Biochemical variability of olive-orchard soils under different

Biochemical variability of olive-orchard soils under different

management systems

Emilio Benitez *, Rogelio Nogales, Mercedes Campos, Francisca Ruano

Departamento de Agroecologıa y Proteccion Vegetal, Estacion Experimental del Zaidın,

CSIC, c/Profesor Albareda 1, 18008 Granada, Spain

Received 22 November 2004; received in revised form 15 June 2005; accepted 15 June 2005

Abstract

This work undertakes the biochemical characterization of olive-orchard soils cultivated under three different management

systems: conventional, integrated, and organic. The orchards are located in two districts of Andalusia (S Spain): Pedroches

Valley (Cordoba province) and Montes Orientales (Granada province). In each soil, the activities of various enzymes were

determined – oxide reductases (dehydrogenase, o-diphenol oxidase), hydrolytic activities linked to the C- and P-cycles (b-

glucosidase and phosphatase) and indolacetic acid production (auxins) – as were phenol concentrations, pH, and total organic

carbon of the soil.

The biochemical activity of the soils studied differed depending on the cultivation or weed–control system. The soils

developed under organic management in general presented greater biological activity and greater hydrolytic activity than those

under integrated or conventional cultivation. The data, processed by discriminant analysis, divided the soils into three well-

differentiated groups. Of all the soils considered a priori as soils under organic management, 89% were classified as belonging to

the same group, while the remaining 11% showed characteristics similar to those of the integrated management group. The

discriminant analysis proved especially effective to differentiate olive-orchard soils treated with herbicides from those without

such treatment; the fit between the soils considered as belonging to each of the weed–control systems and those predicted by the

discriminant model was 100%.

The biochemical response of the soil, therefore, differed according to the type of management, and this could be used as a

possible control system of crops under organic cultivation.

# 2005 Elsevier B.V. All rights reserved.

Keywords: Soil enzymes; Organic olive farming; Weed control systems

www.elsevier.com/locate/apsoil

Applied Soil Ecology 32 (2006) 221–231

* Corresponding author. Tel.: +34 958181600;

fax: +34 958129600.

E-mail address: [email protected] (E. Benitez).

0929-1393/$ – see front matter # 2005 Elsevier B.V. All rights reserved

doi:10.1016/j.apsoil.2005.06.002

1. Introduction

In recent years, the greater awareness of sustainable

development in the economic and social sectors has

become associated with a growing interest in organic

agriculture. Agricultural producers, consumers and

.

E. Benitez et al. / Applied Soil Ecology 32 (2006) 221–231222

governmental organizations, research centres, uni-

versities, etc., have been contributing notably to the

development and the study of this type of crop

management. Organic management is defined as a

cultivation system that seeks to produce crops of

maximum nutritional quality while respecting the

environment and conserving soil fertility, by means of

optimal utilization of local resources without the

application of synthetic chemical products.

The surface area of organic agriculture certified in

Andalusia (S Spain) has multiplied by 100 in the last

decade, from 2212 ha in 1992 to more than 225,500 ha

in 2002. In this latter year, the sector added 4024

growers, 214 transformation industries, and 185

farms. Some 75% of the production is exported,

primarily to the European Union, with extra-virgin

olive oil being the largest export.

The cultivation of the olive under the guidelines of

organic management in Andalusia has increased by

270% in the last 6 years (PAAE, 2003). The economic

and social importance of olive cultivation is well

known (2500 oil mills that produce more than 30% of

the world production of olive oil, generating

2,000,000,000 s in Spain), but the ecological impact

of this crop is enormous, as the olive-orchard

constitutes the landscape – or a fundamental part

thereof – throughout vast tracts of land in the

Mediterranean Basin. Therefore, olive-orchard man-

agement determines not only the profitability but also

living and working conditions as well as environ-

mental quality in these territories (Pajaron, 2000).

Given this enormous increase, both in the surface

area under cultivation as well as the number of growers,

it has become crucial to develop control systems to

verify whether a management system is truly organic.

