Composting Pen Pack Cattle Manure for Improved Nutrient Transport

Purpose

The overall purpose of this research was to demonstrate the volume, weight and moisture reduction from composting pen pack cattle manure so that organic nutrients can be transported farther from the livestock barn. Simultaneously, through laboratory analysis, the goal was to measure the nutrient density of the compost from the start of the process to the finish. The reduction in volume will allow cattle farmers to store more manure in their dry stack (manure) barns to be land-applied at more ideal times, thus avoiding winter application on frozen and/or snow-covered ground.

Due to the overwhelming weight and volume logistics of unprocessed (raw) manure in general, often the manure is land-applied to fields relatively close to the livestock barn. This phenomenon has historically resulted in some fields or areas within fields that have high or luxury levels of soil test phosphorus and potassium. Manure is a great source of nutrients and organic matter for crop production. Avoiding application of manure on fields that are farther from the livestock barn can result in lower soil health and missed economic opportunity for these fields. Once a drier, more nutrient-dense compost is created, a second purpose of the research is to promote transfer of the compost to fields that are farther from the livestock barn or to fields with lower soil test phosphorus or potassium levels.

A final purpose of the research is to utilize compost in corn production systems to evaluate its benefit when applied at the same nutrient rate as its raw manure or commercial fertilizer counterparts. When manure or compost are added to a crop production system, the health and biology of the soil are improved.

What Did We Do

The study began by working with local cooperators who currently raise cattle and manage manure nutrients. This peer learning group included five (5) cooperators. Each cooperator was asked to build at least one windrow of pen pack (solid, dry bedded) manure removed from their cattle barn. The windrow was not to exceed 6 feet in height by 12 feet in width and could be of any length. All manure was weighed at the start of the composting process and then at the end of the process to measure weight reduction. To measure volume, windrows were measured (height x width x length) at the start and finish; cooperating farmers recorded ‘trucks in’ and ‘trucks out’. The five cooperators built eight (n=8) windrows for the purpose of this study.

For baseline data, all cooperators were asked to dedicate one windrow for weekly mechanical compost turning inside a dry stack barn for eight (8) weeks. Any additional windrows composted were to address research questions raised by cooperators. Two ‘additional’ windrows were turned every 2 weeks and a third ‘additional’ windrow was turned weekly, but in an outdoor setting. Mechanical composting was achieved with an HCL Machine Works pull-type compost turner (Figure 1). The compost turner accomplished two key things: consistently mixing compost ingredients (manure, sawdust, wheat straw), and adding oxygen into the composting system. The compost turner was pulled by a Case IH 190 Magnum tractor equipped with a continuously variable transmission (CVT). The CVT allowed for critical ultra-slow speeds (.05-.15 mph) necessary for early mixing passes with the compost turner and raw ingredients.

Figure 1. A pull-type compost turner (6 foot x 12 foot) used for this study

Another significant part of the research was manure nutrient analysis. Every windrow site (n=8) had 3 samples pulled for analysis: once at the start of composting, after every compost turn (6-8 turns on average) and when the compost was land applied or at the last turn. Key nutrients analyzed were nitrogen, phosphorus, potassium, sulfur and calcium. Additionally, temperatures were monitored using a 36” dial compost thermometer (Figure 2) prior to every turn to ensure adequate composting temperatures (120-140 deg F ideally) were maintained. Each windrow also had a HOBO temperature logger inserted in the center of the pile for temperature logging every 15 minutes for the duration of the process.

Figure 2. Compost thermometers (36”) were used to double-check pre-turn temperatures each week

Finally, cooperators were asked to work with the researcher to develop a replicated field trial in field corn utilizing the finished compost product from their farm. Generally, the goal of the field trials were to compare a ‘normal’ rate of manure against a half rate of compost (Figure 3). Yield and moisture data from field trials were collected and analyzed.

Figure 3. Land application of manure (light in color) and compost (dark in color) for replicated strip trials in corn.

What Have We Learned

This research began with an aggregated 258 tons of unprocessed (raw) pen pack cattle manure among 8 sites (windrows) and yielded 121 tons of finished compost, a 53% reduction in weight. However, the volume reduction was less significant than the reduced weight. The number of ‘trucks in’ versus ‘trucks out’ resulted in 28% reduction in volume. The average initial moisture of raw manure was 66% as compared the average final moisture of 48%.

Cooperators turned compost for a minimum of five weeks with some turning up to eight weeks. The average number of turns was seven weeks for each of the eight windrow sites.

The starting nutrient analysis of the manure on a per ton basis was 8 lbs total nitrogen (TKN), 8 lbs phosphorus (P), 14 lbs potassium (K), 1.5 lbs sulfur (S), and 4.5 lbs Calcium (Ca). The finished compost averaged 7.5 lbs TKN, 20 lbs P, 31 lbs K, 3 lbs S, and 12 lbs Ca per ton. Except for total nitrogen, nutrient density more than doubled for these key nutrients as a result of the composting process (Figure 4). It is assumed that nitrogen was consumed in the composting process resulting in increased organic matter and organic carbon.

Figure 4. Density of key nutrients doubled for phosphorus, potassium, sulfur and calcium from the start of composting to the finished product (n=8 sites)

Temperatures were monitored weekly and temperature data indicated that only one windrow dropped below 100 degrees Fahrenheit during the 8-week process. This windrow was smaller than the others and the compost was happening in below freezing temperatures that occurred in the month of February 2021.

