where θFC and
θPWPare water content at field capacity, here considered at -33 kPa, and
permanent wilting point (PWP) at -1500 kPa, respectively
(m3 m-3).
The air capacity, AC (m3 m-3),
indicates the soil aeration (Reynolds et al., 2009) and was defined by
White (2006):
\begin{equation}
\text{AC}=\theta_{s}-\theta_{-10kpa}\nonumber \\
\end{equation}where θs and θ-10kPa are water content
at saturation and at a matric potential of -10 kPa. The macroporosity,
MacPOR (m3 m-3), gives the volume of
large pores (i.e., > 300 µm equivalent diameter according
to the capillary equation), and represents the ability of the soil to
quickly drain excess water and facilitate root proliferation (Reynolds
et al., 2009). It can be formulated as:
\begin{equation}
\text{MacPOR}=\theta_{s}-\theta_{m}\nonumber \\
\end{equation}where θm is the soil-matrix porosity (MatPOR,
m3 m-3). According to Greenland
(1977), MacPOR can be seen as drained pores with an equivalent diameter
of >50 µm. Then, using the capillary equation, the
soil-matric potential corresponding to this pore diameter is -6 kPa.
2.5. Soil biological analysis
Two composite soil samples per plot were taken at 0-10 cm depths in the
first week of August 2013 when the soil moisture was at about field
capacity to determine the soil microbial biomass carbon (SMBC) using
substrate-induced respiration (SIR) method (Anderson and Domsch, 1978).
Soil microbial biomass carbon was calculated according to (Schinner et
al., 1996) using the following equation.
\begin{equation}
\text{RQ}=\frac{\left(B-D\right)x2.2x100}{4xSWxDM}\nonumber \\
\end{equation}Where RQ is respiratory quotient in milligram of CO2 per
100 gram of soil per hour, B is volume of HCl consumed by blanks (ml), S
is volume of HCl consumed by samples (ml), 4 is incubation time (h), 100
is conversion factor (100 g dm), 2.2 is a conversion factor (1 ml 0.1 M
HCl corresponds to 2.2 tolerant mg CO2), SW is the
initial soil weight (g), DM is the soil dry matter (%). It is assumed
that a respiratory quotient of 1.1 mg CO2 .100
g-1 dm. h-1 corresponds to 20.6 mg
biomass-C.100g-1 dm (Erkossa et al., 2007). Thus, this
factor was used to convert CO2 to SMBC.
Two soil samples per plot were also taken at 0-10 cm soil depth during
the growing season in mid-August 2013 to determine soil bacteria and
fungi. The bacteria and fungi populations in the soil were determined by
culture methods as described in Benson (2001).
Two composite soil samples per plot were taken during the growing season
in mid-August 2013 to assess AMF spore density. AMF spores were
separated from each dried soil sample by wet sieving and decanting
process, followed by flotation centrifugation in 50% sucrose as
described in Brundrett et al. (1996). Spore density was enumerated as
spore number per gram of air-dry soil.
Samples of wheat roots were taken mid-August 2013 to measure percent
root length colonized by AMF structures such as AMF hyphae, arbuscules
and vesicles. A portion of each root sample was washed with 10% KOH and
autoclaved (121 0C for 15 min), acidified with 3% HCl
(v/v) for 30 min at room temperature. Cleared roots were transferred
into a staining solution of Trypan blue (0.05% w/v) in lactoglycerol
(1:1:1, lactic acid: glycerol: distilled water), and autoclaved at 1210C for 15 min (Brundrett et al., 1996). Then, stained
roots were left in a de-staining solution (50% glycerol). Finally,
percentage of root AMF colonization was assessed using the gridline
intersect method (Giovannetti and Mosse, 1980) under a compound
microscope (100–400 times magnification) (Mc Gonigle et al., 1990).
Presence of arbuscules, internal hyphae and vesicules was recorded from
each intersect and expressed as a percentage of total root intersect.
At
each intersection, there were six possible mutually exclusive outcomes
(Brundrett et al., 1996) and the line might intersect at p, q, r, s, t
and u.
where p is the intersection at no fungal structures are seen, q is
arbuscules, r is mycorrhizal vesicles, s is arbuscules and mycorrhizal
vesicles, t is mycorrhizal hyphae but no arbuscules or mycorrhizal
vesicles, and u is hyphae not seen to be connected to arbuscules or
mycorrhizal vesicles.
A reasonable estimate of percentage of root length colonization was done
from 100 or more intersections for each root sample:
\(G=p+q+r+s+t+u\)
where G is the total intersect inspected. The percentage of root length
colonization by hyphae of all types was calculated as hyphal
colonization (HC):
\begin{equation}
HC=100\left\lceil\frac{G-P}{G}\right\rceil\nonumber \\
\end{equation}The percentage of root length colonized by AMF hyphae (MHC) was
calculated as:
\begin{equation}
\ MHC=100\left[\frac{q+r+s+t}{G}\right]\nonumber \\
\end{equation}The percentages of root length colonization by arbuscules, and the
percentage of root length colonized by mycorrhizal vesicles, was
calculated as arbuscular colonization (AMC)
\(AMC=100\left[\frac{q+s}{G}\right]\) and vesicle
colonization (VC):\(VC=100\left[\frac{r+s}{G}\right]\).
2.6. Crop yield and straw
The crop grown in rotation during this study period in 2013 was wheat.
Grain and straw yield of wheat was determined at harvest from areas of 1
m × 1 m in three replicates per plot.
2.7. Data Analysis
ANOVA was used with student t-tests at α = 0.05 to test for statistical
differences in soil properties and crop yield between the treatments.
The data on percentage of AMF colonization and spore density was
transformed using a logarithmic function followed by ANOVA analysis to
test the statistical differences on the effects of treatments on AMF.
Data were analyzed using the SAS statistical software (JMP version
11.0), and the standard error of treatment means was used for separation
of means.
