RESIDUE MANAGEMENT TO MAXIMIZE CROP PRODUCTION AND ENVIRONMENTAL PERFORMANCE: A SYSTEMS APPROACH

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: COMPLETE
• Funding Source: AFRI COMPETITIVE GRANT
• Reporting Frequency: Annual
• Accession No.: 1022182
• Grant No.: 2020-67013-30858
• Cumulative Award Amt.: $499,644.00
• Proposal No.: 2019-06653
• Project Start Date: May 1, 2020
• Project End Date: Apr 30, 2025
• Grant Year: 2020
• Program Code: [A1102]- Foundational Knowledge of Agricultural Production Systems

Recipient Organization
IOWA STATE UNIVERSITY
2229 Lincoln Way
AMES,IA 50011

Performing Department
Agronomy

Non Technical Summary
Historical yield gains have increased crop residue production to unprecedented levels. Contemporary residue inputs have become so large that they can reduce the yield and nitrogen use efficiency of subsequent crops. This project will develop innovative solutions to the increasing challenge of sustainable crop residue management. Our overarching hypothesis is that a systems level understanding of crop residue decomposition dynamics can lead to residue management strategies that: 1) increase yield, 2) reduce N fertilizer inputs, and 3) improve soil health. Because yield can be increased while reducing N fertilizer inputs, both farmers and the environment will benefit.As crop residue production continues to grow, there is a critical need for improved crop residue management strategies. Nevertheless, we lack an understanding of how multiple systems components interact to control residue decomposition dynamics and cascading effects on yield, nitrogen use efficiency, and environmental nitrogen losses. Moreover, residue management has inconsistent effects on yield and nitrogen dynamics across space and time. Process-based simulation models can improve the efficiency of cropping systems by explaining these inconsistencies, but current models rely on outdated residue decomposition algorithms that result in low prediction accuracy. Here, we propose a coupled experimental-modeling approach that will: 1) use experiments to develop new fundamental knowledge on corn and soybean residue decomposition dynamics; 2) incorporate experimental results into a dynamic simulation model to improve prediction and explanatory power; and 3) deploy the improved model to extrapolate knowledge across environments, identifying residue management strategies that are profitable in the near-term and sustainable in the long-term.

Animal Health Component

33%

Research Effort Categories

Basic

34%

Applied

33%

Developmental

33%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
205	1510	1000	34%
205	0110	1000	33%
205	1599	1000	33%

Knowledge Area
205 - Plant Management Systems;

Subject Of Investigation
1510 - Corn; 0110 - Soil; 1599 - Grain crops, general/other;

Field Of Science
1000 - Biochemistry and biophysics;

Keywords

crop residue

environmental quality

yield

Goals / Objectives
Objective 1: Conduct residue decomposition experimentsto improve our fundamental understanding of crop residuedecomposition. Specifically, we will measure decomposition of individual plant organs(leaves, stems, and cobs/pods). We hypothesize that decomposition rates vary moreacross organs than they do across two very different crop species (i.e., corn and soybean).Objective 2: Develop new residue decomposition equations within the APSIM modelthat operate at the organ level, and thus better predict and explain residue effects on soilplant-atmosphere processes including drainage, fertilizer NUE, grain yield, NO3 leaching, denitrification, and soil N mineralization. We hypothesize that plant organ basedrepresentation of crop residue decomposition dynamics will improve predictive andexplanative power of these processes.Objective 3: Use the improved model to identify residue management strategies that areeconomically sustainable in the near-term and environmentally sustainable in the long term.We hypothesize that the improved model can reduce the risk and uncertaintycurrently associated with the effects of residue harvest on crop production andenvironmental performance.

