Research Project: Optimizing Photosynthesis for Global Change and Improved Yield

Location: Global Change and Photosynthesis Research

2022 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 toward all relevant project milestones including improving photosynthetic, nitrogen and water use efficiency for greater food and bioenergy crop yields, 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 abiotic stress into mechanistic crop models. Objective 1: ARS scientists in Urbana, Illinois, made progress toward the goal to improve nitrogen use efficiency by identifying and manipulating transcriptional regulators that integrate nitrogen (N) availability with photosynthesis, the ultimate determinant of yield. Plants were grown at varying nitrogen and light (arabidopsis) or carbon dioxide (CO2) concentrations (alfalfa) and characterized gene expression and physiological traits, such as photosynthesis, N content and biomass, across a matrix of N supply, light levels, and CO2 concentrations. Importantly, by manipulating factors influencing nitrogen and energy supply, we observed interactive effects on both gene expression and plant traits that were previously unknown in both species. Gene network modeling was then used to identify transcription factors that regulate genes correlated to the physiological outcomes. These regulators provide targets for researchers and breeders interested in optimizing nitrogen-photosynthesis interactions to generate crops that will have improved nitrogen use efficiency under lower nitrogen supply and at future CO2 concentrations. Objective 3: ARS scientists advanced a population of soybean recombinant inbred lines (RILs) for testing at ambient and elevated CO2 concentrations in the field at the Soybean Free Air Concentration Enrichment (SoyFACE) facility. The F6 population of 200 RILs was planted in May 2022 and is being grown in replicated plots (n=4) at ambient (~410 ppm) and elevated (600 ppm) CO2 concentrations. Development, growth, leaf area index, photosynthesis, biomass production and yield will be measured in the population. The population was genotyped with an assay that identifies ~6000 core single nucleotide polymorphisms (SNPs) and future work will identify quantitative trait loci associated with soybean responses to elevated CO2. The population has also been planted at different densities in ambient CO2 to test the hypothesis that density can be used as a proxy for carbon availability. Objective 3: ARS scientists examined the impact of sustained elevated ozone concentration on the leaf transcriptome of five diverse maize inbred genotypes, which varied in physiological sensitivity to ozone (B73, Mo17, Hp301, C123, and NC338), using long reads to assemble transcripts and short reads to quantify expression of these transcripts. More than 99% of the long reads, 99% of the assembled transcripts, and 97% of the short reads map to both B73 and Mo17 reference genomes. Approximately 95% of the genes with assembled transcripts belong to known B73–Mo17 syntenic loci and 94% of genes with assembled transcripts are present in all temperate lines in the nested association mapping pan-genome. While there was limited evidence for alternative splicing in response to ozone stress, there was a difference in the magnitude of differential expression among the five genotypes. The transcriptional response to sustained ozone stress in the ozone resistant B73 genotype (151 genes) was modest, while more than 3,300 genes were significantly differentially expressed in the more sensitive NC338 genotype. Genes with a common response across the five genotypes (83 genes) were associated with photosynthesis, in particular photosystem I. The functional annotation of genes not differentially expressed in B73 but responsive in the other four genotypes (789) identified reactive oxygen species. This suggests that B73 had a different response to long-term ozone exposure than the other four genotypes. Objective 3: In 2020, a new experiment was established at SoyFACE to investigate the interaction of ozone pollution and drought stress. These experiments consisted of setting up rain exclusion awnings inside of the Free Air Ozone Enrichment plots. Awnings were deployed during night-time rain events and captured ~40% of seasonal rainfall in both 2020 and 2021. However, there was greater precipitation in 2021, and the decrease in soil moisture, especially in the surface layers was less apparent. Canopy growth, estimated by leaf area index, was significantly reduced by elevated ozone, but not affected by rainfall exclusion. Across both growing seasons, soybean yields were significant lower in elevated ozone, but less affected by rainfall exclusion. This experiment is being repeated for a third growing season in 2022, and soybeans were planted in May. Objective 4: In 2021, a new experiment was initiated to test the interactive effects of rising temperatures and vapor pressure deficit. An open-air humidifying system consisting of a high-pressure air compressor coupled with horizontal pipes each outfitted with misting nozzles was assembled. The combination of misting nozzles with compressed air allowed the misted vapor to mix with the atmosphere and increase the humidity within the plots. This system was coupled with infrared heaters so soybeans could be exposed to both heating and misting, enabling crops to be grown at different temperatures and vapor pressure deficits. 2021 was a pilot year for the new system. and it is being tested again in 2022. Objective 4: ARS scientists made progress towards advancing phenotyping efforts by developing high-throughput estimates of height and leaf area index from Light Detection and Ranging (LIDAR). Algorithms were developed to estimate canopy height and leaf area index from LIDAR scans. The instrument and algorithms were used for ~900 plots at 14 dates throughout a growing season, providing an unprecedented amount of data. Since these traits correlate well with yield, this approach could be used in genetic analysis to identify genes associated with these traits, which could guide breeding programs. The data can also be used in modeling to identify sets of traits that optimize yield, or to predict end-of-season yield from early season data.

