Research Project: Optimizing Photosynthesis for Global Change and Improved Yield

Location: Global Change and Photosynthesis Research

2020 Annual Report

Objectives
Objective 1: Improve photosynthetic efficiency along with water/nitrogen use efficiency in crops for greater food production and bioenergy crop yields. 1.1 Decrease leaf chlorophyll content to maximize water and nitrogen use efficiency without reduction in the daily integral of canopy carbon. 1.2 Lower energetic costs of photorespiration by installing improved engineered chloroplast photorespiratory bypass pathways. 1.3 Stack best performing reduced chlorophyll and photorespiratory traits to combine efficiencies. 1.4 Determine the heritability of photosynthetic traits in maize, and map QTL for photosynthetic traits and their response to abiotic stress. Objective 2: Identify key regulatory factors controlling carbon and nitrogen assimilation and partitioning in crop plants for improving seed composition and yields. 2.1 Determine the impact of canopy microenvironment on soybean seed composition as affected by canopy position. 2.2 Optimize Rubisco activase (Rca) regulation for dynamic light and temperature environments. Objective 3: Identify new genetic loci for enhancing crop resilience to environmental extremes (higher temperature and increased drought) by determining the major loci and physiological mechanisms that modulate crop performance in response to elevated atmospheric CO2 and tropospheric ozone (GxE). 3.1 Test the response of diverse soybean cultivars to elevated [CO2] and advance genetic populations for mapping CO2 response in soybean. 3.2 Use functional genomic and metabolomic approaches to dissect the mechanistic basis for O3 response in maize. 3.3 Investigate the interactive effects of elevated [O3] and drought stress or high temperature stress on crops. Objective 4: Advance the optimization of central ecosystem services for current and alternative food and bioenergy production systems for carbon, water, nutrient cycling, and energy partitioning, by determining the linkages among genetic, physiological, whole-plant, and ecosystem processes (GxE). 4.1 Quantify direct and indirect ecosystem services for traditional and alternative agroecosystem including but extending beyond harvestable yield. 4.2 Dissociate the impacts of rising temperature and increasing vapor pressure deficit on key ecosystem processes and crop yield. 4.3 Develop techniques for high-throughput phenotyping of leaf and canopy physiological properties to better associate genotype to phenotype. 4.4 Incorporate improved physiological understanding of crop responses to global change and stress conditions into mechanistic crop production models.

Approach
The overall goal of this project is to identify factors affecting food and bioenergy crop production, with an emphasis on photosynthetic performance and intensifying environmental stress. Overall, the experimental approaches combine biophysics, biochemistry, physiology, molecular biology, genetics and genomics. The research will include both laboratory- and field-based studies. Specific approaches for each objective are: Objective 1 – utilize systems biology and transgenic approaches to decrease canopy chlorophyll and reduce flux through photorespiration, as well as to identify genetic variation in photosynthetic traits. Objective 2 – assess the impact of canopy microenvironment on soybean seed composition and engineer Rubisco activase to improve function in dynamic light and temperature environments. Objective 3 – identify genetic loci and the mechanistic basis for enhancing crop responses to global climate change by using free air concentration enrichment and functional genomic and metabolic approaches. Objective 4 – optimize food and bioenergy production systems by high-throughput phenotyping and modeling. Mechanistic crop production models will be developed to improve understanding of carbon, water and nutrient cycling responses to environmental changes.

