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According to the Agriculture Department’s National

Agricultural Statistics Service, there were 69,100

hog operations in 2011 (USDA, 2012). Many crop

farmers rely heavily on the manure that is produced

by these operations as fertilizer. Although there has

been no change in the number of hog operations

since 2010, there is still a concern for the gas

emissions that are being released from the manure

that is being applied to post-harvest soil. This study

will be conducted to measure the amount of GHG

emission from land-applied swine manure in two

studies. One study was conducted in the fall of

2012 (October/November) and the second will

begin in the spring of 2013 (April). The National

Pork Board (NPB) is funding this study and the final

report will be used as a basis for manuscripts for

peer-review.

INTRODUCTION

Assessment of greenhouse gas (GHG) emissions

from land-applied swine manure is needed for

improved process-based modeling of nitrogen and

carbon cycle in animal – crop production systems.

In this research, we developed novel method for

measurement and estimation of greenhouse gas

(CO2, CH4 and N2O) flux (mass/area/time) of land-

applied swine manure. New method is based on

gas emissions collection with static flux chambers

(surface coverage area of 0.134 meters2 and a

head space volume of 6.98 L) and gas analysis

with a GC-FID-ECD. New method is also

applicable to measure fluxes of GHGs from area

sources involving crops and soils, agricultural

waste management, municipal and industrial

waste. New method was used at the Ag 450 Farm

Iowa State University (41.98N, 93.65W) from

October 24, 2012 through December 14, 2012 to

assess GHG emission from land-applied swine

manure on crop land. Gas samples were collected

daily from four static flux chambers. Gas method

detection limits were 1.99 ppm, 170 ppb, and 20.7

ppb for CO2, CH4 and N2O, respectively. Measured

gas concentrations were used to estimate flux

using four different models, i.e., (1) linear

regression, (2) non-linear regression, (3) non-

equilibrium, and (4) revised Hutchinson & Mosier

(HMR). Sixteen days of baseline measurements

(before manure application) were followed by

manure application with deep injection (at 41.2

m3/ha), and thirty seven days of measurements

after manure application.

ABSTRACT

RESULTS

MATERIALS & METHODS CONCLUSIONS

GHG flux estimates are within the range of values

reported in literature for similar studies. It is

noteworthy, that very few studies exist that report

GHG flux from ‘real’ fields like this study. Most of

the reports are based on research plots.

The temperature or the moisture did not have a

strong correlation with the fluxes observed during

the pre-application sampling. Strongest correlations

were found between gases and also between CO2

and environmental parameters. It was observed

that after the manure application with the

temperature dropping low that was followed by a

warm up – the was a significant increase in flux

with the warm up suggesting that gas was building

up in the frozen soil and was then be released

quickly when the ground unfroze.

FUTURE PLANS

The developed greenhouse gas emissions

procedure will be put in to place at the Ag 450 farm

for the Spring 2013 swine manure application (late

March – early May 2013).

Final report including Spring 2013 measurements

will be completed before June 30, 2013.

ACKNOWLEDGEMENT

Appreciation is expressed to National Pork Board

for funding of this experiment.

Kelsey Bruning1, Jacek A. Koziel2, Devin Maurer2, Tanner Lewis2, William Salas3

Greenhouse gas emissions from land applied swine manure:

development of method based on static flux chambers

Side View

Bottom View

Chamber Cover Chamber Anchor

Reflective Insulation

Sampling Port

Vent Tube

Clamping Tab

Thermocouple Port Seal

Handle

Teflon Tubing Sampling Holes

21”

13” 11.5”

19.5”

2.5” 2”

Thermocouple Port

Figure 1: Schematic of static chamber for GHG

sampling

Figure 2: Static Chamber, underside cover and

chamber anchor

Figure 3: Gas sample collection from

static chamber

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Time (d)

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a/d

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SnowManure

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Plowed

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Figure 6: Average Daily Flux Calculated of the four

