SciELO - Scientific Electronic Library Online

 
vol.47 número3Mantenimiento de sistemas agroalimentarios enmarcados mediante la seguridad y la educación del suelo.Innovaciones transformativas en el codiseño de educacion en agroecologia. índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados

Revista

Articulo

Indicadores

Links relacionados

  • En proceso de indezaciónCitado por Google
  • No hay articulos similaresSimilares en SciELO
  • En proceso de indezaciónSimilares en Google

Compartir


International journal of agriculture and natural resources

versión On-line ISSN 2452-5731

Int. j. agric. nat. resour. vol.47 no.3 Santiago dic. 2020

http://dx.doi.org/10.7764/ijanr.v47i3.2290 

Research Paper

Environmental and Ecology

Physical assessment of a Mollisol under agroecological management in the Quillota Valley, Mediterranean Central Chile

Physical assessment of a Mollisol under agroecological management at Quillota valley, Mediterranean Central Chile

Manuel Casanova1 

Berthin Ticona2 

Osvaldo Salazar1 

Eduardo Gratacós3 

Marco Pfeiffer1 

Gonzalo Ávila3 

Yasna Tapia1 

Oscar Seguel1 

Carlo Sabaini3 

1Universidad de Chile, Facultad de Ciencias Agronómicas, Departamento de Ingeniería y Suelos. Santa Rosa 11315, La Pintana, Santiago.

2Universidad de Chile, Facultad de Ciencias Agronómicas, Escuela de Postgrado, Magíster en Manejo de Suelos y Aguas. Santa Rosa 11315, La Pintana, Santiago.

3Centro Regional de Investigación e Innovación para la Sostenibilidad de la Agricultura y los Territorios Rurales. San Francisco 1600, La Palma, Quillota.

Abstract

A number of agroecological practices have been proposed for assessing soil quality. Several physical soil properties have been shown to be important for determining soil quality by using the sustainability index (SI) and the cumulative rating approach. The main aim of the study was to determine the effects of different agroecological managements on the physical properties of a Mollisol in the Mediterranean central Chile. In addition, some physical properties were selected to compare the soil quality among different agroecological management practices and highly mechanized intensive systems by using the SI and cumulative rating approaches. An experimental field was defined in an area of 3.5 ha in 2014. Four sites with different agroecological practices were selected in 2019 to assess soil physical properties: rainfed Mediterranean annual prairie - no tillage (1-S), irrigated perennial prairie with deep-root species - no tillage (2-N), irrigated annual and perennial prairie - conventional tillage (4-S), irrigated vegetables and flowers - minimum tillage (4-N); an avocado orchard with traditional management was used as the control. Soil organic carbon and the following soil physical properties were selected to assess SI and CR: bulk density, total porosity, void ratio, air capacity, fast-drainage pores, relative field capacity, hydraulic conductivity, structural stability index and unavailable water pores. The applicability of the selected physical indicators to the SIs of agroecological management practices compared with the control was demonstrated. The cumulative rating index (CR) for each land use showed that all agroecological practices constituted sustainable soil management (25≤CR<30), whereas the avocado orchard showed the least sustainable management (30≤CR<40), and a change in soil use is recommended.

Keywords: Agroecology; cumulative rating approach; soil health; soil quality; sustainability index

Resumen

Se ha propuesto un número amplio de prácticas agroecológicas para actuar como base para mejorar la calidad del suelo, la salud de las plantas y la productividad de los cultivos. Se ha demostrado que varias propiedades físicas del suelo son importantes para determinar la calidad del suelo utilizando el índice de sostenibilidad (SI) y el enfoque de calificación acumulativa. El objetivo principal del estudio fue determinar los efectos de diferentes manejos agroecológicos sobre las propiedades físicas de un Mollisol en el mediterráneo de Chile central. Se estableció un campo experimental en un área de 3,5 ha en 2014. Se seleccionaron cuatro sitios con diferentes prácticas agroecológicas para evaluar las propiedades físicas del suelo en 2019: pradera mediterránea anual de secano - cero labranza (1-S); pradera perenne y especies de raíces profundas con riego - cero labranza (2-N); pradera anual y perenne - labranza convencional con riego (4-S); hortalizas y flores con riego - labranza mínima (4-N); y como control se utilizó un huerto de paltos con manejo tradicional. Se seleccionaron el contenido de carbono orgánico y las siguientes propiedades físicas del suelo para evaluar las prácticas o la sostenibilidad del manejo agroecológico: densidad aparente, porosidad total, relación de vacíos, capacidad de aire, poros de drenaje rápido, capacidad relativa de campo, conductividad hidráulica, índice de estabilidad de la estructura y poros de agua no disponibles. Se demostró la utilidad potencial de los indicadores físicos seleccionados para SI de prácticas de manejo agroecológico, en comparación con un sistema intensivo altamente mecanizado (huerto de palto convencional). El índice de calificación acumulativa (CR) para cada uso del suelo mostró que todas las prácticas agroecológicas constituyeron un manejo sostenible del suelo (25≤CR<30), mientras que el huerto de paltos fue el manejo menos sostenible (30≤CR<40) recomendándose un cambio de uso del suelo.

Palabras clave: Agroecología; calidad de suelos; enfoque de calificación acumulativa; índice de sostenibilidad; salud del suelo

Introduction

All Mediterranean-type zones worldwide appear to be hotspots of climate change and dwindling biodiversity (Myers et al., 2000; García et al., 2011). Because of their pleasant climate, Mediterranean regions have long been popular sites for human settlement, and the population density is high. Thus, Mediterranean regions are among the longest and most intensively exploited agricultural areas with increasing population pressure, which render their soils already depleted or more fragile. Furthermore, the scope and severity of human impacts in Mediterranean regions are currently accentuating the effects of climate change. These impacts could obliterate the efficient capacity for soil ecological resilience, which has managed to withstand other drastic and rapid changes in the past.

As in other Mediterranean zones, agriculture in central Chile is currently conducted under vulnerable conditions and is characterized by different forms of soil degradation (e.g., soil organic matter decline), water scarcity or overuse, disrupted nutrient cycles, land use change, high dependence on biomass and energy imports, and a prevalence of highly specialized and low-diversity agroecosystems. This scenario of vulnerability is also described by Aguilera et al. (2020), who urged the rapid adoption of systemic measures to increase the resilience of production systems and precision agroecological practices with high adaptation potential through the generation of local knowledge based on the integration of scientific and traditional ecological knowledge. Similarly, Ryan and Peigné (2017) concluded that agroecology, as a scientific discipline, will help facilitate efforts to respond to the actual challenges of agricultural production due to of increasingly applied systems thinking and interdisciplinary research approaches.

A wide range of agroecological practices have already been tested worldwide (Mendez et al., 2015; TWN and SOCLA, 2015) and in Chile (Montalba et al., 2017; Delpino-Chamy et al., 2019) to increase agroecosystem diversity and complexity and to act as a foundation for soil quality, plant health and crop productivity. However, there is a need to assess how these practices impact soil conditions, which are vital for crop production. In particular, soil physical conditions are prone to changes in the field due to management practices that play an integral role in controlling chemical and biological processes (Fuentes et al., 2014). Several physical soil properties (e.g., aggregate stability, available water capacity, and soil strength) have been shown to be important for determining yield and have been utilized as soil health or soil quality tests (Idowu et al., 2008; Schindelbeck et al., 2008).

The hypothesis of this study was that agroecological management improves the ability of soil to store water and improve the air capacity that is necessary for plant growth when compared to conventional management of avocado. Therefore, our objective was to determine the effects of agroecological practices on the physical properties of a Mollisol, which was initially an intensely managed avocado orchard in Mediterranean central Chile. In addition, we identified some physical soil properties for comparing soil quality among agroecological management practices and highly mechanized intensive systems by using the sustainability index (SI) and cumulative rating (CR) approaches.

Material and Methods

The CERES (Regional Center of Research and Innovation for the Sustainability of Agriculture and Rural Territories) is an experimental field that is located near Quillota City, Valparaiso Region, Chile (32°53'SL; 71°12'WL) at 220 masl (Figure 1). It was created to develop agroecological technologies, was established in 2014 and encompasses ≈3.50 ha, but 2.25 ha was designated for polyculture farming. In the previous management of agroecological practices, two subsoils at 0.6 m depth were constructed in perpendicular directions, and three consecutive (June 2015–January 2016–May 2016) high biomass prairies were then sown and incorporated into these soils (Figure 1).

