Copper-litter-soil interaction assessment in fruit tree productive systems

Jorge Tomás Schoffer Navarro

2021

Pontificia Universidad Católica de Chile Facultad de Agronomía e Ingeniería Forestal

Copper-litter-soil interaction assessment in fruit tree productive systems

Jorge Tomás Schoffer Navarro

Thesis

to obtain the degree of

Doctor

en Ciencias de la Agricultura

Santiago, Chile, July 2021

Thesis presented as part of the requirements for the degree of Doctor

in Ciencias de la Agricultura, approved by the

Thesis Committee

Dr. Rosanna Ginocchio, Advisor

Dr. Eduardo Arellano

Dr. Pilar Gil

Dr. Alexander Neaman

Santiago, July 2021

A mi señora e hijo, por su paciencia y apoyo.

Sin ustedes, este proceso no hubiera sido posible.

Los amo.

Financial funding support for J. Tomás Schoffer and all thesis activities was obtained from

ANID Doctoral Grant (21160236/2016), Center of Applied Ecology and Sustainability

(CAPES) by ANID PIA/BASAL FB0002 and Facultad de Agronomía e Ingeniería Forestal

PUC.

Acknowledgements

Esta tesis es resultado de mucho esfuerzo, la cual no hubiese sido posible sin el

apoyo mucha gente. Es por esto que quiero agradecer a mi familia, tanto a la

heredada como a la elegida.

Sobre todo, quisiera agradecer a mi tutora de tesis, Rosanna Ginocchio, no sólo

por su apoyo y mentoría durante este proceso, sino también por su calidad como

persona y preocupación por mí en el ámbito personal. Fue un gusto tenerte como

mentora.

Quisiera agradecer a Hugo Poblete por su amabilidad y ayuda en mi trabajo de

terreno.

Agradezco también a mi comité de tesis, Pilar Gil, Eduardo Arellano y Alexander

Neaman, por su orientaciones y disponibilidad para ayudarme.

Un especial agradecimiento al personal administrativo de la DIP, Marce, Claudia,

Wendy y María José, que desde el inicio del doctorado me acogieron con cariño y

gestionaron con celeridad todo trámite que les solicité. También quisiera

agradecer a Arlene y Kathy del DEMA, por su tiempo para escucharme cuando

necesitaba un respiro.

A todos mis compañeros del laboratorio RESUME. A Luz María por su paciencia y

disponibilidad para ayudarme en el laboratorio. A Fabiola por sus consejos

asociados al doctorado. A Nadia por enseñarme, con cariño y paciencia, temas

asociados al suelo. A Victoria por su ayuda con ArcGIS. A David por su amistad. A

Camila por su calma y simpatía.

Finalmente, te agradezco Dios por darme la oportunidad de poder cumplir un

anhelo y sueño, que significó este proceso

Contents

Chapter 1: General Introduction ........................................................................... 16

Work hypotheses ............................................................................................. 22

Hypothesis 1 .............................................................................................. 22

Hypothesis 2 .............................................................................................. 22

Hypothesis 3 .............................................................................................. 22

Objectives ........................................................................................................ 23

General objective ....................................................................................... 23

Specific objectives ..................................................................................... 23

Objective 1 ................................................................................................. 23

Objective 2 ................................................................................................. 23

Objective 3 ................................................................................................. 23

Figures.............................................................................................................. 28

Chapter 2: Role of leaf litter on the incorporation of copper-containing pesticides into

soils under fruit production: A review .................................................................... 29

Abstract ............................................................................................................ 30

Key words ......................................................................................................... 30

Introduction ....................................................................................................... 31

Use of copper in agriculture and its accumulation in soil ................................... 33

Soil contamination in fruit tree productive systems by copper-based pesticides 34

Soil copper behavior and bioavailability ............................................................ 37

Effects of copper contamination in soil organisms ............................................. 39

Copper-litter relation in soil ............................................................................... 44

Conclusions and future studies ......................................................................... 48

Acknowledgments ............................................................................................. 49

References ....................................................................................................... 50

Figures.............................................................................................................. 60

Tables ............................................................................................................... 62

Chapter 3: Copper content in soils and litter from fruit orchards in Central Chile and

its relationship with soil microbial activity .............................................................. 64

Abstract ............................................................................................................ 65

Key words ......................................................................................................... 65

Introduction ....................................................................................................... 65

Materials and Methods ...................................................................................... 67

Study area ................................................................................................. 67

Soil and litter sampling............................................................................... 69

Physical-chemical characterization of soils ................................................ 70

Leaf litter total Cu determination ................................................................ 70

Soil microbial colonization and respiration ................................................. 70

Statistical analyses .................................................................................... 71

Results and Discussion ..................................................................................... 72

General soil properties............................................................................... 72

Soil and leaf litter copper levels ................................................................. 73

Soil microbial colonization and carbon-induced soil microbial respiration .. 75

Conclusions ...................................................................................................... 78

Acknowledgements ........................................................................................... 79

References ....................................................................................................... 80

Figures.............................................................................................................. 83

Tables ............................................................................................................... 86

Chapter 4: The role of leaf litter as a protective barrier for copper-containing

pesticides in orchard soils .................................................................................... 88

Abstract ............................................................................................................ 89

Key words ......................................................................................................... 89

Introduction ....................................................................................................... 90

Materials and Methods ...................................................................................... 92

Leaf litter sampling .................................................................................... 92

Leaf litter characterization .......................................................................... 93

Treatment of selected leaf litters ................................................................ 94

Leaf litter copper adsorption capacity assay .............................................. 95

Leaf litter degradation assay ...................................................................... 97

Cu determinations in leaf litters, assay solutions and experimental soil ..... 99

Data analysis ............................................................................................. 99

Results and Discussion ................................................................................... 100

Leaf litter characteristics and Lignin/N ratio ..............................................100

Leaf litter Cu adsorption capacity ..............................................................101

Leaf litter degradation assay .....................................................................104

Conclusions .................................................................................................... 105

Acknowledgements ......................................................................................... 106

Authors’ contributions ..................................................................................... 106

Funding........................................................................................................... 107

Data availability ............................................................................................... 107

Declarations .................................................................................................... 107

References ..................................................................................................... 108

Figures............................................................................................................ 113

Tables ............................................................................................................. 115

Chapter 5: General discussion ........................................................................... 119

References ..................................................................................................... 124

Appendix ............................................................................................................ 127

Chapter 2 Supplementary Material.................................................................. 128

Supplementary Tables ..............................................................................128

References ...............................................................................................143

Chapter 3 Supplementary Materials ................................................................ 149

Supplementary Figures .............................................................................149

Supplementary Tables ..............................................................................153

Chapter 4 Supple mentary Materials ............................................................... 161

Supplementary Figures .............................................................................161

Supplementary Tables ..............................................................................162

References ...............................................................................................166

Figure Index

Chapter 1 Figures ................................................................................................ 28

Figure 1. Pesticides of the 2000 series (fungicides and bactericides) sold in

Chile, by region in 2012 .................................................................................... 28

Chapter 2 Figures ................................................................................................ 60

Figure 1. Cu flux in fruit systems ....................................................................... 60

Figure 2. Box-plot for total Cu concentrations of L layer of litter in different fruit

tree species from of the O'Higgins Region, central Chile................................... 61

Chapter 3 Figures ................................................................................................ 83

Figure 1. C-induced soil microbial activity of soil samples taken in the row and in

the interrow in kiwi, table grape, plum, and cherry orchards of selected orchards

of O´Higgins Region, central Chile. ................................................................... 83

Figure 2. Principal component analysis labeled by fruit tree orchard species and

by soil microsite (ridge presence/absence) and the use or not of Cu-based

pesticides .......................................................................................................... 84

Chapter 4 Figures .............................................................................................. 113

Figure 1. The Langmuir adsorption isotherms (solid line) and the Freundlich

adsorption isotherms for Cu2+ adsorption by table grape and kiwi leaf litter .... 113

Figure 2. Transferred Cu from leaf litter to soil as a function of the leaf litter Cu

concentration in table grape and kiwi after 275 days of incubation at 28 °C .... 114

Chapter 3 Supplementary Figures ...................................................................... 149

Figure S1. Principal component analysis labeled by soil sampling site. .......... 149

Figure S2. Principal component analisys labaled by soil sampling microsite in

kiwi, table grape, plum, and cherry orchards. .................................................. 152

Chapter 4 Supplementary Figures ...................................................................... 161

Figure S1. Mass reduction as a function of the leaf litter Cu concentration in

table grape and kiwi after 275 days of incubation at 28 °C. ............................. 161

Table Index

Chapter 2 Tables ................................................................................................. 62

Tables 1. Copper effective concentrations (ECx) for soil organism studies

compiled in this review. As a reference, we considered studies that used field-

contaminated soils. For all studies, the values are expressed as geometric mean,

with the exception of Delgadillo et al. (2017), Konečný et al. (2014) and Oorts et

al. (2006) who present a unique value for their respective ECx......................... 62

Chapter 3 Tables ................................................................................................. 86

Table 1. General soil physicochemical characteristics of selected orchards in the

O´Higgins Region, central Chile ........................................................................ 86

Table 2. Copper concentrations in study soils and leaf litter of selected orchards

in the O´Higgins Region, central Chile. ............................................................. 87

Chapter 4 Tables ............................................................................................... 115

Tables 1. Physicochemical characteristics from the selected avocado orchard

soil used in the leaf litter degradation assay .................................................... 115

Table 2. Leaf litter Cu spiking process results ................................................. 116

Table 3. Leaf litter nitrogen, lignin, cellulose, hemicellulose and lignin/N ratio

content of the sampled leaf litter ..................................................................... 117

Table 4. Copper adsorption model parameters for table grape and kiwi leaf litters

....................................................................................................................... 118

Chapter 2 Supplementary Tables ....................................................................... 128

Table S1. Total and exchangeable soil copper concentrations in vineyards and

fruit orchards worldwide .................................................................................. 128

Table S2. Exchangeable Cu extraction method used by studies presented in

Table S1 ......................................................................................................... 142

Chapter 3 Supplementary Tables ....................................................................... 153

Table S1. Cultivation characteristics of selected orchards in the O´Higgins

Region, central Chile....................................................................................... 153

Table S2. Copper-based pesticide management during 2019 of selected

orchards in the O´Higgins Region, central Chile. ............................................. 154

Table S3. Soil physicochemical characteristics and Cu leaf litter concentration of

selected kiwi orchards in the O´Higgins Region, central Chile......................... 155

Table S4. Soil physicochemical characteristics and Cu leaf litter concentration of

selected table grape orchards in the O´Higgins Region, central Chile ............. 156

Table S5. Soil physicochemical characteristics and Cu litter concentration of

selected plum orchards in the O´Higgins Region, central Chile ....................... 157

Table S6. Soil physicochemical characteristics and Cu litter concentration of

selected cherry orchards in the O´Higgins Region, central Chile ..................... 158

Table S6. Results of principal component analysis for kiwi, table grape, plum and

cherry orchards ............................................................................................... 159

Table S7. Results of principal component analysis for all orchards data ......... 160

Chapter 4 Supplementary Tables ....................................................................... 162

Table S1. Leaf litter nitrogen, lignin, cellulose, hemicellulose and lignin/N ratio

content of the fruit trees according to literature ............................................... 162

Table S2. Previously reported kinetic adsorption model fit and Cu2+ adsorption

capacities in plant leaves studies .................................................................... 164

16

Chapter 1

General Introduction

Metals are naturally present in Earth’s crust and atmosphere (Gall et al., 2015), and

a number of these are essential to all living beings (Maret, 2016). However, soil metal

enrichment is frequently produced by anthropogenic activities, such as mining and

the transformation of metallic minerals, the incineration of waste, road transportation,

and the use of fertilizers and other agrochemicals (Rodríguez et al., 2008). Once

incorporated, they are not subject to degradation processes; therefore, they can

remain in soils for long periods of time (Wang et al., 2017). High concentrations of

these elements in soils of natural and agricultural systems may result in risks

relevant to human health, and toxic and adverse effects in other living beings

(Wuana & Okieimen, 2011; Wang et al., 2017). Therefore, human health is closely

related to soil quality and the risks associated with soil contamination (Sayyed &

Sayadi, 2011). As a consequence, metal soil contamination is currently of global

concern (Doumett et al., 2008; Cao et al., 2009).

In terms of metals, copper (Cu) is one of the oldest and most important, as it has

been part of the development of human civilization (Grass et al., 2011; Murr, 2015;

Pearce, 2019). Due to its high ductility, malleability, thermal and electrical

conductivity (EC), and corrosion resistance, Cu is one of the most important metals

in industry, after iron (Fe) and aluminum (Al), in terms of quantities consumed (Han,

2007). A remarkable fact about Cu is its widespread and long history of use in

17

agriculture as an active component of pesticides (Husak, 2015) to control bacterial

and fungal diseases in crops (Van Zwieten et al., 2007). Its use was generalized

after the accidental discovery of ‘Bordeaux mixture’ in 1880 (Van Zwieten et al.,

2007; Kovačič et al., 2013;). At the end of April 1882, Professor Pierre Marie Alexis

Millardet (1838–1902), from the University of Bordeaux, was walking through a

vineyard in Saint-Julien in Médoc when something caught his eye. Some vines along

the road still had green leaves, while those furthest away were bare. He observed

that the healthy leaves had a bluish-white material on them, as if a chemical had

been applied. He also observed that these vines were not infected with the mildew

fungus, Plasmopara viticola. Millardet found out that the viticulturist had mixed

copper sulfate (CuSO4) with lime (CaO) and put this on the grapes to create the

appearance that they were coated in poison, thus preventing theft by people passing

by (Zimdahl, 2010). By 1885, Millardet had already conducted experiments, which

confirmed that the mixture controlled mildew for a relatively low cost. This is how

Bordeaux mixture became the first Cu-based fungicide to be used on a large scale,

worldwide (Schneiderhan, 1933). It was this fortuitous event that led to the

commercialization of cupric pesticides. However, the first reference of the use of Cu

as a pesticide is Schulthess, in 1761, who used CuSO4 to control the bacterial

disease wheat blight (Pseudomonas syringae pv. syringae) (Richardson, 1997).

Copper's effectiveness as a bactericide and/or fungicide in leaves depends on its

application in inorganic form, which makes it relatively insoluble in water and not

easily washed off foliage. This provides much longer protection against diseases

18

than other compounds (Fishel, 2017). However, it is important to note that the effect

of Cu-based compounds is not that of a fungicide/bactericide (causing the death of

the microorganisms), but rather as a fungistatic/bacteriostatic, as they inhibit spore

or cell germination, which are the precursors responsible for the dissemination and

reproduction of fungi or bacteria (Richardson, 1997). Therefore, their effect is rather

preventive (i.e., prophylactic).

Although Cu is relatively safe, from a management perspective, there is some

concern about its accumulation in agricultural soils (Fishel, 2017), since it may

produce toxic effects in crops (Sayyad et al., 2009) and in soil microorganisms (Giller

et al., 1998; Stowhas et al., 2018), in addition to its biomagnification in terrestrial

trophic webs (Gall et al., 2015). For this reason, Cu pollution in productive

agricultural soils is a highly relevant topic, not only because of its direct effects on

productivity, but also because of its indirect effects on food safety and security (Tóth

et al., 2016). In fact, Zaidi et al. (2005) stated that metals are the main food pollutant,

and that this can be considered to be the most important environmental problem

today. In Chile, this is particularly relevant due to the lack of soil quality regulations

and because Cu-based pesticide applications are carried out only according to the

label’s instructions (Devotto, 2013). In fact, the direct legal aspects of land

degradation have generally been neglected in the past at a national level (Cavieres,

2000). The government of Chile has developed legal tools to promote the

sustainable use of land, but their application is not mandatory (Casanova et al.,

2013). On the other hand, the use of Cu-based pesticides to control Pseudomonas

19

syringae pv. actinidiae pest in kiwi (Actinidia spp.) throughout the national territory is

mandatory, according to the Chilean Ministry of Agriculture through the Agricultural

and Livestock Service (SAG, 2017). Therefore, there is an obligation regarding the

application of Cu-based pesticides in fruit crops, but there is no normative body to

protect soil quality and health. In addition, 32% of total pesticides sold in Chile are

Cu-based (Figure 1) (SAG, 2012).

Figure 1

It is important to emphasize that direct toxic effects of Cu in crops and soil organisms

only occur when there is an effective exposure of organisms to this element. The

exposure degree is variable, as it depends on various factors that determine Cu

bioavailability in soil (Mani & Kumar, 2014). According Sauvé et al. (2000), Ginocchio

et al. (2002) and Mondaca et al. (2015), the most important parameters determining

Cu availability in soils are the total soil Cu concentration, pH and OM. It is well known

that Cu is an immobile element in soil; this is how it accumulates in the upper

horizons of soil (Kabata-Pendias, 2011). This immobility is due Cu in soil being

mainly found as a divalent cation (Cu2+), with a high affinity for soil colloids, such as

the clay fraction (Siddig et al., 2017) and soil organic matter (SOM) (Senesi et al.,

1989). Given the great affinity of Cu for organic matter (OM), one of the main factors

that favor its mobility in soil is its complexation with the dissolved OM (DOM) present

in the soil solution (McBride, 1994). It is important to consider that OM has dual

behaviors, depending on whether the OM is in the soil solid phase (as SOM) or the

20

liquid phase (as DOM) (You et al., 1999). DOM will mobilize Cu, while SOM will

immobilize it (Sauvé et al., 1997; Tipping et al., 2003; Santibañez et al., 2012;

Mondaca et al., 2015). McBride (1994) pointed out that soil metal solubility

decreases when soil pH values increase from 4 to 6–7, but then increases at pH

values higher than 7. This occurs because SOM solubility is enhanced at pH values

higher than 7 (i.e., forming SOM-derived DOM), which, in turn, increases the

concentration of Cu in the solution by the formation of Cu–DOM complexes

(McBride, 1994; Zhou & Wong, 2003; Mondaca et al., 2015). Hence, the dual

behavior of OM must be considered in soil Cu partitioning processes, with its role

being heavily dependent on soil pH.

In applications of cupric pesticides in fruit systems through foliar spraying, the entry

of Cu into the soil is through the fallout and accumulation of Cu-enriched leaves

and/or the dry/wet deposition of Cu-rich aerosols. Indeed, Bergkvist et al. (1989)

determined that litter is the main sink for metals coming from atmospheric fallout,

probably because litter is a good chelating agent for cations (Sauvé, 2002). In these

systems, the decomposition of falling leaves (i.e., litter decomposition) is a critical

step in nutrient cycling and supply (Naik et al., 2018). Nevertheless, litter

decomposition will depend on the soil microbial activity and litter quality (Bani et al.,

2018), understood as the lignin and nitrogen (N) contents and the carbon (C)/N ratio

(Dilly et al., 2004; Dilly & Munch, 2004). Since Cu is a bactericide/fungicide, the rate

of Cu-enriched litter decomposition is reduced, due to a decrease in soil microbial

activity (Freedman & Hutchinson, 1980). This decrease in litter breakdown reduces

21

the formation of low-molecular-weight OM, thus inhibiting its complexation with Cu

and, consequently, the subsequent mobilization and incorporation of this element

into the soil. Therefore, Cu is more likely to be retained in the litter instead of being

incorporated into the soil, thus reducing the exposure risks to soil microorganisms

and plant roots. This protective role of litter was described by Ginocchio et al. (2004)

in sites contaminated by atmospheric Cu deposition coming from a mining smelter,

and in sites with accumulations of Cu-enriched litter where the total soil Cu was

similar to background values and seedling recruitment occurred similar to that in non-

polluted areas.

Based on this background information, it can be summarized that:

- litter is the main sink for metals coming from atmospheric fallout (Bergkvist et

al., 1989) because litter is a good chelating agent of metal cations (Sauvé,

2002);

- litter decomposition depends on its quality (Bani et al., 2018) and on soil

microbial activity (Dilly et al., 2004; Dilly & Munch, 2004; Bani et al., 2018);

and

- soil and litter microbial activity is negatively affected by the presence of Cu

(Fernández-Calviño et al., 2010).

We can then hypothesize that recurring foliar spraying of Cu-based pesticides in

fruit-tree productive systems in Chile will result in the deposition of Cu-enriched litter

on soils that has a reduced breakdown rate and, subsequently, will result in low Cu

22

incorporation into the soil. However, to our knowledge this assumption has not been

evaluated. In this context, the aim of the present doctoral study was to evaluate the

protective effect of leaf litter, in terms of the incorporation of Cu into the soil, and its

effect on soil microorganism toxicity.

