GLOBAL CHANGE, ULTRAVIOLET RADIATION MONITORING AND RESEARCH PROGRAM

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: ACTIVE
• Funding Source: SPECIAL GRANT
• Reporting Frequency: Annual
• Accession No.: 1029379
• Grant No.: 2022-34263-38472
• Cumulative Award Amt.: $3,520,896.00
• Proposal No.: 2022-08081
• Project Start Date: Sep 1, 2022
• Project End Date: Sep 14, 2025
• Grant Year: 2024
• Program Code: [HZ]- Global Change/Ultraviolet Radiation

Recipient Organization
COLORADO STATE UNIVERSITY
(N/A)
FORT COLLINS,CO 80523

Performing Department
USDA-UV-B Program, NREL

Non Technical Summary
Although Ultraviolet (UV) radiation represents only a small fraction of the solar radiation reaching the Earth's surface, the impacts of UV on the biosphere are significant. Stratospheric ozone is one of the main absorbers of the UV portion of the solar spectrum. Measurements in the mid-to-late Twentieth century showed significant stratospheric ozone depletion, with corresponding increases in surface UV radiation over Polar Regions, caused by emissions of ozone-depleting compounds. This discovery prompted a swift international effort to phase out these substances, an action that has resulted in the continuing recovery of the ozone layer - the Antarctic ozone hole appears to be steadily shrinking while the Arctic ozone hole is intermittently expanding. However, occasional new emissions of ozone-depleting compounds and complex interactions between climate change and Ultraviolet-B (UV-B) radiation may increase uncertainty and have implications for future projections of stratospheric ozone and surface UV levels.Responding to these concerns in October 1992, the USDA funded the UV-B Monitoring and Research Program (UVMRP) to monitor UV-B levels over wide geographic areas of the U.S. and to assess the impact of UV radiation on crops and ecosystems. More recently, the UVMRP added a third complementary objective to develop comprehensive climate-crop-economy models that will be able to evaluate the long-term effects of climate change, UV exposure, and their interactive effects, with a focus on the agricultural/economic impacts of these environmental stressors. To address these objectives, the UVMRP pursues research in three program areas:CLIMATOLOGY DATA COLLECTION: The UVMRP has established a 41-station climatological and research network that covers a wide range of ecoregions, elevations and land-use types, including a mix of crop, forest and range lands. Each climatological network station is equipped with instruments capable of measuring ultraviolet and visible solar radiation. Raw instrument signals are examined for quality control (QC) issues and adjusted using calibration factors to produce high-quality solar irradiance data. Irradiance data are subsequently used as inputs to well-established algorithms to calculate a series of relevant derived products (e.g., UV index, biologically-weighted sums). The irradiance data as well as the derived products are made available on the UVMRP website within 48 hours of collection (http://uvb.nrel.colostate.edu). The UVMRP staff, assisted by local site operators, perform routine on-site maintenance and troubleshoot occasional instrument malfunctions to maintain at least 95% data retrieval rates over the entire network. In addition, UVMRP continually investigates new calibration and QC procedures to improve data quality and better meet the needs of our stakeholders. UVMRP researchers also conduct analyses using network data to expand our derived products and contribute to our understanding of the trends in surface UV radiation.EFFECTS RESEARCH: By using computer-controlled-crop-growth chambers, the UVMRP and its collaborators record, analyze, and quantify crop responses to the isolated and interactive effects of environmental stress factors (e.g., drought, temperature, ambient CO2, UV-B radiation). These results help to identify plant characteristics that could be manipulated by plant breeders to develop cultivars that can tolerate extreme and variable environmental conditions. Further, chamber experiments inform the development of functional algorithms that describe crop response to UV-B and other environmental stress factors that can be incorporated into climate-crop-economy models. The work proposed here will expand our analyses to soybean, with a focus on the impact of CO2, drought, and extreme temperatures on its growth and development.INTEGRATED ASSESSMENT: The agricultural community and decision makers require reliable crop yield assessment tools to determine optimal management practices, assess risks and risk management strategies, and define economic impacts. The UVMRP is working with collaborators to develop a comprehensive computational model that will couple state-of-the-art climate, crop, biogeochemical, and economic models to study the influence of environmental change on ecosystems and agricultural productivity, the environmental feedbacks from crop growth, and the resulting economic impacts of these interactions. Modules for corn and soybean are under development and preliminary results show promising agreement between modeled and measured data in the U.S.

Animal Health Component

(N/A)

Research Effort Categories

Basic

100%

Applied

(N/A)

Developmental

(N/A)

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
132	0440	2070	20%
203	2499	1020	10%
202	2499	1080	10%
132	0430	2070	15%
203	0699	1020	10%
203	0799	1020	10%
102	0199	1070	10%
601	1719	3010	15%

Knowledge Area
601 - Economics of Agricultural Production and Farm Management; 102 - Soil, Plant, Water, Nutrient Relationships; 203 - Plant Biological Efficiency and Abiotic Stresses Affecting Plants; 132 - Weather and Climate; 202 - Plant Genetic Resources;

Subject Of Investigation
0430 - Climate; 0799 - Rangelands and grasslands, general; 1719 - Cotton, other; 0199 - Soil and land, general; 0440 - Solar radiation; 0699 - Trees, forests, and forest products, general; 2499 - Plant research, general;

Field Of Science
2070 - Meteorology and climatology; 1070 - Ecology; 1080 - Genetics; 3010 - Economics; 1020 - Physiology;