Figure 1 Time evolution of the study area and distribution of five assessed sites (T0, 1-S, 4-S, 2-N and 4-N) in Mediterranean central Chile (Google Earth images, between 2015 and 2019). 

From the different designed agroecological practices, four sites (e.g., 1-S, 2-N, 4-S and 4-N) were selected to assess the physical properties of soil using an avocado (Persea americana Mill.) orchard (5.50 ha) with traditional management, that was close to the experimental field and was used as the control (Table 1).

Table 1 Treatments defined according to the agroecological practices applied in Mediterranean central Chile. 

Treatment Vegetal cover Management Tillage Irrigation system
Control Conventional irrigated avocado orchard Chemical fertilization. No tillage Microsprinkler
1-S Rainfed Mediterranean annual prairie, growing in winter Cutting and residues left in the field. No tillage –––
2-N Irrigated perennial prairie with deep roots species Cutting, residues removed to compost production. No tillage K-line sprinkler
4-S Irrigated annual and perennial prairie (new apple trees) Winter sowing, cutting and residues left in the field. Conventional Microsprinkler
4-N Irrigated vegetables and flowers Residues removed to compost production, which is later applied to seedbeds. Minimum Drip/trickle

The study area is characterized by deep soils of colluvial origin that are on a slightly inclined plane (piedmont), which exhibit moderate permeability and good drainage and are classified as fine-loamy, mixed, thermic fluventic haploxerolls (CIREN, 1997). Morphological soil profile descriptions for each treatment were initially conducted in pits by collecting soil samples at 0–20, 20–40 and 40–60 cm depths (4 replicates) for laboratory characterization. In general, the dominant climate is Csb2 (Köppen system), i.e., temperate warm with Mediterranean influence with winter rains and an extended dry season (8 months), the average annual temperature is 13°C, annual rainfall is 430 mm and annual potential evapotranspiration is 1,350 mm.

Measured soil physical properties

Soil bulk densities (determined from cylinders and clods); particle densities (Pd, determined with pycnometers); textures (Bouyoucos densimeter) and pF curves (determined with sand beds and pressure-plate devices; at 0.2, 6, 33, 100 and 1,500 kPa) were calculated by following standard Chilean methodologies (Sandoval et al., 2012). Soil saturated hydraulic conductivities, Ks1 and Ks5, were measured at 1 and 5 h, respectively, in undisturbed samples with an Eijkelkamp laboratory constant-head permeameter (Eijkelkamp, 2011).

Soil macroaggregate stability was determined as the mean diameter variation (MDV) by sieving soil samples in wet and dry conditions (Eq. 1, Supplemental Material 1), where ni1 is the dry sieved aggregate fraction (%), ni2 is the wet sieved aggregate fraction (%) and di is the weighted diameter of the aggregates (mm). On the other hand, soil microaggregate stability was assessed as the dispersion ratio (DR) by using Eq. 2 (Supplemental Material 1), which is defined as the ratio of the amount of clay+silt obtained in distilled water-dispersed samples (sd, soft dispersion) to that obtained in sodium hexametaphosphate dispersed samples (dd, drastic dispersion). High values indicate high dispersion of microaggregates and low soil stability.

Supplemental Material 1 Equations for measured and estimated physical soil properties. 

Properties Equations formulas References
Measured
1. Mean diameter variation
MDV=Σ(ni1di)(ni2di)Σni1
Hartge and Horn (2009)
2. Dispersion ratio
DR=(silt+clay)sd(silt+clay)dd
Berryman et al. (1982)
3. Hydro-repellency index
R=1.95sethanolswater
Tillman et al. (1989)
Estimated
4. Total porosity
S=(1cyBdPd)100
Das (2016)
5. Structural porosity
SP=(1cyBdclBd)100
Cerisola et al. (2005)
6. Textural porosity
TP=cyBd(1clBd1Pd)
Cerisola et al. (2005)
7. Void ratio
e=PdcyBd1
Das (2016)
8. Air capacity
AC=WsWfc
Castellini et al. (2019a)
9. Structural stability index
Stl=SOMclay+silt100
Pieri (1992)
10. Relative field capacity to saturation
RFC=WfcWs
Reynolds et al. (2009)
11. Plastic index
PI=LLLP
Das (2016)
12. Optimal soil water contents for agricultural use
Wo=LL(IcPI)
Baumgartl (2016)
13. Activity
Am=PIclay
Baumgartl (2016)

The Atterberg limits (plastic and liquid) of soils were determined following the Test D-4318, standardized by the American Society for Testing and Materials (Das, 2016). The hydrophobicity or repellency index (R) was measured with a microinfiltrometer device (Hallett and Young, 1999; Cosentino et al., 2010) from the sorptivity of aggregates (3 to 5 mm diameter) to deionized water and ethanol (95% vol.). Liquids were supplied to the aggregates through a micropipette tip with a 140 μm radius from a source at a constant hydraulic head (ψ=-1 cm) and according to Eq. 3 (Supplemental Material 1), where the constant (1.95) accounts for the difference in surface tension and viscosities of ethanol and water. Sethanol is the sorptivity for ethanol (mm s-1/2), and Swater is the sorptivity for water (mm s-1/2) of soil aggregates.

At the field level, soil penetration resistance (PR, N cm-2) was measured with a digital force gauge (Enpaix EFG500) and conical tip (1 cm diameter/5 cm length) 24 h after irrigation at each site and included six replicates at each soil depth.

Finally, soil organic matter content (SOM) was determined through dry calcination at 360 °C for 16 h (Sadzawka et al., 2006).

Estimated soil physical properties

Soil porosity, which is dependent on management treatments, was evaluated by examining several properties:

Total porosity (S) was obtained by using the cyBd and Pd values shown in Eq. 4 (Supplemental Material 1).

Textural (TP) and structural (SP) porosities: SP includes macropores (structural pores) that result from tillage, traffic, weather and biological activity, while TP includes micropores (textural pores) that result from the arrangement of elementary soil particles (Nimmo, 2004). Structural pores are subjected to short-term variations such as compaction by wheeling, whereas compaction does not affect textural porosity (Pereira et al., 2019). Using soil density values, TP and SP were estimated using Eqs. 5 and 6 (Supplemental Material 1).

Void ratio (e): expresses changes in soil porosity for the same mass of soil regardless of the bulk density (Eq. 7, Supplemental Material 1). The e value may range from 0.25 to 0.80 for subsoils and from 0.80 to 1.40 for surface soils (Lal and Shukla, 2004).

Pore size distribution was derived from pF curves (Hartge and Horn, 2009; Pagliai and Vignozzi, 2002) as fast-drainage pores (FDP, >50 μm and water retention between 0.2 and 6 kPa); slow drainage pores (SDP, 10–50 μm and water retention between 6 and 33 kPa); available water pores (AWP, 0.2–10 μm and water retention between 33 and 1,500 kPa) and unavailable water pores (UWP, <0.2 μm, water retention at 1,500 kPa).

Air capacity (AC): determined by the difference between soil water content at saturation (Ws) and at field capacity (Eq. 8, Supplemental Material 1) and is an indicator of soil ability to store root-zone air (i.e., degree of soil aeration).

Relative field capacity to saturation (RFC): indicates the soil ability to store water and air relative to the total pore volume of the soil and was estimated using Eq. 10 (Supplemental Material 1).

Structural conditions were evaluated through the risks of the structural degradation index (StI, Eq. 9, Supplemental Material 1). Since StI is based on OC and texture, it is directly related to the resilience of the structure (Reynolds et al., 2009).

From the Atterberg limit results, the plastic index (PI, %) was obtained as the difference between LL and PL (Eq. 11, Supplemental Material 1), which is often used as an indicator of soil workability. Furthermore, the consistency index (Ic) was derived from these limits (Eq. 12, Supplemental Material 1), which indicates soil firmness and changes in gravimetric water content that allow the soil to vary from liquid to hard states. Therefore, an optimal range of water content (Wo) for agricultural use was estimated. The difference between PL (optimum conditions for plowing) and field capacity (Wfc or W33) or permanent wilting point (WPWP or W1500) also provides a useful indication of soil workability (Kirby, 2002). If PL is close to Wfc or is much higher than WPWP, soil will be suitable for working soon after drainage or when there is sufficient stored water, respectively. On the other hand, the activity values (Am; Eq. 13, Supplemental Material 1) were also calculated to infer some of the mineralogical properties of soils.