Working hypotheses

Hypothesis 1

The maintenance of leaf litter on soils in orchards where Cu-based pesticides are

applied has a protective effect by limiting the incorporation of Cu into the soil due to

its high Cu-chelating capacity.

Hypothesis 2

The protective effect of leaf litter against soil Cu enrichment depends on a high

lignin/N ratio, as lignocellulosic compounds have Cu-chelating functional groups and

they are more difficult to decompose.

Hypothesis 3

Cu-enriched leaf litter is slowly degraded because high Cu concentrations inhibit soil

microbial activity, thus protecting the soil from Cu enrichment.

23

Objectives

General objective

To evaluate the protective effect of leaf litter against Cu incorporation in soil, in terms

of its quality, in fruit-tree systems in central Chile where Cu-based pesticides are

applied.

Specific objectives

Objective 1

To determine Cu levels in the soils and leaf litter, and the soil microbial activity, in

orchards in the Libertador Bernardo O’Higgins Region, central Chile, where Cu-

based pesticides have and have not been frequently applied (Hypothesis 1).

Objective 2

To determine the capacity of fruit-tree leaf litter to adsorb Cu2+ applied in Cu-based

pesticides relative to their lignin/N ratios (Hypotheses 1 and 2).

Objective 3

To evaluate the microbial decomposition rates of fruit-tree leaf litters with different

lignin/N ratios and Cu contents (Hypotheses 2 and 3).

24

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Figures

Figure 1. Pesticides of the 2000 series (fungicides and bactericides) sold in Chile

by geopolitical region in 2012. Source: SAG (2012).

29

Chapter 2

Role of leaf litter on the incorporation of copper-containing

pesticides into soils under fruit production: A review

J. Tomás Schoffer 1,2, Sébastien Sauvé 3, Alexander Neaman 4 and Rosanna

Ginocchio 1,2

1 Departamento de Ecosistemas y Medio Ambiente, Facultad de Agronomía e

Ingeniería Forestal, Pontificia Universidad Católica de Chile, Santiago, Chile

2 Center of Applied Ecology and Sustainability (CAPES), Pontificia Universidad

Católica de Chile, Santiago, Chile.

3 Department of Chemistry, Université de Montréal, Montréal, QC, H3C 3J7,

Canada.

4 Escuela de Agronomía, Pontificia Universidad Católica de Valparaíso, Quillota,

Chile.

This chapter was published in Journal of Soil Science and Plant Nutrition –

Springer (20, 3, 990–1000)

30

Abstract

High soil copper may result in adverse effects on natural and agricultural systems.

Copper-based pesticides have long been used for control of microbial diseases on

fruit tree productive systems. Although copper is relatively safe from a human health

management point of view, it can be accumulated in agricultural soils, affecting soil

microbiota and litter degradation. The purpose of this review was to collect the

available information to critically discuss the role that litter may play in fruit tree

productive systems, in terms of copper incorporation into the soil, where this element

is used as a pesticide. To achieve this objective, we focused our review on 1) soil

contamination by copper-based pesticides in fruit production systems, 2) soil copper

behavior, 3) effects of copper contamination on soil organisms, and 4) copper-litter

relation in soil. From this review, we can suggest that: 1) litter is the main sink of

metals coming from atmospheric fallout because it is a good complexing agent of

cations, 2) litter decomposition depends on its quality and in soil microbial activity,

and 3) soil and litter microbial activity is negatively affected by soil copper

enrichment. Thus, under uncontrolled applications of copper-based pesticides in fruit

tree productive systems, copper soil enrichment will generate a decrease in

microbial activity, diminishing litter breakdown and decreasing dissolved organic

carbon formation. This process will also decrease the soluble copper incorporation

into the soil; however, this assumption remains unevaluated.

Key words: Metals, pesticides, organic matter, microbial activity, orchards.

31

Introduction

Copper-toxicity effects on crops and soil organisms depend on the bioavailability of

copper (Cu) in soils which depends on several factors (Mani and Kumar 2014).

These factors include pH (Zhang et al. 2018) and soil organic matter (SOM)

(Perminova and Hatfield 2005), among others. It is known that Cu is an immobile

element in soil; thus, higher concentrations are observed in the upper soil horizons

(Kabata-Pendias 2011). Copper is mainly found as a divalent cation (Cu2+) in soil,

which has strong affinity to soil colloids, mainly to the clay fraction (Siddig et al. 2017)

and the SOM (Senesi et al. 1989). On the other hand, one of the main factors that

favor its mobility in the soil is dissolved organic carbon (DOC) (McBride 1994;

Mondaca et al. 2015; Neaman et al. 2009).

It has been stated that the main sink for metals coming from the atmospheric fallout

is the litter (Bergkvist et al. 1989; Ettler 2015; Ginocchio et al. 2004)—likely due to

litter being a good cation-complexing agent (Sauvé 2002). This fact is of great

relevance in fruit tree productive systems, where Cu-based pesticides are applied

through foliar spraying. In these conditions, there can be a direct Cu input to soil

from Cu-based pesticides spraying or an indirect input from rain-removal, leaf drop

and decomposition (Pérez-Rodríguez et al. 2016). Regarding the leaf drop and

decomposition, soil Cu-enrichment drives from the fall and accumulation of Cu-

enriched leaves and dry or wet deposition of Cu-rich aerosols. Decomposition of

litter accumulated in soil is a critical step in the nutrient cycle and supply (Naik et al.

32

2018). Nevertheless, litter decomposition will depend on soil microbial activity and

litter "quality" (Bani et al. 2018) in terms of lignin and nitrogen (N) contents and the

carbon/nitrogen (C/N) ratio (Dilly et al. 2004; Dilly and Munch 2004).

It has been shown that an increase of DOC in soil solution can favor Cu mobilization

(Araújo et al. 2019; Merritt and Erich 2003; Wu et al. 2003). Since Cu is a bactericide

and fungicide, the decomposition rate of Cu-enriched litter could be reduced due to

a decrease in soil microbial activity (Freedman and Hutchinson 1980). A decrease

in litter breakdown could result in reduced formation of DOC, decreasing its

complexation with Cu and the subsequent decreasing mobilization and incorporation

of this element into the soil, showing a possible protective role regarding the Cu

incorporation into the soil (Fig. 1).

Figure1

There are no scientific publications that evaluate or discuss the protective role of

litter in fruit tree productive systems. It is unknown how litter may interact with Cu

applied as a pesticide. For this reason, the objective of this review is to critically

discuss the protective litter role regarding the incorporation of Cu into the soil in fruit

tree systems, where this element is used as pesticide.

33

Use of copper in agriculture and its accumulation in soil

Copper-based pesticides have been used for more than 200 years to control

bacterial and fungal diseases in crops (Rusjan et al. 2007). It use was generalized

after the accidental discovery of the Bordeaux mixture (CuSO4 + Ca(OH)2) by Pierre

Marie Alexis Millardet in 1880 (Kovačič et al. 2013). By 1885, Millardet had already

conducted experiments, which confirmed that the Bordeaux mixture controlled the

disease produced by the mildew fungus (Palmopara viticola) at a relatively low cost.

This is how the Bordeaux mixture became the first fungicide to be used on a large

scale (Schneiderhan 1933). This fortuitous event led the commercialization of Cu-

based pesticides.

The effectiveness of foliar applications of Cu as bactericides or fungicides is due to

the fact that inorganic forms of this element are relatively insoluble in water and are

not easily washed out from the foliage, thus providing much longer protection than

other compounds (Fishel 2017). Nevertheless, there is scientific evidence in

vineyards that Cu washing out from leaves cannot be negligible depending on the

occurrence of rainfall (Pérez-Rodríguez et al. 2016) and its energy (Pérez-Rodríguez

et al. 2015). It is important to note that the real effect of Cu is not as fungicide or

bactericide (death of microorganisms) but, rather, as fungistatic or bacteriostatic

(Richardson 1997). Cu compounds inhibit spore or cell germination, which are

precursors responsible for the dissemination and reproduction of fungus or bacteria

(Celar and Kos 2016; Richardson 1997); therefore, its effect is rather preventive, i.e.,

34

of prophylaxis. Although Cu is relatively safe from a human-management

perspective, there is some concern regarding its accumulation in agricultural soils

(Fishel 2017). Copper soil enrichment can produce toxic effects in crops (Sayyad et

al. 2009) and in soil microorganisms (Stowhas et al. 2018) as well as

biomagnification in the terrestrial trophic web (Gall et al. 2015).

Copper soil pollution becomes more relevant in countries lacking soil quality

guidelines or regulations, such as in Chile, where Cu-based pesticides applications

are carried out only according to product label instructions (Devotto 2013).

Nonetheless, experienced high concentrations in soils arising from past inputs (e.g.

Paradelo et al. 2013) often led to regulations in some countries (e.g. Ballabio et al.

2018; Ministry of the Environment, Finland; MEF 2007).

Soil contamination in fruit tree productive systems by copper-based

pesticides

In Chile, several studies have found that soil metal contamination occurs near Cu

mining industries (e.g. Ginocchio, 2000; González et al., 1984; González and Ite,

1992; Montenegro et al., 2009; Neaman et al., 2009). However, Casanova et al.

(2013) describe that significant levels of Cu may also be found in agricultural soils

away from mining sources. Nevertheless, to date, there are few studies on the

accumulation and toxic effects of agricultural soils due to the application of Cu-based

pesticides. The above information is of great importance since, in Chile a 32% of

35

total pesticides sold (Servicio Agricola y Ganadero; SAG 2012) corresponds to Cu-

based pesticides.

From a review of published literature, Komárek et al. (2010) reported that Cu

concentration in soil surface horizons (up to 30 cm) of vineyards around the world

often exceeded 200 mg kg-1 due to applications of cupric fungicides. This

observation is consistent with a metal survey performed in agricultural soils of

countries of the European Union by Tóth et al. (2016). They found that France, Italy,

Portugal, and Romania are the countries with Cu concentrations above the threshold

value of 100 mg kg-1 proposed by the European Union (MEF 2007), and soil levels

found in France and Italy, specifically, indicate a potential problem for food

production (Tóth et al. 2016). In general, it is observed that soil samples with higher

concentrations of Cu are found in Mediterranean countries, where the European

viticulture is concentrated (Hannah et al. 2013) and where copper fungicide

application rates are higher (Fishel 2017).

Table S1 in supplementary material, showing the total soil Cu concentration in

vineyards worldwide, is based on the compilation of Komárek et al. (2010). In the

present review, only those studies that reported total soil Cu concentration values

higher than 100 mg kg-1 were considered. This value was selected because there is

no common agreement on copper threshold values for the definition of risk, since Cu

mobility and availability depend on soil characteristics (Ballabio et al. 2018).

However, one of the most cited threshold values for Cu is the proposed by the

36

Finnish and Swedish legislations (MEF 2007) for soil contamination, which is also

100 mg kg-1. The compilation was updated with studies published after 2010 and

performed in fruit orchards’ soils contaminated with Cu-based pesticides. Two

columns were also added to these tables; a column with the exchangeable soil Cu

values reported, and another with the calculated Cu partitioning coefficient (Kd-Cu).

This coefficient is the relation between the total soil Cu content (mg kg-1) and the

soluble soil Cu content (mg L-1):

Kd-Cu (L kg-1) =[CuT](mg kg−1)

[Cusol](mg L−1) (1)

Due to soil solution has an ionic strength it is impossible to know what method (H2O,

CaCl2, KNO3, among others) to determine soluble Cu were used by the authors cited

in Table S1. Therefore, Table S2 in supplementary material, resumes the method

used by theses authors.

As can be seen from Table S1, compiled studies cover a period of 58 years (1961-

2019). However, there is a gap between 1961 and 1985, between 1985 and 1991,

and between 1991 and 1996. Furthermore, prior to 1991, no studies were found for

soil Cu concentrations higher than 200 mg kg-1. Also, studies influenced by mining

activities were excluded (e.g., Romero et al., 2012). From the studies shown in Table

S1, only 21 of a total of 65 correspond to areas where different fruit trees are grown

which are not vineyards. The table shows extremely high soil Cu concentrations in

vineyards in Brazil (3,216 mg kg-1) and in Spain (1,438 mg kg-1) and in coffee tree

orchard in Tanzania (1,403 mg kg-1) and in an apple tree orchard in Japan (1,108

37

mg kg-1). Chile is a large fruit producer (e.g., ODEPA-CIREN, 2017 & 2018) and it is

not an exception to this problem where a significant number of Cu-based pesticides

is used (SAG 2012). For instance, the study of Poblete et al. (2017) reports Cu

concentrations close to 250 mg kg-1, mainly explained by the application copper-

based pesticides, in the soil of a yellow kiwi orchard (Actinidia chinensis cv. Kiss)

located in the O'Higgins Region, central Chile.

Soil copper behavior and bioavailability

It is very relevant to understand which soil parameters can affect soil Cu behavior,

especially its bioavailability. According to Ginocchio et al. (2002), Mondaca et al.

(2015) and Sauvé et al. (2000), the most important parameters are total soil Cu

concentration, pH and OM. It is important to consider that OM has a dual behavior

depending on whether it refers to the OM in the soil solid phase (SOM) or the liquid

phase (DOC) (You et al. 1999). Dissolved organic carbon will mobilize Cu, while

SOM will immobilize it (Mondaca et al. 2015; Santibañez et al. 2012; Sauvé et al.

1997; Tipping et al. 2003). McBride (1994) pointed out that soil metal solubility

decreases by increasing the soil pH values from 4 to 6-7, but then rises at pH values

higher than 7. This occurs as SOM solubility enhances at pH values higher than 7

(i.e., SOM-derived DOC), which, in turn, increases the concentration of Cu in the

solution by the formation Cu-DOC complexes (McBride 1994; Mondaca et al. 2015;

Zhou and Wong 2003). Hence, the OM dual behavior must be considered in soil Cu

partitioning and its role is heavily pH-dependent.

38

It worthy to note that not all Cu is transported as uncomplexed Cu2+. As it was stated

before, Cu2+ has strong affinity to soil colloids (Siddig et al. 2017) and due to most

commercial copper-based pesticides are sprayed as colloid-sized water

suspensions of copper oxychloride minerals, their transport in soil may occur as

colloid-size particles and low soluble Cu-hydroxide microparticles (Paradelo et al.

2009; Paradelo et al. 2010), i.e., undergoes to colloid transport mechanisms. The

soil transport dynamics strongly depends on the pore water chemical environment,

porosity and structure, and dynamics of soil interfaces (Bai et al. 2017). The main

governing processes in this environment are the colloid surface-interactions, particle

aggregation dynamics, and entrapment-release of particles in thin films resulting

from wetting-drying cycles on soil pores (Gao et al. 2008). During infiltration

episodes, colloid-surface attachment mechanisms produce hyper-exponential

deposition profiles with depth (Torkzaban et al. 2008), reproducing the typical

decrease of soil Cu concentration in relation with soil depth, found in agricultural soil

where Cu-based pesticides are used (e.g. Flores-Vélez et al. 1996).

Regarding Cu bioavailability, dissolved soil Cu is easier to absorb than soil-bond Cu

for plant roots (Ginocchio et al. 2002; Mondaca et al. 2015) and soil organisms

(Łukowski and Dec 2018). Even more specific, is the free Cu2+ pool in soil pore water

that is considered to be the primary bioavailable form of soil Cu (Thakali et al. 2006),

and the best indicator of Cu phytotoxicity (Sauvé et al. 1998). Furthermore, the solid-

liquid partition coefficient (Kd) has been also described as a good estimate for soil

39

metal bioavailability (Sauvé et al. 2000) and soil leaching losses potential (Luo et al.

2006). The Kd assumes that dissolved metals are mobile and, therefore, interact with

soil organism, including plants (Mondaca et al. 2015; Sauvé et al. 2000). However,

because most of the dissolved Cu is complexed with dissolved organic matter, it is

not necessarily synonym of its bioavailability.

Low molecular weight DOC is ready to solubilize in soils (Phungsai et al. 2016);

hence, it is released from the decomposition of plant residues (such as litter) to soil

pore water and can greatly increase Cu availability for plants (Zandonadi et al. 2013)

and other soil organisms. Inaba and Takenaka (2005) report that litter breakdown

results in low molecular weight DOC compounds such as phenolic acids and sugar

acids, among others (Adeleke et al. 2017), which increase soil Cu solubility

(Zandonadi et al. 2013). However, Adeleke et al. (2017) informed that only 20-30%

of the leaf litter decomposition correspond to low molecular weight organic

compounds (i.e., DOC); the rest corresponds to humic substances (i.e., SOM), which

decrease Cu bioavailability (Soler-Rovira et al. 2010).

Effects of copper contamination in soil organisms

Metals play an integral role in vital processes of living organisms (Olaniran et al.

2013). In the case of Cu, it participates in the functioning of more than 30 enzymes

(Wright and Welbourn 2002). Nonetheless, Cu becomes toxic at concentrations that

exceed certain limits (Banu et al. 2004).

40

In terms of quantity and variability, it has been described that the increase in soil Cu

concentrations adversely affect microbial communities present in vineyard soils (e.g.

Dell’Amico et al. 2008; Díaz-Raviña et al. 2007; Dumestre et al. 1999; Lejon et al.

2008) as well as litter decomposition processes (Freedman and Hutchinson 1980).

Tyler et al. (1989) reported a decrease between 20% and 40% in litter and soil

biological activity, respectively, in a forest when metal concentrations (among others

Cu) rise between 2 and 10 times in comparison to a control soil. Komárek et al.

(2010) pointed out that Cu distribution in vineyard soils is quite heterogeneous,

generating sites where Cu concentrations are higher (Cu hotspots). Soil

microorganisms would be able to avoid these hotspots (Jacobson et al. 2007), while

Cu-tolerant microbial species colonize them (Hiroki et al. 1985). However, other

studies have shown that there are other factors, such as soil pH, soil OM, soil clay

content, and tillage practices (Mackie et al. 2013), more important than soil Cu

concentration in determining soil microbial distribution and enzyme activity.

It is well described that metal solubility and its potential toxicity is higher in metal-

spiked soils (artificially contaminated soils), compared to field-contaminated soils

(Ginocchio et al. 2006; Hamels et al. 2014; Senkondo et al. 2015; Smolders et al.

2009; Zhuang et al. 2009). This discrepancy could be explained by soil aging

processes. Toxicological studies in soil organism, such as microbes (Oorts et al.

2006), plants (Hamels et al. 2014) and worms (Scott-Fordsmand et al. 2000)

demonstrated the difference in metal solubility and its potential toxicity between

41

metal-spiked soils and field-contaminated soils. Lu et al. (2009) showed, in a

laboratory experiment, that Cu mobility estimated on the basis of exchangeable,

water and acid soluble decreases with aging as well as its toxicity to earthworms and

wheat. Similar results were exposed by Oorts et al. (2006), who showed that Cu

effective concentrations (ECx) thresholds to microbial processes diminished with

aging time. These results can be explained because metal toxicity depends on the

soil residence time, among other factors (Guo et al. 2011; Ma et al. 2006; Martı́nez

and McBride 2000; McBride and Cai 2016). A possible explanation is that metal

cations, such as Cu2+, are initially sorbed on the surfaces of variable charge minerals

that slowly form precipitates with time and/or penetrate into micropores of the

sorbents (Violante et al. 2010).

Despite the importance of using filed-contaminated soil to determine soil organisms

toxicity, Mondaca et al. (2017) highlights certain difficulties in the use of these soils.

First, being field samples, it is difficult to establish toxicity thresholds due to the co-

occurrence of several contaminants. Second, the physicochemical properties (pH,

SOM, texture, among others) can affect the degree of toxicity caused by metals.

Third, side effects of anthropogenic activities, such as acidification by mining, could

exacerbate toxicity. Finally, the quality and availability of soil nutrients could affect

the toxicity thresholds for different soil organisms. However, a detailed

characterization of soil properties allows confoundig soil factors be separated from

metal-toxicity factors (Mondaca et al. 2017; Verdejo et al. 2015), and so estimate an

acurate metal induce toxicity threshold. For this reason, artificially contaminated soils

42

have limited relevance from an environmental point of view and for the decision-

making on soil quality (Lillo-Robles et al. 2020). Therefore, to better determine

toxicity thresholds of metals in soils, studies should use field-collected contaminated

soil samples instead of metal-spiked soil preparations (Mondaca et al. 2017). For

this reason, in Table 1 we summarize Cu toxicity studies performed using real field-

contaminated soils for plants, worms and microorganisms. Surprisingly, we found

that there is little data available on Cu toxicity thresholds—like effective

concentration (ECx), lowest observed effect concentration (LOEC) and no observed

effect concentration (NOEC)—for field-contaminated soils. Six studies were found

for plants (Hamels et al. 2014; Kolbas et al. 2014; Kolbas et al. 2018; Lillo-Robles et

al. 2020; Mondaca et al. 2017; Verdejo et al. 2015), 5 for earthworms (Delgadillo et

al. 2017; Konečný et al. 2014; Mirmonsef et al. 2017; Scott-Fordsmand et al. 2000;

Van Zwieten et al. 2004) and only 3 for soil microorganisms (Arthur et al. 2012; Oorts

et al. 2006; Sauvé 2006). A specific ECx value for a given species, endpoint, and

bioassay exposition time may have limited relevance from an agricultural or

ecological point of view (Delgadillo et al. 2017; Mondaca et al. 2017). Given this

limitation, Checkai et al. (2014) suggested to average effective concentration values

to take into account the effects of different species and endpoints. Geometric means

are preferable to arithmetic ones, since the data is often log-normally distributed

(Checkai et al. 2014). Therefore, geometric means were calculated for each study

presented in Table 1, (with the exception of Delgadillo et al. (2017), Konečný et al.

