Abstract

Establishment of groundcovers (GC) is an efficient practice to reduce soil and nutrient losses in olive orchards, so they can act as a sink of atmospheric carbon and improve soil fertility. The aim of this study was to assess the carbon sequestration potential of several species used as groundcovers in two olive orchards. The experiment was conducted during three growing seasons in two olive orchards in Andalusia (Spain). In an experimental field, a grass (Brachypodium distachyon) and two crucifers (Eruca vesicaria and Sinapis alba) were used; in the other experimental field, three legumes were sown: common vetch (Vicia sativa), bitter vetch (Vicia ervilia) and hairy vetch (Vicia villosa). In both fields the species were sown and compared with the spontaneous vegetation of the area. The carbon release from groundcovers was studied and soil organic carbon (SOC) analysed during the decomposition period to assess the atmospheric carbon fixation. The increments of SOC in the first 20 cm of soil reached higher values with crucifers and grass than legumes. Sinapis alba obtained the best result with 2.56 Mg SOC ha−1 yr−1. Establishment of groundcovers are an efficient tool for atmospheric carbon sequestration and to protect the soil from erosion.

INTRODUCTION

The Mediterranean climate is characterised by very hot and dry summers and a gap of precipitation in this season. Moreover, the annual variability of precipitation is high, with droughts and intense rainfall events (Aguilera et al. 2013; Taguas & Gómez 2015). Olive is well-adapted to this environment and it is not particularly demanding in terms of water and nutrients, thus it has been traditionally cultivated in marginal areas of low fertility and shallow soils located on hills (Taguas & Gómez 2015). Furthermore, European subsidies coming from the Common Agricultural Policy have promoted the expansion of olive trees on inadequate areas with steep slopes (Martínez-Fernández & Esteve 2005; Nekhay et al. 2009). These soils have been gradually degraded due to erosion, which is favoured by some inappropriate practices such as intensive tillage (Rodríguez-Entrena et al. 2014). In addition, erosive processes have caused the loss of soil organic matter (Martínez-Mena et al. 2008).

Mediterranean soils are poor in organic matter (Kassam et al. 2012), which is essential in soil fertility, but organic matter also makes the soil more water permeable (Franzluebbers 2002). The removal of plant residues decreases the soil organic carbon (SOC) (Pulleman et al. 2005), at the same time increasing soil erodibility (Beniston et al. 2015).

Groundcovers (GC) in the inter-row of olive trees have proven to be an efficient practice to reduce soil and nutrient losses (Ordóñez-Fernández et al. 2007a; Gómez et al. 2009a). Moreover, GC can play an important role in increasing SOC, while improving the environmental quality of production systems (Reicosky & Archer 2007). The use of GC between rows of woody crops, seeded or spontaneous, has been extended in the last decade as an environmentally sustainable tool to control erosion and reduce pollutants (Francia et al. 2006; Rodríguez-Lizana et al. 2007), improve soil structure and soil water storage (Palese et al. 2014), and soil microbial activity and diversity (Sofo et al. 2014).

On the other hand, agricultural soils play an important role in regulating the carbon dioxide (CO2) content in the atmosphere, and can act either as carbon sources or sinks. SOC is one of the most important terrestrial carbon sinks and sources of exchange with atmospheric CO2 (Lal 2004; Baker et al. 2007; Farina et al. 2011).

Some authors (Lal 1997; Smith et al. 2000; Jarecki & Lal 2003) have reported that changes in soil management practices increase SOC and carbon sequestration. The introduction of alternative soil-management practices to tillage, such as leaving the ground untilled and sowing groundcovers, has decreased erosion and increased fertility (Hernández et al. 2005; Rodríguez-Lizana et al. 2007; Castro et al. 2008; Gómez et al. 2009a; Nieto et al. 2010; Montanaro et al. 2017). Over the last 25 years, tests have been carried out comparing different systems of soil management (Pastor 2008; González-Sánchez et al. 2015). Some of the most notable positive results have been seen with no-till and reduced-till systems or cultivation with cover crops. Thus, this conservative practice improves SOC by atmospheric fixation and by the reduction of tillage that increases carbon emissions due to the rupture of the soil aggregates (Repullo-Ruibérriz de Torres et al. 2012; Márquez-García et al. 2013).

Most studies compare soil management systems, but a few works make a comparison between different GC species belonging to different plant families. The aim of this study has been to assess the CO2 sequestration potential of six different species used as GC and two groups of spontaneous vegetation in two olive orchards. The initial hypothesis of the work considers that the more C input provided by groundcovers the more SOC is fixed into the soil. It is also expected that seeded groundcovers would produce a higher amount of biomass than spontaneous vegetation.

MATERIALS AND METHODS

Experimental field 1

The experiment was conducted at ‘Arenillas’ olive orchard farm, in Fernán Núñez, Córdoba, southern Spain (37°40′2″ N, 4°47′5″ W and 300 m above mean sea level). The plot has an 11% average slope and the soil is a Vertic haploxerept (Soil Survey Staff 2014). The physicochemical characteristics of the soil are shown in Table 1.

Table 1

Physicochemical characteristics of the soil of experimental field 1

Depth (cm) pH (H2O) pH (CaCl2Sand (%) Silt (%) Clay (%) Textural class 
0–10 8.14 7.66 6.0 43.5 50.5 Silty clay 
10–20 8.23 7.66 9.8 39.1 51.1 Silty clay 
20–40 8.28 7.68 8.4 41.7 49.9 Silty clay 
40–60 8.36 7.74 8.8 41.8 49.4 Silty clay 
Depth (cm) OM (%) CaCO3 (%) N (%) P (mg kg−1) K (mg kg−1) CEC (molc kg−1) 
0–10 0.85 29.88 0.04 06.51 326.20 0.24 
10–20 0.72 28.50 0.03 13.60 369.45 0.22 
20–40 0.65 31.75 0.02 09.86 271.75 0.23 
40–60 0.58 33.06 0.02 10.95 209.67 0.22 
Depth (cm) pH (H2O) pH (CaCl2Sand (%) Silt (%) Clay (%) Textural class 
0–10 8.14 7.66 6.0 43.5 50.5 Silty clay 
10–20 8.23 7.66 9.8 39.1 51.1 Silty clay 
20–40 8.28 7.68 8.4 41.7 49.9 Silty clay 
40–60 8.36 7.74 8.8 41.8 49.4 Silty clay 
Depth (cm) OM (%) CaCO3 (%) N (%) P (mg kg−1) K (mg kg−1) CEC (molc kg−1) 
0–10 0.85 29.88 0.04 06.51 326.20 0.24 
10–20 0.72 28.50 0.03 13.60 369.45 0.22 
20–40 0.65 31.75 0.02 09.86 271.75 0.23 
40–60 0.58 33.06 0.02 10.95 209.67 0.22 