All of these measurements and estimations were considered to be the total data set (TDS), and principal component analysis (PCA) was used to select more effective soil physical indicators of management sustainability and conform to a minimum data set (MDS).

Relationships within the TDS were investigated by using parametric correlation analysis and by computing Pearson correlation coefficients. To assess soil sustainability in different agricultural management systems, a cumulative rating (CR) approach was also utilized (Shukla et al., 2006). Selected soil physical indicators were categorized on the basis of critical levels from none to extreme limitation on a scale of 1 to 5, respectively, by using a relative weighting factor (RWF) based on the limitations for crop production (Landon, 1984; Lal, 1994; Nwosu and Okon, 2020). Finally, the physical soil sustainability values for each site and soil depth were calculated by summing the RWFs (Table 2).

Table 2 Sustainability of a land use in relation to the cumulative rating index (CR, 10 indicators), according Lal (1994)

Cumulative rating index Sustainability
<20 Highly sustainable (HSU)
≤ 20–<25 Sustainable (SUS)
≤ 25–<30 Sustainable with high inputs (SHI)
≤ 30–<40 Sustainable with another land use (SAU)
≤ 40 Unsustainable (USU)

Results and Discussion

The soils at the surface and at different depths are mainly medium textured, and the particle density (Pd) varies within a narrow range (Supplemental Material 2). A positive, significant correlation of bulk density (Bd), as determined from cylinders and clods, was observed with sand contents but the correlation was negative with soil clay and silt contents (Supplemental Material 3).

Supplemental Material 2 Mean (± standard deviation) values of measured soil properties by site and depth (n= 4) 

Soil depth Properties Units Sites
T0 1-S 2–N 4–S 4–N
0 - 20 cm Sand (%) 44.41± 2.88 46.21± 4.36 37.97± 2.17 42.02± 3.26 38.36± 0.84
Silt (%) 32.49± 2.05 29.87± 2.05 33.37± 2.00 33.20± 0.48 36.00± 1.22
Clay (%) 23.10± 1.51 23.92± 3.40 28.67± 3.50 24.78± 3.09 25.64± 0.90
Pd (Mg m-3) 2.66± 0.11 2.69± 0.13 2.60± 0.18 2.78± 0.28 2.87± 0.13
SOM (%) 4.60± 0.94 5.61± 0.59 3.71± 0.37 5.76± 1.36 3.99± 0.61
R (-) 2.68± 0.64 6.67± 2.65 3.36± 2.53 5.38± 1.53 4.69± 0.96
cyBd (Mg m-3) 1.53± 0.12 1.36± 0.11 1.28± 0.02 1.34± 0.07 1.25± 0.04
clBd (Mg m-3) 1.80± 0.07 1.76± 0.13 1.67± 0.09 1.63± 0.06 1.53± 0.26
DR (%) 53.77± 4.64 45.22± 7.25 56.97± 4.89 52.68± 7.67 62.64± 5.94
MDV (mm) 4.86± 1.94 1.69± 0.86 2.23± 1.14 2.70± 0.94 3.39± 1.31
LL (%) 28.69± 1.46 31.86± 1.36 25.12± 1.94 28.74± 2.10 27.25± 0.89
LP (%) 19.78± 3.35 24.56± 3.99 18.04± 2.52 21.88± 1.33 20.42± 1.42
PR (N cm-2) 206.64± 4.99 201.97± 17.67 147.06± 31.71 161.22± 48.94 64.03± 18.70
θ33 (cm3 cm-3) 0.292± 0.040 0.232± 0.011 0.242± 0.022 0.252± 0.022 0.248± 0.010
θ1500 (cm3 cm-3) 0.218± 0.028 0.171± 0.012 0.148± 0.021 0.158± 0.015 0.128± 0.004
Ks1 (cm h-1) 18.23± 19.26 69.77± 12.48 26.21± 5.84 10.98± 10.98 14.27± 10.83
Ks5 (cm h-1) 19.09± 18.28 67.52± 12.27 26.02± 7.22 13.09± 7.56 14.90± 9.60
20 - 40 cm Sand (%) 49.53± 6.12 46.24± 3.43 36.10± 2.21 42.96± 2.51 37.84± 2.13
Silt (%) 28.47± 4.18 27.18± 2.59 33.58± 1.27 31.56± 0.80 36.00± 1.04
Clay (%) 22.00± 2.04 26.57± 2.14 30.32± 2.96 25.48± 2.45 26.15± 1.26
Pd (Mg m-3) 2.75± 0.11 2.65± 0.13 2.79± 0.18 2.76± 0.28 2.72± 0.13
SOM (%) 2.63± 0.48 3.73± 0.56 2.43± 0.50 3.37± 0.15 3.25± 0.43
R (-) 1.55± 0.44 2.40± 1.10 1.50± 0.11 3.37± 0.72 3.10± 1.51
cyBd (cm3 cm-3) 1.63± 0.07 1.47± 0.10 1.43± 0.04 1.51± 0.10 1.45± 0.09
clBd (cm3 cm-3) 1.88± 0.05 1.83± 0.06 1.77± 0.10 1.74± 0.08 1.80± 0.14
DR (%) 53.79± 6.90 49.75± 13.68 56.54± 9.88 44.05± 12.20 59.37± 4.76
MDV (mm) 8.05± 1.79 3.66± 1.12 4.71± 0.69 5.53± 2.78 5.57± 0.96
LL (%) 25.32± 1.72 26.03± 1.45 23.30± 0.65 25.34± 1.06 25.22± 0.96
LP (%) 18.55± 2.11 17.82± 1.51 15.34± 3.29 17.81± 1.05 18.99± 0.39
PR (N cm-2) 527.79±52.44 284.04± 24.79 248.56± 78.95 270.10± 48.79 152.05± 38.52
θ33 (cm3 cm-3) 0.274± 0.022 0.230± 0.022 0.229± 0.023 0.245± 0.014 0.256± 0.014
θ1500 (cm3 cm-3) 0.185± 0.012 0.156± 0.005 0.155± 0.014 0.157± 0.009 0.150± 0.005
Ks1 (cm h-1) 8.57± 7.91 22.63± 13.76 3.83± 1.97 6.95± 6.95 2.80± 1.37
Ks5 (cm h-1) 14.82± 15.39 20.82± 11.14 5.45± 4.46 7.60± 5.46 3.73± 0.54
40 - 60 cm Sand (%) 61.05± 5.88 54.72± 7.04 34.44± 2.24 48.18± 3.62 41.04± 1.88
Silt (%) 21.22± 4.70 23.70± 7.16 33.96± 1.68 29.10± 4.88 32.96± 2.16
Clay (%) 17.73± 1.22 21.58± 3.72 31.60± 2.43 22.72± 2.17 26.00± 0.85
Pd (Mg m-3) 2.69± 0.09 2.73± 0.12 2.70± 0.22 2.85± 0.06 2.70± 0.27
SOM (%) 2.17± 0.46 2.74± 0.34 2.51± 0.08 2.99± 0.64 2.51± 0.25
R (-) 1.45± 0.81 1.61± 0.61 1.80± 0.50 2.07± 0.77 1.45± 0.81
cyBd (Mg m-3) 1.64± 0.10 1.48± 0.14 1.51± 0.10 1.50± 0.09 1.48± 0.03
clBd (Mg m-3) 1.81± 0.07 1.81± 0.09 1.83± 0.09 1.77± 0.04 1.83± 0.17
DR (%) 66.20± 4.16 52.20± 12.80 62.39± 3.98 57.73± 16.77 60.77± 6.02
MDV (mm) 7.03± 1.05 3.87± 2.40 5.69± 2.34 7.64± 1.65 6.91± 1.05
LL (%) 21.10± 1.44 22.47± 1.83 22.23± 0.56 22.62± 1.42 22.82± 0.41
LP (%) 17.85± 1.44 15.06± 0.82 15.90± 0.82 17.65± 1.40 16.50± 1.75
PR (N cm-2) 612.29± 23.21 282.47± 28.35 381.02± 139.33 370.54± 71.39 226.50± 74.53
θ33 (cm3 cm-3) 0.258± 0.038 0.199± 0.045 0.258± 0.009 0.216± 0.015 0.243± 0.016
θ1500 (cm3 cm-3) 0.158± 0.019 0.123± 0.016 0.156± 0.029 0.137± 0.002 0.133± 0.007
Ks1 (cm h-1) 2.36± 1.16 12.87± 1.86 4.43± 1.99 4.68± 4.68 2.30± 0.89
Ks5 (cm h-1) 2.27± 1.46 11.10± 1.47 4.08± 0.83 5.34±1.89 2.51± 0.89

Pd, particle density; SOM, soil organic matter content; R, water repellency index; clBd, bulk density determined from clods; cyBd, bulk density determined from cylinders; DR, dispersion ratio; MDV, mean diameter variation; LL, liquid limit; PL, plastic limit; PR, penetration resistance; θ33 and θ1500, volumetric water content at 33 kPa and 1,500 kPa, respectively; and Ks1 and Ks5, hydraulic conductivities at 1 h and 5 h, respectively.