(2014) and Oorts et al. (2006) who present a unique value for their respective ECx)

43

to have a better idea of Cu-toxicity thresholds in plants, earthworms and soil

microorganisms.

Table1

Comparison of total soil Cu concentrations from Table S1 with the obtained EC10

values for all the group of species, suggests that all the soils exhibit some degree of

toxicity. If the same comparison is carried out for the EC50 values, all soils are

subjected to potential toxicity for earthworms. Furthermore, Fernández-Calviño et al.

(2010) stated that the soil microorganism community showed evident changes in the

enzyme activities at total soil Cu concentrations of 150–200 mg kg-1, being this

threshold lower than the obtained in Table 1 (EC50 of 329 mg kg-1). Nevertheless,

there still evidence that soil microorganism function will be potentially affected at

lower levels of total soil Cu-concentration (EC10 = 81 mg kg-1). Regarding the data

for soluble Cu ECx of Table 1, it was only possible to obtain geometric means for

plants. It is important to note that the geometric mean value for soluble Cu of the

EC50 for plants is lower than for its EC25. This is due that only Lillo-Robles et al.

(2020) reported soluble copper values for EC25 (391 µg L-1), hence it is not a real

geometric mean.

44

Copper-litter relation in soil

Litter corresponds to the layer of dead plant material (such as wood and leaf)

accumulated on the soil surface. This layer can be continuous with no defined

boundaries between the obvious structures of the plant and the soil containing

amorphous organic carbon (Bashkin and Howarth 2003). Litter deposited on soil

constitutes a crucial source of nutrients in both natural (Bani et al. 2018) and

agricultural ecosystems (Dilly and Munch 2004). The fresh plant material deposited

on the ground represents an easily degradable substrate for soil microorganisms,

which play a fundamental role in its decomposition (Dilly and Munch 2004). It has

been described that the C content, the nutrient chemistry, and the stoichiometry of

litter (i.e., the litter quality (Dilly et al. 2004)) influence its decomposition (Cornwell et

al. 2008). The quality of the litter is described by several parameters: lignin content,

N content, the C/N and Lignin/N ratio (Bani et al. 2018). In general, a litter with a high

C/N ratio is considered a slow rate degradation substrate; that is, a substrate with

lower lability in the face of microbial degradation (e.g. Purahong et al., 2015). For

example, the leaves of coniferous trees decay more slowly than those of deciduous

trees, as broad-leaved litter covers less lignin (Krishna and Mohan 2017). Palviainen

et al. (2004) suggested that low-weight molecular N compounds could react with

decomposing products, creating recalcitrant complexes. The formation of these

recalcitrant complexes explains the low decomposition rates of litter during the last

stages of the process. Given the high lignin content and the high C/N ratio of wood

litter, its slow decomposition rate is expected (Bani et al. 2018). The above is of

45

interest because the chemical nature, i.e., leaf litter quality, in fruit systems is very

different from that of forests. In a monitoring carried out by our group, we observe

that the Lignin/N ratio is several orders smaller (unpublished data) than those of

forest systems. Satti et al. (2003) reported values for Lignin/N ratio for coniferous

species that range between 40 and 94, while for broadleaf species (deciduous and

evergreen) the ratio were between 23 and 28. Given the Lignin/N ratios observed by

our group and the fact that about 70% of the studies in Table S1 are related to

vineyards (broad leaf specie), is possible to assume that the bulk of fruit tree leaf

litter is composed by more easily decomposable elements by soil microorganism

compared with those from forests with high lignin or lignin-cellulose content. This is

how the identity of the arboreal species is a key factor in the configuration of the

decomposing community, both for the decomposition of the leaf litter and the wood

litter (Prescott 2010; Shorohova and Kapitsa 2014; Wang et al. 2014). The above is

explained because the microbial communities are influenced by the characteristics

of the substrate, which reflects the capacity of the microorganisms of different taxa

to metabolize specific structural compounds (Tláskal et al. 2016); therefore, a greater

knowledge of the composition and the activity of the microbial community would help

to explain the decomposition patterns of plant residues in the context of litter quality

and the abiotic environment (Bani et al. 2018). It is important to emphasize the role

that fungi play in leaf litter breakdown. About 90% of the respiration of soil organisms

is a product of fungi (Kjøller et al. 1982; Osono 2007). In addition, they are able to

break down the litter lignocellulose matrix that other soil organisms are rarely

capable of breaking down (Osono 2007). Osono and Takeda (2006) demonstrated

46

that fungi communities are more efficient in decomposing broadleaved leaf litter than

coniferous needles. Therefore, fungi must play a very important role in the

decomposition and formation of DOC in fruit production systems.

Bergkvist et al. (1989) conducted a review on metals fluxes in forest ecosystems.

The authors concluded that the organic layer (litter) is the most important sink for

metals coming from the atmospheric fallout, especially for copper, lead, and

chromium. In the case of fruit tree productive systems with foliar applications of

cupric pesticides, there is no information on litter metal content, which allows the

verification of its role as a sink for metals; however, in a preliminary monitoring

carried out by our group in litter of apricot, kiwifruit, and orange trees fumigated with

cupric pesticides (CuO), we demonstrated that total concentrations of Cu were

between 158 (± 38) mg kg-1 and 646 (± 124) mg kg-1, whereas, in non-fumigated

vines, the total Cu content was only 27 (± 14) mg kg-1 (Fig. 2).

Figure 2

When litter decomposition occurs, metals contained in plant tissues are mobilized

through organic compounds, such as organic acids (Kabata-Pendias 2011), leaching

from the A to the B horizon (Bergkvist et al. 1989). This is very relevant as in systems

with a rich and even coverage of leaf litter, water that leaches through this layer can

be loaded with DOC, enabling the formation of complexes with metals, mobilizing

these contaminants into the lower soil layers (Hesterberg et al. 1992). Cuske et al.

47

(2017) proved that low molecular DOC produced from litter breakdown can increase

Cu solubility in forest soils by forming Cu-DOC complexes; therefore, low molecular

DOC can be an important factor to increase solubility and mobility of Cu accumulated

in soil litter and in soils of fruit tree systems. The Cu-DOC mobilization through the

soil will depend on. On the contrary, Lepp et al. (1984) informed soil and litter Cu

concentrations in 24-, 14-, and 4-year-old coffee orchards. Results showed that Cu

concentrations in litter were several times higher than in the mineral top soils in the

three orchards. Even though researchers did not discuss this finding, a possible

protective effect of litter could be occurring. Likewise, in sites contaminated by

atmospheric deposition coming from a Cu smelter, litter Cu concentration was 7

times higher than soil Cu concentration (Ginocchio et al. 2004). These authors

discuss that the accumulation of Cu-enriched litter showed to have a protective effect

on the recruitment of seedlings. Hirst et al. (1961) reported soil Cu values of a of

apple tree orchard of 120 and 150 mg kg-1. They also determined the value of Cu

the herbaceous layer that covers these soils, reaching values > 1,500 mg kg-1.

Although it does not correspond to leaf litter, this plant material showed to have a

protective effect too. Apart from these three studies, we are unaware of other studies

evidencing a protective role of litter.

The protective effect of litter could be due to microbial activity inhibition caused by

Cu-based pesticides that lead to reduced litter decomposition, and accumulation of

a plant-based material that may adsorb sprayed cupric pesticides.

48

Conclusions and future studies

There is evidence that litter degradation generates low molecular weight dissolve

organic carbon that may solubilize copper accumulated in litter, thus, leaching it into

the soil; however, when elevated copper contents are sprayed in fruit orchards,

higher copper concentrations have been found in litter than in soils. This finding

indicates the possible protective role that litter may play regarding the incorporation

of copper in soils.

In order to further assess the possible protective effect that litter could have on

copper incorporation into soil in fruit systems with frequent sprayings of copper-

based pesticides, it is important to characterize the copper flux paths within these

systems. Therefore, is necessary to perform studies that allow us to understand how

cupric pesticides accumulate in fruit tree systems, what is the capability of copper

adsorption by litters, how this accumulation affects the soil microbial flora and the

subsequent litter decomposition, and what is the effect of copper-enriched litter

regarding incorporation of this element into the soil and potential subsequent

leaching to lower soil layers or the water table.

49

Acknowledgments

Financial support was obtained from CONICYT Doctoral Grant (21160236/2016)

and the Center of Applied Ecology and Sustainability (CAPES) by CONICYT

PIA/BASAL FB0002.

50

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Figures

Figure 2. Cu in flux fruit systems (1, Cu aerosols; 2, Cu adsorption/deposition to

plant tissues; 3, Cu-enriched leaf fall; 4, Cu-enriched litter accumulation; 5, soil

enrichment with Cu) and proposed Cu-litter-soil-microorganisms interaction: Cu-

enriched litter is slowly degraded (A) due to microbial activity inhibition (B), protecting

the soil from Cu enrichment (C).

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Figure 4. Box-plot for total Cu concentrations of L layer of litter in different fruit tree

species from of the O'Higgins Region, central Chile. Apricot, kiwifruit, and orange

trees were fumigated with cupric pesticides while the vine did not. Source:

preliminary results of authors.

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Tables

Table 1. Copper effective concentrations (ECx) for soil organism studies compiled

in this review. As a reference, we considered studies that used field-contaminated

soils. For all studies, the values are expressed as geometric mean, with the

exception of Delgadillo et al. (2017), Konečný et al. (2014) and Oorts et al. (2006)

who present a unique value for their respective ECx.

Study Soil origin Species tested Test

duration

(d)

Total Cu

(mg kg-1)

Soluble Cu

(µg L-1)

EC10 EC25 EC50 EC10 EC25 EC50

Plants

Hamels et

al. (2014)

Sweden Hordeum

vulgare

14 ― ― 1370 ― ― 76

Kolbas et al.

(2014;

2018)

France Helianthus

annuus

30 237 ― 562 221 ― 526

Lillo-Robles

et al. (2020)

Chile Early plant

colonizers

― ― ― ― 255 391 533

Mondaca et

al. (2017)1

Chile Avena sativa +

Brassica rapa +

Lolium perenne

21 356 621 904 ― ― ―

Avena sativa +

Brassica rapa

42-62 355 513 688 ― ― ―

Verdejo et

al. (2015)

Chile Lolium perenne 21 404 750 1050 ― ― ―

Geometric mean 332 398 872 237 391 277

Worms

Delgadillo et

al. (2017)

Chile Eisenia fetida 2 ― ― 200 ― ― ―

Konečný et

al. (2014)

Zambia Enchytraeus

crypticuss

28 ― ― 351 ― ― ―

Mirmonsef

et al. (2017)

Denmark Aporrectodea

tuberculata

21 ― ― 279 ― ― ―

Scott-

Fordsmand

et al. (2000)

Denmark Eisenia fetida 21 131 ― 290 ― ― ―

Van

Zwieten et

al. (2004)

Australia Eisenia fetida 2 ― ― 115 ― ― ―

63

Geometric mean 131

231 ― ― ―

Soil microbial activity

Arthur et al.

(2012)

Denmark Soil microbial

activity

< 1 ― ― 531 ― ― ―

Oorts et al.

(2006)

Netherlands Soil microbial

activity

― ― ― 239 ― ― ―

Sauvé

(2006)

Denmark Soil microbial

activity

― 81 ― 269 ― ― ―

Geometric mean 81 ― 329 ― ― ― 1 Includes Verdejo et al. (2015) ECx values for Lolium perenne

64

Chapter 3

Copper content in soils and litter from fruit orchards in Central

Chile and its relationship with soil microbial activity

J. Tomás Schoffer1,2, Humberto Aponte3, Alexander Neaman4, Luz María de la

Fuente1,2, Eduardo Arellano1,2, Pilar Gil5 and Rosanna Ginocchio1,2.

1 Departamento de Ecosistemas y Medio Ambiente, Facultad de Agronomía e

Ingeniería Forestal, Pontificia Universidad Católica de Chile, Santiago, Chile

2 Center of Applied Ecology and Sustainability (CAPES), Pontificia Universidad

Católica de Chile, Santiago, Chile

3 Laboratorio de Ecología Microbiana y Biogeoquímica de Suelos, Instituto de

Ciencias Agroalimentarias, Animales y Ambientales (ICA3), Universidad de

O'Higgins, San Fernando, Chile

4 Instituto de Ingeniería Agraria y Suelos, Facultad de Ciencias Agrarias y

Alimentarias, Universidad Austral de Chile, Valdivia, Chile

5 Departamento de Fruticultura y Enología, Facultad de Agronomía e Ingeniería

Forestal, Pontifica Universidad Católica de Chile, Santiago, Chile

This chapter was submitted to Plant, Soil and Environment

(May 31, 2021)

65

Abstract

Cu-based pesticides have been used for years to control microbial diseases in fruit

trees, resulting in high soil copper concentrations. Cu-enriched soil can negatively

affect soil microorganisms. Litter is the main sink for the atmospheric fallout of trace

elements. Therefore, this biomass can exert a protective role against the

incorporation of Cu into the soil, where this element is used as a pesticide. The aim

of this work was to assess both the soil and litter Cu levels in areas of fruit-tree

production where Cu-based pesticide application systems are intensively used, in

the O’Higgins Region (Chile), and their relationship with soil microbial activity. In this

context, samples of soil and litter from orchards (kiwi, table grape, plum and cherry)

were collected and analysed for their Cu content and C-induced soil microbial

activity. The mean total Cu level in the soils was 225 mg kg-1. The soluble soil Cu

was less than 0.01% of the total soil Cu concentration. The litter Cu content was 3–

7 times higher than in soil. Despite the soil Cu concentration, no effect was observed

on the C-induced soil microbial activity. Therefore, litter does exert a protective role,

preventing the entry of Cu into the soil.

Key words: Metals, Pesticides, Bioavailability, MicroResp, Microorganisms.

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INTRODUCTION

Cu-based pesticides have been used for more than 200 years to control microbial

diseases in different crops (Rusjan et al. 2007), including fruit trees (e.g., Sonoda et

al. 2019). The prolonged use of cupric pesticides could eventually affect fruit

production due to the phytotoxicity of Cu-enriched soils (Tóth et al. 2016). Increased

soil Cu concentrations may also negatively affect soil microbial communities

(Dell’Amico et al. 2008).

Extremely high total soil Cu concentrations (> 1,000 mg kg-1) have been reported

worldwide as a result of the intensive and prolonged use of cupric pesticides in

vineyards and fruit-tree plantations, as reviewed by Schoffer et al. (2020). They

found that most Cu-enriched soils are found in vineyards, and that only 32% of the

revised studies correspond to other fruit orchards.

Foliar spraying of Cu-based pesticides in fruit systems may result in litter being the

main sink for the atmospheric fallout of trace elements (Bergkvist et al. 1989).

Therefore, the entry routes for Cu into the soil may be through the accumulation of

Cu-enriched leaves and/or through the dry/wet deposition of Cu-rich aerosols. In this

context, and to our knowledge, only one study has reported the Cu values in the leaf

litter together with the Cu levels in the soil in agriculture systems (Lepp et al. 1984).

In Chile, irrigated fruit production is mainly located in the central valley of the

Mediterranan Region in Central Chile. The O’Higgins Region being the most relevant

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with a total surface of 52,467 ha (ODEPA-CIREN 2018). Here, Cu-enriched soils

might be expected due to the significant use of Cu-based pesticides several times

within a growing season (Casanova et al. 2013). However, there is very little

information in the literature on Cu levels from the use of Cu-based pesticides in

Chilean orchard soils and their associated litter. In one of these scarce reports,

Poblete et al. (2017) reported total soil Cu values of up to 250 mg kg-1 in a yellow-

kiwi orchard. No information about the effect on soil microbial communities has been

reported, however.

Due to a global lack of information relating to both soil and litter Cu contents in fruit-

tree production systems and the relationship of Cu with soil microbial activity, the

aim of the present work was to assess both the total soil Cu and litter Cu levels in

fruit production systems in the O’Higgins Region of Chile where the intensive

application of Cu-based pesticides is practiced as part of the regular orchard

management, and determine their relationship with soil microbial activity.

MATERIALS AND METHODS

Study area

Three orchards of four fruit-tree species (kiwi, table grape, plum and cherry) were

selected. All the orchards were located along the Cachapoal River valley in the

O’Higgins Region, central Chile. The cherry orchards were located in the northern

part of Rancagua County (34° 10′ 0″ S, 70° 45′ 0″ W), while the kiwi, table grape and

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plum orchards were located in Peumo County (34° 23′ 46″, 71° 10′ 10″ W). The

studied soils are classified as Haploxeroll with a textural class ranging from loam to

silty loam. The region is characterized by a semi-arid Mediterranean climate (Luebert

and Pliscoff 2017), with winter rains, a prolonged spring/summer dry season, and a

temperate climate of mountain degradation (Chuncho and Arrellano 2018). All the

selected orchards are representative of conventional farming and were all managed

using regular and representative schemes. Tables S1 and S2 (supplementary

material) summarize the cultivation practices and cupric pesticide management of

selected orchards in 2019.

The three plum orchards covered areas of 10–26 ha, with open-vase tree training

systems and a planting density of 278–333 plants m-1. In addition, the three orchards

had ridges to a height of 0.4 m and 1.0 m wide. The areas of the three kiwi orchards

were 19, 6 and 7 ha respectively, with a pergola trellis system and ridges 0.2 m high

by 1.0 m wide. Depending on the orchard, the planting density varied from 416–666

plants m-1. In the table grape orchards, the areas were smaller, varying from 6–12

ha. However, the planting density was 952 plants m-1, higher than kiwi or cherry.

These orchards had a pergola trellis system and none of the table grape orchards

had ridges. Regarding the cherry orchards, the areas varied from 4–6 ha, with a

planting density of 694–889 plants m-1 and an open-vase system. None of the cherry

orchards had ridges.

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Table grapes orchards were not sprayed with Cu-based pesticides, and so these

were considered as control orchards in this study. At the time of the soil and litter

samplings 6.8, 2.0 and 4.5 kg Cu ha-1 were applied in the plum, kiwi and cherry

orchards, respectively. In these three orchards cuprous oxide (Cu2O) 86% (Nordox®

Super 75 WG), was used.

Soil and litter sampling

An initial characterization was made that includes the row and inter-row of the soils

where the textures were loam to silty loam. Litter and soil samples were collected in

the late fall of 2019, when at least two doses of Cu-based pesticides had been

previously applied. The litter corresponded exclusively to the senescent leaves of

each orchard. In all the orchards, the litter had a residence time of 6 months. The

plum, table grape and kiwi soil and litter were sampled on May 22, 23 and 24,

respectively, while the cherry soil and litter were collected on May 28. Eighteen

samples of soil and litter (9 from the row and 9 from the interrrow) were taken from

each selected orchard. The litter samples were collected using 50 x 50-cm quadrats

placed on the soil. The three samples from each fruit species comprised five

subsamples taken from across the selected orchards. Once all the litter had been

completely removed from the quadrat, a soil sample was collected to a depth of 20

cm using an Eijkelkamp® stainless-steel hand auger.

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Physical-chemical characterization of soils

The soil sample were passed through a 2-mm-mesh sieve. An aliquot was stored at

4°C for microbiological determinations. The rest of the samples were dried at 40°C

(Delgadillo et al. 2017) until constant mass was reached, and then physicochemically

characterized using standard methods. Soil pH, EC, OM, total N, and available

phosphorus (P) and potassium (K) were determined, following Sadzawka et al.

(2006). The total and soluble soil Cu were determined according to Zagal and

Sadzawka (2007) and Stuckey et al. (2008) respectively. To determine the soil

texture, the Bouyoucos hydrometer method was used (Sandoval et al. 2011).

