dissertacao washington university 2006 cafe sombreado

7/30/2019 Dissertacao Washington University 2006 Cafe Sombreado

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GEOSPATIAL ANALYSIS AND MULTIVARIATE CLASSIFICATION OF SOIL

PROPERTIES IN NICARAGUAN SUN AND SHADE GROWN COFFEE SYSTEMS

By

NICOLE PAULETTE ANDERSON

A thesis submitted in partial fulfillment of

the requirements for the degree of

Master of Science in Environmental Science

WASHINGTON STATE UNIVERSITY

Program in Environmental Science and Regional Planning

May, 2006


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To the Faculty of Washington State University:

The members of the Committee appointed to examine the thesis of

NICOLE PAULETTE ANDERSON find it satisfactory and recommend that it be

accepted.

_____________________________Chair

_____________________________

_____________________________

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ACKNOWLEDGMENT

My sincere thanks go to Dr. Eldon Franz for his continuous guidance and

encouragement to explore new ideas. I owe special thanks to Dr. Richard Rossi who

challenged me to adventure into geospatial analysis and dedicated considerable amounts

of time and patience to this study. I have also received invaluable support from Dr.

Richard Koenig for which I am very appreciative. I am grateful for the assistance

provided by Dr. Alan Busacca, Kurt Dalman, Ron Bolton and Jeff Boyle in data analysis.

I owe a special thanks to Mercedes Castro, Martin Castro, Chris Sewell, Ryan Hangs,

Rob Currie, Jennifer Olliges and Angie Hunter for their logistical guidance and field

support. Grizelle Gonzalez and Bruce Haines provided valuable comments to early drafts

of this document. Finally, I would like to thank Alberto Mercado and the entire San Luis

Cooperative, as well as Mario Gutierrez, for providing field plots and technical assistance

on their coffee plantations in Nicaragua.

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GEOSPATIAL ANALYSIS AND MULTIVARIATE CLASSIFICATION OF SOIL

PROPERTIES IN NICARAGUAN SUN AND SHADE GROWN COFFEE SYSTEMS

ABSTRACT

By Nicole Paulette Anderson, M.S.Washington State University

May 2006

Chair: Eldon H. Franz

The global economic and ecological significance of coffee production has been

increasing over the last several decades as coffee has become a major commodity in

international agricultural trade. The trend of reducing shade cover to increase crop yield

has raised important concerns regarding the long-term sustainability of sun grown coffee

production. This study presents a geospatial approach, using geostatistical and

multivariate analysis techniques to characterize soil properties in both sun and shade

grown coffee production systems. Two small, equal-sized plots near Masatepe,

Nicaragua were chosen to intensively sample soilin both the wet and dry seasons.

Collected data included soil pH, % water content, total C, N and S, and available nutrient

concentrations (N, P, K, Ca, Mg and Al). Results indicate larger percentages of organic

C and N in the sun system, while pH was much more acidic as compared to the shade

grown system. Univariate statistics showed a larger degree of variability, among most

chemical predictors, between the wet and dry season in the sun grown system.

Classification and regression tree (CART) analyses were easily able to distinguish the sun

grown system from the shade grown system while bivariate variogram models showed

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smaller correlation spaces in the shade system, representing more spatial heterogeneity in

the upper 10 cm soil layer. Spatial patterns among soil properties appear to be buffered

by the presence of the canopy and root systems of shade trees as well as traditional

organic management techniques. It is concluded that traditional shade grown

management could provide a more spatially suitable environment for the long-term

sustainability of coffee plantation soils.

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TABLE OF CONTENTS

ABSTRACT...iv

INTRODUCTION.....1

METHODOLOGY........5

2.1 Study Area............5

2.2 Sampling-plot selection............5

2.3 Soil Sampling............6

2.4 Chemical Analyses............9

2.5 Texture Analysis...9

2.6 Ion Exchange Membrane Determination10

2.7 Data Analysis..........10

2.7.1 Univariate Analysis...10

2.7.2 Bivariate Analysis.10

2.7.3 Multivariate Analysis11

RESULTS.........14

3.1 Texture14

3.2 Geostatistical Analysis and Multivariate Classification.14

3.2.1 Univariate Analysis...14

3.2.2 Bivariate Correlation.20

3.2.3 CART Analysis.32

DISCUSSION...36

4.1 Soil Acidity..36

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4.2 Nutrient Cycling and Availability....................................................37

4.3 Spatial Dependence and Heterogeneity...39

CONCLUSION....42

REFERENCES....44

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LIST OF TABLES

1. Univariate statistics for total C, N and S, water content, pH, C:N ratio, andexchangeable nutrients...15

2. Univariate statistics for nutrient availability analyses...16

3. Parameters of the fit spherical variogram model(s) for total C, N, and S, watercontent, pH and C:N ratio analyses ......22

4. Parameters of the fit spherical variogram model(s) for nutrient availabilityanalyses..23

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LIST OF FIGURES

1. A sun plantation near Masatepe, Nicaragua (Left). A traditional shadeplantation near Masatepe, Nicaragua (Right)....1

2. Sample plot of sun system.....7

3. Sample plot of shade system.8

4. Data posting for pH in the sun-dry system..17

5. Data posting for pH in the shade-dry system...18

6. Modeled ellipsoids for pH in the shade system for the wet (A) and dry (B)season...24

7. Semivariogram for soil pH in the sun plot...25

8. Modeled ellipsoid for water content in the sun-wet (A), sun-dry (B), shade-wet (C), and shade-dry (D) systems.26

9. Modeled ellipsoid for total C in the sun-wet (A), sun-dry (B), shade-wet (C)and shade-dry (D) systems...27

10. Modeled ellipsoid for total N in the sun-wet (A), sun-dry (B), shade-wet (C)and shade-dry (D) systems...28

11. Modeled ellipsoid for available N in the sun-wet (A), sun-dry (B), shade-wet(C) and shade-dry (D) systems29

12. Modeled ellipsoid for available P in the sun-wet (A), sun-dry (B), shade-wet(C) and shade-dry (D) systems30

13. Modeled ellipsoid for available K in the sun-wet (A), sun-dry (B), shade-wet(C) and shade-dry (D) systems....31

14. CART tree results for regressor variables shade vs. sun.33

15. CART tree results for regressor variables wet vs. dry.35

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This thesis is dedicated to the small farmers of Nicaraguaand the world, whose wisdom, ingenuity and tireless

struggle to survive has invited us all to share in their

immense vision of justice and sustainability.