Supplemental Material 3 The correlation matrix. Assessed and estimated soil properties in the studied field (n=60) in central Chile. 

sand silt clay Pd
Pd 0.09 0.07 0.09 1 cyBd
cyBd 0.44+ 0.39+ 0.38+ 0.19 1
clBd¯
clBd 0.30 0.39+ 0.10 0.04 0.54+ 1
DR¯
DR 0.02 0.00 0.03 0.15 0.19 0.04 1
MDV¯
MDV 0.18 0.09 0.24 0.05 0.61+ 0.31* 0.24 1
SOM¯
SOM 0.09 0.19 0.05 0.04 0.50+ 0.37+ 0.33+ 0.55+ 1
W33¯
W33 0.22 0.30* 0.06 0.06 0.32* 0.05 0.07 0.24 0.01 1
W1500¯
W1500 0.05 0.09 0.02 0.08 0.38+ 0.19 0.18 0.04 0.26* 0.67+ 1
Ks1¯
Ks1 0.07 0.06 0.05 0.16 0.40+ 0.08+ 0.34 0.57+ 0.59+ 0.16 0.19 1
Ks5¯
Ks5 0.05 0.05 0.04 0.15 0.40+ 0.09+ 0.36 0.52+ 0.57+ 0.12 0.20 0.96+ 1
R¯
R 0.14 0.24 0.02 0.01 0.53+ 0.40+ 0.24 0.51+ 0.73+ 0.11 0.06 0.58+ 0.56+ 1
PR¯
PR 0.57+ 0.55+ 0.42+ 0.03 0.72+ 0.45+ 0.11 0.54+ 0.51+ 0.09 0.15 0.28* 0.26* 0.43+ 1
PL¯
PL 0.20 0.27* 0.05 0.01 0.44+ 0.28* 0.39+ 0.54+ 0.87+ 0.12 0.41+ 0.67+ 0.68+ 0.75+ 0.47+ 1
LL¯
LL 0.00 0.18 0.22 0.12 0.26* 0.38+ 0.17 0.39+ 0.69+ 0.07 0.29* 0.59+ 0.55+ 0.71+ 0.27* 0.77+ 1
StI¯
StI 0.28 0.16 0.35+ 0.01 0.32* 0.22 0.31* 0.47+ 0.92+ 0.12 0.22 0.61+ 0.58 0.64+ 0.28* 0.76+ 0.65+ 1
S¯
S 0.39+ 0.34+ 0.34+ 0.60+ 0.90+ 0.45+ 0.23 0.52+ 0.42+ 0.30 0.34+ 0.26* 0.26+ 0.42+ 0.59+ 0.36+ 0.16 0.27* 1
SP¯
SP 0.20 0.05 0.33+ 0.14 0.60+ 0.34+ 0.28* 0.41+ 0.22 0.32* 0.22 0.38+ 0.37+ 0.22 0.36+ 0.23 0.05 0.16 0.56+ 1
TP¯
TP 0.17 0.28* 0.03 0.43+ 0.21 0.84+ 0.09 0.06 0.17 0.07 0.09 0.17 0.16 0.17 0.18 0.09 0.21 0.07 0.36+ 0.57+ 1
e¯
E 0.38+ 0.34+ 0.32* 0.60+ 0.89+ 0.49+ 0.19 0.50+ 0.42+ 0.26* 0.33* 0.25 0.26* 0.43+ 0.59+ 0.36+ 0.17 0.26* 0.99+ 0.52+ 0.40+ 1
RFC¯
RFC 0.14 0.04 0.22 0.13 0.65+ 0.28* 0.07 0.42+ 0.16 0.81+ 0.69+ 0.14 0.12 0.24 0.39+ 0.02 0.01 0.13 0.59+ 0.46+ 0.06 0.55+ 1
AC¯
AC 0.30 0.26* 0.26* 0.15 0.66+ 0.41+ 0.08 0.34+ 0.13 0.32 0.61+ 0.00 0.00 0.23 0.40+ 0.01 0.01 0.02 0.60+ 0.35+ 0.20 0.59+ 0.74+ 1 AWP
AWP 0.24 0.32* 0.07 0.00 0.01 0.17 0.25 0.26* 0.28* 0.53+ 0.27* 0.40+ 0.37+ 0.16 0.06 0.29* 0.20 0.38+ 0.00 0.18 0.20 0.03 0.26* 0.29* 1
FDP¯
FDP 0.22 0.17 0.22 0.26* 0.81+ 0.38+ 0.15 0.53+ 0.38+ 0.56+ 0.50+ 0.28* 0.26* 0.39+ 0.53+ 0.28* 0.16 0.30* 0.77+ 0.54+ 0.16 0.76+ 0.87+ 0.72+ 0.16 1
SDP¯
SDP 0.36 0.27* 0.35+ 0.08 0.20 0.22 0.05 0.04 0.11 0.11 0.31* 0.27* 0.25 0.03 0.14 0.20 0.12 0.23 0.13 0.05 0.08 0.11 0.43+ 0.65+ 0.24 0.17 1
UWP¯
UWP 0.05 0.09 0.01 0.08 0.38+ 0.19 0.18 0.04 0.26* 0.67+ 1.00+ 0.19 0.20 0.06 0.15 0.41+ 0.29* 0.22 0.34+ 0.22 0.09 0.33* 0.69+ 0.61+ 0.27* 0.50+ 0.31* 1

Values in bold indicate negative Pearson's correlation coefficients (r<1).

* and +represent the p<0.05 and p<0.01 significance values, respectively. AC, air capacity; AWP, available water pores; clBd, bulk density determined from clods; cyBd, bulk density determined from cylinders; DR, dispersion ratio; FDP, fast-drainage pores; R, hydro-repellency index; LL, liquid limit; MDV, mean diameter variation; MDS, minimum data set; Wo, optimal range of gravimetric water content for agricultural use; Pd, particle density; PR, penetration resistance; PI, plastic index; PL plastic limit; RFC, relative field capacity to water saturation; SDP, slow drainage pores; SOM, soil organic matter; Ks1 and Ks5, hydraulic conductivities at 1 h and 5 h, respectively; SP, structural porosity; TP, textural porosity; TDS, total data set; S, total porosity; UWP, unavailable water pores; e, void ratio; W33, gravimetric water content at 33 kPa; and W1500, gravimetric water content at 1,500 kPa.

Both soil bulk densities showed similar trends in soils when agroecological practices were developed, which exhibited increases with depth and showed higher variations in surface areas. Reynolds et al. (2009) reported that the Bd (Mg m-3) ranges for most soil textures were optimal (0.90≤Bd≤1.20), near optimal (0.85≤Bd<0.90 and 1.20<Bd≤1.25) and at critical limits (0.85<Bd and Bd>1.25).

Soils under traditional management (i.e., T0, avocado trees) maintained the highest Bd values, regardless of the determination method (Supplemental Material 2), suggesting increasing soil compaction at depth. In the same manner, in the upper soil horizons of all sites, penetration resistance levels (PR, N cm-2) varied from medium (50<PR<125) to very dense (200<PR<300) according to Hazelton and Murphy (2016). However, in the subsurface soils of some sites (e.g., T0, 2-N and 4-N), the degree of soil consolidation was classified as extremely dense (PR>300) (Supplemental Material 2).

