Intercropping with legumes is an important component of climate-smart
agriculture (CSA) in sub-Saharan Africa, but little is known about its
effect on soil greenhouse gas (GHG) exchange. A field experiment was
established at Hawassa in the Ethiopian rift valley, comparing nitrous oxide
(N2O) and methane (CH4) fluxes in minerally fertilized maize (64 kg N ha-1) with and without Crotalaria (C. juncea) or lablab (L. purpureus) as intercrops
over two growing seasons. To study the effect of intercropping time,
intercrops were sown either 3 or 6 weeks after maize. The legumes were
harvested at flowering, and half of the aboveground biomass was mulched. In
the first season, cumulative N2O emissions were largest in 3-week
lablab, with all other treatments being equal to or lower than the fertilized
maize mono-crop. After reducing mineral N input to intercropped systems by
50 % in the second season, N2O emissions were comparable with the
fully fertilized control. Maize-yield-scaled N2O emissions in the first
season increased linearly with aboveground legume N yield (p=0.01), but
not in the second season when early rains resulted in less legume biomass
because of shading by maize. Growing-season N2O-N emission factors
varied from 0.02 % to 0.25 % in 2015 and 0.11 % to 0.20 % in 2016 of the
estimated total N input. Growing-season CH4 uptake ranged from 1.0 to
1.5 kg CH4-C ha-1, with no significant differences between
treatments or years but setting off the N2O-associated emissions by up
to 69 %. Our results suggest that leguminous intercrops may increase
N2O emissions when developing large biomass in dry years but, when
mulched, can replace part of the fertilizer N in normal years, thus
supporting CSA goals while intensifying crop production in the region.
Introduction
With a rapidly increasing population and declining agricultural land in
sub-Saharan Africa (SSA), increasing productivity per area (intensification)
is the only viable alternative for producing sufficient food and feed
(Hickman et al., 2014a). Intensification entails the increased use of inorganic
fertilizers, which may cause emissions of nitrous oxide (N2O). Abundant
ammonium (NH4+) may also reduce the soil CH4 sink by
competing with CH4 for the active binding site of methane
monooxygenase, the key enzyme of CH4 oxidation (Bédard and Knowles,
1989). Climate-smart
agriculture (CSA) is an approach to transform agricultural practices in a
changing climate with the triple objective of increasing agricultural
productivity, building climate resilience and reducing greenhouse gas (GHG) emissions
(Neufeldt et al., 2013). Potential CSA practices include improved water
management, use of improved livestock and crop species, conservation
farming, agroforestry, and crop diversification as well as improved soil
fertility management practices (Makate et al., 2019). Legume intercropping
is one way to diversify and intensify cropping systems while contributing
to the food and nutritional security of smallholder farmers (de Jager et al.,
2019). Legume intercropping can also be used to add biologically fixed
nitrogen to soils, build soil carbon and improve soil quality
(Bedoussac et al., 2015). As such, it is a powerful approach to reduce
greenhouse gas emissions by replacing inorganic fertilizers and GHG
emissions associated with their production. However, GHG measurements in SSA
crop production systems in general, and in legume intercropping systems in
particular, are scarce and proof of concept for the mitigation potential of
legume intercropping is missing (Kim et al., 2016; Hickman et al., 2014b).
Moreover, modeling studies predict significant negative impacts of climate
change on crop productivity in Africa (Blanc and Strobl, 2013) and it is
largely unknown how these and the countermeasures taken to maintain
agricultural productivity will affect GHG emissions.
Crop production is a major source of N2O, the third-most important
anthropogenic GHG after CH4 and CO2 (IPCC, 2014). Emission rates
of N2O reported for SSA crop production so far are low (Kim et al.,
2016) owing to low fertilization rates, but they may increase with increasing
intensification. Inorganic and organic N added to soil provide ammonium
(NH4+) and nitrate (NO3-) for nitrification and
denitrification, respectively, which are the two main processes of microbial
N2O production in soil (Khalil et al., 2004). The rate of N2O
formation depends greatly on the extent and distribution of anoxic
microsites in soils, which is controlled by moisture, texture and the
distribution of decomposable organic matter and NH4+ fueling
heterotrophic and autotrophic respiration, respectively (Schlüter et
al., 2019; Wrage-Mönnig et al., 2018). The magnitude of soil N2O
emissions depends on O2 availability as controlled by soil moisture and
respiration, the availability of mineral N and readily decomposable C
(Harrison-Kirk et al., 2013), and soil pH (Russenes et al., 2016), all of
which are affected by management practices. Other important factors are soil
type (Davidson et al., 2000) and temperature (Schaufler et al., 2010). The
N2O yield of nitrification and the production and reduction of N2O
during denitrification are further controlled by soil pH (Bakken et al.,
2012; Nadeem et al., 2019) and by the balance between oxidizable carbon and
available NO3- (Wu et al., 2018). Mulching and the incorporation of
crop residues lead to increased N mineralization and respiratory O2
consumption, thus potentially enhancing N2O emissions both from
nitrification and denitrification (Drury et al., 1991) if soil moisture is
sufficient to support microbial activity and restrict O2 diffusion into
the soil. Accordingly, N2O emissions are variable in time, often
following rainfall events (Schwenke et al., 2016).
Crop diversification by combining legumes with cereals, both in rotation and
intercropping, enhances overall productivity and resource use efficiency if
managed properly (Ehrmann and Ritz, 2014). Intercropping maize with grain
legumes is common in the Great Rift Valley of Ethiopia and a central component in
CSA (Arslan et al., 2015). In low-input systems common to the Great Rift Valley,
the integration of legumes with cereals diversifies the produce and improves
farm income and nutritional diversity for smallholder farmers (Sime and
Aune, 2018). Moreover, by partially replacing energy-intensive synthetic N,
intercropping with legumes may increase the sustainability of the
agroecosystem as a whole (Carranca et al., 2015). However, to make the best use
of the resource complementarity of intercrops and main crops, the planting
time of the intercrop has to be optimized so that the maximum nutrient
demand of the two components occurs at different times (Carruthers et al.,
2000). The timing of intercrops could also affect N2O emissions if N
mineralization from legume residues is poorly synchronized with the N
requirement of the cereal crop. This can be counteracted by reducing mineral
N additions to intercropping systems, but the timing of the intercrop
(sowing date relative to the cereal crop) remains an issue that has, to the
best of our knowledge, not been studied with regard to N2O emissions.
