Soil quality: Evaluation of on-farm assessments in relation to analytical index

Soil & Tillage Research 198 (2020) 104565

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Soil quality: Evaluation of on-farm assessments in relation to analytical index

T

Gustavo Pereira Valania,*, Fabiane Machado Vezzanib, Karina Maria Vieira Cavalieri-Polizelic a

Postgraduate Programme in Soil and Plant Nutrition, University of São Paulo, 11 Pádua Dias Av, 13418-900, Brazil Postgraduate Programme in Soil Science, Federal University of Paraná, 1540 Funcionários St, 80035-050, Brazil c Postgraduate Programme in Soil Science, Federal University of Paraná, 1540 Funcionários St, 80035-050, Brazil b

ARTICLE INFO

ABSTRACT

Keywords: SMAF DRES Participative evaluation

Although soil quality assessments are mostly based on analytical approaches, on-farm evaluations help farmers, advisors and researchers to analyse soils rapidly and inexpensively. This study’s hypothesis was that two on-farm soil quality assessments, the Rapid Diagnosis of Soil Structure (DRES) and the Practical Guide for Participative Evaluation of Soil Quality (PGPE) are able to distinguish the quality of soils under different management systems as effectively as the widely-used and analytical strategy SMAF (Soil Management Assessment Framework). Thus, this study aimed to test DRES, PGPE and SMAF in soils under different management systems, as well as to determine the correlation between the results from each on-farm assessment (DRES and PGPE) and SMAF results. Cambisols of conventional farming, no-tillage farming, organic farming, agroforestry systems and native vegetations were sampled in the 0−25 cm layer in two different municipalities with subtropical climate in southern Brazil. SMAF assessment was performed by integrating six soil quality indicators (total organic carbon, microbial biomass carbon, macroaggregate stability, bulk density, soil pH and available soil P) into a final soil quality index. DRES assessment combined on-farm information about soil aggregates, compaction, rupture resistance, root system and biological activity into a final soil quality index. PGPE assessment integrated the on-farm observation of organic matter, root system, soil structure, soil compaction and infiltration, erosion, water retention, soil macrofauna and soil cover into a final soil quality index. The on-farm strategies to assess soil quality DRES and PGPE were proven to be able to distinguish different soil management systems, as well as was the analytical method SMAF. The PGPE distinguished a wider range of sites than DRES, regardless the municipality or soil texture. The PGPE was more correlated with the SMAF than the DRES, especially in clayey and clayey loam soils. These results highlight the value of on-farm soil quality assessments, providing timely results for ease of interpretation.

1. Introduction Soil quality is “the capacity of a specific kind of soil to function, within natural or managed ecosystem boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality, and support human health and habitation” (Karlen et al., 1997). Due to its complexity, soil quality per se cannot be directly measured neither on-farm nor at laboratories. Nevertheless, it can be construed from measuring a number of soil properties considered as soil quality indicators (Cardoso et al., 2013; Zornoza et al., 2015). Several efforts to address the challenge of assessing soil quality resulted in different approaches to integrate soil quality indicators into a final soil quality index. Such methodologies are developed to guide farmers, advisors and researches to understand soil processes and ⁎

ecosystem services to manage soils in order to promote sustainability (Bünemann et al., 2018; Palm et al., 2007). The Soil Management Assessment Framework (SMAF), described by Andrews et al. (2004) has been widely used with a high level of sensitivity to distinguish soils under different management systems. It is an analytical methodology which integrates soil biological, physical and chemical indicators into a single soil quality index. Although a single soil quality index was deemed “unattainable” by Sojka and Upchurch (1999), the SMAF are turning to a standard analytical method to assess soil quality as it has been successfully used in different management systems worldwide (Gelaw et al., 2015; Kalu et al., 2015; Swanepoel et al., 2015; Cherubin et al., 2016; Apesteguía et al., 2017; Şeker et al., 2017). Despite its efficiency, it is costly and time-consuming, which may decrease it suitability under certain circumstances where these

Corresponding author. E-mail addresses: [email protected] (G.P. Valani), [email protected] (F.M. Vezzani), [email protected] (K.M.V. Cavalieri-Polizeli).

https://doi.org/10.1016/j.still.2019.104565 Received 6 September 2019; Received in revised form 20 December 2019; Accepted 24 December 2019 0167-1987/ © 2019 Elsevier B.V. All rights reserved.

Soil & Tillage Research 198 (2020) 104565

G.P. Valani, et al.

factors are limiting (Wienhold et al., 2009). Although SMAF and most approaches to assess soil quality are often based primarily on analytical methods (Bünemann et al., 2018), onfarm assessments of soil quality are considered to be important in management programs as well as in yield gaps analysis (McKenzie et al., 2015). These strategies may help farmers, advisors and researches in a quicker and more affordable analysis of soil quality, with timely results (Emmet-Booth et al., 2016). Among such strategies, the DRES Rapid Diagnosis of Soil Structure (Ralisch et al., 2017a) and the PGPE Practical Guide for Participative Evaluation of Soil Quality (Comin et al., 2016) are examples of practical and rapid assessments of soil quality. These are on-farm approaches which result in a final soil quality index to assess soil quality. DRES consists of a spade test to assess topsoil structure in relation to visual features (Ralisch et al., 2017a). The method was recently published by the Brazilian Agricultural Research Corporation and it was included as a book chapter within the Brazilian Manual of Methods of Soil Analysis (Ralisch et al., 2017b). It is based in other strategies, as the Visual Evaluation of Soil Structure – VESS (Ball et al., 2007; Guimarães et al., 2011) and the Cultural profile methodology (Tavares Filho et al., 1999). According to the authors, the DRES is a feasible and rapid alternative of soil structural assessment, with minimal intervention in the site and vast sensitivity to detect differences in soil management changes. Furthermore, the authors suggest that DRES is easy to perform and totally suitable for tropical and subtropical conditions. However, as the DRES is a new method of soil structure assessment, it needs to be tested to confirm the vast sensitivity and suitability stated by the authors. As soil structure is described in terms of structural form, stability and resiliency (Kay, 1990), different soil management system may affect these three dimensions of soil structure, which are closely related with soil biological, physical and chemical properties (Askari et al., 2015; Rabot et al., 2018; da Silva et al., 2014). Therefore, the use of reliable methods to monitor the contrasting aspects of soil structure is important for soil quality assessments. The PGPE - Practical Guide for Participative Evaluation of Soil Quality (Comin et al., 2016) is another on-farm strategy to assess soil quality. Apart from soil structure, the PGPE also assesses organic matter, root system, soil compaction and infiltration, erosion, water retention, soil macrofauna and soil cover. Although this strategy requires previous reading, understanding and training to the method, it is more accessible than analytical methods, therefore it can be performed by a wider range of the public, including farmers themselves. The PGPE was proposed as a methodology to assess soil quality under no-tillage vegetables (Comin et al., 2016), referred as SPDH in the method description; however, it has a potential to be tested in other agricultural or native ecosystems. Furthermore, the PGPE includes a wider range of soil quality indicators than DRES, which may permit a greater sensitivity to assess soil quality in different management systems, requiring, however, comparative studies. It is interesting to note that the different approaches to assess soil quality may take into consideration similar indicators with different interpretations. Soil aggregates scored with SMAF, for example, interpret the macroaggregates (> 250 μm) stability and consider a maximum score whenever the macroaggregate stability is more than 0.50 (Andrews et al., 2004). DRES evaluation, in contrast, assess aggregates up to 10 cm and consider aggregates between one and four centimetres as the ideal range of aggregate sizes (Ralisch et al., 2017a). Differently from SMAF and DRES, the PGPE assess soil structure as the abundance of visual aggregates and its ease to disruption (Comin et al., 2016). Despite the individuality of each of these assessments, most strategies to assess soil quality integrate soil quality indicators (including biological, physical and chemical) in order to distinguish soil quality under different management systems (Bünemann et al., 2018; Karlen et al., 2019). Therefore, it is important to test different soil quality strategies to investigate whether their different approaches to the same indicators results in similar outcomes.