Project Methods
First, we will develop new fundamental knowledge on corn and soybean residue decomposition dynamics at the organ level (Objective 1; tasks 1.1 and 1.2). Second, we will incorporate this fundamental knowledge into a dynamic simulation model to improve predictive and explanatory power (Objective 2; tasks 2.1 to 2.4). Third, we will deploy the improved model to extrapolate gains in fundamental knowledge and model simulations across environments and cropping systems to explain how situation-specific residue management strategies can maximize profit in the short-term and environmental sustainability in the long-term (Objective 3; tasks 3.1 to 3.2).Task 1.1: Measure organ-level residue decomposition (surface and incorporated residue) in field experiments. Across 2 years, we will measure decomposition at three field sites. In the field, we will place different residue tissues (corn cobs, stems, leaves + husks and soybean stem, pod walls and leaves) at the top of the soil surface (no-till) and at 15 cm below the soil surface (tilled systems) decomposition bags. In each destructive litter-bag samplings we will measure: 1) residue dry and fresh weight, 2) surface residue specific area (weight / surface area ratio), 3) residue C and N concentrations using dry combustion, 4) soluble C, acid and neutral detergent fiber, and acid detergent lignin to characterize carbohydrate-like, cellulose + hemicellulose-like, and lignin-like residue components, 5) soil nitrate and ammonium, and 6) soil pH.Task 1.2: Analyze existing field residue decomposition time-series: As described in "preliminary results" the PDs have developed two years of residue decomposition data (without separation to organ level) from two central Iowa fields, one with tillage (site name: Boone) andanother without tillage (site name: Kelley). Both crops are present each year in each field. Each dataset is replicated three times and has at least 10 field surface residue measurements. At each measurement, surface residue in a 1.5 m2 was sampled by hand; litterbags were not used. Measurements included fresh and dry surface residue weight, soil cover by surface residue, residue C/N ratio, soil nitrate and ammonium, soil moisture and temperature, and groundwater table depth. The above data were collected in years 2016/2017 and 2017/2018 (period October to July).Task 2.1 - Develop and incorporate organ-level decomposition algorithms into the model. We will formulate and incorporate into APSIM new organ-level algorithms similar to the approach used by Archontoulis et al. (2016) for the development of new algorithms for the biochar module within APSIM. As a starting point we will use first order kinetics theory for each organ, which is supported by literature (Freschet et al. 2012). We will simultaneously fit in APSIM all the data (temperature, moisture, residue dry matter, residue N, residue cover, soil inorganic nitrogen) and by using inverse modeling techniques and APSIM's moisture and temperature modifiers already present in the model we will estimate the "potential" decomposition rates. The C/N ratio and the dry matter of each material will derive from measurements (Task 1.1). A drawback of this "organ-level" approach is the cost for the extra parameters that will be introduced. Through sensitivity analysis of model parameters, data generated in Objective 1, and review of the literature (Shepherd et al., 2005;Beyaert and Vorone, 2011), we will develop generalized parameters and a range of values to guide model users.Task 2.2 - Develop algorithms for chemical compositions of residues. We will test equations available in the literature that describe residue decomposition kinetics as a function of