Intercropping and mulching may also affect the soil's capacity to oxidize
atmospheric CH4 as abundant NH4+ might inhibit methanotrophs
(Laanbroek and Bodelier, 2004). However, field studies with the incorporation of
leguminous or non-leguminous catch crops have been inconclusive (e.g.,
Sanz-Cobena et al., 2014). In a meta-study on CH4 fluxes in non-wetland
soils, Aronson and Helliker (2010) concluded that N inhibition of CH4
uptake is unlikely at fertilization rates below 100 kg N ha-1 yr-1
and that, much to the contrary, N addition may stimulate CH4 uptake in
N-limited soils. Ho et al. (2015) found that the incorporation of organic
residues stimulated CH4 uptake even in fairly N-rich Dutch soils. Apart
from providing reactive nitrogen to the soil, leguminous intercrops may also
affect CH4 uptake by lowering soil moisture and thus increasing the
diffusive flux of atmospheric CH4 into the soil. For instance, Wanyama et al. (2019) found that CH4 uptake in soil was negatively correlated
with mean annual water-filled pore space in a study on different land use
intensities in Kenya.
In a review on N2O fluxes in agricultural legume crops, Rochette and
Janzen (2005) concluded that the effect of legumes on N2O emissions is
to be attributed to the release of extra N by the rhizodeposition of soluble N
compounds and the decomposition of nodules rather than to the process of
nitrogen fixation itself. Intercropped legumes may thus affect N2O
emissions in two ways: by directly providing organic N or by modulating the
competition between plants and microbes for soil N, for example by acting as
an additional N sink prior to nodulation. Compared to mineral fertilizers, N
supply from biological fixation is considered environmentally friendly as it
can potentially replace industrially fixed N (Jensen and Hauggaard-Nielsen,
2003), provided that crop yields remain the same. However, combining easily
degradable crop residues with synthetic N can lead to elevated N2O
emissions (Baggs et al., 2000), potentially compromising the environmental
friendliness of intercropping in CSA. It is well known that the effect of
crop residues on N2O emission depends on a variety of factors such as
residue amount and quality (C : N ratio, lignin and cellulose content), soil
properties (e.g., texture), placement mode (mulching, incorporation), and soil
moisture and temperature regimes (Sanz-Cobena et al., 2014; Li et al.,
2016). So far,
only a limited number of studies address the
effect of legume intercropping on N2O emissions and CH4 uptake in
SSA crop production (Baggs et al., 2006; Millar et al., 2004; Dick et al.,
2008).
The main objective of the present study was to evaluate the effects of
forage legume intercropping with maize on N2O and CH4 emissions
during maize production in the Ethiopian Great Rift Valley. We hypothesized that
forage legumes increase N2O emissions and decrease CH4 uptake
depending on aboveground biomass, legume species and sowing date; legumes
intercropped 3 weeks after sowing maize would result in higher yields
than those intercropped 6 weeks after maize and lead to increased N2O
emissions if used with full-dose mineral fertilization. With late
intercropping, legume yields would be suppressed, having little to no effect
on N2O emissions. Hence, choosing legume species and the sowing date as well as
accounting for potential N inputs from legume intercrops could allow for a
better management of legume intercropping in SSA with reduced GHG emissions.
Materials and methodsStudy area
The field experiment was conducted during 2 years (2015–2016) at the
Hawassa University Research Farm (7∘3′3.4′′ N, 38∘30′20.4′′ E) at an altitude of 1660 m a.s.l. The mean annual rainfall is 961 mm, with a bimodal pattern. The rainy season between June and October
accounts for close to 80 % of the annual rainfall. Average maximum and
minimum monthly temperatures are 27.4 and 12.9 ∘C, respectively. The
soil is clay–loam (46 % sand, 26 % silt, 28 % clay) derived from
weathered volcanic rock (Andosols), with a bulk density of 1.25±0.05 g cm-3, a total N content of 0.12 %, an organic C content of
1.64 %, an available Olsen P content of 175 mg kg-1 and a pHH2O
of 6.14.
Experimental design and treatments
Experimental plots (20 m2) were laid out in a completely randomized block
design (RCBD) with four replicates and six treatments: unfertilized maize
mono-crop (M-F), fertilized maize mono-crop (M + F), Crotalaria intercropping
3 (M + Cr3w) and 6 (M + Cr6w) weeks after sowing maize, and lablab
intercropping 3 (M + Lb3w) and 6 (M + Lb6w) weeks after sowing maize
(Table 2). The seed bed was prepared in both years by a mold board plow to a depth
of 0.25 m followed by harrowing by a tractor. A hybrid maize variety, BH-540
(released in 1995), was sown on 30 May 2015 and 7 May 2016. Maize was
planted at a density of 53 333 plants ha-1. Following national
fertilization recommendations, diammonium phosphate (18 kg N, 20 kg P) was
applied manually at planting and urea (46 kg N) was applied 4 weeks after sowing
maize to all treatments except for the unfertilized control. The N
fertilization rate was halved for the intercropping treatments in the 2016
season to account for carryover of N from forage legumes grown in the
previous year. The forage legumes Crotalaria (C. juncea) and lablab (L. purpureus) were planted
between maize rows at a density of 500 000 and 250 000 plants ha-1,
respectively.