Keywords

solar ultraviolet-b (uv-b) irradiance

agriculture

modeling

monitoring

networks

research programs

economic assessment

Goals / Objectives
The USDA UV-B Monitoring and Research Program (UVMRP) maintains three distinct and complementary program areas:(1) Monitoring and evaluating surface levels of solar Ultraviolet-B (UV-B) and Visible (VIS) irradiances across the U.S.;(2) Studying the isolated and interactive effects of UV-B and other environmental stress factors on crops; and(3) Coupling state-of-the-art climate, crops, biogeochemical and economic assessment models to investigate the impact of environmental change on the agricultural system and assess the subsequent economic impacts.Each program area specific goals are described below.CLIMATOLOGY DATA COLLECTIONCurrently the project staff supports 37 climatological stations, and 4 long-term research stations that are equipped with four basic solar measuring instruments: a UV and a visible (VIS) Multi-filter Rotating Shadowband Radiometer (MFRSR), a broadband UV-B radiometer and a Photosynthetically Active Radiation (PAR) sensor.Objectives for this project area are to:♦ deliver continuous maintenance of the 41-station network, and preserve high rates of data collection;♦ improve data quality by developing more accurate and efficient quality-control and calibration algorithms/procedures;♦ provide primary irradiance products and derived secondary products in near-real time through the program's website;♦ investigate potential replacement sensors that can be substituted for the existing 7-channel sensors and still maintain the shadowband components;♦ perform analyses using network data to investigate the spatial and temporal trends in surface radiation and the factors that influence the amount of radiation reaching the surface;♦ compare UVMRP data products with satellite UV radiation measurements and satellite retrievals of atmospheric constituents.EFFECTS RESEARCHResearch has shown that UV-B radiation alone or in combination with other abiotic factors such as temperature, moisture, nutrients, and atmospheric CO2 concentration adversely impacts crops. Experiments will be conducted to explore these effects on the growth and development of major crops including rice, soybean, corn, sweetpotato, and cotton, and to identify the tolerance of various cultivars/lines under future environmental conditions.Objectives for this project area are to:♦ study the interactive effects of UV-B radiation and other environmental factors (e.g., temperature and ambient CO2 concentration) on growth and development of major crops, cover crops, and horticultural crops with the current focus on soybean; and♦ develop quantitative algorithms describing the effects of elevated UV-B and environmental stress factors and incorporate these algorithms into climate-crop-economy simulation models.INTEGRATED ASSESSMENTProjected environmental change is expected to impact crop production and resource availability in key agricultural regions. UVMRP is developing a coupling system that will assess and quantify the interactions between climate patterns, soil, plant processes, agricultural practices, and identify the potential economic impacts of these interactions.Objectives for this program area are to:♦ couple the Climate-Weather Research and Forecasting model (CWRF) with the latest version of Decision Support System for Agrotechnology Transfer (DSSAT) for corn, soybean, and cotton;♦ study climatic feedback from crop growth, investigate future crop yields using the CWRF-CROP model, and identify the underlying physical processes and mechanisms that contribute to changes in crop productivity;♦ enable CWRF-CROP model with economic assessment by linking climate and crop predictions with economic model simulations of aggregate agricultural productivity at the state and national levels;♦ understand the connections between U.S. TFP changes and environment, technology, and adaptation factors using historical data;♦ develop the UV-B module in the biogeochemical model, DayCent, and couple CWRF with DayCent to better simulate the UV-B impacts on soil carbon and nitrogen dynamics in ranchlands and dry lands in the western U.S.;♦ incorporate the Tropospheric Ultraviolet and Visible Radiation Model (TUV) into CWRF and generate all sky UV-B data for crop-impact studies with CWRF-CROP; and♦ use physically-based models with both field and remote sensing observations to investigate patterns in environmental conditions and to assess model performance.

Project Methods
Broadly, the UVMRP follows standard scientific approaches to address the objectives in the three aforementioned program areas:♦ convert questions/concerns into one or several testable hypotheses;♦ design experiments or develop models to test the hypotheses;♦ validate the hypotheses with high quality measurements with replications; and♦ conclude and publish the findings in technical reports, peer-reviewed scientific journals, and conference proceedings.Although the three areas of the UVMRP serve one overarching goal, they have distinctive objectives and require unique implementation methods.CLIMATOLOGY DATA COLLECTIONMethods and procedures for operation of the UVMRP network are mature and proven, and exist for all facets of network operation including:♦ instrument servicing;♦ routine on-site maintenance and troubleshooting;♦ data collection, transfer, and storage;♦ database management;♦ quality control and quality assurance;♦ calibration; and♦ data products generation and dissemination.While mature data collection procedures are routinely employed, the UVMRP continually investigates potential improvements for instrument calibration, data collection and data QC procedures to better meet the needs of stakeholders. For example, the UVMRP will improve the efficiency and accuracy of our in-house spectral response measurements through software and hardware upgrades. Routine calibration of shadowband and broadband instruments at the Mauna Loa Observatory (MLO), Hawaii facility are ongoing. The UVMRP will also continue to investigate state-of-the-art data processing (e.g., quality control) and calibration methodologies. This includes the development of deep anomaly detection models (e.g., transformer-based algorithms) for automatic QC labeling using cross-instrument (e.g., 368 and 415nm) measurements. This also includes the deployment of the Gaussian Process regression based method to smooth historic in-situ calibration factors and predict future in-situ calibration factors at all UVMRP sites. In addition, UVMRP research will address research questions using network data. This includes a comparison between diffuse-to-total irradiance ratios measured during the Summer 2018 California wildfire events and those modeled for smoke-free conditions using radiative transfer model to better understand the impact of smoke on surface radiation. The patterns of seasonal peak irradiances at UV-MFRSR channels over the past 20+ years will also be examined using calibration factors derived from monthly direct beam measurements.EFFECTS RESEARCHCrop stress/response experiments in computer-controlled Soil-Plant-Atmosphere-Research (SPAR) chambers will be conducted to quantify the effects of abiotic stress factors including elevated UV-B radiation, high concentration of CO2, high temperature, and drought on agricultural crops, with the current focuses on soybean. For soybean, we will specifically investigate the impacts of (high and low) temperature stress (including in nighttime) and drought stress at ambient (420 ppm) and elevated (720 ppm) CO2 conditions on gas exchange parameters, yield components, and transgenerational seed germination of two soybean cultivars. In addition, functional algorithms describing these effects will be derived from the extensive experiments.INTEGRATED ASSESSMENTEfforts to couple CWRF with crop (e.g., The Decision Support System for Agrotechnology Transfer (DSSAT)), biogeochemical (e.g., DayCent-UV, Dynamic Land Ecosystem Model (DLEM)), and biomass (e.g., BioCro) models (hereafter referred to as CROP) are ongoing. The coupling work can be summarized by four main steps: 1) replacing the soil dynamic module of the CROP model with the CWRF counterpart; 2) preparing the parameters of the CROP model on the regional scale; 3) providing the CROP model with the daily weather inputs from CWRF; and 4) returning plants' physical properties (e.g. height, coverage, and root profile) from the CROP model to CWRF (e.g. for the calculation of albedo and infrared emissivity, precipitation interception, and evapotranspiration). Efforts to build a more robust model that predicts the economically-focused agricultural total factor productivity change (TFPC) are also under development by incorporating the physical understanding of the TFPC-environment relationships with up-to-date historical data. In addition, the influence of agricultural climate adaptation on productivity growth will be quantified through a comprehensive decomposition (e.g., PCA) of planting and harvesting areas, crop yields, climate statistics, and national and state-level TFPC data.

