requiring only a single VNIR camera. The analysis pipeline and methods can be used in any cropping system with modified species-specific PLSR analysis to offer a high-throughput field phenotyping screening for germplasm with improved photosynthetic performance in field trials.

Quantifying irrigation cooling benefits to maize yield in the US Midwest.

Irrigation is an important adaptation strategy to improve crop resilience to global climate change. Irrigation plays an essential role in sustaining crop production in water-limited regions, as irrigation water not only benefits crops through fulfilling crops' water demand but also creates an evaporative cooling that mitigates crop heat stress. Here we use satellite remote sensing and maize yield data in the state of Nebraska, USA, combined with statistical models, to quantify the contribution of cooling and water supply to the yield benefits due to irrigation. Results show that irrigation leads to a considerable cooling on daytime land surface temperature (-1.63°C in July), an increase in enhanced vegetation index (+0.10 in July), and 81% higher maize yields compared to rainfed maize. These irrigation effects vary along the spatial and temporal gradients of precipitation and temperature, with a greater effect in dry and hot conditions, and decline toward wet and cool conditions. We find that 16% of irrigation yield increase is due to irrigation cooling, while the rest (84%) is due to water supply and other factors. The irrigation cooling effect is also observed on air temperature (-0.38 to -0.53°C) from paired flux sites in Nebraska. This study highlights the non-negligible contribution of irrigation cooling to the yield benefits of irrigation, and such an effect may become more important in the future with continued warming and more frequent droughts.

Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States.

Increasing drought and extreme rainfall are major threats to maize production in the United States. However, compared to drought impact, the impact of excessive rainfall on crop yield remains unresolved. Here, we present observational evidence from crop yield and insurance data that excessive rainfall can reduce maize yield up to -34% (-17 ± 3% on average) in the United States relative to the expected yield from the long-term trend, comparable to the up to -37% loss by extreme drought (-32 ± 2% on average) from 1981 to 2016. Drought consistently decreases maize yield due to water deficiency and concurrent heat, with greater yield loss for rainfed maize in wetter areas. Excessive rainfall can have either negative or positive impact on crop yield, and its sign varies regionally. Excessive rainfall decreases maize yield significantly in cooler areas in conjunction with poorly drained soils, and such yield loss gets exacerbated under the condition of high preseason soil water storage. Current process-based crop models cannot capture the yield loss from excessive rainfall and overestimate yield under wet conditions. Our results highlight the need for improved understanding and modeling of the excessive rainfall impact on crop yield.