The aboveground forage legume biomass was harvested at flowering, and half
of it was removed. The remaining half was spread manually between the maize
rows after cutting the fresh biomass into ∼10 cm pieces.
The 3- and 6-week intercrops were mulched on 27 July and 4 September 2015
and 2 August and 8 September 2016. As the mulching was done plot-wise,
plots within the same treatment received different amounts of mulch
depending on the legume yield of each plot. In the 2016 growing season, all
treatments were kept on the same plots as in 2015, capitalizing on
plot-specific N and C input from previous mulch. Aboveground dry matter
yield was determined by drying a subsample at 72 ∘C for 48 h, and C
and N contents were measured by an element analyzer.
N2O and CH4 fluxes and ancillary data
GHG exchange was monitored weekly at different spots within the middle maize
row by static, non-vented chambers (Rochette and Eriksen-Hamel, 2008). We used
custom-made aluminum chambers with an internal volume of 0.144 m3 and a
cross-sectional area of 0.36 m2 (Fig. S1 in the Supplement). The chambers were pushed
gently ∼3 cm into the soil, including two to five legume plants in
the headspace. The septum was left open during deployment; once the chamber
was inserted into the soil, the septum was closed and the base of the
chamber was sealed around the circumference using moist clay.
Sampling was carried out weekly during the period June to September 2015
and May to September 2016 on 15 and 17 sampling dates, respectively. Gas
samples were collected between 09:00 and 14:00 EAT (UTC+3). For each flux estimate,
four gas samples were drawn from the chamber headspace at 15 min intervals,
starting immediately after deployment. Samples were taken with a 20 mL
polypropylene syringe equipped with a three-way valve. Before transferring the
sample to a pre-evacuated 10 cc serum vial crimp-sealed with butyl septa,
the sample was pumped five times in and out of the chamber to obtain a
representative sample. Overpressure in the septum vials was maintained to
protect the sample from atmospheric contamination during storage and
shipment to the Norwegian University of Life Sciences, where the samples
were analyzed by gas chromatography. Helium-filled blank vials were included
to evaluate contamination, which was found to be less than 3 % of ambient.
All samples were analyzed on a gas chromatograph (GC; model 7890A, Agilent, Santa Clara, CA,
USA) connected to an auto-sampler (GC-Pal, CTC, Switzerland). Upon piercing
the septum with a hypodermic needle, ca. 1 mL of sample is transported via a
peristaltic pump (Gilson minipuls 3, Middleton, W1, USA) to the GC's
injection system before reverting the pump to back-flush the injection
system. The GC is configured with a Poraplot U wide-bore capillary column
connected to a thermal conductivity, flame ionization and electron
capture detector (ECD) to analyze CO2, CH4 and N2O, respectively.
Helium 5.0 was used as a carrier and Ar/CH4 (90 : 10 vol / vol) as a makeup gas
for the ECD. For calibration, two certified gas mixtures of CO2,
N2O and CH4 in helium 5.0 (Linde-AGA, Oslo, Norway), one at
ambient concentrations and one ca. 3 times above ambient, were used. A
running standard (every tenth sample) was used to evaluate drift of the ECD
signal. Emission (CO2, N2O) and uptake (CH4) rates were
estimated by fitting linear or quadratic functions to the observed
concentration change in the chamber headspace and converting them to area
flux according to Eq. (1):
FGHG(µgm-2h-1)=dcdt⋅VcA⋅MnVn⋅60,
where FGHG is the flux (µg N2O-N m-2 h-1 in the case
of N2O; µg CH4-C in the case of CH4), dcdt
the rate of change in concentration over time (ppm min-1), Vc the
volume of the chamber (m3), A the area covered by the chamber (m2),
Mn the molar mass of the element in question (g mol-1) and Vn the
molecular volume of gas at chamber temperature (m3 mol-1). A
quadratic fit was only used in cases in which N2O accumulation in the
chamber showed a convex downwards and CH4 uptake a convex upwards trend
(i.e., decreasing emission or uptake rates with time) to estimate time-zero
rates. R2 values for fluxes >3µg N2O-N or
CH4-C m-2 h-1 were generally ≥0.85; fluxes <3µg had lower R2 values in some cases but were still included to
capture periods with low flux activity. Fluxes were cumulated plot-wise by
linear interpolation for each growing season.
In 2016, soil moisture and temperature at 5 cm of depth were monitored hourly
using data loggers (Decagon EM50, Pullman, WA, USA) together with ECH2O
sensors (Decagon) for volumetric soil water content (VSWC) and temperature
at five points across the experimental field. The sensors were placed in the
experimental field at five random spots. No data are available for the 2015
season due to equipment failure.
Soil bulk density was measured at 10 random spots in the experimental field
using 100 cm3 steel cylinders and drying them at 105 ∘C for 24 h. To calculate daily water-filled pore space values for the 2016
growing season, a particle density of 2.65 g cm-3 was assumed:
WFPS=VSWC/1-BDPD⋅100,
where WFPS is the water-filled pore space, VSWC the volumetric soil water content,
BD the bulk density and PD the particle density. Daily rainfall data were
collected using an on-site rain gauge.
Estimating N inputs and N2O emission factors
N input from forage legume crop residues was estimated from measured
aboveground dry matter yield, its N content and the amount of mulch
applied. To account for belowground inputs a shoot-to-root ratio of 2 was
assumed for both Crotalaria and lablab (Fageria et al., 2014). Dry matter
yields of forage legumes differed greatly depending on sowing time, with
yields generally larger in the 3-week than in the 6-week intercropping. Also, forage
legumes sown 3 weeks after maize grew faster and were harvested and
mulched earlier than those sown 6 weeks after maize. We assumed that
50 % of the legume N (mulched and belowground) was released during the
growing season but reduced this amount to 30 % for the aboveground
component (mulch) of the 6-week treatments to account for the later mulching
date. The proportions becoming available during the growing seasons are
conservative estimates based on Odhiambo (2010), who reported that about
50 % of N contained in Crotalaria, lablab and Mucuna was released during a
16-week incubation experiment at optimal temperature and moisture
conditions. Placing litter bags into dry surface soil, Abera et al. (2014)
found that legume residues decomposed rapidly under in situ conditions in the
Ethiopian Great Rift Valley, releasing up to 89 % of the added N within 6 months.