Progress 09/01/23 to 08/31/24

Outputs
Target Audience:The UVMRP mission is to provide high quality data, data products, and services in support of agricultural research, and to facilitate the use of these measurements with climate and crop models. Research topics span the geographic distribution of UV-B solar irradiance and the effects of increased or diminished UV-B radiation on crops, native and invasive plants, and animals. The program participates in international instrument inter-comparisons to ensure quality control and a common reference standard for comparing U.S. data with the rest of the world. Over the past years there have been between 6,000 and 10,000 visits per month to the web site (and average 500 downloads per month) by users to obtain data and/or related information. Over the years, 54% of web data downloads have come from .EDU domains, 23% from .GOV domains, 21% from .COM domains and the balance from other domains. Both national and international educators and students have retrieved data or related information via the program website. Most academic data use is for agricultural research. The UVMRP measurements and tools have been the source for numerous research studies conducted by universities and agricultural research facilities across the nation. Fourteen land grant universities have prior and/or current research studies in collaboration with the UVMRP, and all of them have a UVMRP instrument array at their location. In addition, the UVMRP has prior and/or current research collaborations with government agencies such as NASA, NOAA, DOE, EPA, and NSF. UVMRP stakeholders include: a) USDA personnel at all levels who fund, participate, or have an interest in the program, b) agricultural researchers from governmental, academic, and other institutions, c) researchers from health communities, and d) educators and students at all levels who make use of network data and tutorials. Collaboration and support of research in the effects of UV-B radiation on plants and materials has resulted in papers that are presented at scientific conferences. As ground-based measurements of UV-B radiation and study of UV-B effects on agriculture continue to be important, and predictive crop yield tools come of age, an increasing number of researchers could make use of UVMRP products. Perhaps the best measure of the effectiveness of the UVMRP in serving stakeholders is the large number of publications that have come directly from the program or from collaboration with affiliated researchers. There have been over 300 publications to date in the 31-year history of the UVMRP. Agricultural effects studies account for two-thirds of publications, with UV climatology making up most of the remainder. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Graduate and post-doctoral participation and mentoring are taking place in all phases of the project, through co-authorship of papers in scientific journals, proceedings, and transactions, as well as presentations of research work at professional society events. Two student interns develop data-driven models for predicting cloud fraction from ground-based shortwave solar irradiance. One student intern assists with the QC labeling effort. One Postdoctoral fellow contributes to the calibration of the DayCent-UV model and the coupling between the DayCent-UV and CWRF models to simulate the grasslands dynamics in North American. Several Postdoctoral fellow(s) and student(s) focus on effects of environmental stress factors on economically import crops. One postdoctoral associate contributes to the bibliometric analysis of climate change's impact on agriculture. One Ph.D. student works on surface albedo parameterization. One Ph.D. student works on modeling UV-B radiation from reanalyzed meteorological data. How have the results been disseminated to communities of interest?Data from all agroclimatologic and research network sites are available on the project's web site (http://uvb.nrel.colostate.edu). Our scientifically peer-reviewed papers and associated conference presentations are also available on the website for scientific and/or wider communities of interest. Additionally, UVMRP staff routinely respond directly to data inquiries and requests for UV-related expertise via telephone and email. Currently the project has users from more than 300 different organizations, with 48% from national educational institutions; 33% from national governmental agencies; 2% from commercial enterprises; and 17% from international institutions. Our paper in determining climate effects on US total agricultural productivity that is published in the Proceedings for the National Academy of Sciences of the United States of America (PNAS), attracts significant media attention, and which is covered by Nature Research Highlights in 2017. The 5-year impact factor of the PNAS is 12.0, while the current altmetric score of the article is 136, putting it in the top 5% of research outputs, revealing the significance of its findings. Furthermore, it has been cited 162 times, downloaded/full-paper-assessed over 18,000 times, picked up by 9 news outlets, 6 blogs, tweeted by 48 users, and have 334 readers on Mendeley. Our effects work facilitates breeders in selecting crop (e.g., sweetpotato and soybean) varieties that can handle realistic future conditions like UV exposure, drought, warmer temperature, and more carbon dioxide. What do you plan to do during the next reporting period to accomplish the goals?The UVMRP will deliver continuous maintenance of the network instruments, preserve high rates of data collection, develop up-to-date calibration protocols, and provide reliable primary and derived data products. We will continue to work with Mississippi State University and utilize their SPAR facility to evaluate the isolated effect of UV-B and combined effects of UV-B and other environmental stress factors on crops. Further progress towards the completion of the Integrated Agricultural Impact Assessment System [now the Climate-Agroecosystem-UV Interactions and Economic system (CAIE)] framework is expected through ongoing collaborations with the University of Maryland. This includes advancing efforts to couple the Agroecosystem framework (CROP) with the CWRF model as well as developing and integrating an economic module into the impact assessment system to provide insight for farm management practices and policy decisions. For climatology data collection, we will continue to update the old Small Board Computers (SBC) at each site, and schedule annual site visits to perform necessary instrument maintenance. We will continue to work with site operators to ensure rapid response to instrument issues. We will continue to follow our established QC protocols. Evaluation of the in-house calibration facility software and hardware will be conducted to improve the accuracy of the (UV-) MFRSR spectral response measurements. We will continue to explore new mathematical and statistical algorithms including machine learning to create more reliable and automatic data processing workflows. These workflows include smoothing and uncertainty estimation for calibration factor time series using Gaussian Process Regression, transformer-based QC algorithms, and efficient QC labeling within limited timeframes. We will continue to develop the new UVMRP website and modernize software management to enhance efficiency and reliability. We will also continue to evaluate and compare our shadowband radiometers with a Biospherical Instruments model GUVis-3511 bioshade radiometer. For effects research, we will continue to evaluate the isolated effect of UV-B radiation and combined effects of UV-B radiation and other environmental stressor factors on the growth and development of important crops and to identify the cultivars/lines tolerance for future environmental conditions. This includes conducting SPAR chamber experiments to investigate effects of elevated UV-B radiation exposure and other environmental stress factors (e.g., elevated CO2 and high temperature, and drought) on growth and development of rice. Specifically, we will investigate how early-season exposure to UV-B radiation (control: 0 kJ, elevated: 10 kJ) affects growth of indica and japonica rice varieties. These experiments will facilitate the development of quantitative algorithms that will be incorporated into climate-crop simulation models for the Climate-Agroecosystem-UV Interactions and Economic system. For integrated assessment, we will continue to develop/upgrade the coupled CWRF-CROP system by reengineering the latest DSSAT version for distributed modeling and to simulate the growth and yields of corn, soybean, and cotton over the U.S. We will continue to investigate the uncertainty of CWRF-DSSAT on soybean modeling and perform sensitivity analysis of soybean cultivars, farm management practices, soil conditions, and climate variations. We will continue to use the coupled CWRF-DSSAT system to quantify the roles of climate-crop interactions and agricultural management practices, focusing on corn production. We will continue to combine statistical analysis and dynamic modeling to determine quantitative relationships between crop yields and environmental, technological, and adaptation factors. We will also continue to develop DayCent-UV and the CWRF/DayCent-UV interface for their one-way and two-way coupling. The coupled model will simulate the grasslands ecosystem dynamics under future climate scenarios across the Great Plains.