In fact, divergences within on-farm and analytical methods of soil quality assessment in relation to time, costs and knowledge can be verified. On-farm assessment techniques are known to provide quicker results than analytical measurements (Ball et al., 2007; Batey, 2000), and although they require some training, no specialised pedological knowledge is necessary (Guimarães and Ball, 2017). Analytical measurements, in contrast, are generally costly, time-consuming and require expertise in order to be performed accordingly (Bünemann et al., 2018), especially when soil biological assessments are included (O’Sullivan et al., 2017). In this point of view, it can be inferred that both DRES and PGPE (on-farm methods) might be less costly and less time-consuming than the SMAF (which integrates analytical soil biological, physical and chemical measurements). This study hypothesised that the on-farm methods DRES (Rapid Diagnosis of Soil Structure) and PGPE (Practical Guide for Participative Evaluation of Soil Quality) are able to distinguish the quality of soils under different management systems to a similar level of accuracy as the widely-used SMAF (Soil Management Assessment Framework). Thus, this study aimed to test DRES, PGPE and SMAF in soils under different management systems, as well as to analyse the correlation between on-farm assessments (DRES and PGPE) and SMAF results. 2. Material and methods 2.1. Study sites and soil sampling The study was carried out in the Atlantic Forest biome located on the coast of the Brazilian state of Paraná. Soil samples were taken in sites of two municipalities, Morretes (subtropical Cfa climate) and Lapa (subtropical Cfb climate), totalling 15 sites. Detailed information about each site, its municipality, date of sampling, its coordinates, soil texture, as well as the crop of each agricultural site and the farming managements can be found in Table 1. All sites were located in farms owned by smallholder farmers. Soil order was Cambisol for all sites (IUSS Working Group WRB, 2015). The sites in Morretes included conventional farming, organic farming, agroforestry systems and native vegetation. The sites in Lapa included no-tillage, organic farming, agroforestry system and native vegetation. These sites were chosen for the reason that the soils in each site are managed differently and therefore the soil quality is expected to be contrasting. Unfortunately, it was not practically possible to sample the exactly same management systems in both municipalities, (no notillage system was sampled in Morretes and no conventional farming was sampled in Lapa). The agricultural sites sampled in Morretes were located far from each other, thereafter, every agricultural site was contrasted with an adjoining native vegetation. The study sites of Lapa, on the other hand, were located close to each other, then only one native vegetation was considered as a reference for all other agricultural sites. The agroforestry systems in both municipalities were composed of a range of different crop species combined. It consisted of horticultural crops grown in beds in-between tree rows. As the crops in the rotation and the farming management within the agroforestry systems were different in the horticultural beds and in the three rows (Table 1), soil samples were taken both in the horticultural beds and in the tree rows, wherein each sampling position (horticultural beds and tree rows) was considered as a different site in this study. All sites of native vegetation in the municipality of Morretes were part of the Serra do Mar coastal forest, which is an ecoregion of the Atlantic Forest biome. The native vegetation of the municipality of Lapa was consisted of the mixed ombrophilous forest, also known as araucaria moist forest, which is a coniferous forest ecoregion of the Atlantic Forest Biome. These sites of native vegetation were chosen for the reason that the soils were expected to have greater soil quality and therefore could be used to contrast with the agricultural fields in order to test the sensitivity of the soil quality assessments to distinguish 2

3

07/12/2017

16/05/2018

16/05/2018

04/01/2018

04/01/2018

04/01/2018

04/01/2018

04/01/2018

04/01/2018

NVCF

OF

NVOF

HB7

TR7

NV7

HB11

TR11

NV11

13/03/2018

13/03/2018

13/03/2018

HB

TR

NVL

25°38′31″S 49°41′57″W 25°38′27″S 49°41′57″W 25°38′26″S 49°41′49″W 25°38′26″S 49°41′49″W 25°38′26″S 49°41′44″W

25°30′57″S 48°52′10″W 25°30′53″S 48°52′01″W 25°30′53″S 48°52′01″W 25°30′52″S 48°52′00″W

25°29′48″S 48°48′39″W 25°30′58″S 48°52′07″W 25°30′58″S 48°52′07″W

25°28′20″S 48°50′12″W 25°29′47″S 48°48′40″W

25°28′21″S 48°50′13″W

Coordinates

172

300

264

288

384

262

322

328

353

341

350

506

516

497

537

Clay (g kg−1)

Sandy Loam Sandy Loam Sandy Loam Sandy Loam

Clay

Clay

Clay

Sandy Clay

Sandy Clay Loam Sandy Clay Loam Sandy Clay Loam

Clay Loam

Clay Loam

Clay Loam

Silty Clay

Clay

Clay

Clay

Soil Texture

–

Pine and eucalyptus (until 2000)

–

Sugarcane (until 1950) followed by pasture (until 2007)

–

Sugarcane (until 1950) followed by pasture (until 2007)

Vegetables under conventional farming (until 200) –

–

Sugarcane (until 2007)

Previous crop

Cattley guava, cedar, eucalyptus, lemon, loquat and orange.

