Final report for LNC18-402
Project Information
Farmer interest in organic or reduced input no-till soybean production systems planted into roller-crimped cereal rye residues is steadily increasing. Such systems are good for improving the sustainability of soybean production, through soil quality improvements and decreased hand labor requirements for weed management. However, adoption has been limited by farmer concerns about how to predict, and respond to, the impact of variable planting date and weather on the timing of spring rye development and biomass production. Will the system produce enough biomass early enough in a given year to support a rolled-crimped system, or should the cover crop be turned under as a green manure? Our team's recent advances in the quantitative understanding of cereal rye phenology and biomass production in relation to local environmental and management factors will allow stronger support of adaptive management strategies for effective use of cereal rye cover crops in no-till soybeans. We propose to develop an online decision support tool for managing cereal rye phenology and growth, and to work with a farmer advisory council and the IDEA Farm Network, to undergo an iterative evaluation and design process to improve the tool for public release. In addition to a core group of 30 farmers who will take on-farm measurements of spring rye phenology and biomass and weed growth, over 200 farmers will evaluate the tool, improving farmer understanding of adaptive rye management, improving adoption rates of this soil building, labor saving approach to organic and reduced input no-till soybean production.
PROJECT OUTCOMES
1) Learning: Over 200 farmers will learn how soil and environmental variability at regional scales causes variation in timing of rye development.
2) Action: Beta version of web-based decision support tool will be used by at least 50 growers to optimize their planting and termination dates for adaptive use of cereal rye in organic and conventional no-till soybean.
3) System-wide: Constructing and evaluating the decision support tool with the IDEA Farm Network will help over 200 farmers understand and act on regional-scale variation in cover crop performance, demonstrating how learning communities can synergize ongoing improvement in sustainable farming practices.
Over the past decade, production of organic soybeans no-till planted into roller-crimped residues of fall planted cover crops has received increased attention from researchers and growers for use in both organic and conventional production systems. When implemented well, this system has the potential to combine good soybean yields with effective weed suppression, N scavenging and soil organic matter benefits in both organic and conventional production systems. Many growers are reluctant, however, to try this approach due to uncertainty about its repeatability from year to year (e.g., whether planting dates and weather in a given year support the use of a rolled-crimped system, or whether the rye cover crop should be incorporated as a green manure). This hesitance, on the part of growers, to adopt this technique highlights a knowledge gap that we propose to address: predicting variability in cereal rye phenology and biomass accumulation in relation to planting date, management information (seeding rate, cultivar, fertilizer application) and weather for a given site-year in the north central U.S. production region.
Cooperators
- (Researcher)
- (Educator)
- (Educator and Researcher)
- (Researcher)
- (Researcher)
- (Researcher)
Research
We hypothesize a new decision support tool will help farmers use cereal rye cover crops to more successfully achieve adaptive management objectives by modeling rye phenology and biomass as a function of planting window, rye cultivar and local environment.
Data was gathered from farmers in 2019 to validate the Mirsky et al. (2009) model for rye phenology prediction. In fall 2018, farmer participants installed Hobo temperature loggers in their rye fields to capture temperature data throughout the growing season. Beginning in March 2019, farmer participants sent in pictures of their rye cover crop to allow researchers to follow rye development. At the boot stage, the number of rye tillers within a 20” x 20” quadrat were recorded. At the boot and late anthesis stages, rye height was measured from a 20” x 20” quadrat and the rye within the quadrat was cut at ground level, dried, and weighed to obtain dry biomass values. The dates that the rye cover crop reached boot and late anthesis stages were recorded. In addition to rye development and biomass measurements, farmers reported rye planting date, variety, and fertilization practices.
Data was gathered from 10 farmers in 2020 to provide additional validation for the rye phenology prediction model, using the same process as in 2019. Unfortunately, data gathering was hindered by Covid-19, which made it difficult for project cooperators to assist farmers with sample collection.
Ploughman Analytics developed a web-based application for the decision support tool that farmers were able to test during the 2020 and 2021 growing seasons. The application was launched on April 15, 2020. This application allows farmers to confirm the development stage of their cereal rye and provides predictions for when the cereal rye will reach late anthesis based on the current development stage of their cereal rye.
The data collected from farmer’s till date was used to perform regional simulation of cover crop impacts on crop yield and soil organic carbon (SOC) using process-based crop models. We simulated the impact of adding cereal rye to the two most dominant crop rotations in the state of Illinois. Site-level farmer’s data were specifically used for calibrating the rye growth and development in APSIM model (Holzworth et al. 2014). We assessed the impact of cereal rye as a cover crop on crop production and soil organic carbon by estimating the probability and expected value of change in SOC and yield. The calibrated model was used to simulate the impact of integrating cereal rye as cover crop into corn-corn and corn-soybean crop rotation for the entire state of Illinois. The study used pSIMS modeling platform (Elliot et al., 2014) for gridded simulation at regional level at a spatial resolution of 5 × 5 km from 2000-2020. To obtain robust results from the simulations, we propagated the uncertainty around initial conditions, weather (10 ensemble members), soil (2 soil databases), crop cultivar parameters, and management operations across the state of IL. We calculated the probability and expected value of change in SOC (pSOC and ESOC, respectively) when corn-corn (CC) rotation was compared to corn-rye-corn-rye (CRCR), and when corn-soybean (CS) rotation was compared to corn-rye-soybean-corn (CRSR).
