Iron Deficiency Chlorosis (IDC) is one of the most yield-damaging maladies of soybean in western Minnesota. Farmers valiantly battle IDC with a wide range of strategies to reduce losses. Crop rotations, variety selection, seeding rates, row spacing, iron chelates, tillage, drainage, and even cover crops or companion crops are utilized today. However, each of these strategies comes at some cost. Today’s most tolerant IDC varieties come with some yield penalty relative to susceptible varieties. Increased seeding rates significantly reduce IDC, but soybean seed has become increasingly expensive. Iron chelates have been a game changer for soybean producers in the most heavily affected areas but are quite costly when utilized at effective rates.
This proposal represents a funding request for year two of a two-year project. This study is designed to examine three of the most often used management strategies for IDC from a systems approach: variety selection, seeding rate, and iron chelates. We will examine tradeoffs in cost and yield response to these strategies across a wide range of IDC levels. Coupled with an economic analysis, this work will allow us to provide producers with more refined recommendations for managing IDC, to support them in their efforts to achieve the greatest economic return on every farm. In addition, we are working with Bayer Crop Science to allow us to extend dissemination of research findings throughout the entire Bayer network in addition to University and MSR&PC’s networks.

Goal 1: Examine tradeoffs and interactive effects between varieties, populations, and iron chelate rates across a range of IDC levels.

Objective 1: Examine each of the three factors individually across a range of IDC levels. Objective 2: Examine all two- and three-way interactions between these factors.
Objective 3: Examine interactions to define relative effects of each factor on yield.

Goal 2: Develop an economic model informing producers about ROI for each management strategy individually and collectively, to maximize economic returns across fields and farms.
Objective 1: Utilize yield response data against a range of grain prices and input costs to define optimum combinations of the three factors for IDC management farm-wide.

Goal 3: Collaborate with Bayer to leverage investments into research and outreach activities.
Objective 1: Coordinate with Bayer Technology Development team to develop identical treatments across platforms for data sharing. Bayer will put out multiple on-farm trials in the Benson area with medium-scale precision planting equipment.
Objective 2 Coordinate throughout the season to capture weekly drone imagery, ground-based measurements, and yield data from small plots, strip plots, and field-scale research studies.

Goal 4: Determine whether a Variable Rate Approach may be most feasible based on in-field and field- to-field variability in responses.

As mentioned above, preliminary results of this project have already been shared at a half dozen events including the international Agronomy meetings, Prairie Grains, Best of the Best, and MN Soybean Expo. This topic is in great demand by producers. An additional year of data with summarization, including an economic analysis, will make this a hot topic for discussion at many meeting within and beyond Minnesota. We anticipate writing a series of blog articles about this work and we will develop final summaries to be published on www.soybeans.edu. In addition, this work will be published in peer- reviewed publications.
Protocol Overview: Overall, we had an extremely successful research season in 2021. We were able to capture yield responses to common IDC management strategies at six unique field sites on 3 farms.
Results were covered in recent progress reports, at the Prairie Grains Conference, at the CPM Short Course, at the Best of the Best, and at the MN Ag Expo. We were able to carry out research as planed in 2021, and we will repeat these same protocols in 2022. Details can be found below.
We plan to evaluate all combinations of two varieties (tolerant and highly tolerant), two seeding rates, three rates of iron chelate (SoyGreen), and two levels of IDC intensity by supplemental N. The study (24 treatments x 4 replicates) will be planted in 10x30’ plots in two areas in each producer field. One area will be placed in the ‘hottest’ IDC part in the field, and the other area will be planted nearby in a representative part of the field. These two paired studies will be planted in three farm fields in western Minnesota.
Bayer Collaboration: The Bayer Crop Science Regional Technology team has an intense interest in this work. They have agreed to continue to cooperate on this project. Although, Bayer has agreed to cooperate with us on this project, funding from the MSR&PC for a portion of this work is a critical foundation for this project to go forward. Any collaboration will allow for two-way data sharing without any intellectual property restrictions.
As in 2021, the Naeve project will conduct small plot research trials in six field locations on the full set of treatments (2 x 2 x 3 x 2). Specific treatments are shown below. The Bayer Technology Development team has a complement of medium-scale research equipment to examine the same treatments outlined here; however, their locations are limited to the Benson area. This research plan leverages the strengths of each organization to test the same questions at additional locations.
Graduate Student: An MS student, Maykon da Silva, has assisted with managing this research trial and coordinating with Bayer. Maykon will continue to collect from each research site high-resolution, multispectral drone imagery on a weekly basis and to examine leaf area and NDVI accumulation over time. This work is critical for developing robust yield estimation models for IDC with remote-sensed data. While he did not have time to do so in 2021, Maykon may also contribute to remote sensing on Bayer research sites and at on-farm research trials in 2022. While this project will be Maykon’s primary responsibility and the focus of his MS degree, this proposed grant only supports 50% of his appointment. Other funds will be sought to supplement his graduate study funding from other sources.

