ORCID Profile
0000-0002-2397-425X
Current Organisations
University of Illinois Urbana-Champaign
,
USDA Agricultural Research Service
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Publisher: American Geophysical Union (AGU)
Date: 03-2023
DOI: 10.1029/2022JG007187
Abstract: Earth System Models (ESMs) are increasingly representing agriculture due to its impact on biogeochemical cycles, local and regional climate, and fundamental importance for human society. Realistic large scale simulations may require spatially varying crop parameters that capture crop growth at various scales and among different cultivars, as well as common crop management practices, but their importance is uncertain, and they are often not represented in ESMs. In this study, we examine the impact of using constant versus spatially varying crop parameters using a novel, realistic crop rotation scenario in the Energy Exascale Earth System Model (E3SM) Land Model version 2 (ELMv2). We implemented crop rotation by using ELMv2's dynamic land unit capability, and then calibrated and validated the model against observations collected at three AmeriFlux sites in the US Midwest with corn soybean rotation. The calibrated model closely captured the magnitude and observed seasonality of carbon and energy fluxes across crops and sites. We performed regional simulations for the US Midwest using the calibrated model and found that spatially varying only a few crop parameters across the region, as opposed to using constant parameters, had a large impact, with the carbon fluxes and energy fluxes both varying by up to 40%. These results imply that large scale ESM simulations using spatially invariant crop parameters may result in biased energy and carbon fluxes estimation from agricultural land, and underline the importance of improving human‐earth systems interactions in ESMs.
Publisher: IOP Publishing
Date: 28-04-2022
Abstract: Understanding agroecosystem carbon (C) cycle response to climate change and management is vital for maintaining their long-term C storage. We demonstrate this importance through an in-depth examination of a ten-year eddy covariance dataset from a corn–corn–soybean crop rotation grown in the Midwest United States. Ten-year average annual net ecosystem exchange (NEE) showed a net C sink of −0.39 Mg C ha −1 yr −1 . However, NEE in 2014 and 2015 from the corn ecosystem was 3.58 and 2.56 Mg C ha −1 yr −1 , respectively. Most C loss occurred during the growing season, when photosynthesis should dominate and C fluxes should reflect a net ecosystem gain. Partitioning NEE into gross primary productivity (GPP) and ecosystem respiration (ER) showed this C ‘burp’ was driven by higher ER, with a 51% (2014) and 57% (2015) increase from the ten-year average (15.84 Mg C ha −1 yr −1 ). GPP was also higher than average (16.24 Mg C ha −1 yr −1 ) by 25% (2014) and 37% (2015), but this was not enough to offset the C emitted from ER. This increased ER was likely driven by enhanced soil microbial respiration associated with ideal growing season climate, substrate availability, nutrient additions, and a potential legacy effect from drought.
Publisher: Elsevier BV
Date: 09-2019
Publisher: American Geophysical Union (AGU)
Date: 02-2018
DOI: 10.1002/2017JG004180
Publisher: Springer Science and Business Media LLC
Date: 29-02-2016
DOI: 10.1038/NCLIMATE2942
Publisher: American Geophysical Union (AGU)
Date: 04-2020
DOI: 10.1029/2019WR026058
Abstract: The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station on 29 June 2018 by the National Aeronautics and Space Administration (NASA). The primary science focus of ECOSTRESS is centered on evapotranspiration (ET), which is produced as Level‐3 (L3) latent heat flux ( LE ) data products. These data are generated from the Level‐2 land surface temperature and emissivity product (L2_LSTE), in conjunction with ancillary surface and atmospheric data. Here, we provide the first validation (Stage 1, preliminary) of the global ECOSTRESS clear‐sky ET product (L3_ET_PT‐JPL, Version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against the site measurements (clear‐sky instantaneous/time of overpass: r 2 = 0.88 overall bias = 8% normalized root‐mean‐square error, RMSE = 6%). ET uncertainty was generally consistent across climate zones, biome types, and times of day (ECOSTRESS s les the diurnal cycle), though temperate sites are overrepresented. The 70‐m‐high spatial resolution of ECOSTRESS improved correlations by 85%, and RMSE by 62%, relative to 1‐km pixels. This paper serves as a reference for the ECOSTRESS L3 ET accuracy and Stage 1 validation status for subsequent science that follows using these data.
Publisher: Wiley
Date: 17-08-2023
DOI: 10.1111/GCB.16903
Publisher: Wiley
Date: 23-01-2020
DOI: 10.1111/GCB.14978
Publisher: Wiley
Date: 12-04-2021
DOI: 10.1111/GCB.15603
Abstract: 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 (Φ F ) signals. Φ F further excludes the canopy structural impact than SIF yield and indicates plant physiological variability, and we found that Φ F outperformed SIF yield in responding to physiological stress ( r = −0.37). Our findings highlight that Φ F sensitively responded to the physiological downregulation of soybean gross primary productivity under high temperature. Φ F , if reliably derived from satellite SIF, can support monitoring regional crop growth and different ecosystems' vegetation productivity under environmental stress and climate change.
