Introduction
Increased atmospheric concentrations of greenhouse gases (GHGs: CO2,
N2O and CH4) over the last century have been correlated to
increasing mean global temperature (IPCC, 2013); while N2O is also the
primary ozone-depleting anthropogenically emitted gas (Ravishankara et al.,
2009). Globally, agriculture is directly responsible for approximately
14 % of anthropogenic GHG emissions while indirect emissions due to
conversion of natural landscapes to agricultural systems may contribute an
additional 17 % (Vermeulen et al., 2012). In developing countries,
however, agriculture can account for up to 66 % of a country's total GHG
emission (Tubiello et al., 2014), with African GHG emissions from agriculture
and other land uses estimated to be 61 % of total continental GHG
emissions (Valentini et al., 2014).
In parts of the developing world, such as sub-Saharan Africa (SSA),
smallholder farms (farm size < 10 ha) comprise almost 80 % of
farmland and up to 90 % of farms (Altieri and Koohafkan, 2008). Thus, it
is likely that smallholder farms have a large effect on the GHG inventories
of SSA. Unfortunately, there is a dearth of knowledge on agricultural soil
GHG emissions from smallholder systems as only a handful of empirical studies
(Table 1) have measured these (e.g., Baggs et al., 2006; Brümmer et al.,
2008; Dick et al., 2006; Predotova et al., 2010). Previous studies in Africa
were also limited in scope, measuring emissions from a low number of sites
(generally less than 10) for a short time period (i.e., less than 1 year),
often with low temporal resolution. This shortage of baseline data makes it
impossible for many developing countries to accurately assess emissions from
soils used for agriculture or to use Tier 2 methodology, which requires the
development and documentation of country or regionally specific emission
factors, to calculate GHG inventories (IPCC, 2006). Also, Tier 1 methodology
assumes a linear response to fertilizer, which may not accurately reflect
emissions in low input systems (Shcherbak et al., 2014). Finally, because
most of the research behind the development of the Tier 1 methodology has
been completed in temperate zones, the differences in climate, soils, farm
management and nutrient balances (Vitousek et al., 2009) seem to result in
consistent overestimates of GHG fluxes (Hickman et al., 2014b; Rosenstock et al., 2013b). This
likely translates to inflated national agricultural GHG inventories in SSA
that may result in incorrect targeting and inefficient mitigation measures.
List of in situ empirical studies of greenhouse gas fluxes from
agricultural systems in sub-Saharan Africa.
Reference
Location (and croptype/treatment)
Sites
Time of measurement
Samplingfrequency
Flux ratesd
Annual flux estimates
Brümmer et al. (2008, 2009)
Burkina Faso(sorghum, cottonor peanut)
4
Jun–Sep 2005 Apr–Sep 2006
1–3 times per week
N2O: 0.19–0.67 kg ha-1 yr-1 CO2: 2.5–4.1 Mg ha-1 yr-1 CH4: -0.67 to -0.7 kg ha-1 yr-1
Dick et al. (2008)a
Mali (pearl milletwith/without legumeintercropping)
3
Jan 2004–Feb 2005
Monthly
N2O: 0.9–1.5 kg ha-1 yr-1
Hickman et al. (2015)
Kenya (maize)
1
Mar 2011–Jul 2011 Apr 2012–Jan 2013
Daily to weekly
N2O: 0.1–0.3 kg ha-1 yr-1
Koerber et al. (2009)b
Uganda (vegetables)
24
Jul 2005–Sep 2006
Monthly
CO2: 30.3–38.5 Mg ha-1 yr-1
Lompo et al. (2012)c
Burkina Faso(urban gardens)
2
Mar 2008–Mar 2009
Twice a day (“several” times percropping period)
N2O: 80.5–113.4 kg ha-1 yr-1 CO2: 22–36 Mg ha-1 yr-1
Makumba et al. (2007)
Malawi (maize withagroforestry)
1
Oct 2001–Apr 2002
Weekly
CO2: 2.6–7.8 Mg ha-1 yr-1
Predotova et al. (2010)c
Niger (urban and peri-urban gardens)
3
Apr 2006–Feb 2007
Twice a day for 6days (repeated8–9 times per year)
N2O: 48–92 kg ha-1 yr-1 CO2: 20–30 Mg ha-1 yr-1
Sugihara et al. (2012)b
Tanzania (maize,with/without residue)
2
Mar 2007–June 2010
1–2 times permonth
CO2: 0.9–4.0 Mg ha-1 yr-1
This study
Kenya (annual crops,grazing land, woodlots, fodder grasses)
59
Aug 2013–Aug 2014
Weekly
N2O: -0.13–1.83 kg ha-1 yr-1 CO2: 2.8–15.0 Mg ha-1 yr-1 CH4: -5.99–2.44 kg ha-1 yr-1
Seasonal flux estimates
Baggs et al. (2006)
Kenya (maize withagroforestry, till/no till)
1
Feb–June 2002 (rainy season)
Weekly
N2O: 0.2–0.6 kg ha-1 CO2: 1.8–2.3 Mg ha-1 CH4: 0.1–0.3 kg ha-1
Chapuis-Lardy etal. (2009)
Madagascar (maizewith soybean)
1
Nov 2006–Apr 2007 (rainy season)
Weekly
N2O: 0.3 kg ha-1
Chikowo et al. (2004)
Zimbabwe (maize/improved fallow)
1
Dec 2000–Feb 2001 (rainy season)
Weekly
N2O: 0.1–0.3 kg ha-1
Hickmann et al.(2014a)
Kenya (maize with dif-ferent fertilizer rates)
1
Mar–Jul 2010
Daily to weekly
N2O: 0.62–0.81 kg ha-1 yr-1