To understand the pathways linking management practices (CT, CF and PRB)
to soil health (soil biology and soil properties) and ultimately to crop
productivity, we used piecewise structural equation modelling.
Structural equation modelling (SEM) has been used extensively in
psychology (MacCallum and Austin, 2000) and to a lesser extent in
ecology (Pugesek et al., 2003). Its use remains limited in soil science,
though it is a powerful method to develop causal understanding of the
drivers of ecological interactions and processes in soils, instead of
focusing solely on patterns, as argued by Eisenhauer et al. (2015). SEM
is based on the use of cause-effect (i.e., structural) equations (two or
more) to model multivariate relationships (Grace, 2006). It is related
to regression, principal components analysis, and path analysis, but in
addition to these methods, SEM provides a means to evaluate the
structure of the model (i.e., direct and indirect relationships among
variables) as well as the model parameters using observed data (McCune
and Grace, 2002). As such, SEM can be used to test construct models
(i.e., hypothesized models) and quantify relationships between model
components (Grace, 2006).
A construct model was developed to test the pathways linking management
practices to indicators of soil biology, indicators of soil properties,
and wheat grain yield (Fig. 4). Because of the limited degree of
freedom, only indicators of soil biology and soil properties could be
used, and not the whole range of variables measured in this study. The
density of bacteria and the density of fungi were used as proxy of soil
biology. Regarding soil properties, the soil organic carbon in the top
10 cm of soil was used as proxy of soil fertility, the KFS as proxy of
soil hydraulic properties, and the SI as proxy of soil structural
properties.
Management practices were represented by two binary variables: contour
furrowing (yes/no) and permanent raised bed (yes/no). Due to these two
factorial variables and because SEM assumed a normal distribution for
all the variables included in the model, we used piecewise structural
equation modelling (PSEM). In this extension to SEM, paths are estimated
in individual models and then pieced together to construct the causal
model
(www.jonlefcheck.net/2014/07/06/piecewise-structural-equation-modeling-inecological-research).
This was performed using the R package piecewise SEM .
3. Results
3.1. Effects on biotic soil properties
Soil microbial biomass C, bacterial colony and fungi population were
higher in PRB compared to CF systems and lowest CT (Table 1). Soil
microbial biomass C was highest with PRB and least with CT at 0-10 cm
soil depth, while CF showed intermediate values. Higher populations of
bacteria colonies and fungus mycelia were recorded with PRB followed by
CF compared to CT treatment (Table 1). The bacterial and fungi
populations were highest under PRB and lowest in CT (Table 1 and 2).
AMF spore abundance and root colonization of wheat crops by the
different AMF structures were significantly (P<0.05) affected
by the treatments (Table 2). Higher AMF colonization was observed in PRB
in HC, MHC, AC and VC as compared to CT (Table 1). There was
significantly higher percentage of wheat root length colonized by AMF
hyphae with PRB (88%) compared with CF (74%) and CT (58 %) systems.
3.2. Effects on abiotic soil properties
Soil
aggregate stability index was significantly higher in CA-based
treatments as compared with CT at 0-15 cm depth (Table 3) but not at
15-30 cm depth. The highest soil aggregate stability was observed on PRB
and CF compared to CT on both soil depths (Table 3). PRB had higher KFS
than CT treatment. However, CF did not show a significant different KFS
in comparison with PRB and CT. The bulk density of the topsoil (0-10 cm)
as derived from SSCC was, at higher soil water potential with higher
soil moisture content, lower with PRB compared to CF and CT treatments.
However, there was no significant difference in bulk density among
treatments at low soil water potential when the soil was dry. The bulk
density changed from about one Mg m-3 (at higher soil
matric potential, swelling) to 1.71 Mg m-3 (at lower
soil matric potential, shrinking) (Fig. 5).
The SWRC in Fig. 6 indicates significantly higher soil water content in
PRB followed by CF and lowest in CT at higher soil matric potential
while there was no significant difference at lower soil matric
potentials.
The SOC was significantly higher (p < 0.05) with PRB
than CF and CT at zero – 10 cm soil depth (Fig. 7). Similarly, SOC was
significantly higher with CA-based treatments as compared to CT at both
the subsoil depths (10-20 cm and 20-30 cm). Also higher total soil N was
recorded in CA-based treatments (PRB and PC) than CT treatment (Table
4). The highest soil C: N ratio was found in CT followed by CF and the
lowest with PRB (Table 4). Besides, available P was significantly higher
in PRB followed by CF as compared to CT at 0-10 cm soil depth (Table 4).
As expected, neither CEC nor texture was affected by the treatments, and
change in soil pH was not significant (Table 3).
3.3. Effects on soil functions
The average SOC sequestration rates (Mg C ha-1yr-1) within the 0–10 cm soil depth was higher in PRB
(0.62) followed by CF (0.49) and lowest with CT (0.25) over 8 years
between 2006 and 2013 following the implementation of CA-based practices
(Table 4). The total SOC added to the soil over 8 years (between 2006 to
2013) in the topsoil (0–10 cm soil depth) was significantly higher with
PRB (4.31 Mg ha-1) followed by CF (3.45 Mg
ha-1) and lowest with CT (1.72 Mg
ha-1) (Table 4).
PAWC, MacPOR and AC were significantly higher with PRB compared to CT
(Table 4) indicating the improvements in water storage capacity,
internal drainage and soil aeration in PRB. Soil N and available P were
significantly higher in PRB as compared to CT signifying the
improvements in nutrient availability in the CA-based systems.
Wheat grain and straw yields were
significantly higher (P < 0.05) with PRB followed by CF as
compared to CT (Table 5) demonstrating the improvement in soil
functioning in terms of food production in the CA-based systems (Table
6).
3.4 Structural equation modeling
Outputs of the PSEM revealed two pathways linking CF and PRB to improved
wheat yield in the conditions of the study: (1) via an increase of the
density of bacteria and an improvement of KFS, and (2) via an increase
in the density of fungi and an increase of soil organic carbon content
in the topsoil.
