Source: IOWA SOYBEAN ASSOCIATION submitted to NRP
ON-THE-GO NIR MANURE CONTENT SENSING TO IMPROVE MANAGEMENT IN MANURED CROPPING SYSTEMS
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
ACTIVE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1028772
Grant No.
2022-67021-37859
Cumulative Award Amt.
$582,391.00
Proposal No.
2021-11074
Multistate No.
(N/A)
Project Start Date
Aug 1, 2022
Project End Date
Jul 31, 2026
Grant Year
2022
Program Code
[A1521]- Agricultural Engineering
Recipient Organization
IOWA SOYBEAN ASSOCIATION
1255 SW PRAIRIE TRAIL PKWY
ANKENY,IA 500237068
Performing Department
Center for Farming Innovation
Non Technical Summary
This project uses engineered devices and engineering technology to solve significant problems in manured cropping systems. While animal manure improves soil health and is a valuable source of crop nutrients, its inherent heterogeneity leads to systemic cropping systems problems such as under and over-application of manure and synthetic nutrients. Under application of nutrients within fields leads to yield losses while over-application of nutrients impairs water resources.On the go, Near-Infrared Manure Content Sensing (MCS) systems measure liquid manure nutrient levels in real-time during application to apply manure nutrients more precisely across the landscape. The promise of MCS systems is increased crop yield with reduced nutrient losses, but these systems have yet to be fully calibrated and engineered into a complete cropping system.The goal of this project is to improve manure management with engineering and engineered devices. Specific objectives for this goal are listed below.Calibrate MCS Systems output with lab manure nutrient analysis to validate the system's accuracy from swine finishing, gestation/farrowing, dairy, and liquid beef manure systems.Expand the use of MCS Systems to solid manure sources, including bedded pack beef, open lot beef, layer, turkey, and broiler litters.Characterize and model benefits of MCS Systems on crop yield, water quality, and Nitrogen Balance across the landscape.Develop and demonstrate engineering approaches for optimizing nutrient management in manured cropping systems using crop modeling coupled with variable rate technology.This project fits into the Engineering for Agricultural Production Systems priority because it focuses on groundbreaking engineered devices and engineering technologies to increase yield and conserve natural resources in manured cropping systems.Stakeholder farmers, livestock integrators, manure applicators, and conservationists are excited about the potential of this project to improve water quality and farmer profitability.
Animal Health Component
70%
Research Effort Categories
Basic
(N/A)
Applied
70%
Developmental
30%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
10272102020100%
Knowledge Area
102 - Soil, Plant, Water, Nutrient Relationships;

Subject Of Investigation
7210 - Remote sensing equipment and technology;