Mapanda et al. (2011)b
Zimbabwe (maize with different fertilizer rates and types)
2
Nov 2006–Jan 2007 Nov 2007–Apr 2008 Nov 2008–Apr 2009 (rainy season)
Once every 2months
N2O: 0.1–0.5 kg ha-1 CO2: 0.7–1.6 Mg ha-1 CH4: -2.6 to +5.8 kg ha-1
Millar et al. (2004)
Kenya (maize withregular and improvedfallow)
Sep–Dec 1999 Mar– Jun 2000 (rainy season)
1–2 times per week
N2O: 0.1–4.1 kg ha-1 CO2: 0.7–1.7 Mg ha-1
Mean flux rates from short duration studies
Kimetu et al. (2007)
Kenya (maize)
1
Mar–Jun 2000(rainy season)
3 times per month
N2O: 1.3–12.3 µg m-2 h-1
Mapanda et al. (2010)b
Zimbabwe (grassland/grazing, treeplantations and maize)
12
Nov 2006–Mar 2007 (rainy season)
Twice a monthto once every 2months
N2O: 1.0–4.7 µg m-2 h-1 CO2: 22.5–46.8 mg m-2 h-1 CH4: -9.4 to +6.9 µg m-2 h-1
Thomas (2012)
Botswana (grazing)
2
Feb, Apr, Jul, Nov 2010 (both rainy and dryseason)
7 times per day, 12separate days only
CO2: 1.1–42.1 mg m-2 h-1
a Study includes fertilization up to
200 kg N ha-1.b Sampling is too infrequent for accurate estimates of cumulative
fluxes (Barton et al., 2015).c Uses photoacoustic spectroscopy, which has recently had questions
raised about its accuracy (Rosenstock et al., 2013a); also, these studies
used exceptionally high N application rates from 473 to approximately
4000 kg N ha-1 yr-1.d Note: flux rates are given as the range of values from the
various replicates used in the studies, i.e., the spatial variability and,
where available (Mapanda et al. 2011 and Thomas 2012), the temporal
variability, are reported as N-N2O, C-CO2 and C-CH4. Please
also note units: where possible, annual cumulative fluxes are presented,
however, in cases with insufficient data to estimate cumulative annual fluxes,
we present either mean flux rates or the cumulative for the given period.
Soil GHG emission rates have been related to soil properties such as pH,
soil organic carbon (SOC)
content or texture (Khan et al., 2011; Chantigny et al., 2010; Rochette et
al., 2008; Stehfest and Bouwman, 2006) but also to vegetation (crop)
type (Stehfest and Bouwman, 2006) and management operations, e.g., tillage,
fertilizer type or crop rotation (Baggs et al., 2006; Drury et al., 2006;
Grageda-Cabrera et al., 2004; Halvorson et al., 2008; Yamulki and Jarvis,
2002). In contrast to agricultural systems in most OECD (Organisation for
Economic Co-operation and Development) states, smallholder farmers
differentially allocate resources based on distance from homestead and
perceived soil fertility, specifically manure and fertilizer applications, to
their fields resulting in strong gradients in soil fertility (Tittonell et
al., 2013). The differences in soil fertility can be predicted using remote
sensing tools like the “normalized difference vegetation index” (NDVI) to
determine the magnitude and temporal variability of primary productivity
(Paruelo et al., 2001). Differences in fertility can also be predicted using
farmer questionnaires to determine how farmers allocate resources to the
fields and then using this typology of farming activities (hereafter “field
typology”) to estimate where soil GHG fluxes are more likely to be high. If
strong correlations can be empirically observed, such fertility gradients may
then be upscaled based on either the NDVI or farmer interviews, further
allowing for effective landscape-level predictions based on the field-scale
GHG measurements.
Map of study area showing the sampling location by the different
vegetation cover types.
Soil properties (± 1 SEM (standard error of the mean)) for
0 to 20 cm depth, sampled immediately before initiation of gas sampling for
the different land classes.
Land class
C contentb (%)
N content (%)
C / N ratio
pH
Bulk density (g cm-3)
(1) Lowland small (< 2 ha) mixed farms with degradation signsa (n= 7)
1.38 ± 0.13
0.10 ± 0.01
13.18 ± 0.51
6.61 ± 0.09
0.86 ± 0.03
(2) Lower slopesc, moderate-sized (2–5 ha) mixed farms with degradationsigns (n= 8)
1.18 ± 0.14
0.10 ± 0.01
11.60 ± 0.58
6.58 ± 0.16
1.14 ± 0.08
(3) Mid-slopes, moderate-sized grazingland (n= 10)
2.27 ± 0.37
0.18 ± 0.03
12.16 ± 0.42
6.02 ± 0.21
0.98 ± 0.07
(4) Upper slopes/highland plateau,mixed farms (n= 22)
2.67 ± 0.17
0.21 ± 0.02
12.69 ± 0.52
5.46 ± 0.24
0.80 ± 0.06
(5) Mid-slopes, isolated moderate-sized farms (n= 12)
2.83 ± 0.36
0.24 ± 0.02
13.02 ± 0.81
5.84 ± 0.20
0.71 ± 0.04
a Degradation signs were bare soil and evidence of erosion visible on
MODIS images.b Due to lack of carbonates, total C equals organic C.c Sloped areas went from the lowlands (approx. 1200 m a.s.l.) up to the
highlands (approx. 1800 m a.s.l.) ranging from 10 to 30 %.