Lettuce

Lettuce

Maize

Banana, cedar, eucalyptus, lemon and orange

Yams

Acerola, banana, cedar, custard apple, eucalyptus, lemon, orange and tangerine

Lettuce

–

Fallow

–

Aubergine

Current Crop

Carrot, cabbage, courgette and lettuce Chicory, lettuce, rocket, spinach, spring onion and yams Cattley guava, cedar, eucalyptus, lemon, loquat and orange. –

Maize and beans

Banana, cedar, eucalyptus, lemon and orange –

Ginger, lettuce and yams

Chicory, lettuce, rocket, spinach, spring onion and yams Acerola, banana, cedar, custard apple, eucalyptus, lemon, orange and tangerine –

–

Courgette, ginger and lettuce

–

Aubergine, beans, courgette, cucumber, ginger and okra

Crops in the crop rotation

No-tillage, continual inputs of synthetic chemical fertilisers, pesticides. Intense soil tillage, organic preparations are frequently used and weeds are removed from crop fields manually Intense soil tillage, use of organic preparations, use of grass straw for weed control. No-tillage, use of organic preparations, fruit trees grown for profit-making and timber trees for biomass purposes –

Intense soil tillage, use of organic preparations, use of grass straw for weed control. No-tillage, use of organic preparations, fruit trees grown for profit-making and timber trees for biomass purposes –

–

Intense soil tillage, use of organic preparations, use of grass straw for weed control No-tillage, use of organic preparations, fruit trees grown for profit-making and timber trees for biomass purposes

Intense soil tillage; organic preparations are frequently used and poultry litter is applied sporadically; weeds are removed from crop fields manually –

Intense soil tillage; continual inputs of synthetic chemical fertilisers, pesticides and herbicides are performed; poultry litter is applied sporadically –

Farming Management

CF: conventional farming, NVCF: native vegetation adjoining to CF, OF: organic farming, NVOF: native vegetation adjoining to OF, HB7: horticultural beds in the 7-year-old agroforestry system, TR7: tree rows in the 7year-old agroforestry system, NV7: native vegetation adjoining to 7-year-old agroforestry system, HB11: horticultural beds in the 11-year-old agroforestry system, TR11: tree rows in the 11-year-old agroforestry system, NV11: native vegetation adjoining to 11-year-old agroforestry system, NT: no-tillage, HB: horticultural beds in the agroforestry system, TR: tree rows in the agroforestry system. NVL: native vegetation in the municipality of Lapa.

24/03/2018

OF

Sites in Lapa NT 24/03/2018

Sites in Morretes 07/12/2017

Date of Soil Sampling (dd/mm/ yyyy)

CF

Site

Table 1 Location of the study sites, soil texture, crops and farming management.

G.P. Valani, et al.

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different soil management systems. The agricultural sites studied ranged from 0.05 to 2.60 ha. Within each site, a plot of about 200 m2 was chosen for soil sampling. The samples were taken randomly in four points within the chosen plot, with a distance of about 8 m from each other. The soil samples were taken at the depth of 0−25 cm. The reasons for sampling the top 25 cm are i) it is the recommended sampling depth for one of the methodologies studied (the Rapid Diagnosis of Soil Structure – DRES), ii) more than 70 % of the soil microbial biomass are in the topsoil and this portion of microbial biomass is the most active along soil profile (Fierer et al., 2003; Xu et al., 2013), iii) this depth is highly influenced by tillage operations, which may alter soil structural form and total organic carbon content (Zheng et al., 2018) and iv) it concentrates soil nutrients strongly cycled by plants, such as P and K (Jackson and Jobbagy, 2001). All samples were taken after two or three days after a saturation rain, in order to sample the soil as close to the field capacity as possible. Undisturbed aggregate samples, disturbed soil samples and soil cores were taken at each site. The disturbed samples were sieved through a 2 mm sieve and kept in a refrigerator at 4 °C prior to microbiological analysis, which commenced within a week after sampling and were concluded in four days.

point optima) used were based on site-specific algorithms according to analytical methods, climate, crop, season at the moment of sampling, soil iron oxide class, soil mineralogy, soil organic matter, soil texture, soil weathering class and surface slope. Upper and lower limits or optima values in the curves represented the indicators threshold values outside of which soil functions are impaired (Wienhold et al., 2009). The scoring algorithms and the site-specific factor for each indicator used are reported by Andrews et al. (2002). As the soil quality indicators were interpreted according to the sitespecific factors, the use of SMAF was performed accordingly for this study’s conditions. The P method code was 1 (P extracted by Mehlich-I); the climate factor was 1 (≥ 170° days and ≥ 550 mm of mean annual precipitation); season code was 2 (summer / mid-growing season); soil iron oxide class was 2 (related to Cambisols); soil mineralogy code was 3 (1:1 clay and Fe and Al oxides); soil organic matter ranged from class 3–4, according to the sampling sites; texture factors ranged from 1 to 2 and from 4 to 5, according to each site; soil weathering code was 3 (slightly weathered) and the surface slope factor was 2 for all sites (2–5 % slope). Crop factors were the ones related to the current crop at the moment of sampling and they affected soil pH and soil available P scores. In this study, the factor for native vegetation (Atlantic Forest) was the same as described by Cherubin et al. (2016). New crop factors were added to the SMAF spreadsheet in order to include the crops of this study. These new crop factors were set using regional recommendations (Pauletti and Motta, 2017). Optimum soil pH and soil available P values were considered as the ones that support up to 90 % of crop yield. Maximum values were considered as the ones that support up to 100 % of crop yield, and if increased, it may limit crop production (Pauletti and Motta, 2017). Optimum and maximum soil pH values for all new crops were set as 5.7 and 6.2. Optimum and maximum soil available P (mg dm−3) were 13.0 and 18.0 for the no-tillage site and 51.0 and 100.0 for the sites where horticultural crops were grown. As the agroforestry systems were composed of a range of different crop species combined, it was challenging to set a new crop factor for them. Considering that the agroforestry systems were sampled both in the horticultural beds and in the tree rows, the crop factors were established according to each sampling position (one crop factor for the horticultural beds and other for the tree rows). The crop factor for the horticultural beds (the horticultural crops in the agroforestry systems) was set as the same for the other horticultural sites. The crop factors for tree rows were set as described for forestry systems by Pauletti and Motta (2017), where optimum and maximum soil pH values were 5.7 and 6.2 and optimum and maximum soil available P were 6.0 and 7.0 mg dm−3. Each indicator score was thereafter integrated into an overall soil quality index through an arithmetic mean. Whenever the overall soil quality index was not able to clearly distinguish the management systems in each site, the index was separated into its soil biological, physical and chemical components (in order to check whether one of the components could distinguish the different management systems) and a cluster analysis from the soil quality indicators was performed using Euclidian distance and the unweighted pair group method with arithmetic mean (UPGMA).