Impacts
What was accomplished under these goals? The USDA UV-B Monitoring and Research Program (UVMRP) focuses on three primary goals: (1) collecting high-quality and geographically-distributed solar ultraviolet (UV) and visible radiation measurements, and to make these data publicly available to the agricultural, ecological, atmospheric, and other scientific communities; (2) quantifying the isolated effect of UV and combined effects of UV and other environmental stressor factors on important crops through experiments; and (3) developing a coupled modeling framework of climate-agroecosystem-UV interactions and economic impacts (CAIE) to support science-informed decisions toward sustainable US agriculture. UVMRP manages the only nationwide network which measures surface UV-B irradiance, and provides 8 other data products, at 37 agro-climatological sites and 4 long-duration research sites that together encompass 20 ecoregions and a variety of land types. UVMRP staff check the instrument data daily, and when necessary, work with local site operators to correct any problems. Through the UVMRP web page, we continue to provide quality UV and visible surface radiation measurements and data products, including calibrated and biologically-weighted UV irradiances, UV index, synthetic spectra, instantaneous optical depths, daily and hourly sums, and photosynthetically active radiation (PAR). We are updating the old Small Board Computers (SBC), which communicate and download data at each site. Conversion from analog telephone modem connections to Ethernet/cellular modem connections has been completed. Development and maintenance of the UVMRP website for information dissemination and data delivery is ongoing. UVMRP is continuously improving the accuracy and quality of its climatological data. Staff manually check instruments daily and work with site operators to resolve issues. Automated quality control (QC) algorithms are being developed to detect noise and instrument failures. For complex QC challenges, a two-stage deep neural network is under development. The success of this approach hinges on the quality of QC labels. Therefore, we are in the process of meticulously labeling the band motion and obstruction issues in an 8-year time window across UVMRP climatological sites. Calibration procedures are continually refined and deployed. Monthly in-situ calibrations derived from the Langley Analyzer (supplemented by the global-pairing cloud screening algorithm) are smoothed across sites using Gaussian Process Regression. All radiometers are periodically recalibrated at MLO, Hawaii. PAR sensors are manufacturer-calibrated. Data accuracy is assessed through intercomparison with a GUVis-3511 bioshade radiometer. We use network irradiances for agricultural, ecological, and atmospheric research. A deep neural network accurately predicts opaque and total cloud fractions from ground-based shortwave solar irradiance (R2 = 0.959). For effects research, we are working with our collaborators at Mississippi State University on experiments using its Soil-Plant-Atmosphere-Research (SPAR) environmental chambers to investigate impacts of UV-B radiation and other environmental stressor factors [e.g., elevated CO2 (+CO2), high/low temperature (±T), drought stress (DS), waterlogging (+WL)] on the growth and development of economically important crops (Corn and Cotton). For corn, a quadratic model incorporating air temperature is developed to predict seedling emergence time and it is found that newer hybrids require 16% more growing degree days (GDD) to emerge compared to older hybrids. For cotton, a novel fiber quality simulation module is developed to predict fiber quality indices (length, strength, micronaire, uniformity) based on temperature, water, and nutrient availability. Additionally, it is found that +WL significantly impairs root growth and nutrient uptake, increases stress-protective compounds, and reduces photosynthetic parameters (ETR, PhiPS2, Fv'/Fm'), consequently negatively impacting plant growth parameters (biomass, leaf area, node number) and reproductive development. The findings of these studies could allow for revealing underlying mechanisms, selection of stress tolerant cultivars with the best coping ability for future climate, optimization of commercial crop productions or specific traits and simulation of crop responses in models. For integrated assessment, we are working with our collaborators at the University of Maryland to develop a comprehensive predictive system for risk analysis, evaluation of economic impacts, and strategic planning concepts to achieve sustainable agricultural development in a changing environment, using the CWRF-CROP coupling system, where CWRF refers to the Regional Climate-Weather Research and Forecasting Model and CROP represents a universal abstraction for following crop models such as GOSSYM (a dynamic cotton (Gossypium hirsutum L.) crop simulation model), DSSAT (Decision Support System for Agrotechnology Transfer model for crop simulation that includes cotton, corn, soybean, etc. ), and DayCent (a daily time-step biogeochemical model for ecosystems that include grasslands). As a part of the CWRF-CROP coupling effort, we continue to develop the CWRF-DSSAT system to be included in this integrated assessment system. We continue to enhance the coupled CWRF-CROP system to simulate and predict the growth, development, and yields of cotton, and corn in the US. An effort is to couple and optimize DSSAT with CWRF to simulate cotton variations at the 30km scale over the U.S. cotton belt. The initial results show that the model realistically simulates the spatial distribution and interannual pattern of cotton yields across the Cotton Belt during 1980-2019. It is also found that integrating crop feedback mechanisms significantly impacts crop growth, development, and regional climate, leading to a cooling effect that mitigates heat stress and enhances precipitation, ultimately benefiting crop growth. The coupled simulation also shows that corn yields decrease from 8.9% to 14.6% under different climate scenarios. To improve the capability of the coupled CWRF-CROP system, we continue to improve the simulation accuracy of the CWRF with current focus on improving its cumulus scheme. To evaluate the economic impacts, we continue to incorporate the trend of total factor productivity (TFP) and its correlation with environmental variables. A bibliometric analysis of climate change's impact on agriculture indicates that quantifying the impact of research on crop yields and productivity is important. These studies emphasize food security, water resources, and sustainability, highlighting concerns about global food shortages and the need for adaptation strategies. Development of DayCent-UV is underway to quantify UV impacts on ecosystems and to be coupled with CWRF ultimately. To improve the long-term simulation of important US crop rotation systems, we continue to couple the CWRF land surface model with the DayCent-UV model by developing an interface between reanalyzed meteorological data grids and DayCent-UV. We are developing the interface for DayCent-UV to utilize weather data in regional grids, and are preparing the crop fractional area and the corresponding crop management files for every grid. In the meantime, DayCent-UV is applied to various Ecosystems in North America to simulate their dynamics. A modified variant of the DayCent model, calibrated with 16 years of field data, demonstrates that applying organic waste products biennially enhances soil carbon sequestration, nutrient content, and crop uptake, while reducing fertilizer needs. However, reducing application frequency diminishes carbon sequestration rates.