For the second year, 50 % of the N left after the growing season (belowground
and aboveground) was assumed to become available, on top of the N input from
the newly sown forage legumes. Dry matter yields of forage legumes and
estimated N input for the 2 years are presented in Table 1.
N inputs from forage legumes and fertilization per treatment. Shown
are mean values (n=4± standard error).
LegumeDMYAbovegroundBelowgroundN fromMineral NCarryoverdTotal NN yieldaN yieldbmulchcinputkg N ha-12015 Crotalaria3w1516±18353.3±6.417.7±2.126.6±3.26475.86w345±6512.1±2.34.0±0.86.1±1.16466.4Lablab 3w2221±34096.8±14.832.3±4.948.4±7.46482.96w467±13720.3±6.06.8±2.010.2±3.06467.72016 Crotalaria3w468±8516.4±3.05.47±1.08.21±1.53211.1±1.356.86w65±442.3±1.50.75±0.51.13±0.8322.5±0.536.4Lablab 3w1256±22154.7±9.618.25±3.227.4±4.83220.2±3.197.86w186±608.1±2.62.70±0.94.06±1.3324.2±1.243.0
a N content of Crotalaria and lablab was 3.51 and 4.36 %,
respectively, measured in two representative samples. DMY: dry matter yield.
b Assuming a shoot-to-root ratio of 2 and an average belowground N input from the standing legumes of 50 % during the growing season.
c Returning half of the aboveground yield as mulch; assuming an average
N release of 50 % and 30 % for 3-week and 6-week treatments,
respectively, during the growing season.
d Assuming that 50 % of the remaining N becomes available in the
following cropping season.
Treatment-specific, growing-season N2O emission factors were calculated
as
EFN2O=(N2Otreatment-N2Ocontrol)Ninput⋅100,
where EFN2O is the N2O emission factor (% of N input lost as
N2O-N), N2Otreatment the cumulative N2O-N emission (from
sowing to harvest) in the fertilized and intercropped treatments,
N2Ocontrol the emission from the M-F treatment (background emission)
and Ninput the estimated total input of N.
Non-CO2 GHG emissions were calculated as CO2 equivalents, balancing
the cumulative seasonal N2O-N emissions with CH4 uptake on the plot
level and averaging them for treatments (Table 2, Fig. 5).
Grain yields, growing-season N2O emission factors, and
non-CO2 GHG emissions associated with N2O, CH4, and N2O
emission intensities for fertilized treatments with and without legume
intercropping during 107 d in 2015 and 123 d in 2016. N input was
estimated as outlined in Table 1. Shown are mean values (n=4± standard error). Different letters indicate statistical differences at p<0.05.
Maize grain yield was determined by manually harvesting the three middle
rows (to avoid border effects) of each plot and was standardized to
12.5 % moisture content using a digital grain moisture meter. All values
were extrapolated from the plot to the hectare. To estimate yield-scaled
N2O emissions (g N2O-N t-1 grain yield), cumulative
emissions were divided by grain yield.
Statistical analysis
Differences in cumulative CH4 and N2O emissions between treatments
in each cropping season were tested by analysis of variance (ANOVA) with least significant difference (LSD)
used for mean separation after testing the data for normality and
homoscedasticity. Cumulative seasonal N2O emissions for 2015 were log-transformed. Statistical significance was declared at p≤0.05.
ResultsWeather conditions
The year 2015 was one of the most severe drought years in decades and, as a
result, sowing in 2015 was delayed by 3 weeks compared to 2016. Rain fell
late during the growing season, and the cumulative rainfall for April to
October was about 100 mm lower in 2015 than in 2016 (Fig. 1d, g).
Mean N2O emission rates (n=4; error bars: standard error of the mean – SEM) in 2015
(left column) and 2016 (right column), with daily rainfall and water-filled
pore space (in 2016 only). Panels (a) and (e) show emission rates in the
absence of intercrops, panels (b) and (f) with Crotalaria, and panels (c) and (g) with lablab
intercrops.
N2O fluxes
N2O emission rates in 2015 (treatment means, n=4) ranged from 1.1 to
13.7 µg N m-2 h-1 for the control treatment (Fig. 1a).
Similarly, for fertilized maize, N2O emissions ranged from 2 to 23.5 µg N m-2 h-1. Emission fluxes were generally larger for the
3-week intercropping treatments; the 3-week Crotalaria treatment emitted
N2O at rates of 1.7–34.3 and the 3-week maize–lablab emitted 1.9–62.7 µg N m-2 h-1, whereas the 6-week
maize–Crotalaria emitted 2.1–24.2 µg N m-2 h-1 and the
corresponding rate for the 6-week maize–lablab intercrop was 1.5–10.7 µg N m-2 h-1. The generally low emission rates in the
6-week lablab intercropping systems corresponded to poor growth of lablab
due to shading by the maize plants. Irrespective of legume species, the
highest emission rates were found for intercrops planted 3 weeks after
maize (Fig. 1b, c). A peak in N2O emissions occurred in the 3-week
intercropping systems around mid-August 2015, which was significantly
larger than in the unfertilized control (p=0.013), the fertilized maize
mono-crop (p=0.001), and the 6-week Crotalaria (p=0.021) and lablab
(p=0.002) intercrops.
