Source: TENNESSEE STATE UNIVERSITY submitted to NRP
ENHANCING CROP PRODUCTIVITY AND REDUCING NITROUS OXIDE EMISSIONS IN CORN AND SOYBEAN CROPPING SYSTEMS
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
ACTIVE
Funding Source
OTHER GRANTS
Reporting Frequency
Annual
Accession No.
1028525
Grant No.
2022-38821-37341
Cumulative Award Amt.
$502,853.00
Proposal No.
2021-12899
Multistate No.
(N/A)
Project Start Date
Jun 1, 2022
Project End Date
May 31, 2026
Grant Year
2022
Program Code
[EQ]- Research Project
Recipient Organization
TENNESSEE STATE UNIVERSITY
3500 JOHN A. MERRITT BLVD
NASHVILLE,TN 37209
Performing Department
Department of Biological Scien
Non Technical Summary
As global population continues to increase, one of the great challenges we are facing today is to increase food, feed, fiber, and fuel crop production while reducing environmental impacts. To meet food and energy demands, large amounts of inorganic nitrogen fertilizer are appplied each year to agricultural lands leadingto serious problems with soil greenhouse gas (i.e., nitrous oxide N2O, carbon dioxide CO2 and methane CH4) emissions to the atmosphere. In the past decades, climate-smart agriculture(CSA) practices have been proposed to simultaneously increase agricultural productivity, improve nitrogen use efficiency, build agricultural resilience to climate and environmental changes, and reduce greenhouse gas emissions. Those agricultural practices include technologies such as the use of legume crops and nitrogenfixer to reduce nitrogenapplication, the use of nitrification inhibitor, biochar, and no-tillage to reduce greenhouse gas emissions, particularly N2O emission. Croplands are considered biogeochemical "hotspots" of N2O emission, asa significant portion of the total N2O emission to the atmosphere comes from croplands. However, how the CSA pracrices inflluence soil N2O emission in corn and soybean cropping systems are still not entirely clear.This project is designed to investigate the impacts of several CSA practices (i.e., reduce nitrogenapplication, no-tillage, nitrification inhibitor, biochar, and chicken manure) on crop production, nitrogenuse efficiency, and soil N2O emission in corn and soybean cropping systems using both field experiments, meta-analysis, and ecosystem models, and develop decision support systems and economic impact analysis tools to improve resource use efficiency and economic benefits for farmers. The completion of this project will provide better understanding of the effects and mechanisms of CSA practices on crop production and soil N2O emission, and significantly build and strengthen our research and extension capacities in agro-ecology, crop production, global climate change, sustainable agriculture, and environmental sciences.
Animal Health Component
50%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
2051510107035%
2051820107035%
6011510301030%
Knowledge Area
601 - Economics of Agricultural Production and Farm Management; 205 - Plant Management Systems;

Subject Of Investigation
1510 - Corn; 1820 - Soybean;