The lack of good information on GHG fluxes related to agricultural activities
in SSA and specifically on smallholder farming systems is a large data gap
that needs to be addressed. The objectives of this study were to gather GHG
flux data from smallholder farms of the western Kenyan Highlands that
represent both the diversity in farming practices and the landscape
heterogeneity typically found for many highland regions in eastern Africa. We
hypothesized that (a) in view of low rates of fertilizer applications by
smallholders, the GHG fluxes are generally at the low end of published fluxes
from agricultural land, (b) the seasonality of rainfall is mirrored by fluxes
and (c) differences in land productivity as reflected by NDVI and field
typology as well as differences in vegetation can be used to explain spatial
variability in field-scale soil GHG fluxes.
Materials and methods
The study site was a 10 km × 10 km area in Kisumu County, western
Kenya (centered at 35.023∘ E, 0.315∘ S), north of the town
of Sondu (Fig. 1) and ranged from a lowland area at approximately
1200 m a.s.l. to a highland plateau at approximately 1800 m a.s.l. The
site is one of the sentinel sites for the CGIAR Research Program on Climate
Change, Agriculture and Food Security (CCAFS) and is described in detail in
Sijmons et al. (2013). This site was selected as it was found to be broadly
similar in terms of demographics (e.g., population density, income) and
agro-ecological characteristics (e.g., elevation, temperature, precipitation)
of other eastern African tropical highlands (Braun et al., 1997), allowing us
to scale up the results to other countries in the region (Sijmons et al.,
2013). Mean annual temperature is approximately 23 ∘C and the
average annual rainfall is 1150 mm (Köppen classification of a tropical
savanna climate). Temperatures tend to be slightly cooler and precipitation slightly
higher in the highlands compared to the lower regions of the study site.
Precipitation patterns are bimodal with the “long rains” occurring from
April to June (42 % of annual precipitation) and the “short rains”
occurring from October through December (26 % of annual precipitation).
The site is primarily composed of smallholder rain-fed farms typically
growing maize (Zea mays) and sorghum (Sorghum bicolor)
during the long rains and beans during the short rains. Based on farmer
interviews, approximately 27 % of them applied fertilizers (i.e., manure
or synthetic fertilizers) to their plots, with application rates being very
low. For manure, application rates were approximately
200 kg manure ha-1, which corresponds to approximately 95 kg of C
and 5 kg of N given typical N contents for cattle in this region (Pelster et
al., 2016). While application rates for synthetic fertilizer (two farmers
applied diammonium phosphate and one applied urea) were < 50 kg
fertilizer ha-1 (< 25 kg N ha-1). These fertilizer
rates are much lower than rates typical for industrial production where
application rates often exceed 150 kg N ha-1 for maize production.
Soil types in the study area are highly heterogeneous, ranging from
well-drained, acidic Nitisols in the upper part of the landscape, to eutric
and dystric Cambisols in mid-altitude areas and poorly drained Planosols in
the lower parts (IUSS Working Group WRB, 2015). Selected topsoil
characteristics for the different land classes identified in the study region
are provided in Table 2.
Landscape stratification
Differences in management intensity and vegetation were expected to affect
GHG fluxes, and so the landscape was stratified to account for the expected
variability. The stratification was based on a mixed method land use
classification combining remote sensing and household surveys.
For the land classification we followed an approach based on vegetation
functioning in terms of the magnitude and the temporal variability of primary
productivity (Paruelo et al., 2001), assessed through the proxy variable
NDVI, which allows approximate but widespread characterizations of
productivity across space and time and across different ecosystems (Lloyd,
1990; Xiao et al., 2004). We acquired 2001–2012 NDVI data from MODIS
(Moderate Resolution Imaging Spectroradiometer). We selected only those NDVI
values indicating good to excellent quality conditions (i.e., pixels not
covered by clouds and with a low to intermediate aerosol contamination).
Then, we used the program TIMESAT version 3.1 to reconstruct temporal series
(Jönsson and Eklundh, 2002).
From the reconstructed temporal series we assessed six functional metrics
depicting the magnitude, seasonality and interannual variability of
productivity. The metrics used were as follows: (1) the mean annual NDVI,
(2) the minimum NDVI, (3) the browning rate (rate of NDVI decrease), (4) the
peak of the NDVI, (5) the intra-annual coefficient of variation (CV) of the
NDVI and (6) the interannual CV of the NDVI. These metrics allow us to
differentiate between land cover types (e.g., cultivated vs. uncultivated)
and between different cultivation management approaches (e.g.,
commercial intensive
vs. subsistence) (Baldi et al., 2015). The different elevation bands and soil
types resulted in different magnitudes, seasonality and interannual
variability of productivity with the highlands generally having higher
productivity due to the higher rainfall and more fertile soils. We then ran
an ISODATA unsupervised classification algorithm (Jensen, 1996), and the
resulting spectral classes were aggregated to create patches. After combining
minor or sparsely distributed patches, we ended up with five classes
characterized by the following features: (1) lowland subsistence farms with
degradation signs (n= 7), (2) lower slopes, moderate-sized mixed farms
(n= 8), (3) mid-slopes, moderate-sized, primarily grazing/shrubland
(n= 10), (4) upper slopes/highland plateau, mixed farms (n= 22) and
(5) mid-slopes, moderate-sized mixed farms (n= 12).