During the 2016 season, N2O emission rates in the M-F treatment
(unfertilized control) varied between 2.5 and 22.8 µg N m-2 h-1, peaking at the beginning of the season when WFPS was >50 %. There were no significant differences in WFPS values between
treatments (data not shown). Fertilized maize had similar rates (3.1–24.2 µg N m-2 h-1), peaking at around 4 weeks after planting.
Maize–forage legume treatments had larger emission rates, ranging from 1.8
to 40.2 for 3-week Crotalaria, 3.2 to 58.6 µg N m-2 h-1
for 6-week Crotalaria, 3.9 to 38.0 for 3-week lablab and 1.9 to 45.2 µg N m-2 h-1 for 6-week lablab. In general, emission
rates were higher at the beginning than at the end of the cropping season
(Fig. 1e–h). Despite higher fluxes for intercropping treatments than in the
unfertilized control in week 1 (p=0.162) and 4 (p=0.061), there were no
statistically significant differences in flux rates between the treatments.
Cumulative N2O emissions
During the 2015 growing season, all treatments had equal or higher
cumulative N2O emissions than the unfertilized control, with the 3-week
lablab intercropping system emitting significantly more N2O than the
unfertilized control (p=0.006) and the 6-week lablab intercrop (Fig. 2a).
Comparing intercropping treatments with the fertilized control, lablab sown
3 weeks after maize clearly increased N2O emissions but not
significantly (p=0.35), whereas all other intercropping treatments had
cumulative N2O emissions comparable with the fertilized maize control.
Regarding sowing date, 3-week lablab had significantly higher N2O
emissions (p<0.01) than its 6-week counterpart, whereas no such
effect was seen for Crotalaria.
Cumulative seasonal N2O-N (g N ha-1 per season) in
2015 (a) and 2016 (b) throughout 107 and 123 d, respectively, in
treatments with and without legume intercropping. Error bars denote SEM
(n=4). Different letters indicate significant differences at p<0.05. M + F: fertilized maize; M + Cr3w: fertilized maize with Crotalaria
sown 3 weeks after maize; M + Cr6w: fertilized maize with Crotalaria sown 6
weeks after maize; M + Lb3w: fertilized maize with lablab sown 3 weeks after
maize; M + Lb6w: fertilized maize with lablab sown 6 weeks after maize.
During the 2016 growing season, lablab intercropping 3 weeks after maize
showed significantly higher (p<0.01) cumulative N2O emissions
than the unfertilized control, but there was no difference between the fully
fertilized maize mono-crop and intercropped maize treatments fertilized with
50 % of the mineral N applied in 2015, nor was there any effect of
intercropping date (3 vs. 6 weeks; Fig. 2b).
Legume and maize yields
Aboveground yields of lablab were generally higher than those of Crotalaria
(Table 1). Intercropping 3 weeks after maize resulted in higher biomass
yields compared to 6 weeks for both legume species. Both legumes grew
poorly during the second growing season, particularly Crotalaria. Maize
grain yields differed greatly between the years and were roughly 20 %
higher in the wetter year of 2016 (Table 2). Better growth conditions for
maize in the second year resulted in smaller yields of intercrop legumes.
N2O emission factor and intensity
Growing-season emission factors (EFN2O) varied from 0.02 % to 0.25 % in 2015 and
0.11 % to 0.20 % in 2016 (Table 2). Of the intercropped treatments, lablab
intercropped 3 weeks after maize resulted in a significantly larger emission
factor than fertilized maize and other intercropping treatments, whereas
there was no significant difference in 2016. Overall, growing-season
N2O emission factors were ∼40 % higher in 2016 than in
2015, which is mainly due to the smaller N input in 2016, which was 25 % to
45 % lower than in 2015, except for the 3-week lablab system, which had an
estimated 18 % higher N input in 2016 than 2015 (Table 1). The latter was
due to the extraordinary high lablab yield in the previous year and its
stipulated carryover (Table 1).
Mean yield-scaled N2O emissions in 2015 varied between 25 and 55 g N2O t-1 grain yields. In 2015, 3-week lablab had a higher
N2O intensity than 6-week lablab, whereas all other differences were
insignificant. In 2016, with the mineral N fertilization reduced to 50 %,
N2O emission intensities varied from 26 to 37 g N2O t-1
grain, with no significant effect of legume species, sowing date or N
fertilization (Table 2).
To further explore the variability of N2O emissions, we plotted
cumulative N2O emissions plot-wise against legume N yield but found no
relationship (not shown). However, when plotting yield-scaled N2O
emissions over legume N yield, a significant positive relationship (p=0.01)
emerged for 2015 but not 2016 (Fig. 3a, b), suggesting that leguminous N
input increased N2O emissions more than maize yields in the dry year of
2015.
Relationship between N2O emission intensity and aboveground
intercrop legume N yield in intercrop treatments in 2015 (a) and 2016 (b).
Shown are single-plot values for each treatment (n=4).
CH4 fluxes
All treatments acted as a net sink for CH4, with uptake rates ranging
from 31 to 93 µg C m-2 h-1 in 2015 (Fig. 4a–c). Uptake
rates in 2015 were rather constant in time, with somewhat elevated uptake
rates towards the end of the season. There were no obvious treatment
effects. By contrast, in the wetter year of 2016, CH4 uptake showed a
pronounced maximum at the beginning of June with uptake rates of up to 140 µg C m-1 h-1 irrespective of treatment (Fig. 4e–g), when
WFPS values declined to below 25 % (Fig. 4h). Methane uptake during
this period tended to be greatest in the unfertilized control, while
intercropping treatments had smaller uptake rates, but these were not
significantly different from maize mono-crop treatments. Differences between
treatments on single sampling dates were insignificant throughout the
season. The highest CH4 uptake in 2016 was recorded with the lowest WFPS
(∼10 %).