Accomplishments
1. Identified transcription factors to improve nitrogen use efficiency in rice. Water and nitrogen availability are two of the most important factors for plant growth. In order to address global climate change the development of crop varieties with increased nitrogen and water use efficiency is necessary. Rice is a staple crop for 3.5 billion people and ARS researchers in Urbana, Illinois, used trait and gene expression data from 19 varieties of rice grown in a matrix of controlled nitrogen and water conditions to identify 18 transcription factors predicted to be important for grain yield. The expression of these transcription factors was dependent on nitrogen-by-water interactions, indicating they integrate nitrogen and water signals. These results can now be applied to develop/breed rice plants with improved yields in marginal, low N-input, drought-prone soils which are increasing in the face of climate change.

2. Established the world’s largest outdoor plant phenotyping facility that enables response to variable growing conditions. Field-based measurements of experiments on crops is burdened by the inability to accurately, precisely, and frequently measure important growth and physiological variables over multiple experimental plots. ARS researchers and university colleagues in Urbana, Illinois, developed a cable-based aerial phenotyping system with instruments to monitor the growth and physiology of up to 10 acres of field planted crops, which is an order of magnitude larger than any other existing field phenotyping system. This system will benefit breeders by advancing understanding of how efforts can translate into improved crop varieties while allowing for high temporal resolution measurements that can help understand how different crops and crop genotypes respond to variable growing conditions within and between growing seasons.

3. Identified ecosystem fluxes of bioenergy sorghum have similarities to perennial biomass crops. Maize is the dominant bioenergy feedstock in the United States; however, other potential feedstocks are being evaluated to assess whether they can yield more while providing additional ecosystem services. ARS researchers in Urbana, Illinois, measured ecosystem scale fluxes of carbon, water, and energy throughout a growing season to assess whether bioenergy sorghum behaved more similarly to maize or to the perennial grass feedstock miscanthus. Despite being an annual plant, bioenergy sorghum showed water and energy fluxes that were more similar to miscanthus than maize suggesting that this crop may have additional ecosystem services compared to maize. At the same time, the carbon fluxes for bioenergy sorghum were as high as for maize yet sorghum had higher total yield. These results indicate the potential for bioenergy sorghum to behave as an ideal bioenergy crop with more production and beneficial ecosystem services relative to existing feedstocks. These results are beneficial to farmers considering the ecosystem consequences of alternative cropping systems.

4. Modified software for modular model development. Models have been used to identify physiological modifications that might improve crop yield with the potential to forecast crop yield like weather models forecast weather. Development of crop models has been hindered by hard coding with software limited in its modularity. Modular model development allows people to work on individual parts separately and run a model programmatically, which facilitates automation. ARS researchers in Urbana, Illinois, modified the BioCro crop model to be modular and accessible via a command-line interface. This allows other groups to develop submodels related to their areas of expertise and combine their work into a larger model. This tool is valuable to the community of researchers who are working to develop better crops and test their performance under different management practices and environmental conditions in silico.

