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.
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 was made towards all relevant 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 abiotic stress into mechanistic crop models. Objective 1: Research to improve transcriptional regulators and networks that coordinate photosynthesis with nitrogen assimilation have progressed along two fronts. Arabidopsis plants are currently growing in new growth chambers with high light capability in a matrix of intensities and nitrogen availabilities. Additionally, snap bean lines with demonstrated variation in nitrogen fixation capacity are being grown in the field and greenhouse for pilot experiments. Objective 2: Research to identify key regulatory factors controlling carbon and nitrogen assimilation and partitioning in crop plants has progressed by collecting data from two soybean cultivars to parameterize a soybean model that newly includes a sub-model of nitrogen uptake and distribution. Objective 3: A new experiment was established at the Soybean Free Air Concentration Enrichment (SoyFACE) facility in 2020 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 resulting in significant reductions in soil moisture, especially at the surface. Roots were imaged over the growing season and a machine learning approach is being used to identify roots in images, stitch those images and data together over the growing season and test how ozone and drought impact root distribution. Preliminary analysis of yield data suggests that drought reduced yields in ambient ozone, but to a much lesser extent in elevated ozone. The experiment is being repeated in summer 2021. Objective 3: Research also identified mechanisms of crop responses to elevated ozone concentrations. Research discovered that variation in ozone response of maize was due to variation in gene expression, not genic content as the leaf transcriptome of five diverse genotypes was largely shared. The most ozone tolerant inbred line had only 151 genes significantly altered in expression with growth at elevated ozone, while the most sensitive had more than 3300 genes changing. In all five maize inbred lines, 81 genes were commonly altered by growth at elevated ozone, including down-regulation of photosynthetic genes and up-regulation of aquaporins and heat shock proteins. Metabolite analysis of maize identified that alpha-tocopherol, a chloroplastic scavenger of reactive oxygen species, was up-regulated. This work identifies potential genetic and molecular targets for improving ozone tolerance. Objective 4: Another new experimental system that will both heat soybean canopies and alter vapor pressure deficit is in development at the Soybean Free Air Concentration Enrichment (SoyFACE) facility in 2021. The system combines infrared heating arrays with high-pressure air compressors coupled with horizontal pipes each outfitted with misting nozzles. The combination of misting nozzles with compressed air allows the misted vapor to mix with the atmosphere and increase the humidity across the soybean canopy. Objective 4: Research improved capacity for high-throughput phenotyping at many scales. This includes developing models for species-specific prediction of photosynthesis, leaf nitrogen content, and specific leaf area for food and energy crops. Additionally, pipelines for rapidly processing hyperspectral and Light Detection and Ranging (LIDAR) data have been developed, and a new cable mounted phenotyping system is under construction. Additionally, progress has been made to use hyperspectral imaging to estimate soil water content. Traditionally, soil water content measurements require sampling, weighing, drying and reweighing known volumes of soil. This is time-consuming and low throughput. Recent results suggest that partial least squares regression can be used to build accurate models of soil water content from reflectance data. Objective 4: Advances in improving mechanistic representation in models was made by including a circadian clock model into a soybean crop model. Day length and temperature determine when soybean flowers. In current crop models, day length is calculated based on the position of the earth around the sun. This calculation does not represent inherent biological processes making it difficult to couple flowering with environmental conditions such as temperature or drought. In plants, day length is sensed by a network of genes that naturally oscillate and are synchronized with daylight, a system called the circadian clock. There are detailed genetic models that capture much of the nuance of the true biological response, but they are too computationally expensive to use in crop models. A simplified clock model that represents the oscillatory nature of the circadian clock without attempting to model the gene network was developed. The simplified clock model accurately estimated day length, and when used as an input to a flowering time model, accurately predicted soybean flowering time. This improvement enables flowering time to easily be predicted in a variety of environments and to interact with other environmental factors, which will improve soybean yield prediction.