Leaf litter total Cu determination

The leaf litter samples were oven-dried at 65°C for 48 h (Tu et al. 2014) in a Binder

forced-convection drying oven, and then ground using a food processor with

stainless steel blades (model TH-9010V, Thomas). The ground samples were

calcined at 500°C and digested using hydrochloric acid (HCl). The total Cu was

determined by atomic absorption spectrometry according to Sadzawka et al. (2007).

Soil microbial colonization and respiration

To quantify the soil microbial growth, a modified dilution and plating method was

performed, following Pepper & Gerba (2009). The plates were incubated at 25°C for

3 days to determine the colony-forming unit (CFU) per gram of soil.

The soil microbiological respiration was measured using the MicroResp™ bioassay

(MicroResp™, James Hutton Ltd., Aberdeen, UK), following Campbell et al. (2003).

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Ten C-sources were dosed to a known quantity of each soil stored at 4°C. The C-

sources used were α-ketoglutaric acid (AKG), glucose (GLU), fructose (FRU), malic

acid (MAL), citric acid (CIT), L-arabinose (ARA), N-acetyl glucosamine (NAG), oxalic

acid (OXA), L-arginine (ARG) and cysteine (CYS). The samples were dosed with

water (WAT) to measure their basal respiration. After dosing, they were incubated

at 25°C for 4 h in 96-well detection microplates, which had a pre-measured

absorbance of λ = 570 nm (Biobase EL10 microplate spectrophotometer). Following

the incubation period, the detection microplates were read in the microplate

spectrophotometer to estimate the soil microorganismal C dioxide (CO2) emissions

through the colorimetric shift of the detection microplates. Also, the average well

color development (AWCD) was calculated (the mean amount of CO2 respired for

the MicroResp test).

Statistical analyses

The data were analyzed using microsite (row and interrow) and species (kiwi, table

grape, plum and cherry) as factors. Firstly, the Kolmogorov–Smirnoff and Leven

tests were applied to evaluate the normality and homoscedasticity assumptions,

respectively. ANOVA and Tukey’s honest significant difference (HSD) test were

applied when the variables met previous statistical assumptions in order to

determine the effect of the factors. Where these assumptions were not met, log and

root transformations were performed. A Kruskal–Wallis test, with a Holm p-value

adjustment, was performed in cases where the assumptions were ultimately not met.

Additionally, principal component analysis (PCA) was performed on the entire data

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set, with labels by site, species, the presence/absence of a ridge in the row, and the

application or not of the Cu-based pesticide. PCA was also performed for each

species in order to explore the relationships between the physicochemical and

biological soil properties, and their associations with the treatments, per species.

Analyses were done using R version 3.5.1.

RESULTS AND DISCUSSION

General soil properties

The soil pH of the selected orchards varied from slightly acidic (6.33) to slightly

alkaline (7.93), with a mean value of 7.2 (Table 1); values and ranges higher than

those indicated by Luzio (2010) for the area (6.0–6.5). Significant statistical

differences were observed (F = 68.84, p < 0.001), where kiwi had the highest pH

(7.54; Table S3) and cherry the lowest (6.65; Table S6). Only table grape showed

significant pH differences (F = -2.90, P = 0.011) between the row (7.3) and interrow

(7.1) soils (Table S4).

The mean OM value was 2.5% (Table 1), with the statistically highest (F = -4.63, P

= 0.005) being for the kiwi soil (2.9%) and the lowest for the table grape soil (2.2%);

values lower than the indicated by Luzio (2010) for the area (> 5%). No orchard

showed significant differences in OM values between the row and interrow soils.

The mean EC value reached 1.14 dS m-1 (Table 1). Despite this high EC value, the

soils were classed as non-saline (Gartley 2011). The plum orchard soil EC (1.67 dS

73

m-1; Table S5) was statistically higher than in the other three orchards (Tables S3,

S4 and S6). No orchard soil showed any significant differences in EC values

between the row and interrow soils.

Table 1

Soil and leaf litter copper levels

The mean total Cu level in the soils was 225 mg kg-1, with a range from 131 to 432

mg kg-1 (Table 2). This mean value is higher than threshold value (100 mg kg-1;

further evaluation is needed in the area where the limit was exceeded) and guideline

value (150 mg kg-1; which denotes an ecological or health risk) proposed by the

Finnish government (MEF 2007). The mean total soil Cu values per orchard type

were 243, 233, 228 and 196 mg kg-1 for plum, cherry, table grape and kiwi,

respectively (Table 2). Lower total soil Cu levels might be expected in kiwi orchards

compared to plum and cherry orchards due to the lower Cu2O doses applied (130

versus 200 g 100 L-1). Surprisingly, the total soil Cu levels in the table grape

orchards, where cupric pesticides were not applied, were not significantly different

from the Cu levels in the soils of the cherry and plum orchards. This might be

explained by the selected table grape orchards having used to be citrus orchards, in

which Cu-based pesticides were applied (INDAP-PRODECOP INIA Intihuasi, 1998).

The soluble soil Cu levels in all the orchards were less than 0.01% of the total soil

Cu levels, indicating low availability and thus toxicity (Ginocchio et al. 2002).

Specifically, the soluble soil Cu concentrations were 0.20 mg kg-1 in the cherry

74

orchards and 0.14 mg kg-1 in the plum, kiwi and table grape orchards, only being

significantly different (F = 3.21, P = 0.029) among the cherry and table grape soils

(Table 2). When the soil microsite factor was considered, only the soils of the cherry

orchards showed significant differences (F = 3.025, P = 0.012), with the soluble Cu

being 1.7 times higher in the interrow than in the row samples (Table S6). This result

is the opposite to that of (Mackie et al. 2013), who showed significant differences in

soluble Cu in a vineyard soil, with higher levels within the rows than in the interrows.

Komárek et al. (2008), however, reported no significant differences between soils

from the rows and interrows in a vineyard, similarly to the kiwi, plum and table grape

orchards in the present study. In kiwi orchard, we expect this behavior since being

in pergola trellis system loses the row and interrow effect, as it has complete

coverage of the soil through the canopy. The same occurs with table grape orchards

that also do not apply Cu-based pesticides. Nevertheless, due the tree training

system of plum orchards we expected differences between the row and the interrow.

The leaf litter Cu levels of the cherry, plum and kiwi were statistically 22, 10 and 5

times higher (F = 213.02, P < 0.001) than in the table grape leaf litter (Table 2), as

expected, as no Cu-based pesticides were applied to the table grape orchard.

Furthermore, the cherry (1,583.44 mg kg-1) and plum (687.28 mg kg-1) leaf litters

showed higher Cu values than the kiwi (380.6 mg kg-1) leaf litter due to the higher

doses of Cu-based pesticide applied (200 g 100 L-1 versus 130 g 100 L-1). With the

exception of the table grape orchards, the Cu levels in the leaf litters were much

higher than the total soil Cu levels (Table 2). Specifically, the CuT_L/S ratio values

75

were 0.3, 2.1, 3.2 and 7.1, for grape, kiwi, plum and cherry, respectively. Significant

difference (F = 122.25, P < 0.001) were observed between the table grapes and the

other species in terms of the value of the CuT_L/S ratio. This result supports the

hypothesis proposed by Schoffer et al. (2020), that the leaf litter exerts a protective

role against the incorporation of Cu into the soil of orchards where this element is

applied as a Cu-based pesticide. Lepp et al. (1984) reported Cu litter levels of 884

and 320 mg kg-1 in coffee orchards that were 24 and 14 years old, respectively,

which our finding also supports. These Cu levels correspond to approximately twice

the total soil Cu concentration also reported by these researchers. To our

knowledge, Lepp et al.’s (1984) study is the only one in which litter and total soil Cu

concentrations are jointly reported from a fruit production system.

Table 2

Soil microbial colonization and carbon-induced soil microbial respiration

There was no significant difference between the fruit-tree species (F = 2.37, P =

0.08) and the soil microsites (F = 0.78, P = 0.38) in terms of soil microbial

colonization. This may be the result of the high variability in the data. In this context,

all the orchards are drip irrigated, that causes the soils to be constantly mixing.

Hence, the changes in the soil are very noticeable under the dripper, but not the rest

of the soil (Osorio and Césped, 2000). In the kiwi orchards (Figure 1A), no effect of

the microsite was observed for any C source, nor for WAT and AWCD. In the table

grape orchards (Figure 1B), higher microbial activity was observed in the row than

76

in the interrow for ARG (F = -2.46, P = 0.027), NAG (F = -2.93, P = 0.010) and AWCD

(F = -2.256, P = 0.023). As mentioned above, Cu was not applied in the table grape

orchards, and so the concentrations of total and soluble Cu in the soil do not differ

between the rows and interrows. Similar to this, in the plum orchards (Figure 1C),

there was greater activity in the rows in terms of AKG (F = -2.37, P = 0.033), NAG

(F = -2.28, P = 0.044) and AWCD (F = -2.67, P = 0.017). None of the parameters

related to Cu, either in the soil or litter, differed between the soils of the rows and

interrows (Table S5). The cherry orchards (Figure 1D) were where the microsite

effect affected most sources of C, as well as WAT and AWCD. In this case OXA (F

= 4.68, P < 0.001), CYS (F = 6.44, P < 0.001), ARG (F = 5.81, P < 0.001), GLU (F =

3.42, P = 0.004), NAG (F = 3.78, P = 0.002), FRU (F = 2.86, P = 0.016), WAT (F =

3.41, P = 0.004) and AWCD (F = 3.08, P = 0.007) showed greater activity in the

interrows than the rows. Even though the available Cu fraction was low, it was

significantly higher in the interrows in the cherry orchards (Table S6), where a

decrease in soil microbial activity might be expected; nevertheless, the opposite was

the case. According to Aponte et al. (2021), differences between the rows and

interrows may be due to a shift in the utilization of the C substrate by microbial

communities that may be associated with soil Cu contamination. However, we found

no relation to the soil total Cu, soil soluble Cu, litter Cu content or CuT_L/S ratio

values. Therefore, it is possible there are other variables responsible for the shift in

C-source utilization by the soil microbial community that are not Cu related, such as

soil tilling practices (Mackie et al. 2013) or DOM, which is available as a source of

energy and nutrients to the soil microorganisms, accelerating the succession of the

77

soil microbial community after a period of stress caused by Cu toxicity (Brandt et al.

2010). The AWCD values were highest in the kiwi orchard soils (3.59 μg CO2-C g-1

h-1), followed by plum (3.40 μg CO2-C g-1 h-1) and cherry (2.89 μg CO2-C g-1 h-1).

The lowest AWCD value was observed in the table grape soils (2.03 μg CO2-C g-1

h-1). Significant differences in the AWCD (F = 3.44, P < 0.022) were only found

between the plum and table grape soils. As explained above, the plum orchard soils

had high total Cu concentrations and also high AWCD values. In this context,

Donoso et al. (2016) stated that AWCD is not a good bioindicator for Cu soil toxicity,

hence, this protective role against Cu that litter exert should be evaluated using a

better indicator species for Cu toxicity, such as earthworms (Delgadillo et al. 2017).

Figure 1

The PCA showed clear discrimination between the orchards (Figure 2A), resulting in

the following order: kiwi = table grape ≠ plum ≠ cherry. By contrast, the PCA did not

show any differences by soil microsite (Figure S1). The principal component (PC) 2

showed higher positive eigenvector for pH (Table S7) with the kiwi and table grape

orchards (Figure 2A). In this context, PC1 explained the most variability, being

positively associated with almost all the microbiological indicators (except for CFU),

the soil Cu concentration and the litter Cu concentration, especially with the cherry

orchard, where the highest values for Cu in the litter were found. It is important to

note that PC1 and PC2 only explained 48% of the total variance (Figure 2), but with

PC3, the explanation of the variance increased to 59% (Table S7). In the PCA

78

carried out with labels by the presence/absence of ridge in the rows and the

application or not of the Cu-based pesticide (Figure 2B), a clear discrimination was

found associated with these labels, with the following groupings: no ridge–no Cu

application ≠ no ridge–Cu application ≠ ridge–Cu application. In this context, the

table grape––the only orchards to which Cu was not applied––was grouped alone,

and no vector (variable) was observed to explain this grouping. The grouping of the

kiwi and plum orchards (Figure 2B), where Cu was applied and which had ridges,

can be explained in similar ways due to the microbial activity of the soils induced by

all the C-sources, whose eigenvectors had similar weights in PC1 (Table S7). For

the cherry orchards, which did not have ridges and where Cu was applied (Figure

1B), the variability can mainly by both, microbiological variables (PC1), sand content

and the Cu levels in the litter (PC1 and PC3; Table S7).

Figure 2

CONCLUSIONS

As a result of the large annual amount (4.0 to 9.2 k ha-1 yr-1) of Cu-based pesticide

applied, we can conclude that:

- copper has been accumulated mainly in the litter, which exerted a protective

role regarding copper incorporation into the soil;

- although soil has been enriched in copper, its bioavailability and ecotoxicity

were relatively low;

79

- it has been indicated that AWCD is not a good indicator of Cu toxicity in soils

and that to assess Cu soil toxicity a better bioindicator should be uses; and

- it is important to study the adsorption capacity and degradability of Cu-

enriched litter, since this could be a way of incorporating the element into the

soil.

To date, this is the first work that describes the Cu content in the soils of orchards in

central Chile. It is also one of the few studies, worldwide, carried out on fruit trees

other than vineyards. The study has highlighted the importance of litter as a

protective layer against the incorporation of Cu in the soil.

Acknowledgments

Financial support was obtained from ANID Doctoral Grant (21160236/2016), Center

of Applied Ecology and Sustainability (CAPES) by ANID PIA/BASAL FB0002 and

Facultad de Agronomía e Ingeniería Forestal PUC.

80

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Figures

Figure 1. C-induced soil microbial activity of soil samples taken in the row (black) and in the interrow (grey) in A) kiwi, B)

table grape, C) plum, and D) cherry orchards of selected orchards of O´Higgins Region, central Chile. Different letters per

species denote significant differences at p ≤ 0.05 among soil microsite.

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84

Figure 2. Principal component analysis labeled by fruit tree orchard species (A) and by soil microsite (ridge

presence/absence) and the use or not of Cu-based pesticides. EC = electrical conductivity; OM = organic matter; N =

available nitrogen; P = available phosphorous; K = available potassium; CuT_S = total copper; CuS = soluble copper; CFU

= colony-forming units; MAL = malic acid; ARA = L-arabinose; CIT =citric acid; AKG = α-ketoglutaric acid; OXA = oxalic acid;

WAT = microbial basal respiration; CYS =cysteine; ARG = L-arginine; GLU = glucose; NAG = N-acetyl glucosamine; FRU

= fructose; AWCD = average well color development; CuT_L = litter total copper; CuT_L/S = litter Cu concentration to soil

85

Cu concentration ratio; No–No = no ridge and no Cu-base pesticide use; No–Yes = no ridge and Cu-base pesticide use;

Yes–Yes = ridge and no Cu-base pesticide use.

86

Tables

Table 1. General soil physicochemical characteristics of selected orchards in the

O´Higgins Region, central Chile

Soil property Mean ± SEM Range

pH 7.17 ± 0.05 6.33–7.93

Electrical conductivity (dS m-1) 1.14 ± 0.07 0.46–3.11

Organic matter (%) 2.53 ± 0.07 1.52–5.54

Available N (mg kg-1) 18.58 ± 0.96 6.37–43.00

Available P (mg kg-1) 31.20 ± 2.54 2.33–88.10

Available K (mg kg-1) 353.70 ± 19.70 128–801

Sand (%) 26.42 ± 0.79 15–44

Clay (%) 19.08 ± 0.27 14–25

Silt (%) 54.50 ± 0.79 38–68

Total Cu - soil (mg kg-1) 225.10 ± 6.65 131–432

Soluble Cu – soil (mg kg-1) 0.16 ± 0.01 0.06–0.42

Total Cu – Leaf litter (mg kg-1) 680.9 ± 72.90 16.3–2290

Litter Cu:Total soil Cu 3.17 ± 0.35 0.07–13.4

87

Table 2. Copper concentrations in study soils and leaf litter of selected orchards in

the O´Higgins Region, central Chile.

Fruit species Plum Kiwi Table grape Cherry

Mean ± SEM Mean ± SEM Mean ± SEM Mean ± SEM

Total Cu–soil

(mg kg-1)

243.44 ± 18.57

195.80 ±11.60 227.94 ± 8.01

233.22 ± 11.01

Soluble Cu–

soil (mg kg-1)

0.14 ± 0.02

0.14 ± 0.02 0.14 ± 0.01

0.20 ± 0.02

Total Cu–leaf

litter (mg kg-1)

687.28 ± 41.08

380.6 ± 48.20 72.39 ± 22.19

1583.44 ± 96.35

Litter Cu:Total

soil Cu

3.16 ± 0.33

2.13 ± 0.33 0.31 ± 0.08

7.11 ± 0.60

88

Chapter 4

The role of leaf litter as a protective barrier for copper-containing

pesticides in orchard soils

Jorge Tomás Schoffer 1,2, Mónica Antilén 3, Alexander Neaman 4, María Francisca

Díaz 1,2, Luz María de la Fuente 1,2, Cristian Urdiales 3 and Rosanna Ginocchio 1,2

1 Departamento de Ecosistemas y Medio Ambiente, Facultad de Agronomía e

Ingeniería Forestal, Pontificia Universidad Católica de Chile, Vicuña Mackenna

4860, Santiago, Chile

2 Center of Applied Ecology and Sustainability (CAPES), Pontificia Universidad

Católica de Chile, Av. Libertador Bernardo O’Higgins 340, Santiago, Chile

3 Departamento de Química Inorgánica, Facultad de Química y de Farmacia,

Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Santiago, Chile

4 Instituto de Ingeniería Agraria y Suelos, Facultad de Ciencias Agrarias y

Alimentarias, Universidad Austral de Chile, Valdivia, Chile

This chapter was published in Environmental Science and Pollution Research–

Springer (March 24, 2021)

89

Abstract

We studied the capacity of leaf litters to adsorb copper ions applied as a copper-

based pesticide. Leaf litters of two fruit tree species with different lignin/N ratios were

examined to determine their protective role in the incorporation of Cu into the soil

after a degradation process. A leaf-litter Cu adsorption capacity assay and a

degradation assay were performed using table grape (lignin/N = 2.35) and kiwi

(lignin/N = 10.85) leaf litters. Results showed that table grape leaf litter had a

significantly (p = 0.001) higher Cu adsorption capacity (15,800 mg kg-1) than kiwi leaf

litter (14,283 mg kg-1). After the degradation assay, significant differences (p = 0.011)

were observed in the transfer of Cu from Cu-enriched leaf litter into soil, showing

that kiwi litter has a greater protective effect on the incorporation of Cu into soil,

regardless of the amount of Cu applied. This protective role is reflected in that a

significantly (p = 0.015) higher mean of Cu is observed in table grape soil (41.71 ±

2.14 mg kg-1) than in kiwi soil (35.87 ± 0.69 mg kg-1). Therefore, although a higher

concentration of lignin/N does not mean that the leaf litter has a greater Cu

adsorption capacity, it to indicate a greater protective role, in relation to the

incorporation of Cu into soil.

Key words: Metals, pesticides, degradation, microbial activity, orchards, lignin

90

Introduction

Copper-based pesticides have been used for more than 200 years in agriculture to

control fungal and bacterial diseases (Rusjan et al. 2007). As a result of this long-

term use, high soil copper (Cu) levels can be found in some agricultural lands

worldwide (e.g., Komárek et al. 2010; Tóth et al. 2016; Schoffer et al. 2020). In fruit

systems, soil Cu enrichment is mediated by the dry/wet deposition of Cu-rich

aerosols and by the fallout of Cu-enriched leaves (i.e., leaf litter) derived from cupric

pesticide fumigation on the tree canopy (Schoffer et al. 2020).

According to Bergkvist et al. (1989), Ginocchio et al. (2004) and Ettler (2015), leaf

litter is one of the main sinks of soil trace elements from the atmospheric fallout,

likely due to its cation-complexing capacity (Sauvé 2002). For this reason, Schoffer

et al. (2020) proposed that leaf litter exerts a protective role on the incorporation of

Cu into the soil of fruit tree orchards, where this element is used as a pesticide.

Likewise, Lepp et al. (1984) demonstrated that Cu concentration was several times

higher in litter than in the mineral topsoil in a study conducted in coffee tree orchards

of different ages. Furthermore, Ginocchio et al. (2004) reported that Cu

concentration in leaf litter was seven times higher than Cu concentration in topsoil in

an area affected by atmospheric deposition from a Cu smelter.

The protective role of leaf litter on the incorporation of Cu into the soil of fruit tree

orchards will depend on both its adsorption capacity and its rate of decomposition.