Publications

• Status: Published Year Published: 2023 Citation: Lerma, J., E.K. Blackaby, M. Chen, S. Madronich, and Wei Gao. 2023. Predicting Cloud Fraction with Instantaneous Direct and Diffuse Shortwave Solar Irradiance. The Geographical Bulletin, Vol. 64: Iss. 2, Article 1, 121-130, ISSN 2163-5900. Status: Published Year Published: 2023 Citation: Adegoye G. A., O. J. Olorunwa, F. A. Alsajri, C. H. Walne, C. Wijewandana, S.R. Kethireddy, K.N. Reddy, and K.R. Reddy. 2023. Waterlogging Effects on Soybean Physiology and Hyperspectral Reflectance during the Reproductive Stage. Agriculture. 2023; 13(4):844. doi: 10.3390/agriculture13040844.
• Status: Published Year Published: 2023 Citation: Lerma, J., E.K. Blackaby, M. Chen, S. Madronich, and Wei Gao. 2023. Predicting Cloud Fraction with Instantaneous Direct and Diffuse Shortwave Solar Irradiance. The Geographical Bulletin, Vol. 64: Iss. 2, Article 1, 121-130, ISSN 2163-5900. Status: Published Year Published: 2023 Citation: Adegoye G. A., O. J. Olorunwa, F. A. Alsajri, C. H. Walne, C. Wijewandana, S.R. Kethireddy, K.N. Reddy, and K.R. Reddy. 2023. Waterlogging Effects on Soybean Physiology and Hyperspectral Reflectance during the Reproductive Stage. Agriculture. 2023; 13(4):844. doi: 10.3390/agriculture13040844. Status: Published Year Published: 2023 Citation: Beegum S., C. H. Walne, K. N. Reddy, V. Reddy, and K. R. Reddy. 2023. Examining the Corn Seedling EmergenceTemperature Relationship for Recent Hybrids: Insights from Experimental Studies. Plants. 12(21):3699. doi: 10.3390/plants12213699.
• Status: Published Year Published: 2023 Citation: Lerma, J., E.K. Blackaby, M. Chen, S. Madronich, and Wei Gao. 2023. Predicting Cloud Fraction with Instantaneous Direct and Diffuse Shortwave Solar Irradiance. The Geographical Bulletin, Vol. 64: Iss. 2, Article 1, 121-130, ISSN 2163-5900. Status: Published Year Published: 2023 Citation: Adegoye G. A., O. J. Olorunwa, F. A. Alsajri, C. H. Walne, C. Wijewandana, S.R. Kethireddy, K.N. Reddy, and K.R. Reddy. 2023. Waterlogging Effects on Soybean Physiology and Hyperspectral Reflectance during the Reproductive Stage. Agriculture. 2023; 13(4):844. doi: 10.3390/agriculture13040844. Status: Published Year Published: 2023 Citation: Beegum S., C. H. Walne, K. N. Reddy, V. Reddy, and K. R. Reddy. 2023. Examining the Corn Seedling EmergenceTemperature Relationship for Recent Hybrids: Insights from Experimental Studies. Plants. 12(21):3699. doi: 10.3390/plants12213699. Status: Published Year Published: 2023 Citation: Taduri S., R. Bheemanahalli, C. Wijewardana, A. A. Lone, S. L. Meyers, M. Shankle, W. Gao, and K. R. Reddy. 2023. Sweetpotato cultivars responses to interactive effects of warming, drought, and elevated carbon dioxide. Frontiers in Genetics. 13. doi: 10.3389/fgene.2022.1080125.
• Status: Published Year Published: 2023 Citation: Lerma, J., E.K. Blackaby, M. Chen, S. Madronich, and Wei Gao. 2023. Predicting Cloud Fraction with Instantaneous Direct and Diffuse Shortwave Solar Irradiance. The Geographical Bulletin, Vol. 64: Iss. 2, Article 1, 121-130, ISSN 2163-5900. Status: Published Year Published: 2023 Citation: Adegoye G. A., O. J. Olorunwa, F. A. Alsajri, C. H. Walne, C. Wijewandana, S.R. Kethireddy, K.N. Reddy, and K.R. Reddy. 2023. Waterlogging Effects on Soybean Physiology and Hyperspectral Reflectance during the Reproductive Stage. Agriculture. 2023; 13(4):844. doi: 10.3390/agriculture13040844. Status: Published Year Published: 2023 Citation: Beegum S., C. H. Walne, K. N. Reddy, V. Reddy, and K. R. Reddy. 2023. Examining the Corn Seedling EmergenceTemperature Relationship for Recent Hybrids: Insights from Experimental Studies. Plants. 12(21):3699. doi: 10.3390/plants12213699. Status: Published Year Published: 2023 Citation: Taduri S., R. Bheemanahalli, C. Wijewardana, A. A. Lone, S. L. Meyers, M. Shankle, W. Gao, and K. R. Reddy. 2023. Sweetpotato cultivars responses to interactive effects of warming, drought, and elevated carbon dioxide. Frontiers in Genetics. 13. doi: 10.3389/fgene.2022.1080125. Status: Published Year Published: 2023 Citation: Beegum, S., V. Reddy, and K. R. Reddy. 2023. Development of a cotton fiber quality simulation module and its incorporation into cotton crop growth and development model: GOSSYM. Computers and Electronics in Agriculture. 212, 108080. doi: 10.1016/j.compag.2023.108080
• Status: Published Year Published: 2023 Citation: Lerma, J., E.K. Blackaby, M. Chen, S. Madronich, and Wei Gao. 2023. Predicting Cloud Fraction with Instantaneous Direct and Diffuse Shortwave Solar Irradiance. The Geographical Bulletin, Vol. 64: Iss. 2, Article 1, 121-130, ISSN 2163-5900.
• Status: Published Year Published: 2023 Citation: Lerma, J., E.K. Blackaby, M. Chen, S. Madronich, and Wei Gao. 2023. Predicting Cloud Fraction with Instantaneous Direct and Diffuse Shortwave Solar Irradiance. The Geographical Bulletin, Vol. 64: Iss. 2, Article 1, 121-130, ISSN 2163-5900. Status: Published Year Published: 2023 Citation: Adegoye G. A., O. J. Olorunwa, F. A. Alsajri, C. H. Walne, C. Wijewandana, S.R. Kethireddy, K.N. Reddy, and K.R. Reddy. 2023. Waterlogging Effects on Soybean Physiology and Hyperspectral Reflectance during the Reproductive Stage. Agriculture. 2023; 13(4):844. doi: 10.3390/agriculture13040844. Status: Published Year Published: 2023 Citation: Beegum S., C. H. Walne, K. N. Reddy, V. Reddy, and K. R. Reddy. 2023. Examining the Corn Seedling EmergenceTemperature Relationship for Recent Hybrids: Insights from Experimental Studies. Plants. 