Field Of Science
1070 - Ecology; 3010 - Economics;
Goals / Objectives
The overall goal of this project is to investigate six different climate-smart agriculture(CSA) practices (including different nitrogen fertilizations, use of nitrification inhibitor, no-tillage, chick litter, and biochar) on the yields of corn and soybean and the emissions of greenhouse gases (GHG), particularly N2O, and to provide farmers the best CSA practices to improve their incomes. The specific objectives are: 1) to quantify crop productivity and soil N2O emissions in corn and soybean cropping systems under six CSA practices; 2) to conduct meta-analysis of the impacts of CSA practices on soil N2O emission in corn and soybean cropping systems; 3) to simulate crop productivity and soil N2O emissions in the southeastern US using the DLEM-Ag and DNDC models; and 4) to build a decision support system and conduct economic impact analysis for farmers to increase resource use efficiencies and their incomes. The project will build and strengthen research and extension capacities and train minority graduate and undergraduate students in Agro-ecology, Global Climate Change, and Sustainable Agriculture. The findings of this project will be presented in national and international conferences, published in peer-review journals, and disseminated to farmers through workshops.
Project Methods
To accomplish the goal and project objectives, an integrated approach will be applied including field experiments, meta-analysis, ecosystem modeling, and decision support system and economic impact models. For the field experiments, crop production and soil GHG emissions will be measured in corn and soybean cropping systems under six CSA practices over three years. Ecosystem models (DLEM-Ag and DNDC) will be applied and parameterized to simulate the CSA practices on crop productivity and soil GHG emissions at site and regional levels. A decision support system will be developed, and economic impact analysis will be conducted to provide guidelines for farmers to implement CSA practices in their cropping systems. Four specific tasks will be executed to address these project objectives.2.2.1 Field experiments to improve crop production and N use efficiency, and reduce soil N2O emission in corn and soybean cropping systemsThe field experiments will be performed at TSU Agricultural Research and Education Center. A split-plot experimental design with four blocks will be applied in the field experiments. Cropping system (two levels, corn and soybean) is the main plot factor. The CSA practices are the split plot factors and include six treatments: normal N fertilization, reduced (2/3) N fertilization, fertilization with a nitrification inhibitor (dicyandiamide, DCD), fertilization with biochar, fertilization with no-tillage, and fertilization with ½ of N as chicken manure). Normal N fertilization levels for corn and soybean are based on typical practice by farmers in Tennessee. The reduced N fertilization will be 2/3 of the typical N application levels. Plot size will be 4.3×15.2 m2 (i.e. 14×50 ft2) with four replications. The total number of plots will be 48. Planting date, density and weed control, and other management practices will follow typical practices in Tennessee.Field measurements: Field measurtementsinclude Soil sampling and soil physical, chemical and microbial properties analysis, leaf photosynthesis rate, transpiration, leaf area index (LAI), phenology and biomass, and greenhouse gas N2O, CO2, CH4 emission measurements. Soil N2O, CO2, CH4 and NH3 will be measured using the Picarro G2508 Multiple Gas Analyzer together with 6 Eosense Automatic Smart Chambers. We will measure 24 hours a day for two weeks in corn field and two weeks in soybean field. The setting of the GHG emission measurements is illustrated in Fig. 3a below. Soil fluxes of N2O, CO2, and CH4 will be calculated using the Eosense software. Simultaneous measurements of soil temperature and soil moisture content will be made with soil flux measurements.Data Analyses: The crop physiology, biomass and growth, grain yield, N use efficiency, and GHG emissions will be analyzed for the CSA practices effects and between two cropping systems using the Analysis of Variance (ANOVA) method, and multiple comparisons will be conducted if significant effects are detected.2.2.2 Meta-analysis of the impact of CSA practices on crop yield and greenhouse gas N2O emissionMeta-analysis is a quantitative review that synthesizes results from multiple independent studies to address a common question (Luo et al. 2006; Jian et al. 2016; Deng et al. 2015; Duruy et al. 2019). For example, Huang et al. (2018) investigated crop yield and greenhouse gas emissions in a no-tillage system. Bai et al. (2019) studied the effects of CSA (i.e., tillage cover crop and biochar) on soil carbon sequestration. Recently, Kichamu-Wachira et al. (2021) synthesized the impacts of CSA on crop yield and soil C and N pools in Africa. These studies have improved our understanding of the CSA impacts on crop yield and soil GHG emissions, but a more focused study on more CSA practices, corn and soybean crop yields, and soil N2O emissions in US is needed.In this study, we will focus on the impacts of CSA practices on soil N2O emission in corn and soybean cropping systems. We will follow the protocol for meta-analysis as we did before (e.g., Deng et al. 2005; Huang et al. 2018). The key words we will use to search literature include "nitrous oxide or N2O, climate-smart agriculture, tillage, nitrogen fertilizer, nitrification inhibitor, litter or manure, soybean or corn". The criteria of paper selection will be set and data of crop yield, N2O emission and other relevant variables will be extracted from the papers. We will quantify the effect of CSA practices on crop yield and soil N2O (and CO2 and CH4) emissions by calculating the natural log of the response ratio (RR), a metric commonly used in meta-analyses (Hedges and Olkin 1985, Deng et al. 2015). A weighted RR will be computed from individual RR (Hedges 1992, Luo et al. 2006). Data analysis will be carried out using SAS software (Deng 2015).2.2.3 Model simulations of crop production, nutrient use efficiency, and greenhouse gas N2O emission using DLEM-Ag and DNDC models Ecosystem models are powerful tools that are increasingly used to examine potential impacts of management practices on crop yields and GHG emissions (Gilhespy et al. 2014). In this proposal, two models will be used to simulate crop productivity and estimate GHG, particularly N2O emissions: the DLEM-Ag (Tian et al. 2016) and the DNDC model (Giltrap et al., 2010). Both models have been successfully applied to different crop types in many places around the world (Li et al. 1992; Beheydt et al., 2007; Ren et al. 2012; Tian et al. 2015, 2020; Zhang et al. 2021). The data collected in the field experiments above will be used to improve, parameterize, and validate these two models, and simulate crop production and soil N2O emission at the site and scale up to the southeastern US.Both the DLEM-Ag and DNDC models can be applied at site and regional levels. The DNDC is run mostly at the site level, and the DLEM-Ag can provide better estimation at the regional scales. We will apply both models to improve the accuracy of model estimation and quantify the uncertainty of model outputs. The models will first be parameterized based on the measured data and the initial soil physical and chemical parameters to simulate crop production and soil N2O emission in corn and soybean at plot level under the CSA practices. The model performance will be evaluated by quantifying the discrepancy of crop production and soil N2O emissions between modeled and measured values. The parameterized models will then be used to simulate crop growth and daily ecosystem soil N2O emissions for different CSA practices. The improved model structure and optimized parameters for the DLEM-Ag and DNDC could be further scaled up with regional climate data, crop areas, soil types, and agricultural practices to quantify crop production and soil N2O emission in the southeastern US in various cropping systems managed with CSA practices (Saikawa et al. 2013; Ren et al. 2012).2.2.4 Decision support system and economic impact analysis for farmers to implement CSA practices under different cropping systems Based on the DLEM-Ag and DNDC models, a decision support system will be built to allow the farmers/producers, researchers, and natural resource managers to select different cropping systems and N fertilization and other CSA practices, and simulate crop yield and GHG emissions. An economic impact analysis will be conducted to illustrate the economic benefits of different production systems under CSA practices. Workshops will be conducted to disseminate findings from this project to farmers in a timely way.

