Spectroscopy is becoming an increasingly powerful tool to alleviate the challenges of traditional measurements of key plant traits at the leaf, canopy, and ecosystem scales. Spectroscopic methods ...often rely on statistical approaches to reduce data redundancy and enhance useful prediction of physiological traits. Given the mechanistic uncertainty of spectroscopic techniques, genetic modification of plant biochemical pathways may affect reflectance spectra causing predictive models to lose power. The objectives of this research were to assess over two separate years, whether a predictive model can represent natural and imposed variation in leaf photosynthetic potential for different crop cultivars and genetically modified plants, to assess the interannual capabilities of a partial least square regression (PLSR) model, and to determine whether leaf N is a dominant driver of photosynthesis in PLSR models. In 2016, a PLSR analysis of reflectance spectra coupled with gas exchange data was used to build predictive models for photosynthetic parameters including maximum carboxylation rate of Rubisco (Vc,max), maximum electron transport rate (Jmax) and percentage leaf nitrogen (N). The model was developed for wild type and genetically modified plants that represent a wide range of photosynthetic capacities. Results show that hyperspectral reflectance accurately predicted Vc,max, Jmax and N for all plants measured in 2016. Applying these PLSR models to plants grown in 2017 resulted in a strong predictive ability relative to gas exchange measurements for Vc,max, but not for Jmax, and not for genotypes unique to 2017. Building a new model including data collected in 2017 resulted in more robust predictions, with R2 increases of 17% for Vc,max. and 13% Jmax. Plants generally have a positive correlation between leaf nitrogen and photosynthesis, however, tobacco with reduced Rubisco (SSuD) had significantly higher N despite much lower Vc,max. The PLSR model was able to accurately predict both lower Vc,max and higher leaf N for this genotype suggesting that the spectral based estimates of Vc,max and leaf nitrogen N are independent. These results suggest that the PLSR model can be applied across years, but only to genotypes used to build the model and that the actual mechanism measured with the PLSR technique is not directly related to leaf N. The success of the leaf-scale analysis suggests that similar approaches may be successful at the canopy and ecosystem scales but to use these methods across years and between genotypes at any scale, application of accurately populated physical based models based on radiative transfer principles may be required.
•We built PLS models to predict photosynthesis from hyperspectral reflectance.•The models work for plants with genetic modification to photosynthesis.•Predicted photosynthetic capacity is uncoupled from leaf nitrogen.•PLS photosynthesis models must be built with representation of all genetic material.
This review explores current understanding in crop photosynthesis and temperature across scales, linking enzyme processes within the leaf to stomata, plant transport systems, individual plants, and ...ecosystem-scale responses.
Abstract
As global land surface temperature continues to rise and heatwave events increase in frequency, duration, and/or intensity, our key food and fuel cropping systems will likely face increased heat-related stress. A large volume of literature exists on exploring measured and modelled impacts of rising temperature on crop photosynthesis, from enzymatic responses within the leaf up to larger ecosystem-scale responses that reflect seasonal and interannual crop responses to heat. This review discusses (i) how crop photosynthesis changes with temperature at the enzymatic scale within the leaf; (ii) how stomata and plant transport systems are affected by temperature; (iii) what features make a plant susceptible or tolerant to elevated temperature and heat stress; and (iv) how these temperature and heat effects compound at the ecosystem scale to affect crop yields. Throughout the review, we identify current advancements and future research trajectories that are needed to make our cropping systems more resilient to rising temperature and heat stress, which are both projected to occur due to current global fossil fuel emissions.
The eddy covariance technique, commonly applied using flux towers, enables the investigation of greenhouse gas (e.g., carbon dioxide, methane, nitrous oxide) and energy (latent and sensible heat) ...fluxes between the biosphere and the atmosphere. Through measuring carbon fluxes in particular, eddy covariance flux towers can give insight into how ecosystem scale photosynthesis (i.e., gross primary productivity) changes over time in response to climate and management. This chapter is designed to be a beginner's guide to understanding the eddy covariance method and how it can be applied in photosynthesis research. It introduces key concepts and assumptions that apply to the method, what materials are required to set up a flux tower, as well as practical advice for site installation, maintenance, data management, and postprocessing considerations. This chapter also includes examples of what can go wrong, with advice on how to correct these errors if they arise. This chapter has been crafted to help new users design, install, and manage the best towers to suit their research needs and includes additional resources throughout to further guide successful eddy covariance research activities.
Substantial uncertainty exists in daily and sub-daily gross primary production (GPP) estimation, which dampens accurate monitoring of the global carbon cycle. Here we find that near-infrared radiance ...of vegetation (NIRv,Rad), defined as the product of observed NIR radiance and normalized difference vegetation index, can accurately estimate corn and soybean GPP at daily and half-hourly time scales, benchmarked with multi-year tower-based GPP at three sites with different environmental and irrigation conditions. Overall, NIRv,Rad explains 84% and 78% variations of half-hourly GPP for corn and soybean, respectively, outperforming NIR reflectance of vegetation (NIRv,Ref), enhanced vegetation index (EVI), and far-red solar-induced fluorescence (SIF760). The strong linear relationship between NIRv,Rad and absorbed photosynthetically active radiation by green leaves (APARgreen), and that between APARgreen and GPP, explain the good NIRv,Rad-GPP relationship. The NIRv,Rad-GPP relationship is robust and consistent across sites. The scalability and simplicity of NIRv,Rad indicate a great potential to estimate daily or sub-daily GPP from high-resolution and/or long-term satellite remote sensing data.