12(21):3699. doi: 10.3390/plants12213699. Status: Published Year Published: 2023 Citation: Taduri S., R. Bheemanahalli, C. Wijewardana, A. A. Lone, S. L. Meyers, M. Shankle, W. Gao, and K. R. Reddy. 2023. Sweetpotato cultivars responses to interactive effects of warming, drought, and elevated carbon dioxide. Frontiers in Genetics. 13. doi: 10.3389/fgene.2022.1080125. Status: Published Year Published: 2023 Citation: Beegum, S., V. Reddy, and K. R. Reddy. 2023. Development of a cotton fiber quality simulation module and its incorporation into cotton crop growth and development model: GOSSYM. Computers and Electronics in Agriculture. 212, 108080. doi: 10.1016/j.compag.2023.108080 Status: Published Year Published: 2023 Citation: Beegum S., V. Truong, R. Bheemanahalli, D. Brand, V. Reddy, and K. R. Reddy. 2023. Developing functional relationships between waterlogging and cotton growth and physiology-towards waterlogging modeling. Front. Plant Sci. 14:1174682. doi: 10.3389/fpls.2023.1174682.
• Status: Published Year Published: 2023 Citation: Lerma, J., E.K. Blackaby, M. Chen, S. Madronich, and Wei Gao. 2023. Predicting Cloud Fraction with Instantaneous Direct and Diffuse Shortwave Solar Irradiance. The Geographical Bulletin, Vol. 64: Iss. 2, Article 1, 121-130, ISSN 2163-5900. Status: Published Year Published: 2023 Citation: Adegoye G. A., O. J. Olorunwa, F. A. Alsajri, C. H. Walne, C. Wijewandana, S.R. Kethireddy, K.N. Reddy, and K.R. Reddy. 2023. Waterlogging Effects on Soybean Physiology and Hyperspectral Reflectance during the Reproductive Stage. Agriculture. 2023; 13(4):844. doi: 10.3390/agriculture13040844. Status: Published Year Published: 2023 Citation: Beegum S., C. H. Walne, K. N. Reddy, V. Reddy, and K. R. Reddy. 2023. Examining the Corn Seedling EmergenceTemperature Relationship for Recent Hybrids: Insights from Experimental Studies. Plants. 12(21):3699. doi: 10.3390/plants12213699. Status: Published Year Published: 2023 Citation: Taduri S., R. Bheemanahalli, C. Wijewardana, A. A. Lone, S. L. Meyers, M. Shankle, W. Gao, and K. R. Reddy. 2023. Sweetpotato cultivars responses to interactive effects of warming, drought, and elevated carbon dioxide. Frontiers in Genetics. 13. doi: 10.3389/fgene.2022.1080125. Status: Published Year Published: 2023 Citation: Beegum, S., V. Reddy, and K. R. Reddy. 2023. Development of a cotton fiber quality simulation module and its incorporation into cotton crop growth and development model: GOSSYM. Computers and Electronics in Agriculture. 212, 108080. doi: 10.1016/j.compag.2023.108080 Status: Published Year Published: 2023 Citation: Beegum S., V. Truong, R. Bheemanahalli, D. Brand, V. Reddy, and K. R. Reddy. 2023. Developing functional relationships between waterlogging and cotton growth and physiology-towards waterlogging modeling. Front. Plant Sci. 14:1174682. doi: 10.3389/fpls.2023.1174682. Status: Published Year Published: 2023 Citation: Wu, Y., S. Meng, C. Liu, W. Gao, X.-Z. Liang. 2023. A bibliometric analysis of research for climate impact on agriculture. Frontiers in Sustainable Food Systems. 7. doi: 10.3389/fsufs.2023.1191305.
• Status: Published Year Published: 2023 Citation: Lerma, J., E.K. Blackaby, M. Chen, S. Madronich, and Wei Gao. 2023. Predicting Cloud Fraction with Instantaneous Direct and Diffuse Shortwave Solar Irradiance. The Geographical Bulletin, Vol. 64: Iss. 2, Article 1, 121-130, ISSN 2163-5900. Status: Published Year Published: 2023 Citation: Adegoye G. A., O. J. Olorunwa, F. A. Alsajri, C. H. Walne, C. Wijewandana, S.R. Kethireddy, K.N. Reddy, and K.R. Reddy. 2023. Waterlogging Effects on Soybean Physiology and Hyperspectral Reflectance during the Reproductive Stage. Agriculture. 2023; 13(4):844. doi: 10.3390/agriculture13040844. Status: Published Year Published: 2023 Citation: Beegum S., C. H. Walne, K. N. Reddy, V. Reddy, and K. R. Reddy. 2023. Examining the Corn Seedling EmergenceTemperature Relationship for Recent Hybrids: Insights from Experimental Studies. Plants. 12(21):3699. doi: 10.3390/plants12213699. Status: Published Year Published: 2023 Citation: Taduri S., R. Bheemanahalli, C. Wijewardana, A. A. Lone, S. L. Meyers, M. Shankle, W. Gao, and K. R. Reddy. 2023. Sweetpotato cultivars responses to interactive effects of warming, drought, and elevated carbon dioxide. Frontiers in Genetics. 13. doi: 10.3389/fgene.2022.1080125. Status: Published Year Published: 2023 Citation: Beegum, S., V. Reddy, and K. R. Reddy. 2023. Development of a cotton fiber quality simulation module and its incorporation into cotton crop growth and development model: GOSSYM. Computers and Electronics in Agriculture. 212, 108080. doi: 10.1016/j.compag.2023.108080 Status: Published Year Published: 2023 Citation: Beegum S., V. Truong, R. Bheemanahalli, D. Brand, V. Reddy, and K. R. Reddy. 2023. Developing functional relationships between waterlogging and cotton growth and physiology-towards waterlogging modeling. Front. Plant Sci. 14:1174682. doi: 10.3389/fpls.2023.1174682. Status: Published Year Published: 2023 Citation: Wu, Y., S. Meng, C. Liu, W. Gao, X.-Z. Liang. 2023. A bibliometric analysis of research for climate impact on agriculture. Frontiers in Sustainable Food Systems. 7. doi: 10.3389/fsufs.2023.1191305. Status: Published Year Published: 2023 Citation: Parton, W. J., R. H. Kelly, M. D. Hartman, A. Revallier, A. B. B. de Faria, G. Naves-Maschietto, M. Orvain, S. Houot, M. Albuquerque, and S. Kech. 2023. Agricultural and municipal organic waste amendments to increase soil organic carbon: How much, how often, and to what end? Soil Science Society of America Journal. 87(4), 885901. doi: 10.1002/saj2.20529.

