1. This research is part of a regional collaborative project supported by the USDA-NIFA, Award No. 2011-68002-30190:
Cropping Systems Coordinated Agricultural Project: Climate Change, Mitigation, and Adaptation in Corn-based Cropping Systems
Project Web site: sustainablecorn.org
Soil Water Potential Control of the Relationship
between Moisture and Greenhouse Gases Fluxes in
Corn-Soybean Field
Dinesh Panday* and Nsalambi V. Nkongolo
Department of Agriculture and Environmental Sciences, Lincoln University, MO

2. Introduction
• Agriculture is a significant source of three main biogenic GHGs
(CO2, N2O and CH4) fluxes and the ways of management of soils,
it can constitute as either a net source or sink of these GHGs.
• Greenhouse gases produced in soils move through the exchange
of gas between the soil surface and the adjacent atmosphere.
This exchange can occur by means of two mechanisms:
diffusion and advection and it is done through the pore space.
• Both temperature and moisture are important controls on
decomposition processes for GHGs but is more strongly related
to soil water content (Conrad, 1989).

3. • The water status in soils is characterized by the amount of
water present, called as soil water content (SWC, θ) and its
energy state, called as soil water potential (SWP, Ψ).
• SWC is expressed on a gravimetric or volumetric basis and
represented by percent whereas SWP measurement is pressure
(Pa or bar) usually the expressed as a negative number.
• The term “suction” and “tension” are definitions developed to
avoid using the negative sign and to represent the SWP as
positive numbers.

4. • Several authors have studied the relationship between SWC and
GHGs fluxes and most of them have quantified SWC but the
energy state of that water or the potential at which this soil
water was held was not measured.
• Tremendous efforts are devoted to understanding the
relationship between GHGs and soil controlling factors such as
soil moisture on daily, weekly, bi-weekly and monthly basic.
• However, to save energy, time and financial resources it would
have been better if such measurements could have been done
only when soil moisture was held at certain water potentials.

5. Objectives
To assess the relationship between soil moisture (θ) held at
different water potentials () and GHGs fluxes in corn-soybean
rotational field.

6. Methodology
• The experiment was conducted on silt loam soil at the Freeman
farm of the Lincoln University of Missouri in 2011.
• Total of 48 plots with 12.19 m width by 21.34 m length for each,
3 factorial experiment in a RCBD with 16 treatment
combinations and 3 replications.
• The 3 factors were (i) Tillage at 2 levels (No-Tillage vs
Conventional Tillage), (ii) Cover crop at 2 levels (Rye vs No-Rye)
and (iii) Cropping sequence or rotation at four levels
(Continuous Corn, Continuous Soybean, Corn-Soybean and
Soybean-Corn rotations).

7. Soil and Soil Air Sampling
• Soil samples were collected at four depths: 0-10, 10-20, 20-40
and 40-60 cm, then air-filled porosity (AFP) and other soil
properties were calculated.
• Soil air samples for gas analysis were collected using 48
individual PVC static and vented chambers (30 cm Ht * 20 cm
Dia) in each plot. Concentrations of CO2, N2O and CH4 from soil
air samples were measured with a Shimadzu GC-2014 gas
chromatograph.

8. Soil Moisture Measurement
• Soil samples were placed onto a ceramic porous plate and
wetted for overnight, the pressure chambers were closed and a
specified pressure was applied by an air compressor.
• The sample started losing water that moved through the porous
plate. After the water ceased to drain, the samples were
collected at the specified pressure (SWP,  = 0, -0.05, -0.1, -0.33
and -15 bar).
• The soil sample was then removed from the plate, weighted and
placed into an oven, for gravimetric determination of soil water
content.

9. Result and Discussions
Summary statistics for soil moisture at different matric potentials
Statistics m =0 m =-0.05 m =-0.1 m =-0.33 m =-15
Mean 0.33 0.21 0.13 0.12 0.09
SD 0.07 0.04 0.01 0.01 0.02
C.V. 21.86 21.61 9.25 8.61 18.47
Minimum 0.18 0.11 0.10 0.09 0.06
Median 0.34 0.21 0.13 0.12 0.09
Maximum 0.45 0.31 0.16 0.14 0.12
Skew -0.33 -0.12 -0.09 0.39 0.07
Kurtosis -1.09 0.71 -0.39 0.30 -1.40
Considerable differences were found in the mean soil moisture contents across the potential at
0 and -0.05 bar where moisture level was 33 and 21 percent respectively.

10. Statistics CO2 (mg m−2 h−1 ) N2O (μg m−2 h−1 ) CH4 (μg m−2 h−1 )
Mean 477.83 21.02 16.82
SD
125.67 100.87 292.21
C.V. 26.30 479.85 1737.10
Minimum 318.05 -108.49 -315.27
Median 462.37 -15.65 -110.04
Maximum 764.78 285.82 751.47
Skew 0.94 1.04 1.07
Kurtosis 0.03 0.45 0.20
Summary statistics for soil moisture at different matric potentials

11. y = 1535.4x - 277.91
R² = 0.4096
-50
0
50
100
150
200
250
300
350
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
Nitrousoxide(μgm−2h−1)
Soil moisture at 0 matric potential
Fig.3. Relationship between m = 0 and N2O emissions
Relation between soil moisture (at m =0) and GHGs fluxes

12. Summary
When soil matric potential (m) was close to 0 bar i.e., saturated
conditions at 0-10 cm depth, soil moisture was positively
correlated with CO2 and N2O fluxes with correlation coefficients
ranging from 0.49 to 0.64, but negatively correlation with CH4 (-
0.43) fluxes at p<0.05.

13. Conclusions
We conclude that when soil water was held at matric potentials
close to zero, there were higher CO2 and N2O emissions and higher
CH4 uptake in 0–10 cm soil depth.
Since soil moisture availability is controlled by the matric potential
at which this water is held, this study stresses the need to monitor
soil water potential when monitoring greenhouse gases fluxes.