Both CF and PRB were found to have a positive effect on the density of
both fungi and bacteria, though the effect of PRB was around twice the
effect of CF on these microorganisms (Table 6; Fig. 8). A positive
relationship between the density of bacteria and KFS, and a positive
relationship between, the density of fungi and SOC content in the
topsoil, were found. Both KFS and SOC content in the topsoil were found
to have a positive impact on wheat grain yield. No statistically
significant relationship between SI and wheat grain yield was found.
Similarly, no statistically significant relationship between the
densities of bacteria and fungi on SI was found (thus, SI doesn’t
feature on Fig. 8).
4. Discussion
4.1. Effects on biotic soil properties
The highest SMBC was obtained in PRB at the upper soil surface followed
by CF, confirming that CA-based systems can improve microbial activities
(Madejón et al., 2007). The increase in SMBC in PRB and CF corresponds
to the increased SOC and soil N. Higher SMBC may indicate increased
potentially available N (Hart, et al., 1986). Retaining crop residue was
not only relevant to improve SOC and N but also to enhance soil water
storage by mulching the soil surface that limits water loss in the form
of evaporation (Araya et al., 2015). The main sources of crop residue
during the experimental period that have better soil surface cover were
crops with high C: N ratios that include wheat (80:1) and barley (85:1)
(Flower et al., 2012), while grass pea and tef residues (low C: N ratio
crops) had short life span as soil surface cover due to fast
decomposition rate. The residue of tef retained from previous season
(2012) was completely decomposed before planting in June 2013 while
residue of wheat and barley resist decomposition until next cropping
season (personal observation). Wheat and barley had higher impact on SOC
while lower on soil N as the crop straw has higher C and smaller N
content. On the other hand, the legume crops have higher capacity to
generate soil N whereas its role in terms of soil surface cover was
negligible. Tef residue covers only about 20% of the target soil
surface immediately after harvest as compared to the 60% residue cover
in barley and wheat, although there was no straw retained during grass
pea cropping. Growing dryland legumes such as grass pea in rotation or
as cover crop with low C: N ratio as part of the CA-based systems can
improve soil N availability (Flower et al., 2012). However, the fresh
crop residue retained in CA-based systems might increase immobilization
of soil N substantiating the need for N fertilizer application as part
of the early stages of CA-based systems (Chávez-Romero et al., 2016).
There was significant difference in bacteria and fungi population among
treatments indicating the improvements in CA-based systems. The highest
bacterial and fungi population in PRB followed by CF could be attributed
to the increased SOC and N source and reduced soil disturbances in the
CA-based treatments. Similar results were reported in other CA studies
(Six et al., 2002; Lienhard et al., 2013). Wang et al. (2011) and Fierer
et al. (2012) reported that CA had significantly higher SOC, soil N and
microbial biomass and higher association of AMF with the crops as
compared to CT.
Higher AMF spore density and colonization of wheat roots in CA-based
treatments as compared to CT indicates that minimizing soil disturbance
in CA-based treatments increases the spore (Table 2; Fig. 8). Jansa et
al. (2002) reported an increase in mycorrhizal spores and root
colonization on several crops with minimum tillage compared with CT
practices. Minimal soil disturbance and the stability of the soil
surface in CA-based treatments could prevent the fungi mycelium from
being fragmented and promote production and expansion of AMF (Mc Gonigle
et al., 1990; Kabir, 2005). If the AMF hyphal network is not disrupted,
the next crop could be more rapidly connected to the network and have
higher nutrient absorption capacity. Helgason et al. (2010) also
reported AMF hyphae are sensitive to physical disruption by tillage. AMF
spores germinate under suitable conditions of the soil matrix,
temperature, CO2 concentration, pH and P concentration
(Helgason et al., 2010), and increase in the carbon supplied by the
plant to the AMF can increase the uptake of P (Miller, 1992). AMF has
reportedly increased nutrient uptake, salinity tolerance, drought
tolerance, water uptake, root disease resistance, and photosynthesis
(Srivastava, 1996; Sharmaet al., 1994). AMF extension of the plant root
surface facilitates potential uptake and translocation of P, N, K, Ca,
S, Cu, Mo, and Zn (Srivastava, 1996; Frey and Ellis, 1997). However,
nutrient uptake by plants grown in CT can be lower than grown in
CA-based systems (Miller, 1992). Kabir (2005) also reported that P
concentrations were significantly greater with CA than with CT. The
higher association AMF in the PRB and CF could help to increase the
availability of P in the root zone and direct uptake by the crops.
Al-Karaki et al. (2004) AMF colonization of roots has been shown to
increase the uptake of water and drought resistance of wheat. Similarly,
the wheat root colonization of AMF in this study was found to be higher
in CA-based systems as compared to CT.
4.2. Effects on abiotic soil properties
The soil aggregate stability was significantly higher in CA-based
systems as compared to CT (Table 3) implicating improvements for a
number of soil physicochemical properties including water infiltration,
water holding capacity, oxygen supply, and organic matter mineralization
rates (Feller et al., 1996; Six et al., 2004). Minimum soil disturbance
and increased SOC through retaining crop residue can improve aggregate
stability, bulk density and porosity as well as the soil moisture and
air regime that, altogether, stimulate the activities of the soil
organisms. The bulk density was decreased significantly with PRB
followed by CF planting systems compared to CT at higher soil water
potential but non-significantly at lower matric potentials. The lowest
bulk density at higher soil water potential with PRB planting system
indicates higher capacity to conserve water in the root zone and
improves soil productivity. It is well established that addition of SOM
can not only reduce bulk density and increase water holding capacity,
but also effectively increase soil aggregate stability (Kay and Angers,
2001). Similarly, higher soil water content were observed in CA-based
systems as compared to CT at higher soil matric potentials indicating
the impacts of SOC. Field saturated hydraulic conductivity were shown to
increase with PRB and CF as compared to CT (Table 3) confirming the
improvement in porosity of the soil at higher soil matric potentials.