It is known that Bd indirectly provides a measure of soil porosity and has an inverse relationship; in this sense, we found a strong negative correlation (Supplemental Material 3) between both of these properties. Likewise, void ratios (e), which have an advantage over total porosity (S) because their changes only result from changes in pore volumes with the volume of solids remaining unaltered and soil compaction findings, particularly for soil profiles of the avocado orchard, were corroborated (Figure 2). In fact, Li and Zhang (2009) report that e values become smaller (i.e., reduction of open pore space available for water flow) as compaction increases; then, soil permeability is directly proportional to e. Nevertheless, these results also indicate that for sites with agroecological management, subsoiling operations are effective only at the soil surface, considering that looser soils are those with higher e values than dense soils.

Figure 2 Assessment of soil porosity with depth following six methods, with conventional avocado orchard management (T0) and with agroecological management sites (1-S, 2-N, 4-S and 4-N) in Mediterranean central Chile. 

Textural porosity (TP) is only slightly affected by soil management, whereas structural porosity (SP) is sensitive to management factors such as tillage, compaction and cropping. Both Richard et al. (2001) and Kutilek et al. (2006) detected that soil compaction by intense management occurred mainly at the expense of SP. In this sense, similar trends among S, e and SP (but not TP) at all sites assessed in our study were observed (Figure 2). According to Pitts (1985), for the range of e values between 0.35 and 1.00, the soil skeleton remains stable (for most of the assessed sites), but if e values >1.00 are recorded, then the soil may be collapsible (for only the surface soils of 4-N and 4-S sites).

Air capacity (AC, cm3 cm-3) values were also estimated. Reynolds et al. (2015) concluded that high AC values (≤0.20) are considered ideal for maintaining atmospheric concentrations of O2 and CO2 in fine-textured soil; AC≈0.14 is equivalent to the lower optimal limit for adequate aeration of fine-textured soil and AC≈0.09 corresponds to the lower critical limit where fine-textured soil becomes susceptible to periodic anaerobiosis. At irrigated sites (e.g., 2-N, 4-N and 4-S), the AC values obtained were optimal and varied between 0.19 and 0.22 cm3 cm-3 (Figure 2), but at T0 and 1-S (rainfed site), the AC values fluctuated between 0.14 and 0.18 cm3 cm-3, which were closer to values indicating poor aeration. Recently, Castellini et al. (2019b) suggested that optimal AC values are in the range of 0.10–0.26 cm3 cm-3, while higher or lower values represent inadequate soil aeration conditions.

RFC values indicate a soil's primary limitation with respect to water (droughtness) and air storage, and the optimal range (0.6≤RFC≤0.7) was defined by Reynolds et al. (2009). Lower values (RFC<0.6) can reduce microbial activity and nitrate production because of insufficient water (water-limited soil), whereas greater values (RFC>0.7) may indicate reduced microbial activity because of insufficient air (aeration-limited soil). In agreement with Castellini et al. (2019a), with RFC being a key soil physical quality indicator, Supplemental Material 3 shows that there are high negative correlations (p<0.01) of RFC with FDP, SDP, UWP and AC but there is a positive correlation (p<0.05) with AWP. Figure 2 describes the variations in soil depth at the assessed sites and shows that there are no values that are in the undesirable aeration range.

High to very high values of SOM in the upper horizons were observed, which decreased with depth (60 cm) to medium values (Supplemental Material 2). However, those sites where residues were left in the field (1-S and 4-S sites) showed higher SOM values than other sites. On the other hand, the SOM contents at the surface of the old (20 years) avocado orchard (T0) were higher than those at sites where organic residues were removed (2-N and 4-N).

Similar to the results of other studies (Lichner et al., 2018; Mao et al., 2019), vegetation cover strongly influences surface SOM and impacts the level and distribution of soil water repellency (R); in fact, a significant correlation (Supplemental Material 3) between both variables was observed.

Soil water repellency is a common phenomenon that is observed postfire in Mediterranean forest soils but also in agricultural soils in which hydrophobic organic substances are produced during plant decomposition in rotations including legumes (Garcia-Chevesich, 2010; Casanova et al., 2013; Fuentes et al., 2015). Considering the thresholds for the water repellency index (R) as defined by Iovino et al. (2018), all sites, particularly those with agroecological management, are included in the class of slight repellents (1.95≤R<10) at the soil surface (Supplemental Material 2), while at depth, the general trend changed to wettable soils (R<1.95). The highest R values were measured in sites where harvesting residues were incorporated into soil (1-S and 4-S), and following dry periods, exacerbated water repellency should be expected, and increasing summer droughts could worsen the problem.

Trafficability and workability (i.e., optimum conditions suitable for plowing) are soil capabilities which support the operations of agricultural machinery while avoiding soil degradation risk (Müller et al., 2011). Soils that have poor trafficability and a narrow range of water contents in which cultivation is beneficial (Wo) are difficult to manage and are susceptible to compaction (Kirby, 2002).

Optimal soil water contents (Wo, Eq. 12, Supplemental Material 1) for soil cultivation are estimated to occur when soils are stiff and have an Ic (index of consistency) between 0.75 and 1.00; drier soils increase the energy input needed for cultivation, which can be a serious problem for fine-textured soils as plowing can become difficult. For the case of lower than optimal Ic, soil structures can easily be destroyed when the soil is kneaded by trafficking, and cultivation can have serious effects on plant growth and soil biological activity (Baumgartl, 2016). In this sense, a low soil degradation risk is expected when Wo values that are favorable for workability are estimated and differentiated by site. Therefore, during tillage and according to Figure 3, we estimate that the rainfed site (1-S) should have a high Wo (17 to 19%), 4-S site should have a medium Wo value (15 to 17%) and 2-N and 4-N sites should have low Wo values (10 to 15%) in the plowed layer.

Figure 3 Gravimetric water contents (Wo) for optimum agricultural use (avoiding physical soil degradation risk) that were estimated with index of consistency (Ic) values of 0.75 and 1.00 for each site and soil depth. Agroecological management (1-S, 2-N, 4-S and 4-N) and conventional avocado orchard management (T0) in Mediterranean central Chile. 

For fine-textured soils, it is possible to use clay contents and the plastic index (Eq. 11, Supplemental Material 1) to compute some mineralogical features of soils because there is a fairly close correlation between clay mineral type and activity (Lambe & Whitman, 1969). Activity of clay (Am) values allow differentiation of active soils with a high capacity for swelling and shrinking (Am≤1.25), illitic (0.75<Am<1.25) and kaolinitic soils (Am≤0.75). The values obtained at all sites (0.18 to 0.40, Supplemental Material 2) confirm the homogeneity of the studied field as well as the kaolinitic mineralogy domain in the soils of Mediterranean central Chile informed by CIREN (1997).

It is often argued that agroecological management tends to favor and enhance soil structure (Lozano et al., 2015; Ryan & Peigné, 2017) by using practices that are oriented to preserving soil stability. Pulido Moncada et al. (2014) described the structural stability index (StI, Eq. 9 in Supplemental Material 1), which allowed us to classify cultivated soils as structurally degraded (StI≤5%), with high structural degradation risk (5%<StI<7%), with low structural degradation risk (7%≤StI < 9%) and with good conditions for maintaining structural stability (StI≤9%). In this sense, 1-S and 4-S sites showed higher values, while 2-N and 4-N sites fell in the degraded range.

In our study and in agroecological management, reductions in macroaggregate stability (higher values of MDV, Figure 4) occurred in depth, which were explained by the small influence of pedogenetic processes (e.g., wetting-drying cycles and SOM dynamics) at these depths. Therefore, more labile organic compounds would promote bonding among soil mineral particles, which would improve macroaggregate stability in the upper horizons. On the other hand, SOM contents were affected by subsoiling and favored its oxidation, which took place mainly in the lower horizons with a subsequent decrease in macroaggregate stability.

Figure 4 Structural degradation index and aggregate stability with soil depth for each agroecologically managed (1-S, 2-N, 4-S and 4-N) and conventional avocado orchard management (T0) systems in Mediterranean central Chile. 