Grasslands can significantly contribute to climate mitigation. However, recent trends indicate that human activities have switched their net cooling effect to a warming effect due to management ...intensification and land conversion. This indicates an urgent need for strategies directed to mitigate climate warming while enhancing productivity and efficiency in the use of land and natural (nutrients, water) resources. Here, we examine the potential of four innovative strategies to slow climate change including: 1) Adaptive multi-paddock grazing that consists of mimicking how ancestral herds roamed the Earth; 2) Agrivoltaics that consists of simultaneously producing food and energy from solar panels on the same land area; 3) Agroforestry with a reverse phenology tree species, Faidherbia (Acacia) albida, that has the unique trait of being photosynthetically active when intercropped herbaceous plants are dormant; and, 4) Enhanced Weathering, a negative emission technology that removes atmospheric CO2 from the atmosphere. Further, we speculate about potential unknown consequences of these different management strategies and identify gaps in knowledge. We find that all these strategies could promote at least some of the following benefits of grasslands: CO2 sequestration, non-CO2 GHG mitigation, productivity, resilience to climate change, and an efficient use of natural resources. However, there are obstacles to be overcome. Mechanistic assessment of the ecological, environmental, and socio-economic consequences of adopting these strategies at large scale are urgently needed to fully assess the potential of grasslands to provide food, energy and environmental security.
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•Four strategies to mitigate warming and enhance efficiency of grasslands were evaluated.•Adopting some of these strategies could offer almost exclusively environmental benefits.•These strategies have the potential to enhance the resilience of grasslands to climate change.•Their implementation in grasslands could be combined.•Future research work is needed to secure food and energy from sustainable grasslands using these strategies.
Terrestrial enhanced weathering (EW) through the application of Mg‐ or Ca‐rich rock dust to soil is a negative emission technology with the potential to address impacts of climate change. The ...effectiveness of EW was tested over 4 years by spreading ground basalt (50 t ha−1 year−1) on maize/soybean and miscanthus cropping systems in the Midwest US. The major elements of the carbon budget were quantified through measurements of eddy covariance, soil carbon flux, and biomass. The movement of Mg and Ca to deep soil, released by weathering, balanced by a corresponding alkalinity flux, was used to measure the drawdown of CO2, where the release of cations from basalt was measured as the ratio of rare earth elements to base cations in the applied rock dust and in the surface soil. Basalt application stimulated peak biomass and net primary production in both cropping systems and caused a small but significant stimulation of soil respiration. Net ecosystem carbon balance (NECB) was strongly negative for maize/soybean (−199 to −453 g C m−2 year−1) indicating this system was losing carbon to the atmosphere. Average EW (102 g C m−2 year−1) offset carbon loss in the maize/soybean by 23%–42%. NECB of miscanthus was positive (63–129 g C m−2 year−1), indicating carbon gain in the system, and EW greatly increased inorganic carbon storage by an additional 234 g C m−2 year−1. Our analysis indicates a co‐deployment of a perennial biofuel crop (miscanthus) with EW leads to major wins—increased harvested yields of 29%–42% with additional carbon dioxide removal (CDR) of 8.6 t CO2 ha−1 year−1. EW applied to maize/soybean drives a CDR of 3.7 t CO2 ha−1 year−1, which partially offsets well‐established carbon losses from soil from this crop rotation. EW applied in the US Midwest creates measurable improvements to the carbon budgets perennial bioenergy crops and conventional row crops.
Enhanced weathering in bioenergy cropping systems reduces carbon loss in annual row crops and increases carbon storage in perennial crops, while increasing grain yields in maize and soybean. A new rare earth element method allows for calculation of weathering rates and carbon capture by ground silicate rock soil amendments. These amendments have the potential to sequester 3.7–8.6 t CO2 ha−1 year−1 in bioenergy crop systems of the US Midwest, altering the carbon balance of one of the largest ecosystems of North America.
To enable enhanced paper-based diagnostics with improved detection capabilities, new methods are needed to immobilize affinity reagents to porous substrates, especially for capture molecules other ...than IgG. To this end, we have developed and characterized three novel methods for immobilizing protein-based affinity reagents to nitrocellulose membranes. We have demonstrated these methods using recombinant affinity proteins for the influenza surface protein hemagglutinin, leveraging the customizability of these recombinant “flu binders” for the design of features for immobilization. The three approaches shown are: (1) covalent attachment of thiolated affinity protein to an epoxide-functionalized nitrocellulose membrane, (2) attachment of biotinylated affinity protein through a nitrocellulose-binding streptavidin anchor protein, and (3) fusion of affinity protein to a novel nitrocellulose-binding anchor protein for direct coupling and immobilization. We also characterized the use of direct adsorption for the flu binders, as a point of comparison and motivation for these novel methods. Finally, we demonstrated that these novel methods can provide improved performance to an influenza hemagglutinin assay, compared to a traditional antibody-based capture system. Taken together, this work advances the toolkit available for the development of next-generation paper-based diagnostics.