2.2. Soil management assessment framework (SMAF) The Soil Management Assessment Framework (SMAF) was performed to assess soil quality in the agricultural sites as well as in the soils under native vegetation. Soil biological, physical and chemical properties were analysed to integrate the minimum data set for the SMAF analysis. Six soil quality indicators were used: total organic carbon, microbial biomass carbon, macroaggregate stability, soil bulk density, soil pH and soil available P. These indicators were selected for the reason that they comprise six out of the 13 soil quality indicators with scoring curves or interpretations available in SMAF (Andrews et al., 2004; Wienhold et al., 2009; Stott et al., 2010) and they are related to a range of soil functions and ecosystem services well reviewed in the literature (Andrews et al., 2004; Zornoza et al., 2015). Furthermore, the use of these indicators follow the suggestion of the SMAF protocol, to use a minimum of five indicators, including soil biological, physical and chemical properties or processes (Karlen et al., 2008). Total organic carbon content was determined by dry combustion on a Vario EL III CHNOS elemental analyser. Microbial biomass carbon was determined by the fumigation-extraction method (Vance et al., 1987) and calculated as the difference between the carbon in fumigated and non-fumigated replicates, with a k-factor of 0.40, as indicated to be more appropriated for Brazilian soils (Kaschuk et al., 2010; Roscoe et al., 2006). The soil bulk density was determined as the relation between the dry mass of soil and the bulk volume of the core used for sampling (Blake and Hartge, 1986), which was approximately 60 cm3. Macroaggregate stability (water stable macroaggregates) was determined using an apparatus for vertical oscillation (Yoder, 1936) with three sieve sizes (2000, 250 and 53 μm) operating at 42 oscillations per minute for 15 min. The macroaggregate stability was calculated as the ratio between the mass of aggregated larger than 250 μm and the total soil mass. Chemical analysis included the soil pH, which was determined in a 1:2.5 soil:water solution by using a combined pH electrode and soil available P, extracted using Mehlich-I and measured in an ultravioletvisible spectrophotometer after adding ammonium molybdate and ascorbic acid (Mehlich, 1953; van Raij, 1994). After assessing the soil quality indicators, the mean value of each indicator was interpreted by transforming it into a unitless 0–1 value using non-linear scoring curves (0 being the lower quality and 1 the highest). The scoring curves (either more-is-better, less-is-better or mid-

2.3. On-farm evaluations of soil quality Soil quality was evaluated on-farm by two methods: the Rapid Diagnosis of Soil Structure, known as DRES (Ralisch et al., 2017a) and by the Practical Guide for Participative Evaluation of Soil Quality PGPE (Comin et al., 2016). All on-farm evaluations were performed with a group of three to four people, composed of master students in Soil Science and undergraduate students in BSc Agronomy from the Federal University of Paraná (Brazil), who have previous training to the methods. 4

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Table 2 Brief description of the grading system of DRES (Rapid Diagnosis of Soil Structure). Soil structural grade

Aggregate size and percentage within the sample

Characteristics of soil structure

1

More than 70 % of aggregates with less than 1 cm and more than 7 cm

2

50 to 70 % of aggregates with less than 1 cm and more than 7 cm

3

Less than 50 % of aggregates with less than 1 cm and more than 7 cm

4

Less than 50 % of aggregates from 1 to 4 cm

5

50 to 70 % of aggregates from 1 to 4 cm

6

More than 70 % of aggregates from 1 to 4 cm

Cohesive structure in the large aggregates and very loose within the aggregates smaller than 1 cm. Aggregates surfaces are mostly flat, no visible pores and no visible biological activities. Strong restriction of root development, with most roots flat, growing preferentially in the cracks between aggregates. Cohesive structure in the large aggregates and very loose within the aggregates smaller than 1 cm. Aggregates surfaces are mostly flat, with few pores and few biological activities. Limited root development, with most roots flats and showing restrictions to penetrate into the aggregates. Cohesive structure in the large aggregates and very loose within the aggregates smaller than 1 cm. Aggregates surfaces are mostly flat, with few pores and few biological activities. Roots may be flat and show restrictions for its full development. Aggregates with friable structure, moderately porous and fairly-structured, with poor biological activity. Roots may show obstacles for root development. Aggregates with friable structure, porous and well-structured, with median biological activity. Roots are well-distributed, and may show few signs of root restriction. Roots grow mostly through the aggregates Aggregates with friable structure, porous and well-structured, with high biological activity. Roots are abundant and well-distributed, with no deformations caused by physical impediments, growing through the aggregates

Table 3 Brief description of the grading system of PGPE (Practical Guide for Participative Evaluation). Soil quality indicator

Grade for soil quality assessment

Characteristics related to the grade

Soil organic matter

1 5 10 1 5 10 1 5 10 1 5 10 1 5 10 1 5 10 1 5 10 1 5 10

Lighter colour, unpleasant odour, very low organic matter content Darker colour, no distinct odour, average organic matter content Dark colour, odour of forestry soil, high organic matter content Few or no visible roots in the sample Presence of some roots in the sample Abundance in roots in the entire sample Powderly soil, with no visible aggregates Few soil aggregates, which break after slightly pressured Abundance in aggregates, maintained shape after slightly pressured. Compacted soil, highly resistant to knife penetration and no water infiltration Compacted soil, some resistance to knife penetration and slow water infiltration Absence of soil compaction, not resistant to knife penetration and rapid water infiltration Severe erosion, presence of rill erosion and streamlets Erosion not easily noticeable (sheet erosion) with no rill erosion No visible signs of erosion Dry soil after a short time without rain Moist soil after some time without rain Moist soil after long drought No signs of worms and/or arthropods Presence of some worms and/or arthropods Abundance of worms and/or arthropods No cover (green cover or straw) in the field Half of the field covered Field fully covered with green cover and/or straw

Root System (adapted) Soil structure Soil compaction and infiltration Soil erosion Soil water Soil

macrofauna

Soil cover (adapted)

2.3.1. Rapid diagnosis of soil structure (DRES) Undisturbed soil samples of 10 × 20 × 25 cm (length x width x height) were placed in plastic trays and disintegrated into smaller aggregates. Structural quality was assessed according to the size, abundance and shape of soil aggregates, presence or absence of compaction or other soil degradation related process, rupture resistance, distribution, appearance of the root system and evidence of biological activity. The samples were thereafter graded by using the scoring table ranging from 1 (lowest quality, with flat aggregate surfaces, flat roots and disaggregated or compacted soil) to 6 (highest quality, with well-structured aggregates, with evidences of biological activity and well-developed roots) provided by Ralisch et al. (2017a). A brief description of the grading system is shown in the Table 2. Whenever the soil had different layers in the 25 cm depth, a score was given for each of them. The final soil structural index was calculated as a weighted average between the score and the depth of each layer. As soil structure is an integration of soil biological, physical and chemical properties (Askari et al., 2015; Rabot et al., 2018; da Silva et al., 2014), the structural index DRES was considered as a soil quality index for assessing changes in the different sites of this study.