Most soils contain microaggregates that are composed of a vast variety of organic and inorganic material that are bound together during pedogenesis by several processes, which enable them to withstand strong stresses, survive slaking in water and persist in soils for decades (Totsche et al., 2018). Although little is known regarding how microaggregates and their properties change over time (Ritschel and Totsche, 2019), which strongly limits our understanding of microscale soil structure dynamics, similar behavior between the structure stability index (StI) and dispersion ratio (DR, Eq. 1 in Supplemental Material 1) was observed but not between StI and MDV. Higher DR values (i.e., lower stability), even than those for T0, were detected for sites where harvesting residues were exported (2-N and 4-N), which indicates low resistance of soil microaggregates to breakdown by water; instead, for those sites where residues were left in the field, lower DR (high microaggregate stability) values were observed, but few cases were below the threshold level of DR<0.3 reported for highly stable soils (Brunel et al., 2016).

The highest values of fast-drainage pores (FDP≤17%) were detected at the surfaces of sites under different agroecological practices, whilst in T0 were lower than 13% (Figure 5). Moreover, the average difference between available water pores (AWP) and unavailable water pores (UWP) showed contrasts among the soil profiles and followed a trend of T011% > 1-S8% > 4-S7% > 2-N6% > 4-N3%. Significant negative correlations, with p values <0.01 (n=60) between the measured properties and FDP were observed (water retention between 6 and 1,500 kPa; MDV; Bd and PR). Additionally, significant positive correlations with p values <0.05 (n=60) were detected for Ks1, Ks and Pd (Supplemental Material 3).

Figure 5 Soil pore size distributions under different agroecological management practices (1-S, 2-N, 4-S and 4-N) and conventional avocado orchard management (T0) in Mediterranean central Chile. (UWP: unavailable water pores, AWP: available water pores, SDP: slow drainage pores, and FDP: fast-drainage pores). 

There were fewer slow drainage pores (SDPs) in the avocado orchard than in agroecological practice sites (Figure 5). In addition, it is known that for soils with few slow drainage pores (SDPs) and conversely, with abundant FDPs, soil water will decrease very rapidly; in this case, among the studied sites, it was notable that the nonirrigated site (1-S) exhibited an average value of 13% in its soil profile.

Most measured and/or estimated soil properties were included in the principal component analysis (PCA) to extract the smallest number of factors that could explain most of the total variation. Two factors extracted by PCA explained 55% of the total variance in the samples (Figure 6). The first factor accounted for 33%, and the second accounted for 2%. The highest loadings in the first factor group were bulk density (cyBd), total porosity (S), void ratio (e), air capacity (AC), fast-drainage pores (FDP) and relative field capacity (RFC). The second factor had a high factor loading for soil organic carbon (SOC), hydraulic conductivity (Ks5), structure stability index (StI) and unavailable water pores (UWP). Thus, we selected those properties to assess practices and agroecological management sustainability (Table 3).

Figure 6 Principal component analysis of soil properties (e.g., AC, air capacity; AWP, available water pores; UWP, unavailable water pores; clBd, bulk density determined from clods; cyBd, bulk density determined from cylinders; DR, dispersion ratio; FDP, fast-drainage pores; R, hydro-repellency index; LL, liquid limit; MDV, mean diameter variation; Pd, particle density; PR, penetration resistance; PI, plastic index; PL plastic limit; PC1, first principal component; PC2, second principal component; RFC, relative field capacity to water saturation; SDP, slow drainage pores; SOC, soil organic carbon; SOM, soil organic matter; SP, structural porosity; TP, textural porosity; S, total porosity; e, void ratio; θ0.2, volumetric water content at 0.2 kPa; θ33, volumetric water content at 33 kPa; θ1500, volumetric water content at 1,500 kPa; W33, gravimetric water content at 33 kPa; and W1500, gravimetric water content at 1,500 kPa) at 0–40 cm soil depth in sites with different agroecological managements (1-S, 2-N, 4-S and 4-N) and conventional avocado orchard management (T0) in Mediterranean central Chile (n=40). 

Table 3 Relative weighting factors (RWF) based on threshold values of soil physical indicators by using the cumulative rating (CR) approach; adapted from Landon (1984), Lal (1994), Cass (1999), Hazelton and Murphy (2016), Nwosu and Okon (2020)  

Limitation RWF AC S cyBd e RFC SOC StI FDP UWP Ks5
cm3 cm-3 Mg m-3 - % cm h-1
None 1 > 0.20 >0.50 < 1.25 >1.2 ≤0.60 to ≤0.70 >5 > 9 > 20 <15 < 2
Slight 2 >0.18 to ≤0.20 >0.45 to ≤0.50 ≤1.25 to <1.35 >1.0 to ≤1.2 >0.50 to ≤0.60 ≤0.70 to <0.75 >3 to ≤5 >7 to ≤9 >18 to ≤20 ≤15 to <18 ≤2 to <6
Moderate 3 >0.15 to ≤0.18 >0.40 to ≤0.45 ≤1.35 to <1.55 >0.8 to ≤1.0 >0.40 to ≤0.50 ≤0.75 to <0.80 >1 to ≤3 >6 to ≤7 >15 to ≤18 ≤18 to <20 ≤6 to <8
Severe 4 >0.10 to ≤0.15 >0.35 to ≤0.40 ≤1.45 to <1.55 >0.6 to ≤0.8 >0.35 to ≤0.40 ≤0.80 to <0.90 >0.5 to ≤1 >5 to ≤6 >10 to ≤15 ≤20 to <25 ≤8 to <12.5
Extreme 5 ≤ 0.10 ≤ 0.35 ≤ 1.55 ≤0.6 ≤0.35 to ≤0.90 ≤0.5 ≤ 5 ≤ 10 ≤25 ≤12.5

AC, air capacity; S, total porosity; cyBd, bulk density determined from cylinders; e, void ratio; RFC, relative field capacity to water saturation; SOC, soil organic carbon; FDP, fast-drainage pores; UWP, unavailable water pores; and Ks5, hydraulic conductivity at 5 h.

Considering all soil profiles (0–60 cm), cumulative ratings of 33, 29, 29, 26 and 25 were obtained for T0, 1-S, 2-N, 4-S and 4-N sites, respectively, which indicated that T0 is sustainable only with another use but for the other sites, the current land uses and management systems are sustainable with high inputs (Table 4). Similar results were observed when 0–40 cm soil depths were assessed. Only surface soils (0–20 cm) at the 4-S and 4-N sites showed greater sustainability under current agroecological land use and management.

Table 4 Selected soil physical indicators, their relative weighing factors and the cumulative rating approach (CR, 10 factors) for each site and soil depth in Mediterranean central Chile. 

Sites Depth AC S cyBd e RFC SOC StI FDP UWP Ks5 Cumulative rating (CR, 10 factors)
cm cm3 cm-3 Mg m-3 - % cm h-1 Horizons Soil profile (0-60 cm) Soil profile (0-40 cm)
T0 0–20 4 3 4 4 1 3 2 3 4 5 33 (SAU) 33 (SAU) 35 (SAU)
20–40 4 3 5 4 1 3 4 4 3 5 36 (SAU)
40–60 3 4 5 4 1 3 3 4 2 2 31 (SAU)
1-S 0–20 3 2 3 4 1 2 1 3 2 5 26 (SHI) 29 (SHI) 29 (SHI)
20–40 3 3 4 4 1 3 3 3 2 5 31 (SAU)
40–60 3 2 4 4 2 3 4 3 1 4 30 (SAU)
2-N 0–20 2 1 2 3 2 3 4 4 1 5 27 (SHI) 29 (SHI) 28 (SHI)
20–40 2 2 3 4 2 3 5 3 2 2 28 (SHI)
40–60 2 3 4 4 2 3 5 4 2 2 31 (SAU)
4-S 0–20 2 1 2 3 2 2 1 2 2 5 22 (SUS) 26 (SHI) 25 (SHI)
20–40 1 2 4 4 2 3 4 3 2 3 28 (SHI)
40–60 2 2 4 4 2 3 4 3 1 2 27 (SHI)
4-N 0–20 1 1 1 3 2 3 4 1 1 5 22 (SUS) 25 (SHI) 25 (SHI)
20–40 1 2 4 3 3 3 5 4 1 2 28 (SHI)
40–60 1 2 4 3 2 3 4 3 1 2 25 (SHI)

AC, air capacity; S, total porosity; cyBd, bulk density determined from cylinders; e, void ratio; RFC, relative field capacity to water saturation; SOC, soil organic carbon; FDP, fast-drainage pores; UWP, unavailable water pores; and Ks5, hydraulic conductivity at 5 h. (SAU, sustainable with another land use; SHI, sustainable with high inputs; and SUS, sustainable).