Progress 06/01/23 to 05/31/24

Outputs
Target Audience:The target audience of this project included scientists, professors, students, gerenal public, and farmers. Results were disseminated through presentations and and journal publications. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?We continued to provide training to students. One Ph.D. student, Avedananda Ray, and three MS students, Navneet Kaur, Funmilayo Akintunde, and Lovish Kasrija, at Tennessee State University, have been working on this project. Navneet Kaur and Funmilayo Akintunde graduated with MS in Biology. Two undergraduate students are also participating in the project. Students were trained on meta-analysis, ecosystem modeling, and field experiments. All of them participated in field experiments and data analysis. Avedananda Ray, Navneet Kaur, and Lovish Kasrija conducted literature review, data collection from published papers, worked on data synthesis. Navneet Kaur conducted modeling simulation using DNDC. Yongfa You at Boston College with Dr. Tian conducted model simulation using the DLEM mode. They were also trained for conference presentation and writing manuscripts for publications. How have the results been disseminated to communities of interest?The findings of this project were disseminated through presentation and publications. Preliminary findings from the meta-analysis and mega-analysis have been presented at TSU Research Symposium 2024. The DLEM paper has been published in a peer-review journal. One review paper on the climate smart agricultural was published in Agriculture (Hui et al. 2024). One modeling manuscript has been accepted for publication (Kieffer et al. 2024). Two abstracts have been accepted for presentation at the ESA annual meeting 2024. What do you plan to do during the next reporting period to accomplish the goals?We will continue the field experiments with corn and soybean, conductmeta-analysis and ecosystem modeling, provide research skills training to graduate and undergraduate students, disseminate our findings to farmers, and present our results in scientific meetings and write manuscripts for publications.