Soil biological constituents such as bacteria and fungi can influence
the formation and stabilization of soil aggregates (Table 6; Fig. 8; Six
et al., 2004; Lavelle and Martin, 1992). Beare et al. (1997) showed that
fungal hyphae were responsible for about 40% of the macroaggregation
(>2000 um) and significantly greater retention of SOM under
no-tillage soils than in CT. Mathew et al. (2012) reported that the
population of bacteria and fungi like AMF and actinobacteria was higher
in CA systems due to improvement in physicochemical and microbiological
characteristics of the soil.
4.3. Effects on soil functions
Soil function indicators encompass carbon transformation, nutrient
cycling, soil water and nutrient availability, soil structure, gas
exchange, adequate rooting depth and food production (Adhikari and
Hartemink, 2016). SOM provides nutrients and habitat to soil biota and
contributes to particle aggregation, enhancing the physical structure of
soils and then promoting aeration, water infiltration, and resistance to
erosion and crusting (Horwath, 2007). The SOC sequestration rate (Mg C
ha-1 yr-1) was higher with PRB
(0.62) followed by CF (0.49) and lowest with CT (0.25) (Table 4)
indicating the potential of CA-based systems to play a role in the
mitigation of greenhouse gases. The highest SOC sequestration rate
reported in humid climates regions under no-till system was 0.22 Mg C
ha–1 y–1 while 0.10 Mg C
ha–1 y–1 in arid climates (Six et
al., 2002). Lal (2004a) reported that an average long-term rate of SOC
sequestration with no-tillage practices is 0.2 to 1 Mg C
ha–1 y–1 for humid temperate
regions while 0.05 to 0.25 Mg C ha–1y–1 for dry tropical regions. These rates are low
compared to the average sequestration rate of 0.48 Mg C
ha–1 y–1 reported by West et al.
(2002). The weed biomass retained after killing by glyphosate in
CA-based systems in our study and incorporated by tillage in CT instead
of being grazed by livestock as at nearby farmers’ crop fields might
contribute to increase the SOC stock and sequestration rates in all
treatments. Also the crop rotations and fertilizers added in all
treatments might enhance root biomass and absence of aftermath
overgrazing after harvesting in CT like that of CA-based systems might
have increased the SOC stock in CT. The rate of SOC sequestration is
also affected by the quantity and quality of biomass returned to the
soil. Several studies revealed that the high potential of CA system for
SOC sequestration (Lal, 2004a; Lal, 2004b; de M et al., 2001). Soil
organic matter plays an important role in soil function determining soil
biological quality (provision of substrate and nutrients for microbes),
chemical quality (buffering and pH changes) and physical quality (water
holding capacity and stabilization of soil structure) properties and
susceptibility of soil to degradation (Kabir et al., 1998). Soil
aeration is one of the most important determinants of soil productivity
because it determines the level of oxygen in the soil (Kibblewhite et
al., 2007). CA-based systems had higher AC as compared to CT indicating
a better wheat growth rate in CA-based systems. Plants are sensitive to
the soil aeration status of the soil (Hatfield et al., 2017).
The higher bulk density in PRB followed by CF as compared to CT at
higher soil water potential and a similar trend in aggregate stability
among treatments confirms the improvement in soil structure. The higher
aggregate stability in CA-based systems as compared to CT could imply
high resistances of the soil to erosion, raindrop and surface sealing.
Thus, PAWC was significantly affected by treatments due to the improved
soil structure in PRB in comparison with CT. The higher bulk density in
CA-based systems as compared to CT at higher soil water potential while
there was no significant change at lower soil water potential indicates
as the improvement in PAWC was due to enhanced soil structure but not
soil texture. Higher PAWC in CA-based systems as compared to CT system
shows the importance of CA-based systems for climate change adaptation.
Higher macroporosity (MacPOR) was observed in PRB than CF and CT proving
the improvement in the ability of the soil to quickly drain excess water
and facilitate root proliferation (Reynolds et al., 2009). Improving
drainage in Vertisols can reduce the denitrification process while it
can enhance aerobic condition in the soil and nutrient availability. The
study area receives the highest amount of rainfall in August every year
that made waterlogging a common phenomenon in the study area with poorly
drained crop fields (Araya et al., 2015). On the other hand, the high
oxygen demands due to crop residue retention that increased microbial
activities in general might be a cause for oxygen deficiency and nitrate
respiration in CA-based systems. MacPOR refers to the void spaces with
diameter > 50 µm formed by soil aggregate formation in
finer textured soil with higher SOM. The higher SOC sequestration rates
and storage with an increase in PAWC in CA-based systems demonstrates
that they can play a vital role to reduce net CO2emissions and protect the livelihoods of the poor through adapting and
mitigating climate change. Adaptation strategies of CA-based systems are
related to improvement in PAWC in the root zone and thus minimize the
effects of declining rainfall and dry spells and high soil surface
temperature through retaining crop residue on crop yields (Giller et
al., 2009). In addition, the improvements in MacPOR in CA-based systems
contribute to draining excess rainfall and reduce crop yield loss due to
waterlogging.
The suitability of soil for s ustaining plant growth and
biological activity is a function of its physical properties (porosity,
infiltration rate, water holding capacity, soil structure). Food
production such as crop yield is an indicator of the soil functioning
(Hatfield et al., 2017). The highest grain and straw yields were
recorded in PRB compared to CF and lowest in CT (Table 5 and 6)
indicating an improvement in soil properties of the PRB treatments
(Table 6; Fig. 8). The higher wheat yield with CA-based practices as
compared to CT was due to cumulative positive changes that occurred on
bacteria, fungi, SOC and KFS in the CA-based systems (Table 6; Fig. 8).
