Experimental addition of olive mill waste compost in ...

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Experimental addition of olive mill waste compost in an old agroecosystem: identifying main shortterm vegetation responses Claudia Angiolini, 1✉ Email [email protected] José Vesprini, 2 Paolo Castagnini, 1 Patricia Torres, 3 Alessia Nucci, 1 Claudia Perini, 1 1 Dipartimento di Scienze della Vita, Universita degli Studi di Siena, via P.A. Mattioli 4, I-53100 Siena, Italy 2 Facultad de Ciencias Agrarias, UNR, IICAR CONICET, cc14 S2125, ZAA, Zavalla, Argentina 3 Facultad de Ciencias Agrarias, UNR, CIUNR, cc14 S2125, ZAA, Zavalla, Argentina Received: 27 October 2016 / Accepted: 7 January 2018

Abstract Olive mill wastewater (OMW) is the main residual product of olive processing and its disposal can represent a relevant environmental issue in Mediterranean countries. OMW is characterised by high pollutant load, salinity and phytotoxic levels of polyphenols, but also by a high amount of organic compounds and plant mineral nutrients. In this perspective, a technology named MATReFO was developed, with a final dry product easy

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to transport for commercial use. Here, we assessed the short-term effects of OMW and MATReFO applications on spontaneous vegetation in an old agroecosystem. Following a randomized block design soil was amended with different quantities of OMW or MATReFO and vegetation was sampled for 4 years after the treatments. Multivariate and univariate analyses of plant data showed that: (1) only high volume of MATReFO affected spontaneous vegetation, whereas OMW and low volume mixture addition did not determine significant effects; (2) plant species composition and abundance varied significantly among years and exhibited considerable variation over the study period, particularly with high volume of MATReFO; (3) vegetation dynamic had already undergone first steps of natural succession in control and almost all treatments. Our results revealed no negative effects of olive mill waste compost addition in plant community assemblage, since vegetation changes can be mainly related to the abandonment of soil tillage. Therefore, we can assert that OMW and MATReFO can be discharged in abandoned agroecosystems without shortterm effects on natural vegetation.

Keywords Uncultivated fields Olive mill wastewater (OMW) Plant community Soil amendments Vegetation succession PERMANOVA

Introduction Olive oil production is a very important economic activity, particularly for Spain, Italy and Greece. Moreover, olive oil production is no longer restricted to the Mediterranean basin, and new producers such as Australia, USA, South America and North Africa will also have to face the environmental problems posed by olive-mill wastes. AQ1 AQ2

The chemical composition of olives, which is the raw material for olive oil extraction, is very variable and depends on factors such as the olive variety,

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soil type and climatic conditions, but in general it consists of 18–28% oil, 40–50% water and stone (pit), 30–35% pulp (Niaounakis and Halvadakis 2004). Following olive oil extraction a large quantity of liquid and solid residues is produced, with a high organic load, the nature of which depends on the extraction system employed. The three-phase system, introduced in the 1970s to improve extraction yield, produces three streams: pure olive oil, olive-mill wastewater (OMW) and a solid by-product called olive cake. From an environmental point of view, OMW is considered the most critical waste emitted by olive mills in terms of both quantity and quality (Niaounakis and Halvadakis 2004) in relation to its high organic load and its chemical composition, which makes it resistant to degradation. The OMW contains almost all of the water-soluble chemical compounds present in the olive fruit, showing a high organic load with a high C/N ratio and a pH between 4 and 6. The organic fraction is characterised by a large amount of proteins, lipids and polysaccharides. Unfortunately OMW also contains toxic components that inhibit microbial growth (Capasso et al. 1995; Ramos-Cormenzana et al. 1996), as well as the plant germination and growth (Linares et al. 2003). Phenolic compounds are the main determinants of antimicrobial and toxic actions of olive-mill wastes. Due to their instability, OMW phenols tend to polymerise during storage into condensed high-molecular-weight polymers that are difficult to degrade (Ayed et al. 2005; Crognale et al. 2006). The uncontrolled disposal of OMW has become a great problem in Mediterranean countries just because of their polluting effects on soil and water (Sierra et al. 2001; Piotrowska et al. 2006). On the other hands, many beneficial effects of OMW application for soil have been reported by several authors (D’Addabbo et al. 1997; Kotsou et al. 2004; Alburquerque et al. 2007; Brunetti et al. 2007). Although several techniques for OMW management exist (Niaounakis and Halvadakis 2006), they appear to be too expensive to implement for most olive-oil producers; the only viable technology for managing organic waste such as OMW that is both environment-friendly and cost-effective was recognized to be composting (Paredes et al. 2005), even if recent studies showed that some macrofungi, as Abortiporus biennis and Hericium erinaceus, demonstrated the potential to detoxify OMW (Koutrotsios and Zervakis 2014; Koutrotsios et al. 2016). The Italian law 574/96 regards the spread of OMW in agricultural lands limiting the volume of discharge. In cases of risks of provision-water pollution, damages to living resources or to