Impacts
What was accomplished under these goals? During the project year 2023-2024, we have conducted a series of activities to accomplish the project goals. We conducted field experiments with corn and soybean, collected more data of corn and soybean yield and soil N2O emissions for meta-analysis, conducted a mega-analysis with 30 meta-analyses on crop yield and tillage treatments, and continued the DNDC and DLEM model simulations. The major achievements include: 1. Field experiments. We conducted field experiments with corn and soybean under different CSA practices. The Experiment plots were set up in the prior switchgrass and gamagrass fields. Seeds of corn and soybean were planted after distilled field. Six CSA practices including biochar application, nitrification inhibitor, chicken manure, different nitrogen fertilizer application rates were considered. Each treatment was replicated 4 times as blocks for a total of 48 plots. Field measurements of plant physiology, growth, and yield and soil greenhouse gas emissions (CO2 and N2O) were measured during the growing season. Leaf photosynthesis was measured using Li-7800 Photosynthesis Analyzer, soil GHG emissions were measured using Li-8100A CO2 and Li-7280 N2O analyzers. Plants of corn and soybean were sampled in each plot and yield and yield components were quantified. We will analyze these data and prepare for presentation and publications. 2. Meta- and mega-analysis and data collection. 1) We expanded the dataset of the meta-analysis to including 153 papers (adding 73 more papers). Preliminary results showed that inorganic fertilizers and CSA practices, such as Organic amendments (animal manure and straw) reduced tillage, and cover crop and biochar application significantly influenced soil N2O emission. For example, reduced tillage increased soil N2O emission by 6.88%, while cover crops reduced N2O emission by 9.9%. Water-saving irrigation significantly decreased N2O emission by 138.24% compared to flooding. Nitrification inhibitors led to a significant decrease in N2O emission by 266.47%, whereas the application of biochar reduced N2O emission by 16.66 %. 2) We performed a mega-analysis (i.e., a meta-meta-analysis), synthesizing data from 30 meta-analysis studies to provide a comprehensive quantification of the impacts of the no-till (NT) and reduced tillage (RT) on crop yield and GHG emissions. Results showed large variation in NT and RT effects on crop yield and GHG emissions among meta-analysis studies. Overall, the NT treatment slightly reduced crop yield by 3.3%, varying from a reduction of 11.9% to an increase of 10.7%. The mixed NT+RT and RT treatments did not change crop yield. The NT treatment increased soil CO2 emission by 10.5%, increased soil N2O emission by 8.7%, and had little overall impact on CH4 emissions. But studies with the NT+RT treatment showed a decrease of CH4 emission by 6.0% and increased soil N2O emission by 5.9%, whereas the RT treatment did not influence soil N2O emission. Our mega-analysis offers new insights by providing more comprehensive estimations than individual meta-analyses of the impacts of conservation tillage practices on crop yield and GHG emissions. These results were presented at TSU Research Symposium 2024 and have been accepted for presentation in the ESA 2024 annual meeting. We also reviewed the impacts of CSA on nitrogen processes and N2O emission (Hui et al. 2024) and submitted a book chapter of "Effects of climate-smart agricultural practices on greenhouse gas emissions in croplands" for publication. 3. Ecosystem modeling. We simulated net greenhouse gas emissions in U.S. croplands using the DLEM model, and the manuscript has been published in Global Change Biology (You et al. 2024). The DNDC model simulations of the impact of precipitation on corn yield and N2O emission were publish in Ecological Processes (Kaur et al. 2023). We continued our model simulation with DNDC on plant productivity and greenhouse gas emissions and simulated different precipitation changes. We will perform more simulations on climate smart agricultural practices using the DNDC model.