Conclusions

Our results indicate that subsoiling and other initial operations at sites with agroecological management are effective only at the soil surface (0–20 cm), which emphasizes the lower bulk densities and penetration resistance values obtained with agroecological management, as well as the higher values of porosity indicators when compared to intensive management. In this sense, the potential usefulness of measured physical indicators for integrated assessments of the sustainability of agroecological management practices when compared with highly mechanized intensive systems (conventional avocado orchard) was demonstrated.

Soil organic carbon content and nine physical soil quality indicators (air capacity, bulk density, relative field capacity to saturation, structural stability index, total porosity, void ratio, fast-drainage pores, unavailable water pores and saturated hydraulic conductivity at 5 h) were identified as being important for the sustainable management of natural resources. Therefore, the cumulative ratings index (CR) for each land use showed that all agroecological practices constituted sustainable soil management (25≤CR<30), although with high input requirements, while T0 (avocado orchard) exhibited the least sustainable management (30≤CR<40) with a recommended change in soil use.

Acknowledgments

The authors would like to thank the Agreement between Programa de Magíster en Manejo de Suelos y Aguas (Universidad de Chile) and CERES (Centro Regional de Innovación Hortofrutícola de Valparaíso) for their financial and institutional support. This study was also partially financed by Regular FONDECYT project N° 1201497.

This manuscript summarises the authors' intended contribution at the Workshop on Challenges for Agroecology Development for the Building of Sustainable Agri-Food Systems (CRP), which was due to take place at the Faculty of Agricultural Sciences, University of Chile, Santiago de Chile, on 11–13 November 2019, and which was sponsored by the OECD Co-operative Research Programme: Biological Resource Management for Sustainable Agricultural Systems. Although due to the circumstances the workshop did not take place as a physical meeting and contributions intended to be supported by the OECD CRP are published in this Thematic Issue.

Disclaimer

The opinions expressed and arguments employed in this manuscript are the sole responsibility of the authors and do not necessarily reflect those of the OECD or of the governments of its Member countries.

References

Aguilera, E., Díaz-Gaona, C., García-Laureano, R., Reyes-Palomo, C., Guzmán, G. I., Ortolani, L., Sánchez-Rodríguez, M., & Rodríguez-Estévez V. (2020). Agroecology for adaptation to climate change and resource depletion in the Mediterranean region. A review. Agricultural Systems, 181:102809. doi: 10.1016/j.agsy.2020.102809 [ Links ]

Baumgartl, T. (2016). Atterberg Limits. In: R. Lal (Ed.) Encyclopedia of Soil Science, 3rd Ed., CRC Press, Taylor & Francis Group. pp: 171–174. [ Links ]

Berryman, C., Davies, D., Evans, C., Harrod, M., Hughes, A., Skinner, R., Swain, R., & Soane, D. (1982). Techniques for Measuring Soil Physical Properties. Formerly Advisory Paper N°18. Reference Book 441. Ministry of Agriculture, Fisheries and Food, Sweden. [ Links ]

Brunel, N., Martínez, I., Seguel, O., Ovalle, C., & Acevedo, E. (2016). Structural characterization of a compacted alfisol under different tillage systems. Journal of Soil Science and Plant Nutrition 16(3):689–701. doi: 10.4067/S0718-95162016005000050 [ Links ]

Casanova, M., Salazar, O., Seguel, O., & Luzio, W. (2013). Human-Induced Soil Degradation in Chile. In: A. Hartemink (Ed.) The Soils of Chile. Springer Serie, Soils of the World. The Netherlands. pp: 121–158. [ Links ]

Cass, A. (1999). Interpretation of some soil physical indicators for assessing soil physical fertility. In: K. I. Peverill, L. A. Sparrow, & D. J., Reuter (eds.) Soil Analysis - an interpretation manual. CSIRO Publishing. Collingwood, Australia. pp: 95–102. [ Links ]

Castellini, M., Stellacci, A.M., Barca, E., & Iovino, M. (2019a). Application of Multivariate Analysis Techniques for Selecting Soil Physical Quality Indicators: A Case Study in Long-Term Field Experiments in Apulia (Southern Italy). Soil Science Society of America Journal, 83:707–720. doi: 10.2136/sssaj2018.06.0223 [ Links ]

Castellini, M., Stellacci, A. M., Tomaiuolo, M., & Barca, E. (2019b). Spatial Variability of Soil Physical and Hydraulic Properties in a Durum Wheat Field: An Assessment by the BEST-Procedure. Water, 11:1434. doi: 10.3390/w11071434 [ Links ]

Cerisola, C.I., García, M.G., & Filgueira, R.R. (2005). Soil porosity distribution of a clay loam soil (alfisol) in semi-arid conditions after 15 years under direct drilling. Ciencia del Suelo, 23:167–178. [ Links ]

CIREN. (1997). Soil Survey, Valparaiso Region. Center Natural Resources Information. CIREN Publication N° 116. Santiago, Chile. (In Spanish). [ Links ]

Cosentino, D., Hallett, P. D., Michel, J. C., & Chenu, C. (2010). Do different methods for measuring the hydrophobicity of soil aggregates give the same trends in soil amended with residue? Geoderma, 159:221–227. [ Links ]

Das, B. M. (2016). Principles of Foundation Engineering, 8th Edition. Cengage Learning. Boston, MA. USA. [ Links ]

Delpino-Chamy, M., Alarcon, M., Fernández, S., & Soto, J. (2019). Methodology to identify and assess agroecological practices in metropolitan areas. Case study, Concepción, Chile. International Journal of Design & Nature and Ecodynamics, 14(2):119–130. 10.2495/DNE-V14-N2-119-130 [ Links ]

Eijkelkamp. (2011). Operating instructions. 09.02 Laboratory-permeameters. Retrieved from http://www.eijkelkamp.com/files/media/Gebruiksaanwijzingen/EN/m1-0902elab-permeameters.pdfLinks ]

Fuentes, I., Casanova, M., Seguel, O., Nájera, F., & Salazar, O. (2014). Morphophysical pedotransfer functions for groundwater pollution by nitrate leaching in Central Chile. Chilean Journal of Agricultural Research, 74:340–348. [ Links ]

Fuentes, I., Casanova, M., Seguel, O., Padarian, J., Najera, F., & Salazar, O. (2015). Preferential flow paths in two alluvial soils with long-term additions of pig slurry in the Mediterranean zone of Chile. Soil Research, 53:433–447. [ Links ]

Garcia-Chevesich, P., Pizarro, R., Stropki, C.L., Ramirez de Arellano, P., Ffolliott, P.F., DeBano, L.F., Neary, D.G., & Snack, D.C. (2010). Formation of post-fire water-repellent layers in Monterrey pine (Pinus radiata D. DON) plantations in south-central Chile. Journal of Soil Science and Plant Nutrition, 10(4):399–406. [ Links ]

Hallett, P.D., & Young, I.M. (1999). Changes to water repellence of soil aggregates caused by substrate-induced microbial activity. European Journal of Soil Science, 50:35–40. [ Links ]

Hartge, K.H., & Horn, R. (2009). Measuring the physical parameters of soils: Methods, Application, and Assessment. Schweizerbart, Stuttgart, Germany. (In Deutsch). [ Links ]

Hazelton, P., & Murphy, B. (2016). Interpreting soils test results, what do all the numbers mean? Third edition. CSIRO Publishing. Australia. [ Links ]

Idowu, O.J., van Es, H.M., Abawi, G.S., Wolfe, D.W., Ball, J.I., Gugino, B.K., Moebius, B.N., Schindelbeck, R.R., & Bilgili, A.V. (2008). Farmer-oriented assessment of soil quality using field, laboratory, and VNIR spectroscopy methods. Plant and Soil, 307:243–253. [ Links ]

Iovino, M., Pekárová, P., Hallett, P.D., Pekár, J., Lichner, L., Mataix-Solera, J., Alagna, V., Walsh, R., Raffan, A., Schacht, K., & Rodný, M. (2018). Extent and persistence of soil water repellency induced by pines in different geographic regions. Journal of Hydrology and Hydromechanics, 66(4):360–368. doi: 10.2478/johh-2018-0024 [ Links ]