Note: OM: organic matter; CEC: cation exchange capacity; N: total nitrogen; P: available phosphorus; K: exchangeable potassium.

The experimental design was created using randomised complete blocks sited perpendicular to the slope, with four replicates. The ‘Picual’ variety olive trees are 15 years old and were planted at a distance of 4 m × 8 m. The single plot measured 192 m2 and it consisted of two central olive trees with a groundcover strip of 12 m × 4 m to each side. The weeds in the olive trees row strip (4 m wide) were controlled by systemic herbicide (glyphosate 36%) in spring at 4–5 L ha−1 dose. As regards the soil management before the experiment started, soil had been conventionally tilled with a disc harrow two or three times per year.

The groundcovers were: a commercial grass cover called ‘Vegeta’ (Brachypodium distachyon L.) (BRA), and two cruciferous species, rocket (Eruca vesicaria (L.) Cav.) (ERU) and common mustard (Sinapis alba L. subsp. Mairei (H. Lindb. Fil.)) (SIN). A spontaneous cover consisting of typical weed flora of the area was the control treatment. The sowing dates were between October and November depending on weather conditions. Eruca vesicaria and Sinapis alba seeds were previously collected from spontaneous wild populations and replicated in the Andalusia Research Centre, IFAPA Alameda del Obispo (Córdoba, Spain). Cruciferous seeds were sown and buried at 0.5 cm depth following the procedures established in previous field studies (Alcántara et al. 2009). The doses of seeds were 10 and 3 kg ha−1 for common mustard and rocket, respectively, which were sown for 3 years. Brachypodium distachyon was only sown the first year at a rate of 100 kg ha−1 of commercial product, 30 kg seeds ha−1, and left on the surface following commercial recommendations. After the first year, Brachypodium was established from a groundcover strip which had been left to self-seed the first year; therefore this species was self-seeded the following seasons. The selected GC were compared with the spontaneous vegetation of the area (Spon1), mainly composed by mallows, Convolvulus arvensis, Diplotaxis virgata, Lolium rigidum and Taraxacum officinale.

Experimental field 2

In the other experimental field, three leguminous plants, usually used as GC in Mediterranean areas, were sown: common vetch (Vicia sativa L.) (VS), bitter vetch (Vicia ervilia L.) (VE) and hairy vetch (Vicia villosa Roth.) (VV). They were studied and compared with vegetation that grew naturally in the field (Spon2); Medicago polymorfa, Bromus sp., Diplotaxis virgata, Hordeum leporinum and Anagallis arvensis were identified as the most abundant species. The legumes were sown at a rate of 200 kg ha−1 and buried by cultivator each season in October or November depending on the weather conditions. Before sowing in the first season, the soil had been tilled with a disc harrow.

Field 2 is located in the IFAPA (Andalusian Institute for Research and Training in Agriculture, Fisheries and Food) ‘Alameda del Obispo’ Experimental Station near the Guadalquivir River in Cordoba (Spain) (37°51′25″ N, 4°48′28″ W). The average slope of the experimental plot is 2% and it has 95 m of elevation. The soil is classified as Typic calcixerept according to Soil Survey Staff (2014). The physicochemical characteristics of the soil are shown in Table 2.

Table 2

Physicochemical characteristics of the soil of experimental field 2

Depth (cm) pH (H2O) pH (CaCl2Sand (%) Silt (%) Clay (%) Textural class 
0–5 8.69 7.73 56.6 26.2 17.3 Sandy loam 
5–10 8.73 7.72 58.7 26.8 14.5 Sandy loam 
10–20 8.86 7.83 57.0 27.3 15.7 Sandy loam 
20–40 8.82 7.84 56.2 28.2 15.5 Sandy loam 
40–60 9.11 8.05 62.6 25.3 12.0 Sandy loam 
Depth (cm) OM (%) CaCO3 (%) N (%) P (mg kg−1) K (mg kg−1) CEC (molc kg−1) 
0–5 2.25 15.51 0.05 05.95 266.23 0.13 
5–10 2.34 15.94 0.06 07.98 259.98 0.14 
10–20 1.96 15.76 0.04 02.79 178.03 0.13 
20–40 2.07 15.38 0.03 05.12 150.73 0.13 
40–60 1.37 20.90 0.03 02.32 108.73 0.13 
Depth (cm) pH (H2O) pH (CaCl2Sand (%) Silt (%) Clay (%) Textural class 
0–5 8.69 7.73 56.6 26.2 17.3 Sandy loam 
5–10 8.73 7.72 58.7 26.8 14.5 Sandy loam 
10–20 8.86 7.83 57.0 27.3 15.7 Sandy loam 
20–40 8.82 7.84 56.2 28.2 15.5 Sandy loam 
40–60 9.11 8.05 62.6 25.3 12.0 Sandy loam 
Depth (cm) OM (%) CaCO3 (%) N (%) P (mg kg−1) K (mg kg−1) CEC (molc kg−1) 
0–5 2.25 15.51 0.05 05.95 266.23 0.13 
5–10 2.34 15.94 0.06 07.98 259.98 0.14 
10–20 1.96 15.76 0.04 02.79 178.03 0.13 
20–40 2.07 15.38 0.03 05.12 150.73 0.13 
40–60 1.37 20.90 0.03 02.32 108.73 0.13 

Note: OM: organic matter; CEC: cation exchange capacity; N: total nitrogen; P: available phosphorus; K: exchangeable potassium.