Figure 5. Buried temperature loggers monitored compost temperatures throughout the research. Temperature drops resulted when loggers were removed for compost turning and then replaced

Finally, three replicated field trials were conducted in field corn to compare full rates of manure versus half rates of compost (Tables 1, 2, 3). One more comprehensive trial included a university recommended fertilizer rate as well (Table 4). On average, the compost was hauled 4.5 miles from the livestock barn, thus giving some promise to improved transport of manure/compost to farther field locations. The results below are from one year of data at each respective site and should be interpreted as such.

Table 1: Site 1 – Corn for grain
Treatments Harvest Moisture (%) Yield (bu/acre)
10 tons/ac MANURE 17.5 252 a
5 tons/ac COMPOST 17.8 245 a
LSD: 11.5, CV 2.0
Table 2: Site 2 – Corn for grain
Treatments Harvest Moisture (%) Yield (bu/acre)
Check (no manure or compost) 18.0 258 a
6 tons/ac MANURE 17.9 259 a
3 tons/ac MANURE 18.1 258 a
LSD: 9.7, CV 2.1
Table 3: Site 3 – Corn for silage
Treatments Harvest Moisture (%) Yield (bu/acre)
10 tons/ac MANURE 57.8 23.8 a
5 tons/ac COMPOST 57.8 22.7 a
LSD: 1.7, CV 3.1
Table 4: Site 4 – Corn for grain
Treatments Harvest Moisture (%) Yield (bu/acre)
Fertilizer (22-52-120-12s/ac) 17.6 190 b
10 tons/ac MANURE 17.7 213 a
5 tons/ac MANURE 17.5 202 ab
LSD: 14.9, CV 4.3

Future Plans

Future plans include adding 4-5 more windrow sites before this 2023 grant expires. In 2022 and 2023, the hope is to compare static windrows versus those that are turned mechanically. In the first 8 sites, compost turning was based on time (weekly or bi-weekly turn). In the future, oxygen level or temperatures should be evaluated to help determine timing of turning. From a crop yield perspective, measuring soybean yields in the year following corn where the compost, manure or fertilizer was applied would be informative for growers as they make decisions about improving placement (transport) of manure or compost further from the livestock barn or to fields that have low soil test phosphorus or potassium. Finally, a complete economic analysis of the composting plus further transport needs to be conducted via a case study model.

Authors

Eric A. Richer, Assistant Professor and Extension Educator, Ohio State University Extension
richer.5@osu.edu

Additional Authors

-Jordan Beck, Water Quality Extension Associate, Ohio State University Extension
-Glen Arnold, Field Specialist, Manure Nutrient Management, Ohio State University Extension

Additional Information

Hawkins, E. et al. 2021 eFields Report. Retrieved from https://digitalag.osu.edu/efields

OSU Extension Facebook and Twitter pages: www.fulton.osu.edu

Acknowledgements

This work is supported by a Great Lakes Sediment and Nutrient Reduction Program grant. Thanks to the five cooperating farmers who participated in this research study with Ohio State University Extension. Thanks to Stuckey Brothers Farms for use of compost turner and Redline Equipment for rental of Case IH 190 Magnum tractor.

 

The authors are solely responsible for the content of these proceedings. The technical information does not necessarily reflect the official position of the sponsoring agencies or institutions represented by planning committee members, and inclusion and distribution herein does not constitute an endorsement of views expressed by the same. Printed materials included herein are not refereed publications. Citations should appear as follows. EXAMPLE: Authors. 2022. Title of presentation. Waste to Worth. Oregon, OH. April 18-22, 2022. URL of this page. Accessed on: today’s date.

Overview of ODA’s Division of Livestock Environmental Permitting

Purpose

The purpose of this presentation is to provide a complete overview of ODA’s Division of Livestock Environmental Permitting (“ODA-DLEP”). ODA-DLEP regulates any livestock facility in Ohio that has the following number of animals or greater:

    • 700 mature dairy cows
    • 1,000 beef cattle or dairy heifers
    • 2,500 swine weighing more than 55 pounds
    • 10,000 swine weighing less than 55 pounds
    • 82,000 layers
    • 125,000 broilers or pullets
    • 500 horses
    • 55,000 turkeys

What Did We Do

Ohio Department of Agriculture’s Division of Livestock Environmental Permitting (“ODA-DLEP”) regulates the siting, construction, and operation of Ohio’s largest livestock facilities, referred to as Concentrated Animal Feeding Facilities (“CAFF”). ODA-DLEP’s primary objective is to minimize any water quality impacts, including both surface and ground waters, associated with the construction of new or expanding CAFFs, as well as implementation of best management practices once a CAFF becomes operational. These best management practices include management of manure, insect and rodent control, mortality management, and emergency response practices. ODA-DLEP issues Permits to Install (for construction) and Permits to Operate (for operations).

In addition, ODA-DLEP conducts routine inspections of each CAFF at least once a year, responds to complaints, and participates in emergency response. Inspections are conducted to review a CAFF’s compliance with Ohio Revised Code 903 and Ohio Administrative Code 901:10, the laws and regulations governing Concentrated Animal Feeding Facilities.

Finally, ODA-DLEP administers the Certified Livestock Manager program. Any individual in the State of Ohio that manages 4,500 dry tons of solid manure or 25 million gallons of liquid manure is required to be a Certified Livestock Manager (“CLM”).

What Have We Learned

Livestock operations continue to get larger and more concentrated and as a result, regulations are necessary to ensure proper handling and management of manure, particularly with land application of manure.

Future Plans

Over the past several years, DLEP has started to see more interest in manure treatment technologies. This could include, but is not limited to, anaerobic digestion, nutrient recovery, solids separation, and wastewater treatment. Technologies like this could greatly alter the landscape of the livestock industry by fundamentally changing the way manure is handled and how nutrients from manure are applied. DLEP does have regulations in place to account for manure treatment technologies. However, regulations, and specifically changes to regulations, cannot maintain the same pace as these technological advancements.