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INTRODUCTION

Coffee (Coffea Arabica L.) plantations are of great importance both ecologically

and economically to many developing countries in Latin America, including Nicaragua.

In these sub-humid tropics, coffee and other perennial crops are grown using one of two

management techniques. In some areas, modern coffee plantations are intensively

maintained in a monoculture system under direct sunlight. Alternatively, coffee can be

found in traditional low-input agroforestry systems in the presence of shade and other

tree species (Figure 1).

Figure 1. A sun plantation near Masatepe, Nicaragua (left) (photo by Nicole Anderson).

A traditional shade plantation near Masatepe, Nicaragua (right) (photo by Nicole

Anderson).

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Beginning in the 1970s, many traditional coffee systems underwent a transition

to intensive monoculture management under direct sunlight. This model, used to produce

higher yields, has been increasing over the last half century. By 1990 it was estimated

that more than half of the area planted in coffee in Latin America was being managed

under one species of shade tree, or under direct sunlight (Perfecto et al., 1996). Tropical

soils are known to be much more fragile than those of temperate origin. In many cases

inappropriate management of these acidic, deeply weathered soils has caused severe

degradation of soil quality in tropical agroecosystems (Zhang, 2005). Understanding the

impact of different farming techniques is important for land users when making decisions

regarding management techniques for coffee plantations.

Shade trees can buffertemperature extremes by as much as 5C, buffer humidity

and soil moisture availability, improve or maintain soil fertility, and reduce nutritional

imbalances (Beer, 1998). Runoff and soil loss are larger in unshaded than shaded

plantations, threatening the long-term sustainability of the system (Wiersum, 1984).

Shade trees contribute to erosion control and produce natural litter fall, providing better

soil protection during high intensity rainfall (Rice, 1990; Gomez-Aristizabal, 1992).

Heuveldop et al. (1985) reported that between 4.5 and 18.1 metric tons of litterfall and

deadfall can enter a shade managed system annually, depending on the type of shade

trees used. Several studies have reported that species diversity may affect nutrient cycling

and the heterogeneity of forest floor nutritional characteristics under mixed species

canopies (Ferrari, 1999; Rothe et al., 2001).

It has been found that shade trees reduce the stress on coffee by ameliorating

adverse climatic conditions and nutritional imbalances, but they may compete for growth

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resources. While nitrogen (N) fixation by leguminous shade trees is an important

resource to the non-N fixing coffee bush, there may be a time period each year when N is

not being fixed and the leguminous tree must compete with the coffee bush for soil N

(Kass, 1997).

The canopy of shade production systems influences nutrient cycling and the

availability of soil nutrients. Diverse shade structures can increase the range of temporal

distribution of organic matter into the soil. It has been shown that conventional and

traditional management systems can affect processes for N loss differently. Positive

correlations between total N and organic matter have been found in shade coffee systems

(Romero-Alvarado, 2002). However, while increased organic matter and root density

lead to improved soil structure, they may increase leaching of N below the coffee root

zone (Arya et al., 1999). Conversely, Babbar and Zak (1995) reported a three-fold greater

annual loss of nitrate (NO3-) in Costa Rican sun grown than shade grown coffee

plantations.

In a study done by Sadeghian et al. (2001), total soil organic matter was greater

under shade grown coffee plantations when compared to sun grown plantations. Another

study has shown shade plantation and sun plantation soils to have the same amount of

total soil carbon (C) (Hoyos, 2005). An increase in soil properties such as pH,

exchangeable calcium (Ca), magnesium (Mg), potassium (K), phosphorous (P), zinc

(Zn), boron (B), and base saturation (BS), and a decrease in exchangeable aluminum (Al)

has been reported in coffee systems that are managed with little to no application of

chemical fertilizers, pesticides and herbicides (Theodoro et al., 2003).

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Understanding relationships among soil properties within tropical agroecosystem

settings is needed to better address the long-term sustainability of such systems. This

includes investigating spatial and temporal patterns and the interdependence of chemical

indicators of soil quality between the two types of coffee production systems. Traditional

statistical procedures are often based upon the assumptions of sample independence, a

normal sample distribution and a randomized experimental design that can obviate spatial

and temporal dependence and non-normally distributed samples (Rossi et al., 1992).

Many soil properties vary at scales of meters or less and can directly affect plant growth

(Bell, 1994). Geostatistical analysis techniques have become resourceful tools in

addressing spatial correlations and interdependence among variables in such ecological

settings (Rossi et al., 1992), and can be used to distinguish further the similarities or

differences between sun and shade grown coffee systems.

Sustainability of coffee production is inherently linked to successful management

practices, which in turn are dependent upon the available soil resources. The objective of

this study was to classify soil properties in Nicaraguan shade grown and sun grown

coffee systems using geostatistical analysis and multivariate characterization techniques.

Information gathered will help determine how management practices influence the long-

term sustainability of coffee production.

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METHODOLOGY

2.1 Study Area

The research sites were located in the San Luis community near the Municipality

of Masatepe in the South of the department of Masaya in Nicaragua. The climate is

subtropical and humid with a rainy season lasting from May to December, an average

annual temperature of 25C, and an average annual rainfall of 1102 mm. Elevation in the

study area is approximately 453 m above sea level.