Review Publications
Wedow, J.M., Ainsworth, E.A., Li, S. 2021. Plant biochemistry influences tropospheric ozone formation, destruction, deposition, and response. Trends in Biochemical Sciences. 46(12):992-1002. https://doi.org/10.1016/j.tibs.2021.06.007.
Christian, N., Basurto, B., Toussaint, A., Xu, X., Ainsworth, E.A., Busby, P.E., Heath, K.D. 2021. Elevated carbon dioxide reduces a common soybean leaf endophyte. Global Change Biology. 27(17):4154-4168. https://doi.org/10.1111/gcb.15716.
Zhou, W., Guan, K., Peng, B., Wang, Z., Fu, R., Li, B., Ainsworth, E.A., DeLucia, E., Zhao, L., Chen, Z. 2021. A generic risk assessment framework to evaluate historical and future climate-induced risk for rainfed corn and soybean yield in the U.S. Midwest. Weather and Climate Extremes. 33. Article 100369. https://doi.org/10.1016/j.wace.2021.100369.
Montes, C.M., Demler, H.J., Li, S., Martin, D.G., Ainsworth, E.A. 2021. Approaches to investigate crop responses to ozone pollution: from O3-FACE to satellite-enabled modeling. Plant Journal. 109(2):432-446. https://doi.org/10.1111/tpj.15501.
Li, S., Moller, C.A., Mitchell, N.G., Lee, D., Sacks, E.J., Ainsworth, E.A. 2022. Testing unified theories for ozone response in C4 species. Global Change Biology. 28(10):3379-3393. https://doi.org/10.1111/gcb.16108.
Peng, F., Montes, C.M., Siebers, M.H., Gomez-Casanovas, N., McGrath, J.M., Ainsworth, E.A., Bernacchi, C.J. 2022. Advances in field-based high-throughput photosynthetic phenotyping. Journal of Experimental Botany. 73(10):3157-3172. https://doi.org/10.1093/jxb/erac077.
Kimm, H., Guan, K., Jiang, C., Miao, G., Wu, G., Suyker, A.E., Ainsworth, E.A., Bernacchi, C.J., Montes, C.M., Berry, J.A., Yang, X., Frankenberg, C., Chen, M., Koehler, P. 2021. A physiological signal derived from sun-induced chlorophyll fluorescence quantifies crop physiological response to environmental stresses in the U.S. Corn Belt. Environmental Research Letters. 16. Article 124051. https://doi.org/10.1088/1748-9326/ac3b16.
Kumagai, E., Burroughs, C., Pederson, T., Montes, C.M., Peng, B., Kimm, H., Guan, K., Ainsworth, E.A., Bernacchi, C.J. 2022. Predicting biochemical acclimation of leaf photosynthesis in soybean under in-field canopy warming using hyperspectral reflectance. Plant Cell and Environment. 45(1):80-94. https://doi.org/10.1111/pce.14204.
Digrado, A., Gonzalez-Escobar, E., Owston, N., Page, R., Mohammed, S., Umar, M.L., Boukar, O., Ainsworth, E.A., Carmo-Silva, E. 2022. Cowpea leaf width correlates with above ground biomass across diverse environments. Legume Science. https://doi.org/10.1002/leg3.144.
Pelech, E.A., Alexander, B.C.S., Bernacchi, C.J. 2021. Photosynthesis, yield, energy balance, and water-use of intercropped maize and soybean. Plant Direct. 5(12). Article e365. https://doi.org/10.1002/pld3.365.
Cavanagh, A.P., South, P.F., Bernacchi, C.J., Ort, D.R. 2022. Alternative pathway to photorespiration protects growth and productivity at elevated temperatures in a model crop. Plant Biotechnology Journal. 20(4):711-721. https://doi.org/10.1111/pbi.13750.