Nowadays, these controls consist of testing whether the

soils and crops are free of residues from synthetic

chemicals. Such controls ensure that the resulting foods

are free of such contaminants, and additionally that the

management procedures are environmentally sound

(European Union Directives 2092/91).

These controls, however, do not examine the

sustainability of the agrosystem or specifically the

continued capability of the soil to produce crops that

are economically profitable for the grower and

beneficial to public health, without danger to future

generations. To meet this objective, controls on quality

and soil health are vital.

Soil quality signifies the capacity of the soil, within

the natural or anthropic limits of an agroecosystem, to

maintain crop productivity, water and air quality, and

consequently human as well as environmental health

(Papendick and Parr, 1992). The concept of soil health

includes the ecological qualities of the soil, which

have implications beyond its quality or capacity to

produce a particular crop.

In this sense, the biochemical state of the soil has

been proposed as an indicator of the real biological

state of this medium as well as recovery processes, in

natural ecosystems as well as agroecosystems (Bolton

et al., 1985; Dick and Tabatabai, 1993; Dick, 1994).

Enzymes catalyse all the biochemical reactions and

thereby play a key role in the nutrient cycles of the

soil. Therefore, enzymes have been suggested as

potential indicators of soil quality, due to their

relationship with the biological activity of the

medium, ease of measurement, and rapid response

to management changes (Dick et al., 1996, Benitez

et al., 2004). Thus, various indices, combining

chemical, physical, and biological characteristics,

have been proposed and used to determine the impact

of different soil management systems on long-term

crop productivity (Doran and Parkin, 1994). Research

has shown that the agricultural practises affect the

biochemistry of the soil, but there is still a broad array

of enzymes that have not been adequately investigated

as indicators of soil quality or as indices of long-term

effects of such practises (Dick, 1994).

The aim of the present work is to evaluate the effect

of the different types of crop management (conven-

tional, integrated, and organic) and weed control

(tillage, no-tillage, and herbicide application) on the

biochemical characteristics of the soil in order to

evaluate the quality of the management system.

2. Materials and methods

In the conventional and integrated olive-orchard,

synthetic chemical fertilizers, and insecticides are

applied, though with a much lower application rate of

insecticides in the integrated system, usually with a

maximum of one annual application as opposed to two

to four in the conventional orchard. In the organic

orchard, by regulation, only natural fertilizers can be

used together with infrequent applications of per-

E. Benitez et al. / Applied Soil Ecology 32 (2006) 221–231 223

mitted insecticides of natural origin (no such

insecticides were applied during the study period).

However, soil management, which can directly

influence soil quality, is not well characterized in the

regulations referring to different crop systems, so that

some organic olive-orchards use different mechanical

means to control weeds (from deep tillage at different

frequencies to weed choppers), although herbicides

are not allowed. In conventional and integrated

management, deep tillage, and herbicide application

are used to control weeds. On this basis, we classified

the samples from soils with tillage (ploughing more

than twice yearly), those without tillage (orchards in

which a weed chopper or harrow was used) and those

with herbicide application.

A total of 14 orchards were sampled, 6 of these in

the district of Montes Orientales (Granada province)

and 8 in the Valle de los Pedroches (Cordoba

province). The classification of each of the orchards,

with respect to the type of crop management

(conventional, integrated, and organic) and to the

type of weed control (tillage, no-tillage, and herbicide

application), is presented in Table 1.