2.3.2. Practical guide for participative evaluation (PGPE) On-farm assessment of soil quality was also performed with the Practical Guide for Participative Evaluation (PGPE) of Soil Quality, proposed by Comin et al. (2016). The soil was dug up to 25 cm to maintain the same assessment depth of the others methodologies used in this study (SMAF and DRES). Scores from 1 to 10 were given to the following indicators of soil quality: i) organic matter, ii) root system, iii) soil structure, iv) soil compaction and infiltration, v) erosion, vi) water retention, vii) soil macrofauna and viii) soil cover. Higher scores indicated higher soil quality for each assessment. A brief description of the grading system is shown in the Table 3. The final soil quality index for the Practical Guide for Participative Evaluation of Soil Quality was calculated as the mean average of all indicators. 2.4. Data analysis The normality of the data for each soil quality index in each site was tested with the Shapiro-Wilk test (p > 0.05) and no data transformation was needed. An analysis of variance (Anova) was performed for 5

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Fig. 1. Soil quality indices determined by the Soil Management Assessment Framework - SMAF (A and B), Rapid Diagnosis of Soil Structure - DRES (C and D) and Practical Guide For Participative Evaluation of Soil Quality - PGPE (E and F) in conventional farming (CF), organic farming (OF) and each of their adjoining native vegetation (NV) in Morretes - PR. Sites with different lowercase letter significantly differ from each other according to Tukey test with its p-value shown below each graph.

each site and each soil quality index determined (SMAF, DRES and PGPE), contrasting the agricultural site(s) with the adjoining native vegetation. If the Anova F statistics was significant (p < 0.05), the means were compared through the Tukey test. The Pearson correlation coefficient was calculated between the results from each on-farm soil quality index and SMAF. The results were separated according to soil texture, as the correlation between visual observations and standards measurements are type dependent, as well as should be its interpretations (Mueller et al., 2009; van Leeuwen et al., 2018). All statistical analysis were performed at the R studio environment version 3.5.0 (R Core Team, 2018), using the packages ‘car’ (Fox and

Weisberg, 2011) and ‘vegan’ (Oksanen et al., 2018). 3. Results and discussion 3.1. Soil quality indices in Morretes Soil quality according to SMAF, DRES and PGPE in conventional and organic farming in the municipality of Morretes were lower than each of their adjoining native vegetation, regardless of the assessment strategy (Fig. 1). It is important to recapitulate that the agricultural sites sampled in Morretes were located far from each other, and thereafter every agricultural site was contrasted with an adjoining 6

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Fig. 2. Soil quality indices determined by the Soil Management Assessment Framework - SMAF (A and B), Rapid Diagnosis of Soil Structure - DRES (C and D) and Practical Guide for Participative Evaluation of Soil Quality - PGPE (E and F) in agroforestry systems of 7 (A, C and E) and 11 (B, D and F) years old sampled in the horticultural beds (HB) and in tree rows (TR) contrasted with each adjoining native vegetation (NV) in Morretes – PR. Sites with different lowercase letter significantly differ from each other according to Tukey test with its p-value shown below each graph. Ns: not significant in Anova.

native vegetation. Tillage operations in both agricultural sites may have lowered soil quality indices, as the structural cracks caused by tillage negatively influence soil biological, physical and chemical properties (Bronick and Lal, 2005), as well as the visual observations. These results suggest the suitability of the different methods to assess soil quality, as all analytical and on-farm strategies were effective to distinguish different soil management systems. SMAF and PGPE were able to assess soil quality in the agroforestry systems, with higher soil quality indices for the native vegetations (Fig. 2). However, SMAF could not distinguish between sampling positions in the 11-year-old agroforestry system. DRES results for the 7year-old agroforestry system showed lower soil quality indices in the tree rows than in the horticultural beds or in the native vegetation, in contrast to SMAF and PGPE.

PGPE indices in the agroforestry systems seemed to show clearer and more understandable results than both SMAF and DRES (Fig. 2), as it was the most able method to distinguish the sampling position in the agroforestry systems of the municipality of Morretes. The greater capability of PGPE to distinguish the sites under different management systems might be related with a wider range of soil quality indicators taken in consideration in PGPE analysis than in SMAF and DRES. Considering SMAF, DRES and PGPE results for the agroforestry systems, soil quality seems to be different according to the sampling position, being higher in tree rows than in the horticultural beds. These results are probably related with no-tillage in the tree rows along with the variety of tree species (Table 1) that remain in the soil for a longer period of time. This management of constant crops in combination with no-tillage is known to promote a greater aggregate stability, adding 7

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Fig. 3. Soil quality indices determined by the Soil Management Assessment Framework - SMAF (A), Rapid Diagnosis of Soil Structure - DRES (B) and Practical Guide for Participative Evaluation of Soil Quality - PGPE (C) in no-tillage farming (NT), organic farming (OF), horticultural beds of the agroforestry system (HB), tree rows of the agroforestry system (TR) and in the native vegetation (NVL) in Lapa - PR. Sites with different lowercase letter significantly differ from each other according to Tukey test with its p-value shown below each graph.

Fig. 4. Soil Management Assessment Framework (SMAF) results from its biological, physical and chemical components in Lapa - PR. AGF: agroforestry system. Sites with different lowercase letter significantly differ from each other according to Tukey test with its p-value shown below each graph. Ns: not significant in Anova.

more carbon in the soil and enhancing the microbial activity, which reflect a greater soil capacity to execute its ecosystem functions (Vezzani et al., 2018). Differences between on-farm and analytical results can be seen amongst DRES and SMAF indices for the 7-year-old agroforestry system (Fig. 2). These differences might lead to the assumption that the methodology of each assessment results in unconnected information between indices, as discussed for other methodologies by Emmet-Both et al. (2016). Under such circumstances, both on-farm and analytical analysis should be taken into consideration in soil quality assessment frameworks (Pulido Moncada et al., 2014). It is important to discuss how the results of on-farm methods of soil quality assessment may help farmers, advisors and researches to

manage the soil in order to promote soil quality. The DRES methodology, for example, provides a table for interpreting its results with recommendations of possible changes to promote soil quality. Considering the soil management system with the lowest soil quality assessed by DRES in this study (the conventional farming, with an index of 3.0), the authors suggest that the production system should be ameliorated with crop diversification, and tillage should be reduced or eliminated. Such recommendations certainly contribute to the ease of using and interpreting on-farm methods of soil quality and thus help users of DRES to better manage soil degradation. In summary, the soil quality indices tested in the municipality of Morretes showed that both on-farm methods (DRES and PGPE) were able to distinguish management systems, with generally the same 8

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Fig. 5. Cluster Analysis of all sampling sites in the Municipality of Lapa - PR using Euclidian distances and the unweighted pair method with arithmetic mean (UPGMA). Cophenetic correlation coefficient = 0.69. Soil quality indicators used: total organic carbon, microbial biomass carbon, macroaggregate stability, soil bulk density, soil pH and soil P. NT: no-tillage farming. OF: organic farming. HB: horticultural beds of agroforestry system. TR: tree rows of agroforestry system. NV: native vegetation.