We also stratified the plots by field typology using the following variables
to define a field type score as follows. (1) Crop: this score is the sum of
the crop types each household is cultivating in one plot. (2) Fertilizer
use: this score distinguishes organic and inorganic fertilizers. (3) Number
of subplots: allowing us to capture the spatial and temporal allocation of
land to crops, crop mixtures, and combination of annual and perennial crops
in intercropping, permanent and seasonal grazing land. (4) Location of
field: the assumption being that fields close to the homestead receive
preferential land management (fertilization, addition of organic amendments,
weeding etc.) when compared to fields that are far away (Tittonell et al.,
2013). (5) Signs of erosion: fields obtained a different score depending on
the severity of the visible signs of erosion. Plots were scored based on the
preceding information and those with a higher score were considered field
type 1 (n= 17), those with a low score were considered field type 3
(n= 19) and those plots with intermediate scores were assigned a field
type 2 (n= 23). It was assumed that field type 1 was the most intensively
managed (i.e., more fertilizer/manure additions) and field type 3 the least managed (i.e., none to
very low fertilizer additions, degraded, low soil C). For a more detailed
description of the stratification process; see Rufino et al. (2016).
Finally, the plots were stratified by vegetation (cover) type: treed/bush
(generally plantations of either Grevillea spp. or
Eucalyptus spp.) (n= 7), perennial grasses/grazing (n= 15) and
annual cropping (n= 37). Initially, the total number of sample plots
was 60, with the number per category based partly on the area covered by each
specific land classification/field type/vegetation type combination and
partly on logistical constraints (i.e., access). One plot however, was
converted into a construction site in late 2013, resulting in only 59 plots
being measured for the full year.
Field soil GHG flux survey
At the 59 field sites (see above and Fig. 1) soil CO2, N2O and
CH4 fluxes were measured weekly, starting at the week of 12 August 2013
through to 12 August 2014 (one full year including two growing seasons) using
non-flow-through non-steady-state chambers (Rochette, 2011; Sapkota et al.,
2014). Given the large number of sites and the difficult access, this
required four 2-person crews sampling 4 days per week. Briefly, four
rectangular (0.35 m × 0.25 m) hard plastic frames per site were
inserted 0.10 m into the ground. Fields planted with annual crops were
ploughed, either using an ox-pulled plough or by hand, twice during this
period, which meant that the frames needed to be removed and then
reinstalled; however, where possible, the chamber frames were left
undisturbed for the entire period. For fields planted with annual crops, the
frames were installed between the rows and were weeded the same week the
farmers weeded the rest of the field. The chambers in the grazing and treed
plots would have included some vegetation (primarily grasses), but these were
kept short (< 5 cm long) by the continual grazing. On each sampling
date, an opaque, vented and insulated lid (0.125 m height) covered with
reflective tape was tightly fitted to the base (Rochette, 2011). The lid was
also fitted with a small fan to ensure proper mixing of the headspace and air
samples (15 L) were collected from the headspace at 0, 15, 30 and 45 min
after deployment using a syringe through a rubber septum. Even with the
insulation and reflective tape on the chambers, the air temperature inside
the chambers still increased during deployment (approximately 10 ∘C
on average), which may slightly affect microbial and root activity in the
soil underneath the chamber. The increase in temperature inside the chamber
headspace would also cause some bias in the calculation of mixing ratios,
which was estimated to be about 3 %.
To increase the number of sites measured while still accounting for the
representativeness of flux measurements in view of expected high spatial
variability of fluxes at field-scale samples were pooled from the four
replicate chambers at each plot (Arias-Navarro et al., 2013) to form a
composite air sample of 60 mL. This method has been found to provide flux
estimates within 8 and 4 % (for CO2 and N2O respectively) of
the estimates calculated by separate sampling, although it is unclear which
is the more accurate depiction of the true mean. Also, as noted by
Arias-Navarro et al. (2013), this precludes the ability to examine
within-site variability. However, we believed that the trade-off between
on-site variability and sampling a broader range of sites was worthwhile
given our aims of characterizing emissions in a way that captured both the
diversity in farming practices and landscape heterogeneity typically found
for many highland regions in eastern Africa. The first 40 mL of the sample
was used to flush a 10 mL sealed glass vial through a rubber septum, while
the final 20 mL was transferred into the vial to achieve an overpressure to
minimize the risk of contamination by ambient air. The gas samples were
analyzed within 10 days of sample collection
for CO2, CH4 and N2O in an SRI 8610C gas chromatograph (2.74 m Hayesep-D column) fitted with a
63Ni-electron capture detector for N2O and a flame ionization
detector for CH4 and CO2 (after passing the CO2 through a
methanizer). The flow rate for the carrier gas (N2) was
20 mL min-1. Every fifth sample analyzed on the gas chromatograph was
a calibration gas (gases with known CO2, CH4 and N2O
concentrations in synthetic air) and the relation between the peak area from
the calibration gas and its concentration was used to determine the CO2,
CH4 and N2O concentrations of the headspace samples.
Calculation of soil GHG fluxes
Soil GHG fluxes were calculated by the rate of change in concentration over
time in the chamber headspace (corrected for mean chamber temperature and
air pressure) after chamber deployment, as shown in Eq. (1).