The olive trees belong to the ‘Picual’ variety, 15 years old, and the plantation pattern is 6 m × 5 m. The experimental design was created using randomised complete blocks with five replicates. The single plots consisted of one central olive tree with a groundcover strip of 10 m × 3.5 m to each side. The width of the cover strip, 3.5 m, corresponded to the width of the cultivator. The weeds in the olive trees row strip (2.5 m wide) were controlled by systemic herbicide (glyphosate 36%) in spring at 4–5 L ha−1 dose. Before the experiment started, soil had been treated with pre-emergence herbicide, but the previous season, no management had been done on the soil.

Sampling and analysis of samples

In field 1, residue biomass and soil samplings were taken after the mechanical mowing of the GC at the beginning of May every year. In field 2, the first biomass sampling was collected a few days before the mechanical mowing of the GC. From that date onwards and up to the autumn sowing of the new GC, plant residues were periodically sampled. In both fields, the mowing of the GC was performed with a hammer cutter. This type of machine with a horizontal axis allows a more homogeneous distribution of the residues than those that have a vertical axis, which have a certain windrower effect (Ordóñez-Fernández et al. 2018). The study was carried out during three different growing seasons in each experimental field: 2008–2010 in field 1 and 2013–2015 in field 2.

The residue biomass was estimated from the stubble collected in a 0.25 m2 metal frame, which served to delimit the sampling area and was placed at all the selected points. In field 1, three residue collection points were established per block, which made a total of 12 samples per type of cover and sampling date. In field 2, one residue biomass sample was collected per block, which means five samples per GC on each sampling date.

The collected residue was sent to the laboratory, where it was weighed to measure the fresh weight, washed with deionised water to prevent contamination in the subsequent analysis, and placed in an oven at 65 °C until it reached a constant weight to estimate the amount of dry matter.

The soil surface (0–5 cm) was sampled at the beginning and end of the decomposition period of the groundcover every year. At the beginning and at the end of 3 years of study, soil samples were taken at depths of 0–5, 5–10 and 10–20 cm from each subplot to assess the effect of groundcover decomposition at each depth. The soil samples were taken with an Edelman auger. Core cylinders of known volume (100 cm3) were used to measure the bulk density. The soil samples were air-dried and sieved through a 2-mm mesh sieve for their subsequent analysis.

Total C and total N in the residue samples was analysed in a LECO (TRUSPEC, CNS; St. Joseph, MI, USA) elemental analyser. The determination of SOC is based on the Walkley-Black chromic acid wet oxidation method. Oxidisable matter in the soil is oxidised by 1N K2Cr2O7 solution. The reaction is assisted by the heat generated when two volumes of H2SO4 are mixed with one volume of the dichromate. The remaining dichromate is titrated with ferrous sulfate. The titre is inversely related to the amount of C present in the soil sample (Sparks et al. 1996).

Carbon release from residues

The residual amounts of organic carbon (kg ha−1) remaining at each residue sampling were calculated using the product of the dry matter of the residues and the concentration of carbon on the same sampling date.

The C release from the different groundcover residues was calculated every year, understanding as such the difference between the content of this element in the residues when they were mowed and that estimated in the residue samples collected on the different dates, both on a dry weight basis: 
formula
(1)
where Ct (kg ha−1) is the amount of C remaining in the residue at time t, and C0 (kg ha−1) is the amount of C remaining in residues when these were mowed (kg ha−1).
In order to describe the reduction of the amount of remaining C in the residue, a single negative exponential model (Olson 1963) has been fitted to the data, which are described by Equation (2): 
formula
(2)
where Ct is the amount of C remaining at time t, C0 is the estimated amount of C remaining in residues when these were mowed, k (days−1) is the C release rate constant and t is time (in days after mowing).

Soil organic carbon and fixed carbon

The amount of fixed carbon in soil was estimated from the increment of SOC from the beginning of the experiment (0) to a determined date (t): 
formula
(3)
SOC was calculated according to Equation (4) in each depth interval (0–5, 5–10 and 10–20 cm): 
formula
(4)
where ρB is the bulk density of soil.
For the 0–20 cm interval depth, the accumulated SOC was calculated using Equation (5): 
formula
(5)
where i is a determined sampled depth interval (0–5, 5–10 and 10–20 cm) and n the number of depth intervals.

CO2 sequestration was determined from the values of SOC increments using the molecular weight ratio (1 g C = 3.67 g CO2).

Analysis of data

An analysis of variance (ANOVA) was performed for all the measured parameters, which are decomposed biomass, carbon release, and SOC. In addition, a comparison of means was carried out by the LSD-test with P ≤ 0.05. Previously, the homogeneity of variance, the random distribution of residuals and the normal distribution of errors had been tested.

The meteorology of both areas was monitored during the 3-year study in each field, assessing rainfall and maximum and minimum daily temperature data. The data were taken from the network of agricultural weather stations (RIA) of the Andalusian Regional Ministry of Agriculture, Fisheries and Rural Development (Spain).

RESULTS AND DISCUSSION

Carbon release from groundcovers

Decomposition of residues from the studied species was different. The patterns of decomposition can be explained considering the specific soil and climate conditions, the soil management practice and the composition of the different species. Crop residue decomposition is generally described by single exponential models whose decay constants are empirically determined (Quemada 2004). Figures 1 and 2 show the patterns described by the remaining carbon in fields 1 and 2.

Figure 1

Temporal evolution of the remaining carbon content in residue during the decomposition periods in every season considered in field 1. The data have been fitted to a single exponential model.

Figure 1

Temporal evolution of the remaining carbon content in residue during the decomposition periods in every season considered in field 1. The data have been fitted to a single exponential model.