Progress 09/01/22 to 08/31/23

Outputs
Target Audience:The UVMRP mission is to provide high quality data, data products, and services in support of agricultural research, and to facilitate the use of these measurements with climate and crop models. Research topics span the geographic distribution of UV-B solar irradiance and the effects of increased or diminished UV-B on crops, native and invasive plants, and animals. The program participates in international instrument inter-comparisons to ensure quality control and a common reference standard for comparing U.S. data with the rest of the world. Over the past years there have been between 6,000 and 10,000 visits per month to the web site (and average 500 downloads per month) by users to obtain data and/or related information. Over the years, 54% of web data downloads have come from .EDU domains, 23% from .GOV domains, 21% from .com domains and the balance from other domains. Both national and international educators and students have retrieved data or related information via the program website. Most academic data use is for agricultural research. The UVMRP measurements and tools have been the source for numerous research studies conducted by universities and agricultural research facilities across the nation. Fourteen land grant universities have prior and/or current research studies in collaboration with the UVMRP, and all of them have a UVMRP instrument array at their location. In addition, the UVMRP has prior and/or current research collaborations with government agencies such as NASA, NOAA, DOE, EPA, and NSF. UVMRP stakeholders include: a) USDA personnel at all levels who fund, participate, or have an interest in the program, b) agricultural researchers from governmental, academic, and other institutions, c) researchers from health communities, and d) educators and students at all levels who make use of network data and tutorials. Collaboration and support of research in the effects of UV-B on plants and materials has resulted in papers that are presented at scientific conferences. As ground based measurements of UV-B and study of UV-B effects on agriculture continue to be important, and predictive crop yield tools come of age, an increasing number of researchers could make use of UVMRP products. Perhaps the best measure of the effectiveness of the UVMRP in serving stakeholders is the large number of publications that have come directly from the program or from collaboration with affiliated researchers. There have been over 300 publications to date in the 30-year history of the UVMRP. Agricultural effects studies account for two-thirds of publications, with UV-B climatology making up most of the remainder. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Graduate and post-doctoral participation and mentoring are taking place in all phases of the project, through co-authorship of papers in scientific journals, proceedings, and transactions, as well as presentations of research work at professional society events. One undergraduate student is investigating the relationship between diffuse irradiance and cloud coverage using our shadowband measurements. One Postdoctoral fellow contributes to the calibration of the DayCent-UV model and the coupling between the DayCent-UV and CWRF models to simulate the grasslands dynamics in North American. One Postdoctoral fellow and two undergraduate students focus on effects of environmental stressors on economically import crops. One Postdoctoral fellow focuses on developing the coupled climate-crop model, conducting model calibration and experiments, analyzingresults, and writing journal articles. One Postdoctoral fellow focuses on building the high-resolution crop-specific agricultural production database. How have the results been disseminated to communities of interest?Data from all agroclimatologic and research network sites are available on the project's web site (http://uvb.nrel.colostate.edu). Our scientifically peer-reviewed papers and associated conference presentations are also available on the website for scientific and/or wider communities of interest. Additionally, UVMRP staff routinely respond directly to data inquiries and requests for UV-related expertise via telephone and email. Currently we the project has have users from more than 300 different organizations, with 48% from national educational institutions; 33% from national governmental agencies; 2% from commercial enterprises; and 17% from international institutions. Our paper in determining climate effects on US total agricultural productivity that is published in the Proceedings for the National Academy of Sciences of the United States of America (PNAS), attracts significant media attention, and which is covered by Nature Research Highlights in 2017. The 5-year impact factor of the PNAS is 12.3, while the current altmetric score of the article is 137, putting it in the top 5% of research outputs, revealing the significance of its findings. Furthermore, it has been cited 123 times, downloaded/full-paper-assessed over 18,000 times, picked up by 9 news outlets, 6 blogs, tweeted by 50 users, and have 307 readers on Mendeley. Our effects work facilitates breeders on selecting crop (e.g., sweetpotato and soybean) varieties that can handle realistic future conditions like UV exposure, drought, warmer temperature, and more carbon dioxide. What do you plan to do during the next reporting period to accomplish the goals?The UVMRP will deliver continuous maintenance of the network instruments, preserve high rates of data collection, develop up-to-date calibration protocols, and provide reliable primary and derived data products. We will continue to work with Mississippi State University and utilize their SPAR facility to evaluate the isolated and combined effects of UV-B and other environmental stressors on crops. Further progress towards the completion of the Integrated Agricultural Impact Assessment System framework is expected through ongoing collaborations with the University of Maryland. This includes advancing efforts to couple the Agroecosystem framework (CROP) with the CWRF model as well as developing and integrating an economic module into the impact assessment system to provide insight for farm management practices and policy decisions. For climatology data collection, we will continue to update the old Small Board Computers (SBC) at each site. we will continue to follow our established QC protocols and schedule annual site visits to perform necessary instrument maintenance. Ongoing evaluation of the shadowband and pyranometer QC flags and methodology is expected to simplify and improve the efficiency and accuracy of the automated component of the QC procedure. We will continue to work with site operators to ensure rapid response to instrument issues. Evaluation of the in-house calibration facility software and hardware will be conducted to improve the accuracy of the (UV-) MFRSR spectral response measurements. We will continue to investigate state-of-the-art methodologies for data processing and to deploy the validated new algorithms in production (e.g., Gaussian Process Regression). We will continue to develop the new UVMRP website with the focus on the data interface and the backend for UVMRP data products. We will also continue to seek replacement of the aging shadowband radiometers. For effects research, we will continue to evaluate the isolated and combined effects of UV-B and other environmental stressors on the growth and development of major crops and to identify the cultivars/lines tolerance for future environmental conditions. This includes conducting SPAR chamber experiments to investigate effects of temperature stress, drought, and CO2 levels on soybean and sweetpotato growth, development, and yield. These experiments will facilitate the development of quantitative algorithms that will be incorporated into climate-crop simulation models for the Climate-Agroecosystem-UV Interactions and Economic system. For integrated assessment, we will continue to develop/upgrade the coupled CWRF-CROP system by reengineering the latest DSSAT version for distributed modeling and to simulate the growth and yields of corn, soybean, and cotton over the U.S. We will continue to investigate the uncertainty of CWRF-DSSAT on soybean modeling and perform sensitivity analysis of soybean cultivars, farm management practices, soil conditions, and climate variations. We will continue to combine statistical analysis and dynamic modeling to determine quantitative relationships between crop yields and environmental, technological, and adaptation factors. We will also continue to develop DayCent-UV and the CWRF/DayCent-UV interface for their one-way and two-way coupling. We will set up a process to validate simulated plant productivity for the CWRF grassland domain using MODIS NDVI.