Authors

Samuel Mullins, Chief of ODA-Division Livestock Environmental Permitting
Samuel.mullins@agri.ohio.gov

Additional Information

https://agri.ohio.gov/divisions/livestock-environmental-permitting
https://codes.ohio.gov/ohio-administrative-code/901:10
https://codes.ohio.gov/ohio-revised-code/chapter-903

Videos, Slideshows and Other Media

ODA Division Spotlights – Division of Livestock Environmental Permitting 1

ODA Division Spotlights – Division of Livestock Environmental Permitting 2

 

The authors are solely responsible for the content of these proceedings. The technical information does not necessarily reflect the official position of the sponsoring agencies or institutions represented by planning committee members, and inclusion and distribution herein does not constitute an endorsement of views expressed by the same. Printed materials included herein are not refereed publications. Citations should appear as follows. EXAMPLE: Authors. 2022. Title of presentation. Waste to Worth. Oregon, OH. April 18-22, 2022. URL of this page. Accessed on: today’s date.

Assessing the implications of chloride from land application of manure for Minnesota waterways

Purpose

Rising chloride contamination in ground and surface waters is a growing concern in Minnesota. Previous studies estimate 87% of the chloride load originated from road salts, fertilizers, and wastewater treatments plants, and 6% from livestock manure. However, these estimates may be outdated as the livestock industry and manure application practices have evolved since these estimates of manure chloride concentrations were calculated in 2004. It also remains unclear how varying soil types affect the movement of chloride leaching following manure application. The aim of this study is to understand the movement of manure-based chloride from liquid and solid manures in Minnesota soils through a series of intact core leaching studies. Specifically, this project examines the magnitude of chloride leaching from swine and turkey manure application and compares it with synthetic potassium chloride fertilizer and a no nutrient control. The soil cores represent fine and medium textured soil.

What Did We Do?

    • Collected 24 12-inch soil columns from medium and fine-textured soils in Minnesota (Figure 1).
    • Collected swine and turkey manure from Minnesota farms
    • Analyzed soil pre- and post-leaching study for nutrient analysis (Cl, Bray P, NH4+, NO3, K, Organic Matter, pH, and Exchangeable Ca, Mg, Na, K)
    • Analyzed manure samples for nutrient analysis pre-application (Total N, P2O5, K2O, Cl)
    • Added water to cores until they reached field capacity
    • Applied manure using N-based application rates, and fertilizer using a K-based rate to 3 replicates
    • Simulated 2-in rainfall events on days 4, 12, and 18 post nutrient application
    • Collected and analyzed leachate for Cl, NH4+-N, and NO3-N
Figure 1: Setup of 12-inch PVC soil cores for leaching study

What Have We Learned?

    • How chloride concentration varies based on manure type and species
    • How the total amount of chloride applied via fertilizer application to cores varies by treatment
    • How manure-based chloride moves through soil
    • How fine and medium textured soil influences the movement of manure-based chloride
    • How chloride storage changed by soil type following the experiment (Figure 2)
      1. Medium textured soils had a greater change in chloride storage in both top and bottom layers compared to fine textured soils
      2. Manure additions increased chloride storage in both medium and fine textured soils
      3. Control soil cores experienced a loss in chloride storage following leaching
Figure 2: Change in soil chloride storage in each medium textured (left) and fine textured (right) soils by treatment. Positive values indicate a net gain in soil chloride, while negative values indicate a net loss in soil chloride following leaching.
Table 1: Total Cl concentration of liquid swine manure (lbs/1000 gallons), solid turkey litter, and synthetic KCl (lbs/ton) followed by total weight (g) of Cl added per core via application.
Treatment

Cl (lbs/1000 gallons)

Cl (lbs/ton)

Cladded per core (g)

Liquid

26

1.49

Solid

2.7

0.179

KCl

940

0.576

Control

0

0

Future Plans

Our group would like to complete a second round of this study the following year on newly identified liquid and solid manure and an additional coarse textured soil type. Future attempts in creating chloride-based mass balances for the state of Minnesota will benefit from this study.

Authors

Matthew Belanger, Graduate Research Assistant, Dept of Soil, Water, and Climate, University of Minnesota

Corresponding author email address

belan081@umn.edu

Additional authors

Dr. Erin L. Cortus, Associate Professor and Extension Engineer, Dept of Bioproducts and Biosystems Engineering, University of Minnesota

Dr. Gary W. Feyereisen, Research Agricultural Engineer, USDA-ARS Soil & Water Mgt. Research Unit

Nancy Bohl Bormann, Graduate Research Assistant, Dept of Soil, Water, and Climate University of Minnesota

Dr. Melissa L. Wilson, Assistant Professor and Extension Specialist, Dept of Soil, Water, and Climate, University of Minnesota

Additional Information

Wilson Manure Management and Water Quality Lab Site

Acknowledgements

This project is funded through the University of Minnesota Water Resource Center’s Watershed Innovation Grants Program. We’d also like to thank Scott Cortus, Eddie Alto, Todd Schumacher, Dr. Pedro Urriola, and Thor Sellie for their assistance.

Assessment of method of photo analysis for demonstrating soil quality

Purpose

The use of livestock manure as a soil amendment to benefit soil health by improvements to soil physical, chemical, and biological properties, has been documented. However, quantification of the impact of improved soil health metrics on nutrient cycling has lagged. The soil your undies experiment has been implemented in the past to visually demonstrate microbial activity (Figure 1). However, this demonstration is seldom quantified, and does not have the capacity to statistically show that the effects of different management practices are distinct. The goal for this study was to quantify the degradation of fabric on a similar experiment, using cotton fabric on agricultural soils through photographic editing software. This study was designed to assess a visual method for quantifying carbon cycling in soil, observed through the degradation of buried organic materials.