1. Genetic mapping of photosynthesis and leaf functional traits in soybean. Photosynthesis is a key target for improving crop production, but measuring photosynthetic capacity is time-consuming and laborious. ARS scientists and university colleagues in Urbana, Illinois, developed machine learning models to predict photosynthetic capacity, leaf carbon, leaf nitrogen content and the mass of the leaf per unit area (specific leaf area) from leaf reflectance in soybean. Measurements of leaf reflectance are rapid and were taken in a large multiparental population of soybean, which enabled genetic mapping of photosynthesis and leaf traits. Photosynthetic capacity estimated from reflectance mapped to a region of chromosome 19 containing multiple copies of the genes encoding the primary carboxylase enzyme for photosynthesis (Rubisco). Leaf carbon and nitrogen content and specific leaf area of soybean were strongly correlated with yield and are promising markers for breeders. This study is among the first to use leaf reflectance spectrometry to map the genetic architecture of photosynthesis.
2. A new circadian clock model allows more robust modeling of crop flowering time and yield. Soybean flowers based on day length and temperature, which in current crop models is calculated based on celestial mechanics – that is, day length is calculated based on the position of the earth around the sun. This calculation does not represent inherent biological processes making it difficult to couple flowering with environmental conditions such as temperature or drought. In plants, day length is sensed by a network of genes that naturally oscillate and are synchronized with daylight, a system called the circadian clock. There are detailed genetic models that capture much of the nuance of the true biological response, but they are too computationally expensive to use in crop models. A simplified clock using a Poincaré oscillator to represent the nature of the circadian clock without attempting to model the complex gene network was developed by ARS researchers at Urbana, Illinois. The simplified oscillator clock model accurately estimates day length, and when used as an input to a flowering time model, accurately predicts soybean flowering time. This improvement enables flowering time to easily be predicted in a variety of environments and to interact with other environmental factors, which will improve soybean yield prediction.
3. Genetic basis for ozone response in maize. Ozone is a secondary air pollutant formed from the reactions of sunlight with nitrogen oxides and volatile organic compounds. Ozone is damaging to human health and to crops and is estimated to reduce U.S. maize production by up to 10%. Ozone enters leaves through stomatal pores and rapidly forms other damaging reactive oxygen molecules. ARS scientists at Urbana, Illinois, discovered that variation in ozone response of diverse maize lines was caused by variation in gene expression, not genic content. The most ozone tolerant inbred line had only 151 genes significantly altered in expression with growth at elevated ozone, while the most sensitive had more than 3300 genes changing. In all five maize inbred lines, 81 genes were commonly altered by growth at elevated ozone, including down-regulation of photosynthetic genes and up-regulation of membrane channels and proteins involved in heat shock response. Metabolite analysis of maize identified that alpha-tocopherol, a chloroplastic scavenger of reactive oxygen molecules, was up-regulated likely to scavenge the damaging molecules. This work identifies potential genetic and molecular targets for improving ozone tolerance in maize.
4. Bioenergy sorghum has ecosystem attributes that mimic both traditional row crops and perennial bioenergy feedstocks. Bioenergy sorghum is increasingly studied as a bioenergy crop because of its high productivity. However, replacing traditional crops on the landscape could have other environmental benefits or consequences, including altered hydrological, nutrient or carbon cycles. ARS researchers at Urbana, Illinois, measured growth, water use, and ecosystem photosynthetic rates during the growing season for bioenergy sorghum and compared it to traditional row crops and perennial grasses. Sorghum planting, emergence, and establishment was similar to traditional row crops, but its productivity, photosynthesis and water use were more similar to perennial grasses. This study provides a critical data set for parameterizing models to extrapolate sorghum water and carbon fluxes beyond the field scale and to predict the regional implications for shifting from traditional row crops to bioenergy crops.
Wedow, J.M., Burroughs, C., Rios Acosta, L., Leakey, A.D.B., Ainsworth, E.A. 2021. Age-dependent increase in a-tocopherol and phytosterols in maize leaves exposed to elevated ozone pollution. Plant Direct. 5(2). Article e00307. https://doi.org/10.1002/pld3.307.
Wang, S., Guan, K., Wang, Z., Ainsworth, E.A., Zheng, T., Townsend, P., Li, K., Moller, C., Wu, G., Jiang, C. 2021. Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy. Journal of Experimental Botany. 72(2):341-354. https://doi.org/10.1093/jxb/eraa432.