maintained even when a slight degree of visual degradation of soil structure is found under no-tillage, as stated by Cavalieri et al. (2009), which relates with higher SMAF results. In order to further discuss the effectiveness of SMAF results to distinguish the sites of Lapa, its final results were separated into its soil biological, physical and chemical components (Fig. 4). Although there were no differences within sites for the soil biological and physical components, the soil chemical component of the organic farming was lower than most sites (Fig. 4). The soil chemical component of SMAF is an integration of soil pH and soil available P interpretations. Lower soil chemical quality indices in SMAF in the organic farming are related with higher soil pH in the system, with values far beyond the mid-point optima in the scoring curve of this soil quality indicator. Furthermore, the soil P content may also have influenced the lower result in the organic farming due to its crop factor, as the soil P content required for horticultural crops (in the conventional farming system, organic farming system and in the horticultural beds in the agroforestry systems) of Morretes and were the highest for all sites studied. An example of the P interpretation can be seen between the organic and no-tillage farming, despite higher quantities of P found in the organic farming than in the no-tillage farming, the P index for the organic farming were lower than the no-tillage farming, due its crop factors. Detailed information about this can be found in the appendices A and B. Regarding the soil physical component of SMAF, the macroaggregates stability in the soils sampled in Lapa ranged from 0.79 to 0.98, which led to the maximum interpretation score (1.00) for all sites. The SMAF scoring curve for this soil quality indicator was previously considered not sensitive in Brazilian soils by Cherubim et al. (2016) in tropical soils, probably due to the dominance of Fe and Al oxides as well as 1:1 minerals (Six et al., 2000). Thus, although the macroaggregate stability scoring curve in SMAF takes into consideration the soil organic matter, soil texture and the Fe oxide content (Andrews et al., 2004), it was also not able to detect different management systems in the subtropical conditions of this study. As the overall SMAF was not fairly as able to distinguish between different soil management systems in Lapa even when separated into its soil biological, physical and chemical components, the soil quality indicators chosen might be questioned. Considering the consistency of such soil properties as soil quality indicators and the low performance of these indicators when interpreted with SMAF, a cluster analysis of the indicators was performed (Fig. 5). Considering a cut-off point of 3.2 in the dendrogram, the groups are formed as follow: group 1: all samples from no-tillage farming and one

Table 4 Correlations between soil quality indices in relation to soil texture.

Clay (n = 13) DRES x SMAF PGPE x SMAF Clay Loam (n = 11) DRES x SMAF PGPE x SMAF Sandy Clay Loam (n = 21) DRES x SMAF PGPE x SMAF

Pearson’s Correlation

p-value

0.5416 0.8414

0.0559 0.0003

0.2365 0.9089

0.4838 0.0001

0.1681 0.1125

0.4664 0.6275

DRES: Rapid Diagnosis of Soil Structure, SMAF: Soil Management Assessment Framework, PGPE: Practical Guide for Participative Evaluation of Soil Quality.

trends to SMAF. In such environmental conditions, the PGPE results were more able than DRES to distinguish between different management systems compared with SMAF results. 3.2. Soil quality indices in Lapa As distinct from the results in the municipality of Morretes, most results from Lapa did not show the same trend for the different soil quality indices tested. According to SMAF, for example, soil quality in the horticultural beds in the agroforestry was lower than in the notillage farming (Fig. 3). DRES results, in contrast, indicated a higher soil quality in the horticultural beds than in the no-tillage. It is important to recapitulate that the study sites of Lapa were located close to each other, therefore only one native vegetation was considered as a reference for all other agricultural sites. In contrast to both SMAF and DRES results, soil quality according to PGPE results was higher in the native vegetation, lower in the no-tillage farming and intermediate in the organic farming and in the agroforestry system (Fig. 3). Higher results in the agroforestry system and in the organic farming system confirm the importance of such agroecosystems in Brazil, as they promote soil quality, support agricultural production and contribute to agricultural sustainability (Costa et al., 2017). It is important to note the differences between the visual and analytical assessments in the no-tillage system. The soil quality indices were lower in the visual assessments and higher in the SMAF analysis (Fig. 3). The scores in the DRES methodology can be grouped in soils under degradation (scores 1–3) and under conservation or recovery (scores 4–6). It is possible that the soil structure in the no-tillage system was in-between those classes, furthermore, the soil functionality may be 9

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correlation between the methodologies tested. Considering the database for correlation, PGPE was more suitable to assess soil quality in order to predict SMAF results than DRES, especially in clayey and clayey loam soils. This result is possibly related with the wider range of soil quality indicators taken into consideration in the PGPE, which permitted a greater sensitivity to assess soil quality in the different management systems.

sample from the organic farming, group 2: all samples from the native vegetation and one sample from the tree rows in the agroforestry system; group 3: all samples from the horticultural beds in the agroforestry system, most samples from the organic farming and most samples from the tree rows in the agroforestry system. It is interesting to note that the group 2 and 3 are closer related to each other than to group 1 in the cluster analysis shown in Fig. 5 (because the height in the dendrogram that joins group 2 and 3 together is lower than the height that links to group 1), which suggests that soil quality in the organic farming and in the agroforestry system (group 3) is generally closer to soil quality of the native vegetation (group 2) than of the no-tillage farming (group 1). These results highlight the capability of the soil quality indicators chosen to integrate the SMAF minimum data set (the analytical measurements) in this study, as they were able to detect different management systems. In summary, the soil quality indices tested in the municipality of Lapa showed that none of the methods were able to distinguish management systems at all time. Most results from Lapa did not show the same trend for the different soil quality indices tested. Moreover, within the on-farm assessments, the PGPE distinguished a wider range of sites than DRES. These results might be related with the texture of the soils sampled in Lapa, which were either sandy loam, sandy clay or sandy clay loam (Table 1). In order to further discuss the correlation between soil quality indices in relation to soil texture, a closer analysis was thenceforth performed.

4. Conclusions The on-farm methods used to assess soil quality, the Rapid Diagnosis of Soil Structure (DRES) and the Practical Guide of Participative Evaluation (PGPE) were able to distinguish the soil quality of soils with different management systems, as well as was the Soil Management Assessment Framework (SMAF). However, none of the methods were able to distinguish the management systems at all times. In relation to the on-farm methods, the PGPE distinguished a wider range of sites than DRES, regardless the municipality or soil texture. In relation to the soils of the two municipalities studied (Morretes and Lapa), the results from Morretes showed that both on-farm methods (DRES and PGPE) were able to distinguish management systems, with generally the same trends to SMAF. Most results from Lapa, in its turn, did not show the same trend for the different soil quality indices tested. For on-farm methods to assess soil quality, the Practical Guide of Participative Evaluation (PGPE) was more correlated with the Soil Management Assessment Framework (SMAF) than the Rapid Diagnosis of Soil Structure (DRES), especially in clayey and clayey loam soils. This study’s results suggest that the on-farm soil quality assessments provide reliable results with the advantage of providing timely results that are relatively easy to interpret. DRES and PGPE are valuable alternatives for teaching and research purposes, especially for the reason that it can be performed by a wider range of the public, including farmers themselves. However, it is important to note that although no specialised pedological knowledge is required, such on-farm strategies may require previous training, thus the user becomes familiar with the methods and may perform them accordingly. As this work focused on studying soil quality in the topsoil (up to 25 cm), future investigations about deeper layers would be important to infer about soil quality in the entire soil profile, including the subsoil. Although the on-farm methods of soil quality assessment tested in this work were spade tests proposed to assess soil quality in the topsoil, existing methodologies could be used to assess soil quality further in the soil profile (Ball et al., 2015; Tavares Filho et al., 1999) or maybe the DRES and PGPE methods tested could be developed to include subsoil assessments.