2.2 Sampling-plot selection

Shade and sun grown coffee plantations were chosen in close proximity to ensure

a similar soil development history. Two, 15 x 15 m sampling plots were established, one

in each coffee plantation, each with a uniform slope, in order to measure ecological

attributes of soil properties in both systems with consistency. The sun grown coffee

plantation consisted of monocropped coffee bushes grown under direct sunlight and

managed with intensive chemical inputs of N, P and K, as well as conventional fungicide,

insecticide and herbicide applications. Dosage and application frequency vary yearly

according to the economic status of the coffee market. In 2005, the sun plot selected was

fertilized three times in late June, early August and late September. The shade grown

coffee plantation consisted of coffee bushes grown under the shade of several tree species

including, banana (Musa acuminata), aceituno (Simarouba glauca), laurel (Cordia

alliodora), guacimo (Guazuma ulmifolia), ceiba (Pseudobombax septenatum), guarumo

(Cecropia peltata) and guanacaste (Enterolobium cyclocarpum). Management practices

in the shade grown plantation were primarily organic, using composted manure for

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fertilizer, and with pruning and weeding carried out by machete. In both plantations,

coffee bushes were planted approximately 1.5 m apart in rows spaced approximately

every 3 m. The coffee bushes on both plantations had been managed using these

techniques for at least eight years.

2.3 Soil Sampling

Soil samples were collected from the shade and sun managed plots during both

dry (April, 2005) and wet (July, 2005) seasons. Every meter interval was marked and

sampled for a total of 256 soil cores in each 225 m

2

measurement plot (Figures 2 and 3).

Resin exchange probes were placed at 72 locations, represented by the large dot in the

grid. Soil samples were collected from the top 10 cm of the soil profile at each

intersecting point (small dot) in the grid. Leaf litter or deadfall was moved away from the

sampling site before soil cores were taken. Field-moist soil was passed through a 2 mm

mesh size sieve to remove larger pieces of organic material and homogenize the samples.

A portion of the sample was subsequently air-dried for later chemical analysis. The

remainder of the sample was oven-dried for moisture determination as described below.

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Figure 2. Sample plot of sun system

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Figure 3.Sample plot of shade system

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2.4 Chemical Analyses

Standard laboratory analyses were conducted on samples collected at each grid

point, including pH, using a 1:1 soil to water ratio (Thomas, 1986); gravimetric water

content (Gardner, 1986), and total C (Nelson, et al., 1996), N (Bremner, 1996) and sulfur

(S)(Tabatabai,1996) measured by dry combustion in a CNS analyzer (LECO Corp., St.

Joseph, MI). Exchangeable Ca, K and Mg were extracted with 0.5Mammonium acetate

(NH4C2H3O2)and filtered through a Whatman #42 paper. Exchangeable cations weredetermined by atomic absorption spectrometry (Varian, Inc., Palo Alto, CA).

2.5 Texture Analysis

A set of five homogenized sub samples was taken from composite soil sets from

both sun and shade plots. Each sample was analyzed in triplicate to quantify the

distribution of particle sizes within the mineral fraction. Each sub sample was treated

with 30% hydrogen peroxide (H2O2)at 65C to remove organic material. Following

pretreatment, 50 ml of deionized (DI) water was added and each sample centrifuged

twice at 2000 rpm for 10 minutes, decanting the supernatant with a vacuum bottle in

between cycles. Samples were then dispersed using sodium hexametaphosphate

(Na(PO3)6) and DI water before being placed on a reciprocating low speed shaker for 12-

16 hours. The paste method was utilized by oven drying each sample until a concentrated

muddy paste resulted. The Malvern Mastersizer S (Malvern Instruments, Inc.,

Gilbertsville, PA) was used to measure particle size by passing a collimated laser through

each sample.

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2.6 Ion Exchange Membrane Determination

Plant Root SimulatorTM

probes (Western Ag Innovations, Saskatchewan, Canada)

were used to examine the bioavailability of total N, NO3-, ammonium (NH4

+), Ca, Mg, K,

P, iron (Fe), manganese (Mn), copper (Cu), Zinc (Zn), B, S, lead (Pb) and Al in the soil.

A total of 72 pairs of ion exchange resins were placed in each plot at locations that were

selected to ensure adequate numbers of pairs at selected lag distances (Figures 2 and 3).

This is to quantify the mass of nutrient ion per unit ion-exchange surface area over the

determined burial period. Burial locations remained the same for both wet and dry

season sampling for 4 and 6 weeks, respectively. After removal from the soil, each

PRSTM

probe was washed thoroughly using deionized water and frozen at -20C. The

PRSTM

probes were eluted with 0.5Nhydrocholoric acid (HCl) and the eluate was

analyzed for nutrient concentrations using automated colorimetry for N and inductively-

coupled plasma emission spectrometry (ICP) for all other nutrients.

2.7 Data Analysis

2.7.1 Univariate Analysis

Data postings, cumulative frequency diagrams (CFD), and summary statistic

tools were used in the univariate analysis procedure. Mean, median, and variance values

were determined for each data set.

2.7.2 Bivariate Analysis

Spatial dependence of the samples was analyzed using geostatistical analyses

(Isaaks, 1989). Analyses of bivariate correlation using variography was carried out to

identify how each variable changes as a function of distance and direction in both the sun

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and shade systems. Nested spherical variogram models were used to fit the experimental

scatter of points calculated using VCALC and modeled using VMOD (Rossi, 1995). An

isotropic nugget effect and one or two spherical models comprise the nested structures.

A spherical model is defined as,

(1)

where (h) represents the degree of spatial continuity of data points separated by vectorh,

a is the effective range for a spherical model,and Cis the sill. Standardized nonergodic

correlograms in variogram formwere calculated for the 0, 30, 60, 90, 120, and 150-

degree directions. The variogram remains essentially flat for spatially uncorrelated data

because there is little change in the semivariance with increasing distance. When

samples are more similar in value and autocorrelated, the semivariogram rises as

separation distances become larger and then level off at a sill, indicating the distance

(known as range) beyond which samples are uncorrelated. Spatial variability, at a scale

finer than the minimum sampling lag and experimental error, contribute to what is known

as nugget variance (C0). A nugget is the apparent discontinuity at 0 distance. Modeled

ellipsoid orientation and size were used to describe spatial dependence.