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ecological systems, and in cases of torrent, this practice is restricted. Besides the mentioned toxic effects by themselves, there are physical problems associated with voluminous OMW spreading. In this perspective, a new patented composting technology using OMW for agronomic purposes was developed (Fontanazza et al. 2000; Altieri et al. 2005). The process termed MATReFO (patent WO/2005/082814) consists essentially of mixing raw, destoned OMW with appropriate hygroscopic organic wastes (such as straw, wool waste, sawdust, leaves and twigs from olive mill, pruning residues). The resulting mixture is packaged in net sacks and stored outdoors in stacks protected from rain for a period of 3 months, allowing natural aerobic microbial activity. The final product is a stable organic matter rich in humuslike compounds and relatively dry, easy to transport for commercial use (Altieri et al. 2008). Land application of OMW is reported as an economical solution (Lucas and Peres 2009), and the most frequent used is on agroecosystems (grasslands, extensive and intensive crops, agricultural soils) as an organic fertiliser (Nieto and Hoyos 1994; de Ursinos and Padilla 1992; Paredes et al. 1999; Roig et al. 2006). AQ3

Very frequently, OMWs are discharged on uncultivated lands, old fields, or non-suitable areas for agricultural uses and mainly devoted to livestock, sometimes with plant communities with high conservation value. Spreading can affect in a selective way the community components enhancing or threating prairies productivity. As far as we know, the field applications and impact of OMW on spontaneous vegetation remain restricted to severely degraded habitat and still little studied (Clemente et al. 2012; Pardo et al. 2014). The vegetation responses of this amendment under field conditions and its effect on plant community composition and succession must be studied further in order to provide accurate information improving the management of OMWs and the agroecosystems where they are discharged. The purpose of this work was to test the hypothesis that a single addition of MATReFO or OMW at different quantities on the spontaneous species can have effects in short-term on vegetation composition, abundance and succession, conducting an experiment in an old field under Mediterranean climatic conditions. Our main interest was to find a level of oil wastes that

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could be applied to field without influence the plant communities assemblages and causing a reduction of biodiversity. Additionally, the effects of OMW and MATReFO addition in short-term vegetation dynamics was documented, in relation to the different treatments.

Materials and methods Study site Investigations were carried out in an area belonging to the Botanical Garden of the University of Siena (Tuscany, Italy), 43.313540 Lat N, 11.330611 Long E (UTM WGS 84). The area has not been used for agricultural activities in the past 20 years. Soil tillage was conducted sporadically in order to preserve the grassland dynamics and to avoid the growth of shrubs and trees, thus maintaining the typical Tuscan landscape. The last tilling was performed in spring 2006 before the starting of the present study, and hereafter no more modifications were done. Climatic conditions were typically Mediterranean, with rainfall concentrated −1 mainly from autumn to spring (mean 810 mm year ) and mean monthly air temperature ranging from 5 °C in January to 22.3 °C in July (mean of 13.2 °C −1 year ) (see http://agrometeo.arsia.toscana.it/). The vegetation consisted of an annual, ephemeral, weed ruderal community that hosts, in microhabitats with soils richer in organic matter, a perennial pioneer vegetation rapidly colonizing open areas.

Experimental design and OMW application Twenty-five plots (2 × 2 m) separated with buffer zones in order to avoid contamination were assigned following a randomized block design. Five treatments with five replicates were performed in April 2006 and consisted in: 2 (1) addition of MATReFO mixture at 8 kg/m (M), (2) addition of MATReFO 2 2 mixture at 40 kg/m (MM), (3) addition of OMW at 8 kg/m (S), (4) addition 2 of OMW at 40 kg/m (SS), (5) non addition (C). As described before the MATReFO mixture was obtained from a previously destined two-phase mill (72%), wool waste (11%), wheat straw (8.5%) and saw dust (8.5%). The 2 dosage of 40 kg/m , exceeding five times the limit fixed by law (at the time of the experiment) for OMW dispersal, was an attempt to improve the knowledge about the resistance of wild plants and to test if a much higher dosage could be however used without risks for natural vegetation.