Progress Report
Progress was made towards all milestones of the project including improving photosynthetic, nitrogen and water use efficiency for greater food and bioenergy crop yields, identifying regulatory factors controlling seed composition, identifying mechanisms of crop responses to elevated carbon dioxide and ozone, developing high throughput phenotyping techniques, and incorporating improved physiological understanding of crop responses to global change and stress into mechanistic crop models. Several nutrients critical to human health are reduced in crops grown in elevated carbon dioxide (CO2) concentrations. Statistical re-analysis of unrelated studies suggests that one mechanism for the decline is reduced transpiration in elevated CO2, but there is little direct experimental evidence for this. Experiments using the Soybean Free Air Concentration Enrichment (SoyFACE) facility showed that in leaves, stems, and pods, the more strongly a nutrient was acquired by transpiration, the more its concentration declined in elevated CO2, providing experimental support for a direct role of transpiration in nutrient responses to elevated CO2. Advances in understanding C4 crop responses to elevated ozone were made by studying the metabolic profile of maize leaves during exposure to season-long elevated ozone in the field. The hybrid line, B73 x Mo17, showed an acceleration of chlorophyll loss under elevated ozone accompanied by a significant change in the metabolite profile. In contrast, the metabolite profile, although significantly different between the two inbred lines (B73 and Mo17), was not different in ambient and elevated ozone treatments. Phytosterols and alpha-tocopherol levels increased in B73 x Mo17 leaves as they aged, and to a significantly greater degree in elevated ozone stress. This experiment established that stabilization of membranes and quenching of chloroplast reactive oxygen species were key mechanisms of response, suggesting a target for maize tolerance to ozone. Additionally, ten genotypes of sorghum were grown at elevated ozone, and all ten showed significant tolerance to ozone stress, suggesting that sorghum has greater tolerance to ozone than other bioenergy grasses and could be used to enhance biomass productivity in ozone polluted regions. A plot-level screening tool for quantification of photosynthetic parameters and pigment content in crops using hyperspectral reflectance from sunlit leaf pixels was developed. This tool provides an advancement from leaf-level photosynthetic and phenotyping efforts and can rapidly assess many photosynthetic traits and pigment content of a crop canopy. Advances in identifying the most cost effective and computationally efficient methods to integrate proximal remote sensing techniques for photosynthetic phenotyping were also published. In addition to hyperspectral reflectance, laser imaging, detection and ranging (LIDAR) approaches for measuring canopy height, 3D structure, and leaf angle have been established. Few global land models explicitly represent crops or crop management practices given the complexity of interactions between human decisions, crop phenology, and land processes at global scales. A collaborative project introduced specific crop types, as well as irrigation and fertilization, into the Community Land Model (CLM). The introduction of crops increased the amount of carbon that plants drew out the atmosphere and changed patterns of evapotranspiration in simulations. The study modeled the impact that crop expansion and management had on climate, and highlighted that global models should represent specific crop types and crop management to accurately capture carbon, water, and energy fluxes from the land surface. Additionally, the BioCro crop model was re-written to follow common mathematical design used by modelers in all scientific fields, and crop-specific models for energy sorghum and soybean have been developed.

Accomplishments
1. Age-dependent increase in a-tocopherol and phytosterols in maize leaves exposed to elevated ozone pollution. Ground-level ozone is a damaging air pollutant that reduces crop yields around the world. The metabolic responses to increasing ozone pollution have not been widely studied in crops, especially in crops grown in the field. ARS researchers in Urbana, Illinois, used a unique field facility to grow two classic maize inbred lines (B73 and Mo17) and their hybrid cross (B73 x Mo17) in the field at elevated ozone, and to study the response of leaf metabolites. Results show that the hybrid line was more sensitive to ozone stress than the inbred lines. Two key metabolic changes in the hybrid at elevated ozone included increased sterol production, which may help stabilize membranes, and increased alpha-tocopherol, which could quench damaging reactive oxygen species in chloroplasts. These discoveries provide key metabolic targets for scientists to improve maize response to pollution.

2. Bioenergy sorghum maintains photosynthetic capacity in elevated ozone concentrations. Current ozone in the United States decreases maize production by up to 10%, thereby threatening global food and energy security. However, it is unknown how ozone affects plant growth, development, and productivity in sorghum (Sorghum bicolor L.), an emerging C4 bioenergy crop. ARS researchers in Urbana, Illinois, grew ten lines of bioenergy sorghum in the field under elevated ozone pollution to investigate the effects of elevated ozone on photosynthesis and biomass production. All of the bioenergy sorghum lines were tolerant to elevated ozone pollution, suggesting that sorghum has greater tolerance to ozone than other bioenergy grasses and could be used to enhance biomass productivity in ozone polluted regions.