2.7.3 Multivariate Analysis

The classification and regression tree (CART) analysis, a multivariate

technique, was used to identify the interrelationships among all variables for both

management systems and seasonal comparisons. CART uses all available information to

partition a regressor variable based on all possible combinations of all predictor variables

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into the most homogeneous groups within and the most heterogeneous groups between

(Breiman, et al., 1984). The output results in a bivariate tree structure using the regressor

sums of squares (SS),

(2)

where large SS values of the regressor are more heterogeneous and small SS values of the

regressor are more homogeneous.

Initially, CART computes the total SS (SST) of the regressor variable. Next,

CART sorts each predictor variable while permuting the regressor. CART then

iteratively splits all possible ordered predictor-regressor pairs into two groups and

computes the SS of the regressor for each side of the split. Subsequently, CART searches

formax,

(3)

where SSR and SSL are the SS for each side of the split. The first split in the bivariate

tree structure is made where CART locates the largest max for all split values and all

predictors. The first node in a CART bivariate tree contains all the regressor cases. The

predictor and split value that results in the largest max value splits all the regressor cases

initially into two groups. The subgroup with the smaller mean on one side of the max

split tends to split to the left (SSL) while the subgroup with the larger mean tends to split

to the right (SSR). The two child nodes are then each split again using all predictors and

possible split values. CART uses a surrogate predictor to make the split for missing data.

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A surrogate predictor sends the largest percentage of the same cases to the left and right

as the best predictor.

The resulting bivariate tree structure can be grown and pruned by changing the

minimum total number of cases required in each node to prevent any further splits. The

classification of both the most homogenous groups within and the most heterogeneous

groups between is thus satisfied to the degree permitted by the minimumnumber of cases

per node restriction. The objective is to maintain the greatest level of specificity while

sustaining the most number of cases in each terminal node.

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RESULTS

3.1. Texture

The average particle size for soil samples taken from the sun plot was 50.26 mm

being comprised of 21.35% sand, 41.62% silt and 37.02% clay. Soil samples from the

shade plot had an average particle size of 45.34 mm and were comprised of 18.77% sand,

45.65% silt and 35.60% clay. The textural class was determined to be silt loam for both

sets of soils.

3.2. Geostatistical Analysis and Multivariate Classification

3.2.1 Univariate Analysis

The soil in the sun plots was generally acidic, with a mean pH ranging from 5.22

in the dry season to 5.23 in the wet season. Soil in the shade plots was more neutral, with

pH ranging from a mean of 6.15 in the dry season to 6.25 in the wet season. Variance is

small at all four samplings with the largest variance value being 0.18 in the sun-dry

system (Table 1). However, data postings for pH revealed a relationship between coffee

bushes and low pH readings in the sun plot (Figure 4). It should be noted that a row of

coffee bushes occurs just above the 15 m grid line in the sun system, and is associated

with the row of low pH values at the top of the figure. The row of coffee bushes at 6 m

had been pruned. Figure 5 shows that the relationship between low soil pH and the

presence of coffee bushes is not the same in the shade system.

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Table 1. Univariate statistics for total C, N and S, water content, pH, C:N ratio

and exchangeable nutrients

Sun Shade

Wet Dry Wet Dry

m 4.46 4.08 3.46 2.86Total C (%)

2 0.63 0.55 0.27 0.18

m 0.38 0.34 0.31 0.26Total N (%)

2 0.01 0.00 0.00 0.00

m 0.06 0.06 0.04 0.03Total S (%)

2 0.00 0.00 0.00 0.00

m 28.86 20.78 28.64 21.48Water Content (%)

2 5.95 11.96 4.58 4.36

m 5.23 5.22 6.25 6.15pH

2 0.11 0.18 0.14 0.11

m 11.89 11.89 11.32 10.99C:N Ratio

2

0.23 0.29 0.47 0.30m 2.88 3.11 4.73 4.42Exchangeable Ca

(mg/kg soil) 2 0.37 0.81 0.77 1.04

m 3.27 3.29 1.56 1.49Exchangeable K

(mg/kg soil) 2 0.36 0.61 0.12 0.11

m 0.63 0.65 0.77 0.66Exchangeable Mg

(mg/kg soil) 2 0.02 0.04 0.02 0.01

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Table 2. Univariate statistics for nutrient availability analyses

Sun Shade

Wet** Dry** Wet** Dry**

M 38.53 62.79 18.00 22.44Available Al*

2 458.42 585.74 25.53 44.71

M 0.60 0.66 0.72 0.93Available B*

2 0.19 0.10 0.24 0.22

M 1626.3 1946.0 2556.8 2682.8Available Ca*

2 279873.2 128614.5 54267.7 111739.5

M 9.22 14.39 7.25 5.25Available Cu*

2 82.63 84.23 50.16 13.52

M 26.43 54.11 36.30 26.22Available Fe*

2 597.06 577.36 1516.5 375.02

M 293.94 260.97 43.51 96.29

Available K* 2 27385.9 17268.1 322.78 2918.5

M 378.18 471.26 309.28 360.40Available Mg*

2 10339.2 9160.9 3990.4 7740.5

M 6.314 9.34 17.08 6.48Available Mn*

2 55.34 48.44 1274.24 33.07

M 4.29 4.43 2.41 4.76Available

NH4+-N* 2 6.93 6.05 0.88 3.32

M 337.50 729.04 233.56 375.50Available

NO3-N* 2 49741.7 134151.4 19015.1 27177.3

M 4.52 3.67 2.68 4.17Available P*

2 9.01 8.09 2.96 11.61

M 0.12 0.26 0.11 0.20Available Pb*

2 0.01 0.05 0.02 0.00

M 47.06 78.03 66.79 48.97Available S*

2 405.36 1399.3 453.58 243.08

M 333.90 733.51 235.09 380.31Available Total

N* 2 46427.0 133914.9 19009.3 27324.0

M 2.84 3.17 1.15 1.21Available Zn*

2 6.18 2.92 1.12 0.22

* Available nutrient figures represent data taken from PRS

TM

probe results** Units are reported as ug/10cm2/length of burial period

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Figure 4. Data posting for pH in the sun-dry system

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Figure 5. Data posting for pH in the shade-dry system

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As expected, soil water content is greater under both management systems in the

wet season compared to the dry season (Table 1). The mean % water content was

approximately 21% in the dry season and 29% in the wet season for both management

systems. The variability in the sun system is large in the dry season with variance equal

to 11.96 while it is much lower in the wet season at 5.95. Variability in the shade system

remains quite consistent between dry and wet seasons with variance values of 4.36 and

4.58, respectively.