Kirby, J.L. (2002). Liquid and Plastic Limits. In: N. McKenzie, K. Coughlan, & H. Cresswell (Eds.) Soil physical measurement and interpretation for land evaluation. CSIRO Publishing. Collingwood, Australia. pp. 261–270. [ Links ]

Kutilek, M., Jendele, L., & Panayiotopoulos, K.P. (2006). The influence of uniaxial compression upon pore size distribution in bi-modal soils. Soil and Tillage Research, 86: 27–37. [ Links ]

Lal, R. (1994). Methods and guidelines for assessing sustainable use of soil and water resources in the tropics. Soil Management Support System, USDA-NRCS, Washington, DC. USA. [ Links ]

Lal, R., & Shukla, M. (2004). Principles of soil physics. Marcel Dekker, New York, USA. [ Links ]

Lambe, T.W., & Whitman, R.V. (1969). Soil Mechanics. Series in Geotechnical Engineering. John Wiley & Sons. New York, USA. [ Links ]

Landon, J.R. (1984). Booker Tropical Soil Manual. A handbook for soil survey and agricultural land evaluation in the tropics and subtropics. Routledge, Taylor & Francis Group. NY, USA. [ Links ]

Li, X., & Zhang, L.M. (2009). Characterization of dual-structure pore-size distribution of soil. Canadian Geotechnical Journal, 46:129–141. [ Links ]

Lichner, L., Felde, V.J.M.N.L., Büdel, B., Leue, M., Gerke, H.H., Ellerbrock, R.H., Kollár, J., Rodný, M., Šurda, P., Fodor, N., & Sándor, R. (2018). Effect of vegetation and its succession on water repellency in sandy soils. Ecohydrology, 11:e1991. doi: 10.1002/eco.1991 [ Links ]

Lozano, J. D., Armbrecht, I., & Montoya, J. (2015). Arbuscular mycorrhiza and their effect on the soil structure in farms with agroecological and intensive management. Acta Agronómica, 64(4):289–296. [ Links ]

Mao, J., Nierop, K.G.J., Dekker, S.C., Dekker, L.W., & Chen, B. (2019). Understanding the mechanisms of soil water repellency from nanoscale to ecosystem scale: a review. Journal of Soils and Sediments, 19:171–185. doi: 10.1007/s11368-018-2195-9 [ Links ]

Mendez, V.E, Bacon, C.M., Cohen, R., & Gliessman, S.R. (2015). Agroecology. A Transdisciplinary, Participatory and Action-oriented Approach. CRC Press. New York, USA. [ Links ]

Montalba, R., Infante, A., Contreras, A., & Vieli, L. (2017). Agroecology in Chile: precursors, pioneers, and their legacy. Agroecology and Sustainable Food Systems, 41(3–4):416–428. doi: 10.1080/21683565.2017.1288671 [ Links ]

Müller, L., Lipiec, J., Kornecki, T.S., & Gebhardt, S. (2011). Trafficability and workability of soils. In: J. Gliński, J. Horabik, & J. Lipiec. (Eds.) Encyclopedia of Agrophysics. Springer, Dordrecht, The Netherlands. pp: 912–924. [ Links ]

Myers, N., Mittermeier, R.A., Mittermeier, C.G., da Fonseca, G.A.B., & Ken, J. (2000). Biodiversity hotspots for conservation priorities. Nature, 403:853–858. [ Links ]

Nimmo, J. R. (2004). Porosity and Pore Size Distribution. In: D. Hillel (Ed.) Encyclopedia of Soils in the Environment. v. 3. Elsevier. London, UK. pp: 295–303. [ Links ]

Nwosu, N.J., & Okon, P.B. (2020). Impacts of Some Climatic Factors on Soil Quality of Tropical Acid-Sand Soils. In: W. Leal Filho (Ed.) Handbook of Climate Change Resilience. Springer Nature, Switzerland. pp: 295–317. doi: 10.1007/978-3-319-93336-8_72 [ Links ]

Pagliai, M., & Vignozzi, N. (2002). The soil pore system as an indicator of soil quality. Advances in Geoecology, 35:71–82. [ Links ]

Pereira J.O., Dantas, V.B., Silva, S., Batista, R., Pordeus, R.V., Diniz, M.J., de Oliveira, M.A., & Linhares, P.C.F. (2019). Soil structural behavior as a function of the amount of coverage of maize and oat straw on oxisol on subtropical region. Journal of Agricultural Science, 11(7):246–255. doi: 10.5539/jas.v11n7p246 [ Links ]

Pieri, C.J.M.G. (1992). Fertility of Soils: A Future for Farming in the West African Savannah. Springer-Verlag, Berlin, Germany. [ Links ]

Pitts, J. (1985). A Manual of Geology for Civil Engineers. World Scientific. Singapore. [ Links ]

Pulido Moncada, M., Gabriels, D., Lobo, D., De Beuf, K., Figueroa, R., & Cornelis, W.M. (2014). A comparison of methods to assess susceptibility to soil sealing. Geoderma, 226–227:397–404. [ Links ]

Reynolds, W.D., Drury, C.F., Tan, C.S., & Yang, X.M. (2015). Temporal effects of food waste compost on soil physical quality and productivity. Canadian Journal of Soil Science, 95:251–268. [ Links ]

Reynolds, W.D., Drury, C.F., Tan, C.S., Fox, C.A., & Yang, X.M. (2009). Use of indicators and pore volume-function characteristics to quantify soil physical quality. Geoderma, 152:252–263. [ Links ]

Richard, G., Cousin, I., Sillon, J.F., Bruand, A., & Guérif, J. (2001). Effect of compaction on soil porosity: consequences on hydraulic properties. European Journal of Soil Science, 52:49–58. [ Links ]

Ritschel, T., & Totsche, K.U. (2019). Modeling the formation of soil microaggregates. Computers & Geosciences, 127:36–43. doi: 10.1016/j.cageo.2019.02.010 [ Links ]

Ryan, M.R., & Peigné, J. (2017). Applying Agroecological Principles for Regenerating Soils. In: A. Wezel (Ed.) Agroecological Practices for Sustainable Agriculture. World Scientific Publishing Europe Ltd. New Jersey, USA. pp: 53–84. doi: 10.1142/9781786343062_0003 [ Links ]

Sadzawka, M.A., Carrasco, M.A., Grez, R., Mora, M.L., Flores, H., & Neaman, A. (2006). Métodos de análisis de suelos recomendados para los suelos de Chile (Recommended analysis methods for Chilean soils). Instituto de Investigaciones Agropecuarias, Serie Actas INIA N° 34, Santiago, Chile. (In Spanish). [ Links ]

Sandoval, M., Dörner, J., Seguel, O., Cuevas, J., & Rivera, D. (2012). Métodos de análisis físicos de suelos (Soil physical analyses methods). Departamento de Suelos y Recursos Naturales. Universidad de Concepción. Chillán, Chile. (In Spanish). [ Links ]

Schindelbeck, R.R., van Es, H.M., Abawi, G.S., Wolfe, D.W., Whitlow, T.L., Gugino, B.K., Idowu, O.J., & Moebius-Clune, B.N. (2008). Comprehensive assessment of soil quality for landscape and urban management. Landscape and Urban Planning, 88:73–80. [ Links ]

Tillman, R.W., Scotter, D.R., Wallis, M.G., & Clothier, B.E. (1989). Water repellency and its measurement using intrinsic sorptivity. Australian Journal of Soil Research, 27(4):637–644. [ Links ]

Totsche, K.U., Amelung, W., Gerzabek, M.H., Guggenberger, G., Klumpp, E., Knief, C., Lehndorff, E., Mikutta, R., Peth, S., Prechtel, A., Ray, N., & Kögel-Knabner, I. (2018). Microaggregates in soils. Journal of Plant Nutrition and Soil Science, 181:104–136. doi: 10.1002/jpln.201600451 [ Links ]

TWN & SOCLA. (2015). Agroecology key Concepts, Principles and Practices. Trainingcourses on agroecology. 3rd World Network and the Latin American Society of Agroecology. Malasya. [ Links ]

Received: September 01, 2020; Accepted: November 23, 2020

Corresponding author: mcasanov@uchile.cl

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License