Field Of Science
2020 - Engineering;
Goals / Objectives
On-the-go, NIR Manure Content Sensing (MCS) is a potential step-change technology for reducing nutrient losses and increasing whole farm yield in manured cropping systems.The main goal of this project toimprove manure management with engineering and engineered devices. Specific objectives are toCalibrateMCS systems output with lab manure nutrient analysis to validate the system's accuracy from swine finishing, gestation/farrowing, dairy, and liquid beef manure types.Expand the use of MCS Systems to solid manure sources, including bedded pack beef, open lot beef, layer, turkey, and broiler litters.Characterize and model benefits of MCS Systems on crop yield, water quality, and Nitrogen Balance across the landscape.Develop and demonstrate engineering approaches for optimizing nutrient management in manured cropping systems using crop modeling coupled with variable rate manure application technology.
Project Methods
The project involves several phases as listed below.Calibration MCS Systems output with lab analysis To understand the robustness of the NIR technology in MCS Systems, the HarvestLabs3000 NIR sensor will be calibrated against lab analysis of both liquid and dry manure sources from commercial livestock production. John Deere has agreed to provide a full-spectral reading standalone HarvestLabs NIR sensor for the project.For liquid manure calibration, nutrient contents will be determined on 2000 gallons of manure by pumping from the manure storage through the standalone sensor and into the storage tank. A recirculation pump will mix manure within the tank, and a sample of manure collected and analyzed for solids, total N, Ammoniacal N, Total P, and Potassium. We will select a subset (40) of samples to send to a second laboratory to determine lab-to-lab variability in sample results, and 20 samples will be collected in duplicate to compare within lab variability in nutrient concentration. This calibration will occur at 120 deep-pit swine finishing buildings, 30 gestation-farrowing buildings, 30 liquid beef buildings, and 20 dairies.The standalone sensor will also be used during manure removal from five field sites. We will take 15-paired manures samples at various stages of pump-out (beginning, end, and approximately evenly spaced in between) to evaluate how much nutrient concentration varied during manure removal. This technique allows assessing the sensor's ability to measure within manure removal event variability and estimate potential value by maintaining consistent N application rate to help conceptualize value for the technology.For dry manure calibration, samples of cattle (50), laying hen (50), and turkey (50) manure will be analyzed using a standalone HarvestLabs3000 system modified to work in batch mode. The NIR spectra from samples will be collected and used to identify which spectra wavelengths are essential for estimating nutrient content. These wavelengths will be used to develop calibration curves for the solid manure types as compared to lab analysis.In 9 on-farm replicated trials over 3 years, the MCS machine will be assessed for its spatial application accuracy. The MCS machine, owned by commercial applicators, will be stopped during application of manure and raw manure collected for lab analysis. These samples will be measured by a commercial lab for full constituent analysis. Each point in the field where manure samples are collected will be measured with high accuracy GPS and compared to the sensor reading at the same GPS points. Fifteen calibration points per field will be collected in equally spaced intervals during application for a total of 135 calibration points for the project.In analysis of sensor capabilities, correlation coefficients and equations will be determined via linear and/or non-linear regressions.Crop Yield, Nitrogen Budget and Nitrogen Use EfficiencyTo understand the effects of MCS Systems on corn yield and manure nitrogen use efficiency, MCS Systems will be compared with standard gallons per acre applications of manure in on-farm, replicated strip trials. These assessments will occur at 6 locations per year for a total of 18 locations for the project.To understand and predict the effects of MCS Systems on nitrogen balance (loss, crop uptake and nitrogen transformation), APSIM models will be calibrated with soil sampling and plant analysis at 9 locations. Soil samples will be taken at planting time, two growth stages (V8 and R1) and measured for soil organic carbon, soil texture, ammonium-N and nitrate-N. nitrate and ammonium. Plant analysis will occur at the R1 growth stage. Once APSIM models are calibrated with data, stochastic simulations with historical crop weather will occur to compare nutrient losses in an MCS System versus standard practices. The APSIM modeling will also estimate daily and annual estimates of crop N uptake, N loss by leaching and denitrification, mineralization of soil organic matter and animal manure.Data analysis of field trials and reporting is as follows. Yield monitor observations will be cleaned for outliers using late-season aerial imagery, distributions of combine speed, grain moisture. Yield data will be analyzed in two stages. First, statistical differences between treatments will be determined in each on-farm replicated trial. Second, if appropriate, data from the on-farm experiments will be combined and analyzed using mixed-effects model procedures with Bayesian and economic analysis (Laurent et al., 2019). Summaries of individual on-farm trials and all trials within a year or region will be presented online using the Interactive Summaries of On-Farm Strip Trial Tool or ISOFAST (https://analytics.iasoybeans.com/cool-apps/ISOFAST/) and Economic Value of On-Farm Studies (EVOS) (https://analytics.iasoybeans.com/cool-apps/EVOS/). These interactive tools provide summaries of on-farm experiments in terms of yield, economics and impact of various factors on yield response in a format that is easy for farmers and agronomists to comprehend.