Publisher: American Geophysical Union (AGU)
Date: 2023
DOI: 10.1029/2022MS003171
Abstract: Perennial bioenergy crops are increasingly important for the production of ethanol and other renewable fuels, and as part of an agricultural system that alters the climate through its impact on biogeophysical and biogeochemical properties of the terrestrial ecosystem. Few Earth System Models (ESMs) represent such crops, however. In this study, we expand the Energy Exascale Earth System Land Model to include perennial bioenergy crops with a high potential for mitigating climate change. We focus on high‐productivity miscanthus and switchgrass, estimating various parameters associated with their different growth stages and performing a global sensitivity analysis to identify and optimize these parameters. The sensitivity analysis identifies five parameters associated with phenology, carbon/nitrogen allocation, stomatal conductance, and maintenance respiration as the most sensitive parameters for carbon and energy fluxes. We calibrated and validated the model against observations and found that the model closely captures the observed seasonality and the magnitude of carbon fluxes. The validated model represents the latent heat flux fairly well, but sensible heat flux for miscanthus is not well captured. Finally, we validated the model against observed leaf area index (LAI) and harvest amount and found modeled LAI captured observed seasonality, although the model underestimates LAI and harvest amount. This work provides a foundation for future ESM analyses of the interactions between perennial bioenergy crops and carbon, water, and energy dynamics in the larger Earth system, and sets the stage for studying the impact of future biofuel expansion on climate and terrestrial systems.
Publisher: American Geophysical Union (AGU)
Date: 09-2019
DOI: 10.1029/2019EF001282
Publisher: Springer Science and Business Media LLC
Date: 28-03-2017
Publisher: Wiley
Date: 09-2007
DOI: 10.1111/J.1365-3040.2007.01710.X
Abstract: Photosynthetic responses to carbon dioxide concentration can provide data on a number of important parameters related to leaf physiology. Methods for fitting a model to such data are briefly described. The method will fit the following parameters: V(cmax), J, TPU, R(d) and g(m)[maximum carboxylation rate allowed by ribulose 1.5-bisphosphate carboxylase/oxygenase (Rubisco), rate of photosynthetic electron transport (based on NADPH requirement), triose phosphate use, day respiration and mesophyll conductance, respectively]. The method requires at least five data pairs of net CO(2) assimilation (A) and [CO(2)] in the intercellular airspaces of the leaf (C(i)) and requires users to indicate the presumed limiting factor. The output is (1) calculated CO(2) partial pressure at the sites of carboxylation, C(c), (2) values for the five parameters at the measurement temperature and (3) values adjusted to 25 degrees C to facilitate comparisons. Fitting this model is a way of exploring leaf level photosynthesis. However, interpreting leaf level photosynthesis in terms of underlying biochemistry and biophysics is subject to assumptions that hold to a greater or lesser degree, a major assumption being that all parts of the leaf are behaving in the same way at each instant.
Publisher: Wiley
Date: 29-09-2020
DOI: 10.1111/GCBB.12743
Publisher: IOP Publishing
Date: 18-02-2020
Abstract: Substantial uncertainty exists in daily and sub-daily gross primary production (GPP) estimation, which d ens accurate monitoring of the global carbon cycle. Here we find that near-infrared radiance of vegetation (NIR v,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, NIR v,Rad explains 84% and 78% variations of half-hourly GPP for corn and soybean, respectively, outperforming NIR reflectance of vegetation (NIR v,Ref ), enhanced vegetation index (EVI), and far-red solar-induced fluorescence (SIF 760 ). The strong linear relationship between NIR v,Rad and absorbed photosynthetically active radiation by green leaves (APAR green ), and that between APAR green and GPP, explain the good NIR v,Rad -GPP relationship. The NIR v,Rad -GPP relationship is robust and consistent across sites. The scalability and simplicity of NIR v,Rad indicate a great potential to estimate daily or sub-daily GPP from high-resolution and/or long-term satellite remote sensing data.
Publisher: Wiley
Date: 22-12-2020
DOI: 10.1111/GCBB.12788
Publisher: Oxford University Press (OUP)
Date: 23-02-2021
DOI: 10.1093/JXB/ERAB090
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.
Publisher: Portland Press Ltd.
Date: 02-02-2021
DOI: 10.1042/ETLS20200292
Abstract: 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.
Location: United States of America
Location: United States of America
No related grants have been discovered for Carl Bernacchi.