Protocol Detail: This proposal covers the deployment of six small-plot research studies on three cooperating famers’ fields. To vary the intensity of IDC, small-plot studies will be located in a ‘hot-spot’ on each producer field as well as in an area of each field that is representative of the majority of the acres in that field or less chlorotic. We developed an efficient protocol whereby we were able to increase the intensity of IDC symptoms through the application of supplemental N. Therefore, in addition to the primary treatments, each study will be blocked with and without supplemental N application.
In total, we will replicate this study across 12 unique IDC environments (3 fields, 2 areas within each field, and 2 levels of IDC intensity within each study area). With four replications, we are planning for 768 individual plots.
• 2 Variety treatments
o Moderately tolerant
o Highly tolerant
• 2 Populations
o 125,000
o 175,000
• 4 Iron Chelate (SoyGreen) rates
o 0
o 2 lbs
o 4 lbs
• 2 IDC levels
o With N
o Without N
• 2 Locations within fields
• 3 Producers

Three cooperating producers will be selected to represent distinct IDC environments so that unique soil and climate factors are examined annually to allow a broad inference of research findings. The six field studies will be planted in production soybean fields by the Naeve project. A split-block design will be utilized, where iron chelate rate will be the main block and varieties, populations, and N will be randomized within the main block.
After emergence, plots will be evaluated weekly for IDC symptoms using visual scoring, ground-based NDVI as well as though high-resolution drone imagery. These data will be utilized to evaluate each treatment for timing and intensity of IDC symptomology relative to yield.

Upon completion of field activities, a complete economic analysis will be conducted. Yield data will be examined relative to a range of grain prices and appropriate input costs. Economists will be consulted on the most appropriate analyses based on the data.

Update:
This study is being conducted at four unique field sites on two farms in Western Minnesota, within the counties of Wilkin and Swift (Table 1). Locations were determined based on previous soybean IDC history provided by farmers’ cooperators’ knowledge. Planting the same trial within a farm field with varying IDC intensities is needed to determine whether management strategies should be changed based on the severity of IDC. A list of activities already performed and yet to be completed is listed below (List 1).




Table 1. Location, county, site, and field coordinates for all four unique sites planted at two locations in Western Minnesota.
Location / County Site Field coordinates
Foxhome / Wilkin Hotspot 46°20'00.3"N 96°25'10.9"W
Foxhome / Wilkin Non-Hotspot 46°20'00.6"N 96°25'00.9"W
Danvers / Swift Hotspot 45°15'18.2"N 95°41'38.8"W
Danvers / Swift Non-Hotspot 45°15'28.0"N 95°41'39.1"W

List 1. List of activities already performed and yet to be completed:
• Planting and treatment application: 05/28/22 at Foxhome and 06/03/22 at Danvers. The Iron Chelate (trade name Soygreen AST) was applied in-furrow at the time of planting. Nitrogen (1 lb. urea/plot) was manually applied on-furrow at planting as well.
• Soil sampling: On 05/28/22 at Foxhome and 06/03/22 at Danvers. A total of 32 soil samples were collected for common soil chemical factors and nematode egg counts (SCN).
• Weather data collection: Weather stations were installed at each location at the time of planting. Weather stations are being checked weekly and will remain in the field until harvest.
• Field maintenance (weed control) and growth stages record keeping: On a weekly basis after planting. At Foxhome, herbicide was sprayed only where there was a high density of weeds. Escapes and other weeds spread throughout the study were manually removed.
• IDC severity assessment including greenness scores (1 to 5 visual rating), drone imagery (using a multispectral sensor), and Crop Circle (ground-based NDVI and NDRE): On a weekly basis after symptoms started to appear (V2). Until the date this report was written, data was collected 9 times at Foxhome (6/22/22, 6/28/22, 7/5/22, 7/12/22, 7/21/22, 7/28/22, 8/3/22, 8/8/22, and 8/17/22) and 7 times at Danvers (6/28/22, 7/19/22, 7/28/22, 8/3/22, 8/8/22, 8/16/22, and 8/23/22). Two more weeks of data collection will be performed at Danvers.
• Ground Control Points (GCPs) installation: Six GCPs were installed at each of the four field sites. GPS coordinates from each GCP were collected with a centimeter-level GPS and will be used for geometric calibration of point clouds and orthomosaics. Done at both locations on 06/22/22.
• Stand counts: The number of plants in 1 linear meter (3.28 feet) was counted from the two central rows and was utilized to estimate plant population (plants/acre). On 6/28/22 at Foxhome, and on 7/19/22 at Danvers.
• Plant height: Yet to be done. The height of 10 randomly selected plants will be measured per plot.
• Harvest: Yet to be done. Plots will be harvested for grain yield estimation when plants reach maturity (R8).
• Data analysis and interpretation: Will be done as soon as the study is harvested.
• Prepare data summary and reports: As of 2021, the results of this study will be presented at the ASA annual meeting and other conferences.