Figure 2

Temporal evolution of the remaining carbon content in residue during the decomposition periods in every season considered in field 2. The data have been fitted to a single exponential model.

Figure 2

Temporal evolution of the remaining carbon content in residue during the decomposition periods in every season considered in field 2. The data have been fitted to a single exponential model.

Significant differences in the amount of remaining C between the species within the same experimental field were statistically studied (Tables 3 and 5). In field 1, the species BRA usually provided statistically higher amounts except in the third season when the four GC species had similar behaviour. Only Spon1 was higher than BRA in the first sampling and ERU lower at the last one.

Table 3

ANOVA performed on the C remaining in residue for the different treatments on field 1 in each season and sampling dates compared using LSD-test (P ≤ 0.05)

Season 1, days after mowing 22 46 60 108 136 157 
Brachypodium distachyon 
Eruca vesicaria bc ab 
Sinapis alba 
Spontaneous 1 
Season 2, days after mowing 30 46 67 109 129 172 
Brachypodium distachyon 
Eruca vesicaria 
Sinapis alba ab 
Spontaneous 1 ab 
Season 3, days after mowing 37 82 107 136 163  
Brachypodium distachyon  
Eruca vesicaria ab  
Sinapis alba ab  
Spontaneous 1 ab  
Season 1, days after mowing 22 46 60 108 136 157 
Brachypodium distachyon 
Eruca vesicaria bc ab 
Sinapis alba 
Spontaneous 1 
Season 2, days after mowing 30 46 67 109 129 172 
Brachypodium distachyon 
Eruca vesicaria 
Sinapis alba ab 
Spontaneous 1 ab 
Season 3, days after mowing 37 82 107 136 163  
Brachypodium distachyon  
Eruca vesicaria ab  
Sinapis alba ab  
Spontaneous 1 ab  

Different letters between species at each date represent significant differences.

Weather conditions influenced the development of the GC and their decomposition after mowing, varying the amount of released C every year (Tables 4 and 6). In the third season of field 1, excessive rainfall in the autumn months, when GC were recently planted, limited the growth of the species. The high rainfall recorded in the autumn months caused waterlogged soil conditions unsuitable for the establishment and development of the plants and reduced their growth in the developing stage in comparison with the second season. Only during the autumn (September–December) of 2009, was there recorded rainfall of 450 mm in Córdoba, which is slightly lower than a year of standard rainfall levels in this area.

Table 4

Decomposed biomass residue, carbon release and percentage of remaining C at the end of decomposition period in every season of study in field 1. Fit of a single exponential model to the data of C remaining in every season of study and groundcover

Groundcover Biomass loss [kg ha−1C rele. [kg ha−1Rem. C [%] C0 [kg ha1k (×103) [days1R2 
 Season 1 (156 days)
 
Exponential model season 1
 
BRA 5,252.9 2,157.0 28.0 3,590.05 7.456 0.85 
ERU 1,350.3 588.1 50.5 1,381.03 3.412 0.57 
SIN 1,540.3 665.2 46.9 1,553.36 4.049 0.56 
Spon1 1,063.6 461.6 47.6 912.03 3.516 0.59 
 Season 2 (171 days)
 
Exponential model season 2
 
BRA 3,640.0 1,910.8 58.5 4,323.75 2.710 0.85 
ERU 3,411.7 1,471.3 ab 52.6 3,273.24 3.993 0.94 
SIN 1,160.3 534.2 79.1 2,713.06 1.434 0.40 
Spon1 1,745.2 ab 741.2 bc 73.4 2,989.36 2.303 0.84 
 Season 3 (162 days)
 
Exponential model season 3
 
BRA 1,630.1 614.4 60.4 1,519.87 3.211 0.98 
ERU 2,937.7 ab 1,145.6 ab 31.8 1,689.45 5.457 0.78 
SIN 3,477.0 1,372.6 38.4 1,983.22 4.841 0.87 
Spon1 3,809.4 1,493.6 34.9 2,110.35 5.892 0.96 
Groundcover Biomass loss [kg ha−1C rele. [kg ha−1Rem. C [%] C0 [kg ha1k (×103) [days1R2 
 Season 1 (156 days)
 
Exponential model season 1
 
BRA 5,252.9 2,157.0 28.0 3,590.05 7.456 0.85 
ERU 1,350.3 588.1 50.5 1,381.03 3.412 0.57 
SIN 1,540.3 665.2 46.9 1,553.36 4.049 0.56 
Spon1 1,063.6 461.6 47.6 912.03 3.516 0.59 
 Season 2 (171 days)
 
Exponential model season 2
 
BRA 3,640.0 1,910.8 58.5 4,323.75 2.710 0.85 
ERU 3,411.7 1,471.3 ab 52.6 3,273.24 3.993 0.94 
SIN 1,160.3 534.2 79.1 2,713.06 1.434 0.40 
Spon1 1,745.2 ab 741.2 bc 73.4 2,989.36 2.303 0.84 
 Season 3 (162 days)
 
Exponential model season 3
 
BRA 1,630.1 614.4 60.4 1,519.87 3.211 0.98 
ERU 2,937.7 ab 1,145.6 ab 31.8 1,689.45 5.457 0.78 
SIN 3,477.0 1,372.6 38.4 1,983.22 4.841 0.87 
Spon1 3,809.4 1,493.6 34.9 2,110.35 5.892 0.96 

Different letters with the mean values indicate significant differences between species in each season compared with LSD-test (P ≤ 0.05).

Note: C0: remaining C at the beginning provided by the model; k: C release rate constant; R2: coefficient of determination.

In the first season, in which the different GC were established, the BRA germinated very badly but improved over time after some rainfall in the winter season. This delay in the growth of BRA in the first season was also reported by Saavedra & Alcántara (2011). In the subsequent season, BRA was the species that reached the highest biomass in both favourable and adverse weather conditions. At the end of the decomposition period of the three seasons, BRA was the treatment that maintained a higher residue biomass. Thus BRA was the groundcover which provided the best soil protection, in agreement with Repullo-Ruibérriz de Torres et al. (2012).