Publications

  • Type: Journal Articles Status: Published Year Published: 2024 Citation: Hui, D., A. Ray, L. Kasrija, and J. Christian. 2024. Impacts of Climate Change and Agricultural Practices on Nitrogen Processes, Genes, and Soil Nitrous Oxide Emissions: A Quantitative Review of Meta-Analyses Agriculture 14(2), 240.
  • Type: Journal Articles Status: Accepted Year Published: 2024 Citation: Kieffer, C., N. Kaur, J. Li, R. Matamala, P. Fay, H. Dui. 2024. Photosynthetic responses of switchgrass to light and CO2 under different precipitation treatments. GCB-Bioenergy 10.1111/gcbb.13138.
  • Type: Book Chapters Status: Under Review Year Published: 2025 Citation: Hui, D., Q. Deng, H. Tian, Y. Luo. 2025. Effects of climate-smart agricultural practices on greenhouse gas emissions in croplands. In Lackner, M.; Sajjadi, B.; Chen, W.-Y. (Eds.) Handbook of Climate Change Mitigation and Adaptation, Springer
  • Type: Conference Papers and Presentations Status: Awaiting Publication Year Published: 2024 Citation: Ray, A., L. Kasrija, D. Hui. 2024. Impact of climate-smart agricultural practices on nitrous emission from corn and soybean fields. A meta-analysis. ESA Annual Meeting 2024, August 4-9, 2024, Long Beach, CA.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Kasrija, L., D. Hui. 2024. An evaluation of meta-analyses of greenhouse gas emissions and crop yield under conservation tillage practices. TSU 2024 Research Symposium. March 25-29, 2024.
  • Type: Other Status: Published Year Published: 2024 Citation: Hui, D., J. Christian, F. Hayat. 2024. Impacts of precipitation change on switchgrass productivity and soil CO2 emission: A modeling study. TSU 2024 Research Symposium. March 25-29, 2024.
  • Type: Conference Papers and Presentations Status: Awaiting Publication Year Published: 2024 Citation: Kasrija, L., A. Ray, W. Ren, L. Wang, P. Fay, D. Smith, J. Li, P. Illukpitiya, H. Tian, D. Hui. 2024. Effects of no-till and reduced tillage on crop yield and greenhouse gas emissions: A mega-analysis. ESA Annual Meeting 2023, August 4-9, 2024, Long Beach, CA.
  • Type: Journal Articles Status: Published Year Published: 2024 Citation: You, Y., Tian, H., Pan, S., Shi, H., Lu, C., Batchelor, W.D., Cheng, B., Hui, D., Kicklighter, D., Liang, X.Z. and Li, X., 2024. Net greenhouse gas balance in US croplands: How can soils be part of the climate solution? Global Change Biology, 30(1), p.e17109


Progress 06/01/22 to 05/31/23

Outputs
Target Audience:The target audience of this project included scientists, professors, students, gerenal public, and farmers. Results were disseminatedthrough presentations andand journal publications. Changes/Problems:Dr. Hanqin Tian, an ecosystem modeler and co-PI of this project, moved from Auburn University to Boston College. This change will not influence the implementationof the project. He and his team will continue to conduct ecosystem modeling with the DLEM-Ag to simulate the impacts of climate smart agricultural practices on soil greenhouse gas emissions in corn and soybean fields/ and official transition procedure will be submitted to NIFA for further approval. What opportunities for training and professional development has the project provided?One Ph.D. student, Avedananda Ray, and two MS students, Funmilayo Akintunde and Navneet Kaur, at Tennessee State University, have been working on this project. Two undergraduate students are also participating in the project. They were trained on meta-analysis, ecosystem modeling, and field experiments. Students conducted literature review, collected data from published papers, worked on data analyses, and conducted model simulation. They were also trained for conference presentation and writing manuscripts for publications. How have the results been disseminated to communities of interest?The findings of this project were disseminated through presentation and publications. Parts of the meta-analysis have been presented at TSU Annual Research Symposium. One paper with the DNDC model simulation of corn yield and soil N2O emission has been published in a peer-review journal. One review paper on meta-analysis and mega-analysis was published. One DLEM model paper is under review. Two abstracts have been accepted for presentation at the ESA annual meeting 2023. What do you plan to do during the next reporting period to accomplish the goals?We will start the field experiments with corn and soybean, continue the meta-analysis and ecosystem modeling, provide research skills training to graduate and undergraduate students, provide documents for farmers, and present our results in scientific meetings and write manuscripts for publications.