Three separate plots (100 m � 50 m) were selected

from within each orchard. In each plot, 10 samples

were collected along straight lines laid out at random

10 m apart. A hole was made roughly 20 cm in

Table 1

Orchards sampled

Crop management Soil management Soil classification

(FAO, 1998)

Valle de los Pedroches (Cordoba province)

Conventional Herbicide Eutric Cambisol

Conventional Herbicide Eutric Regosol

Organic Tillage Chromic Luvisol

Organic Tillage Eutric Cambisol

Organic No-tillage Chromic Luvisol

Organic No-tillage Chromic Luvisol

Integrated Herbicide Chromic Luvisol

Integrated Herbicide Eutric Regosol

Montes Orientales (Granada province)

Conventional Herbicide Calcic Regosol

Conventional Tillage Calcic Cambisol

Integrated Tillage Calcic Cambisol

Integrated Tillage Calcic Regosol

Organic Tillage Calcic Regosol

Organic No-tillage Calcic Cambisol

Values are the means of three replicates. S.E., standard error.

diameter, and then the first 10 cm of the soil surface

was separated in order to avoid the effects of farmers’

activities, fallen leaves, etc. The soil was collected

from the underlying layer (to 30 cm). Samples were

bulked to give three replicates per plot. Field-moist

soil was stored at 4 8C until enzymes analyses were

determined. A subsample was air-dried, sieved, and

ground (<0.5 mm) prior chemical analyses were

carried out.

2.1. Chemical analysis

The pH was measured with a glass electrode using a

1:2.5 sample:water ratio and the total organic-carbon

content was determined using the Walkley-Black wet

dichromate oxidation method (M.A.P.A., 1986).

Extractable phenols were determined from samples

after extracting with a mixture of aqueous acetone:-

formic acid (1:1), adding Folin–Ciocalteu’s Reagent,

and then measuring absorbances at 725 nm (Julkunen

Tiito, 1985).

2.2. Enzyme assays

For the assessment of dehydrogenase activity, 1 g

of each sample were incubated during 20 h at 25 8Cwith 0.2 ml of 0.4% 2-p-iodophenyl-3 p-nitrophenyl-5

pH Total organic

carbon (g kg�1)

Phenols

(mg kg�1)

6.20 � 0.07 10.31 � 0.31 69.65 � 6.25

6.94 � 0.04 5.56 � 0.24 58.75 � 0.83

6.68 � 0.17 13.24 � 0.75 99.05 � 5.27

6.66 � 0.11 10.81 � 1.02 139.60 � 10.68

6.25 � 0.27 10.97 � 0.30 60.49 � 2.44

6.43 � 0.04 6.54 � 0.44 99.81 � 7.54

6.11 � 0.25 8.81 � 0.52 141.24 � 11.24

6.34 � 0.10 11.82 � 0.86 68.68 � 4.69

8.36 � 0.03 10.65 � 0.52 56.56 � 1.74

8.21 � 0.05 15.29 � 1.17 86.43 � 1.58

8.42 � 0.13 15.04 � 0.96 54.77 � 2.39

8.45 � 0.15 11.34 � 0.57 91.02 � 4.25

8.38 � 0.24 12.72 � 1.26 45.03 � 4.53

8.49 � 0.02 12.59 � 0.56 73.92 � 4.13


tetrazolium chloride (INT) as a substrate. Iodonitrote-

trazolium formazan (INTF) produced in the reduction

of INT was extracted with a mixture of acetone:

tetrachloroetene (1.5:1) and measured in a spectro-

photometer at 490 nm (Garcia et al., 1997). Assays

without soil and without INT were carried out

simultaneously as controls.

For the measurement of indole-3-acetic acid (IAA)

production in the soils, a colorimetric method was

used (Wohler, 1997). Two grams of fresh soil were

placed in a 50-ml centrifuge flask. Six mililiters of

phosphate buffer (pH 7.5) with glucose (1 g glucose

100 ml�1 phosphate buffer) and 4 ml of 1% L-

tryptophan were added. Assays without L-tryptophan

were made at the same time as controls. The soil

solutions were mixed and incubated at 37 8C for 24 h

in the dark. After this, L-tryptophan was added to

controls. Two milliliters of 5% trichloroacetic acid

solution were added to inactivate the enzymes

involved in the bioassay of auxin, and then 1 ml of

0.5 M CaCl2 solution was added. The soil solution was

centrifuged at 5000 � g. Three milliliters of the

supernatant were placed in a test tube, 2 ml of salper

solution (2 ml of 0.5 M FeCl3 and 98 ml of 35%

perchloric acid) were added, and the mixture was

incubated for 30 min at 25 8C in the dark. The

absorbance of the resulting red solution was measured

with a spectrophotometer at 535 nm. Assays without

soil were performed at the same time as controls.

