Progress 02/15/24 to 02/14/25
Outputs
Target Audience:The target audience of this project include farmers, researchers, NGOs, privivate industries who are interested in soil and water conservation, soil health, and regernerative agriculture. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?The project has trained 2 graduate students, 2 postdoc scholars, and 1 undergraduate interns relatated meta-analysis, satellite remote sensing, and ecosystem process-based modeling. How have the results been disseminated to communities of interest?The research outcome and results have been diseminated to the communities through academic conferences and extension/outreach activities. What do you plan to do during the next reporting period to accomplish the goals?In the next year, we will focus on analyzing the process-based model simulation results of tillage practices on soil carbon squestration and GHG emission and prepare the related mansucripts on process-based model simulation. We will also focus on extension and outreach activities related to regerative agriculture management.
Impacts
What was accomplished under these goals?
To address objective 1, a meta-analysis was conducted to quantify the tillage effects on nitrogen loss. 151 peer-reviewed publications were collected using a specific filter condition on the Web of Science. These results were further refined to ensure each crop yield data is obtained from field experiments, and the yield records are comparable between tillage treatments. The supporting information such as crop species and fertilization was also collected for the sub-scenario analysis. More than 151 articles and 604 nitrogen loss records were collected in the meta-analysis. The results show that compared to conventional tillage, conservation tillage has differential effects on nitrogen loss. Nitrate load (RR = 1.0018) shows no significant change, indicating no substantial difference in total nitrogen loss between the two tillage practices. However, nitrate concentration (RR = 1.1420) increases by 14.2%, potentially enhancing nitrification and resulting in higher nitrate accumulation in soil solution. Meanwhile, N?O flux (RR = 0.8157) decreases by 18.43%, suggesting that by reducing soil disturbance, conservation tillage improves soil aeration, which in turn limits denitrification under oxygen-deficient conditions and reduces N?O emissions. Additionally, cumulative N2O emission increases slightly by 3.31% (RR = 1.0331), suggesting that conservation tillage has a relatively minor effect on total N2O emission. If the primary goal is to reduce greenhouse gas emissions (N?O), conservation tillage proves to be more effective than conventional tillage. However, if water pollution due to increased nitrate concentration is a concern, additional management practices such as cover cropping and precision fertilization should be implemented to mitigate nitrate accumulation. While conservation tillage has a limited impact on total nitrogen loss, further optimization is required to mitigate localized nitrate accumulation. To address objective 2, we built a dynamic feature threshold framework utilizing satellite data, environmental variables, and machine learning to map out tillage practices for corn and soybean fields in the Midwest from 2000 to 2022. The framework can effectively consider the confounding impact from local soil, vegetation, and environmental effects on crop residues. We compared tillage mapping accuracy using dynamic feature threshold or fixed feature threshold with either broadband angle index (BAI) or normalized difference tillage index (NDTI) as the key feature for tillage. The results showed that the dynamic feature threshold framework paired with BAI feature can accurately estimate tillage percentage for three categorical practices (no-till, reduced-till, and conventional-till), with a R2 value of 0.84, 0.62, and 0.76 for no-till, reduced-till, and conventional-till in corn fields, and a corresponding R2 value of 0.84, 0.52, and 0.70 in soybean fields, respectively. Dynamic feature threshold framework paired with BAI feature led to slightly better performance than that paired with NDTI feature. The fixed feature threshold framework has a much lower performance, with a R2 value compared to the latter increased by 0.41, 0.42, and 0.61 for no-till, reduced-till, and conventional-till in corn fields, and 0.35, 0.23, and 0.50 in soybean fields, mainly because it does not consider the confounding factors. We found significant regional differences of tillage adoption in the US Midwest. We also found that the adoption of conservation tillage (no-till or reduced-till) increases slowly from 2000 to 2022. Northern counties in Dakotas, southern Minnesota, eastern Nebraska, and Iowa showed an increase trend in no-till percentage, which largely comes from the decreased adoption of reduced-till percentage. Some counties in Indiana and Ohio owed an increase trend in reduced-till percentage, which is mainly contributed by thedecrease of conventional- and no-till practices. This study provides a highly applicable and cost-effective way for regional tillage practice mapping. The manuscript is under-review now. To address objective 3, we have been conducting ecosys simulations of different tillage practices over the US Midwest to quantify their impacts on soil carbon sequestration and GHG emissions. The model runs have been finished and we are now analysing the results to generate the impact quantification results. The project team has also been actively participating in extension/outreach activities at University of Illinois and Indiana University Indianapolis.