FGHG =(∂c/∂t)*(M/Vm)*(V/A),
where FGHG is the flux of the GHG in question, ∂c/∂t is the change in concentration over time, M is the molar mass of the
element in question (N for N2O and C for CO2 and CH4),
Vm is the molar volume of gas at the sampling temperature and
atmospheric pressure, V is the volume of the chamber headspace and A is
the area covered by the chamber.
For calculating the change in the GHG concentration over time, nonlinear
models are generally less biased than linear models; however, they also tend
to be very sensitive to outliers (Rochette, 2011). Therefore, when there was
a strong correlation for the nonlinear model (R2 > 0.95) we
used a second-order polynomial; otherwise, we used a linear model. See
Rochette and Bertrand (2008) for details on these models. If however the
R2 < 0.95 for the nonlinear model and < 0.64 for the
linear model, we assumed there was no valid flux measurement and the data
point was thrown out. We validated the data for each chamber measurement by
examining the dynamics of the CO2 concentrations over the 45 min
deployment period. Chambers that experienced a decrease in CO2 greater
than 10 % between any of the measurement times were assumed to have a
leak and all GHG fluxes were discarded unless the decrease occurred in the
last measurement; in this latter case, the flux rate was calculated with the
first three measurement points. In cases where the change in concentration
was lower than the precision of the instrument, we assumed zero flux. The
minimum flux detection limits (Parkin et al., 2012) were 3.61 and
12.46 µg N2O-N m-2 h-1, and 0.015 and
0.051 mg CH4-C m-2 h-1 for the linear and nonlinear models
respectively. Also, negative fluxes for CO2 were deleted while negative
CH4 and N2O fluxes were accepted as uptake of either in upland
soils is feasible. To minimize measurement error and uncertainty, we used
methods that were ranked as either “good” or “very good” for 15 of the 16
criteria selected by Rochette and Eriksen-Hamel (2008), with only the
deployment time exceeding the recommended time by about 10 %. Cumulative
annual fluxes were estimated for each plot using trapezoidal integration
between sampling dates.
Soil analysis
At the beginning of the experiment and for each sampled site, five replicate
soil samples were taken both at 0–5 and 5–20 cm depths with a stainless
steel corer (40 mm inner diameter). Samples were individually placed in
labeled zip-lock bags. All soil material was oven-dried at 40 ∘C
for a week with large clumps being progressively broken by hand. Carbon and
N concentrations were determined on powdered samples using an elemental
combustion system (Costech International S.p.A., Milan, Italy) fitted with
a zero-blank auto-sampler. Soil pH was measured in a 2 : 1 water : soil
solution. Soil texture was determined gravimetrically as described by
van Reeuwijk (2002).
In addition, soil samples were collected periodically (every 2 months) for
determination of inorganic N concentrations. Briefly, the topsoil (0–10 cm
depth) was collected using a soil auger. Three samples from each plot were
collected and pooled to form one composite sample. These were taken back to
the lab and stored (4 ∘C) for less than 1 week before extraction
(1 : 5 soil : solution w:v ratio) with 2M KCl. Extracts
were kept frozen until analyzed. Analysis for NO3-N was done via
reduction with vanadium, with development of color (540 nm) using sulfanilic
acid and naphthyl ethylenediamine and
measurement of light absorbance on an Epoch microplate spectrophotometer
(BioTek, Winooski, Vermont, USA). The NH4-N concentrations were measured
using the green indophenol method (660 nm) using the same spectrophotometer
(Bolleter et al., 1961).
Environmental data
Environmental data were collected at two sites, one in the uplands
(35.056∘ E, 0.351∘ S, 1676 m a.s.l.) and the other in the
lowlands (34.988∘ E, 0.308∘ S, 1226 m a.s.l.). Each of
the two weather stations was installed at a farm where we also measured GHG
emissions. Air temperature was measured using a Decagon ECT (Decagon Devices,
Pullman, Washington, USA) air temperature sensor (measurement every 5 min),
while precipitation data were collected with a Decagon ECRN-100
high-resolution, double-spoon tipping bucket rain gauge. Soil moisture and
temperature were measured using a Decagon MPS-2 water potential and
temperature sensor. Data were logged on a Decagon Em50 data collection system
and downloaded periodically (typically monthly).
Air temperature, soil temperature and soil moisture (5 cm depth) were also
measured at each site at the time of gas sampling using a ProCheck handheld
datalogger outfitted with a GS3 sensor (Decagon Devices).
Plant production
To estimate crop yields and crop N content of annual crops in the study area,
we randomly selected nine of the study plots including annual crops. There
were four plots with maize, four with sorghum and one with green grams
(Vigna radiata (L.) R. Wilczek). In June 2013, all the plants within
a 2.5 m × 2.5 m square near the center of the field (i.e., to
avoid edge effects) were harvested and the grains were removed from the
plant. Both the stover and grains were dried for 48 h at 60 ∘C and
then weighed. A subsample of the grains was then ground and analyzed for C
and N content on the same elemental combustion system described above for
soil analysis. Yield-scaled N2O emissions (g N2O-N kg-1
aboveground N uptake) were calculated for each site by dividing the
cumulative emissions of the growing season by the grain yields. The growing
season lasted from mid-March to August, which corresponds to the period
between preparation of fields for the long rains through harvest and up to
the preparation of the fields for the following growing season. No estimate
of crop yields (or yield-scaled emissions) was done for the second growing
season.