3.3. Correlation between soil quality indices in relation to soil texture Considering that interpretations of soil quality indices are site dependent and that soil texture in particular is crucial for a meaningful soil quality assessment (Karlen et al., 2017), correlations between soil quality indices in relation to the most common soil textural classes of this study (clay, clay loam and sandy clay loam) in both municipality are presented in Table 4. DRES was not significantly (p < 0.05) correlated with SMAF, regardless soil texture. Higher correlations were found between SMAF and PGPE in clay loam (r = 0.91) and clay (r = 0.84) soils (Table 4). These results highlight the capability of the on-farm method PGPE to produce similar results to SMAF with the additional advantage that PGPE may be performed more quickly and affordable than SMAF. Their correlation is important in such conditions as described by Batey (2000), where specific tests to assess soil quality cannot be performed or the number of soil samples make it unfeasible to tackle spatial and temporal variability appropriately. Both DRES and SMAF methodology adjust their assessments for soil texture. The highest score of DRES (six), for example, is related to wellstructured and highly porous aggregates, with abundant biologic activity (Table 2). As well-structured aggregates are less common in sandy soils when compared to other texture classes, the other characteristics would be the main aspects driving to the score six in such sandy samples (Ralisch et al., 2017a). Thereafter, it can be seen that soil texture is taken into consideration while assessing the soil with DRES and it should not affect its results. The SMAF methodology also emphasises the importance of the soil texture, as it is one of the factors related to the indicator’s interpretation (Andrews et al., 2004). Despite such efforts to overcome the effect of soil texture in the soil quality assessments, the results of this study testify the influence of soil texture in the

Declaration of Competing Interest None. Acknowledgments This work was supported by the Brazilian National Council for Scientific and Technological Development (CNPq). A special thanks is given to all people involved in this work, from soil sampling to writing this paper, including the farmers whose properties were sampled.

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Appendix Table A1 and Table B1

Table A1 Values of each soil quality indicator used to integrate the minimum data set in the SMAF analysis. Municipality

Site

Replicate

Total Organic Carbon (g kg−1)

Microbial Biomass Carbon (mg kg−1)

Macroaggregate Stability

Bulk Density (g cm−3)

pH

soil P (mg dm−3)

Morretes

Conventional Farming

1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

0.14 0.12 0.14 0.14 0.16 0.29 0.22 0.22 0.20 0.20 0.21 0.20 0.32 0.25 0.29 0.19 0.19 0.15 0.14 0.16 0.16 0.15 0.13 0.15 0.21 0.29 0.21 0.39 0.20 0.14 0.19 0.16 0.34 0.19 0.15 0.24 0.19 0.29 0.21 0.23 0.33 0.36 0.36 0.40 0.29 0.38 0.30 0.31 0.21 0.26 0.28 0.20 0.32 0.24 0.23 0.18 0.13 0.18 0.14 0.24

226.84 263.69 269.91 268.38 133.70 346.62 201.38 313.86 186.92 128.26 154.60 42.89 210.65 263.12 277.69 167.80 209.58 229.11 223.79 201.07 225.23 173.46 204.17 199.19 201.72 248.89 320.39 409.35 135.12 116.97 159.06 180.39 172.93 138.75 162.30 127.16 237.06 271.52 296.15 249.40 184.67 153.40 160.38 170.08 78.93 211.43 106.22 188.94 118.86 109.32 143.10 99.63 171.83 187.43 165.58 100.22 122.08 157.03 164.74 212.56

0.78 0.78 0.84 0.79 0.86 0.95 0.93 0.90 0.89 0.81 0.78 0.80 0.98 0.96 0.75 0.94 0.91 0.82 0.90 0.89 0.96 0.86 0.97 0.92 0.97 0.88 0.90 0.91 0.80 0.86 0.80 0.90 0.89 0.84 0.81 0.92 0.93 0.95 0.95 0.94 0.98 0.88 0.83 0.88 0.79 0.90 0.84 0.83 0.81 0.85 0.85 0.84 0.86 0.86 0.88 0.81 0.81 0.86 0.85 0.81

1.18 1.22 1.27 1.28 1.06 0.89 1.00 0.98 0.99 0.92 0.82 0.82 1.05 0.96 0.90 1.01 1.31 1.51 1.48 1.39 1.29 1.29 1.27 1.34 1.05 1.19 1.12 0.85 1.24 1.33 1.24 1.15 1.21 1.49 1.45 1.17 1.30 0.87 1.26 1.07 1.12 1.26 1.15 1.14 1.37 1.12 1.30 1.32 1.48 1.36 1.32 1.47 1.50 1.39 1.45 1.41 1.38 1.14 1.25 1.30

5.87 6.12 6.14 6.27 4.54 4.42 4.43 4.33 6.68 6.52 6.69 6.84 5.57 5.32 5.63 5.77 5.05 5.14 5.05 5.38 5.56 5.65 5.81 5.84 4.73 4.48 4.70 4.71 4.96 5.48 5.43 5.37 5.33 5.73 6.08 6.08 4.82 4.81 4.57 4.51 5.13 5.25 5.28 5.33 6.66 6.68 6.71 6.65 6.58 6.64 6.60 6.48 6.58 6.71 5.95 5.90 4.29 4.30 4.17 4.36

27.20 16.20 24.40 45.60 2.70 6.60 1.50 3.10 73.10 57.30 104.00 55.30 4.70 6.10 4.50 4.70 3.10 3.00 2.00 3.00 3.50 3.50 3.80 5.40 5.20 4.80 4.50 9.40 36.70 26.10 29.10 30.30 14.90 14.90 8.00 29.50 3.60 8.00 6.10 4.10 3.50 5.70 6.60 4.30 18.80 16.20 15.90 11.70 18.00 20.30 44.90 33.80 15.70 8.00 6.50 3.50 2.80 2.80 2.20 3.80

Adjoining Native Vegetation

Organic Farming

Adjoining Native Vegetation

7-year-old Agroforestry System Horticultural Beds 7-year-old Agroforestry System Tree rows Adjoining Native Vegetation

11-year-old Agroforestry System Horticultural Beds 11-year-old Agroforestry System Tree rows Adjoining Native Vegetation