Over the 3-year study period, the mineralisation of the plant residues of the different GC in field 1 showed carbon release figures of 4,682.2 kg C ha−1 for BRA, 3,205 kg C ha−1 for ERU, 2,572 kg C ha−1 for SIN and 2,696.4 kg C ha−1 for Spon1 (Table 4). The gramineous species released 1.5, 1.8 and 1.7 more C than ERU, SIN and Spon1, respectively.

Regarding the empirically calculated decay constants, all species from field 1 provided values ranging between 2 × 10−3 and 6 × 10−3 days−1, except BRA in the first season, which presented residual biomass and C release values significantly higher than the other species due to low residue level at the end of the decomposition period. SIN was hardly decomposed in the second season, a decay rate of only 1.4 × 10−3 days−1 was obtained. In that season, SIN reached a higher amount of biomass in the developing stage than in the first season. Furthermore, SIN regrew in the middle of the decomposition period, limiting the decomposition because the new biomass had a shorter decay period. The fit of this species in this season adjusted very poorly to the exponential model. Similarly, bad adjusts were obtained in the first season for all treatments, except BRA. The other species did not reach an adequate development and they produced relatively low biomass at mowing that determined their decomposition.

In the second and third seasons BRA was not seeded and there was residue from the previous season that avoided contact with the soil surface, limiting its decomposition. However, the first year without this residue layer, the decomposition was accelerated.

In field 2 the leguminous plants showed greater amounts of remaining carbon in the residue in comparison with the Spon2 in the whole study period (Figure 2). In the first season this was statistically corroborated by the performed ANOVA (Table 5). During most of the season, Spon2 was significantly lower than the legumes. In the following season the differences remained, except in the last sampling when Spon2 regrew and was only significantly different from VS. In the third season, these differences were not statistically significant except with VV, which provided the biggest amount of biomass.

Table 5

ANOVA performed on the C remaining in residue for the different treatments on field 2 in each season and sampling dates compared using LSD-test (P ≤ 0.05)

Season 1, days after mowing LBM 12 57 77 117 146 172 
Vicia sativa 
Vicia ervilia 
Vicia villosa ab 
Spontaneous 2 
Season 2, days after mowing LBM 23 50 84 121 160  
Vicia sativa ab 
Vicia ervilia ab ab 
Vicia villosa ab 
Spontaneous 2 
Season 3, days after mowing LBM 26 81 146    
Vicia sativa ab    
Vicia ervilia bc    
Vicia villosa    
Spontaneous 2 bc    
Season 1, days after mowing LBM 12 57 77 117 146 172 
Vicia sativa 
Vicia ervilia 
Vicia villosa ab 
Spontaneous 2 
Season 2, days after mowing LBM 23 50 84 121 160  
Vicia sativa ab 
Vicia ervilia ab ab 
Vicia villosa ab 
Spontaneous 2 
Season 3, days after mowing LBM 26 81 146    
Vicia sativa ab    
Vicia ervilia bc    
Vicia villosa    
Spontaneous 2 bc    

Different letters between species at each date represent significant differences.

Note: LBM: last sampling before mowing.

Leguminous plants produced a large amount of biomass in the developing stage. The large biomass produced before mowing in conjunction with the faster decomposition of these plants due to the low C:N ratio, produced high biomass losses and carbon releases (Table 6). VV was the species which provided the greatest amounts of released carbon, which was significantly bigger than for other species in the second and third seasons. The carbon release for the 3-year study period was in total 5,250 kg C ha−1 for VS, 3,146.7 kg C ha−1 for VE, 8,437.1 kg C ha−1 for VV and 1,961.2 kg C ha−1 for Spon2 (Table 6). VV released 1.6, 2.7 and 4.3 more C than VS, VE and Spon2, respectively.

Table 6

Decomposed biomass residue, carbon release and percentage of remaining C at the end of decomposition period in every season of study in field 2. Fit of a single exponential model to the data of C remaining in every season of study and groundcover

Groundcover Biomass loss [kg ha−1C rele. [kg ha−1Rem. C [%] C0 [kg ha1k (×103) [days1R2 
 Season 1 (172 days)
 
Exponential model season 1
 
VS 4,486.4 1,952.4 24.6 1,757.94 6.039 0.67 
VE 4,179.2 1,859.4 27.5 2,152.84 5.713 0.90 
VV 4,374.2 2,016.9 22.5 1,748.71 5.715 0.62 
Spon2 921.6 471.2 39.1 785.65 2.572 0.27 
 Season 2 (160 days)
 
Exponential model season 2
 
VS 4,595.2 1,784.8 34.7 2,326.29 5.420 0.92 
VE 1,872.0 709.2 53.7 1,567.89 4.484 0.91 
VV 6,745.6 2,645.2 23.2 2,541.09 6.892 0.83 
Spon2 1,913.6 742.2 25.4 835.78 9.986 0.91 
 Season 3 (146 days)
 
Exponential model season 3
 
VS 3,465.6 1,512.8 24.9 1,777.01 8.676 0.96 
VE 1,507.2 578.1 40.4 986.03 6.343 0.99 
VV 8,772.8 3,775.0 15.3 4,192.01 11.518 0.91 
Spon2 1,936.0 747.8 bc 29.2 748.39 7.502 0.71 
Groundcover Biomass loss [kg ha−1C rele. [kg ha−1Rem. C [%] C0 [kg ha1k (×103) [days1R2 
 Season 1 (172 days)
 
Exponential model season 1
 
VS 4,486.4 1,952.4 24.6 1,757.94 6.039 0.67 
VE 4,179.2 1,859.4 27.5 2,152.84 5.713 0.90 
VV 4,374.2 2,016.9 22.5 1,748.71 5.715 0.62 
Spon2 921.6 471.2 39.1 785.65 2.572 0.27 
 Season 2 (160 days)
 
Exponential model season 2
 
VS 4,595.2 1,784.8 34.7 2,326.29 5.420 0.92 
VE 1,872.0 709.2 53.7 1,567.89 4.484 0.91 
VV 6,745.6 2,645.2 23.2 2,541.09 6.892 0.83 
Spon2 1,913.6 742.2 25.4 835.78 9.986 0.91 
 Season 3 (146 days)
 
Exponential model season 3
 
VS 3,465.6 1,512.8 24.9 1,777.01 8.676 0.96 
VE 1,507.2 578.1 40.4 986.03 6.343 0.99 
VV 8,772.8 3,775.0 15.3 4,192.01 11.518 0.91 
Spon2 1,936.0 747.8 bc 29.2 748.39 7.502 0.71 

Different letters with the mean values indicate significant differences between species in each season compared with LSD-test (P ≤ 0.05).