Progress 08/01/23 to 07/31/24

Outputs
Target Audience:ISA hasreached farmer participants through recruitment efforts as well as manure applicators, equipment providers and engaged with John Deere to better understand how the NIR sensor works to better apply trials on farmer fields. We have also had several meetings with Prestage pork to discuss the value proposition of this system and to understand pains and gains from the pork industry. We have had similar meetings with manure applicators who have used this technology in the past to better understand the economics of using an NIR sensor compared to the standard application methods. ISU is working to develop a module for 2025 manure applicator training. Changes/Problems:We do not anticipate any issues, but as with all on farm research trials recruiting farmers to participate in trials is always a risk of failure. This particular project has an additional level of risk because of the required technology to execute the trials and also recruiting manure applicators who have the NIR sensors to apply the treatments required. What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest?Initial Results have been shared to technical service provides at the Iowa State Crop Advantage Series and at the Iowa State Integrated Crop Management Conference. ISA has shared the results from the 2023 trials with farmers that participated in this project. What do you plan to do during the next reporting period to accomplish the goals?Dissemination of preliminary results to 2025 Iowa manure applicator training. Finalize performance on swine farms and start evaluation on deep pit beef barns and liquid manure dairy facilities. Complete uncertainty evaluation tool and submit work for peer review. Develop an extension publication on value and uses of real-time manure nutrient sensing. ISA will continue to recruit farmers and applicators to test this technology, and take both manure and soil samples to aid in the modeling of both yield and nutrient outcomes. Secondly, as we get more data from multiple fields and years we will work with ISU to develop farmer friendly information that we can use to disseminate the results to farmers through our research publications page, weekly news letter, in person research meetings, and our monthly publication and podcast.

Impacts
What was accomplished under these goals? Iowa State hired a graduate assistant to focus exclusively on this project starting in June. Our primary objective is to evaluate the performance of the John Deere Harvest Lab at sensing manure nutrient concentrations in both liquid and solid manures. Current focus has been on liquid manure from swine barns with work on liquid beef and dairy to start in August and swine manure to continue throughout the summer. Work is two-fold, how well does constituent sensing perform across sites and within sites. Across all sites the sensor has shown a strong linear relationship between its reported nitrogen concentration and what was found in the paired sample sent to laboratory for wet chemical analysis. This indicates that across sites the sensor is performing well has strong potential to estimate nitrogen concentrations on the go, though slight adjustments or calibration is required. Within a single farm performance was more variable, as typically the concentrations reported by the Harvest Lab varied less than those found using wet chemistry. Duplicate wet sample analysis is needed and will be used on select samples in future tests to understand how much of the wet sample concentration is laboratory variation and what represents physically measured nutrient concentration changes. Overall, the sensor is showing great promise for potential use to help provide confidence in fertility manure is providing. This has great potential value as we estimate that if calibrated well it could provide an economic value of $10-20 per hectare in fertility value and stabilized yields depending on the crop rotation, weather, and manure properties. A secondary objective was to collect data on farmers perspectives on nutrient consistent sensing and where they believe the greatest variability and uncertainty in using manure as a fertilizer comes. During the 2024 Iowa Manure Applicator Verification program we surveyed 2500 manure applicators (both commercial manure applicators and farmers applying manure from their farm on their land) to determine their perspective on what the limitations were. We are currently summarizing this data and will have it available to report next quarter. In this vein, we believe it is important to provide farmers with an evaluation of how different uncertainties and variabilities interact when selecting manure application rates and their manure application equipment. Selecting appropriate fertilization rates is critical for optimizing crop yield and protecting the environment. Optimal nitrogen fertilizer rates are typically calculated assuming average growing conditions and assuming "perfect" information. In practice, uncertainty about critical impact variables adds risk to fertilization decisions. Here we evaluate how the uncertainty of different process variables (crop nitrogen response, manure nitrogen content, application rate, nutrient content variability, manure nitrogen availability, volatilization losses, and application uniformity) impact the optimal application strategy. This work demonstrates that when accounting for uncertainty in different variables, the economic optimum nitrogen fertilization rate increases. More specifically, we show that higher uncertainty in any term causes the optimum economic rate to increase proportionally. As such, while much attention has been given to providing farmers with tools and methods to select appropriate rates, just as important is providing them with the confidence and technology to trust their choice and equipment. Moreover, we demonstrate, using the concept of uncertainty, why split fertilizer application only has limited potential to improve nitrogen management. When used as a tool to manage risk and uncertainty, its benefits have traditionally been underestimated. We have developed a primary spreadsheet tool to test using Monte Carlo simulation to evaluate the impact of variability on farm economics and have since ported this to a python based model to enable quicker evaluation of various manure system combinations. In so doing we will develop information on how farmers could and should be adjusting manure application rates relative to commercial fertilizer to help provide improved recommendations for both maintaining crop production and enhancing environmental performance. A first approximation of results for deep pit swine manure applied in spring to corn in a corn soybean rotation with representative variation in nitrogen concentration, ability to hit specified application rate, nitrogen volatilization and nitrogen availability, with knife-to-knife flow variation of 0 (perfect) to 100% evaluated. Results indicated that adding variability or uncertainty always lowered the expected partial budget income and that for knife-to-knife variabilities of less than 40% it was rational for farmers to apply 5-15 lb N/acre more than they would using commercial fertilizer. If knife-to-knife variability exceeded 40% farmers could no longer make up for the variability by applying more nitrogen due to the non-uniform manure application and should instead by applying less with manure. In practice what this means is farmers should split apply two forms of nitrogen to cover the application variability. ISA collected yield and management data from 3 sites that were planted in 2023, and has 4 fields that were planted in 2024 that will have yield data collected at the end of 2024 or early 2025. Sampling has taken place in the 2024 growing season for both the manure that was applied as well as multiple timepoints of soil sampling to aid in the crop modeling component of this research. We have started recruiting locations for the 2025 growing season as well as working with John Deere to facilitate training on best practices for using the technology to provide better field prescriptions to farmers and manure applicators.