CA-based systems compared to CT improved SMBC and bacteria and fungi
density of microorganisms (Table 1 and 2). An increase in SMBC, bacteria
and fungi density can improve nutrient cycling and absorption of
nutrients by plant roots (Kabir et al., 1998; Chávez-Romero et al.,
2016) that might support the improvements in crop yields under CA-based
systems. FAO (2018) also reported from the same experimental plots that
the greater yield in CA-based systems was due to lower weed density,
reduced tillage, crop residue management, in-situ water and soil
conservation and less runoff and higher rainwater use efficiency.
Chávez-Romero et al. (2016) reported similar findings.
4.4 Structural equation modeling
CA-based practices (PRB and CF) influenced soil biological properties
such as density of bacteria and fungi significantly and thus, enhanced
KFS and SOC, respectively (Table 6; Fig. 8; Six et al., 2004). Increased
density of bacteria in CA-based systems positively affected KFS and
improved wheat yield with PRB followed by CF as compared to CT (Table 6;
Fig. 8). Frankenberger (1979) and many other authors reported that an
increase in bacteria density reduced KFS in short-term experiments
because of blockage of pores by the bacteria. However, long-term effects
of bacteria improved KFS that led to improvements in wheat yield (Fig.
8) in this study might be associated to the incorporation of dead
bacteria into the soil. Similar to the bacteria effects, increased fungi
density have a positive effect on SOC accumulation (Table 6; Fig. 8). On
other hand, an increased SOC have improved wheat yield in CA-based
systems as compared to CT. Plants allocate a substantial portion of
their photosynthetic products belowground to support their root systems
(Gill and Finzi, 2016). The fate of this carbon includes root
structures, root symbionts, autotrophic (root and root symbiont)
respiration, storage compounds, exudates, volatile organic compounds,
and the extraradical fungal hyphae associated with mycorrhizal roots.
The fungal hyphae provides an efficient mechanism for distributing plant
carbon throughout the soil, facilitating its deposition into soil pores
and onto mineral surfaces, where it can be protected from microbial
attack (Frey, 2019). According to Frey (2019), mycorrhizal exudates and
dead tissues contribute to play a dominant role in SOC formation and
stabilization.
5. Conclusions
The present study suggests clearly that
CA-based systems can significantly
alter physical, chemical and biological characteristics of the soil and
influence the functional capacity of the soil as compared to CT systems.
Abiotic soil properties such as bulk density, aggregate stability and
KFS were significantly higher in CA-based systems compared to CT while
there was no significant difference in pH and CEC. SMBC, bacteria and
fungi population and AMF spore density were significantly improved with
PRB followed by CF and lowest in CT. SOC sequestration rates, nutrient
availability such as soil N and P, water availability (PAWC), drainage
for excess rainwater (MacPOR), soil aeration (AC) and food production
(wheat grain yield) were significantly improved with CA-based systems as
compared to CT through minimizing soil disturbance and retention of crop
residues at harvesting in CF and PRB systems. AMF spore abundance and
root length colonization was also enhanced due to low level of soil
disturbance in PRB and CF. However, the full benefit of permanent raised
beds plus CA can only be expected after several years. Notwithstanding
the overall better results of PRB compared to CF, the latter tillage
system can be recommended as a first step for improving soil health
whilst increasing crop yield. The long-term goal should be to achieve a
permanent raised bed planting system along with the use of crop residues
(PRB). Hence, CA-based systems (PRB and CF) can be recommended for
large-scale dissemination and implementation to improve soil health and
wheat yield on Vertisols and possibly on other soils in northern
Ethiopia.
References
Adhikari, K., Hartemink, A.E. 2016. Linking soils to ecosystem
services—A global review. Geoderma. 15;262: 101-11.
Al-Karaki, G., McMichael, B.Z.A.K.J., Zak, J., 2004. Field response of
wheat to arbuscular mycorrhizal fungi and drought
stress. Mycorrhiza , 14 (4), pp.263-269.
Anderson, J.P.E., Domsch, K.H. 1978. A physiological method for the
quantitative measurement of microbial biomass in soils. Soil biology and
biochemistry, 10(3), pp.215-221.
Anderson, T.A., Guthrie, E.A., Walton, B.T. 1993. Bioremediation in the
rhizosphere. Environ. Sci. Technol. 27, 2630–2636
Araya, T., Nyssen, J., Govaerts, B., Deckers, J. Cornelis, W.M. 2015.
Impacts of medium term conservation agriculture-based farming systems on
optimizing seasonal rainfall partitioning and productivity on Vertisols
in northern Ethiopia. Soil Tillage Res. 148, 1-13.
Araya T, Nyssen, J, Govaerts B, Deckers, J, Sommer, R., Bauer, H,
Gebrehiwot, K, Cornelis, W.M. 2016a. Seven years resource-conserving
agriculture effect on soil quality and crop productivity in the
Ethiopian drylands. Soil and Tillage Research. 163:99-109.
Beare MH, Hu S, Coleman DC, Hendrix PF. 1997. Influences of mycelial
fungi on soil aggregation and organic matter storage in conventional and
no-tillage soils. Applied Soil Ecology. 5(3):211-9.
Benson, H. J. 2001. Microbiological Applications: A Laboratory Manual in
General Microbiology. /Harold J. Benson. USA: The McGraw− Hill
Companies, pp 76-200.
Bezuayehu, T., Gezahegn, A., Yigezu, A., Jabbar, M.A., Paulos, D. 2002.
Nature and causes of land degradation in the Oromiya Region: a review.
Socio-economics and Policy Research Working Paper 36. ILRI
(International Livestock Research Institute), Nairobi, Kenya, 34 pp.
Bremner, J.M., Mulvaney, C.S. 1982. Nitrogen-total. In: Black, C.A.
(Ed.), Methods of Soil Analysis. Part II. Agronomy Series No. 9. ASA,
Madison, WI, USA, pp. 595– 624.
Brundrett, M., Bougher, N., Dell, B., Giove, T., Malajczuk, N.