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Table 1. Uncertainty factors considered for simulating each scenario. |
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Name |
Options |
Definition |
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Initial Conditions |
Residue type (RT) |
RT ~ sample(corn, soybean) |
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Residue weight (RW) |
RW ~ U(100,2500) |
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Water fraction (WF) |
WF ~ U(0.05,0.95) |
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Soil |
GSDE/SoilGrid |
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Weather |
10 ensembles from ERA5 |
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Management |
Planting date (pdate) |
pdate + N(0,sd(pdate)) |
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Harvesting date (hdate) |
hdate + N(0, sd(hdate)) |
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Parameters |
Corn: Random selection for 6 parameters |
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Soybean: Random selection from set number of cultivars depending upon latitude (30 total genotypes varying from MG 2 to MG 4) |
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Rye: Random selection from 7 calibrated genotypes |
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References:
Elliott, J., Kelly, D., Chryssanthacopoulos, J., Glotter, M., Jhunjhnuwala, K., Best, N., Wilde, M., Foster, I., 2014. The parallel system for integrating impact models and sectors (pSIMS). Environmental Modelling & Software, 62, 509–516.
Holzworth, D. P., Huth, N. I., deVoil, P. G., Zurcher, E. J., Herrmann, N. I., McLean, G., Chenu, K., van Oosterom, E. J., Snow, V., Murphy, C., Moore, A. D., Brown, H., Whish, J. P. M., Verrall, S., Fainges, J., Bell, L. W., Peake, A. S., Poulton, P. L., Hochman, Z., … Keating, B. A. (2014). APSIM – Evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software, 62, 327–350
Mirsky, S. B., Curran, W. S., Mortensen, D. A., Ryan, M. R., & Shumway, D. L. (2009). Control of cereal rye with a roller/crimper as influenced by cover crop phenology. Agronomy Journal, 101(6), 1589-1596.
Data collected in 2020 and 2021 were added to the existing rye phenology prediction model by Dr. Hamze Dokoohaki.
The results across 2019-2021 seasons provided baseline information for termination date and biomass predictions and were used as guidelines for the decision support tool. Termination date varied widely across and within states (Table 1). The variation in termination dates highlights the importance of a decision support tool, which will facilitate farmers’ management decisions regarding rye cover crop termination.
Table 1. Earliest and latest dates at which cereal rye cover crop reached late anthesis, by state.
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State |
Earliest Date Late Anthesis Reached |
Latest Date Late Anthesis Reached |
N |
|
IL |
May 17, 2019 |
June 5, 2019 |
6 |
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IN |
May 29, 2019 |
June 6, 2019 |
13 |
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OH |
June 5, 2019 |
|
1 |
|
WI |
June 4, 2019 |
June 28, 2019 |
6 |
Average dry biomass at boot stage (Figure 1) and late anthesis (Figure 2) varied widely across states. The minimum recommended biomass for roller crimping cereal rye at late anthesis is 8,000 lbs/acre. Looking only at averages, this level of biomass production was reached in IL and WI but not in IN or OH. However, it should be noted that biomass production varied greatly within states as well, meaning that some farmers would have benefitted from weed-suppressive effects of high rye biomass at termination while others would not.
Soil organic carbon in the top layer (0-45 cm) appeared to have benefitted from the addition of cover crops to the crop rotation. Due to a strong temporal autocorrelation factor in the SOC, we estimated the proportion of pixels that fall into different categories (4 classes of 1. high probability and expected value of change, 2. high probability and low expected value, 3. low probability high expected value and 4.low probability and low expected value) over based on pSOC and ESOC in 2010, 2015, and 2020. For the CRCR rotation, 57 % of the cropland area in 2010 was found to be under low pSOC and low ESOC class, which then this value was reduced to 31 % in 2015, and to 20 % in 2020 suggesting an increase in both probability and expected value of increase in SOC across the state IL after incorporating cereal rye. In the meantime, the area under high pSOC and high ESOC increased from 14 % in 2010 to 64 % in 2020. Majority of the area that benefitted from cover crop adoption, with respect to SOC, lied in the southern region of the state.
Interest in, and use of cover crops, continues to grow in North Central U.S. agriculture. Currently, cereal rye is likely the single most popular cover crop used in soybean production. The intent of this project was to develop a decision support tool to aid growers in determining planting date of cereal rye in the fall and termination date in the spring, by predicting cereal rye growth on their farm. The COVID-19 pandemic disrupted not only the original plan to engage growers in the project, but also the ability to recruit and retain a graduate student for the project. Nonetheless, the tool was successfully developed, launched, and made available to growers. Feedback, although far limited compared to the original expectations, was positive. The extent to which the project improves decision-making on cereal rye cover crop management remains to be seen. Assuming greater adoption of cereal rye cover crops in the future, a modeling activity quantified the extent of potential improvements in soil health in Illinois. We found that lower quality soils in the state are likely to benefit the most from cereal rye cover crops, as measured by soil organic carbon.
Project Activities
Educational & Outreach Activities
Participation Summary:
An online training was launched spring 2020 to accompany the launch of the decision support tool. This online training provides information on the importance of timing rye cover crop termination as well as instructions on the use of the decision support tool. This online training will immediately be made available to the 30 farmer participants. After their feedback on the decision support tool is obtained, the online training will be sent to the Idea Farm Network listserv to encourage other farmers to use the decision support tool. Additionally, we presented research results at the ASA-CSSA-SSSA meeting in November 2022 to peer-researchers.
Learning Outcomes
- Farmers have reported a greater understanding of the biomass and timing requirements for terminating a rye cover crop using the roller crimping method.
Project Outcomes
Understanding rye phenology and a prediction tool for guiding cover crop choices
Information Products
- Probabilistic Assessment of Cereal Rye Cover Crop Impacts on Regional Crop Yield and Soil Carbon (Peer-reviewed Journal Article)