91

The adsorption capacity, which is the effectiveness of plant tissue to bind trace

elements from aqueous solution, is due to the affinity between water molecules and

the cell wall components (Malik et al. 2017). Many researchers have reported that

materials such as coconut shell charcoal, hazelnut shell, almond shell, cashew nut

shell and oil palm leaf have great trace element adsorption capacity (e.g., Babel and

Kurniawan 2004; Pehlivan et al. 2009a, b). In general, the adsorption capacity of

plant tissue will depend on the availability of functional groups towards trace

elements (Saxena et al. 2017), such as hydroxyl and carboxyl (Wan Ngah and

Hanafiah 2008a) and phenolic (Habib-ur-Rehman et al. 2006) groups. Biopolymers

such as cellulose, hemicellulose, and lignin are precursors of these functional groups

(Karnitz et al. 2007) and show adequate sorption properties due to their multi-

component composition and the presence of various active functionalities (Halysh et

al. 2020). Results obtained by Basso et al. (2002, 2005), Sirviö and Visanko (2020)

and Halysh et al. (2020) suggest that the ability of lignocellulosic materials to adsorb

trace elements depends on their lignin content.

Leaf litter decomposition rate depends, among other factors, on litter quality, a

concept based on concentrations of nitrogen (N), phosphorus (P), polyphenol and

lignin, in addition to the C/N, lignin/N, and polyphenol/N ratios (Liu et al. 2007; Naik

et al. 2018). In general, leaf litter with a high lignin/N ratio is a slow-rate degradation

substrate (e.g., Purahong et al. 2015). Cu-enriched leaf litter should also have a

slower microbial decomposition rate because Cu inhibits the dissemination and

reproduction of fungi and bacteria (Richardson 1997; Celar and Kos 2016).

92

Based on available information, we hypothesized that a leaf litter with a high lignin/N

ratio would have higher Cu adsorption than one with a low lignin/N ratio due to its

higher content of lignocellulosic compounds, and thus a higher protective role of

copper incorporation into the orchard soil. Therefore, the objectives of this study

were 1) to determine the capacity of fruit tree leaf litters to adsorb copper ions (Cu2+)

applied as a copper-based pesticide (Hidroxicobre® 50WG; Cu(OH)2 and

coformulants) relative to the lignin/N ratio of the leaf litters and 2) to assess if fruit

leaf litter plays a protective role in the incorporation of Cu into the soil after the

degradation process.

Materials and Methods

Leaf litter sampling

In April 2019 (early fall), when the leaves begin to fall, three samples of the leaf litter

of five fruit trees species (Prunus avium L. cv. Royal Dawn, Prunus domestica L. cv.

D’Agen, Actinidia deliciosa (A. Chev.) C. F. Liang & A. R. Ferguson, cv. Hayward,

Prunus americana L. cv. Dina, Vitis vinifera L. cv. Crimson) were taken from orchards

located in the O’Higgins Region of central Chile. Each sample per species was taken

from one selected fruit orchard. Samples were collected using 50 × 50 cm quadrats

placed on the soil and were stored in double Ziplock bags. Samples per species

were composed of five subsamples taken randomly from selected orchards, which

were then mixed to form a composite sample. All leaf litter samples were taken to

the laboratory.

93

Leaf litter characterization

All fruit tree leaf litter samples were oven-dried at 65° C for 48 h (Tu et al. 2014) in

an air-forced convection drying oven (Binder, model: FD260). The content of lignin,

cellulose, hemicellulose, and total nitrogen (N) was analyzed in all fruit tree species

to select the two most contrasting leaf litters in terms of lignin/N ratios. An ANKOM

Fiber Analyzer (model: ANKOM 200) was used to determine lignin, cellulose, and

hemicellulose contents according to the manufacturer’s instructions. Neutral

detergent fiber (NDF: the remaining residue after digesting biomass in a detergent

solution) and acid detergent fiber (ADF: the remaining residue after digesting the

biomass with sulfuric acid and cetyl trimethylammonium bromide) were used for

determining hemicellulose and cellulose leaf litter contents. The NDF is

predominantly hemicellulose, cellulose, and lignin, while the ADF is predominantly

cellulose and lignin. Lignin concentration was obtained by the acid detergent lignin

procedure, which is determined gravimetrically as the residue remaining upon

ignition after a 72% H2SO4 treatment. The ADF results were subtracted from the

NDF results to obtain the hemicellulose concentration, and the results for lignin were

subtracted to the ADF results to obtain cellulose concentrations. Total N was

determined by the Kjeldahl digestion method (Schumacher et al. 1995). Lignin is a

precursor of functional groups (Basso et al. 2002, 2005; Karnitz et al. 2007; Sirviö

and Visanko 2020; Halysh et al. 2020) that can interact with trace metals. For this

reason, we selected the lignin/N ratio as an indicator of the leaf litter’s Cu adsorption

capacity and degradation rate. Therefore, leaf litters with the highest and the lowest

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lignin/N ratio (kiwi and table grapes respectively) were selected for the assays

described below.

Treatment of selected leaf litters

Leaf litter samples of each selected fruit tree species (kiwi and table grapes) were

ground using a food processor with stainless steel blades (Thomas, model: TH-

9010V). An aliquot of each leaf litter sample was analyzed for total Cu (Cut) to

establish initial concentrations. The rest of the sample was subjected to sequential

acid washings, according to the methodology described by Schumacher et al.

(1995). Acid washings were intended to decrease potential Cu loads remaining in

leaf litters due to cupric pesticide fumigations on tree canopies under field conditions.

Briefly, sequential acid washes used distilled water, 0.01 M hydrochloric acid (HCl),

distilled water, 0.05 M ethylenediaminetetraacetic acid (EDTA), and distilled water.

This sequence was repeated three times. After washings, leaf litters were oven-dried

at 65° C for 48 h (Tu et al. 2014) in an air-forced convection drying oven (Binder,

model: FD260). Samples were analyzed for Cut after washing to establish the Cu

load decrease. Total Cu concentrations in leaf litters before and after washings were

determined by atomic absorption spectrophotometry. After washing treatments, Cut

contents decreased from 60 ± 4 mg kg-1 to 22 ± 4 mg kg-1 in kiwi leaf litter and from

22 ± 2 mg kg-1 to 7 ± 1 mg kg-1 for table grapes. After washing the leaf litters, their

Cu concentration can be considered normal for several crops (Gupta 1997) and for

plant leaves (Oorts 2013). The initial Cu concentration indicates that a considerable

amount of Cu was indeed adsorbed on the surface of selected leaf litters due to

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copper-based pesticide (Cu2O) fumigation management practices performed on

these fruit tree canopies. All acid-washed leaf litter samples were sifted through a 1-

mm mesh sieve and used in the assays described below.

Leaf litter copper adsorption capacity assay

Cu adsorption capacity on selected leaf litters (kiwi and table grapes) followed a

modification of the methodology described by Gardea-Torresdey et al. (1996). A Cu

solution (0.3 mM copper or 19 mg L-1) was made from the Hydroxicobre® 50WG

pesticide used in the fruit tree orchards of central Chile and buffered with 0.01 M

sodium acetate. The solution’s pH was adjusted to pH 5.0 with 0.1 M NaOH or 0.1

M HCl to avoid Cu precipitation. For each selected leaf litter, an aliquot of 40 mg was

put into a 15 mL centrifuge tube and suspended with 8 mL of the 0.3 mM Cu solution

in triplicate. The tubes were allowed to equilibrate for 15 minutes in a Labtech

(model: LSI-3016A) orbital shaker at 200 rpm (room temperature) and then

centrifuged at 2,500 rpm for 5 minutes. Supernatants were collected and saved for

subsequent determination of Cut by atomic absorption spectrophotometry. The

resulting pellets were then resuspended with 8 mL of the 0.3 mM Cu solution, and

the procedure described above was repeated 14 times (runs). The pH was recorded

at the beginning and the end of each run. Control centrifuge tubes consisting of 15

mL with 8 mL of the 0.3 mM copper solution, but without leaf litter biomass, were

subjected to the same process. Copper adsorption values were obtained by

subtracting the average Cu concentration of the control tubes (14 mg L-1) from the

Cut present in the supernatant of each run.

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Freundlich (Freundlich 1906) and Langmuir (Langmuir 1918) adsorption models

were used to fit the experimental data obtained by the leaf litter Cu adsorption

capacity assays and evaluate how Cu was absorbed in the biomass of leaf litters

with contrasting lignin/N ratios (kiwi and table grapes).

Freundlich model: This model does not have a mechanistic interpretation; it

represents an empirical approach to predict the distribution of species between a

solid phase and a solution phase (Limousin et al. 2007). The model is described by:

𝐶𝑆 = 𝐾𝐹𝐶𝑆𝑒

1𝑛 (1)

Where CS is the concentration of adsorbed species (mg kg-1), CSe is the

concentration in the supernatant solution (mmol L-1), KF is the empirical Freundlich

adsorption coefficient, and n is a linearity factor.

Langmuir model: This model is applicable to L-type isotherms, characterized by a

high affinity between adsorbate and adsorbent, commonly implying chemical

adsorption with the formation of inner-sphere complexes (Giles et al. 1974). The

Langmuir model is described by the following mathematical expression:

𝐶𝑆 = 𝐶𝑚−𝑐𝑎𝑙

𝐾𝑙𝐶𝑆𝑒

1 + 𝐾𝑙𝐶𝑆𝑒 (2)

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Where CS and CSe have the same meaning as in the Freundlich model, Cm-cal is the

maximum adsorption capacity (mg kg-1) calculated by the model, and KL is the

empirical affinity Langmuir coefficient.

Leaf litter degradation assay

This assay was based on the leaf litter decomposition method using litterbags,

adapting the field methodology described by Coleman et al. (2004) to a laboratory

experiment.

Soil sample: About 2 kg of soil was collected from an avocado orchard (central Chile)

to a depth of 20 cm using an Eijkelkamp® stainless-steel hand auger, and the sample

was stored in double Ziplock bags. The sample was taken to the laboratory in a

cooler filled with an icepack to avoid any increase of soil microbial activity. At the

laboratory, moist soil was rid of coarse materials and then passed through a 2 mm

mesh sieve. The sample was maintained at 4° C. Before the experiment setup, a

subsample of the soil was analyzed for pH, electric conductivity (EC), soil organic

matter (SOM), P, K (Sadzawka et al. 2006), total N (Schumacher et al. 1995) Cut

and (Zagal and Sadzawka 2007). The results of these analyses are given in Table

1.

Table 1

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Experimental design: Polyethylene containers (400 mL) with perforated lids were

used for the experiment. All containers were filled with 50 g of the sampled soil. The

soil was kept at 70% of its water holding capacity throughout the experiment.

Litterbags of 5 × 5 cm were made from a non-woven polypropylene fabric (donated

from the Tea Bag Index project) and filled with approximately 2 g of either table grape

or kiwi leaf litters spiked with increasing Cu concentrations. Acid-washed leaf litters

were spiked with 0, 150, 300, 600 and 1200 mg kg-1 of Cu(OH)2. This range was

selected according to previous results for Cu leaf litter concentrations found in

different fruit tree species from the O’Higgins Region in central Chile (Schoffer et al.

2020). Results of the spiking process are shown in Table 2.

Table 2

Filled litterbags were weighed and placed inside the polyethylene containers, on top

of the experimental soil. The containers were covered and incubated at 28° C for

275 days in a Biobase incubator (model: BJPX-L250) to allow the leaf litter

decomposition process take place. At the end of the incubation period, Cut in the

experimental soils was determined per container. The Cu transfer factors (FT) from

leaf litters to the soil were estimated from the soil Cut values at the beginning and

the end of the assay according to Dean (2007) as follows:

𝐹𝑇 =(𝐶𝑢𝑡−𝑠𝑜𝑖𝑙,𝑡 − 𝐶𝑢𝑡−𝑠𝑜𝑖𝑙,0)

𝐶𝑢𝑡−𝑙𝑖𝑡𝑡𝑒𝑟 (3)

99

where Cut-soi,0 is the initial Cut concentration (mg kg-1) of Cut in the soil, Cut-soil,t is the

Cut concentration (mg kg-1) in the soil at the end of the experiment (i.e., 275 d), and

Cut-litter is the concentration of the same element in the leaf litter located above the

soil. Then, the obtained FT was multiplied with the real level of Cu in leaf litter at the

beginning of the experiment to obtain the real concentration of the transferred Cu

into the soil.

Cu determinations in leaf litters, assay solutions and experimental soil

The amount of Cut in leaf litters and experimental soil was determined by acid

digestion followed by Cu determination with a GBC (model: SENSAA SERIES AAS)

Atomic Absorption Spectrophotometer (Walinga et al. 1995; Kalra 1998; Zagal and

Sadzawka 2007). Briefly, the samples were calcinated at 500° C and then digested

with 2M HCl. Treated samples were then measured in an atomic absorption

spectrometer. The wavelength used for Cu for maximum absorbance was 324.7 nm.

The concentration of Cut in assay solutions was directly measured with the atomic

absorption spectrometer (SISS 2007).

Data analysis

An F test was performed for the isotherm model parameters to determine which leaf

litter had the highest Cu adsorption capacity. The normality assumptions were

previously checked using the Shapiro–Wilk test, and the assumptions of

homoscedasticity were checked using the Levene test. ANOVA and simple linear

100

regression analysis were used for the leaf litter degradation assay data. All ANOVA

and linear regression analysis assumptions were met except for normality.

Simulation studies of non-normal distributions have shown that the false positive rate

is not affected by the violation of the normality assumption (Glass et al. 1972; Harwell

et al. 1992; Lix et al. 1996). Due to the above and the sample size (n = 30), the use

of these analyses is justified using the central limit theorem. An F test was performed

on the resultant slope parameter of the resultant models of the simple linear

regression analysis to compare which leaf litter had a better protection characteristic

with respect to the incorporation of Cu into the soil. The analyses and graphs were

performed in R version 3.5.1 and GraphPad Prism version 8.0.

Results and Discussion

Leaf litter characteristics and Lignin/N ratio

Table 3 shows the lignin/N ratio and the other relevant parameters determined in

leaf litters. The main results indicate that total N content is quite similar among

selected leaf litters (1% to 2%), but lignin, hemicellulose and cellulose broadly vary

among them. Specifically, the former variables are much higher (14–17%) in kiwi

leaf litter than in the others (2.4–11%). The same trends are described in the

literature for leaf litters of a variety of fruit trees (see Table S1), with total N content

varying from 1% to 2% (Musvoto et al. 2000; Green et al. 2006; Tagliavini et al. 2007;

Sariyildiz 2008; Neto et al. 2009; Ventura et al. 2010; Murovhi et al. 2012; Murovhi

and Materechera 2015; Naik et al. 2018). Broader variation has been described for

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the other parameters: 21 times for lignin (3–62%), 4.6 times for cellulose (10–46%),

8.7 times for hemicellulose (10–86%), and 31.1 times for the lignin/N ratio (1–40)

(Table 1 and Table S1). Although the kiwi leaf litter had the highest lignin/N ratio

found in this study, the lignin/N ratio has been reported to be higher in other leaf

litters: 31.3 in avocado, 36.1 in mango, 36.8 in litchi (Murovhi et al. 2012) and 40.4

in pear (Neto et al. 2009). Tagliavini et al. (2007) reported a similar lignin/N ratio (9.8)

for apple leaf litter. In our study, the lignin/N ratio ranged from 2.35 to 10.85 for study

leaf litters (Table 3). This range is narrower than the range described in the literature

(Table 3 and Table S1). Kiwi leaf litter had the highest value (lignin/N = 10.85), while

the lowest was found in table grape leaf litter (lignin/N = 2.35; Table 3). Therefore,

these leaf litters were selected for the assays described below.

Table 3

Leaf litter Cu adsorption capacity

Experimental data of Cu adsorption capacities for leaf litters of both selected species

fitted to the Langmuir and Freundlich models are shown in Fig. 1 and Table 4. Of the

two models, the Langmuir model has a better fit to experimental data, with an R2 of

0.991 and 0.997 for table grape and kiwi leaf litter, respectively, versus R2 of 0.977

and 0.982 for the Freundlich model (Table 4). Because a good fit using the Langmuir

model commonly implies chemical adsorption with the formation of inner-sphere

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complexes, it appears that Cu adsorption occurs at specific homogenous binding

sites located on the surface of leaf litters (Kecili and Hussain 2018).

Figure 1

Table 4

An F test was performed to compare the parameters of the table grape and kiwi leaf

litters obtained by the Langmuir model to determine which leaf litter had more Cu

adsorption capacity. Results showed that table grape leaf litter has a significantly

higher Cu adsorption capacity (Cm-cal; Table 4) than kiwi leaf litter (p = 0.001). These

results contradict what we hypothesized in the introduction, suggesting that leaf litter

with a low lignin/N ratio has better Cu adsorption capacity. However, results show

that the Cu affinity (KL) of leaf litters does not depend only on its quality (p = 0.324),

at least in our range of lignin/N ratio values. In general, the availability of functional

groups such as hydroxyl, carboxyl, phenolic or lactonic (e.g., Habib-ur-Rehman et

al. 2006; Mesquita et al. 2006; Wan Ngah and Hanafiah 2008b; Xie et al. 2017) is

quantified to characterize the adsorption capacity of metals by biomass. We used

the lignin/N ratio as an indicator of the presence of these functional groups because

lignin is a precursor of these (Karnitz et al. 2007) in this study. We expected that the

higher the lignin/N ratio, the greater the availability of functional groups capable of

adsorbing Cu and, therefore, the greater the adsorption capacity. However, our

results indicate that the lignin/N ratio is not a good estimator of the availability of

103

functional groups and therefore of the copper adsorption capacity, at least in our

study range (2.35 to 10.85).

The Cu adsorption capacity of selected leaf litters in the present study is not the

highest, but neither is it the lowest compared to the literature results. When the leaf

litter Cu adsorption capacity values estimated by the Langmuir model (Cm-cal) in the

present study (15,800 mg kg-1 for table grape and 14,283 mg kg-1 for kiwi) are

compared to those published in the literature (see Table S2), they are near to those

reported in 2015 and 2006 by Çekim et al. (Cm-cal = 17,180 mg kg-1) and Prasanna

Kumar et al. (Cm-cal = 15,430 mg kg-1), respectively. However, our Cm-cal results are

much lower than the mean value (59,563 mg kg-1) of the studies presented in Table

S2. Notice that results for the Cu adsorption capacity of the studies presented in

Table S2 were obtained through batch-method experiments, while in the present

study, the same biomass was subjected to 14 runs at a constant concentration of Cu

(0.3 mM). Our experimental approach may be a more realistic representation of what

could be happening in fruit production systems, where Cu is applied as a cupric

pesticide several times onto the same leaves (repeated exposures). Of the studies

summarized in Table S2, this is the first that uses leaf litter from fruit trees as a

possible biosorbent material for Cu.

Results obtained in the present study support the hypothesis discussed by Schoffer

et al. (2020) that fruit leaf litters have a protective role on the incorporation of Cu into

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the soil of fruit tree orchards, at the very least, considering leaf litter’s ability to bind

Cu from a Cu-based pesticide.

Leaf litter degradation assay

Fig. 2 presents the linear regressions of Cu transferred to the soil from the leaf litters

as a function of the Cu level in the leaf litter. This figure shows significant differences

(p = 0.011) between the kiwi and grape leaf litter curves. Therefore, a litter with a

higher lignin/N ratio, such as kiwi leaf litter, has a greater protective effect on the

incorporation of Cu into the soil, irrespective of the amount of Cu applied to the leaf

litter, as we hypothesized in the introduction. A significantly higher mean (p = 0.015)

of net Cu contribution to the soil from the Cu-enriched leaf litter after 275 days of the

degradation process is observed in table grape soil (41.71 ± 2.14 mg kg-1) than in

the kiwi soil (35.87 ± 0.69 mg kg-1). These values represent a soil increase in Cut

concentrations of 43% and 23% in table grapes and kiwi, respectively. If we consider

this last result and that the grape litter, with a lignin/N ratio lower than kiwi, presented

a better Cu adsorption capacity (Fig. 1 and Table 4), it can be inferred that the Cut

is desorbed faster from litter with a lower lignin/N ratio. Therefore, a leaf litter with a

higher lignin/N concentration confers greater protection to the soil, in terms of Cu

incorporation, after a 275-day leaf litter degradation process.