Publications
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2024
Citation:
Huang, Yawen, Wei Ren, Laura E. Lindsey, Lixin Wang, Dafeng Hui, Bo Tao, Pierre-Andre Jacinthe, and Hanqin Tian. "No-tillage farming enhances widespread nitrate leaching in the US Midwest." Environmental Research Letters 19, no. 10 (2024): 104062.
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2024
Citation:
Peng, Yu, Lixin Wang, Pierre-Andr� Jacinthe, and Wei Ren. "Global synthesis of cover crop impacts on main crop yield." Field Crops Research 310 (2024): 109343.
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2024
Citation:
Wang, Lixin. "Global plant nitrogen use is controlled by temperature." nature communications 15.1 (2024): 7651.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Hu, Tongxi, Kaiyu Guan, Bin Peng, Robert F. Grant, Ziqi Qin, Ziyi Li, and Rongzhu Qin. "Modeling impacts of climate-smart practices on soil carbon sequestration in the US Midwest at field scales." AGU24 (2024).
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Progress 02/15/23 to 02/14/24
Outputs
Target Audience:The target audience of this project include farmers, researchers, NGOs, privivate industries who are interested insoil and water conservation, soil health, and regernerative agriculture. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?We provide trainning to 1 postdoc, 2 graduate students, and 1 undergraudate student in Year 1 in this project. How have the results been disseminated to communities of interest?The research results have been communicated to local farmers through extension activities. What do you plan to do during the next reporting period to accomplish the goals?The project team will continue to work on meta analysis, remote sensing detection of tillage practices, and modeling tillage impacts with ecosys model.
Impacts
What was accomplished under these goals?
In objective 1, the project team conducted a meta-analysis to quantify the tillage effects on crop yield. 3275peer-reviewed publications were collected using a specific filter condition on the Web of Science. These results were further refined to ensure each crop yield data is obtained from field experiments, and the yield records are comparable between tillage treatments. Specifically, the text string "TI =(mulch-till or mulch tillage or ridge tilling or ridge-till or reduced tillage or Intensive tillage or soil management or cover crop* or tillage practice or residue or residues or fertilizer or manure or amendment or liming or compost or biochar or intercropping or conservation tillage or crop rotat* or no tillage or no-till or no-tillage) AND TS= agriculture AND TS= (soil NOT forest NOT urban) AND (TI=(yield OR fertil* OR irrgat* OR product*)" was used to search the published literature using Web of Science. This search returned 3284 results. After removing duplicates, we got 3275 articles. However, only 718 of these articles obtained data from field experiments so these articles were used in the data collection and subsequent meta-analysis. The supporting information such as crop species and fertilization were also collected for the sub-scenario analysis. We also tried to geo-reference these studies whenever possible. In Objective 2, the project team workedonbuildinga dynamic feature threshold framework utilizing satellite data, environmental variables, and machine learning to map out tillage practices for corn and soybean fields in the Midwest from 2000 to 2022. The framework can effectively consider the confounding impact from local soil, vegetation, and environmental effects on crop residues. We are comparingtillage mapping accuracy using dynamic feature threshold or fixed feature threshold with either broadband angle index (BAI) or normalized difference tillage index (NDTI) as the key feature for tillage. In Objective 3, the project team worked on testing the ecosys model performance on simulating the impacts of tillage onon yield, GHG emission (CO2 and N2O in particular), N leaching, and changes in soil organic carbon (SOC).
Publications
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