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Soil data Soil data are those indicated in the report of Altieri et al. (2008) for the study area. In Table 1 are reported the main physico-chemical features of the soil before the treatments. It is a loam (USDA classification) subalkaline soil with a medium percentage of limestone, a good presence of organic matter and + nitrogen and a high presence of exchangeable K . Table 1 Features of sampled soil at the starting of the study at Botanical Garden of Siena (from Altieri et al. 2008) Coarse sand

3%

Fine sand

49%

Loam

27%

Clay

21%

Total limestone

19%

Organic matter

3.20%

Total nitrogen

0.19%

Exchangeable potassium

0.73 meq/100 g

Cation exchange capacity

15.0 meq/100 g

pH

8.00

On April 2007, 1 year after the study started, soil was sampled in order to analyze the main modifications occurred at the organic component of the superficial soil. The results of these soil analyses in 2007 (Table 2) underlined an increase of Total Organic Carbon (TOC), Extracted Total Carbon (TEC) and humic acid + fulvic acid (HA + FA) in all the plots with Mill waste and MATReFO mixture with respect to the Control, even if with no statistical evidence (Student–Newman–Keuls test performed with p < 0.05), due to the high variability. Also for soil humification no significant differences were 2 found, except for HI value of Mill waste at 8 kg/m , resulted to be the lower with respect to the others.

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Table 2 Carbon analysis in superficial (0–20 cm) sample soil 1 year after the starting of the study (from Altieri et al. 2008) TOC

TEC

HA + FA

DH

HR

Control

1.40

1.23

0.90

71.1

65.1

0.46

181.5a

OMW at 8 kg/m2

1.78

1.46

1.20

82.0

68.0

0.22

190.6ab

OMW at 40 kg/m2

1.89

1.49

1.07

72.0

56.5

0.40

238.6ab

MATReFO mixture at 8 kg/m2

1.79

1.41

1.03

72.1

58.6

0.41

253.8b

MATReFO mixture at 40 kg/m2

1.81

1.62

1.18

72.7

65.7

0.42

274.7b

HI

DOM

−1

TOC total organic carbon, TEC extracted total carbon, HA + FA humic acid + fulvic acid, HD humification degree = (HA + FA) × 100/TEC, HR humification rate = (HA + FA) × 100/TOC, HI humification index = [TEC − (HA + FA)]/(HA + FA), DOM organic matter dissolved in water. The different letters indicate significant differences among mean DOM values of the treatments (p < 0.05; from Altieri et al. 2008)

The analysis of variance reported only for Organic Matter Dissolved in water (DOM) in some cases showed significant differences; particularly, highest 2 DOM was found in MATReFO mixture at 40 kg/m with respect both to the 2 Control and to Mill waste at 40 kg/m . A higher level of this parameter has a positive influence on plant nutrition, even if could create pollution risk for aquifer. However, the highest DOM value was found only in plots with a dose of MATReFO exceeding five times the maximum level allowed by Italian Law (n°574/96).

Vegetation sampling and data Since June 2006, vegetation was sampled yearly (2006–2009 in June) recording abundance estimated as cover percentage. In Appendix I the species recorded during the sampling period and their frequency in the 5 different treatments are reported. Nomenclature of vascular plant species and families followed Conti et al. (2005) and The Plant List (2013). In order to characterize the ecological and dynamic changes of vegetation 11/01/2018, 10:10

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present in the study area in successive years we attributed to each species: (1) some Ellenberg Indicator Values (EIVs) (Ellenberg et al. 1991), recalibrated and completed for the Italian Flora according to Pignatti (2005) and expressed as integer number in a range of values (1–9 or 1–12). EIVs have been tested and validated for ecological studies by numerous authors (Gilardelli et al. 2015). Specifically, the Ellenberg indicator values considered for this study were: ‘Moisture’ (M) and ‘Nutrients’ (N). Following Diekmann and Dupré (1997), species with moisture (M) ≥ 5.0 were considered linked to mesic sites while the others to dry or semi-dry sites, while following Meltzer and Van Dijk (1986), species with nitrogen demand (N) ≥ 5 were considered nitrophilous; (2) growth forms (that often implicate the life span of a species) discerned in annual species, perennial herbaceous species, woody species; (3) vegetational categories. Collecting species of phytosociological classes with same ecological conditions as follows: ruderal species (Stellarietea and Artemisietea species), semi-natural grassland species (MolinioArrhenateretea, Trifolio-Geranietea and Festuco-Brometea species), forest species (Querco-Fagetea and Rhamno-Prunetea species). For this purpose phytosociological classes were attributed according to their indication as ‘diagnostic’, “constant or abundant” for classes (or related lower syntaxa) in Biondi and Blasi (2015) and Biondi et al. (2014).