Ainsworth, E.A., Long, S.P. 2020. 30 years of free air carbon dioxide enrichment (FACE): What have we learned about future crop productivity and the potential for adaptation? Global Change Biology. 27(1):27-49. https://doi.org/10.1111/gcb.15375.
Peng, F., Meacham-Hensold, K., Siebers, M.H., Bernacchi, C.J. 2021. The inverse relationship between solar-induced fluorescence yield and photosynthetic capacity: benefits for field phenotyping. Journal of Experimental Botany. 72(4):1295-1306. https://doi.org/10.1093/jxb/eraa537.
Moore, C., Berardi, D., Blanc-Betes, E., Dracup, E., Egenriether, S., Gomez-Casanovas, N., Hartman, M., Hudiburg, T., Kantola, I., Masters, M., Parton, W., Van Allen, R., von Haden, A., Yang, W., DeLucia, E., Bernacchi, C.J. 2020. The carbon and nitrogen cycle impacts of reverting perennial bioenergy switchgrass to an annual maize crop rotation. Global Change Biology Bioenergy. 12(11):941-954. https://doi.org/10.1111/gcbb.12743.
Ely, K., Rogers, A., Agarwal, D.A., Ainsworth, E.A., Albert, L., Ali, A., Anderson, J., Aspinwall, M., Bellasio, C., Bernacchi, C.J., Bunce, J.A., et al. 2021. A reporting format for leaf-level gas exchange data and metadata. Ecological Informatics. 61. Article 101232. https://doi.org/10.1016/j.ecoinf.2021.101232.
Leung, F., Williams, K., Sitch, S., Tai, A., Wiltshire, A., Gornall, J., Ainsworth, E.A., Arkebauer, T., Scoby, D. 2020. Calibrating soybean parameters in JULES 5.0 from the US-Ne2/3 FLUXNET sites and the SoyFACE-O3 experiment. Geoscientific Model Development. 13(12):6201-6213. https://doi.org/10.5194/gmd-13-6201-2020.
Zeri, M., Yang, W.H., Cunha-Zeri, G., Gibson, C.D., Bernacchi, C.J. 2020. Nitrous oxide fluxes over establishing biofuel crops: Characterization of temporal variability using the cross-wavelet analysis. Global Change Biology Bioenergy. 12(9):756-770. https://doi.org/10.1111/gcbb.12728.
Digrado, A., Mitchell, N.G., Montes, C.M., Dirvanskyte, P., Ainsworth, E.A. 2020. Assessing diversity in canopy architecture, photosynthesis, and water-use efficiency in a cowpea magic population. Food and Energy Security. 9(4). Article e236. https://doi.org/10.1002/fes3.236.
Kimm, H., Guan, K., Burroughs, C.H., Peng, B., Ainsworth, E.A., Bernacchi, C.J., Moore, C.E., Kumagai, E., Yang, X., Berry, J.A., Wu, G. 2021. Quantifying high-temperature stress on soybean canopy photosynthesis: The unique role of sun-induced chlorophyll fluorescence. Global Change Biology. 27(11):2403-2415. https://doi.org/10.1111/gcb.15603.
Moore, C.E., von Haden, A.C., Burnham, M.B., Kantola, I.B., Gibson, C.D., Blakely, B.J., Dracup, E.C., Masters, M.D., Yang, W.H., DeLucia, E.H., Bernacchi, C.J. 2021. Ecosystem-scale biogeochemical fluxes from three bioenergy crop candidates: How energy sorghum compares to maize and miscanthus. Global Change Biology Bioenergy. 13(3):445-458. https://doi.org/10.1111/gcbb.12788.
Chu, H., Luo, X., Ouyang, Z., Chan, W., Dengel, S., Biraud, S.C., Torn, M.S., Metzger, S., Kumar, J., Arain, M.A., Arkebauer, T.J., Baldocchi, D., Bernacchi, C.J., Knowles, J.F., Prueger, J.H., et al. 2021. Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites. Agricultural and Forest Meteorology. 301-302. Article 108350. https://doi.org/10.1016/j.agrformet.2021.108350.
Lochocki, E.B., McGrath, J.M. 2021. Integrating oscillator-based circadian clocks with crop growth simulations. in silico Plants. 3(1). Article diab016. https://doi.org/10.1093/insilicoplants/diab016.