Impacts
What was accomplished under these goals? The USDA UVB Monitoring and Research Program (UVMRP) activities focus on three primary goals: (1) to collect high-quality and geographically-distributed solar ultraviolet (UV) and visible radiation measurements, and to make these data publicly available for the agricultural, ecological, and scientific communities; (2) to quantify the isolated and combined effects of UV and other environmental stressors on economically important crops through experiments; (3) to develop a coupled modeling framework of climate-agroecosystem-UV interactions and economic impacts for science-informed decision support toward sustainable US agriculture. UVMRP manages the only nationwide network which measures surface UVB irradiance, and provides 8 other data products, at 37 agro-climatological sites and 4 long-duration research sites that together encompass 20 ecoregions and a variety of land types. UVMRP staff check the instrument data daily, and when necessary, work with local site operators to correct any problems. Through the UVMRP web page, we continue to provide quality UV and visible surface radiation measurements and data products, including calibrated and biologically-weighted UV irradiances, UV index, synthetic spectra, instantaneous optical depths, daily and hourly sums, and photosynthetically active radiation (PAR). We are updating the old Small Board Computers (SBC), which communicate and download data at each site. Development of the new UVMRP website is ongoing but is available to the public. UVMRP continues to improve the accuracy and quality of climatological data in several ways. Quality control (QC) and quality assurance (QA) algorithms are continuously revised/redesigned to reduce the human labeling effort and improve the labeling accuracy. Automatic statistics-based algorithms are developed, which utilizes a series of range checks, signal-to-noise limits, as well as sub-hourly correlation between 368 and 415 channels to catch QC issues such as noise and instrument failures. In addition, with self-supervised pre-task training on unlabeled data and supervised classification, a two-stage deep neural network is under development to capture QC issues that are subtle or obscured by broken clouds and aerosols. Monthly in-situ calibrations are performed using the Langley Analyzer and the supplemental global-pairing cloud screening algorithm. The Matern kernel was introduced to the Gaussian Process Regression algorithm, which has been used to smooth all sites' calibration factors generated by the Langley Analyzer. For effects research, we are working with our collaborators at Mississippi State University on several experiments using its Soil-Plant-Atmosphere-Research (SPAR) environmental chambers to investigate impacts of UVB radiation and other environmental stressors on the growth and development of economically important crops (basil, sweetpotato, rice, soybean). For basil, the experiment shows that elevated UVB (+UVB) increases leaf temperature (by ~1°C) while elevated CO2 (+CO2) amplifies superoxide dismutase and ascorbate peroxidase activity in leaves. Additionally, effects of +UVB and +CO2 interactions are observed for net photosynthesis, maximal quantum efficiency, pigment concentrations, and malondialdehyde. For sweetpotato, the effects of drought (-W), high temperature (+T), +CO2, low nitrogen (-N), and/or +UVB on traits like morphology, physiology, gas-exchange, photosynthesis, root storage are investigated and its stress-tolerant/sensitive cultivars are identified. +CO2 shows mitigation effects on sweetpotato. For rice, the proteomic response of leaves to +UVB is investigated and finds that the expression of its proteins involved in photosynthesis, protein biosynthesis, signal transduction, and stress response is changed. For soybean, it is found that +T decreases the gas exchange parameters and the transgenerational seed germination ability. Another study phenotypes 18 soybean cultivars for yield and quality traits and identifies the high-yielding short-duration cultivars (539-T3, GT-477CR2) and high-protein high-oil high-yield cultivars (MS 4616 RXT and 7547XT). The findings of these studies could allow for revealing underlying mechanisms, selection of stress tolerant cultivars with the best coping ability for future climate, optimization of commercial crop productions or specific traits (e.g., protein/oil contents), and simulation of crop responses in models. For integrated assessment, we are working with our collaborators at the University of Maryland to develop a comprehensive predictive system for risk analysis, evaluation of economic impacts, and strategic planning concepts to achieve sustainable agricultural development in a changing environment, using the CWRF-CROP framework, where CWRF refers to the Regional Climate-Weather Research and Forecasting Model and CROP represents a universal abstraction for many crop models such as GOSSYM (a dynamic cotton (Gossypium hirsutum L.) crop simulation model), DSSAT (Decision Support System for Agrotechnology Transfer model for crop simulation that includes cotton, corn, soybean), and DayCent (a daily time-step biogeochemical model for ecosystems that include grasslands). As a part of the CWRF-CROP effort, we continue to develop the CWRF-DSSAT system to be included in this integrated assessment system. We continue to enhance the coupled CWRF-CROP system to simulate and predict the growth and yields of soybean in the US. One effort is to optimize the ensemble of soybean cultivars and farm management practices (e.g., irrigation, fertilization) to reduce the uncertainty on the yield prediction at the county-level. The preliminary result shows that the soybean yields are underestimated in most areas with the initial set of 5 cultivars, justifying the optimization effort on a larger set of cultivars with more computational resources. To improve the capability of the coupled CWRF-CROP system, we continue to improve the simulation accuracy of the CWRF with current focus on improving its cumulus scheme. The proposed solutions to the summer Central US warm-and-dry biases in CWRF include triggering mesoscale convective systems, lowering cloud base and increasing cloud depth, suppressing penetrative cumuli, and increasing water detrainment. These improvements produce consistently heavier precipitation and colder T, essentially eliminating the bias. To evaluate the economic impacts, we continue to incorporate the trend of total factor productivity (TFP) and its correlation with environmental variables. Development of DayCent-UV is underway to quantify UV impacts on ecosystems and to be coupled with CWRF ultimately. To improve the long-term simulation of important US crop rotation systems, we continue to couple the CWRF land surface model with the DayCent model by developing an interface between reanalyzed meteorological data grids and DayCent-UV. In the meantime, DayCent-UV is applied to various Ecosystems in North America to simulate their dynamics. We continue to simulate ecosystem dynamics for the US Great Plains using climate change projections provided by 3 GCM models and 2 scenarios of future greenhouse gas emissions, with the current focus on translating weather data from GCM grids to the DayCent-UV file format. Another statistical analysis using long-term data from over 100 sites in Canada and the US finds that growing season AET and precipitation are the best predictors of grassland plant productivity in the region.

Publications

• Type: Journal Articles Status: Published Year Published: 2022 Citation: Taduri, S., R. Bheemanahalli, C. Wijewardana, A.A. Lone, S.L. Meyers, M. Shankle, W. Gao, K. R. Reddy. 2022. Sweetpotato cultivars responses to interactive effects of warming, drought, and elevated carbon dioxide. Front. Genet. 13:1080125. doi: 10.3389/fgene.2022.1080125
• Type: Journal Articles Status: Published Year Published: 2022 Citation: Ramamoorthy, P., R. Bheemanahalli, S.L. Meyers, M.W. Shankle, K.R. Reddy. 2022. Drought, Low Nitrogen Stress, and Ultraviolet-B Effects on Growth, Developmental, and Physiology of Sweetpotato Cultivars during Early Season. Genes, 13, 156. doi: 10.3390/genes13010156
• Type: Journal Articles Status: Published Year Published: 2022 Citation: Sah, S. K., S. R. Jumaa, J. Li, K. R. Reddy. 2022. Proteome analysis response of rice (Oryza sativa) leaves to ultraviolet-b radiation stress. Frontiers in Plant Science, 13. doi: 10.3389/fpls.2022.871331
• Type: Journal Articles Status: Published Year Published: 2022 Citation: Alsajri, F. A., C. Wijewardana, R. Bheemanahalli, J.T. Irby, J. Krutz, B. Golden, V.R. Reddy, K.R. Reddy. 2022. Morpho-Physiological, Yield, and Transgenerational Seed Germination Responses of Soybean to Temperature. Frontiers in Plant Science, 13. doi: 10.3389/fpls.2022.839270
• Type: Journal Articles Status: Published Year Published: 2022 Citation: Barickman, T. C., S. R. Brazel, A. Sehgal, C. H. Walne, W. Gao, and K. R. Reddy. 2022. Daily UV-B treatments and elevated CO2 increase pigment concentrations and net photosynthesis of basil (Ocimum basilicum L.). Technology in Horticulture. 2:2. doi: 10.48130/TIH-2022-0002
• Type: Journal Articles Status: Published Year Published: 2022 Citation: Sun C., and Liang X.-Z. 2022. Understanding and Reducing Warm and Dry Summer Biases in the Central United States: Improving Cumulus Parameterization. J. Climate, 1-43. doi: 10.1175/JCLI-D-22-0254.1
• Type: Journal Articles Status: Published Year Published: 2022 Citation: Bheemanahalli, R., S. Poudel, F. A. Alsajri, K.R. Reddy. 2022. Phenotyping of Southern United States Soybean Cultivars for Potential Seed Weight and Seed Quality Compositions. Agronomy, 12(4), 839. doi: 10.3390/agronomy12040839