Data analysis The dataset was analyzed using permutational multivariate analysis of variance (PERMANOVA) (Anderson 2001; Mc Ardle and Anderson 2001, 2005). In this study we tested the simultaneous response of plant species (considered as variables) with respect to two factors in an ANOVA experimental design on the basis of Euclidean distance, using permutation methods. The two factors (fixed) were “treatment” (five levels) and “year” (four levels) and the interaction between both of them (treatment × year) was also considered. The tests for each factor and the interaction were calculated using 5000 permutations with unrestricted permutation method (Torres et al. 2010). We included pair-wise comparisons of levels for factors. A canonical analysis of principal coordinates (CAP) (Anderson and Robinson 2003; Anderson and Willis 2003; Anderson 2004) based on Euclidean distance was also performed to examine differences among treatments levels, years levels and interactions in a constrained ordination diagram. The aim was to find the axis (or axes) that best discriminated among the a priori groups (factor’s levels) with the advantage that the analysis takes into account the

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correlation structure among variables. The method includes a test by permutation, as described by Anderson and Willis (2003) and Anderson and Robinson (2003). We run 5000 unrestricted permutations to test the hypothesis that treatments could influence plant species composition and plant succession in the community. Principal Response Curves (PRC) were performed to focus the effect of treatments on species assemblage changes over time (Lepš and Šmilauer 2003) using Canoco 4.5 for Windows (ter Braak and Šmilauer 2002). For the interpretation of multivariate analysis results on vegetation ecology and dynamic, Ellenberg indicator values, growth forms and vegetational categories cover were also calculated as weighted averages (Diekmann 1995). Differences between the means of ecological attributes of vegetation condition data from first and last sampling time were tested using ANOVA (p < 0.05; performed with STATISTICA 6.0, StatSoft Inc. 1995).

Results PERMANOVA analysis showed significant differences of vegetation composition among treatments and among years, but there were no interactions among years and treatments (Table 3). The lack of interactions between factors allowed to compare them separately. There were statistically significant differences in the composition and abundance-cover between the year 2006, when tilling and waste addition was performed, and the following ones. Differences between 2006 and 2007 were marginally significant and also the year 2007 differed significantly from the year 2009 (Table 4). When treatments were compared, we found that high volume mixture treatment (MM) differed from all the other treatments; the control differed from high volume treatments (SS and MM treatments). High volume mixture and waste (MM and SS) were significantly different; hence high volume mixture (MM) differed significantly from low volume treatments (M and S). There were not significant differences between control and low volume treatments as prescribed by law (S and M) (Table 5). Table 3 PERMANOVA based on Euclidean distance of vegetation composition of 25 plots in response to treatment and time (significance level at 0.05)

Treatment

4

505.1

126.3

1.6964

0.0082

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Year

3

518.6

172.8

2.3222

0.0004

Interaction

12

762.2

63.5

0.8533

0.8754

Residual

80

5954.8

74.4

Total

99

7740.7

Table 4 Pairwise comparisons for factor “year” (significance level at 0.05)

2006 versus 2007

1.2922

0.0830

2006 versus 2008

1.5719

0.0162

2006 versus 2009

2.0969

0.0004

2007 versus 2008

1.0731

0.2660

2007 versus 2009

1.6124

0.0064

2008 versus 2009

1.2437

0.1004

Table 5 Pairwise comparisons for factor treatment (significance level at 0.05)