Models for Methane, Net Flux Mean = 1.50 +/- 2.58

g/ha/d, Cumulative Net Flux = 55.5 +/- 95.5 g/ha/d

Figure 7: Average Daily Flux Calculated of the

four Models for Carbon Dioxide, Net Flux Mean

= 13,400 +/- 12,300 g/ha/d, Cumulative Net Flux

= 494,000 +/- 456,000 g/ha/d

Figure 8: Average Daily Flux Calculated of the

four Models for Nitrous Oxide, Net Flux Mean =

94.3 +/- 81.0 g/ha/d, Cumulative Net Flux =

3,490 +/- 3,000 g/ha/d

Figure 4: Vial cleaning system, vials purged

with helium and evacuated for seven cycles

before field sampling

Figure 5: GC-FID-ECD

Flux Estimation Models

Linear Regression Model

Microsoft Excel linear regression was

used to determine the flux of each gas

by taking the slope of the linear

regression line (µL gas L-1 h-1) (Eq. 1),

multiplying it by the chamber volume (L)

and dividing by the chamber surface

area (m2) resulting in flux (µL gas m-2 h-

1) (Eq. 2).

𝐶 𝑡 = 𝑆𝑡 + 𝑏 𝐸𝑞. 1

Where: C(t) is concentration (µL gas L-1)

t is time (h)

S and b are best fit coefficients . with S being the slope

𝐽 =𝑆𝑉

𝐴 𝐸𝑞. 2

Where: J is Flux (µL gas m-2 h-1)

S is slope (µL gas L-1 h-1)

V is chamber volume (L)

A is chamber surface area (m2)

First Order Linear Regression Model

For the first order linear regression

model the same was done as the linear

regression model but only on time 0 and

0.25 h data points (Eq. 3).

𝐽 =

𝐶𝑡 0.25 − 𝐶𝑡 0𝑡0.25 − 𝑡0

𝑉

𝐴 𝐸𝑞. 3

Where: J is Flux (µL gas m-2 h-1)

Ct0.25 is target gas

concentration (µL gas L-1) at

time 0.25 h

Ct0 is the target gas

concentration (µL gas L-1) at

time 0 h

t is time (h)

V is chamber volume (L)

A is chamber surface area (m2)

HMR Model

The HMR model calculations were done

using the HMR package in R statistical

software. The HMR model uses non

linear regression (Eq. 4) or linear

regression (Eq. 1) to best fit the data

and outputs the slope of the regression

line at t0 (µL gas L-1 h-1), the chamber

volume and chamber surface area may

also be plugged in to the program to

output flux (µL gas m-2 h-1), this was not

done in this study, the calculation from

µL gas L-1 h-1 to µL gas m-2 h-1 was done

outside of R to minimize unit confusion

(Eq. 2).

𝐶 𝑡 = 𝑎 + 𝑏 1 − 𝑒𝑐𝑡 𝐸𝑞. 4

Where: C(t) is concentration (µL gas L-1)

t is time (h)

a, b and c are best fit

coefficients

Hyperbolic Regression Model

The hyperbolic regression calculations

were done using Sigma Plot software to

best fit data to a hyperbolic function (Eq.

5), data was shifted on the y axis to

force the data to start at 0,0 by

subtracting concentrations by t0

concentration (Eq. 6). The resulting

derivative at t0 (Eq. 7) of the best fit

hyperbolic function was the slope of the

line at t0 (µL gas L-1 h-1), which can be

used as HMR calculations to determine

µL gas m-2 h-1 using known volume and

surface area of static chambers (Eq. 2).

𝐶 𝑡 =𝑎𝑡

𝑏 + 𝑡 𝐸𝑞. 5

𝐶 = 𝐶𝑡 − 𝐶0 𝐸𝑞. 6

𝑆 =𝑎𝑏

𝑏2 𝐸𝑞. 7

Where: C(t) is concentration (µL gas L-1)

t is time (h)

a and b are best fit coefficients

S is the slope (µL gas L-1 h-1) at

t0

1Iowa State University, Department of Civil,

Construction and Environmental Engineering 2Iowa State University, Department of Agricultural and

Biosystems Engineering 3Applied GeoSolutions

LITERATURE CITED

United States Department of Agriculture (USDA).

February 2012. Farms, Land in Farms, and

Livestock Operations. ISSN:1930-7128.