Total C ranged from a mean of 2.86% of soil mass in the shade-dry system to

4.46% for the sun-wet system (Table 1). Total soil N ranged from a mean of 0.26% of

soil mass in the shade-dry system to 0.38% in the sun-wet system while total soil S

ranged from a mean of 0.03% in the shade-dry system to 0.06% in the sun-wet system.

While total soil C, N and S were higher in the sun system, values for the shade system

showed the smallest variance (Table 1).

Mean values and variance were also calculated for available total (NO3 + NH4)-N,

NO3, NH4, Ca, Mg, K, P, Fe, Mn, Cu, Zn, B, S, Pb and Al, and exchangeable Ca, Mg and

K (Table 2). However, upon review of the summary statistics, it was decided that this

paper would concentrate on the following variables: available (NO3 + NH4)-N, Ca, Mg,

K, P, and Al. This decision was based on the agronomic importance of these variables in

defining plant nutrition and also the presence of spatial trends in the data. Exchangeable

cations were measured to examine the credibility of the PRSTM

probe results. Based on

these results, it was concluded that results from the PRSTM

probes was supported by more

traditional exchangeable cation methodology.

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The mean values for available total N, P and K are larger in the sun system for

both the wet and dry seasons, due to application of chemical fertilizers to the soil (Table

2). Variability of these three nutrients in the shade system is larger in the wet season than

in the dry season. Variability among sampling locations for these nutrients is expected to

occur in the sun system due to the method of site injection of fertilizers applied in

measured quantities at the base of each coffee plant rather evenly distributing the

nutrients.

The mean values of available Al are smaller in the shade systems for both seasons

(Table 2), as they are expected to decrease as soil pH approaches neutrality. Mean values

of the basic cation Ca are larger in the shade system for both the wet and dry season

(Table 2).

3.2.2 Bivariate Correlation

In all cases, the bivariate analysis of the soil variables resulted in spatial

dependence as a function of distance and direction for both the sun and shade systems

(Tables 3 and 4). Model range sizes represent the distance beyond which samples are

spatially independent and are an indicator of patch size. Modeled ellipsoids were not

created for sun pH because all calculated variogram directions were dominated by a

pronounced hole effect, except in the 90-degree direction. The hole effect is

representative of spatial correlation changes as a function of distance in a sinusoidal

behavior (i.e., greater correlation, then less correlation, then more, etc.). Positive

direction is defined as degrees clockwise from 0-degree or grid top or across the coffee

bush rows.

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Modeled ellipsoids for pH in the shade system show more spatial continuity in

the 90-degree direction without a hole effect. The red and blue ellipses represent the

spatial orientation and sizes of the best-fit models for all calculated directional

variograms. If two models fit better to the scatter of points in all directions then two

nested structures were created to accommodate additional smaller spatial patterns within

larger patches. As seen by the numerical box size measurement at the bottom of both

ellipsoid figures the range for pH in the shade system is over 50% larger in the dry season

than it is in the wet season (Xa2 = 53.16 vs. 88.77)(Figure 6).

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Table 3. Parameters of the fit spherical variogram model(s) for total C, N, and S, water content, pH

Co1 XC1

2 C22 Rotation1

3 Rotation23

a14 Ya1

4

Sun Wet 0.29 0.33 0.38 0.02 81.76 81.70 1.4

Sun Dry 0.29 0.26 0.45 2.19 5.38 4.34 33.2Shade Wet 0.56 0.17 0.28 28.89 0.57 1.85 5.0

Total C (%)

Shade Dry 0.45 0.24 0.31 88.81 0.10 5.72 2.7

Sun Wet 0.35 0.36 0.28 -0.01 -2.02 68.73 0.9

Sun Dry 0.15 0.36 0.49 57.53 0.44 1.56 2.0

Shade Wet 0.12 0.67 0.14 -4.03 8.84 2.28 0.9

Total N (%)

Shade Dry 0.27 0.50 0.23 0.53 0.62 3.63 1.0

Sun Wet 0.21 0.32 0.47 -0.54 5.80 28.49 1.5

Sun Dry 0.23 0.57 0.21 -1.41 -0.31 34.89 0.9

Shade Wet 0.55 0.15 0.31 31.78 17.61 7.79 110.5

Total S (%)

Shade Dry 0.47 0.28 0.25 1.88 -34.54 13.50 0.8

Sun Wet 0.25 0.56 0.19 -38.62 0.00 1.01 2.5

Sun Dry 0.36 0.32 0.32 8.10 -1.57 1.38 2.4Shade Wet 0.56 0.13 0.31 24.22 -3.20 5.39 46.1

WaterContent (%)

Shade Dry 0.69 0.18 0.13 -5.44 43.91 18.69 1.6

Sun Wet **** **** **** **** **** **** ***Sun Dry **** **** **** **** **** **** ***Shade Wet 0.24 0.06 0.70 68.27 -1.66 1.21 1.9

pH

Shade Dry 0.46 0.12 0.43 47.32 7.49 0.79 1.3

Sun Wet 0.12 0.43 0.45 10.16 -0.43 3.12 6.9

Sun Dry 0.16 0.43 0.41 -13.78 0.77 1.70 2.7

Shade Wet 0.19 0.14 0.67 3.06 -0.04 1.99 0.9

C:N Ratio

Shade Dry 0.26 0.17 0.58 0.38 -30.33 10.22 0.6

1Nugget size; 2 Model coefficients; 3 Rotation clockwise from degree 0; 4 Model range for rotated x/y****Modeled ellipsoids were not created for sun pH because all calculated variogram directions were dominated by pr

the 90-degree direction.