Figure 1. Soil your undies soil health demonstration. Credit Clackamas Soil and Water Conservation District.

What Did We Do?

White, 100% cotton fabric cloths were cut into 29.21 × 29.84 cm (871.62 cm2) (11.5 x 11.75 in, 135 in2) pieces and placed flat inside a non-degradable mesh bag (48 cm × 48 cm, 18.9 in x 18.9 in). Sixty of the mesh bags were buried at 5 cm (2 in) depth in a field planted with corn in May of 2021 (Figure 2). The sixty bags were arranged in 12 plots to which one of three soil treatments (swine slurry, swine slurry + woodchips, and control plots with no amendments) with four replications per treatment were also applied. Swine slurry was applied at a rate of 39,687.06 L-ha-1 (4,242 gal-ac-1) and woody biomass was applied at a rate of 21.52 Mg-ha-1 (9.6 tons-ac-1).

Figure 2. Fabric and mesh bag burial in research plots

Five times during the growing season (25, 54, 81, 99 and 128 days after establishment), one bag was retrieved from each plot and returned to the lab for analysis. For each bag, soil was gently removed from the surface of the mesh and then the bag was cut open to observe the cotton fabric remaining. All the fabric pieces were photographed after retrieval. Photographs of the fabric were taken with an iPad mounted on a tripod. Fabric samples were photographed in a premeasured area of 29.21 × 29.84 cm (11.5 x 11.75 in) on a black surface (Figure 3).

Figure 3. Fabric sample placement inside pre-measured area (29.21 × 29.84 cm) for photographing

Manual evaluation of percent fabric degradation for each sample was performed by overlaying a clear plastic grid (Figure 4) with primary graduations (darker lines) of 2.54 cm (1 in) and secondary graduations (lighter lines) of 6.4 mm (0.25 in) on fabric samples and counting grid squares that were void of fabric.

Figure 4. Grid overlayed on fabric sample for manual evaluation of percent fabric degradation

Each photograph was assessed using Adobe Photoshop 2020 and the free license program ImageJ. Briefly, each image was opened in the respective program and the initial fabric area (871.62 cm2) (135 in2) was delineated in the program, based on the premeasured area included in the photo to set a scale for the degradation measurement. The image was converted to black and white, and brightness and contrast were adjusted as needed to remove glare on the black background that might be misread by the program as fabric. Then, all the pixels within a specific color range – which was previously defined as fabric – were selected using the native editing tools in the two programs and this area was compared to the pixels in the initial fabric area to determine the percentage of fabric remaining.

What Have We Learned?

The three methods for estimating the area of the fabric did not show significant differences among each other, which means estimates of fabric degradation obtained with Photoshop and Image J accurately reflect manual hand counts, suggesting that these are reliable visual methods for determining the area of the remaining area of fabric (Figure 5, 6).

Figure 5. Linear regression model for degradation estimation via Photoshop relative to degradation value obtained by hand count
Figure 6. Linear regression model for degradation estimation via ImageJ relative to degradation value obtained by hand count

Future Plans

Future work will seek to validate this method according to standard measures of soil health and biological activity and ensure that the method has enough sensitivity to demonstrate statistical differences between soil treatments. Future studies should also focus on making the process of area estimation with the software an easier, less laborious process. Creating a cellphone app to determine degradation quickly and without the need for a computer could increase the adoption of the fabric degradation assessment method in field settings.

Authors

Amy Schmidt, Associate Professor, University of Nebraska-Lincoln

Corresponding author email address

aschmidt@unl.edu

Additional authors

Karla Melgar Velis, Graduate Research Assistant, University of Nebraska-Lincoln

Mara Zelt, Research Technologist, University of Nebraska-Lincoln

Andrew Ortiz Balsero, Undergraduate Research Assistant, University of Nebraska-Lincoln

Acknowledgements

Funding for this study was provided by the Nebraska Environmental Trust and Water for Food Global Institute at the University of Nebraska-Lincoln. Much gratitude is extended to collaborating members of the On-Farm Research Network, Nebraska Natural Resource Districts, Nebraska Extension Agents and Michael Hodges and family for providing the land, manure, and effort for this research project. Much appreciation to members of the Schmidt Lab who supported field and laboratory work: Juan Carlos Ramos Tanchez, Nancy Sibo, Andrew Lutt, Seth Caines and Jacob Stover.

Life Cycle Assessment methodology to evaluate environmental impact of beef manure management: a comparison

Purpose

Manure from beef feedlot productions can be managed through a diversity of strategies. When choosing from the possible scenarios the main factors influencing the decision are financial, logistical or from a regulatory fulfillment focus, however it is necessary to consider the environmental impact generated from the manure management system in order to generate less burdens on behalf of meat production. One of the most reliable methodologies for this matter is Life Cycle Assessment (LCA), which considers every input and output throughout the process and will calculate environmental emissions quantitatively. In this study we compared various LCA studies of beef lot manure management processes, with the aim of understanding the different systems´ hotspots and global emissions so that these can be considered when establishing a manure management system in similar facilities.

What Did We Do?

We gathered LCA studies published from peer-reviewed scientific journals that assessed the environmental impact of beef manure management. The search terms taken into account were “LCA and beef manure” and “LCA and feedlot manure”. To enable comparison between studies the following criterion were considered for inclusion: a) manure collected from intensive feedlot facilities b) results reporting at least global warming potential.