Mean CH4 flux in 2015 (left column) and 2016 (right column), with
daily rainfall and water-filled pore space (in 2016 only). Error bars show
the standard error of the mean (n=4). Panels (a) and (e) show emission rates in
the absence of intercrops, panels (b) and (f) with Crotalaria, and panels (c) and (g) with lablab
intercropping.
Cumulative CH4 uptake
Cropping-season cumulative CH4 uptake exceeded 1 kg C ha-1 in both
years with no significant effect of intercropping, legume species or time of
intercropping (Fig. S2a, b). Maize intercropped with Crotalaria tended to
take up less CH4, but this effect was not statistically significant in
2015 or 2016 (p=0.056). Plotting cumulative CH4 uptake plot-wise over
legume dry matter yield did not result in a significant relationship, but
the highest seasonal uptake rates occurred in plots with the lowest legume dry
matter yield (data not shown).
Total non-CO2 GHG emissions
The relative contribution of CH4 to the non-CO2 GHG emissions of
the different cropping systems varied between 22 % and 69 % and was the highest
in the non-fertilized maize mono-crop. The 3-week lablab intercropping
resulted in significantly higher total emissions compared with 6-week lablab
intercropping and maize mono-cropping (Table 2). By contrast, in 2016,
legume species but not intercropping time affected the non-CO2 GHG
emission balance (p<0.05). Lablab intercropped 3 weeks after maize
resulted in significantly higher (p<0.05) total GHG emissions than
the unfertilized control but was indistinctive from the fertilized maize
mono-crop or other intercrop treatments (Table 2, Fig. 5a, b).
Relative contribution of CH4 uptake and N2O emissions to
seasonal total non-CO2 GHG emissions in mono-cropping and intercropping
treatments in 2015 (a) and 2016 (b). Error bars indicate standard deviation
(n=4).
DiscussionMaize–legume intercropping and N2O emissions
Background N2O emissions (in unfertilized maize mono-crop) fluctuated
between 1.1 and 23.0 µg N2O-N m-2 h-1, which is in the
range of previously reported emission rates for soils in SSA with low N
fertilizer input (0–20 µg N2O-N m-2 h-1; Pelster et
al., 2017). Baseline emissions were somewhat higher in the wetter season of
2016 owing to ∼100 mm more rainfall at the beginning of the
season (Fig. 1d, g). Elevated emission rates >30µg N2O-N m-2 h-1 occurred in 2015 on a few occasions in intercrop
treatments, notably in mid-August when rain fell right after mulching of the
3-week intercrops. Mulching of the 6-week intercrops did not affect N2O
emissions, probably because the mulched legume biomass was too small to
affect the flux (Fig. 1b, c; Table 1). In 2016, mulching of the 3-week
legumes was followed by rainfall, increasing the WFPS to 50 % (Fig. 1h) but without resulting in elevated N2O emission rates (Fig. 1f, g).
Together, this suggests that the direct effect of mulching on N2O
emissions is highly dependent on soil moisture and the amount of mulch and
cannot be generalized, contrary to our hypothesis that legume intercrops
would invariably increase N2O emissions.
Legume dry matter yields varied strongly (100 to 3000 kg ha-1)
throughout the two experimental years (Table 1, Fig. 3) depending on
species, intercropping time and weather. Lablab grew more vigorously and
realized larger dry matter yields than Crotalaria (Table 1). Moreover,
lablab is known to be a better N2 fixer than Crotalaria (Ojiem et al.,
2007), presumably leading to higher N input, which would explain larger
N2O emissions with this intercrop (Fig. 2). The 3-week intercrops
performed generally better than the 6-week intercrops. This was particularly
apparent for the low-growing lablab (Table 1). Weather at the beginning of
the season played a major role for the growth performance of the intercrops
by controlling maize growth, which in turn controlled legume growth by
shading. Together, this resulted in a wide range of potential leguminous
N inputs in our experiment, which could be used to examine their overall
effect on N2O emissions on a seasonal basis under the semiarid
conditions of the central Ethiopian rift valley. Surprisingly, we did not
find any significant relationship between estimated total N input or legume
N yield and cumulative N2O emissions. This may be due to the notoriously
high spatial and temporal variability of N2O emission rates (Flessa et
al., 1995) or reflect the fact that intercropping had no effect or opposing
effects on N2O-forming processes. Cumulative N2O emissions and
legume N yields integrate over the entire season and do not capture the seasonal
dynamics of soil N cycling and N uptake, which could obscure or cancel out
transient legume effects on N2O emissions. Possibly, N released in
intercropping treatments was efficiently absorbed by the main crop, even
though intercropping did not lead to significantly higher maize grain yields
in our experiment. Alternatively, changes in physicochemical conditions
brought about by intercrops, such as potentially lower soil moisture due to
more evapotranspiration, may have counteracted the commonly observed
stimulating effect of legume N on N2O emissions (Almaraz et al., 2009;
Sant'Anna et al., 2018).
We found a significant positive relationship between N2O intensity and
legume N yields in 2015, suggesting that intercropped legumes indeed
increase N2O emissions relative to maize yields (Fig. 3a). It is
impossible to say, however, whether this relationship was driven by the
extra N entering the system through biological N fixation or whether an
increasing legume biomass affected physicochemical conditions in the
rhizosphere favoring N2O formation. In 2016, legume dry matter yields
were much lower than in 2015 owing to early rains favoring maize growth,
and no significant relationship with N2O intensity was found (Fig. 3b).
This illustrates that the effect of legume intercropping on N2O
emissions is highly dependent on sowing date and weather, both of which
control the growth of legume and main crops and ultimately the amount and
fate of leguminous N in the intercropping system. Our data suggest that
excessive accumulation of leguminous biomass in SSA maize cropping enhances
the risk for elevated N2O emissions.