Impacts
What was accomplished under these goals? During the first project year (2022-2023), we have conducted a series of activities to accomplish the project goals. The major achievements include: 1. Meta-analysis and data collection. To quantify the impacts of nitrogen fertilizer, tillage, irrigation, crop rotation, and biochar application on soil N2O emissions, we searched peer-reviewed publications using Web of Science and Google Scholar. We have compiled datasets of 530 observations of corn and 352 observations of soybean from 80 peer-reviewed publications up to February 2023. Preliminary results showed that soil pH played a crucial role in regulating the N2O emissions after application of soil amendments. The C:N ratios of manure, straw, biochar application also significantly affected soil N2O emission. In addition, the reduced N fertilizers, water-saving irrigation, reduced or no tillage, and applying enhanced efficiency fertilizers significantly decreased soil N2O emissions. Parts of these results have been presented at TSU Annual Research Symposium. We also compiled a comprehensive dataset of biochar application and soil N2O emission based on 18 meta-analyses. Meta-analysis is ongoing with this dataset. We also published a review on literature review, meta-analysis, and mega-analysis in ecological and agricultural sciences (Hui et al. 2023). 2. Ecosystem modeling. Using the DNDC model, we simulated the impacts of precipitation on corn yield and soil N2O emission (Kaur et al. 2023). We found that both corn yield and soil N2O emission showed near linear relationships with precipitation based on the long-term precipitation data, but with different response patters of corn yield and soil N2O emission to precipitation manipulation treatments. The extreme drought treatments reduced corn yield sharply in both normal and wet years. In contrast, soil N2O emission mostly responded linearly to precipitation manipulations. This study revealed different response patterns of corn yield and soil N2O emission to precipitation and highlighted that mitigation strategy for soil N2O emission reduction should consider different background climate conditions. We also simulated net greenhouse gas emissions in U.S. croplands using the DLEM-Ag model, and found that U.S. croplands sequestered 13.2 Tg CO2-C/yr in SOC during 1960-2018 and emitted 0.39 Tg N2O-N/yr and 0.21 Tg CH4-C/yr, respectively. The estimated national net GHG emission rate was 121.9 Tg CO2-eq/yr. This study highlights the importance of simultaneous quantification of SOC-sequestered CO2 and non-CO2 greenhouse gas emissions for developing effective agricultural climate change mitigation measures and assessing regional climate impacts. 3. Field experiment and preparation. As setting up account and purchasing supplies took longer time, we will start the field experiment in spring 2023. We have purchased supplies including biochar, seeds, and fertilizer for field experiment. A six climate-smart (CSA) field experiment with corn and soybean is going to be established in 2023. Field measurements of plant physiology, growth, and yield and soil greenhouse gas emissions are going to be conducted.

Publications

  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Kaur, N., Hui, D., Riccuito, D.M., Mayes, M.A. and Tian, H., 2023. Response patterns of simulated corn yield and soil nitrous oxide emission to precipitation change. Ecological Processes, 12(1), pp.1-13.
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Hui, D., N. Kaur, A. Ray, L. Kasrija, Q. Li. 2023. Literature Review, Meta-Analysis, and Mega-Analysis in Ecological and Agricultural Sciences. Agricultural Sciences, 14, 474-484.
  • Type: Journal Articles Status: Under Review Year Published: 2023 Citation: You, Y., H. Tian, S. Pan, H. Shi, C. Lu, W.D. Batchelor, B. Cheng, D. Hui, D., Kicklighter, X-Z Liang, X. Li, J. Mellilo, N. Pan, S. A. Prior, J. Reilly. 202x. Net greenhouse gas balance in U.S. croplands: How can soils be a part of the climate solution? Global Change Biology (under review).
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Hui, D., N. Kaur, C. Kieffer, C., W. Ren. 2023. Response patterns of simulated corn yield and soil nitrous oxide emission to precipitation change. ESA Annual Meeting 2023, August 6-11, 2023, Portland, Oregon.
  • Type: Other Status: Published Year Published: 2023 Citation: Ray, A., D. Hui. Impacts of nitrogen fertilizer, irrigation and biochar application on yield-scaled nitrous oxide emission from corn and soyabean agri-fields: A meta-analysis. TSU Annual Research Symposium. March 2023.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Kaur, N., C. Kieffer, Y. Huang, W. Ren, D. Hui. 2023. Effects of climate-smart agricultural practices on crop yield and greenhouse gas nitrous oxide emissions: Meta-analyses. ESA Annual Meeting 2023, August 6-11, 2023, Portland, Oregon.