Figure 2

105

The leaf litter mass reduction (Fig. S1) did not vary between species (p = 0.702),

reaching values between 37% and 44%. However, the Cu level in the leaf litter

reduces the leaf-litter-mass reduction as shown by its coefficients of determination

(R2) of 0.788 for table grape and 0.699 for kiwi, after simple linear regression

analysis. The decrease in biomass reduction with the increase of Cu levels in leaf

litters are in agreement with results of literature as Cu has been shown to inhibit soil

microbial activity (e.g., Fernández-Calviño et al. 2012; Stowhas et al. 2018; Aponte

et al. 2020) and thus the leaf litter decomposition. These results are also consistent

with degradation experiments in the leaf litter of fruit species found in the literature.

For example, Musvoto et al. (2000) report a ≈40% reduction in dry matter in mango

litter (lignin/N = 15.4) after 275 d of the assay. A clear decrease in leaf litter mass

reduction is observed when the lignin/N ratio increases in the study by Murovhi and

Materechera (2015). In this study, both avocado leaf litter (lignin/N = 16.3) and

mango (lignin/N = 12.3) suffered a reduction of ≈40% in mass, while litchi leaf litter

(lignin/N = 25.2) showed a reduction of ≈20%, after 272 d of the assay. Naik et al.

(2018) report a similar trend, where mango (lignin/N = 13.2), guava (lignin/N = 16.3)

and litchi (lignin/N = 23.6) presented a reduction in mass of 40%, 15%, and 5%,

respectively.

Conclusions

Contrary to what we hypothesized, a leaf litter with a high lignin/N ratio (at least in

the present study range) is more suitable for the adsorption of Cu when this element

106

is applied as a pesticide. On the other hand, our results suggest that a higher lignin/N

ratio is more relevant to determine the protective role that leaf litter can have on the

incorporation of Cu into the soil after a long-term degradation process. Regardless

of the lignin/N ratio, the leaf litter decomposed less as the Cu concentration in the

litter increased because Cu inhibits the microorganisms in the soil.

Field experiments with different species and different lignin/N ratios need to be

performed to better understand how different lignin/N ratios have a protective effect

on the incorporation of Cu into the soil. In addition, leaf litter with a higher lignin/N

ratio range (> 20) than was used in the present study could be examined to verify

whether a higher lignin/N ratio shows a greater Cu adsorption capacity and greater

protective effect on the incorporation of Cu into the soil.

Acknowledgements

We are grateful to the Center of Applied Ecology and Sustainability (CAPES) for the

opportunity to carry out this research and to ANID PIA/BASAL FB0002 and ANID

Doctoral Grant (21160236/2016) for funding

Authors’ contributions

J. Tomás Schoffer: Conceptualization, Methodology, Formal analysis, Writing—

original draft. Mónica Antilén: Writing—review and editing, Formal analisys.

107

Alexander Neaman: Writing—review and editing, Supervision. María Francisca

Díaz—Investigation. Luz María de la Fuente: Investigation. Cristian Urdiales: Formal

analysis. Rosanna Ginocchio: Writing—review and editing, Supervision

.

Funding

Financial support was obtained from ANID Doctoral Grant (21160236/2016), Center

of Applied Ecology and Sustainability (CAPES) by ANID PIA/BASAL FB0002 and

Facultad de Agronomía e Ingeniería Forestal PUC.

Data availability

The datasets used and/or analyzed during the current study are available from the

corresponding author on reasonable request.

Declarations

Ethical approval: Not applicable.

Consent to participate: Not applicable.

Consent for publication: Not applicable.

Competing interest: The authors declare no competing interests.

108

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113

Figures

Figure 1. The Langmuir adsorption isotherms (solid line) and the Freundlich

adsorption isotherms (dashed) for Cu2+ adsorption by table grape (black) and kiwi

(grey) leaf litter (40 mg/mL) subjected to 14 exposures of 0.3 mM Cu2+: pH 5, tube

shaken at 200 rpm at room temperature during the 15 min equilibrium time.

114

Figure 2. Transferred Cu from leaf litter to soil as a function of the leaf litter Cu

concentration in table grape (black) and kiwi (grey) after 275 days of incubation at

28 °C. The solid lines show the obtained regression (Y = aX+b), whereas the dashed

lines demonstrate the 95% confidence intervals.

115

Tables

Table 1. Physicochemical characteristics from the selected avocado orchard soil

used in the leaf litter degradation assay.

Soil parameter Avocado orchard soil

pH 6.50

Electrical conductivity (dS/m) 0.97

Soil organic matter (%) 1.25

Total N (mg kg-1) 23.2

P (mg kg-1) 21.4

K (mg kg-1) 158

Total Cu (mg/kg) 29.1

Soluble Cu (mg/kg) <0.009

116

Table 2. Leaf litter Cu spiking process results.

Leaf litter specie Nominal Cu Concentration

(mg kg-1)

Cu spiking results

(mg kg-1)

Table grape 0 7

150 269

300 426

600 720

1200 1421

Kiwi 0 22

150 330

300 449

600 1110

1200 1339

117

Table 3. Leaf litter nitrogen (N), lignin (Lig), cellulose (Cel), hemicellulose (HC) and

lignin/N ratio content (mean ± standard error) of the sampled leaf litter (n = 3).

Fruit tree specie N (%) Lig (%) Cel (%) HC (%) lignin/N

Prunus avium L.

cv. Royal Dawn

(Cherry)

1.68 ± 0.14 11.45 ± 0.74 11.20 ± 0.29 2.66 ± 1.23 6.88 ± 0.70

Prunus domestica

L. cv. D'Agen

(Plum)

1.82 ± 0.06 7.22 ± 0.79 9.26 ± 1.34 7.57 ± 0.83 3.94 ± 0.30

Actinidia deliciosa

(A. Chev.) C. F.

Liang & A. R.

Ferguson, cv.

Hayward (Kiwi)

1.31 ± 0.10 14.25 ± 1.76 16.67 ± 0.33 13.05 ± 2.43 10.85 ± 0.67

Prunus americana

L. cv. Dina

(Apricot)

1.99 ± 0.03 12.47 ± 0.36 11.07 ± 0.82 2.33 ± 0.97 6.25 ± 0.10

Vitis vinifera L. cv.

Crimson (Table

grape)

1.86 ± 0.03 4.35 ± 1.53 10.80 ± 0.52 8.16 ± 1.50 2.35 ± 0.84

Ranges described

in literature a

0.71 – 2.3 3.0 – 63.1 10 – 46.4 9.9 – 86 1.3 – 40.4

a According to detailed information of Table S1, supplementary material

118

Table 4. Copper adsorption model parameters for table grape and kiwi leaf litters.

Table Grape Kiwi

Cm-exp (mg kg-1) 11063 9515

Freundlich model

KF (mg kg-1) 7273 6285

N 0.525 0.548

R2 0.977 0.982

Langmuir model

Cm-cal (mg kg-1) 15800 14283

KL (L mmol-1) 0.939 0.862

R2 0.991 0.997

119

Chapter 5

General discussion and conclusions

To date, no scientific study has reported the situation regarding soil Cu levels derived

from the foliar application of Cu-based pesticides in fruit-tree productive systems in

Chile, with the exception of the conference paper by (Poblete et al., 2017).

Furthermore, to our knowledge, this work is one of the very few studies, worldwide,

carried out in fruit-tree soils other than vineyard soils, along with the study of (Lepp

et al., 1984) that reported the Cu content in the soils and litter of a coffee plantation

in Kenya.

This work––as part of my doctoral thesis––describes the soil Cu levels (total and

soluble) and Cu levels in leaf litter (Specific Objective 1) in a selected group of

orchards (plum, cherry, kiwi and table grape) in the Libertador Bernardo O’Higgins

Region. The plum, cherry and kiwi orchards are frequently sprayed with a Cu-based

pesticide, while the table-grape orchard is not (Chapter 3). The mean total soil Cu

concentration of the studied orchards was 225 mg kg-1, with a range of 131–432 mg

kg-1, similar to that reported by other studies on fruit-tree and vineyard soils (e.g.,

Jacobson et al., 2005; Arias et al., 2006; Vitanović et al., 2010; Gómez-Armesto et

al., 2015; Avramidis et al., 2019). However, all the total soil Cu concentrations

determined in the orchards in this study exceed the threshold value (100 mg kg-1)

proposed by the Finnish government (MEF, 2007), which is one of the most cited in

120

the literature (e.g., Tóth et al., 2016; Ballabio et al., 2018; Panagos et al., 2018;

Avramidis et al., 2019). No extreme values of total soil Cu were determined, such as

those reported by Aoyama & Kuroyanagi (1996), Loland & Singh (2004), Mirlean et

al. (2007) and Fernández-Calviño et al. (2013), who reported 1,108, 1,403, 3,216

and 1,438 mg kg-1, respectively. The reason such higher Cu values were not found

in the soils of the selected orchards in this study, despite the large amounts of Cu-

based pesticides applied (1.0 to 2.3 kg ha-1 application-1), is that the litter was able

to adsorb a large amount of the Cu, thus preventing its entry into the soil. In fact, the

Cu levels determined in the leaf litter of the orchards were up to seven times higher

than the soil Cu levels. The protective role of leaf litter against Cu soil enrichment

was manifested in the soil microbial activity of the orchards, which was not affected

by levels of total Cu found in the soils. In this context, the effective half maximum

soil Cu concentration (EC50) determined for soil microorganisms of 329 mg kg-1,

estimated herein based on values published in the literature (Chapter 2; Schoffer et

al., 2020), is higher than the mean value of the total soil Cu determined in the

orchards in the present study. Also, the soluble soil Cu levels in all the orchards were

less than 0.01% of the total soil Cu levels, indicating its low availability and, thus,

totoxicity (Ginocchio et al., 2002)

In Chapter 4, the capacity of the leaf litter of the selected fruit-tree species to adsorb

Cu2+ ions, applied as a Cu-based pesticide, was determined in relation to their

lignin/N ratios (Specific Objective 2). It was found that the leaf litter had elevated Cu-

adsorption capacities (Cm-cal = 15,800 mg kg-1 for table grape and Cm-cal = 14,283 mg

121

kg-1 for kiwi), according to the Langmuir adsorption model, irrespective of their

lignin/N ratio (kiwi, lignin/N = 10.9; table grape, lignin/N = 2.4), as opposed to what

we hypothesized. These values are close to those reported in Prasanna Kumar et

al. (2006) (Cm-cal = 15,430 mg kg-1) and Çekim et al. (2015) (Cm-cal = 17,180 mg kg-

1). However, our Cm-cal results were much lower than the mean value (59,563 mg kg-

1) of the studies compiled in Table S2 (Chapter 4), possibly due to differences in the

methodological approaches used. Despite what we hypothesized (Hypothesis 2), the

table-grape leaf litter had a high Cu adsorption capacity. In general, the availability

of functional groups, such as the hydroxyl, carboxyl, phenolic and lactonic groups

(e.g., Habib-ur-Rehman et al., 2006; Mesquita et al., 2006; Wan Ngah & Hanafiah,

2008; Xie et al., 2017), is quantified in order to characterize the adsorption capacity

of metals by biomass. Herein, the lignin/N ratio was used as an indicator of the

presence of these functional groups because lignin is a precursor to these (Karnitz

et al., 2007). We expected that the higher the lignin/N ratio, the greater the

availability of functional groups capable of adsorbing Cu would be, and therefore the

greater the adsorption capacity. However, our results indicate that the lignin/N ratio

is not a good predictor of the availability of functional groups, and therefore the Cu

adsorption capacity, at least in the range in this study (2.35 to 10.85).

Although the table-grape leaf litter had a higher Cu adsorption capacity than the kiwi

leaf litter, the latter had a greater protection capacity against the incorporation of Cu

into the soil after a degradation period of 275 days (Specific Objective 3). The

lignin/N ratio appeared not to have had an effect on the reduction of the litter mass.

122

However, the Cu levels in the leaf litter decreased the reduction of the leaf-litter

mass, as indicated by the coefficients of determination (R2) of 0.788 for table grape

and 0.699 for kiwi, after simple linear regression analysis. The decrease in biomass

reduction with the increase in Cu levels in the leaf litter support reports in the

literature, as Cu has been shown to inhibit soil microbial activity (Fernández-Calviño

et al., 2012; Stowhas et al., 2018; Aponte et al., 2020), and thus leaf-litter

decomposition. These findings are also consistent with degradation experiments

reported in the literature, performed using the leaf litter of fruit-tree species. For

example, Musvoto et al. (2000) determined a ≈40% reduction in dry matter in mango

litter (lignin/N = 15.4) after 275 days of assay. A clear decrease in leaf-litter mass

reduction correlated with an increase in the lignin/N ratio in a study by Murovhi &

Materechera (2015). They found that both avocado (lignin/N = 16.3) and mango

(lignin/N = 12.3) leaf litters experienced a reduction in mass of ≈40%, while litchi leaf

litter (lignin/N = 25.2) showed a reduction of ≈20%, after 272 days of the assay. Naik

et al. (2018) reported a similar trend, where mango (lignin/N = 13.2), guava (lignin/N

= 16.3) and litchi (lignin/N = 23.6) litters presented a reduction in mass of 40%, 15%

and 5%, respectively.

In summary, the leaf litter of fruit-tree species seems to play a protective role against

the incorporation of Cu into soil, when this element is applied as a Cu-based

pesticide. This protection is manifested by a lack of change in the soil microbial

activity, and is mainly due to the Cu adsorption capacity of the leaf litter. This

adsorption capacity seems to depend on the lignin/N ratio, but in the opposite way

123

to what we hypothesized (i.e., the higher the lignin/N ratio, the higher the Cu

adsorption capacity). The lignin/N ratio has an effect on the incorporation of Cu into

the soil during the degradation process of the litter biomass (i.e., the higher the

lignin/N, the less the Cu is incorporated into the soil, after the litter degradation

process). In conclusion, we fulfilled our general aim to evaluate the protective effect

of leaf litter in terms of soil Cu incorporation, with respect to its quality, in fruit-tree

systems where Cu-based pesticides are applied. However, to better understand how

the Cu-enriched leaf litter affects the microbial activity of the soil, it is necessary to

carry out laboratory tests where an indicator of the microbial activity of such soil

(respiration or enzymatic activity) is periodically examined. Along with this, field

experiments involving different fruit-tree species and different lignin/N ratios need to

be performed in order to better understand how different lignin/N ratios have a

protective effect against the incorporation of Cu into the soil.

124

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127

Appendix

128

Chapter 2 Supplementary Material

Supplementary Tables

Table S1. Total (Cut) and exchangeable (Cue) soil copper concentrations in

vineyards and fruit orchards worldwide.

Country Orchard Orchard

age

(years) 1

Soil

Depth

(cm)

Cut

(mg kg-

1)

Cue

(mg kg-1) 2

Kd-Cu

(L kg-1)

Reference

Australia

Avocado

tree

25

0-2

280 2.15

[1075]

260 Merrington et

al. (2002)

345 1.29 [645] 535

2-10 176 0.76 [380] 463

301 0.71 [355] 848

Australia Vineyard 20-30 0-1 136 0.60 [60] 2267 Pietrzak and

McPhail

(2004) 249 2.90 [290] 859

1-5 107 0.40 [40] 2675

148 4.30 [430] 344

Australia

Avocado

tree

> 20 0-10

103 ― ― Van Zwieten

et al. (2004) 737 ― ―

≈ 50 436 ― ―

Australia Vineyard 25-80 0-10 119 3 ― ― Wightwick et

al. (2008) 30-100 128 3 ― ―

38-84 136 3 ― ―

30-100 150 3

20-38 223 3 ― ―

Brazil

Vineyard

40

0-20

506 4 4.70 4

[588]

861 Casali et al.

(2008)

665 4 9.50 4

[1188]

560

20-40 106 4 0.40 4

[50]

2120

Brazil

Vineyard

95 0-10 602 5 2.20 5

[275]

2189 da Rosa

Couto et al.

(2015) 10-20 324 5 0.36 5

[45]

13378

129

20-30 326 5 0.04 5 [5] 65.200

30-40 306 5 < 0.001 5

[0.125]

>

2448000

Brazil Vineyard 5 0-5

354 5 5.00 5

[500]

708 Mirlean et al.

(2007)

211 6 ― ―

509 3 ― ―

40 1019 5 4.20 5

[420]

2426

509 6 ― ―

1505 3 ― ―

45 537 5 2.50 5

[250]

2148

434 6 ― ―

689 3 ― ―

61 1838 5 1.30 5

[130]

14138

1508 6 ― ―

2450 3 ― ―

100

2198 5 2.20 5

[220]

9991

1214 6 ― ―

3216 3 ― ―

Brazil

Vineyard ≈ 50 0-5 473 5.80 [588] 816 Mirlean et al.

(2009) 324 6 ― ―

543 3 ― ―

499 6.40 [640] 780

298 6 ― ―

564 3 ― ―

550 5.50 [550] 1000

342 6 ― ―

672 3 ― ―

468 5 ― ―

432 5 ― ―

517 5 ― ―

Chile Yellow

Kiwifruit

tree

6 0-30 250 7 ― ― Poblete et al.

(2017)

China Apple tree 30 0-5 210 3 ― ― Li et al.

(2005)

China Apple tree n.i. 0-20 229 3 ― ― Wang et al.

(2015a)

130

China Apple tree 50 0-20 148 5 ― ― Wang et al.

(2015b)

Croatia Vineyard n.i. 0-20 217 5 ― ― Miko et al.

(2007) 105 6 ― ―

553 3 ― ―

Croatia

Vineyard > 40 0-10 368 5 ― ― Romić et al.

(2004) 700 3 ― ―

Croatia Vineyard 40-70 0-20 106 ― ― Vitanović et

al. (2010) 113 ― ―

139 ― ―

162 ― ―

164 ― ―

166 ― ―

182 ― ―

183 ― ―

196 ― ―

205 ― ―

211 ― ―

214 ― ―

248 ― ―

253 ― ―

298 ― ―

302 ― ―

626 ― ―

Czech

Republic

Vineyard ≈ 600 0-10 114 ― ― Komárek

et al. (2008) 156 ― ―

≈ 60 11-20 168 ― ―

England Apple tree 40-60 0-10 120 ― ― Hirst et al.

(1961) 150 ― ―

France Vineyard n.i 0-2 323 5 ― ― Flores-Vélez

et al. (1996) 2-5 209 5 ― ―

5-28 127 5 ― ―

France Vineyard 33 0-3 264 6 ― ― Besnard et al.

(2001) 519 3 ― ―

10-15 149 5 ― ―

France Vineyard 8 n.i 0-15 100 2.86 [286] 350 Brun et al.

(1998) 118 0.23 [23] 5130

126 4.46 [446] 283

128 0.16 [16] 8000

134 0.17 [17] 7882

165 0.22 [22] 7500

131

210 0.43 [43] 4884

251 0.56 [56] 4482

France Vineyard 36 0-15 102 3.76 [376] 271 Brun et al.

(1998 2001) 103 4.25 [425] 242

127 6.4 [640] 198

168 9.24 [924] 182

173 0.39 [39] 4436

177 0.35 [35] 5057

251 0.56 [56] 4482

France Vineyard n.i 0-15 158 ― ― Brun et al.

(2003)

France Vineyard n.i 0-2 145 ― ― Chaignon et

al. (2003) 146 1.00 [100] 1460

173 39.00

[3900]

44

398 ― ―

0-15 157 7.00 [700] 224

2-15 140 ― ―

225 1.00 [100] 2250

288 ― ―

15-30 110 ― ―

114 ― ―

117 40.00

[4000]

29

117 ― ―

123 ― ―

129 6.00 [600] 215

136 1.00 [100] 1360

329 3.00 [300] 1097

346 ― ―

France Vineyard 26 5-10 232 5 ― ― Chopin et al.

(2008) 35-40 227 5 ― ―

France Vineyard n.i. 0-10 120 0.12 [60] 2000 Jacobson et

al. (2005) 156 4.60

[2300]

68

202 0.16 [80] 2525

232 0.16 [80] 2900

428 0.20 [100] 4280

430 0.14 [70] 6143

France Vineyard n.i. 0-2.5 492 ― ― Jacobson

et al. (2007)

France Vineyard 8 n.i. n.i. 100 ― ―

132

103 ― ― Michaud et al.

(2007) 103 ― ―

106 ― ―

111 ― ―

114 ― ―

117 ― ―

117 ― ―

120 ― ―

123 ― ―

128 ― ―

140 ― ―

141 ― ―

146 ― ―

147 ― ―

150 ― ―

150 ― ―

152 ― ―

155 ― ―

158 ― ―

163 ― ―

165 ― ―

186 ― ―

540 9 ― ―

1030 9 ― ―

France Vineyard n.i 0-20 108 1.40 [140] 771 Parat et al.

(2002) < 50 112 1.50 [150] 747

> 50 107 2.60 [260] 412

210 2.80 [280] 750

332 4.70 [470] 706

20-50 234 2.50 [250] 936

France Vineyard n.i. 0-10 243 5 ― ― Probst et al.