Kivi, M., Blakely, B., Masters, M., Bernacchi, C.J., Miguez, F., Dokoohaki, H. 2022. Development of a data-assimilation system to forecast agricultural systems: A case study of constraining soil water and soil nitrogen dynamics in the APSIM model. Science of the Total Environment. 820. Article 153192. https://doi.org/10.1016/j.scitotenv.2022.153192.
Wu, G., Jiang, C., Kimm, H., Wang, S., Bernacchi, C.J., Moore, C.E., Suyker, A., Yang, X., Magney, T., Frankenberg, C., Ryu, Y., Dechant, B., Guan, K. 2022. Difference in seasonal peak timing of soybean far-red SIF and GPP explained by canopy structure and chlorophyll content. Remote Sensing of Environment. 279. Article 113104. https://doi.org/10.1016/j.rse.2022.113104.
Fu, P., Jaiswal, D., McGrath, J.M., Wang, S., Long, S.P., Bernacchi, C.J. 2022. Drought imprints on crops can reduce yield loss: Nature's insights for food security. Food and Energy Security. 11(1). Article e332. https://doi.org/10.1002/fes3.332.
Varela, S., Pederson, T., Bernacchi, C.J., Leakey, A.D.B. 2021. Understanding growth dynamics and yield prediction of sorghum using high temporal resolution UAV imagery time series and machine learning. Remote Sensing. 13(9). Article 1763. https://doi.org/10.3390/rs13091763.
Jaikumar, N.S., Stutz, S.S., Fernandes, S.B., Leakey, A.D.B., Bernacchi, C.J., Brown, P.J., Long, S.P. 2021. Can improved canopy light transmission ameliorate loss of photosynthetic efficiency in the shade? An investigation of natural variation in Sorghum bicolor. Journal of Experimental Botany. 72(13):4965-4980. https://doi.org/10.1093/jxb/erab176.
Montes, C.M., Fox, C., Sanz-Saez, A., Serbin, S.P., Kumagai, E., Krause, M.D., Xavier, A., Specht, J.E., Beavis, W.D., Bernacchi, C.J., Diers, B.W., Ainsworth, E.A. 2022. High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population. Genetics. 221(2). Article iyac065. https://doi.org/10.1093/genetics/iyac065.
Boughton, E., Gomez-Casanovas, N., Swain, H., Bernacchi, C.J., Boughton, R.K., Brinsko, K., Li, H., Rivero, A., DeLucia, E.H., Sparks, J. 2022. Patch-burn grazing impacts forage resources in subtropical humid grazing lands. Rangeland Ecology and Management. 84:10-21. https://doi.org/10.1016/j.rama.2022.05.004.
Xia, L., Lam, S., Kiese, R., Chen, D., Luo, Y., van Groenigen, K., Ainsworth, E.A., Chen, J., Liu, S., Ma, L., Zhu, Y., Butterbach-Bahl, K. 2021. Elevated CO2 negates O3 impacts on terrestrial carbon and nitrogen cycles. One Earth. 4(12):1752-1763. https://doi.org/10.1016/j.oneear.2021.11.009.
Wu, G., Guan, K., Jiang, C., Kimm, H., Miao, G., Bernacchi, C.J., Moore, C.E., Ainsworth, E.A., Yang, X., Berry, J.A., Frankenberg, C., Chen, M. 2022. Attributing differences of solar-induced chlorophyll fluorescence (SIF)-gross primary production (GPP) relationships between two C4 crops: corn and miscanthus. Agricultural and Forest Meteorology. 323. Article 109046. https://doi.org/10.1016/j.agrformet.2022.109046.
Nanni, A., Morse, A., Newman, J., Choquette, N., Wedow, J., Liu, Z., Leakey, A.D.B,, Conesa, A., Ainsworth, E.A., McIntyre, L.M. 2022. Variation in leaf transcriptome responses to elevated ozone corresponds with physiological sensitivity to ozone across maize inbred lines. Genetics. Article iyac080. https://doi.org/10.1093/genetics/iyac080.