We expected N2O emissions to respond more strongly to intercropping in
the second year (2016), as legume mulches were applied according to their
plot-wise aboveground yields in the previous year. Indeed, N2O emission
rates were clearly higher in intercropping treatments on the first sampling
date in 2016 (Fig. 1f, g), indicating increased N cycling in mulched plots
(Campiglia et al., 2011). This difference vanished quickly, however,
suggesting that the effect of intercrop mulches, even at high amounts (Table 1), on N2O emissions in the subsequent year was negligible. It is
noteworthy that our estimates of the fraction of N carried over between the
years were based on literature data (Table 1) and that a considerable part
of the mulched N may have been lost during abundant rainfall (300 mm) early
in the 2016 season before crops were sown.
Cumulative N2O emissions from intercrops, with the mineral fertilization
rate halved, were comparable to those in the fully fertilized maize mono-crop
in 2016. This may be partly due to the 50 % reduction in mineral N
application to intercrop treatments, as found by others (Tang et al., 2017).
Another reason may be that a considerable proportion of the cumulative
emissions in 2016 occurred before or shortly after 3-week intercrops were
sown and was thus unaffected by growing legumes. Overall, cumulative
N2O emissions in 2016 were equal to or higher than those in 2015, despite
reduced mineral N addition to intercrops and lower legume biomass.
Ultimately, the lack of a clear emission response to legume intercropping in
the second year calls for studies tracing cumulative mulching effects over
multiple years and exploring their driving factors in more detail. In our
study, the amount and timing of rainfall appeared to be more important for
N2O emissions in the second year than the amount and carryover of legume N.
Given our finding that N2O intensity responded positively to legume
biomass and its N content in a drought year with poor maize growth,
intercrop species as well as sowing and harvest dates (relative to the main
crop) emerge as viable management factors for controlling the accumulation
of legume biomass between the maize rows and hence the risk for increased
N2O emission. Legume species and cultivar in intercropping systems are
known to be critical for N loss both during the intercropping and the
subsequent seasons (Pappa et al., 2011; Weiler et al., 2018). The
stimulating effect of crop residues on N2O emissions has been reported
to depend on residue quality and soil moisture, with denitrification being
the likely process (Li et al., 2016). Our study provides evidence that
vigorous growth of high-yielding legume intercrops can enhance N2O
emissions in years unfavorable for maize growth, whereas in years with
sufficient water availability early in the growing season, maize growth is
favored, preventing the excessive growth of the intercrop. Our study therefore
points to optimizing the sowing date in response to the expected emergence and
growth of maize as a promising option to control the growth of the intercrop and
hence to deal with the risk of increased N2O emissions.
Seasonal N2O and CH4 emissions, EFN2O, and total GHG emissions
Growing-season N2O emissions in fertilized treatments varied from 0.17
to 0.33 kg N2O-N ha-1 (2015) and 0.23 to 0.3 kg N2O-N ha-1 (2016), covering a
period of 107 d (2015) and 123 d (2016) (Fig. 2) and a range of estimated
total N inputs from 36.4 to 97.8 kg N ha-1 (Table 1). There are no
N2O emission studies for maize–legume intercropping in the Ethiopian
rift valley so far. Hickman et al. (2014a) reported N2O emissions of
0.62 and 0.81 kg N ha-1 over 99 d for 100 and 200 kg N of input
per hectare, respectively, for a maize field without intercropping in humid
western Kenya, which seems to be higher than the seasonal emissions we found.
Baggs et al. (2006), working in the same region with maize intercropped with
legumes in an agroforestry system, reported N2O emissions ranging from
0.2 to 0.6 kg N ha-1 with higher emissions in tilled intercropping
treatments; our values are at the lower end of the range they reported. The
largest seasonal N2O emissions for intercropping reported so far from
SSA are 4.1 kg N ha-1 (84 d) after incorporating 7.4 t ha-1 of a
Sesbania macroptilium mixture in humid western Kenya (Millar et al., 2004). Compared to the
N2O emissions reported for humid tropical maize production systems, our
data suggest that maize–legume intercropping based on mulching in the
subhumid to semiarid rift valley appears to be a minor N2O source,
mainly because of the relatively small amount of legume biomass mulched
(Table 1). Growing-season N2O emission factors (EFN2O) in our study ranged
from 0.02 % to 0.25 % in 2015 and 0.11 % to 0.20 % in 2016 of the estimated
total N input, including assumed N inputs from legume mulch as well as
belowground additions and carryover between the years (Table 1). Even if the
estimated EF is doubled to account for off-season emissions, it is still
lower than the annual IPCC default value of 1 % N2O-N per unit of added
N (IPCC, 2014). Our estimated EFN2O values thus seem to be at the lower end of those
reported by Kim et al. (2016) for SSA smallholder agriculture estimated from
literature data (0.01 % to 4.1 %). The reasons for the low EFN2O values in our study
are probably the high background emissions in the fertile soil of the
Hawassa University Research Farm, which supports high maize yields even in
the unfertilized control (Table 1), and the low levels of N input. The soil
has been used over decades for agronomic trials with various fertilization
rates with and without crop residue retention and legume intercropping (e.g.,
Raji et al., 2019). Thus, our field trial has to be considered
representative for intensive management as opposed to smallholder systems
with minimal or no fertilization history.
Methane uptake by the soil in both seasons varied between 1.0 and 1.5 kg CH4-C ha-1 without showing any significant treatment effect, even
though maize–legume intercrops tended to take up less CH4 than maize
mono-crops (Fig. S1). The observed trend might relate to competitive
inhibition of CH4 oxidation by higher NH4+ availability (Le
Mer and Roger, 2001; Dunfield and Knowles, 1995) in the presence of legume
intercrops, even though estimated total N inputs remained below 100 kg N ha-1, which is considered a threshold for NH4+ inhibition
(Aronson and Helliker, 2010). Alternatively, densely growing legumes may
have lowered CH4 uptake by impeding CH4 and/or O2
diffusion into the soil (Ball et al., 1997). We did not observe the stimulation
of CH4 uptake by legume intercropping, which we attribute to the
absence of N and P deficiency in this fertile soil. Methane uptake rates
varied from 20 to 140 µg CH4-C m-2 h-1, which is in the
range of rates reported previously for SSA upland soils (Pelster et al.,
2017). Seasonal CH4 uptake in our experiment offset between 22 % and
69 % of the CO2 equivalents associated with N2O emissions
without revealing any significant treatment effect (Fig. S2a, b), but the
offset was relatively largest in the unfertilized maize mono-crop and
smallest in lablab intercropping. Hence, CH4 uptake is an important
component of the non-CO2 climate footprint of SSA crop production.