In order to categorize emissions generated from the entire manure life cycle we established four stages of manure management: Feedlot, transport, storage/transformation and use/disposal. Next, we identified which of these stages were taken into account in each study and if emissions were reported for stages individually as well as globally. Lastly, a comparison between LCAs was conducted for which we converted the functional units reported in the references to 1 ton of manure (dry basis). With this we can visualize the emissions generated from every ton of dry manure that enters the system despite the functionality to which it´s destined.

What Have We Learned?

The final review included 14 references which resulted in 19 scenarios evaluated, ranging from 2007 to 2021. Initially we noted that the system with the greatest number of evaluations performed was the transformation of manure into an energetic resource (E), with 12 of the 19 scenarios being focused on energy generation through manure treatment processes, emphasizing that the current trends are not only leaning towards a better manure use but also cleaner energy sources. On the counter part, composting (C) and stockpiling (SP) are the two least evaluated scenarios through LCA (just once in the articles present in this review). Manure composting and stockpiling aren´t perceived as innovative solutions when aiming to mitigate emissions, but shouldn´t be left aside when performing evaluations, since they´re the most applied techniques for feedlot manure management.

The energetic evaluations represented both, the most (E3) and the least emissions (E7) through the whole process. This is because bioenergetic sources, such as the one generated from manure transformation, frequently are given environmental credits and therefore negative emission values considering the substitution of other energetic resources. In this review 10 of the 12 energetic scenarios considered emission reduction by substitution, but not because the actual process generated less amount of greenhouse gases in itself. Energy production from manure is, in many cases assessed as a life cycle for transformation and excluding other stages of the entire management system. In fact, apart from the kind of treatment only 21.4% of all the LCAs considered in this study included all four stages. Since two of them (Lansche et al, 2012; Van Stappen et al, 2016) mentioned that the best mitigation emission option was to reduce storage time, and one (Giwa et al, 2017) reported the largest emissions coming from transportation we can assume that both, storage and transport are important stages when looking at sources of emissions and should not be left aside.

The difference between emissions between different manure management systems can be as extreme as 4,000X depending on system boundaries, allocation procedures, emission factors, environmental credits, amongst others. When evaluating a manure management system, it is necessary to consider every stage and so that emission reduction can be addressed in the whole process hotspots and not only during the transformation of organic matter.

Future Plans

To conduct an attributional LCA of beef feedlot manure management system as a case study. With this we will contribute more data to contrast composting or stockpiling scenarios and address the weight of the different manure management in a feedlot facility. Also, we will report eutrophication potential and water depletion, as their importance in the environmental impacts of manure management is well known and should be considered when decisions are being made.

Authors

Andrea Wingartz, National Autonomous University of Mexico

Corresponding author email address

anwiot@gmail.com

Additional author

PhD. Rafael Olea Pérez, National Autonomous University of Mexico

Potential soil health improvement through the integration of cover crops and manure in the upper Midwest

Purpose

Oftentimes fall manure application is associated with significant offsite transport of nitrogen and phosphorus into nearby bodies of water and the atmosphere. Mechanisms of losses include leaching, runoff, sediment transport, and volatilization processes. This is becoming more common as there has been a trend of increased wet springs that create difficult planting conditions. This prolonged period without an active root system leaves more time for nutrient loss from fall-applied manure to occur.

A strategy to offset nutrient losses in the fall and early spring is to plant a cover crop. The uptake of nutrients during this time in the field, which would otherwise be left fallow, allows for nutrients to be stored in the tissue of the cover crops, minimizing nutrient loss risk. Upon terminating the cover crops, the decomposing residues can supply nutrients to the succeeding row-crop. However, cover crop adoption is low in the upper Midwest US stemming from a short cover crop growing season due to the cold climate. This is especially the case for crops utilizing manure. A strategy to expand the cover crop growing season may be to interseed a cover crop into a maturing row-crop prior to harvest. Previous studies investigating the integration of manure and cover crops have seeded the cover crop after manure application. We wanted to measure the impacts of first planting a cover crop then applying manure once the cover crop has had ample time to get established. This may help expand the cover crop growing season and potentially limit the offsite transfer of pollutants to our water and air.

What Did We Do?

A small plot study was started in fall 2019 at the University of Minnesota West Central Research and Outreach Center near Morris, MN. We tested the effect of nitrogen source and cover crops on soil health, nutrient cycling, and agronomic responses using a randomized complete block design with split plots.

Cover crop mixtures of cereal rye and annual ryegrass were interseeded near corn’s fifth leaf collar (V5) growth stage, physiological maturity (R5 to R6 growth stage), or drilled after corn harvest. Dairy manure was sweep-injected to minimize soil disturbance in early and late fall, when soil temperatures were above and below 10°C (50°F), respectively. Non-manured plots received urea in the spring prior to corn planting. Urea applied plots (no manure) with no cover crops served as the control. Soil samples were taken throughout the cover crop and row-crop growing season from the 0-15, 15-30, and 30-60 cm (0-6, 6-12, and 12-24 in) soil layers. Cover crop biomass samples were taken in the late fall prior to the first frost event and prior to cover crop termination in the spring.

What Have We Learned?

Sweep injection is a reliable method to apply liquid manure to a field with an established stand of cover crops with minimal noticeable damage to the cover crops in the spring (Figure 1). Planting cover crops as soon as possible ensures more biomass is produced; planting after harvest consistently had lower cover crop yield than interseeding. Spring cover crop yield, right before termination, was highest when planted near physiological maturity [110 kg ha-1 (98 lb ac-1)] compared to drilling after harvest [87 kg ha-1 (78 lb ac-1)]. Nutrient source had a significant effect on silage yield. Manure, either applied in the early or late fall, had greater silage yield [58.5 and 58.7 Mg ha-1 (26.1 and 26.2 ton ac-1), respectively] than spring applied urea [53.6 Mg ha-1 (23.9 ton ac-1)]. Plots with cover crops interseeded at V5 had greater silage yield [59.5 Mg ha-1 (26.5 ton ac-1)] than all other treatments [54-56 Mg ha-1 (24-25 ton ac-1)] except no cover crops [57.8 Mg ha-1 (25.8 ton ac-1)].