carbohydrate-like, cellulose-like and lignin-like pools in addition to C/N (Herman et al., 1997; Tian et al., 1995; Kumar and Gog, 2003; Shepherd et al., 2005). As a start, we will incorporate the equation developed by Tian et al. (1995), which defined the plant residue quality index (PRQI). Using experimental data from Objective 1 we will test the predictability of different equations both statistically by fitting regression models (Archontoulis and Miguez, 2015) and also mechanistically via APSIM by testing changes in soil N03-N and NH4-N dynamics.Task 2.3 - Updated model calibration. After incorporating the new equations into the model (Tasks 2.1 and 2.2) we will calibrate the entire model using data from the pot experiments in which APSIM will simulate residue and soil processes over time under controlled conditions.Model calibration will be an iterative process. We will perform an overall systems-level analysis (similar to Martinez-Ferial et al. 2016) to quantify and report interactions.Task 2.4 - Updated model validation. The calibrated model from Task 2.3 will be evaluated against field data already collected by the PDs (see Objective 1) and data from two published studies. The first published study (Puntel et al., 2016) contains data from a 15-year experiment with both corn and soybean. This study reported that the measured CC yield penalty was 1500 Kg/ha (average across 15 years), while the simulated CC yield penalty by the APSIM model was about half that (caused by water stress and delays in emergence). We will re-run simulations with the improved model to quantify and report improvements. Similarly, we will re-run model simulations for the Kelley site that have an 8-year dataset for CC and CS crop yields and N leaching to tile drainage (Martinez-Feria et al., 2018).Task 3.1: We will quantify residue management impacts on systems variables at short (next year's impacts) and long-term time scales (impacts after 30 years). The simulation experiment will be factorial and include 10 environments (part of the FACTS project in which APSIM is already set up), 35 weather years, different tillage practices and N-fertilization rate and timing of application to corn. Scenarios for short-term impacts will be repeated for 35 weather years and a median and standard deviation value will be calculated (Fig. 10). The same intial conditions will be used for each simulation. Long-term scenarios will start in year 1 and end in year 35. The simulation process will be sequential and will include carry-over effects of residue, soil water, and soil nitrate from year to year. We will use the simulations to calculate average fluxes (e.g., N2O emissions, NO3 - leaching and grain yield) as well as change in soil organic matter associated with the different residue managements (Task 1.1) because changes in SOM are notoriously difficult to measure in the field.Task 3.2. The secanario analysis will create two large databases that will be statistically analyzed in R and through optimization we will identify optimum residue management solutions that satisfies both short and long term goals (short-term economic solutions that lead to long term sustainability) per environment. The analysis is scalable to other envirnments too. Then we will use R-shiny to display the results in a user friendly web interface where PIs and users can select inputs and vizualize results together with optimum solutions. Developing R shiny web tools will facilitate effcient communication of the results to stakeholders. PD Archontoulis has already developed two R-shiny web tools, the soybean planting decision tool and the FACTS web tool and has all the softawre, serves, and collaborations needed in place to achieve this objective