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Table 4. Parameters of the fit spherical variogram model(s) for nutrient availability

analyses

Co1 C12 C2

2 Rotation23 Ya1

4

Sun Wet 0.73 0.27 0.05 298.96 1.54

Sun Dry 0.24 0.76 -4.69 3.75 2.42Shade Wet 0.18 0.82 35.20 1.42 2.35

Available Al

Shade Dry 0.56 0.44 -8.84 26.75 3.98

Sun Wet 0.03 0.97 40.79 6.04 1.27

Sun Dry 0.27 0.73 1.89 1.31 1.53

Shade Wet 0.20 0.80 -21.54 1.51 3.30Available Ca

Shade Dry 0.49 0.52 -35.52 10.14 5.58

Sun Wet 0.03 0.97 -31.55 1.88 2.99

Sun Dry 0.60 0.41 16.27 309.08 15.40

Shade Wet 0.04 0.96 48.61 6.54 0.93

Available K

Shade Dry 0.19 0.81 -40.63 3.53 1.32

Sun Wet 0.29 0.71 22.66 1.40 1.60

Sun Dry 0.71 0.29 49.56 88.61 2.73

Shade Wet 0.14 0.86 51.11 19.38 61.13

Available Mg

Shade Dry 0.18 0.82 48.04 16.98 73.62

Sun Wet 0.73 0.27 0.01 101.05 0.72

Sun Dry 0.48 0.52 -4.67 51.30 1.65

Shade Wet 0.18 0.82 1.90 0.85 3.07

Available P

Shade Dry 0.29 0.72 -9.93 48.54 1.88

Sun Wet 0.26 0.74 -10.20 2.70 1.29

Sun Dry 0.10 0.90 -0.36 5.51 2.62

Shade Wet 0.76 0.24 0.70 308.45 6.48

Available S

Shade Dry 0.05 0.95 46.63 6.39 1.58

Sun Wet 0.20 0.80 6.84 7.15 1.10

Sun Dry 0.34 0.66 -12.72 62.27 3.60

Shade Wet 0.48 0.52 33.14 14.35 29.71

Available

Total N

Shade Dry 0.37 0.63 55.14 9.86 17.33

Sun Wet 0.26 0.74 -5.61 2.22 1.08

Sun Dry 0.54 0.46 -0.94 18.74 2.37

Shade Wet 0.16 0.84 9.23 6.77 1.18Available Zn

Shade Dry 0.37 0.63 -0.52 17.13 8.78

1Nugget size; 2 Model coefficients; 3 Rotation clockwise from degree 0; 4 Model range for rotated y

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Figure 6. Modeled ellipsoids for pH in the shade system for the wet (A) and dry (B)season

The non-ergodic correlograms in variogram form resulted in some properties

displaying a distinctive hole-effect behavior. An example of this hole-effect outcome can

be seen most clearly with the sinusoidal behavior of the variogram for pH in the sun

system at 30-degree azimuth (Figure 7). A hole-effects peak-to-trough (amplitude)

variogram value (Y-axis) change corresponds exactly to the proportion of the total

variance that changes as a function of distance. A trough or decreasing variogram value

indicates that the samples are becoming more similar at that distance. Conversely, a peak

or increasing variogram value means the samples are more dissimilar at that distance.

At 30-degree azimuth, the troughs at 4 m and 8 m distances correspond exactly to the

distance between coffee rows

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Figure 7. Semivariogram for soil pH in the sun plot. An example of a hole-effectcan be seen in the sinusoidal behaviors of the variogram in both the wet and dry

seasons

There is clear spatial continuity in the 90-degree direction for water content in the

sun plots in both the wet and dry seasons, as evidenced by Table 3. However, range size

is much smaller in the dry season than the wet season. The orientation of the ellipsoids in

the shade systems show that one nested structure is oriented in the 90-degree direction

while the other nested structures vary in direction. However, the shade system ranges for

both seasons are equal and similar to that of the sun-wet season (Figure 8).

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Figure 8. Modeled ellipsoid for water content in the sun-wet (A), sun-dry (B), shade-wet

(C) and shade-dry (D) systems

Model range sizes for total C and N are both greater in the sun system.

Orientations in the sun system for both seasons are in the 90-degree direction,or down

the coffee bush rows. The orientation of the nested structures in the shade system is

similar and nearly equal; smaller range sizes are present for both seasons (Figures 9 and

10).

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Figure 9. Modeled ellipsoid for total C in the sun-wet (A), sun-dry (B), shade-wet (C) and

shade-dry (D) systems

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Figure 10. Modeled ellipsoid for total N in the sun-wet (A), sun-dry (B), shade-wet (C)

and shade-dry (D) systems

The expressions for the ellipsoid ranges for available N and K show more distinct

differences between seasons in the sun system. The range sizes for the sun-dry plot are

much larger than any of the other plots, while the range size for available P in the shade-

wet plot is much smaller and its orientation is in the 0-degree direction (Figures 11, 12

and 13).

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Figure 11. Modeled ellipsoid for available N in the sun-wet (A), sun-dry (B), shade-wet


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Figure 12. Modeled ellipsoid for available P in the sun-wet (A), sun-dry (B), shade-wet


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Figure 13. Modeled ellipsoid for available K in the sun-wet (A), sun-dry (B), shade-wet


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3.2.2 CART Analysis

Multivariate CART analysis provided a means to determine the presence and

degree of interrelationships among the soil variables. CART is adept at distinguishing

non-linear relationships among variables, is distribution free, can accommodate

continuous as well as categorical variables, and is not constrained by missing data.