1996.Working with mycorrhiza in forestry and agriculture. ACIAR
Monograph, 32
Bünemann, E.K., Bongiorno, G, Bai, Z., Creamer, R.E., De Deyn, G., de
Goede, R., Fleskens, L., Geissen, V, Kuyper, T.W., Mäder, P., Pulleman,
M. 2018. Soil quality–A critical review. Soil Biology and Biochemistry,
1; 120:105-25.
Cerdeira, A.L., Gazziero, D.L., Duke, S.O., Matallo, M.B. 2010.
Agricultural impacts of glyphosate-resistant soybean cultivation in
South America. Journal of agricultural and food
chemistry , 59 (11), pp.5799-5807.
Chávez-Romero, Y., Navarro-Noya, Y.E., Reynoso-Martínez, S.C.,
Sarria-Guzmán, Y., Govaerts, B., Verhulst, N, Dendooven, L, Luna-Guido,
M. 2016. 16S metagenomics reveals changes in the soil bacterial
community driven by soil organic C, N-fertilizer and tillage-crop
residue management. Soil and Tillage Research. 159:1-8.
Cornelis, W.M., Khlosi, M., Hartmann, R., Van Meirvenne, M., De Vos, B.
2005. Comparison of unimodal analytical expressions for the soil-water
retention curve. Soil Science Society of America Journal. 69(6):1902-11.
Cornelis, W.M., Corluy, J., Medina, H., Hartmann, R., Van Meirvenne, M.,
Ruiz, M. 2006. A simplified parametric model to describe the magnitude
and geometry of soil shrinkage. Eur. J. Soil Sci. 57, 258–268.
De Leenheer, L. 1959. Werkwijzen van de analysen aan het Centrum voor
Grondonderzoek. Rijkslandbouwhogeschool Gent, Ghent.
de M, S., Carlos, J., Cerri, C.C., Dick, W.A., Lal, R., Solismar Filho,
P.V., Piccolo, M.C., Feigl, B.E., 2001. Organic matter dynamics and
carbon sequestration rates for a tillage chronosequence in a Brazilian
Oxisol. Soil Science Society of America Journal , 65 (5),
pp.1486-1499.
Doran, J.W., Zeiss, M.R. 2000. Soil health and sustainability: managing
the biotic component of soil quality. Applied soil ecology, 15(1):3-11.
Eisenhauer, N., Bowker, M. A., Grace, J. B., and Powell, J. R. 2015.
From patterns to causal understanding: Structural equation modeling
(SEM) in soil ecology. Pedobiologia (Jena). 58, 65–72.
doi:10.1016/j.pedobi.2015.03.002.
Ellis, J.M., Griffin, J.L. 2002. Soybean (Glycine max) and cotton
(Gossypium hirsutum) response to simulated drift of glyphosate and
glufosinate. Weed Technology , 16 (3), pp.580-586.
Erkossa, T., Itanna, F., Stah, K. 2007. Microbial Biomass carbon as a
sensitive indicator of soil quality changes. Ethiopian Journal of
Natural Reso 9, 141–153.
FAO, 2018. What is conservation agriculture?
http://www.fao.org/ag/ca/laa.htmi,(accessed 28 February 2018)
Feller, C., Albrecht, A., Tessier, D. 1996. Aggregation and organic
matter storage in kaolinitic and smectitic tropical soils. Structure and
organic matter storage in agricultural soils. 309-59.
Feng, P.C., Baley, G.J., Clinton, W.P., Bunkers, G.J., Alibhai, M.F.,
Paulitz, T.C., Kidwell, K.K. 2005. Glyphosate inhibits rust diseases in
glyphosate-resistant wheat and soybean. Proceedings of the
National Academy of Sciences , 102 (48), pp.17290-17295.
Fierer, N., Lauber, C.L., Ramirez, K.S., Zaneveld, J., Bradford, M.A.,
Knight, R. 2012. Comparative metagenomic, phylogenetic, and
physiological analyses of soil microbial communities across nitrogen
gradients. ISME J. 61, 1007–1017
Flower, K.C., Cordingley, N., Ward, PR., Weeks, C. 2012. Nitrogen, weed
management and economics with cover crops in conservation agriculture in
a Mediterranean climate. Field Crops Research. 132:63-75.
Frankenberger Jr, W.T., Troeh, F.R., Dumenil, L.C. 1979. Bacterial
effects on hydraulic conductivity of soils. Soil Science Society of
America Journal, 43(2), pp.333-338.
Frey, S.D. 2019. Mycorrhizal Fungi as Mediators of Soil Organic Matter
Dynamics. Annual Review of Ecology, Evolution, and
Systematics, 50, pp.237-259.
Frey, J.E., Ellis, J.R. 1997. Relationship of soil properties and soil
amendments to response of Glomus intraradices and soybeans. Can. J. Bot.
75:483-491.
Friedrich, T., Derpsch, R., Kassam, A. 2012. Overview of the global
spread of conservation agriculture. Field Actions Science Reports. The
journal of field actions.Special Issue 6.
Giesy, J.P., Dobson, S., Solomon, K.R., 2000. Ecotoxicological risk
assessment for Roundup® herbicide. In Reviews of environmental
contamination and toxicology (pp. 35-120). Springer, New York, NY.
Gill, A.L., Finzi, A.C. 2016. Belowground carbon flux links
biogeochemical cycles and resource-use efficiency at the global scale.
Ecol. Lett. 19:1419–28
Giller, K.E., Witter, E., Corbeels, M., Tittonell, P. 2009. Conservation
agriculture and smallholder farming in Africa: the heretics’ view. Field
crops research. 114(1):23-34.
Giovannetti, M., Mosse, B. 1980. An evaluation of techniques for
measuring vesicular arbuscular mycorrhizal infection in roots. New
Phytol, 84:489–500
Grace, J.B. 2006. Structural Equation Modeling and Natural Systems.