Publications


    Progress 08/01/22 to 07/31/23

    Outputs
    Target Audience:The target audiences during this reporting period have been farmers and commercial manure applicators. Changes/Problems:Staffing changes at bothIowa Soybean Association (ISA)andIowa State University (ISU) affected progress during the reporting period. The original project director, Peter Kyveryga, left ISA, so Christopher Hay has been serving as project director on an interim basis. Dr. Kyveryga's replacement has since beenhired. Scott Nelson, Research Agronomist, has also left ISA. His duties for recruitment, application, and analysis of field trial with MCS systems is being allocated to other agronomy staff at ISA. Brian Daugherty, Field Agricultural Engineer, left ISU during the reporting period. His position will be replaced. With delays in getting the ISU subaward established, a graduate student was not able to be hired for the project during the reporting period. One is being recruited to begin work during the current reporting period. Once the graduate student andMr. Daugherty's replacement are hired, the project will be fully staffed and the pace of progress can increase accordingly. Some of the practical challenges in applying the field trials were identified during this reporting period, so fewer than half were succesfully applied. However, with those lessons learned, additional trial participants have been identified for the 2024 cropping season, and we are still on pace to meet or exceed the proposed number of field trials. What opportunities for training and professional development has the project provided? Nothing Reported 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?Yield data fromthe field trials will be collected following harvest. Once the 2023 yield data have been collected, analysis andcrop model development will begin. Ten fields have been recruited for field trials for the 2024 cropping season. A graduate student at Iowa State is being recruited for the project, and work on MCS calibration with liquid manure sources will continue and expand to dry manure sources.

    Impacts
    What was accomplished under these goals? Goal 1. Samples were run from 10 different swine confinement manure pits to compare nutrient content estimations from the MCS system to lab analyses. Samples were within 12%. Goal 2. Work on expanding use of MCS systems to solid manure sources has not begun yet. Goal 3. Eight field locations were attempted forMCS system trials. Three trials were succesfullyapplied to them during the reporting period. Yield data will be collected in the next reporting period. These trials also included taking manure samples in the field to validate the calibration of the NIR sensor for variable rate manure applications. Goal 4. Iowa Soybean Review article provided examples of using MCS systems for improved nutrient management in manured cropping systems.

    Publications