3. Redesign of the BioCro crop model allows easier and more robust contributions from the community. Crop models are useful for predicting responses in future climates and guiding breeding efforts. Often, they are hard to learn, which hinders contribution from the broader community. ARS researchers in Urbana, Illinois, restructured the BioCro crop model to follow the most common mathematical design used by modelers in all scientific fields. This has already had benefits, as models for two new crops have been developed (energy sorghum and soybean), and researchers in other institutions are using the model. A significant contribution is that the canopy model has been changed to separate the leaf photosynthesis model from the distribution of factors such as light, nitrogen, and humidity in the canopy. This separation will allow study of the effects of nutrient distribution on physiology. These changes enabled modeling climate change impacts on both the quality and quantity of future grain production.

4. Technology to screen for higher-yielding crop traits. Agricultural research is progressing toward the need to measure photosynthesis for thousands of plants each field season. Traditional methods used to measure photosynthesis require as much as 30 minutes per leaf. ARS researchers in Urbana, Illinois, increased measurement efficiency to as little as 15 seconds per plant, allowing for orders of magnitude faster measurements which allows researchers to capture the photosynthetic capacity of hundreds to thousands of plants in a research plot. The researchers also reviewed data from two hyperspectral cameras; one that captures spectra from 400-900 nanometers and another that captures 900-1800 nanometers. The results challenged the previous view that both cameras are required to estimate photosynthetic capacity; this research showed that only one camera is required which lowers the cost of the sensor package and improves the data analysis pipeline. Using these techniques, the team teased out how to identify seven important leaf traits from the hyperspectral data that are related to photosynthesis and of interest to many plant scientists. This technique greatly enhances the capacity for photosynthetic phenotyping of diverse crop genotypes, which is a bottleneck for crop improvement.