Progress 05/01/20 to 04/30/25

Outputs
Target Audience:Corn and soybean growers. Agronomists, scientists, and Industry. Graduate and undergraduate students The study targets farmers, agronomists, and scientists involved with field crops, specifically those growing corn and soybeans in the US Midwest. The focus is on farmers in high-yield environments who face challenges such as excessive crop residue, soil erosion, and the need to conserve moisture in more arid regions. For scientists, the findings will be relevant to those researching crop rotations, carbon and nutrient budgets, residue decomposition dynamics, and crop modeling. Graduate and undergraduate students enrolled at Iowa State University are also project targets. To communicate the results to farmers and agronomists, we organized presentations and field days as part of Iowa State University's extension learning activities and publications in the Iowa State University Research and Demonstration Farms Progress Reports. For the scientific community, we shared project outcomes through poster presentations at conferences (AGU Fall Meeting, and ASA, CSSA, and SSSA Meeting) and through peer-reviewed publications, including one published manuscript and another currently in preparation. Changes/Problems:The only challenge with had with this project was the beginning year (which was the covid year), which caused a delay. Besides that, there were no problems in the approach. This project has generated new information that will last for many years to come and will assist in improving prediction models and agronomic information. What opportunities for training and professional development has the project provided?Several, including attendance with presentations at the international ASA-CCSA-SSSA meetings in 2023 and 2024, AGU fall meeting in 2023, meetings with APSIM model developers and users from Australia and elsewhere, lecture presentations to Agronomy graduate students about crop residue and its impact on cropping system processes. The lecture also covered the effects of management practices on crop residue and strategies for managing corn and soybean residue after harvest. Furthermore, the data collected during this research contributed to the dissertation of a PhD student and is also being utilized for the dissertation of an MSc student. Additionally, we mentored four undergraduate students, guiding them in executing experiments using litter bags to measure the decomposition of individual plant organs in both till and no-till systems. They also gained experience in collecting and processing soil samples. Students learned new techniquesin data collection (mess bag approach), cleaning of samples and processing all the way to CN ratio determination and subsequent statistical analyses. How have the results been disseminated to communities of interest?We published a journal article in 2024 and participated in field days in 2022, 2023, and 2024. Additionally, we took part in a professional conference that was attended by a diverse audience, including undergraduate and graduate students, university scientists (both post-docs and faculty), and researchers from various public and private institutions, such as the USDA and several companies. A portion of the results forthis project were disseminated to Iowa growers through the Integrated Crop Management Conference series, in which project PIs had presentations. Currently, we have one manuscript under review and are in the process of writing a third publication. The paper under review focuses on the historical changes in maize residue carbon inputs and quality in the US Corn Belt. We provide spatial-temporal residue carbon input and quality estimates to inform decision-making. The third paper will report on our field experimentation data and how the new residue decomposition equations improve the simulations when included in the APSIM model. Lastly this project assisted in the development of two additional journal publications regarding corn N fertilizer rate and analysis of yield response to N. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? Objective 1: We collected detailed experimental data from field experiments and we analyzed relevant field data from nearby fields. In particular, we placed a total of 2,100 mesh bags in the fields over two fall seasons, with 1,326 in 2021 and 774 in 2022. After collecting all the mesh bags, we generated 86 residue decomposition curves by testing various combinations of crops, organs, C:N ratios, tillage systems, and weather seasons. Each curve contained 6 to 10 time series measurements and three replicates. Additionally, we grounded and measured the N and C concentrations in a subset of 750 samples and collected soil nitrate samples in the decomposition curves. We also monitored the soil moisture and temperature in the different plots. To complement our study, we installed 500 mesh bags in North Iowa, South Iowa, and Central Nebraska (rainfed and irrigated) to validate the dataset generated in Central Iowa and explore different soil and weather conditions. Our research showed that the CN ratios ranged from 10 to 125, depending on the organ, crop, and starting time. We found that the quality of the organ had more of an impact on decomposition than the crop itself. For example, soybean residue at harvest had the same quality as corn. Furthermore, we discovered that tillage systems (incorporated vs. surface) had a greater effect on decomposition than the type of crop. Lastly, we found a strong correlation between the tissue CN ratio and decomposition coefficient when analyzing all the data together. Currently these datasets are in the final stage of analysis and hence preparation for submission to a journal article. In addition to the detailed decomposition experiment, we collected end of season field data from nearby corn experiments, which included multiple factors such as hybrids, plant density and N-fertilizer rates over two years. This work resulted in hundreds of samples which helped us determine the range of possible corn residue quantity and quality values. The results of this work have been published in the European Journal of Agronomy. Objective 2: During the simulations, we observed that APSIM next-gen was successful in simulating maize residue decomposition both with and without tillage, for different starting dates. However, the model did not perform well in simulating the decomposition of soybean residue. The model was more accurate in simulating the decomposition of incorporated residue, particularly at the start of the simulation. However, towards the later dates, and especially in no-till systems, the model tended to overestimate the decomposition. After trying various parameters, we concluded that there may be a structural flaw in the model, which we intend to improve using our dataset as part of Objective 1. Furthermore, we set the APSIM model to output residue decomposition by organ, which allowed us to compare how well the model simulate residue decomposition by organ. Code changed have been implemented and in the near future these will be submitted for incorporation in the APSIM model public release. Objective 3: Model simulation indicated that future climate scenarios have accelerated the residue decomposition patterns, largely due to an increase in temperature. However, the increased residue amount (by 20%) and the increased CN ratio (by 25%) have slowed the decomposition. It was found that when accounting for future climate and production scenarios together, the acceleration of decomposition due to climate change will be cancelled by the changes in residue, resulting in similar future decomposition as present conditions. Soil evaporation, N mineralization, and N2O emissions will likely increase with future climates in till and no-till systems. Residue quality changes had little effect on soil processes, but residue amount reduced evaporation in no-till systems, reduced N mineralization and N2O emissions in till systems, and increased N2O emissions in no-till systems. Residue quantity and quality have synergistic effects with climate change in the no-till systems, further increasing N2O emissions, but the opposite is found in the tilled systems.