(2008) 227 5 ― ―

Georgia Vineyard

n.i. 0-20

798 ― ― Narimanidze

and Brückner

(1999) 939 ― ―

970

Large-

scale fruit

plantation

290 5 ― ―

1023 3 ― ―

Greece Olive tree 5-60 0-20 131 ― ― Avramidis et

al. (2019) 138 ― ―

133

151 ― ―

165 ― ―

174 ― ―

197 ― ―

198 ― ―

206 ― ―

220 ― ―

228 ― ―

242 ― ―

268 ― ―

276 ― ―

340 ― ―

349 ― ―

354 ― ―

423 ― ―

463 ― ―

514 ― ―

567 ― ―

648 ― ―

671 ― ―

286 5 ― ―

Greece Vineyard n.i. 0-20 111 5 ― ― Kelepertzis et

al. (2018) 291 3 ― ―

0-50 108 0.20 [10] 10800

109 0.40 [20] 5450

110 0.20 [10] 11000

111 0.30 [15] 7400

116 0.50 [25] 4640

123 0.20 [10] 12300

125 0.30 [15] 8333

132 0.20 [10] 13200

140 0.20 [10] 14000

147 0.20 [10] 14700

154 0.50 [25] 6160

168 0.30 [15] 11200

171 0.20 [15] 17100

173 0.20 [15] 17300

204 0.30 [15] 13600

210 0.70 [35] 6000

217 0.80 [40] 5425

134

Greece Almond

tree

≈ 150 0-30 155 ― ― Vavoulidou

et al. (2005)

Cherry tree 129 ― ―

144 ― ―

175 ― ―

Olive tree 112 ― ―

114 ― ―

Orange

tree

105 ― ―

Pistaccio

tree

134 ― ―

Vineyard 110 ― ―

112 ― ―

127 ― ―

129 ― ―

134 ― ―

157 ― ―

202 ― ―

Italy

Vineyard 50-100

0-10 200 5 0.08 5 [8] 25000 Deluisa et al.

(1996) 221 5 0.23 5

[23]

9609

297 5 0.33 5 [33] 9000

10-20 171 5 0.20 5 [20] 8550

275 5 0.29 5 [29] 9482

20-40 114 5 0.12 5 [12] 9500

188 5 0.18 5 [18] 10444

40-60 132 5 0.15 5 [15] 8800

0-60 131 5 0.90 5 [90] 1456

945 3 ― ―

Italy Vineyard > 30 n.i. 215 4 9.00 4

[720]

299 Dell’Amico et

al. (2008)

372 4 30.00 4

[2400]

155

Italy Vineyard 25 0-9 177 4 3.30 4

[132]

1341 Viti et al.

(2008)

Olive tree > 50 146 4 1.70 4 [68] 2147

297 4 4.20 4

[168]

1768

Japan Chestnut

tree

n.i. 0-10 189 0.40 [80] 2363 Aoyama and

Kuroyanagi

(1996) Apple tree 443 17.30

[3460]

128

135

533 38.70

[7740]

69

657 10.90

[2180]

301

658 4.50 [900] 731

830 2.40 [480] 1729

1108 1.70 [340] 3259

631 5 10.84 5

[2169]

291

Japan Apple tree n.i. 0-10 118 0.20 [40] 2950 Aoyama and

Nagumo

(1996) 135 0.90 [180] 750

168 0.30 [60] 2800

443 17.30

[3460]

128

515 2.80 [560] 920

584 2.70 [540] 1081

593 7.80

[1560]

380

632 6.90

[1380]

458

650 33.70

[6740]

96

657 10.90

[2180]

301

658 4.20 [840] 783

679 1.70 [340] 1997

684 4.70 [940] 728

701 0.80 [160] 4381

712 1.50 [300] 2373

827 4.10 [820] 1009

830 2.40 [480] 1729

914 0.70 [140] 6529

1035 1.40 [280] 3696

1108 1.70 [340] 3259

632 5 5.34 5

[1067]

592

Japan Persimmon

Tree

20 0-25 170 5 ― ― Sonoda et al.

(2019)

Vineyard 19 0-25 201 5 ― ―

10-15 177 5 ― ―

Kenia Coffee tree 24 0-20 136 ― ― Lepp et al.

(1984)

136

New

Zealand

Apricot tree 45 n.i. 125 5 ― ― Morgan and

Bowden

(1993)

New

Zealand

Apricot tree 13 10 0-15 100 5 ― ― Morgan and

Johnston

(1991) 217 3 ― ―

New

Zealand

Vineyard 25 0-10 106 3 ― ― Morgan and

Taylor (2004) 156 3 ― ―

167 3 ― ―

304 3 ― ―

Portugal Vineyard 20 0-20 102 ― ― Magalhães

et al. (1985) 121 ― ―

130 ― ―

20-50 107 ― ―

Portugal Vineyard > 50 0-15 211 3 ― ― Patinha et al.

(2018)

Slovenia Vineyard 40 0-20 163 ― ― Kos and

Leštan (2004)

Slovenia Vineyard n.i. 0-45 364 1.70 [170] 2141 Pociecha and

Lestan (2009)

Slovenia Vineyard 4-35 0-20 120 0.05 [10] 12000 Rusjan et al.

(2007) 20-40 110 0.06 [12] 9167

40-60 110 0.06 [12] 9167

Slovenia Fruit

orchards

n.i. 0-20 105 3 ― ― Simončič et

al. (2017) 138 3 ― ―

149 3 ― ―

150 3 ― ―

165 3 ― ―

Vineyard 146 3 ― ―

158 3 ― ―

103 5 ― ―

246 3 ― ―

305 ― ―

317 3 ― ―

359 3 ― ―

129 5 ― ―

301 ― ―

333 ― ―

379 ― ―

379 ― ―

379 ― ―

508 3 ― ―

137

Spain Vineyard > 100 0-20

101 2.00 [400] 253 Arias et al.

(2004) 112 4.70 [940] 119

120 4.20 [840] 143

147 2.50 [500] 294

166 6.40

[1280]

130

173 9.00

[1800]

96

211 3.80 [760] 278

254 6.90

[1380]

184

255 1.90 [380] 671

271 4.70 [940] 288

286 10.20

[2040]

140

301 7.30

[1460]

206

Spain Vineyard n.i. 0-20 107 6.50

[1083]

99 Arias et al.

(2006)

149 3.50 [583] 256

274 8.00

[1333]

206

315 8.00

[1333]

236

560 9.00

[1500]

373

Spain Vineyard n.i. 0-20 120 ― ― Díaz-Raviña

et al. (2007) 155 ― ―

180 ― ―

185 ― ―

195 ― ―

400 ― ―

450 ― ―

500 ― ―

500 ― ―

550 ― ―

Spain Vineyard n.i. 0-20 130 3 ― ― Fernández-

Calviño et al.

(2008a)

Spain Vineyard < 10 0-20 125 3 ― ― Fernández-

Calviño et al.

(2008b; 2012) 30-50 100 5 1.20 5

[240]

417

150 3 ― ―

138

30-50 8 132 5 1.40 5

[280]

417

272 3 ― ―

Spain Vineyard > 50 0-5 133 1.90 [380] 350 Fernández-

Calviño et al.

(2008c) 146 2.50 [500] 292

166 2.90 [580] 286

170 2.40 [480] 354

200 5.40

[1080]

185

205 3.60 [720] 285

231 6.10

[1220]

189

233 3.40 [680] 343

241 4.40 [880] 274

251 6.00

[1200]

209

280 4.60 [920] 304

359 14.30

[2860]

126

583 23.30

[4660]

125

205 5 5.00 5

[1000]

205

Spain Vineyard > 30 0-20 139 5 3.4 [567] 245 Fernández-

Calviño et al.

(2008d) 365 3 ― ―

246 5 7.9 [1317] 187

157 68 ― ―

434 38 ― ―

Spain Vineyard > 100 0-5 368 5 5.20 5

[1040]

354 Fernández-

Calviño et al.

(2013) 235 6 ― ―

1438 3 ― ―

Spain Vineyard n.i. 0-20 102 1.20 [240] 425 Gómez-

Armesto et al.

(2015) 103 1.40 [280] 368

110 1.20 [240] 458

141 1.00 [200] 705

145 1.10 [220] 659

168 0.80 [160] 1050

178 1.70 [340] 524

180 2.80 [560] 321

212 1.70 [340] 624

249 3.50 [700] 356

139

271 4.60 [920] 294

280 5.00

[1000]

280

280 2.20 [440] 636

293 6.10

[1220]

240

313 4.90 [980] 319

321 6.40

[1280]

251

324 4.00 [800] 405

375 2.60 [520] 721

382 6.40

[1280]

298

384 6.70

[1340]

286

396 8.90

[1780]

223

490 9.50

[1900]

258

730 21.80

[4360]

168

224 5 3.92 5

[784]

286

Spain Vineyard n.i. 0-10 603 3 ― ― Nóvoa-Muñoz

et al. (2007) 10-30 621 3 ― ―

> 30 251 5 20.00 5 ―

255 5 29.00 5 ―

271 5 36.00 5 ―

632 3 ― ―

Spain Vineyard > 100 0-5 583 23.00

[4600]

127 Pateiro-

Moure et al.

(2007)

Switzerla

nd

Vineyard n.i. n.i. 142 0.58 ― Celardin et al.

(2004) 174 < 0.45 ―

436 0.48 ―

489 1.20 ―

Thailand Vineyard n.i. 0-10 115 ― ― Joannon et al.

(2001) 158 ― ―

238 ― ―

Tanzania Coffee tree n.i. 0-5 397 5 0.50 5 [50] 7940 Loland and

Singh (2004) 377 5 1.92 5

[192]

1964

333 5 0.36 5 [36] 9250

140

103 6 ― ―

234 6 ― ―

632 3 ― ―

683 3 ― ―

1403 3 ― ―

5-15 202 5 0.26 5 [26] 7769

233 5 1.56 5

[156]

1494

261 5 0.51 5 [51] 5118

140 7 ― ―

301 3 ― ―

452 3 ― ―

617 3 ― ―

15-30 107 5 0.80 5 [80] 1338

113 5 0.10 5 [10] 11300

143 3

236 3 ― ―

246 3 ― ―

Tanzania Coffee tree ≈ 50 0-20 268 5 ― ― Senkondo et

al. (2014) 282 5 ― ―

283 5 ― ―

415 5 ― ―

149 6 ― ―

158 6 ― ―

410 3 ― ―

502 3 ― ―

775 3 ― ―

806 3 ― ―

United

States

Orange

tree

38 10 0-15 102 4 1.40 4

[140]

729 Fan et al.

(2011)

45 10 123 4 0.20 4 [20] 6150

36 10 228 4 0.30 4

[30]

7600

1regarding the article’s publication date 2 in square brackets the value of Cue is shown in µg L-1 when the soil/extractant solution

could be determined 3 samples maximum value 4 replicates mean value 5 samples mean value 6 samples minimum value 7 approximate value estimated from figure 8 former or abandoned vineyard 9 localized contamination with occasional spills of fungicides

141

10 copper use history 11 vineyard in former orchard

n.i. = not informed.

Source: modified from Komárek et al. (2010).

142

Table S2. Exchangeable Cu (Cue) extraction method used by studies presented in

Table S1.

Reference Cue extraction

method

Aoyama and Kuroyanagi (1996) CaCl2

Aoyama and Nagumo (1996) CaCl2

Arias et al. (2004) NH4CH3CO2

Arias et al. (2006) NH4Cl

Brun et al. (1998) CaCl2

Brun et al. (1998; 2001) CaCl2

Brun et al. (2003) NH4CH3CO2

Casali et al. (2008) MgCl2

Chaignon et al. (2003) CaCl2

da Rosa Couto et al. (2015) H2O

Dell’Amico et al. (2008) KNO3

Deluisa et al. (1996) CaCl2

Fernández-Calviño et al. (2008a) NH4CH3CO2

Fernández-Calviño et al. (2008b;

2012)

NH4CH3CO2

Fernández-Calviño et al. (2008c) NH4CH3CO2

Fernández-Calviño et al. (2008d) NH4CH3CO2

Fernández-Calviño et al. (2013) NH4CH3CO2

Gómez-Armesto et al. (2015) NH4CH3CO2

Jacobson et al. (2005) CaCl2

Kelepertzis et al. (2018) NH4CH3CO2

Merrington et al. (2002) CaCl2

Mirlean et al. (2007) CaCl2

Mirlean et al. (2009) CaCl2

Parat et al. (2002) Mg(NO3)2

Pietrzak and McPhail (2004) H2O

Pociecha and Lestan (2009) H2O

Rusjan et al. (2007) CaCl2

Viti et al. (2008) MgCl2

143

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149

Chapter 3 Supplementary Materials

Supplementary Figures

Figure S1. Principal component analysis labeled by soil sampling site (interrow and

row). EC = electrical conductivity; OM = organic matter; N = available nitrogen; P =

available phosphorous; K = available potassium; CuT_S = total soil copper; CuS =

soil soluble copper; CuT_L = litter total copper; CuT_L/S = litter Cu concentration to

soil Cu concentration ratio; CFU = colony-forming units; MAL = malic acid; ARA = L-

arabinose; CIT =citric acid; AKG = α-ketoglutaric acid; OXA = oxalic acid; WAT =

150

microbial basal respiration; CYS =cysteine; ARG = L-arginine; GLU = glucose; NAG

= N-acetyl glucosamine; FRU = fructose; AWCD = average well color development.

151

152

Figure S2. Principal component analysis labeled by soil sampling microsite (between row and row) in A) kiwi, B) table

grape, C) plum, and D) cherry orchards. EC = electrical conductivity; OM = organic matter; N = available nitrogen; P =

available phosphorous; K = available potassium; CuT = total soil copper; CuS = soil soluble copper; L_Cu = litter total

copper; L_Cu.S_Cu = litter Cu concentration to soil Cu concentration ratio; CFU = colony-forming units; MAL = malic

acid; ARA = L-arabinose; CIT =citric acid; AKG = α-ketoglutaric acid; OXA = oxalic acid; WAT = microbial basal

respiration; CYS =cysteine; ARG = L-arginine; GLU = gluose; NAG = N-acetyl glucosamine; FRU = fructose; AWCD =

average well color development.

153

Supplementary Tables

Table S1. Cultivation characteristics of selected orchards in the O´Higgins Region, central Chile.

Fruit species Plum Kiwi Table Grape Cherry

Orchard P1 P2 P3 K1 K2 K3 TG1 TG2 TG3 C1 C2 C3

Cultivar D´Agen D´Agen D´Agen Hayward Hayward Hayward Crimson Crimson Crimson Royal

Down

Bing Santina

Area (ha) 26 17 10 25 19 6 7 6 12 6 4 5

Between plant

spacing (m)

6 5 5 3 4 3 3 3 3 2.5 2.5 3

Between row

spacing (m)

6 6 6 5 6 5 3.5 3.5 3.5 4.5 4.5 4.8

Density (plants

m-1)

278 333 333 666 416 666 952 952 952 889 889 694

Tree training

system

Open

vase

Open

vase

Open

vase

Pergola

trellis

Pergola

trellis

Pergola

trellis

Pergola

trellis

Pergola

trellis

Pergola

trellis

Open Vase Open

Vase

Open

Vase

Ridge height (m) 0.4 0.4 0.4 0.2 0.2 0.2 0 0 0 0 0 0

Ridge width (m) 1 1 1 1 1 1 0 0 0 0 0 0

Microtopography Plane Plane Plane Plane Plane Plane Plane Plane Plane Plane Plane Plane

Planting year 2002 2010 2005 1990 1986 1988 2007 2014 2014 2009 2009 2009

Previous use NI NI NI Peach Wine

vine

Table

grape

Citric Citric Citric Table

grape/Plum

Table

grape

Table

grape

Litter residence

time (month)

6 6 6 6 6 6 6 6 6 6 6 6

154

Table S2. Copper-based pesticide management during 2019 of selected orchards in the O´Higgins Region, central Chile.

Fruit species

Plum Kiwi Cherry

Orchard P1 P2 P3 K1 K2 K3 C1 C2 C3

Cupric pesticide

Nordox Super 75

WG (Cu2O)

Nordox Super 75

WG (Cu2O)

Nordox Super 75

WG (Cu2O)

Nordox Super 75

WG (Cu2O)

Nordox Super 75

WG (Cu2O)

Nordox Super 75

WG (Cu2O)

Nordox Super 75

WG (Cu2O)

Nordox Super 75

WG (Cu2O)

Nordox Super 75

WG (Cu2O) Formulation Cu2O

(86%)a + Coformulant

(12%)

Cu2O (86%)a +

Coformulant (12%)

Cu2O (86%)a +

Coformulant (12%)

Cu2O (86%)a +

Coformulant (12%)

Cu2O (86%)a +

Coformulant (12%)

Cu2O (86%)a +

Coformulant (12%)

Cu2O (86%)a +

Coformulant (12%)

Cu2O (86%)a +

Coformulant (12%)

Cu2O (86%)a +

Coformulant (12%)

1st

application April 22 April 22 April 22 April 4 April 5 April 5 April 26 April 29 April 26

2sd

application May 3 May 3 May 3 May 14 May 13 May 22 May 16 May 16 May 16

3rd

application May 13 May 13 May 13 June 3 June 6 June 4 June 17 June 17 June 17

4tht

application August 8 August 8 August 8 July 5 July 4 July 12 July 23 July 24 July 24

Dose (g 100 L-1)b

200 200 200 130 130 130 200 200 200

Application form

Turbo sprayer

Turbo sprayer

Turbo sprayer

Turbo sprayer

Turbo sprayer

Turbo sprayer

Nebulizer Nebulizer Nebulizer

a Equivalent to 75% Cu.

b Referred to a wetting of 1,500 L of water ha-1 for plum and cherry orchards and of 1,000 L of water ha-1 for kiwi orchards.

155

Table S3. Soil physicochemical characteristics and Cu leaf litter concentration of

selected kiwi orchards in the O´Higgins Region, central Chile.

Soil property Site Mean ± SEM Range

pH Between row 7.54 ± 0.07 7.32–7.93 Row 7.54 ± 0.06 7.26–7.87

Total 7.54 ± 0.05 7.26–7.93 Electrical conductivity (dS m-1) Between row 1.05 ± 0.11 0.66–1.72

Row 1.09 ± 0.07 0.84–1.49 Total 1.07 ± 0.06 0.66–1.72 Organic matter (%) Between row 3.18 ± 0.36 1.89–5.54

Row 2.52 ± 0.16 1.67–3.36 Total 2.85 ± 0.21 1.67–5.54 Available N (mg kg-1) Between row 14.44 ± 1.23 9.80–21.40

Row 13.67 ± 1.34 8.12–18.90 Total 14.01 ± 0.90 8.12–21.40 Available P (mg kg-1) Between row 34.45 ± 6.23 7.33–64.80

Row 16.09 ± 6.95 2.41–69.40 Total 25.27 ± 5.05 2.41–69.40 Available K (mg kg-1)* Between row 354.00 ± 43.30 154.00–623.00

Row 158.00 ± 11.70 135.00–244.00 Total 256.00 ± 32.2 154.00–623.00 Total Cu – soil (mg kg-1) Between row 176.40 ± 14.80 134.00–67.00

Row 215.10 ± 16.00 135.00–283.00 Total 195.80 ± 11.60 134.00–283.00 Soluble Cu - soil (mg kg-1) Between row 0.14 ± 0.03 0.08–0.31

Row 0.15 ± 0.03 0.06–0.32 Total 0.14 ± 0.02 0.06–0.32 Sand (%) Between row 26.67 ± 1.69 20.00–33.00

Row 26.67 ± 2.20 15.00–36.00 Total 26.67 ± 1.35 15.00–36.00 Clay (%) Between row 18.56 ± 0.53 16.00–21.00

Row 18.89 ± 0.82 15.00–21.00 Total 18.72 ± 0477 16.00–21.00 Silt (%) Between row 54.78 ± 1.70 48.00–62.00

Row 54.44 ± 1.94 45.00–64.00 Total 54.61 ± 1.25 48.00–64.00 Total Cu – litter (mg kg-1)* Between row 492.80 ± 76.90 293.00–1035.00

Row 268.30 ± 28.30 154.00–416.00 Total 380.6 ± 48.20 154.00–1035.00 Litter Cu:Total soil Cu* Between row 2.98 ± 0.51 1.23–5.69

Row 1.28 ± 0.15 0.77–2.35 Total 2.13 ± 0.33 0.77–5.69

*significant differences at p ≤ 0.05 among sample site

156

Table S4. Soil physicochemical characteristics and Cu leaf litter concentration of

selected table grape orchards in the O´Higgins Region, central Chile.