Figure 1. Cover crops planted prior to late manure application. Photo was taken in the spring at cover crop termination.

Future Plans

Soil samples collected throughout the study are currently being analyzed for nutrient content and other soil health parameters. Results from this study will be used to develop best management practices for integrating cover crops and liquid injected manure in the upper Midwest.

Authors

Manuel J. Sabbagh, Graduate Research Fellow, University of Minnesota

Corresponding author email address

sabba018@umn.edu

Additional authors

Melissa L. Wilson, Assistant Professor, University of Minnesota; Paulo H. Pagliari, Associate Professor, University of Minnesota

Additional Information

Twitter: @mannyandmanure @manureprof

Lab website: https://wilsonlab.cfans.umn.edu/

Acknowledgements

This work is supported by the Conservation Innovation Grants program at the Natural Resources Conservation Service of the USDA, the Minnesota Corn Research and Promotion Council, and the Foundation for Food and Agriculture Research.

University of Idaho Sustainable Agriculture project seeks to create a bioeconomy from dairy byproducts to increase nutrient recycling

Purpose

This Sustainable Agriculture Systems project is called “Idaho Sustainable Agriculture Initiative for Dairy (ISAID).” Its main purpose is to create a bioeconomy around dairy manure and its byproducts, generating a circular use and economy of nutrients (Figure 1). Idaho is currently the third largest milk-producing state in the USA (USDA-NASS, 2021). Idaho dairy farms typically operate as confined operations that concentrate a significant amount of manure and nutrients in relatively small areas. Over the years, this situation has increased the concentration of nutrients in farms surrounding dairies. Meanwhile, distant farms may not benefit from using those nutrients (Leytem, et al. 2021). Except for its exceptional fertilizer and soil amendment value (USEPA, 2015), dairy manure is seen as a nuisance that needs to be managed well. Manure handling and use generate expenses for the producers and may be a nuisance for the neighboring communities and a potential environmental risk for the areas surrounding dairy production (Berg, et al. 2017; Moore and Ippolito, 2009; Sheffield, et al. 2008). This multidisciplinary project aims to create bioproducts from manure to significantly change the nutrient balance and the economic impact for producers in the region. Implementing the various strategies included in the project will help export nutrients to in-need areas within the region or outside the watershed altogether. In addition, increased income from manure processing would allow for better management and reduction of overall costs associated with nutrient management in the region.  The ISAID project includes three main areas that are integrated to generate the highest impact possible. Research, Extension, and Education are the distinctive areas of work. Still, these areas don’t work as silos, having a lot of integration to get the most of everybody’s work in the project.

What Did We Do?

Figure 1. Dairy bioeconomy

A group of 25 researchers in diverse areas of expertise obtained a USDA-NIFA Sustainable Agricultural Systems grant to conduct long-term (five years or more) projects. On the research side, the multifaceted studies that are under development include: use of amendments in manure composting to increase compost quality and value, reducing air emissions; nutrients’ extraction from various fractions of manure treatment to concentrate specific nutrients for individual commercialization (including nitrogen, phosphorous, and carbon); generation of hydrochar and biochar from dairy manure; bio-plastics production; cover crops use to increase nutrient extraction and soil health; fine-tuning fertilizer guides for crops using manure, compost, and other bioproducts. Analysis of each product’s economic and social impact separately and as a multi-prong approach. The extension component includes outreach to livestock and crop producers, local authorities, and communities to communicate the applicability of researched technologies and techniques, their impacts, benefits and challenges. The development of programs to train producers, allied industry, their workforce government employees on the diverse applications resulting from the project. The education component includes the participation of graduate and undergraduate students in all facets of the project and the development of educational programs for undergraduate and graduate students on topics associated with manure and nutrient management, bioeconomy, and on-farm application and management of these technologies and techniques.

What Have We Learned?

This project just finished the first of its five years; most of the projects are in the inception phase. We are generating baseline data and linking together diverse processes to determine possible interactions and needed extension and instructional needs. The corresponding poster includes a detailed list of projects associated with the grant, their corresponding principal investigators, and any recent advances. Some examples of project outcomes include the Water Machine that extracts phosphorous from waters with high nutrient content. Ammonia extraction from dairy wastewater. Enhanced composting using zeolites, pumice, biochar, and balanced carbon. Cover crops and corn silage as dual and double cropping. Hydrochar production from dairy manure and bioplastics. We are working on obtaining stakeholders’ input through diverse methods to help assess the needs of the industry and communities and guide the evolution of the research, extension, and education processes.

Future Plans

The project will continue to gather data and evolve. Collaborations and graduate student inquiries about inclusion in some projects are welcomed. We will offer updates at various conferences, including the next Waste to Worth.