Cambridge. Cambridge, U.K. Available at:
https://books.google.co.za/books?hl=en&lr=&id=1suuMOChHWcC&oi=fnd&pg=PA326&dq=grace+2006+structural+equation&ots=Fl0DxeykVp&sig=O2H2LJvHxia2fmGrgPB5I_GE7dY#v=onepage&q=grace
2006 structural equation&f=false.
Greenland, D.J. 1977. Soil damage by intensive arable cultivation:
temporary or permanent? Phil. Trans. R. Soc. Lond. B. 281 (980):193-208.
Hart, P.B.S., Sparling, G.P., Kings, J.A. 1986. Relationship between
mineralisable nitrogen and microbial biomass in a range of plant
litters, peats, and soils of moderate to low pH. New Zealand Journal of
Agricultural Res. 29: 681-686.
Hatfield, J.L., Sauer, T.J., Cruse, R.M. 2017. Soil: The forgotten piece
of the water, food, energy nexus. In Advances in Agronomy Vol. 143, pp.
1-46. Academic Press.
He, X.H., Critchley, C., Bledsoe, C. 2003. Nitrogen transfer within and
between plants through common mycorrhizal networks (CMNs). Critical
Reviews in Plant Sciences, 22 (6), pp.531-567.
Helgason, B.L., Walley, F.L., Germida, J.J. 2010. No-till soil
management increases microbial biomass and alters community profiles in
soil aggregates. Applied Soil Ecology, 46: 390-397.
DOI:10.1016/j.apsoil.2010.10.002
Helgason, T., Daniell, T.J., Husband, R., Fitter, A.H., Young, J.P.W.
1998. Ploughing up the wood-wide web?. Nature, 394(6692), pp.431-431.
Horwath, W. 2007. Carbon cycling and formation of soil organic matter,
in: E.A. Paul (Ed.), Soil Microbiology, Ecology and Biochemistry, third
ed., Elsevier, USA, pp. 303-339.
Jansa, J., Mozafar, A., Anken, T., Ruh, R., Sanders, I., Frossard, E.
2002. Diversity and structure of AMF communities as affected by tillage
in a temperate soil. Mycorrhiza. 1;12 (5):225-34.
Kabir, Z. 2005. Tillage or no-tillage: impact on mycorrhizae. Canadian
Journal of Plant Science 1;85 (1):23-9.
Kabir, Z., O’Halloran, I.P., Widden, P., Hamel, C. 1998. Vertical
distribution of arbuscular mycorrhizal fungi under corn (Zea mays L.) in
no-till and conventional tillage systems, Mycorrhiza 8, 53–55.
Kay, B.D., Angers, D.A. 2001. Soil Structure. Soil physics
companion.249.
Kemper, W.D., Rosenau, R.C. 1986. Aggregate stability and size
distribution. In: Klute A (ed) Methods of Soil Analysis, Part I. Soil
Science Society of America, Madison, WI, pp 425–442
Kibblewhite, M.G., Ritz, K, Swift, M.J. 2007. Soil health in
agricultural systems. Philosophical Transactions of the Royal Society B:
Biological Sciences. 363(1492):685-701.
Kremer, R., Means, N. Kim, S. 2005. Glyphosate affects soybean root
exudation and rhizosphere micro-organisms. International Journal
of Environmental Analytical Chemistry , 85 (15), pp.1165-1174.
Laitinen, P., Rämö, S., Siimes, K. 2007. Glyphosate translocation from
plants to soil–does this constitutes a significant proportion of
residues in soil? Plant and Soil , 300 (1-2), pp.51-60.
Lal, R. 2004a. Soil carbon sequestration to mitigate climate change.
Geoderma, 123:1–22
Lal, R. 2004b. Soil carbon sequestration impacts on global climate
change and food security. Science.
304:1623–1627. doi:10.1126/science.1097396 [PubMed]
Lavelle, P., Martin, A. 1992. Small-scale and large-scale effects of
endogeic earthworms on soil organic matter dynamics in soils of the
humid tropics. Soil Biology and Biochemistry. 24(12):1491-8.
Lienhard, P., Tivet, F., Chabanne, A., Dequiedt, S., Lelièvre, M.,
Sayphoummie, S., Leudphanane, B., Prévost-Bouré, N.C., Séguy, L., Maron,
P.A., Ranjard, L. 2013. No-till and cover crops shift soil microbial
abundance and diversity in Laos tropical grasslands. Agronomy for
sustainable development. 33(2):375-84.
MacCallum, R. C., and Austin, J. T. (2000). Applications of Structural
Equation Modeling in Psychological Research. Annu. Rev. Psychol. 51,
201–226. doi:10.1146/annurev.psych.51.1.201.
Madejón, E., Moreno, F., Murillo, J.M., Pelegrín, F. 2007. Soil
biochemical response to long-term conservation tillage under semi-arid
Mediterranean conditions. Soil Tillage Res. 94, 346-352.
Mathew, R., Feng, Y., Githinji, L., Ankumah, R., Balkcom, K. 2012.
Impact of no-tillage and conventional tillage systems on soil microbial
communities. Applied and Environmental Soil Science Article ID 548620, p
1-10.
McCune, B., and Grace, J. B. 2002. Analysis of Ecological Communities.
MjM Softwa. Gleneden Beach, Oregon, USA.
Mc Gonigle, T.P., Miller, M.H., Evans, D.G., Fairchild, D.G., Swann,
J.A. 1990. A new method which gives an objective measure of colonization
of roots by vesicular-arbuscular mycorrhizal fungi. The New
Phytologist,115: 495-501.
Miller, R.M., Jastrow, J.D. 1992. The Role of Mycorrhizal Fungi in Soil
Conservation 1. Mycorrhizae in sustainable agriculture.
(mycorrhizaeinsu):29-44.
Mozafar, A., Anken, T., Ruh, R., Frossard, E. 2000. Tillage intensity,
mycorrhizal and nonmycorrhizal fungi, and nutrient concentrations in
maize, wheat, and canola. Agronomy Journal. ;92(6):1117-24.
