A Beginner's Guide to Eddy Covariance: Methodology and Its Applications to Photosynthesis.

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.

Improved net carbon budgets in the US Midwest through direct measured impacts of enhanced weathering.

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.

Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network.

In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those 'next users' of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers.

A review of transformative strategies for climate mitigation by grasslands.

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.

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.

The effect of increasing temperature on crop photosynthesis: from enzymes to ecosystems.

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.

Emerging approaches to measure photosynthesis from the leaf to the ecosystem.

Measuring photosynthesis is critical for quantifying and modeling leaf to regional scale productivity of managed and natural ecosystems. This review explores existing and novel advances in photosynthesis measurements that are certain to provide innovative directions in plant science research. First, we address gas exchange approaches from leaf to ecosystem scales. Leaf level gas exchange is a mature method but recent improvements to the user interface and environmental controls of commercial systems have resulted in faster and higher quality data collection. Canopy chamber and micrometeorological methods have also become more standardized tools and have an advanced understanding of ecosystem functioning under a changing environment and through long time series data coupled with community data sharing. Second, we review proximal and remote sensing approaches to measure photosynthesis, including hyperspectral reflectance- and fluorescence-based techniques. These techniques have long been used with aircraft and orbiting satellites, but lower-cost sensors and improved statistical analyses are allowing these techniques to become applicable at smaller scales to quantify changes in the underlying biochemistry of photosynthesis. Within the past decade measurements of chlorophyll fluorescence from earth-orbiting satellites have measured Solar Induced Fluorescence (SIF) enabling estimates of global ecosystem productivity. Finally, we highlight that stronger interactions of scientists across disciplines will benefit our capacity to accurately estimate productivity at regional and global scales. Applying the multiple techniques outlined in this review at scales from the leaf to the globe are likely to advance understanding of plant functioning from the organelle to the ecosystem.

High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity.

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 (V c,max ), maximum electron transport rate (J max ) 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 V c,max, J max 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 V c,max, but not for J max, 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 V c,max . and 13% J max . Plants generally have a positive correlation between leaf nitrogen and photosynthesis, however, tobacco with reduced Rubisco (SSuD) had significantly higher [N] despite much lower V c,max. The PLSR model was able to accurately predict both lower V c,max and higher leaf [N] for this genotype suggesting that the spectral based estimates of V c,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.

Seasonal, interannual and decadal drivers of tree and grass productivity in an Australian tropical savanna.

Tree-grass savannas are a widespread biome and are highly valued for their ecosystem services. There is a need to understand the long-term dynamics and meteorological drivers of both tree and grass productivity separately in order to successfully manage savannas in the future. This study investigated the interannual variability (IAV) of tree and grass gross primary productivity (GPP) by combining a long-term (15 year) eddy covariance flux record and model estimates of tree and grass GPP inferred from satellite remote sensing. On a seasonal basis, the primary drivers of tree and grass GPP were solar radiation in the wet season and soil moisture in the dry season. On an interannual basis, soil water availability had a positive effect on tree GPP and a negative effect on grass GPP. No linear trend in the tree-grass GPP ratio was observed over the 15-year study period. However, the tree-grass GPP ratio was correlated with the modes of climate variability, namely the Southern Oscillation Index. This study has provided insight into the long-term contributions of trees and grasses to savanna productivity, along with their respective meteorological determinants of IAV.