Control versus SS

1.3607

0.0510

Control versus MM

1.6432

0.0024

Control versus M

1.0968

0.2286

Control versus S

1.1427

0.1934

SS versus MM

1.4108

0.0316

SS versus M

1.0930

0.2418

SS versus S

0.9732

0.4284

MM versus M

1.2422

0.0834

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MM versus S

1.5206

0.0166

M versus S

1.2572

0.0978

AQ4

Results of PERMANOVA analysis were strongly supported by the graphs obtained by CAP. The first axis of the CAP with plots ordered by year (Fig. 1a) explained a 25% of the total variation. The graph markedly discriminated the plots according to the time elapsed from the treatment, separating the first year from the following three. All the censuses performed in the plots during 2006 were positioned in the right part of the first axis, while the censuses of the third and fourth year were set in the left part of the first axis and clearly overlaid. The second year after treatments occupied an intermediate position closer to the third and the fourth year. The second axis explained 9% of the total variation and separated the second year from the other three. The third and the fourth year since plowing and waste addition were strongly overlaid. Fig. 1 Canonical analysis of principal coordinates (CAP) graph for data. a Year of 2 sampling; b treatments. C no addition (diamond shape); S mill waste (8 kg/m ) 2 2 (X); SS mill waste 40 kg/m (open square); M mixture 8 kg/m (X); MM 2 mixture 40 kg/m (open triangle)

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The results of CAP for treatments (Fig. 1b) clearly divided them along the principal axes. The main axis explained a 26% of the total variation and discriminated on the basis of waste volume. The control treatments plots were positioned in the fourth quadrant, and shared the narrowness to the first axis with the low volume mixture treatment. High volume treatments (MM and SS) occupied the right part of the first axis. The second axis explained a 9.1% of the total variation and separated the plots by the kind of waste, setting the plots treated with mill waste in the upper part while the plots treated with the

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mixture were located in the negative part of the second axis. PCR showed the temporal trajectory of the community composition for the experimental treatments (Fig. 2). The control treatmen non addition overlyed the horizontal position. All the treatments followed the same change direction towards the positive side of the vertical axis during the 4 years. Only the treatment MM deeply diverged from the others in the fourth year of succession, with the decline or increase of very few species, a trend that could be explained with the lack of interactions between years and treatments. Fig. 2 Principal response curves (PRC). Ely rep Elymus repens, Arr ela Arrenatherum elatiu, Bro ere Bromus erectus, Cir arv Cirsium arvense, Dac glo Dactilys glomerata, Dau car Daucus carota, Fes pra Festuca pratense, Gal apa Galium aparine, Med rig Medicago rigidula, Vic hyb Vicia hybrida, Vic sat V.sativa. C 2 2 no addition; S mill waste (8 kg/m ); SS mill waste 40 kg/m ; M mixture 2 2 8 kg/m ; MM mixture 40 kg/m

The standard deviation in the PRC scores among the M, S, SS treatments in the census for the year 2009 was 1.5 times lower than the deviation of the initial scores registered in 2006 (2 month after the treatments were applied),

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suggesting that there were not real differences among the treatments for the last year. The value in the first PRC axis for the MM treatment departured in the fourth year of succession 4.72 times the average of the other three treatments, underlying a real difference with the MM treatment, according with the results of the pairwise comparisons (Table 5). The species weights in the first axis of the redundancy analysis (RDA) reported on the right side of Fig. 2 can be interpreted as the affinity of each species with the curves of principal response. For instance, Elymus repens displayed the highest positive weight, thus the species, with Cirsium arvense and Galium aparine with lower weight, resulted to be associated with MM treatment in the last year of succession. Dactylis glomerata had the lowest negative weight; the species was not present in the MM treatment but showed high values of abundance in the control the third and the fourth year since ploughing and waste addition. Some species of Leguminosae (i.e., Medicago rigidula, Vicia sativa and V. hybrida) showed a same trend, but with lower negative weight. Table 6 showed: (1) EIVs, indicating that in 2006 the vegetation was made up mainly of ruderal species with higher nitrogen demand (N-value) with respect to the 2009. Conversely, the species moisture demand (M-value) remained substantially stable over the time considered, with a slight dominance of species linked to dry habitats; (2) growth forms, with perennial herbs dominating almost all plots in 2009 and their cover increasing significantly from 2006 to 2009, whereas annual plants cover dropped; the only exception was MM treatment, were their cover increases significantly; (3) a higher percentage of ruderal in 2006 than in 2009 and, at the same time, the cover of semi-natural grassland and forest species that increased significantly in 2009 respect to 2006. Table 6

Mean ± SD in the first and last sampling time of cover of Ellenberg indicator value, growth forms and e

M≥5

2006

2009

24.7 ± 19.9

24.8 ± 12.1

p NS

2006

2009

21 ± 9.8

20.4 ± 8.1

p NS

The statistical significance of differences between years were tested by ANOV NS not significant

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2006

2009

M