Publications

• Type: Other Journal Articles Status: Accepted Year Published: 2024 Citation: Alejo Ruiz, Jode W. Edwards, Michael J. Castellano, Brenda L. Gambin, Mark A. Licht, Kenneth J. Moore, Sotirios V. Archontoulis. Nitrogen fertilizer and plant density affect maize residue quantity and quality more than previous crop and genotype. European Journal of Agronomy, Volume 158, August 2024, 127215. https://doi.org/10.1016/j.eja.2024.127215
• Type: Other Journal Articles Status: Accepted Year Published: 2025 Citation: Baum, M.E., Sawyer, J.E., Nafziger, E.D. et al. The optimum nitrogen fertilizer rate for maize in the US Midwest is increasing. Nat Commun 16, 404 (2025). https://doi.org/10.1038/s41467-024-55314-7
• Type: Peer Reviewed Journal Articles Status: Accepted Year Published: 2023 Citation: King K, Archontoulis SV, Baum M, Edwards JW, 2023. From a point to a range of optimum estimates for maize plant density and nitrogen recommendations. Agronomy J
• Type: Other Journal Articles Status: Under Review Year Published: 2025 Citation: Alejo Ruiz, Michael Castellano, Antonella Ferela, Slobodan Trifunovic, Douglas Eudy, Brenda Gambin, Gerasimos Danalatos, Tomas Della Chiesa, Dan Northrup, Sotirios Archontoulis. Large changes in maize residue carbon inputs in the US Corn Belt from 1980 to 2020
• Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Alejo Ruiz, Michael Castellano, Sotirios Archontulis. Crop residue decomposition under current and future climate scenarios. AGU Fall Meeting, Chicago, IL. 2022. Poster presentation. Link: https://ui.adsabs.harvard.edu/abs/2022AGUFMGC42I0808R/abstract
• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Ruiz, Alejo, Slobodan Trifunovic, Clarice Mensah, Michael Castellano, Tony Vyn, Sotirios Archontoulis. 2023. Historical Changes in Maize Stover across Temporal and Spatial Scales. 2023. ASA, CSSA, SSSA International Annual Meeting, San Antonio, TX. 2023. Oral presentation. Link: https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/152140
• Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Ernesto Reboreda, Alejo Ruiz, Michael Castellano, Sotirios Archontoulis. Decomposition Rates Vary More across Organs Than They Do through Corn and Soybean. ASA, CSSA, SSSA International Annual Meeting, San Antonio, TX. 2024. Poster presentation. Link: https://scisoc.confex.com/scisoc/2024am/meetingapp.cgi/Paper/160100
• Type: Other Status: Published Year Published: 2023 Citation: Alejo Ruiz, Jode Edwards, Sotirios Archontoulis. 2023. Understanding Genotype, Crop Sequence, Plant Density, N-Fertilizer Rate, Effects on Corn Stover Quantity and Quality. Extension report https://www.iastatedigitalpress.com/farmreports/article/id/16747/

Progress 05/01/23 to 04/30/24

Outputs
Target Audience:Corn and soybean growers. Agronomists and Scientists and Industry Graduate and undergraduate students Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Several, including attendance with presentations at that international ASA-CCSA meetings, meetings with APSIM model developers from Australia, lecture presentation to Agronomy graduate students about crop residue and its impact on cropping system processes. The lecture also covered the effects of management practices on crop residue and strategies for managing corn and soybean residue after harvest. Additionally, we mentored three undergraduate students, guiding them through how to execute an experiment using litter bags to measure the decomposition of individual plant organs in both till and no-till systems. They also learned how to collect and process soil samples How have the results been disseminated to communities of interest?Journal publication (2024), field days (2002, 2023) and we participated in a professional conference, where we gave an oral presentation about the Historical Changes in Maize Stover: Sustainability Implications. The talk was attended by a diverse group of people, including undergraduate and graduate students, university scientists (post-docs and faculties), as well as researchers from both public and private institutions, including USDA and various companies. Currently we analyze data that would results in two more publications. What do you plan to do during the next reporting period to accomplish the goals?We plan to develop the new residue decomposition equations within the APSIM model that operate at the organ level using our collected field data. Then, we will use the improved model to identify sustainable residue management strategies and assess the impact of future climate and production scenarios on residue dynamics. We plan to submit two peer-reviewed publications related to this topic. The first paper will focus on the historical changes in maize residue carbon inputs and quality in the US Corn Belt. We will provide spatial-temporal residue carbon input and quality estimates to inform decision-making. The second paper will report on our field experimentation data and how the new residue decomposition equations improve the simulations when included in the APSIM model. We will participate in the ASA, CSSA, and SSSA 2024 conference, showing results from objectives 1 and 2.