CO2 (mg C-CO2 m-2 h-1), CH4
(µg C-CH4 m-2 h-1) and N2O
(µg N2O-N m-2 h-1) fluxes over 1 year (August 2013
through July 2014) as well as precipitation (mm), soil water content (SWC) at
5 cm depth (m3 m-3) and inorganic N (IN = NO3 + NH4)
soil concentrations for 59 different fields in western Kenya by land class as
well as soil temperature (∘C) by topography. Note: vertical dotted
lines correspond to planting and vertical dashed lines correspond to
harvesting of annual crops. (Land class 1 is degraded lowland farms; class 2
is degraded farms, lower slopes; class 3 is mid-slopes, grazing; class 4 is
upper slopes/plateau, mixed farms; and class 5 is mid-slopes moderate-sized
farms.) SEM for the various gases ranged from 2.1 to 57.4 for CO2 flux,
0.0 to 106.6 for CH4 flux and 0.2 to 45.6 for N2O flux with the highest
variability occurring between 20 and 27 March 2014 for CO2 and N2O
while the highest variability in CH4 flux occurred during the week beginning 4
August 2013. For all gases the greatest variability occurred in land class 3
(n= 10).
Comparison of mean (±1 SEM) cumulative soil CO2-C,
CH4-C and N2O-N fluxes for 4 weeks during the dry season (February
2014) and rainy season (April 2014) for differently managed sites in western
Kenya.
GHG
Dry season
Rainy season
P values
Annual crop
Other
Annual crop
Other
Season
Managementa
Interaction
(n= 42)
(n= 17)
(n= 42)
(n= 17)
CO2-C (g m-2)
19.4 ± 2.8
20.0 ± 3.8
76.6 ± 5.0
62.7 ± 5.7
< 0.0001
0.393
0.204
CH4-C (mg m-2)
-7.4 ± 4.4
2.2 ± 6.7
-3.7 ± 3.6
-15.0 ± 3.5
0.610
0.873
0.044
Fertilized
Not fertilized
Fertilized
Not fertilized
(n= 16)
(n= 43)
(n= 16)
(n= 43)
N2O-N (mg m-2)
0.52 ± 0.23
1.44 ± 0.40
9.87 ± 4.23
5.35 ± 1.14
< 0.0001
0.562
0.112
a The term management refers to plowing versus no plowing for the
CO2 and CH4 fluxes and to fertilized versus no fertilizer
application for the N2O fluxes.
Statistical analysis
Correlations between GHG fluxes and soil properties were tested using
Pearson's correlation. The cumulative field fluxes for a 4-week period during
the dry season were compared to cumulative fluxes for a 4-week period during
the rainy season using ANOVA (AOV in RStudio version 0.98.953) with the
season and management practices (ploughed versus not ploughed for CO2
and fertilized versus not fertilized for N2O) as fixed factors along
with the two-way interaction terms. Cumulative field annual GHG fluxes
were compared with ANOVA using an unbalanced design and cover type, land
class and field type as fixed factors. In all cases, the distributions of
flux measurements were tested for normality using the Shapiro–Wilk test.
Cumulative soil N2O fluxes were not normally distributed and were
transformed using the natural log.
Box and whisker plots of cumulative annual fluxes of CO2
(Mg CO2-C ha-1 yr-1), CH4
(kg CH4-C ha-1 yr-1) and N2O
(kg N2O-N ha-1 yr-1) from 59 different fields in western
Kenya split by land class, field type or vegetation type. (Land class 1 is
degraded lowland farms; class 2 is degraded farms, lower slopes; class 3 is
mid-slopes, grazing; class 4 is upper slopes/plateau, mixed farms; and class
5 is mid-slopes moderate-sized farms.) Field type is based on Rufino et
al. (2016), with field type 1 being the most highly managed and type 3 being
the least managed plots. Different lower-case letters indicate significant
differences between treatments, while a lack of letters indicates no
difference between any of the treatments.
Discussion
The soil CO2 fluxes were seasonal and it was thought that management
events, such as ploughing or fertilizer applications, would affect the GHG
flux rates throughout the year. However, during the commencement of the rainy
season in March 2014, which coincided with tilling, the ploughed fields did
not show significant increases in soil respiration rates beyond the
enhancement in soil CO2 flux due to rewetting that was also measured in
untilled fields. Increased soil respiration due to ploughing is however
short-term, usually lasting less than 24 h (Ellert and Janzen, 1999;
Reicosky et al., 2005), so because the chambers needed to be removed before
ploughing and were not reinstalled until sites were revisited a week later,
the ploughing-induced increase in soil respiration was probably not fully
captured. Also, root respiration, which at seeding accounts for 0 % of
soil CO2 fluxes but can increase to around 45 % of fluxes (Rochette
et al., 1999), may also result in greater CO2 fluxes during the growing
season for the annual cropping systems. However, the increase in soil
CO2 fluxes from dry to growing season in annual crops was similar to the
increase experienced in the other vegetation types (Table 3; P= 0.39). It
is therefore likely that the low yields for the annual crops corresponded
with poor root growth and low root respiration rates.
Cumulative soil CO2 fluxes (2.7 to
14.0 Mg CO2-C ha-1 yr-1), were well within the range of
other African studies (Table 1) and were not related to land class or field
type, although the higher soil respiration rates from grazing land was
inconsistent with a previous study that found similar rates between perennial
tropical grasslands, croplands and tree plantations (Mapanda et al., 2010).