For the determination of b-glucosidase and

phosphatase activity, 0.5 ml of 0.05 M 4-nitrophe-

nyl-b-D-glucanopyranoside (PNG) and 0.115 M 4-

nitrophenyl phosphate (PNPP) were used as the

substrate, respectively (Tabatabai, 1982). Soil portions

(1 g) were incubated at 37 8C for 2 h with 2 ml of

maleate buffer at pH 6.5. The samples were then kept

at 2 8C for 15 min to stop the reaction, and the p-

nitrophenol (PNP) produced in the enzymatic reac-

tions was extracted and determined at 398 nm

(Nannipieri et al., 1982). Assays without soil and

without PNG or PNPP were made at the same time as

controls.

For the analyses of o-diphenol oxidase, reagent

solutions of 0.1 M phosphate (pH 6.5) containing

0.2 M of catechol or 0.2 M of proline were oxygenated

for 3 min and incubated for 10 min at 30 8C. Then, 1 g

of fresh soil was added to 3 ml of reagent solution

(1.5 ml of catechol solution and 1.5 ml of proline

solution) and 2 ml of phosphate buffer (0.1 M, pH

6.5). The mixture was incubated for 10 min at 30 8Cand the reaction stopped by cooling in an ice-bath and

adding 5 ml of ethanol. The mixture was centrifuged

at 5000 � g at 4 8C for 5 min. The absorbance of the

supernatant fraction was measured at 525 nm. Assays

without soil and without catechol were made at the

same time as controls (Perucci et al., 2000).

2.3. Statistical analysis

For the statistical treatment of the results, an

analysis of variance (ANOVA) was applied together

with discriminant analysis, using the program

STATISTICA (StatSoft Inc., Tulsa, Oklahoma, USA).

Discriminant function analysis was used to

determine which variables discriminate between the

three naturally occurring groups. In stepwise dis-

criminant function analysis (Forward Stepwise Ana-

lysis), a model of discrimination is built step-by-step.

Specifically, at each step all variables are reviewed and

evaluated to determine which one will contribute most

to the discrimination between groups. That variable

will then be included in the model, and the process

starts again. The stepwise procedure is ‘‘guided’’ by

the respective F to enter and F to remove values. The

F-value for a variable indicates its statistical

significance in the discrimination between groups.

Discriminant analysis determine some optimal com-

bination of variables so that the first function provides

the most overall discrimination between groups, the

second provides second most, and so on.

A canonical correlation analysis was performing

determining the two functions and canonical roots.

The summary statistics for all variables in the model is

the following:

2.3.1. Wilk’s Lambda

This is the Wilk’s Lambda for the overall model

that will result after removing the respective variable.

Wilk’s Lambda can assume values in the range of 0

(perfect discrimination) to 1 (no discrimination).

2.3.2. Partial Lambda

This is the Wilk’s Lambda associated with the

unique contribution of the respective variable to the

discriminatory power of the model.


2.3.3. F to remove

This is the F-value associated with the respective

partial Wilk’s Lambda.

2.3.4. p-Level

This is the p-level associated with the respective F

to remove.

2.3.5. Tolerance

The tolerance value of a variable is computed as

1-Tolerance R2 of the respective variable with all other

variables in the model. Thus, the tolerance is a

measure of the respective variable’s redundancy.

2.3.6. 1-Tolerance (R2)

This is the R2-value of the respective variable with

all other variables in the model.

3. Results and discussion

3.1. Biochemical parameters

Dehydrogenase activity was used both in the soils

and in organic wastes as an indirect measurement of

biological activity (Garcia et al., 1997; Benitez et al.,

1999), as intracellular enzymes participate in the

processes of oxidative phosphorylation of microor-

ganisms (Alef and Nannipieri, 1995). The biological

activity of soils under organic cultivation was

significantly greater ( p < 0.05) than in integrated or

Fig. 1. Enzymatic activities in olive-orchard soils under different manage

N = 42).

conventional management (Fig. 1), the latter two

showing no significant differences.