Authors

Mario E. de Haro Martí, Extension Educator, University of Idaho Extension, Central District

Corresponding author email address

mdeharo@uidaho.edu

Additional authors

Mireille Chahine, Extension Dairy Specialist, Department of Animal, Veterinary and Food Science, University of Idaho

Linda Schott, Extension Nutrient and Waste Management Specialist,  Department of Soil and Water Systems, University of Idaho

Additional Information

ISAID Website: https://www.uidaho.edu/extension/nutrient-management/isaid

Facebook: https://www.facebook.com/uofiisaid

Instagram: https://www.instagram.com/uofiisaid/

Acknowledgements

This ISAID project is supported by USDA-NIFA SAS award #2020-69012-31

References

Berg, M., Meehan, M., and Scherer T. 2017. Environmental Implications of Excess Fertilizer and Manure on Water Quality. NM1281. https://www.ag.ndsu.edu/publications/environment-natural-resources/environmental-implications-of-excess-fertilizer-and-manure-on-water-quality

Leytem, A. B., Williams, P., Zuidema, S., Martinez, A., Chong, Y. L., Vincent, A., Vincent, A., et al. 2021. Cycling Phosphorus and Nitrogen through Cropping Systems in an Intensive Dairy Production Region. Agronomy, 11(5), 1005. MDPI AG. http://dx.doi.org/10.3390/agronomy11051005

Moore, A. and Ippolito, J. 2009. Dairy Manure Field Applications—How Much is Too Much? CIS1156. http://www.extension.uidaho.edu/publishing/pdf/CIS/CIS1156.pdf

Sheffield, R. E., Ndegwa, P., Gamroth, M., and de Haro Martí, M. E. 2008. Odor Control Practices for Northwest Dairies. CIS1148. http://www.extension.uidaho.edu/publishing/pdf/CIS/CIS1148.pdf

USDA-NASS. 2021. Quick Stats. Retrieved 02 27, 2022, from National Agricultural Statistics Service: https://quickstats.nass.usda.gov

USEPA. 2015. Beneficial Uses of Manure and Environmental Protection. Fact Sheet. https://www.epa.gov/sites/default/files/2015-08/documents/beneficial_uses_of_manure_final_aug2015_1.pdf

Can we create an accurate manure application map from GPS and manure weight or manure flow data?

Purpose

Manure, fertilizer, and other commercially available precision ag maps are not accurately calculating application rates in turns and turnarounds.  This is significant in turn areas, such as field borders or in small fields. In areas where equipment turns or reverses with a k-turn, more product is applied to the inside turn than the outside turn, resulting in more time and application, and significantly changing the application rates in the turn area. The current version of software supplied with GPS equipment UConn Extension purchased in 2020 does not sum material application rates vertically when overlap occurs. The software also does not adjust application rates on the inside and outside of turns to reflect the change in application area covered during a discrete-time interval.  The photo on the left is an example of an as-applied map from the commercial software. The line drawing on the right in the callout box displays the order of the tractor’s movement during the application. The lines represent the path of the tractor using a dragline to apply manure as it performs a K-turn at the end of the pull.  The lines are formed by connecting the GPS coordinates recorded each second. The red line represents the tractor traveling North until it reached the end of the pass. The green line represents the travel path as the tractor backs up to make the K turn.  The blue line is the path of the tractor as it again travels forward to complete the turn and head South again.  The blue and green lines do not connect because a tractor backed up to some point northeast of the end of the green line, stopped and resumed moving forward again, all during the one-second interval between the ends of the green and blue lines.

The graphic below is the complete map for the field in the example above. If you look closely, you will see a large number of overlaps where a yellow or orange color passes over a darker red color. This should result in the overlapped region changing to one of the green colors if the rates were being summed vertically by the software.

What Did We Do?

When we realized the maps created by the software were inaccurately representing the results, we began by contacting the company’s tech support center. After exchanging emails and several phone conversations with different individuals, the company indicated that their software was unable to calculate the values we needed. Since the software we had purchased didn’t do what we needed we started asking around to all of the commercial applicators of manure and fertilizer that we knew in the region to see if the software they used provided the functionality we needed in New England. We could find no commercial software package that included the calculations needed to allocate the material correctly in our small fields.

Finding no commercially available software we embarked on what turned out to be a long and arduous process to develop a methodology to create adequate spreading maps ourselves. As faculty members, we have access to ESRI ArcMap which we have used for years to generate field boundary maps, calculate hauling distances between fields and storages, and map farmstead layouts for projects.  We incorrectly assumed that ArcMap would have a ready-made solution for our problem. The process wasn’t as simple as playing connect the dots of the GPS coordinates. The GPS antennas on farm equipment are usually located on the cab of the truck or tractor to protect the antenna and in the case of the tractors to make the GPS available to other pieces of equipment such as planters. The material being applied usually comes out of the back, or side of the spreader some distance from the antenna. Then most application equipment throws the material even further behind the equipment with spinning disks or through pressure pumps, adding even more distance between the antenna and where the material actually hits the ground. To determine these distances accurately we measured numerous pieces of equipment and took a series of measurements between the equipment itself and where the material lands. In the case of manure spreaders, this can be a messy proposition. Once these measurements were completed, it was back to the computer to try to put these on a map. After hand digitizing a couple of hundred data points by drawing reference lines and using measuring tools in ArcMap to generate new points representing the distances behind and to the left and right of the equipment centerline, we finally had polygons to work with. Since we knew the amount of material applied each second, and ArcMap has the necessary tools to calculate the areas of the individual polygons, we could now divide the weight by the area to provide a weight or gallons per square foot of application area. ArcMap also has the tools needed to calculate the cumulative applied material for the overlapped areas by summing vertically through the polygons and summing the rate per square foot value of each layer.