Olsen, S.R. 1954. Estimation of available phosphorus in soils by
extraction with sodium bicarbonate. United States Department of
Agriculture, Washington.
Opolot, E., Araya, T., Nyssen, J., Al‐Barri, B., Verbist, K., Cornelis,
W.M. 2016. Evaluating in Situ Water and Soil Conservation Practices with
a Fully Coupled, Surface/Subsurface Process‐Based Hydrological Model in
Tigray, Ethiopia. Land Degradation & Development. 27(8):1840-52.
Powlson, D.S, Stirling, C.M, Thierfelder, C., White, R.P., Jat, M.L.
2016. Does conservation agriculture deliver climate change mitigation
through soil carbon sequestration in tropical agro-ecosystems?
Agriculture, Ecosystems & Environment. 220:164-74.
Pugesek, B. H., Tomer, A., Eye, A. Von (2003). Structural Equation
Modeling: Applications in Ecological and Evolutionary Biology.
Cambridge. Cambridge, U.K.
https://books.google.co.zw/books?hl=en&lr=&id=zgjxnPKoKlAC&oi=fnd&pg=PP1&dq=structural+equation+modeling+biology&ots=ZxDux2_Yzo&sig=iUXj-0twnCLjgE3sqJlMIMKn0Zs&redir_esc=y#v=onepage&q=structural
equation modeling biology&f=false [Accessed January 13, 2020].
Reynolds, W.D., Drury, C.F., Tan, C.S., Fox, C.A., Yang, X.M. 2009. Use
of indicators and pore volume-function characteristics to quantify soil
physical quality. Geoderma. 152 (3-4):252-63.
Sarukhán, J., Whyte, A., Hassan, R., Scholes, R., Ash, N., Carpenter,
S.T., Pingali, P.L., Bennett, E.M., Zurek, M.B., Chopra, K., Leemans, R.
2005. Millenium Ecosystem Assessment: Ecosystems and human well-being.
Sayre, K.D. 1998Ensuring the use of sustainable crop management
strategies by small wheat farmers in the 21st century. Wheat special
report no. 48. Mexico, D.F.: CIMMYT.
Schinner, F., Ohlinger, R., Kandeler, E.R., Margesin. 1996. (eds).
Indirect estimation of microbial biomass. In ‘Methods in soil biology’.
pp. 47–75. Springer-Verlag: Heidelberg
Scholenberger, C.J., Simon, R.H. 1945. Determination of exchange
capacity and exchangeable bases in soil-ammonium acetate method. Soil
Sci.59, 13–24.
Sharma, A.K., Srivastava, P.C., Johri, B.N. 1994. Contribution of VA
mycorrhiza to zinc uptake in plants. Biochemistry of Metal
Micronutrients in the Rhizosphere. Lewis Publishers, Boca Raton, FL ,
pp.111-124.
Six, J., Bossuyt, H., Degryze, S., Denef, K. 2004. A history of research
on the link between (micro) aggregates, soil biota, and soil organic
matter dynamics. Soil and Tillage Research. 79(1):7-31.
Six, J., Feller, C., Denef, K., Ogle, S.M., Sa, J.C.D., Albrecht, A.
2002. Soil organic matter, biota and aggregation in temperate and
tropical soils—effects of no-tillage. Agronomie 22:755–775.
doi:10.1051/ agro:2002043
Srivastava, D., Kapoor. R., Srivastava, S.K., Mukerji, K.J. 1996.
Mycorrhizal research A priority in agriculture. pp. 41-75. In: K.G.
Mukerji (ed.), Concepts in Mycorrhizal Research. Kluwer Academic
Publishers, Dordrecht, The Netherlands.
van Genuchten, M., 1980. A closed form equation for predicting the
hydraulic conductivity of unsaturated soils. Soil Sci. Am. J. 44,
892–898.
Walkley, A., Black, T.A. 1934. An examination of the Degtjareff method
for determining soil organic matter: and a proposed modification of the
chronic acid titration method. Soil Sci. 37, 29–38.
Wang, Y., Tu, C., Cheng, L., Li, C., Gentry, L.F., Hoyt, G.D., Zhang,
X., Hu, S. 2011. Long-term impact of farming practices on soil organic
carbon and nitrogen pools and microbial biomass and activity. Soil.
Till. Res117, 8–16.
West, T.O., Post, W.M. 2002. Soil organic carbon sequestration rates by
tillage and crop rotation. Soil Science Society of America Journal.
66(6):1930-46.
White, R., 2006. Principles and Practice of Soil Science, 4 edition
Oxford, UK.
Zhou, D.M., Wang, Y.J., Cang, L., Hao, X.Z., Luo, X.S., 2004. Adsorption
and cosorption of cadmium and glyphosate on two soils with different
characteristics. Chemosphere , 57 (10), pp.1237-1244.
Table captions Table 1. Soil microbial biomass carbon content, bacteria and
fungi population in the different treatment under CA (PRB and CF) and CT
in Gum SelasaTable 2. Mycorrhizae spore abundance and root colonization of
wheat roots under the different conservation agriculture based systems
in Gum SelasaTable 3. Selected physicochemical soil properties in
conservation agriculture based systems during the 2013 rainy season in
Gum Selasa experimental siteTable 4. Effects of conservation agriculture based systems on
soil function at the topsoil (0-10 cm) in in 2013 at Gum Selasa
experimental siteTable 5. Effects of conservation agriculture based systems on
wheat grain and straw yield during the 2013 rainy season in Gum SelasaTable 6. Estimates and their confidence intervals and
associated P-values derived from piecewise structural equation
modelling for the predictors of wheat grain yield (YLD), soil organic
carbon in the top 10 cm (SOC), hydraulic conductivity (KFS), and soil
aggregate stability index (SI). Predictors with an associatedP-value lower than 0.1 are in bold