The results were even more evident on taking into

account the weed–control system: tillage, no-tillage,

or herbicide application. Soils with herbicide applica-

tion showed lower dehydrogenase activity (Fig. 2);

herbicide use probably inhibited the biological activity

with respect to soils where no synthetic chemicals

were applied (Reinecke et al., 2002). In these cases,

the no-tillage allowed the soils greater microbial

activity, measured as dehydrogenase activity, with

respect to tilled soils ( p < 0.05).

Phosphatase is a broad-spectrum enzyme capable

of hydrolysing organic esters and transforming them

into inorganic phosphate, while b-glucosidase is an

enzyme involved in the C-cycle that catalyses the

conversion of disaccharides into glucose (Alef and

Nannipieri, 1995). Both enzymes presented the

highest levels in soils under organic cultivation

(Fig. 1), while conventional and integrated manage-

ment showed no significant differences. The results

could indicate the higher capacity of the organic

system to cleave organic compounds (Mader et al.,

2002). Since hydrolases are inducible enzymes, their

activity are regulated by the presence of available

substrates (Burns, 1982); the stronger hydrolytic

activity of the organic soils could so reflect greater

nutrient availability (phosphates and carbon com-

pounds) for microorganisms and plants in comparison

to the other two management systems.

When the soils were considered according to the

weed–control system used, similar trends were found

ment systems (thin bars represent the standard error of the sample,


Fig. 2. Enzymatic activities in olive-orchard soils with different weed–control systems (thin bars represent the standard error of the sample,

N = 42).

(Fig. 2). That is, the soils without tillage registered a

higher level of phosphorus organic substrates with

respect to soils that were tilled or treated with her-

bicides ( p < 0.05). Similarly, as the use of synthetic

chemicals diminished an increase in b-glucosidase

activity, and therefore in nutrients available from the

C-cycle was noted.

The o-diphenol oxidase is an enzyme that catalyses

the oxidation of phenolic compounds to quinones,

participates in the formation of humic acids, and

indicates the capacity of the microflora to degrade

recalcitrant organic substances (Perucci et al., 2000).

The activity levels of diphenol oxidase were similar

regardless of the cultivation system used (Fig. 1).

However, when the soils were considered according to

the weed–control system, lower activity ( p < 0.05)

was detected in soils without tillage, or herbicide

application (Fig. 2). These results could indicate, in

the untilled soils, lower content in recalcitrant organic

substances and/or greater quantity of humic precursors

(quinones), inhibitors of this enzyme (Perucci et al.,

2000; Benitez et al., 2004).

Auxins (i.e. indolacetic acid), most of which are

involved in stimulating vegetal growth, are bio-

synthesised by certain groups of microorganisms,

fundamentally bacteria (Patten and Glick, 1996). As

the amount of synthetic chemicals used in the

cultivation systems decreased, the production of

auxins decreased as well (Fig. 1). Also, the

production was significantly greater ( p < 0.05)

when tillage was used for weed control (Fig. 2).

These results could be interpreted more clearly by

considering the ‘‘auxin-production index’’ (API),

which relates the fraction of microorganisms

capable of producing auxins to the total micro-

biological activity—that is, indolacetic acid produc-

tion and dehydrogenase activity. The values of this

index suggest greater microbial biodiversity in the

organic soils with respect to those of the conven-

tional and integrated systems (Fig. 3). In these latter,

auxin-production was greater while total biological

activity was lower than in organic soils (Fig. 1). This

probably indicates that in the conventional and

integrated soils, the diversity of groups of micro-

organisms that produce auxins was lower than in the

organic soil. The results were even more evident

when the soils were considered according to weed

control (Fig. 4); the differences in the API index

could indicate greater diversity of populations of

microorganisms in untilled soils.