Lapa

No-tillage Farming

Organic Farming

Agroforestry System Horticultural Beds Agroforestry System - Tree rows

Native Vegetation

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Table B1 Scored values of each soil quality indicator used to integrate the minimum data set in the SMAF analysis followed by the SMAF biological, physical, chemical and the overall SMAF soil quality index (SQI). Municipality

Site

Replicate

Total Organic Carbon

Microbial Biomass Carbon

Aggregate Stability

Bulk Density

pH

soil P

Biological SQI

Physical SQI

Chemical SQI

Overall SQI

Morretes

Conventional Farming

1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

0.42 0.34 0.46 0.42 0.97 1.00 1.00 1.00 0.81 0.80 0.83 0.79 1.00 1.00 1.00 0.99 0.76 0.53 0.41 0.57 0.54 0.50 0.39 0.50 1.00 1.00 1.00 1.00 0.92 0.60 0.89 0.77 1.00 0.88 0.68 0.91 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.99 1.00 1.00 1.00 0.93 0.99 0.97 0.99 1.00 0.97 0.96 0.84 0.54 0.83 0.60 0.97

0.77 0.88 0.89 0.89 0.65 1.00 0.94 1.00 0.58 0.28 0.41 0.06 0.96 0.99 0.99 0.85 0.69 0.78 0.76 0.65 0.76 0.51 0.67 0.64 0.94 0.99 1.00 1.00 0.56 0.43 0.72 0.83 0.79 0.58 0.74 0.28 1.00 1.00 1.00 1.00 0.84 0.78 0.82 0.86 0.20 0.92 0.35 0.86 0.44 0.37 0.35 0.38 0.79 0.85 0.75 0.31 0.46 0.71 0.75 0.92

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

0.86 0.76 0.63 0.61 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.55 0.30 0.32 0.42 0.58 0.59 0.65 0.50 0.99 0.84 0.95 0.99 0.99 0.95 0.99 0.99 0.99 0.61 0.70 0.87 0.97 0.99 0.99 0.99 0.95 0.66 0.91 0.92 0.89 0.99 0.97 0.96 0.63 0.91 0.53 0.90 0.58 0.84 0.70 0.79 0.87 0.99 0.99 0.97

0.94 0.70 0.68 0.52 0.89 0.86 0.87 0.84 0.15 0.26 0.14 0.07 1.00 1.00 1.00 0.99 0.43 0.53 0.43 0.81 0.96 1.00 0.98 0.96 0.93 0.88 0.92 0.92 0.33 0.91 0.86 0.80 0.76 1.00 0.75 0.75 0.94 0.94 0.90 0.88 0.52 0.67 0.70 0.76 0.16 0.15 0.13 0.16 0.21 0.17 0.20 0.30 0.21 0.13 0.88 0.92 0.83 0.84 0.80 0.85

0.94 0.75 0.91 1.00 0.42 0.92 0.11 0.53 1.00 1.00 0.99 1.00 0.80 0.90 0.77 0.80 0.02 0.02 0.00 0.02 0.66 0.66 0.71 0.88 0.84 0.81 0.78 0.97 0.97 0.93 0.95 0.95 1.00 1.00 0.96 1.00 0.64 0.95 0.90 0.72 0.56 0.85 0.90 0.70 0.82 0.74 0.73 0.52 0.80 0.85 1.00 0.97 1.00 0.96 0.93 0.66 0.45 0.45 0.28 0.67

0.59 0.61 0.68 0.65 0.81 1.00 0.97 1.00 0.69 0.54 0.62 0.43 0.98 1.00 1.00 0.92 0.73 0.65 0.58 0.61 0.65 0.51 0.53 0.57 0.97 0.99 1.00 1.00 0.74 0.51 0.81 0.80 0.90 0.73 0.71 0.59 1.00 1.00 1.00 1.00 0.92 0.89 0.91 0.93 0.60 0.96 0.67 0.93 0.68 0.68 0.66 0.68 0.89 0.91 0.86 0.57 0.50 0.77 0.67 0.95

0.93 0.88 0.81 0.81 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.78 0.65 0.66 0.71 0.79 0.79 0.82 0.75 1.00 0.92 0.98 1.00 1.00 0.98 1.00 1.00 1.00 0.80 0.85 0.93 0.99 1.00 1.00 1.00 0.98 0.83 0.95 0.96 0.95 1.00 0.99 0.98 0.81 0.95 0.76 0.95 0.79 0.92 0.85 0.89 0.94 1.00 1.00 0.99

0.94 0.73 0.80 0.76 0.66 0.89 0.49 0.68 0.57 0.63 0.57 0.54 0.90 0.95 0.89 0.89 0.22 0.28 0.22 0.42 0.81 0.83 0.84 0.92 0.89 0.84 0.85 0.95 0.65 0.92 0.91 0.88 0.88 1.00 0.85 0.87 0.79 0.95 0.90 0.80 0.54 0.76 0.80 0.73 0.49 0.45 0.43 0.34 0.51 0.51 0.60 0.63 0.61 0.54 0.90 0.79 0.64 0.64 0.54 0.76

0.82 0.74 0.76 0.74 0.82 0.96 0.82 0.89 0.76 0.72 0.73 0.65 0.96 0.98 0.96 0.94 0.58 0.53 0.49 0.58 0.75 0.71 0.73 0.75 0.95 0.92 0.94 0.98 0.80 0.80 0.90 0.89 0.92 0.85 0.80 0.80 0.93 0.98 0.96 0.93 0.81 0.83 0.89 0.87 0.68 0.80 0.70 0.75 0.67 0.72 0.67 0.76 0.76 0.79 0.87 0.75 0.69 0.80 0.74 0.90

Adjoining Native Vegetation Organic Farming

Adjoining Native Vegetation 7-year-old Agroforestry System - Horticultural Beds 7-year-old Agroforestry System - Tree rows Adjoining Native Vegetation 11-year-old Agroforestry System - Horticultural Beds 11-year-old Agroforestry System - Tree rows Adjoining Native Vegetation Lapa

No-tillage Farming

Organic Farming

Agroforestry System - Horticultural Beds Agroforestry System - Tree rows Native Vegetation

2017. Tillage effects on soil quality after three years of irrigation in Northern Spain. Sustainability 9, 1476. https://doi.org/10.3390/su9081476. Askari, M.S., Cui, J., O’Rourke, S.M., Holden, N.M., 2015. Evaluation of soil structural quality using VIS–NIR spectra. Soil Tillage Res. 146, 108–117. https://doi.org/10. 1016/j.still.2014.03.006. Ball, B.C., Batey, T., Munkholm, L.J., 2007. Field assessment of soil structural quality – a development of the Peerlkamp test. Soil Use Manag. 23, 329–337. https://doi.org/10. 1111/j.1475-2743.2007.00102.x. Ball, B.C., Batey, T., Munkholm, L.J., Guimarães, R.M.L., Boizard, H., McKenzie, D.C., Peigné, J., Tormena, C.A., Hargreaves, P., 2015. The numeric visual evaluation of

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