Note: C0: remaining C at the beginning provided by the model; k: C release rate constant; R2: coefficient of determination.

The decay rates according to the single exponential model were usually higher with legumes in field 2 than with grass and crucifers in field 1 (Tables 4 and 6). Furthermore, Spon2 obtained higher decay constants than Spon1, except in the first season when very little biomass was reached by Spon2 in the developing stage. The fit of Spon2 in the second season did not take into account the last point due to the growth of new spontaneous vegetation observed at the last biomass sampling. Spon2 grew with the autumn precipitations so this point should not be considered for season 2 since it did not affect the carbon release in that season. It would correspond to the developing stage of the third season where it would be taken into account.

Similarly to field 1, the poorest adjusts were also obtained in the first season in field 2. A relatively high variability in the biomass samples and a regrowth of the groundcovers in the penultimate sampling led to low coefficients of determination.

Ruffo & Bollero (2003) indicated that the decomposition dynamic of hairy vetch in their experiment improves soil fertility, but grasses with slower decomposition were better for a soil conservation strategy. The greater decomposition of the legumes is attributed to the lower C:N ratio in comparison with the species used in field 1. Moreover, the spontaneous vegetation was more similar to the species seeded in the same field; thus, in field 2 there were more leguminous plants, like Medicago polymorfa, that grew naturally as part of the Spon2 treatment. However, Spon1 was composed more abundantly by Convolvulus arvensis and other cruciferous and gramineous plants like Diplotaxis virgata and Lolium rigidum. Therefore, all treatments in field 2 had a lower C:N ratio than in field 1. Figure 3 shows the amount of C and N remaining in the residue in all samplings during the 3-year study period. The fit of data to a lineal model (through origin) provides an average of the C:N ratio in the whole study period.

Figure 3

Relationship between the amount of remaining C and remaining N in residue for the whole study period in experimental field 1 (a) and experimental field 2 (b). The data have been fitted to a linear model (through origin).

Figure 3

Relationship between the amount of remaining C and remaining N in residue for the whole study period in experimental field 1 (a) and experimental field 2 (b). The data have been fitted to a linear model (through origin).

The C:N ratio in the treatments of field 1 ranged between 23.98 and 29.43, which are higher values than those obtained in field 2 (14.29–17.40). VS and VV were the species with the lowest C:N ratios, the greatest decay rates and carbon releases. These treatments released 1.12 and 1.80 more carbon than BRA, the treatment that released more carbon in field 1.

Nguyen & Marschner (2017) indicated a higher microbial activity and soil nutrients availability when residue with low C:N ratio is added. Among other variables, the decomposition is highly dependent on the residues quality as substrate for the decomposer community (Gómez-Muñoz et al. 2014). Sariyildiz & Anderson (2003) demonstrated the relationships of litter quality and decomposition rate for a range of plant species. Ordóñez-Fernández et al. (2007b) indicated lignin and C:N ratio as the most important components for residue decomposition.

Although the experimental plots and the meteorological conditions were different, they do not seem to have had as much influence as the C:N ratio, which should have been the main factor for the differences in release, since the precipitation recorded in both fields was quite similar (Table 7). It was 364.1 mm greater in field 1 than in field 2 where the decomposition was higher. The rainfall in field 1 in season 3 was similar to the total registered in the two previous seasons. In this season the decay rates obtained for the four treatments were higher due to the soil moisture and a smaller amount of biomass reached in the developing stage.

Table 7

Rainfall (mm) recorded in the groundcovers season when the study was carried out

Field 1
 
Field 2
 
Season Rainfall [mm] Season Rainfall [mm] 
2007–2008 644.9 2012–2013 796.4 
2008–2009 448.6 2013–2014 612.8 
2009–2010 1114.7 2014–2015 434.9 
2007–2010 2,208.2 2012–2015 1,844.1 
Field 1
 
Field 2
 
Season Rainfall [mm] Season Rainfall [mm] 
2007–2008 644.9 2012–2013 796.4 
2008–2009 448.6 2013–2014 612.8 
2009–2010 1114.7 2014–2015 434.9 
2007–2010 2,208.2 2012–2015 1,844.1 

The period from 1 November to 31 October was considered.

The groundcover season was established from 1 November to 30 October since the sowing date every year was around the end of October and the beginning of November. In addition, the last soil and biomass sampling was around mid-October every year.

The residue moisture measured in each sampling date during the 3-year study period showed a similar behaviour (Figure 4). A few days after mowing, the residue was still quite wet. Moreover, there were some rainfall events in spring that increased the moisture as well. In autumn, the precipitation increased the moisture again and favoured the decomposition. It must be highlighted that the first biomass sampling in field 2 was carried out before mowing, therefore, the moisture content in the samples was higher than the first biomass sampling in field 1 that was taken 1 day after mowing.

Figure 4

Average of the residue moisture in each sampling date and average of monthly rainfall during the decomposition period of the three seasons in experimental field 1 (a) and experimental field 2 (b). Vertical lines represent the standard error of the precipitation per month in the three seasons.

Figure 4

Average of the residue moisture in each sampling date and average of monthly rainfall during the decomposition period of the three seasons in experimental field 1 (a) and experimental field 2 (b). Vertical lines represent the standard error of the precipitation per month in the three seasons.