3.2. Discriminant analysis

For the discriminant analysis of the olive-orchard

soils sampled, the following variables were deter-

mined: dehydrogenase; o-diphenol oxidase; indola-

cetic acid production; b-glucosidase; phosphatase

activities; total organic carbon; pH; phenols content.

Table 2 shows the results from applying the

algorithm for selecting variables according to the

type of management (conventional, integrated, and

organic). The variables with the greatest discriminat-

ing power were soil enzymatic activities, followed by

pH, total organic carbon, and phenol content.


Fig. 3. Auxin-production index for olive-orchard soils under different management systems (the thin bars represent the standard error of the

sample, N = 42).

The classification matrix (Table 3) indicates that all

of the soils considered a priori as soils under organic

management, 89% were classified as belonging to the

same group, while the remaining 11% showed

characteristics similar to those of the integrated

group. On the other hand, the soils at first considered

to be under integrated management, 17% presented

characteristics similar to those of soils under organic

management, while 8% were similar to those under

conventional cultivation practises.

The representation of the canonical analysis

(Fig. 5) shows the distribution of the soils according

to the two discriminant functions generated taking into

account the type of soil management (conventional,

integrated, or organic). Each soil is represented

according to the values of the variables acquired after

Fig. 4. Auxin-production index for olive-orchard soils with different wee

sample, N = 42).

the discriminant analysis, which gave rise to two

canonical functions. Function 1 (root 1) seems to

discriminate mostly between groups organic, and

conventional and integrated combined (means of the

canonical variables:�1.632, 0.977, and 1.471, respec-

tively). In the vertical direction (root 2), a slight trend

of integrated points to fall below the centerline (0) is

apparent (means of the canonical variables: organic

�0.157, conventional 1.479, and integrated �1.243).

Table 4 shows the results on applying the algorithm

used to select the variables according to the weed–

control system used: herbicides, tillage, and no-tillage.

The variables with the greatest discriminating power

were the indolacetic and dehydrogenase activities,

which gave an idea of the effect caused by the different

weed–control systems on the activity of soil microbes.

d–control systems (the thin bars represent the standard error of the


Table 2

Discriminant function analysis summary of olive-orchard soils under different management systems (conventional, integrated, and organic)

N = 42 Discriminant function analysis summary no. of vars in model: 8 Wilks’ Lambda 14,518 approximately F(16,

64) = 64,979, p<0.0000

Wilks’ Lambda Partial Lambda F-remove (2, 32) p-Level Tolerance 1-Tolerance (R2)

Indolacetic acid 0.2375 0.6114 10.1696 0.0004 0.2454 0.7546

Dehydrogenase 0.2247 0.6462 8.7600 0.0009 0.1499 0.8501

Phosphatase 0.2404 0.6040 10.4880 0.0003 0.1900 0.8100

b-Glucosidase 0.2319 0.6262 9.5519 0.0006 0.2187 0.7813

o-Diphenol oxidase 0.1856 0.7824 4.4506 0.0197 0.2066 0.7934

pH 0.1895 0.7663 4.8798 0.0141 0.1724 0.8276

Total organic carbon 0.1917 0.7573 5.1263 0.0117 0.2724 0.7276

Phenols content 0.1838 0.7897 4.2608 0.0229 0.2296 0.7704

In the first column, variables sorted according to their discriminate power in the model.

Table 3

Classification matrix after the discriminant analysis of olive-orchard soils under different management systems (conventional, integrated, and

organic)

Group Classification matrix rows: observed classifications, columns: predicted classifications

Percent correct Conventional, p = 0.28571 Organic, p = 0.42857 Integrated, p = 0.28571

Conventional 83.33 10 0 2

Organic 88.89 0 16 2

Integrated 75.00 1 1 9

Total 83.33 11 18 13

Fig. 5. Canonical analysis of olive-orchard soils under different management systems.