Impacts
What was accomplished under these goals? Objective 1: We installed and processed2,100 mesh bags over two fall seasons, with 1,326 in 2021 and 774 in 2022. After collecting all the mesh bags in the fall of 2023, we generated 86 decomposition curves by testing various combinations of crops, organs, C:N ratios, tillage systems, and weather conditions. Each curve was generated from 7-10 time series measurements and three replications. Additionally, we ground and measured the N and C concentrations in a subset of 750 samples and we also measured/analyzed soil nitrate samples. We also monitored the top soil moisture and temperature during residue decomposition. To complement our study (validation process), we installed 500 mesh bags in North Iowa, South Iowa, and Central Nebraska (rainfed and irrigated) to validate the dataset generated in Central Iowa and explore different soils and weather conditions. In addition we collected, analyzed and reported data on residue quantiy and quality as affected by genetics, plant density and N-fertilizer. Our research showed that the CN ratios ranged from 10 to 125, depending on the organ, crop, and starting time. We found that the quality of residue was affected more bytheorgan tissue than bythe crop type itself. For example, soybean residue at harvest had the same quality as corn. Furthermore, we discovered that tillage systems (incorporated vs. surface) had a greater effect on decomposition than the type of crop. Lastly, we found a strong correlation between the tissue CN ratio and decomposition coefficient when analyzing all the data together. Objective 2: During the preliminary simulations, we observed that APSIM next-gen was successful in simulating maize residue decomposition both with and without tillage, for different starting dates. However, the model did not perform well in simulating the decomposition of soybean residue. The model was more accurate in simulating the decomposition of incorporated residue, particularly at the start of the simulation. However, towards the later dates, and especially in no-till systems, the model tended to overestimate residue decomposition. After trying various parameters, we concluded that the model may have a structural flaw, which we intend to improve using our dataset as part of Objective 1. Objective 3: It was observed that future climate scenarios have accelerated the residue decomposition patterns, largely due to an increase in temperature. However, the increased residue amount (by 20%) and CN (by 25%) have slowed down the decomposition. It was found that when accounting for future climate and production scenarios together, the acceleration of decomposition due to climate change will be cancelled by the changes in residue, resulting in similar future decomposition as present conditions. Soil evaporation, N mineralization, and N2O emissions will likely increase with future climates in till and no-till systems according to our prelimiary model runs. Residue quality changes had little effect on soil processes, but residue amount reduced evaporation in no-till systems, reduced N mineralization and N20 emissions in till systems, and increased N20 emission in no-till systems. Residue quantity and quality have synergistic effects with climate change in the no-till systems, further increasing N2O emissions, but the opposite is found in the tilled system

Publications

• Type: Journal Articles Status: Accepted Year Published: 2024 Citation: Ruiz, A., J. Edwards, M. Castellano, B. Gambin, M. Licht, K. Moore, S. Archontoulis, 2024. Nitrogen fertilizer and plant density affect maize residue quantity and quality more than previous crop and hybrid. Under review in the European Journal of Agronomy
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Ruiz, A., S. Trifunovic, C. Mensah, M. Castellano, T. Vyn, S. Archontoulis. 2023. Historical Changes in Maize Stover: Sustainability Implications. 2023 ASA, CSSA, SSSA International Annual Meeting. Oral presentation.

Progress 05/01/22 to 04/30/23

Outputs
Target Audience:farmers, scientists, agronomists, students, decision makers, crop-soil modelers. Changes/Problems:There was a delay instarting the project. We started one full year later because of the Covid pandemic. What opportunities for training and professional development has the project provided?The project has provided several professional development opportunities to the grad student, research scientist and undergraduate students working on the project. This includes: Analytical skills: how to analyze complex experimental designs and repeated measurements in time. Modeling skills: Learn how to use and calibrate mechanistic models (APSIM). Oral and writing communication skills. Lab skills: fiber analysis determinations, N and C concentration determinations, soil sampling and nitrates determination, and how to conduct a field experiment How have the results been disseminated to communities of interest?Yes, throughpresentations, field days, journal publication and farm reports. 6 presentations: 1) Archontoulis et al., AgMIP9; 2) Ruiz et al2023 R.F. Baker Plant Breeding Symposium; 3) Ruiz et al., 2023 Maize Genetics 4)2022 AGU Fall Meeting and 5) 2022 ASA, CSSA, SSSA International Annual Meeting (wwo Oral presentations). 2 field days: Boone Farm, IA. Field day to Iowa Certified Crop Advisers; Huxley, IA. Oral presentation to Bayer Crop Science scientists. Formation of a new Initiative (AgMIP-residue) to test 30+ simulation models used for soil C assessments from all over the world using data developed in this project. Launched in June 2023. What do you plan to do during the next reporting period to accomplish the goals?We plan to 1. continue working on the modeling part of this project (calibration and validation and improvements as needed now that the data have been successfully completed) 2. write and submit publications 3. disseminate results at conferences and other events