Review Publications
Choquette, N.E., Ogut, F., Wertin, T.M., Montes, C.M., Sorgini, C.A., Morse, A.M., Brown, P.J., Leakey, A., McIntyre, L.M., Ainsworth, E.A. 2019. Uncovering hidden genetic variation in photosynthesis of field-grown maize under ozone pollution. Global Change Biology. 25(12):4327-4338. https://doi.org/10.1111/gcb.14794.
Cheng, Y., Huang, M., Chen, M., Guan, K., Bernacchi, C.J., Peng, B., Tan, Z. 2019. Parameterizing perennial bioenergy crops in Version 5 of the Community Land Model based on site-level observations in the Central Midwestern United States. Journal of Advances in Modeling Earth Systems. 12(1). https://doi.org/10.1029/2019MS001719.
Bailey-Serres, J., Parker, J.E., Ainsworth, E.A., Oldroyd, G., Schroeder, J. 2019. Genetic strategies for improving crop yields. Nature. 575:109-118. https://doi.org/10.1038/s41586-019-1679-0.
Kimm, H., Guan, K., Jiang, C., Peng, B., Gentry, L.F., Wilkin, S.C., Wang, S., Cai, Y., Bernacchi, C.J., Peng, J., Luo, Y. 2020. Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data. Remote Sensing of Environment. 239:111615. https://doi.org/10.1016/j.rse.2019.111615.
Wu, G., Guan, K., Jiang, C., Peng, B., Kimm, H., Chen, M., Yang, X., Wang, S., Suyker, A.E., Bernacchi, C.J., Moore, C., Zeng, Y., Berry, J.A., Centrero-Mateo, M.P. 2020. Radiance-based NIRv as a proxy for GPP of corn and soybean. Environmental Research Letters. 15:034009. https://doi.org/10.1088/1748-9326/ab65cc.
Gomez-Casanovas, N., DeLucia, N.J., DeLucia, E.H., Blanc-Betes, E., Boughton, E., Sparks, J., Bernacchi, C.J. 2020. Seasonal control of CO2 and CH4 dynamics in a temporarily flooded subtropical wetland. Journal of Geophysical Research-Biogeosciences. 125(3). https://doi.org/10.1029/2019JG005257.
Fu, P., Meacham-Hensold, K., Guan, K., Wu, J., Bernacchi, C.J. 2020. Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression. Plant Cell and Environment. 43(5):1241-1258. https://doi.org/10.1111/pce.13718.
Kimm, H., Guan, K., Gentine, P., Wu, J., Bernacchi, C.J., Sulman, B.N., Griffis, T.J., Lin, C. 2020. Redefining droughts for the U.S. corn belt: The dominant role of atmospheric vapor pressure deficit over soil moisture in regulating stomatal behavior of maize and soybean. Agricultural and Forest Meteorology. 287:107930. https://doi.org/10.1016/j.agrformet.2020.107930.
Wang, C., Guan, K., Peng, B., Chen, M., Jiang, C., Zeng, Y., Wang, S., Wu, J., Yang, X., Frankenberg, C., Koehler, P., Berry, J., Bernacchi, C.J., Zhu, K., Alden, C., Miao, G. 2020. Satellite footprint data from OCO-2 and TROPOMI reveal significant spatio-temporal and inter-vegetation type variabilities of solar-induced fluorescence yield in the U.S. Midwest. Remote Sensing of Environment. 241:111728. https://doi.org/10.1016/j.rse.2020.111728.
Meacham-Hensold, K., Fu, P., Wu, J., Serbin, S., Montes, C., Ainsworth, E.A., Guan, K., Dracup, E., Pederson, T., Driever, S., Bernacchi, C.J. 2020. Plot-level rapid screening for photosynthetic parameters using proximal hyperspectral imaging. Journal of Experimental Botany. 71(7):2312-2328. https://doi.org/10.1093/jxb/eraa068.
Berkelhammer, M., Alsip, B., Matamala, R., Cook, D., Whelan, M.E., Joo, E., Bernacchi, C.J., Miller, J., Meyers, T. 2020. Seasonal evolution of canopy stomatal conductance for a prairie and maize field in the Midwestern United States from continuous carbonyl sulfide fluxes. Geophysical Research Letters. 47(6). https://doi.org/10.1029/2019GL085652.
Thackeray, S.J., Robinson, S.A., Smith, P., Bruno, R., Kirschbaum, M., Bernacchi, C.J., Byrne, M., Cheung, W., Cotrufo, M., Gienapp, P., Hartley, S., Janssens, I., Jones, T., Kobayashi, K., Luo, Y., Penuelas, J., Sage, R., Suggett, D.J., Way, D., Long, S. 2020. Editorial: Civil-disobedience movements such as School Strike for the Climate are raising public awareness of the climate-change emergency. Global Change Biology. 26(3):1042-1044. https://doi.org/10.1111/gcb.14978.
Peng, B., Guan, K., Tang, J., Ainsworth, E.A., Asseng, S., Bernacchi, C.J., Cooper, M., DeLucia, E.H., Elliott, J., Ewert, F., et al. 2020. Towards a multiscale crop modelling framework for climate change adaptation assessment. Nature Plants. 6:338-348. https://doi.org/10.1038/s41477-020-0625-3.
Lombardozzi, D.L., Lu, Y., Lawrence, P., Lawrence, D.M., Swenson, S., Oleson, K.W., Wieder, W.R., Ainsworth, E.A. 2020. Simulating agriculture in the Community Land Model version 5. Journal of Geophysical Research-Biogeosciences. https://doi.org/10.1029/2019JG005529.
Jiménez-Francisco, B., Stirbet, A., Aguado-Santacruz, G.A., Campos, H., Conde-Martínez, F.V., Padilla-Chacón, D., Trejo, J., Bernacchi, C.J., Govindjee, G. 2020. A comparative chlorophyll a fluorescence study on isolated cells and intact leaves of Bouteloua gracilis (blue grama grass). Photosynthetica. 58(Special Issue):262-274. https://doi.org/10.32615/ps.2019.148.