Table 5

Classification matrix of the discriminant analysis of olive-orchard soils with different weed–control systems (herbicide, tillage, and no-tillage)

Group Classification matrix, rows: observed classifications and columns: predicted classifications

Percent correct Herbicide, p = 0.35714 Tillage, p = 0.35714 No-tillage, p = 0.28571

Herbicide 100 15 0 0

Tillage 100 0 15 0

No-tillage 100 0 0 12

Total 100 15 15 12

Table 4

Discriminant function analysis summary of olive-orchard soils under different weed–control systems (herbicide, tillage, and no-tillage)

N = 42 Discriminant function analysis summary no. of vars in model: 8 Wilks’ Lambda 10,960 approximately F(16,

64) = 8.0822, p < 0.0000

Wilks’ Lambda Partial Lambda F-remove (2, 32) p-Level Tolerance 1-Tolerance (R2)

Dehydrogenase 0.1455 0.7535 5.2355 0.0108 0.2956 0.7044

Indolacetic acid 0.1601 0.6847 7.3669 0.0023 0.2738 0.7262

Total organic carbon 0.1885 0.5815 11.5138 0.0002 0.2869 0.7131

Phenols content 0.1374 0.7975 4.0624 0.0268 0.2375 0.7625

Phosphatase 0.1570 0.6981 6.9180 0.0032 0.1691 0.8309

pH 0.1333 0.8221 3.4618 0.0435 0.2158 0.7842

o-Diphenol oxidase 0.1316 0.8329 3.2110 0.0536 0.2366 0.7634

b-Glucosidase 0.1182 0.9274 1.2528 0.2993 0.3797 0.6203

In the first column, variables sorted according to their discriminate power in the model.

Fig. 6. Canonical analysis of olive-orchard soils with different weed–control systems.


The fit between the soils considered as belonging to

each of the three systems and those predicted by the

discriminant model was 100% (Table 5).

Fig. 6 presents the results for the canonical analysis

of the soils according to the two discriminating

functions created taking into account the weed–

control system used (tillage, no-tillage, and herbicide

application). In this case, the two canonical functions

showed great discriminating power; function 1 (means

of the canonical variables: herbicide 2.435, tillage

�0.803, and no-tillage �2.041), separated soils on

which synthetic chemicals were used from those

where such chemicals were not used (tillage or no-

tillage). Function 2 (root 2), on the other hand, was

capable of discriminating tillage from no-tillage (means

of the canonical variables: herbicide 0.321, tillage

�1.161, and no-tillage 1.050).

4. Conclusions

The biochemical activity of the olive-orchard soils

studied differed depending on the cultivation or weed–

control system. The soils developed under organic

management in general presented greater biological

activity and greater hydrolytic activity than those

under integrated or conventional cultivation, thus

translating greater content in the organic soils of

assimilable substrates for microorganisms and plants.

The biochemical activity of the soil diminished with

the use of synthetic chemicals.

The auxin-production index (API) can be useful to

characterize different types of olive-orchard soil

management (conventional, integrated, and organic)

as well as in weed–control systems (herbicides,

tillage, and no-tillage).

The discriminant analysis proved especially effec-

tive to differentiate olive-orchard soils treated with

herbicides from those without such treatment. The

discriminant analysis, used with the variables deter-

mined in this work, characterized the olive-orchard

soils as belonging to one of the three management

systems (conventional, integrated, or organic) as well as

determining whether or not herbicides had been used.

The biochemical response of the soil was different

according to the type of management used, and this

could be used as a possible system to control organic

agriculture.

Acknowledgements

This work has been financed by the Consejerıa de

Agricultura y Pesca de la Junta de Andalucıa (Spain)

through project CO-041 and by the Comision

Interministerial de Ciencia y TecnologIa (CICYT)

through project REN2002-03269. E. Benitez thanks to

Science and Technology Ministry ‘Programa Ramon y

Cajal’ for funding his research. We would also like to

thank David Nesbitt for assisting in the translation of

the original manuscript into English.

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Biochemical variability of olive-orchard soils under different