Impacts
What was accomplished under these goals? All the planned residue decomposition experiments and field measurements have been successfully completed (after having one year delay due to COVID-19). The decomposition experiments generated time-series datasets for two crops (corn and soybean) and two tillage practices (conventional tillage and no-tillage). Within a crop-tillage system, we measured the decomposition of the entire residue over time as well the decomposition of each tissue separately (cobs, leaves and stem) for a total of 4 sub-treatments. Therefore 2 crops x 2 tillage x 4 tissues = 16 treatments. The experiments were repeated across 6 environmentsfor a total of 96 datasets, decomposition time-series datasets. We measured residue decomposition using the mess bag approach and experimental treatments replicated three times. Data measured include the tissue dry weight at the time of harvest, and its corresponding carbon and nitrogen concentration at the first and last sampling and few samples in between. We also collected soil nitrate samples at the time of field sampling and soil moisture. Complementary to this experiment, we installed 420 mesh bags in North and South Iowa (210 bags in each) to explore different weather conditions and validate the data from Central Iowa. Currently, we are analyzing with statistical models the dry weight datasets to estimate the decomposition rate constants and analyzing the tissues in the lab for carbon and nitrogen concentration as well as lignin. In addition to the decomposition experiment, we finalized data collection from a complex field experiment (5 hybrids x 5 N fertilizer rates x 5 plant densities x 2 crop rotations). From this experiment, wegenerated datasets on residue production amount and quality at crop harvest. This is extremely important because the residue decomposition patterns depend on the initial conditions (amount and CN ratio). Currently, we are working on a publication using the results from this experiment. Overall during this reporting period, we accumulated a significant amount of data to increase foundational knowledge on crop residue decomposition and crop residue production. Portion of the experimental datasets have been incorporated within the APSIM crop model and we are currently working to quantify the model performance. Next step is to develop new code in the APSIM model to allow the decomposition of each tissue separately as opposed to all tissues together as a mix. Preliminary model exploration has shown that crop models should move from a single-pool residue decomposition to a multi-pool (by organ, leaf, stem, cob). More to follow in the next report as the modeling objective is in progress (we needed the experimental data to initiate modeling).

Publications

• Type: Journal Articles Status: Published Year Published: 2023 Citation: Ruiz A, Trifunovic S, Eudy D, Sciarresi C, Baum M, Danalatos G, Elli E, Kalogeropoulos G, King K, dos Santos C, Thies A, Olmedo Pico L, Castellano M, Schnable P, Topp C, Graham M, Lamkey K, Vyn T , Archontoulis S, 2023. Harvest index has increased over the last 50 years of maize breeding. Field Crops Research 300, 108991
• Type: Journal Articles Status: Submitted Year Published: 2023 Citation: Baum M, Sawyer J, Nafziger E, Castellano M, McDaniel M, Licht M, Hayes D, Helmers M, Archontoulis, 2023. The optimum nitrogen fertilizer rate for maize in the US Midwest is increasing. (Nature Food, in-review)
• Type: Journal Articles Status: Published Year Published: 2023 Citation: King K, Archontoulis SV, Baum M, Edwards JW, 2023. From a point to a range of optimum estimates for maize plant density and nitrogen recommendations (Agronomy J, in-review)
• Type: Other Status: Published Year Published: 2023 Citation: Ruiz A, Edwards J, Archontoulis S, 2022. Understanding Genotype, Crop Sequence, Plant Density, N-Fertilizer Rate, Effects on Corn Stover Quantity and Quality. Iowa State University Research and Demonstration Farms Progress Reports. Iowa State University Digital Press
• Type: Journal Articles Status: Accepted Year Published: 2023 Citation: Baum M, Sawyer J, Nafziger E, Huber I, Thorburn P, Castellano M, Archontoulis, 2023. Evaluating and improving APSIM's capacity in simulating long-term corn yield response to nitrogen in continuous- and rotated-corn systems. Agricultural Systems 207, 103629