Turns present a different challenge, but the turns cause extreme differences in application rates, so accurate maps require calculating the different application rates on the inside versus the outside of turns. The drawing above illustrates the path of a spreader, in this case a dragline moving North on the right side of the figure, making a 180 degree turn at the end of the row, and heading back south. The numbers in the section of the curve on the upper right reflect the application rates per acre for a fictional piece of equipment with the following characteristics. The descriptive data is real, but it comes from multiple equipment companies. For this example, we used a dragline toolbar that is 60 feet wide with the left and right halves represented by the three arrows on the lines pointing north and south. The dragline has 16 injectors and is fed by a pump capacity of 5,500 gallons per minute. We spent some time watching videos of spreaders at equipment company websites and timing, with a stopwatch, different spreaders as they made turns. This example uses 13 seconds for the toolbar, to make a 180-degree turn. A 5,500 gallon per minute pump pumps 91.7 gallons per second, or a total of 1192 gallons as the vehicle turns around. We divide 1192 in half to separate the left and right sides of the toolbar and you have 596 gallons applied inside and outside the centerline during the turn. Now we need to turn our attention to the area that was covered. The area of a circle is given by the formula Pi times the square of the radius. The area of the 180-degree turn for the inside of a 60-foot toolbar is given by the formula (3.141593 X (30X30))/2 or 1,414 square feet. Using the same formula, the area of the larger 60-foot radius is 5655. To get the actual area covered by the outside half of the toolbar we need to subtract the area of the 30-foot radius half-circle from the 60-foot radius half circle which comes out to 5,655-1,414 = 4,241. Now that we have the areas, we can divide the left and right areas into gallons.

596/1,414 = 0.421 to convert that to gallons per acre multiply by 43,560 = 18,339

 

596/4,241 = 0.141 X 43,560 = 6,142 or almost exactly 1/3 the amount per acre applied to the inside of the curve.

Given the spreading patterns of modern farm equipment and farmers wanting to ensure complete coverage in a field, overlaps are inevitable. If we are really going to follow the 4R’s of nutrient management, we need better as applied maps that will allow us to know where the manure went, and better more agile fertilizing equipment to only apply fertilizer in the areas of fields that truly need the fertilizer – now that we can identify them on accurate maps. This includes the ability of equipment to lower flow rates on the inside of turns and increase the flow rates on the outside of turns automatically to provide more even spreading rates across the entire field.

What Have We Learned?

Current spreading maps are inadequate for small New England farms. Application overlaps and spreading differentials on the inside and outside of turns result in inaccurate application rates. Current equipment does not account for these differing rates along the turn radii. We have worked out an algorithm and a process to correct for both the turn issue and the overlap issue for straight application such as self-propelled spreaders or toolbars attached to the 3-point hitch of a tractor. The algorithm still needs to be adjusted to connect the polygons representing turns with curved lines rather than the straight lines we currently use. This will improve accuracy incrementally, albeit measurably, since the turn radiuses and the distances involved will only add or remove a few square feet from each polygon which in our hand calculation doesn’t affect the rate per acre more than a few hundred gallons at a time.  We understand the math involved to calculate accurate spreading locations and areas for towed equipment if we have adequate measurements of that equipment and with the caveat that the spreader being towed is always moving forward. In certain cases, such as semi-tractor trailers it is possible for the truck cab, with the GPS attached, to be moving in a circular path – while the rear bogey of the trailer spins in place – which can cause the rear wheels on the trailer to back up during a tight turn.  We haven’t worked out the math on this, and so far, we have found no practical way to calculate this given the state of the equipment.  For semi-trailers we would recommend that GPS antennas be mounted at the front and the rear of the trailers. We need two points to determine the straight line between the points so we can calculate the left and right application distances for where the material hits the ground.

The as-applied map on the next page shows how the current version of our software creates a much more accurate application map from GPS data.  This is the same section of the field shown in the first graphic from the commercial software.  As you can see our solution calculates the areas of overlap and sums the application rates vertically through the layers.  If you look closely at the figure, you will see 3-digit numbers representing the calculated positions of the Left, Right, and Center XY coordinates of the points where the material is hitting the ground each second.  The single-digit numbers reflect the number of times that an individual polygon was applied with manure during the turn maneuver.

<7,500 7,500 – 12,500 12,500 – 25,000 25,000 – 100,000 >100,000 gal/ac
880 2060 6600 2211 1427 ft2
7 16 50 17 11 percent
13,177 Sum ft2 Applied
2350 Total ft2

The table above is calculated using the application rates and the areas of the polygons in the as-applied map.  It basically provides a report card of the application.  The farm intended to apply 10,000 gallons per acre to meet the fertilizer needs of the crop.  We placed an arbitrary interval of 2,500 gallons above or below the target rate as an acceptable variation in the application rate.  This allowed us to calculate that ~15.6% of the area applied received the “correct” rate.  Approximately 6.7% of the area received too little manure and 77.7% of the area received too much manure.  In addition, we calculated that the small area on the inside of the turn that received the most manure per acre received 103,383 gallons per acre, rather than the desired 10,000 gallons per acre.

Future Plans

This year we plan to validate our algorithms for the self-propelled and the farm tractor 3-point hitch mounted spreaders.  We hope to expand the algorithm to tractor towed combinations when we receive data from 2 more farms that installed equipment this winter on their equipment.  We will validate our algorithm’s accuracy by applying manure to fields that have had collection pans pre-located throughout the field.  Once we have the spreading map, we can overlay it onto the data from the collection pans that we will geolocate with handheld RTK GPS equipment before applying the manure.  We would also like to rent a pair of RTK GPS loggers to mount on the front and the rear of a semi tanker to verify the movement of the trailer during extreme turns.

Hopefully, we can develop a mathematical model for the semi turns because the alternative – putting 2 farm equipment type GPS antennas on a trailer, will get expensive.

Authors

Richard A. Meinert, Cooperative Extension Educator, University of Connecticut

Corresponding author email address

Richard.Meinert@uconn.edu

Additional author

Qian Lei-Parent, Research Associate, Department of Extension, University of Connecticut

Acknowledgements

Funding to purchase the GPS equipment provided by a USDA NRCS CT CIG Grant.