Update:

View uploaded report

Update:

View uploaded report

Update:

View uploaded report

The present study evaluated the effectiveness of variety selection (highly tolerant [MT] vs. moderately tolerant [MT]), iron chelate application (0, 2, and 4 lbs. Fe-EDDHA A-1), and increased seeding rates (125,000 and 175,000 seeds A-1) as management strategies for soybean iron deficiency chlorosis. Further, profitability and risk analysis were conducted to assess the impact of each of the management practices on overall economic returns. Planting a highly tolerant variety, applying iron chelate in furrow at planting, and increasing the seeding density were all effective at mitigating IDC. As IDC severity increased by one-point in a 1 to 5 visual scale to rate symptoms, the HT variety yielded 5 bu A-1 more, on average, than the MT variety. A similar trend was found with the application of Fe-EDDHA in furrow at planting. Soybean yield increments averaging 6.5 and 7.5 bu A-1 were observed with the application of 2 and 4 lbs. Fe-EDDHA A-1 for every one-point increase in IDC intensity, respectively, relative to the untreated plots. When IDC was severe (average visual chlorosis score of 4 at R5.5 growth stage), iron chelate application at rates of 2 and 4 lbs. A-1 increased yield by 21 and 25 bu A-1, respectively, on average. A smaller but consistent effect was found for increased seeding rates. Compared to 125,000 seeds A-1, increasing the seeding density to 175,000 seeds A-1 significantly increased grain yield by 2.2 bu A-1, regardless of IDC severity or treatment combinations. Our risk efficient frontier analysis revealed that the best management alternative in terms of risk to reward for low-moderate IDC conditions was the combination of a HT variety with 2 lbs. Fe-EDDHA A-1 planted at 125,000 seeds A-1. Conversely, the single best option for severe IDC, both in terms of risk and expected returns, was the combination of a HT variety with 4 lbs. Fe-EDDHA A-1 planted at 175,000 seeds A-1. This study also investigated the utility of UAV-based vegetation indices for estimating grain yield of soybean grown under IDC stress conditions as a tool to aid growers, researchers, industry and policy makers with crop management, market planning, market research, and policy writing. In order to develop the yield estimation model, the most relevant VI’s for soybean yield estimation had to be determined. For that, a path analysis was performed, and indicated that NDRE at R5.5, OSAVI at R5.5, and NDVI at R1 had the strongest positive direct effect on soybean yield under IDC stress conditions. The three VI’s indicated by the path analysis as the most important for yield estimation were used as independent explanatory variables in a regression tree algorithm with grain yield as the principal dependent variable. The resulting regression tree algorithm was able to predict soybean yield with a relatively low RMSE (7.88 bu A-1) and MAE (6.7 bu Aa-1), while explaining more than 93% of the yield variability. Results from this study can help soybean growers increase productivity, improve economic returns while controlling economic risk, and provide an advantage when it comes to agricultural decision making.

This study is designed to examine three of the most often used management strategies for IDC from a systems approach: variety selection, seeding rate, and iron chelates. We will examine tradeoffs in cost and yield response to these strategies across a wide range of IDC levels. Coupled with an economic analysis, this work will allow us to provide producers with more refined recommendations for managing IDC and will support them in their efforts to achieve the greatest economic return on every farm.