CART analyses successfully identified differences between the wet and dry season, and

even more so in shade vs. sun environments. Non-linear relationships were well

addressed and showed a clear distinction when the regressor was sun vs. shade. The

results of this multivariate analysis were supported by the data in both the univariate and

bivariate analysis procedures.

When using sun vs. shade as the regressor variable, CART first split the data at a

pH value of 5.73 at node 1. The next tier of nodes splits the data on total S at 0.05% and

a pH of 6.15 at nodes 2 and 3, respectively. Terminal node 4 resulted in 200 cases where

79% were from the sun plot that had a pH less than 5.73 and a total S less than 0.05%.

Terminal node 5 had 338 cases where 100% were from the sun plot that also had a pH

less than 5.73 but a total S greater than 0.05%. All 282 cases where pH was greater than

6.15 were from the shade plot while approximately 92% of the 204 cases with a pH

greater than 5.73 but less than 6.15 were from the shade plot (Figure 14).

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Figure 14. CART tree results for regressor variables shade vs. sun

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When using wet vs. dryseason as the regressor variable for the same data set,

CART made its first split on water content. Because water content would mask any

change in chemical differences, it was removed from the list of predictors. After the

removal of water content from the predictors, CART first split the data by total S of

0.04% at node 1. The next tier of nodes results in a terminal node 2 where approximately

78% of 299 cases that had a total S less than 0.04 were from the dry season. The data set

is further split at node 3 by a pH at 5.76. The data is split further by total C of 4.43% at

node 4, resulting in terminal nodes 6 where 53% of 296 cases that had total S greater than

0.04, a pH value less than 5.76 and total C less than 4.43 were from the dry season.

Terminal node 7 resulted from 63% of the 210 samples that followed the same criterion

but had a total C greater than 4.43 were from the wet season. In terminal node 5, 81% of

the 219 cases that had a total S less than 0.04 and a pH greater than 5.58 were from the

wet season (Figure 15).

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Figure 15.CART tree results for regressor variables wet vs dry

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DISCUSSION

Since the importance of coffee as a commodity in international agricultural trade

has risen in the last several decades, many studies have investigated management effects

on yield potential and long-term ecological sustainability (Beer et al., 1998; Fournier,

1988). Issues such as nutrient and water competition, pest and disease incidence,

biodiversity, applications of external inputs and methods of shade management have been

at the center of such discussion (Beer et al., 1998). Other investigations on the effects of

coffee management practices such as chemical changes in soil, nutrient balances, litter

production, canopy influence, microbial activity and nutrient cycling have been carried

out using point sampling and standard statistical comparisons (Dechert et al., 2005;

Theodoro et al., 2003; Turgay et al., 2002; Kass et al., 1997). However, there have been

few studies that examine how different plantation management practices affect the spatial

patterns of soil nutrient dynamics.

4.1 Soil Acidity

Soil pH serves as a strong indicator of soil quality in agroecosystems. It can be

influenced by climatic factors such as seasonality, cropping and soil management

practices and biological activity (Smith et al., 1996). While most coffee varieties tolerate

variations in climatic conditions, overall growth and nutrient uptake is optimum at a

narrow range of pH. For example, in a study done on coffee seedlings in Hawaiian acid

soils, results indicated that maximum biomass production occurred at a soil pH of 6.0

(Hue, 2004). The events that follow removal of a shade canopy result in a drastic shift of

hydrogen ion concentration that can influence crop performance by limiting the

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availability of key nutrients to coffee plants. In this study, given that the sun system is

more acidic by nearly 1 pH unit, it is clear that the long-term sustainability and quality of

these soils is affected negatively by conventional management practices. The negative

effects of continued application of ammonium-based fertilizers is shown clearly in Figure

4 where application of ammonium based fertilizers at the base of each coffee plant results

in lower pH values along the coffee rows. Higher effective rainfall and increased soil

erosion, due to a loss of canopy cover, may also contribute to the lower pH in the sun

plantation.

4.2 Nutrient Cycling and Availability

As expected with more acidic soils, values for Al availability in the sun system

are larger. It is known that Ca and Mg are rapidly leached from acidic soils that are

exposed to heavy rain (Jordan, 1985). It is possible that levels of available Ca remain

higher and available Mg lower in the shade system due to application of manure compost

in the shade system. Magnesium could be displaced from the exchange sites by

application of calcium rich chicken and cow manure, or simply leached over time.

While the concentrations of total soil C and N are higher in the sun managed

system, they are positively correlated, as suggested by Romero-Alvarado (2002). The

findings of higher C and N in the sun system could be due to organic matter, and

therefore total C and N and available N, being distributed differently among the two

types of management systems. The sampling scheme employed in this study did not

capture the litter C and N in the shade system, which could potentially account for

substantial amounts of each element. Babbar and Zak (1994, 1995) concluded that while

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there were greater rates of N mineralization in shaded plantations (148 kg N ha-1

yr-1

)

compared to unshaded plantations (111 kg N ha-1

yr-1

), N cycling was more efficient in

shaded plantations because less N was lost through leaching. The results of these studies

suggest that soil N availability is influenced in both systems by the rate at which

microorganisms release organic N through mineralization of organic matter and nutrient

leaching. Even though N availability was somewhat lesser in the shade plantation, N

could be used more efficiently and has less potential to be lost through leaching or

dentrification than in the sun plantation.

Shade management not only plays a critical role in nutrient cycling, but can also

influence the temperature, humidity and moisture of the microclimates under the canopy.

It has been reported that increased microbial activity is favored by shading of turfgrass

canopies (Giesler et al., 2000) due to the presence of canopy microenvironments.