Progress 05/01/21 to 04/30/22

Outputs
Target Audience:agronomists, farmers, researchers, industry, everyone involved in corn and soybean production Changes/Problems:We had no challenges during the reporting period. Our main challenge was covid-19 which delayed the start of the project by 1.5 years. What opportunities for training and professional development has the project provided?graduate students, interns and undergraduate students How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals?1. we will perform a second round of field data collection (year 2) 2. we will initiate chemical analysis of the samples and also residue "size" lab experiments 3. initiate and perform crop model simulations

Impacts
What was accomplished under these goals? During the reporting period, we mostly worked on objective 1 - collection of multi-faceted experimental datasets from field trials. As of today, field experiments are being carried on to develop new fundamental knowledge on corn and soybean residue decomposition dynamics and then incorporate experimental results into a dynamic crop simulation model. We are following the decomposition of leaves, stems, cobs (only for corn), and a representative mix of tissues in till and no-till systems, with three different C:N ratios for soybean and four C:N ratios for corn in Central Iowa. To generate crop residues with different qualities, we terminated plants in different growth stages and levels of N fertilization in the summer/fall of 2021. We installed 1278 mesh bags between July and October 2021, from which we have already collected and processed 810 (63%). The rest of the bags will be collected during the spring and summer of 2022. The experiment will result in 50 decomposition curves generated from 7-10 measurements and three replicates from different crops, organs, C:N ratios, and tillage systems. We also collect nitrate samples in the decomposition curves and register the soil moisture and temperature in the different plots. Complementary to this experiment, we installed 420 mesh bags in North and South Iowa (210 bags in each) to explore different weather conditions and validate the data from Central Iowa. We are following the decompositions of the same crop tissues and tillage systems but with only one C:N ratio. We have already collected and processed 210 bags (50%). The experiment will result in 20 extra curves. Finally, we are working on determining how the most critical corn management decisions affect residue quantity and quality. We carried on 2021 an experiment evaluating two corn sequences (corn-corn, soybean-corn), three commercial hybrids, three N levels, and three stand density levels, totaling 54 individual treatments with two replicates. We collected yield data, biomass samples, harvest index, residue amount, and N concentration in the grain and the residue. It is planned to repeat this experiment in the current year. Overall during this reporting period, we accumulated a significant amount of data to increase foundational knowledge on crop residue decomposition

Publications

Progress 05/01/20 to 04/30/21

Outputs
Target Audience:farmers, agronomists, industry, students, policy makers Changes/Problems:2020 field experiments were not performed due to covid-19 imposed restrictions. This moved the field work a year later (2021) What opportunities for training and professional development has the project provided?we have hired a new PhD student, an undergraduate student. The PhD student will participiate at the ASA conference this year How have the results been disseminated to communities of interest?Results will be disseminated with conference presentations and journal papers and extension newsletters What do you plan to do during the next reporting period to accomplish the goals? Three very detailed field experiments with many plots to accumulate data to parameterize the model. The experiments will include many factors (residue amount x quality x plant tissue x environment)and data will be accumulated every other week from August to next May. Collect field data from an era study that has many genotypes to estimate how the stover amount has increased over the years with plant breeding Collect lab data to investigate the effect of residue size on the decomposition rate Perform chemical analysis of plant samples to accumulate necessary data to develop new modeling algorithms Continue the modeling work and the incorporation of new data into the model

Impacts
What was accomplished under these goals? In year 1 (2020), the scheduled field experiments were cancelled because of covid-19 restrictions. We were not allowed to set up new trials and perform high intensity work. This year (2021) we have designed and implemented many experiments which we will provide accomplishments in the next report.Besides field work, we made significant progress with computer simulation models and processing of existing datasets: We modeled 14 long term N trails with corn following corn and corn following soybean. These are validation datasets. Once the new improvements are made in the APSIM model, we will rerun these simulations to quantify improvements in modeling. We modeled 2 year x 2 location time series residue decomposition datasets. This is another validation dataset. We reviewed the literature on potential avenues to use to improve current modeling - this information helped us to set up the 2021 experiments

Publications