Soil property Site Mean ± SEM Range

pH * Between row 7.05 ± 0.06 6.73–7.30 Row 7.34 ± 0.08 7.06–7.77 Total 7.19 ± 0.06 6.73–7.77 Electrical conductivity (dS m-1) Between row 1.12 ± 0.25 0.53–2.91

Row 0.63 ± 0.04 0.46–0.8 Total 0.88 ± 0.14 0.46–2.91 Organic matter (%) Between row 2.50 ± 0.10 2.04–2.82

Row 1.97 ± 0.15 1.52–2.88 Total 2.23 ± 0.11 1.52–2.88 Available N (mg kg-1) Between row 13.76 ± 3.18 6.37–38.00

Row 14.43 ± 0.48 11.60–15.80 Total 14.10 ± 1.56 6.37–38.00 Available P (mg kg-1)* Between row 25.50 ± 2.58 16.30–39.30 Row 12.79 ± 2.14 4.33–24.20 Total 19.14 ±2.24 4.33–39.30 Available K (mg kg-1)* Between row 375.40 ± 15.70 301.00–451.00

Row 211.4 ± 22.8 128.00–335.00 Total 293.44 ± 23.99 128.00–451.00 Total Cu – soil (mg kg-1) Between row 219.70 ± 11.70 179.00–290.00 Row 236.20 ± 10.91 196.00–290.00 Total 227.94 ± 8.01 179.00–290.00 Soluble Cu – soil (mg kg-1) Between row 0.15 ± 0.02 0.09–0.24

Row 0.12 ± 0.01 0.07–0.18 Total 0.14 ± 0.01 0.07–0.24 Sand (%) Between row 24.67 ± 2.17 16.00–37.00

Row 20.33 ± 1.49 15.00–27.00 Total 22.50 ± 1.38 15.00–37.00 Clay (%) Between row 17.22 ± 0.66 14.00–20.00

Row 18.11 ± 0.59 17.00–22.00 Total 17.67 ± 0.44 14.00–22.00 Silt (%) Between row 58.11 ± 2.14 48.00–66.00 Row 61.44 ± 1.42 56.00–68.00 Total 59.78 ± 1.31 48.00–68.00 Total Cu – leaf litter (mg kg-1) Between row 42.28 ± 4.30 29.00–61.20 Row 102.50 ± 43.00 16.30–409.00 Total 72.39 ± 22.19 16.30–409.00 Litter Cu:Total soil Cu Between row 0.20 ± 0.03 0.10–0.33 Row 0.41 ± 0.15 0.07–1.41 Total 0.31 ± 0.08 0.07–1.41

*significant differences at p ≤ 0.05 among sample site

157

Table S5. Soil physicochemical characteristics and Cu litter concentration of

selected plum orchards in the O´Higgins Region, central Chile.

Soil property Site Mean ± SEM Range

pH Between row 7.23 ± 0.08 6.90–7.57 Row 7.34 ± 0.05 7.06–7.50 Total 7.29 ± 0.05 6.90–7.57

Electrical conductivity (dS m-1) Between row 1.76 ± 0.21 0.93–3.11 Row 1.58 ± 0.16 1.01–2.32

Total 1.67 ± 0.13 0.93–3.11

Organic matter (%) Between row 2.61 ± 0.09 2.19–3.13 Row 2.44 ± 0.10 1.85–2.85

Total 2.23 ± 0.11 1.85–3.13

Available N (mg kg-1) Between row 21.57 ± 1.83 12.70–31.20 Row 26.80 ± 3.71 13.60–43.00

Total 24.18 ± 2.11 12.70–43.00

Available P (mg kg-1) Between row 23.65 ± 5.21 6.50–50.30 Row 28.58 ± 6.18 2.33–60.50 Total 26.11 ± 3.97 2.33–60.50

Available K (mg kg-1) Between row 285.40 ± 37.30 129.00–430.00 Row 345.70 ± 45.00 140.00–499.00

Total 315.56 ± 29.28 129.00–499.00

Total Cu – soil (mg kg-1) Between row 219.40 ± 24.40 131.00–356.00 Row 267.40 ± 26.90 148.00–432.00 Total 243.44 ± 18.57 131.00–432.00

Soluble Cu – soil (mg kg-1) Between row 0.13 ± 0.02 0.07–0.20 Row 0.15 ± 0.03 0.07–0.38

Total 0.14 ± 0.02 0.07–0.38

Sand (%) Between row 28.33 ± 2.03 19.00–37.00 Row 25.44 ± 1.99 17.00–34.00

Total 26.89 ± 1.42 17.00–37.00

Clay (%) Between row 20.44 ± 0.84 15.00–23.00 Row 20.56 ± 0.93 16.00–25.00

Total 20.50 ± 0.61 15.00–25.00

Silt (%) Between row 51.33 ± 1.86 45.00–61.00 Row 54.00 ± 1.34 48.00–60.00 Total 52.67 ± 1.16 45.00–61.00

Total Cu – leaf litter (mg kg-1) Between row 680.60 ± 35.50 505.00–866.00 Row 694.00 ± 76.80 140.00–908.00 Total 687.28 ± 41.08 140.00–908.00

Litter Cu:Total soil Cu Between row 3.51 ± 0.48 1.58–5.66 Row 2.80 ± 0.45 0.57–5.45 Total 3.16 ± 0.33 0.57–5.66

158

Table S6. Soil physicochemical characteristics and Cu litter concentration of

selected cherry orchards in the O´Higgins Region, central Chile.

Soil property Site Mean ± SEM Range

pH Between row 6.73 ± 0.06 6.40–6.91 Row 6.56 ± 0.05 6.33–6.82 Total 6.65 ± 0.04 6.33–6.91 Electrical conductivity (dS m-1) Between row 0.74 ± 0.10 0.48–1.40

Row 1.13 ± 0.17 0.63–2.37 Total 0.94 ± 0.11 0.48–2.37 Organic matter (%) Between row 2.39 ± 0.16 1.66–3.29

Row 2.65 ± 0.07 2.36–2.95 Total 2.52 ± 0.09 1.66–3.29 Available N (mg kg-1) Between row 24.24 ± 1.83 18.60–34.20

Row 19.76 ± 2.64 12.20–38.90 Total 22.00 ± 1.65 12.20–38.90 Available P (mg kg-1) Between row 49.66 ± 6.40 25.50–79.90 Row 58.88 ± 5.39 39.30–88.10 Total 54.27 ± 4.21 25.50–88.10 Available K (mg kg-1) a Between row 615.40 ± 45.20 345.00–801.00

Row 484.10 ± 28.40 301.00–577.00 Total 549.8 ± 30.4 301.00–801.00 Total Cu – soil (mg kg-1) Between row 232.60 ± 18.20 144.00–334.00 Row 233.90 ± 13.60 157.00–296.00 Total 233.22 ± 11.01 144.00–334.00 Soluble Cu – soil (mg kg-1)* Between row 0.25 ± 0.03 0.11–0.42

Row 0.15 ± 0.01 0.09–0.20 Total 0.20 ± 0.02 0.09–0.42 Sand (%)a Between row 24.00 ± 1.35 18.00–31.00

Row 35.22 ± 2.05 25.00–44.00 Total 29.61 ± 1.81 18.00–44.00 Clay (%) Between row 19.33 ± 0.78 15.00–23.00

Row 19.56 ± 0.41 18.00–21.00 Total 19.11 ± 0.44 15.00–23.00 Silt (%)a Between row 56.78 ± 1.14 53.00–62.00 Row 45.11 ± 1.95 38.00–54.00 Total 50.94 ± 1.79 38.00–62.00 Total Cu – leaf litter (mg kg-1)* Between row 1305.40 ± 69.50 961.00–1613.00 Row 1861.00 ± 124.00 1325.00–2290.00 Total 1583.44 ± 96.35 961.00–2290.00 Litter Cu:Total soil Cu* Between row 5.85 ± 0.49 3.97–8.26 Row 8.37 ± 0.95 4.48–13.4 Total 7.11 ± 0.60 3.97–13.4

*significant differences at p ≤ 0.05 among sample site

159

Table S6. Results of principal component analysis for kiwi, table grape, plum and

cherry orchards.

Kiwi Table grape Plum Cherry

Principal components

PC1 PC2 PC3 PC1 PC2 PC3 PC1 PC2 PC3 PC1 PC2 PC3

Eigenvalues 8.64 4.30 3.28 6.40 4.30 3.37 7.74 4.44 3.26 11.13 3.07 2.62

Variance (%) 34.54 17.22 13.10 25.60 17.18 13.47 30.96 17.78 13.04 44.54 12.28 10.48

Cummulative variance (%)

34.54 51.76 64.86 25.60 42.78 56.26 30.96 48.74 61.78 44.54 56.81 78.08

Eigenvectors

pH 0.47 0.36 -0.48 0.35 -0.75 0.19 -0.34 -0.15 0.04 0.61 -0.20 0.36

CE 0.19 -0.31 -0.32 -0.23 0.31 -0.15 0.36 -0.58 0.13 -0.49 0.50 0.33

OM 0.39 -0.30 0.74 -0.34 0.57 0.09 0.19 0.43 -0.35 -0.14 -0.01 0.69

N 0.30 -0.40 -0.37 0.06 0.22 -0.47 0.48 -0.54 0.01 0.09 0.64 -0.06

P 0.24 -0.15 0.64 -0.24 0.64 0.19 0.36 -0.64 0.30 -0.23 -0.20 0.69

K1 0.55 -0.13 0.64 -0.52 0.56 0.21 0.61 -0.60 0.21 0.35 0.41 0.69

CuT 0.22 -0.68 -0.32 0.43 -0.37 0.28 0.60 0.35 -0.57 0.16 -0.73 0.20

CuS 0.46 -0.52 -0.11 -0.46 0.23 -0.49 0.78 -0.04 -0.29 0.56 0.19 0.57

Sand 0.47 -0.29 -0.52 -0.42 0.17 0.75 0.49 -0.67 0.24 -0.79 -0.01 0.32

Clay 0.15 0.25 0.19 0.17 -0.34 -0.43 -0.20 0.69 -0.21 -0.16 0.65 -0.39

Silt -0.58 0.23 0.50 0.38 -0.05 -0.63 -0.51 0.46 -0.19 0.84 -0.15 -0.22

CFU 0.09 0.22 0.12 0.04 -0.17 0.40 -0.43 -0.03 0.32 -0.47 -0.23 -0.06

MAL 0.71 0.64 -0.15 0.35 -0.02 0.56 0.67 0.35 0.42 0.69 -0.52 0.05

ARA 0.69 0.67 -0.12 0.77 0.47 0.22 0.74 0.33 0.45 0.94 -0.08 0.06

CIT 0.79 0.53 -0.21 0.22 -0.38 0.60 0.28 0.54 0.41 0.63 -0.17 -0.22

AKG 0.84 0.38 -0.14 0.43 0.09 -0.29 0.24 0.24 -0.45 0.68 0.18 -0.15

OXA 0.87 0.13 -0.14 0.64 -0.14 0.12 0.66 0.50 0.24 0.90 0.24 -0.08

WAT 0.83 0.41 -0.02 0.29 -0.78 0.27 0.38 0.58 0.60 0.79 0.10 0.41

CYS 0.51 -0.66 0.24 0.77 0.59 -0.06 0.70 0.09 0.43 0.90 0.12 -0.03

ARG 0.87 -0.35 0.13 0.82 0.35 -0.15 0.59 0.44 0.41 0.90 0.27 0.05

GLU 0.84 -0.28 0.21 0.80 0.49 0.14 0.89 -0.10 -0.33 0.93 0.10 -0.05

NAG 0.89 -0.19 0.22 0.91 0.17 0.14 0.83 -0.07 -0.32 0.93 0.22 0.06

FRU 0.82 -0.29 0.27 0.86 0.33 0.01 0.67 -0.23 -0.60 0.81 0.31 -0.06

L_Cu 0.04 0.43 0.46 0.25 -0.42 -0.44 -0.35 0.11 0.12 -0.67 0.31 0.12

L_Cu:S_Cu -0.01 0.64 0.42 0.23 -0.38 -0.47 -0.66 -0.35 0.47 -0.54 0.59 0.03

160

Table S7. Results of principal component analysis for all orchards data

Principal components 7.61 4.83 2.98 Eigenvalues 29.25 18.57 11.47 Variance (%) 29.25 47.82 59.30 Cummulative variance (%) 7.61 4.83 2.98 Eigenvectors pH -0.05 -0.79 -0.09

CE 0.16 0.14 0.08

OM 0.27 0.14 0.08

N 0.37 0.44 -0.09

P 0.33 0.62 0.30

K1 0.48 0.67 0.08

CuT_S 0.26 0.15 -0.45

CuS 0.47 0.33 -0.30

Sand 0.23 0.42 0.50

Clay 0.21 0.03 0.10

Silt -0.31 -0.43 -0.54

CFU -0.07 -0.33 0.34

MAL 0.70 -0.51 0.32

ARA 0.68 -0.49 0.39

CIT 0.73 -0.41 0.36

AKG 0.67 -0.55 0.15

OXA 0.75 -0.42 0.09

WAT 0.76 -0.21 0.15

CYS 0.55 0.32 -0.45

ARG 0.74 0.21 -0.29

GLU 0.78 0.12 -0.50

NAG 0.81 0.08 -0.45

FRU 0.65 0.13 -0.54

AWCD 0.87 -0.45 0.12

CuT_L 0.32 0.72 0.40

CuT_L/S 0.26 0.65 0.51

161

Chapter 4 Supplementary Material

Supplementary Figures

Figure S1. Mass reduction as a function of the leaf litter Cu concentration in table

grape (black) and kiwi (grey) after 275 days of incubation at 28 °C. The solid lines

show the obtained regression (Y =aX+b), whereas the dashed lines demonstrate the

95% confidence intervals

162

Supplementary Tables

Table S1. Leaf litter nitrogen (N), lignin (Lig), cellulose (Cel), hemicellulose (HC)

and lignin/N ratio content (mean ± standard error) of the fruit trees according to

literature

Study Fruit tree specie

N (%) Lig (%) Cel (%) HC (%) lignin/N n

Musvoto et al. (2000)

Mangifera indica L (Mango)

0.71 ± 0.02

10.9 ± 0.17

19.0 ± 0.12

9.94 ± 0.25

15.4 3

Green et al. (2006)

Prunus cerasus L. cv. Stevnsbær (Sour Cherry)1

2.3 ± 0.05

3.0 ± 0.50 10.0 ± 0.5 86.0 ± 1.50

1.3 4

Tagliavini et al. (2007)

Malus domestica Borkh (Apple)

1.33 ± 0.12

13 ± 0.69 33.3 ± 0.52

― 9.8 3

Sariyildiz (2008)

Castanea sativa Mill. (Chestnut)2

1.45 ± 0.24

21.1 ± 0.32

31.7 ± 0.41

― 14.6 10

Neto et al. (2009)

Pyrus communis L. cv. Rocha (Pear)3

1.86 ±0.05

61.5 ± 0.98

― ― 33.1 3

1.71 ± 0.01

41.2 ± 0.46

― ― 24.1 3

1.56 ± 0.08

63.1 ± 1.62

― ― 40.4 3

1.39 ± 0.08

54.1 ± 1.85

― ― 38.9 3

Ventura et al. (2010)

Prunus persica L. (Peach)

1.16 ± 0.03

7.5 ± 1.00 46.4 ± 1.5 ― 6.5 4

Murovhi et al. (2012)

Persea4 americana L. (Avocado)

1.18 34.3 18.3 ― 29.1 5

1.12 35.1 15.7 ― 31.3 5 Mangifera4 indica L. (Mango)

0.94 24.4 16.4 ― 26.0 5

0.94 24.7 22.9 ― 26.3 5 Litchi chinensis L. (Litichi)

1.08 39 21.3 ― 36.1 5 1.09 40.1 18.7 ― 36.8 5

Murovhi and Materechera (2015)

Persea americana L. (Avocado)

1.83 ± 0.08

29.9 ± 5.08

― ― 16.3 3

163

Mangifera indica L. (Mango)

1.18 ± 0.01

14.5 ± 1.33

― ― 12.3 3

Litchi chinensis L. (Litichi)

1.29 ± 0.05

32.5 ± 4.72

― ― 25.2 3

Naik et al. (2018)

Mangifera indica L. (Mango)

1.3 ± 0.03

17.2 ±0.61

45.0 ± 0.73

― 13.2 7

Psidum guajava L. (Guava)

1.3 ± 0.04

21.2 ± 0.96

40.0 ± 1.17

― 16.3 7

Litchi chinensis L. (Litichi)

1.4 ± 0.03

33.0 ± 1.15

36.0 ± 1.00

― 23.6 7

1Leaves severely infected with Cherry leaf spot (B. jaapi); 2Not under fruit

production; 3Sampled from orchards of different ages and subjected to different

fertilization practices; 4Sampled one year apart

164

Table S2. Previously reported kinetic adsorption model fit and Cu2+ adsorption capacities in plant leaves studies

Study Plant leaves Treatment Cu2+ source pH Cu2+ range concentrations (mg

L-1)

Isotherm model fit Cu2+ adsorption capacity (mg kg-

1)a

Bagwan & Patil (2014)

Nigrudi AC CuSO4·5H2O 7.0 50-100 Langmuir (R2 = 0.999)

29410

Mudra 7.0 Langmuir (R2 = 0.999)

35710

Gliricida 8.0 Langmuir (R2 = 0.934)

61660

Çay et al. (2004)

Tea Hot water wash

CuSO4·5H2O 5.5 5-100 Freundlich (R2 = 0.992)

8640b

Çekim et al. (2015)

Tobacco H2SO4 CuSO4·5H2O 4.0 ― Langmuir (R2 = 0.995)

17180

Ghosh et al. (2015)

Tea Ca(OH)2 CuSO4·5H2O 5.5 ― Langmuir (R2 = 0.999)

7813

Hanafiah et al. (2009)

I. cylindrica NaOH Cu(NO3)2 5.0 50-100 D–R (R2 = 0.992) 21740

Kamar et al. (2017)

Cabbage nt CuSO4·6H2O 6.0 0-100 Langmuir (R2 = 0.952)

5746

Makeswari & Santhi (2014)

Castor bean ZnCl2 AC CuSO4·5H2O 6.3 50-200 Langmuir (R2 = 0.995)

333300

nt 5.4 Langmuir (R2 = 0.993)

250000

Nagpal et al. (2011)

Paper mulberry nt ― 6.0 10-500 Langmuir (R2 = 0.999)

26400

Prasanna Kumar et al. (2006)

Teak nt CuSO4·5H2O 5.0 0-100 Langmuir (R2 = 0.985)

15430

Rafatullah et al. (2011)

Oil palm SDBS Cu(NO3)2·2.5H2O 6.0 1-200 Freundlich (R2 = 0.989)

86950c

Reddy et al. (2012)

Moringa C6H8O7 Cu standard 5.0 10-1000 Langmuir (R2 = 0.990)

167900

Sangi et al. (2008)

Smoothleaf elm nt CuNO3)2 5.0 5–5000 Langmuir (R2 = 0.996)

76300

165

European ash AC CuSO4·5H2O 7.0 50-100 Langmuir (R2 = 0.996)

33440

Sawalha et al. (2007)

Saltbush nt CuSO4·5H2O 5.0 0-98 Freundlich (R2 = 0.980)

―

Thilagavathy & Santhi (2014)

Gum arabic tree nt CuSO4·5H2O 5.0 50-200 Freundlich (R2 = 0.996)

38314d

Wan Ngah & Hanafiah (2008)

Rubber nt Cu2+ 5.0 may-50 Langmuir (R2 = 0.993)

8920

Wan Ngah & Hanafiah (2009)

Rubber CH2O Cu2+ 5.0 may-50 Langmuir (R2 = 0.997)

8360

Weng & Wu (2012)

Oil palm High pressure Cu(NO3)2·3H2O 5.0 0-100 Langmuir (R2 = 0.990)

9280

Present study

Table grape Acid wash Hidroxicobre® 50WG

5.0 0-266 Langmuir (R2 = 0.995)

15912

Kiwi

Langmuir (R2 = 0.998)

14163

a Calculated from the reported isotherm model; b Experimentally obtained value: c Value obtained by Langmuir model (R2 = 0.785); d Value obtained

by Langmuir model (R2 = 0.975). AC = leaves calcinated to activated carbon; nt = non treated leaves; SDBS = Sodium dodecylbenzenesulfonate;

D-R= Dubinin–Radushkevich; PSO = Pseuso second order; IPD = Intra-particle diffusion

166

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