Some authors such as Aulakh et al. (1991) and Baggs et al. (2000) indicate that moisture is important to accelerate the decomposition process. In order to include the moisture factor in residue decomposition models, Steiner et al. (1999) and Quemada (2004) suggested using corrected temporal scale, also subsequently used by Eusufzai et al. (2013) and Rodríguez-Lizana et al. (2018). Duong et al. (2009), in a study about the dynamics of plant residue decomposition and nutrient release, observed that frequent residue addition (every 4, 8 and 16 days in their treatments) increased C mineralisation by up to 90% compared with a single addition. According to Duong et al. (2009) this increment is probably due to the more constant supply of water soluble components by repeated additions of residues with enhanced microbial activity.

Carbon release rates of these GC were similar to those estimated by Ordóñez-Fernández et al. (2007b) for sunflower stubble (Helianthus annuus) in a study on the decomposition of residues from three annual crops, which was 4.9 × 10−3 days−1, and those measured by Boniche et al. (2008), which was 6.4 × 10−3 days−1, with the residues decomposition after harvest in heart-of-palm plantations (Bactris gasipaes). They are higher than the release rates obtained in pruning residues from olive trees used as mulching (Repullo et al. 2012), which ranged between 1.6 and 2.0 × 10−3 days−1 depending on treatment. This is due to the high C:N ratio of pruning residues and the decomposition period of 2 years.

Quemada (2004), in an experiment with litter bags, obtained values of 11 × 10−3 and 14 × 10−3 days−1 in two seasons with clover residues. However, the decay constants obtained with grasses (rye, wheat and oat) were lower, ranging between 4 × 10−3 and 9 × 10−3 days−1. The values indicated by this author are similar to those obtained with BRA, the grass used in our study, which provided values between 2.7 × 10−3 and 7.5 × 10−3 days−1 (Table 4). In the case of our legumes, the decay rates ranged between 4.5 × 10−3 and 11.5 × 10−3 days−1 (Table 6), which is a slightly lower range than those obtained with clover in Quemada's experiment. It must be pointed out that more precipitation was recorded in that experimental field during the decomposition period.

Soil organic carbon and carbon fixation

Soil tests conducted at the beginning and end of the decomposition period each year enabled us to assess SOC concentration over the three seasons. Figure 5 shows a series of increases and decreases that correspond to the stages before and after mowing of the different GC residues, and in which the SOC value at the surface (0–5 cm) is influenced by the recorded rainfall. As an example, the heavy rainfall between October 2009 and May 2010 greatly reduced the SOC content in field 1. The rain began at a time of year when the groundcovers were not well established and their coverage was low, facilitating runoff, erosion and SOC loss through eroded sediments. Leaching of soluble organic carbon into the soil profile might occur but this fraction represents about 1% of SOC mineralisation (González-Domínguez et al. 2017).

Figure 5

Evolution of rainfall and SOC on the surface (0–5 cm) for the whole study period in field 1 (a) and field 2 (b). Vertical lines represent the standard error. M: mowing; BM: before mowing; AM: after mowing; EDP: end of decomposition period.

Figure 5

Evolution of rainfall and SOC on the surface (0–5 cm) for the whole study period in field 1 (a) and field 2 (b). Vertical lines represent the standard error. M: mowing; BM: before mowing; AM: after mowing; EDP: end of decomposition period.

Vicente-Vicente et al. (2017), who assessed the SOC fractions of olive orchards managed with GC and compared them with bare soils, obtained higher carbon content in the covered soil. Moreover, they observed a higher percentage of unprotected SOC fraction in the first 5 cm than at 5–15 cm. Plant residues left on the surface usually increase the labile carbon fraction more quickly (Carbonel-Bojollo et al. 2015). Dolan et al. (2006) highlighted the importance of obtaining SOC values for the entire profile, because they are subject to more changes on the surface.

In case of the experiment with legumes, the soil had a higher SOC concentration at the beginning, about double that of field 1, and on many occasions SOC at surface decreased during the decomposition period. The initial content of SOC significantly affected the stock rate. The three legumes provided data with less oscillation than grass and crucifers, which raised the SOC content in the second season.

The C input through residues improved SOC at 20 cm depth (Table 8). The annual averages of C fixation were 1.42 Mg SOC ha−1 yr−1 for BRA, 1.17 with ERU, 2.56 SIN and 1.36 Spon1 in field 1. The legumes in field 2 reached lower values: 0.56 with VS, 0.04 with VE and 0.70 Mg SOC ha−1 yr−1 in VV. There was no fixation with Spon2. The tillage performed at sowing every year produces C emissions and the low biomass produced by Spon2 in the study period was not enough to improve the SOC in this treatment. Furthermore, the soil protection of Spon2 was not likely enough to reduce SOC loss through erosion.

Table 8

Soil organic carbon (SOC) (Mg ha−1) increase in the first 20 cm of soil during three growing seasons, annual increment average and annual CO2 fixation

Field Species SOC origin SOC EDP3 ΔSOC3 Annual ΔSOC Annual CO2 fixed 
BRA 12.37 16.65 4.28 1.43 5.23 
ERU 13.01 16.54 3.52 1.17 4.31 
SIN 13.10 20.79 7.69 2.56 9.40 
Spon1 12.80 16.87 4.07 1.36 4.98 
VS 20.00 21.67 1.67 0.56 2.04 
VE 22.41 22.53 0.12 0.04 0.15 
VV 22.67 24.77 2.10 0.70 2.57 
Spon2 22.35 17.45 −4.90 −1.63 −5.99 
Field Species SOC origin SOC EDP3 ΔSOC3 Annual ΔSOC Annual CO2 fixed 
BRA 12.37 16.65 4.28 1.43 5.23 
ERU 13.01 16.54 3.52 1.17 4.31 
SIN 13.10 20.79 7.69 2.56 9.40 
Spon1 12.80 16.87 4.07 1.36 4.98 
VS 20.00 21.67 1.67 0.56 2.04 
VE 22.41 22.53 0.12 0.04 0.15 
VV 22.67 24.77 2.10 0.70 2.57 
Spon2 22.35 17.45 −4.90 −1.63 −5.99 

Note: EDP3: end of decomposition period at season 3; ΔSOC3: increment of SOC in the 3-year study period.