Increased leaf wetness duration and relative humidity contribute to the presence of

microclimates that favor larger levels of bacterial and microbial populations. In this

study, a portion of the shade systems C and N was laying on the surface of the soil rather

than being incorporated in the sun system. In a study involving Indonesian coffee

plantations, a correlation between the loss of available soil substrates and decreased

microbial biomass was reported (Turgay et al., 2002). Therefore, it is possible that some

of the soil C and N is being consumed by microbes, tied up in their biomass and being

cycled at a much slower rate.

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4.3 Spatial Dependency and Heterogeneity

The presence or absence of a shade canopy had an overall effect on the spatial

distribution of pH, soil moisture, total organic C, N, and S, and available (NO3 + NH4)-N,

NO3, NH4, Ca, Mg, K, P, and Al. This result may in large part be due to manual

application of chemical fertilizer in the sun plantation that creates a pattern of nutrient

distribution and pH during the wet season that is dominated by the coffee row

orientations (Figures 4 and 6). Another possible explanation of such patterns could be

the result of a large degree of nutrient leaching in the sun system during periods of

intense rainfall. As described by Wiersum (1984), it is very likely that the presence of

shade trees in a traditional agroforestry system reduce the potential for runoff and soil

loss. The large degree of spatial variability among several chemical properties in the sun

grown system indicate that the effects of intensive monoculture coffee production, paired

with acidifying fertilizer applications, on soil nutrient properties can affect the long-term

suitability of the production system.

This study was undertaken to examine the small-scale spatial variability of

selected agronomically important soil properties on two coffee plantations with known

but different long-term soil and crop management histories. Results show that the

presence or absence of shade trees can have measurable effects on agronomically

important soil characteristics (Tables 1 and 2), and their spatial distribution (Figures 4 to

13). In comparison with the sun grown study site, where the effective range of the

variograms was often very large, the shade grown site showed small-scale spatial

variability, with smaller effective range sizes (Tables 3 and 4). It is clear that the presence

of coffee bushes had a pronounced affect on the spatial distribution of soil properties in

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the sun plantation. However, the shade system does not appear to be influenced by the

presence or absence of coffee bushes. The shade coffee system was dominated by smaller

correlation regardless of seasonality making soil properties more spatially suitable. Such

results suggest greater overall heterogeneity in the shade system.

Additionally, these results suggest that soil properties in coffee plantations

exhibit spatial autocorrelation and that agricultural management practices impact the

heterogeneity of soil resources. Small nugget sizes and range values in the shade system

suggest the distributions are patchy. It has been hypothesized that strongly spatial

dependent soil variables may be controlled by intrinsic variations of soil characteristics

(Cambardella, 1994). The results presented here suggest that this may be true for sun

coffee production systems with large chemical inputs. However, intrinsic factors, such as

manure application, non-equal distribution of litterfall, differences in microbial activity

and crop pruning may play a larger role in controlling strong spatial dependence for less

intensive traditional agroforestry systems with minimal chemical inputs.

4.4 Temporal Variability

An important finding of this research is the minimal degree of variability between

seasons for most soil properties in the shade system as compared to the sun system. Soil

characteristics and nutrient availability do not change spatially with respect to distance

from coffee bushes between wet and dry seasons in the shade system. In view of this

more temporally stable degree of variability in the shade plantation, traditional

management objectives have been successful in omitting chemical inputs and making

maximum use of the available soil nutrients. Thereby, nutrient leaching and its negative

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side-effects, soil acidification and cation leaching, may be reduced. Although an

increased homogeneity of nutrient distribution may produce productivity benefits in the

sun plantation, it is essential to consider the possible side effects of such an approach on

potential ecosystem services and resistance to environmental and climatic change.

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CONCLUSION

Spatial patterns of soil properties in coffee agroecosystems have been severely

under-addressed in research. Intensive sampling at close distances was needed to capture

the spatial pattern within the two types of management systems studied here. Shorter

range sizes in the shade systems non-ergodic correlograms illustrate a more

heterogeneous spatial distribution among the measured soil properties of the shade

system. This study has described and investigated the use of fine-scale spatial analysis

techniques as useful tools that should be considered when addressing long-term

sustainability of coffee and other agroecosystems.

A limitation to this study is that sampling on two plantations at a small scale may

not address effects of differences in canopy cover of shade plantations and other climatic

or geographic related differences. Unfortunately, sampling over a larger area at the same

intensity used in this study was impracticable. However, increasing the number of

plantations to include a more diverse set of shade management techniques is suggested

for future studies. Future work could also examine the extent to which important small-

scale information is lost by decreasing the number of sampling sites per unit area.

While many studies involving ecological and soil characteristics of sun and shade

grown coffee have been carried out, there is a lack of literature regarding the spatial

relationships and interdependency of agronomically important soil properties. The

results of this study have demonstrated that significant spatial variations exist in soil

characteristics at fine (< 15 m) spatial scales. There is some evidence that plants

influence the spatial variability of soil attributes through litter production, moisture

absorption and nutrient cycling (Robinson, 1998), suggesting the significance of the

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leguminous shade trees and coffee plants to soil heterogeneity, sample variance and

spatial dependency.

Geostatistical and multivariate analysis approaches are necessary for soil based

agroecological research and, based upon results of this study, have proven to be

extremely useful in investigating spatial and temporal patterns and relationships among

sun and shade managed coffee systems. Thus, it is recommended that more research

characterizing the spatial distribution of soil characteristics in sun and shade grown

coffee production studies be undertaken.

Results from the univariate, bivariate and multivariate analyses all suggest that the

canopy, nitrogen-fixing leguminous shade species and organic management of traditional

shade coffee systems can create a complex buffer system against spatial nutrient

imbalances and temporal variability. Therefore, to conserve important spatial soil

property characterizations, management practices should be directed at traditional

techniques that use leguminous shade trees. In time, permanent coffee cultivation under

direct sunlight will result in spatial homogeneity of agronomically important soil

properties. Future investigations should continue to address the effects of spatial

dependency among soil properties on the sustainability of agroecosystems.

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dissertacao washington university 2006 cafe sombreado

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