However, because we did not differentiate between root and microbial
respiration components, we cannot exclude that the continual vegetation cover
in the grazing plots enhanced the root respiration over the year to a higher
extent than in the annual crops and treed plots. It is important to keep in
mind though, that these CO2 emissions were the result of root
respiration and microbial decomposition of organic matter, since plants were
purposely excluded (except for some short grasses; see methods). In order to
obtain the full GHG balance, both photosynthesis and aboveground vegetation
respiration should be considered.
Methane was generally taken up by these upland soils, with rates varying
through the year (Fig. 2b). During August 2013, the soils were sinks for
CH4, however as the soils dried, the emission/uptake rates became more
erratic until the long rains started again in late March 2014. The CH4
flux at the soil–atmosphere interface is the balance between simultaneous
production and consumption of CH4 in different microsites in the soil
profile (Le Mer and Roger, 2001). Thus, the low rates of atmospheric CH4
uptake during the long rains may be caused by greater soil CH4
production due to higher soil moisture and anaerobic conditions at depth
(e.g., Butterbach-Bahl and Papen, 2002) overriding the existing methanotropic
activity; alternatively, the higher water content may have limited the
CH4 diffusion from the atmosphere into the soil.
The CH4 uptakes observed in these sites were consistent with previous
studies in upland agricultural soils and indicate that soils of smallholder
farms are sinks for atmospheric CH4 (Le Mer and Roger, 2001). There were
no differences between field types; however, there were differences between
cover types and land classes, as the grazing plots took up less CH4 than
treed plots and land class 1 (small lowland mixed degraded farms) took up
less than land class 2 (moderate-sized farms with signs of degradation on
lower slopes; see Fig. 3). The difference between cover types is consistent
with previous studies that found that forest soils were greater CH4
sinks than agricultural soils (MacDonald et al., 1996; Priemé and
Christensen, 1999) and high degrees of degradation in land class 1 was likely
responsible for reduced CH4 oxidation rates
The N2O flux rates remained below 20 µg m-2 h-1,
with the exception of the onset of the rainy season in March 2014 (Fig. 3).
According to Linn and Doran (1984) maximum aerobic activity occurs at
approximately 60 % water-filled pore space (approximately 40 %
water-holding capacity (WHC) for our study), above which
anaerobic processes such as denitrification can occur. The soils in the study
area were typically drier than this threshold suggesting that N2O fluxes
were limited by a lack of anaerobic conditions and that the increase in soil
water content was responsible for the increases in N2O fluxes during
March 2014. However, soil moisture was greater than 35 % WHC during
September/October 2013 and March 2014, but it was only in the latter period
large increases in N2O fluxes were observed. The high soil moisture
levels in March coincided with an increase in inorganic N likely caused by
drying and rewetting (Birch, 1960), which can also stimulate N2O fluxes
(Butterbach-Bahl et al., 2004; Davidson, 1992; Ruser et al., 2006).
Commencement of the rainy season was also when farmers fertilized, although
application rates were low (1–25 kg N ha-1) and did not have a
detectable effect on soil inorganic N concentrations, or N2O emissions
(Table 3).
The inability to discern between fertilized and unfertilized plots suggests
that the differences in soil fertility and primary productivity were too low
to have a noticeable effect on the availability of substrate for microbial
activity and the associated GHG emissions. Alternatively, it is possible that
the sensitivity of the monitoring approach was not enough to catch
differences between fields. For instance, the fixed sampling frequency may
have caused us to miss some short-lasting emission peaks following
fertilization, resulting in an underestimation of cumulative emissions.
However, sampling during an emission pulse would result in an overestimate of
emissions due to an extrapolation bias. Previous studies have found that
weekly sampling resulted in an average uncertainty of ±30 % of the
“best estimate” (Barton et al., 2015; Parkin, 2008) and that this
uncertainty changes with the coefficient of variation in measured emission
rates. However, the fertilizer was applied at a low rate
(< 25 kg N ha-1). Application of synthetic fertilizers up to
70 kg N ha-1 at planting in the region had no detectable effect on
annual N2O emissions (Hickman et al., 2015), while another nearby study
found no effect of N fertilization on annual N2O emissions (Rosenstock
et al., 2016), suggesting that our weekly sampling did not miss relevant
N2O/GHG pulses.
The large increase in N2O emission rates after soil rewetting (April
2014) in land class 3 (mid-slopes, grazing land; Fig. 2) was primarily due to
2 (of 10) plots, both located on the same farm that emitted around 4–6 times
more N2O than the rest of the land class 3 plots and 15–23 times more
N2O than the average for all other plots. The reason for the much higher
fluxes after the rewetting compared to other sites is not yet understood as
the topsoil C and N contents were 1.45 and 0.12 % respectively, well
within the range of values for that land class (Table 2). The presence of
livestock on these plots could have resulted in addition of N through either
urine or manure deposition, causing these pulses of N2O. However, the
presence of N2O emission hotspots in general is quite common as
denitrification activity can vary dramatically across small scales (Parkin,
1987).