Recent development of sun‐induced chlorophyll fluorescence (SIF) technology is stimulating studies to remotely approximate canopy photosynthesis (measured as gross primary production, GPP). While ...multiple applications have advanced the empirical relationship between GPP and SIF, mechanistic understanding of this relationship is still limited. GPP:SIF relationship, using the standard light use efficiency framework, is determined by absorbed photosynthetically active radiation (APAR) and the relationship between photosynthetic light use efficiency (LUE) and fluorescence yield (SIFy). While previous studies have found that APAR is the dominant factor of the GPP:SIF relationship, the LUE:SIFy relationship remains unclear. For a better understanding of the LUE:SIFy relationship, we deployed a ground‐based system (FluoSpec2), with an eddy‐covariance flux tower at a soybean field in the Midwestern U.S. during the 2016 growing season to collect SIF and GPP data simultaneously. With the measurements categorized by plant growth stages, light conditions, and time scales, we confirmed that a strong positive GPP:SIF relationship was dominated by an even stronger linear SIF:APAR relationship. By normalizing both GPP and SIF by APAR, we found that under sunny conditions our soybean field exhibited a clear positive SIFy:APAR relationship and a weak negative LUE:SIFy relationship, opposite to the positive LUE:SIFy relationship reported previously in other ecosystems. Our study provides a first continuous SIF record over multiple growth stages for agricultural systems and reveals a distinctive pattern related to the LUE:SIFy relationship compared with previous work. The observed positive relationship of SIFy:APAR at the soybean site provides new insights of the previous understanding on the SIF's physiological implications.
Key Points
Sun‐induced fluorescence was continuously measured at the canopy level across multiple growth stages in a soybean field
The positive relationship between SIF and GPP was dominated by a strong relationship between SIF and APAR
SIF yield was positively correlated with APAR and negatively correlated with LUE under stable sunny conditions
Realistic representations and simulation of mass and energy exchanges across heterogeneous landscapes can be a challenge in land surface and dynamic vegetation models. For mixed life-form biomes such ...as savannas, plant function is very difficult to parameterise due to the distinct physiological characteristics of tree and grass plant functional types (PFTs) that vary dramatically across space and time. The partitioning of their fractional contributions to ecosystem gross primary production (GPP) remains to be achieved at regional scale using remote sensing. The objective of this study was to partition savanna gross primary production (GPP) into tree and grass functional components based on their distinctive phenological characteristics. Comparison of the remote sensing partitioned GPPtree and GPPgrass against field measurements from eddy covariance (EC) towers showed an overall good agreement in terms of both GPP seasonality and magnitude. We found total GPP, as well as its tree and grass components, decreased dramatically with rainfall over the North Australian Tropical Transect (NATT), from the Eucalyptus forest and woodland in the northern humid coast to the grasslands, Acacia woodlands and shrublands in the southern xeric interior. Spatially, GPPtree showed a steeper decrease with precipitation along the NATT compared to GPPgrass, thus tree/grass GPP ratios also decreased from the northern mesic region to the arid south region of the NATT. However, results also showed a second trend at the southern part of the transect, where tree-grass ratios and total GPP increased with decreasing mean annual precipitation, and this occurred in the physiognomic transition from hummock grasslands to Acacia woodland savannas. Total GPP and tree-grass GPP ratios across climate extremes were found to be primarily driven by grass layer response to rainfall dynamics. The grass-containing xeric savannas exhibited a higher hydroclimatic sensitivity, whereas GPP in the northern mesic savannas was fairly stable across years despite large variations in rainfall amount. The pronounced spatiotemporal variations in savanna vegetation productivity encountered along the NATT study area suggests that the savanna biome is particularly sensitive and vulnerable to predicted future climate change and hydroclimatic variability.
•Functional partition of savanna GPP into tree and grass using MODIS was achieved.•Validation of the partitioned results against flux tower data showed good agreement.•Savanna tree and grass ratio varies according to rainfall, soil, and other factors.•Seasonal & inter-annual savanna GPP and tree-grass ratio was driven by C4 grasses.•Savanna is particularly sensitive to climate change and hydroclimatic variability.
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.
The study investigated soybean responses to high‐temperature stress, which is a major threat to crop yield in the U.S. Corn Belt. We utilized an unprecedented experiment, Temperature Free‐Air‐Controlled Enhancement experiment, which enabled detailed investigation by providing four levels of canopy temperature increments. Using advanced hyperspectral remote sensing technique, we collected sun‐induced chlorophyll fluorescence (SIF) data and demonstrated that SIF has the unique capability of quantifying crop physiological variability under high‐temperature stress (i.e., depressions in instantaneous photosynthetic rates per unit leaf area). The unique strength of SIF for quantifying physiological status is expected to contribute to improving vegetation productivity quantification.
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