Legume intercropping and climate-smart agriculture
Legumes are an important N source in smallholder farming systems, where
mineral fertilizers are unaffordable or unavailable. Legume intercrops
maximize resource use efficiency as total productivity is often higher than
in mono-cropping systems (Banik et al., 2006). Moreover, N fixed
biologically by legume intercrops can partly replace synthetic N
fertilizers if the release is synchronized with the nutrient demand of the
cereal crop. On the other hand, surplus N from legumes may result in N
losses as NO3-, NH3 and NO, N2O, or N2. Mulching and the
incorporation of legume biomass has been found to increase N2O
emissions under temperate conditions (Baggs et al., 2000,
2003) and under humid tropical conditions (Millar et al., 2004). Also under
semiarid Mediterranean conditions, vetch (V. villosa) used as a winter catch crop
and mulched in spring significantly increased N2O emissions during the
fallow period, while rape did not (Sanz-Cobena et al., 2014). This was later
confirmed by a 15N study, highlighting the role of N mineralization
from legumes as a source of N2O (Guardia et al., 2016). None of the
studies found an overall N2O-saving effect of catch crops when scaling
up to the entire crop cycle, even though the latter study used reduced
mineral N fertilization rates in treatments with catch crops. By contrast,
reduced NO3- leaching and N2O emissions have been reported from
maize intercropped with legumes in the semiarid North China Plain, which
the authors attributed to enhanced N uptake by both the intercrop and main crop
as well as reduced soil moisture in treatments with intercrops during the rainy
season (Huang et al., 2019). This shows that legume intercrops have the
potential to either increase or reduce N2O emissions with consequences
for the non-CO2 footprint of cereal production and hence for the
viability of intercropping as a central component of CSA (Thierfelder et
al., 2017).
The legume intercrops used in our study had low C : N ratios (Table S1 in the Supplement) and
can be expected to release a significant part of their N through
the decomposition of roots and nodules or root exudation as well as during the
decomposition of mulches (Fustec et al., 2010). The effect of mulching on
N2O emissions depends on the C : N ratio, with increased emissions for residues with a low
C : N ratio (Baggs et al., 2000; Shan and Yan, 2013). In line with
this, N2O emissions in the intercrop treatments of our study exceeded those
in the fertilized maize mono-crop on several sampling dates, both during the active
growth of legumes and after mulching. Another important aspect is the amount
of legume N carried over between years, which depends, among other factors, on
the amount and quality of the legume and the weather between the growing
seasons. Abera et al. (2014) showed that surface-placed residues of haricot
bean and pigeon pea decompose quickly despite relatively dry conditions
during the off-season. Vigorous rainfall at the beginning of the growing season
like in 2016 (Fig. 1) could lead to dissolved N losses, which could lead to
indirect N2O emissions elsewhere; this should be taken into account
when evaluating intercropping as a CSA strategy.
Conclusion
While legume intercrops have the potential to improve cereal yields and
diversify produce for smallholders in the central Ethiopian rift valley, a risk
of enhanced N2O emissions remains, which became apparent as the increased
“N2O intensity” of the main crop in a drought year (2015). At the
same time, our study points at possibilities to counteract this trend by
actively controlling legume biomass development and hence potential N input
through “climate-smart” choices of legume species, sowing date and mulch
amounts in response to prevailing environmental conditions. This approach,
however, is complicated by annual variability in growth conditions and
requires active planning for sowing and mulching time by the farmer. Our
study was conducted on a relatively nutrient-rich soil (compared to
typical smallholder farms), which supports high yields of both maize and
leguminous intercrops. Under these conditions, intercropped legumes can
potentially replace a considerable part of synthetic fertilizer, thus
supporting common CSA goals. However, more studies are needed to fully
explore intercropping options in the framework of CSA in the rift valley,
particularly in nutrient-poor smallholder fields. Future studies on CSA
approaches in the rift valley should address, in addition to greenhouse gas
emissions, N runoff and soil organic matter build-up, ideally in long-term
field trials with and without legume intercropping. Future studies should
also attempt to combine flux measurements with inorganic N dynamics and measurements of biological N fixation. Given that seasonal N2O emission factors and intensities
in our study were in the lower range of published values for SSA,
intercropping appears to be a promising approach to sustainable intensification
in the Ethiopian Great Rift Valley.
Data availability
Flux and yield data can be accessed together with metadata through the NMBU archive at: 10.18710/I6BD3R (Dörsch, 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-17-345-2020-supplement.
Author contributions
SGR and PD designed and SGR carried out the study. Both authors processed the data and wrote the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The study is part of the NORHED program “Research and
capacity building in climate-smart agriculture in the Horn of Africa”. We are
grateful to Teshome Geletu, Teketel Chiro and Tigist Yimer for assistance
during setting up and managing the field experiment, sample collection, and
preparation, as well as to Trygve Fredriksen for assistance during the analysis of
samples in the laboratory at NMBU.
Financial support
This research has been supported by the Norwegian Agency for Development Cooperation (Norad) under the NORHEAD program (grant number ETH-13/0016).
Review statement
This paper was edited by Edzo Veldkamp and reviewed by two anonymous referees.
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