The annual amount of fixed CO2 ranged between 4.3 and 9.4 Mg ha−1 in field 1 (Table 8). In field 2, the initial SOC was higher because the soil was managed without tillage before the experiment started. The SOC increments and the fixation rates were lower in this field since increasing the SOC is more difficult with a high initial C content. González-Sánchez et al. (2012) and Poeplau & Don (2015) found higher rates of SOC accumulation during the first few years when GC were introduced, but they are usually lower when the steady state is reached. The rise in SOC declines with time as the soil approached a new state of equilibrium (Ingram & Fernandes 2001). In addition, experimental field 2 has lower cation exchange capacity, which delays the improvement of soil fertility, and clay content than field 1. The textural composition of the soil can be a determinant factor in terms of the differences in CO2 flows from soil between fields. Comparing CO2 emissions from different cropland locations, Carbonell-Bojollo et al. (2012) found more specific emissions from a soil with less clay content, since having larger-sized pores, like sandy soils, can retain more air and oxidise organic matter. However, it also depends on temperature and moisture and varies between seasons throughout the year.

Furthermore, the root system of legumes is smaller than that of crucifers and grasses (Alcántara et al. 2009; Ola et al. 2015), which was not considered in the residue samples. The results indicated that root system had an influence on the increment of SOC, mainly with crucifers due to their tap root (Wolfe 2000). De Baets et al. (2011) pointed out Sinapis alba has a greater below:above ground biomass ratio than grasses such as rye, ryegrass and oat. Although legumes improved the soil nitrogen, higher carbon sequestration was obtained with the gramineous and cruciferous species.

Our data are in agreement with Smith et al. (2000) who estimated a carbon sequestration between 0.42 and 1.31% year−1 with crop residues incorporation and 0.73% year−1 with no tillage. Hutchinson et al. (2007) obtained average rate of potential C gain from 0.1 to 0.5 Mg ha−1 year−1 with no-till in herbaceous cropping systems.

Higher increments were obtained by Nieto et al. (2013) who indicated an average increase of 4 Mg ha−1 of SOC in the first year of research carried out in three experimental fields comparing GC of weeds and tillage in olive groves. Márquez-García et al. (2013) obtained a fixation of 12.3 Mg CO2 ha−1 year−1 in five different olive orchards, which means 3.35 Mg of SOC comparing GC with tillage systems. Nevertheless, González-Sánchez et al. (2012), in a meta-analysis of carbon capture through the use of conservation agriculture in Spain, reported a carbon sequestration rate of 1.59 Mg C ha−1 year−1 for GC used in woody crops compared with tillage, which is in accordance with our results. In a 7-year experiment carried out by Gómez et al. (2009b), GC doubled SOC with respect to tillage in the first 10 cm of soil. Castro et al. (2008), in a long-term study, obtained a SOC increment of 0.69 Mg C ha−1 year−1 (0–30 cm) with use of GC compared with bare soil controlled by pre-emergence herbicides. They found higher SOC rates when plant residues were incorporated by a pass of a disc harrow which accelerated the decomposition process. The SOC fixation rates provided in those studies compare GC with tillage or bare soil systems, which usually decrease the carbon content. This differs from our experiment that makes comparison between several species and not between systems.

Higher SOC values may be reached with pruning residues mulching as was reported by Repullo et al. (2012) who measured SOC increment ranging between 4.6 and 8.2 Mg C ha−1 year−1 in the first 20 cm of soil with different pruning residues doses in a 2-year study. Nieto et al. (2010) observed how the SOC in two olive groves increased in the first 30 cm of soil from 27.1 Mg ha−1 to 113.6 Mg ha−1 in a calcic vertisol and from 26.4 to 158 in a chromic calcisol as a result of changes in the soil management from tillage to pruning residues application for a period of 6 and 10 years, respectively. Thus, they observed an increase of 14.4 and 13.2 Mg ha−1 in 1 year. Using pruning residues as mulching involves a higher amount of biomass and C input than in our case where only herbaceous plants were used to cover the soil surface.

Vicente-Vicente et al. (2016), in a meta-analysis of C sequestration in Mediterranean woody crops, reported higher C fixation with GC plus organic amendments especially in olive orchards (e.g. pruning debris, composted olive mill pomace). They obtained a C sequestration rate of 1.1 Mg C ha−1 year−1 in olives orchards with GC, which is in agreement with those obtained in our experiment. The fixations provided in the meta-analysis (Vicente-Vicente et al. 2016) were lower in olive orchards than in almond orchards but higher than the one obtained in vineyards.

CONCLUSIONS

The good results observed in terms of increased soil carbon serve to confirm not only the improvement in soil fertility but also the environmental benefits stemming from the establishment of GC between rows of olive trees, in terms of their contribution to reduce erosion and climate change.

Despite the fact that spontaneous vegetation is the most widespread option used by farmers to cover the ground of their olive groves, the results of this study reveal that other types of GC can be more beneficial in order to fix in the soil part of the atmospheric carbon sequestered in plant residues. The selection of species with greater biomass in the shoot and root systems usually increases the C input and, therefore, the SOC. On the other hand, carbon sequestration with GC is usually more effective as the initial carbon content in soil was lower before the change in the soil management practices.

ACKNOWLEDGEMENTS

The authors are grateful to the field and laboratory staff of the Soil Physics and Chemistry team belonging to the Area of Agriculture and Environment at the IFAPA centre ‘Alameda del Obispo’ for their collaboration in the trials, to the project RTA2014-00030-00-00 supported by INIA, FEDER 2014-2020 ‘Programa Operativo de crecimiento inteligente’ and to the project PP.AVA.AVA201601.15 80% co-supported by the European Union via FEDER funds, Operational Program ‘FEDER de Andalucía 2014-2020’.

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