Annual N2O fluxes were low (< 0.6 kg N ha-1 yr-1)
compared with other tropical and subtropical studies, such as a fertilized
field in Brazil (Piva et al., 2014) or China (Chen et al., 2000), with fluxes
up to 4.3 kg N2O-N ha-1 yr-1. On the other hand, our
results were similar to previous studies in low input African agro-ecosystems
(Table 1). The low cumulative fluxes were most likely a result of low
substrate (inorganic N) availability, in addition to low soil moisture
limiting denitrification through much of the year. Similar to the CO2
fluxes, the cumulative N2O fluxes did not differ by cover type, field
type or by land class. However, it is possible that differences between the
classes could be too small to detect given the low cumulative N2O
fluxes, high microsite variability typical of N2O fluxes (Parkin, 1987)
and weekly sampling (Barton et al., 2015; Parkin, 2008).
As shown in the Supplement, maximum N2O and
CO2 fluxes (i.e., flux potentials) from 5 cm soil cores differed by
land class (Fig. S1 in the Supplement), suggesting that there is the
potential for differences in field emissions as well. However, these
potentials in the field appeared to be limited by climatic conditions (i.e.,
lack of precipitation). Also, the maximum N2O flux rates observed within
the soil core study were correlated (Spearman's rank test) with the
cumulative annual fluxes at the field sites (ρ= 0.399, P= 0.040)
while CO2 fluxes followed a similar trend (ρ= 0.349, P= 0.075).
The CH4 fluxes from the soil cores however, were not correlated with
measured flux at the field sites (ρ= -0.145, P= 0.471; see the
Supplement). Therefore, although incubations should not be used to predict
baseline emissions in the field, they may be used as a quick and relatively
inexpensive method to identify locations with potential for high soil
N2O and CO2 fluxes (i.e., emission hotspots).
There are additional sources of uncertainty associated with the sampling
methods (chamber architecture, instrumentation sensitivity etc.). According
to Levy et al. (2011), the uncertainty of the methods then would be about
20 %, which, when combined with the uncertainty around the weekly
sampling, would be about 50 %. Although this may sound high, this is
similar to the majority of other studies (e.g., see Helgason et al., 2005)
measuring GHG emissions and better than many of the studies so far in Africa
(Table 1).
Our study showed no detectable differences in GHG fluxes between the
different field types, contrary to our expectations. We had anticipated
differences in GHG fluxes because of differences among field types in input
use, food production, partial N and C balances, and soil fertility as
previously reported in the region (Tittonell et al., 2013) – these variables
often affect soil GHG fluxes (Buchkina et al., 2012; Jäger et al., 2011).
We further hypothesized that land class and cover type would also have
significant effects on soil GHG fluxes since a significant amount of the
variability in soil CO2 fluxes in agro-ecosystems can be explained by
NDVI (Sánchez et al., 2003) and cover type (Mapanda et al., 2010). We
found, however, no clear effect of field or land type on soil GHG fluxes.
Tittonell et al. (2013) reported important differences between field types
only at each farm individually (Tittonell et al., 2013), which in our case
might have resulted in greater within-type variation that masked differences
between the field types. Moreover, the small differences in the degree of
inputs and labor may have not been enough to provoke distinct GHG fluxes
because the whole region/study site is characterized by low nutrient
availability. For example, manure inputs have previously been found to
increase soil C content (Maillard and Angers, 2014), but the inputs in our
study area were very low (4–6 wheelbarrow loads or approximately
95 kg C ha-1) and probably not enough to cause field-level
differences. Further, considering that a previous study found that N is being
rapidly mined from soils in the Lake Victoria basin (Zhou et al., 2014), it
is likely that soil C is also being lost across the landscape. As most of
this area has been converted from natural forests, and forests generally have
higher SOC stocks than croplands (Guo and Gifford, 2002), time since
conversion could play a larger part in determining the SOC content, which
could mask any effects that management activities have on soil respiration
rates in these low input systems.
Crop yields from the annual cropping systems (100–750 kg ha-1 for one
growing season) were at the lower end of the range for rain-fed smallholder
farms (600 to 3740 kg ha-1) previously reported across SSA (Adamtey et
al., 2016; Sanchez et al., 2009; Tittonell et al., 2008). The farmers in our
study complained of poor timing of the rains that caused low yields. However,
poor timing of the rains tends to be common in eastern Africa, with
estimations that 80 % of growing seasons have critical water shortages
during flowering and grain filling, further resulting in low yields (Barron
et al., 2003). These studies therefore suggest that low yields are common
within this region. Increased nutrient inputs and improved management such as
rainwater harvesting (Lebel et al., 2015) are required to increase yields
(Quiñones et al., 1997), which may also result in increased GHG fluxes.
However, previous studies have found that increases in GHG fluxes tend to be
lower than the corresponding increase in crop yields following addition of
nutrients (Dick et al., 2008), resulting in lower GHG intensities
particularly at lower application rates (Shcherbak et al., 2015). Another
study in western Kenya found that fertilizer applications up to
100 kg N ha-1 provoked no detectable increase in soil N2O
emissions but did increase grain N contents (Hickman et al.,
2014a). The mean
yield-scaled fluxes calculated for the eight maize and sorghum subsamples was
12.9 g N2O-N kg-1 aboveground N uptake (range = 1.1 to
41.6), approximately 54 % higher than the 8.4 g N2O-N kg-1
aboveground N uptake for plots fertilized at 180–190 kg N ha-1 in a
European meta-analysis (van Groenigen et al., 2010). These data further
suggest that improved agronomic performance through better soil, nutrient and
water management in eastern Africa has potential in lowering or at least
maintaining yield-scaled fluxes while increasing food production from
smallholder farms in SSA.