=Paper=
{{Paper
|id=Vol-1152/paper39
|storemode=property
|title=Delineation of Management Zones In an Apple Orchard: Correlations Between Yield and Soil Properties
|pdfUrl=https://ceur-ws.org/Vol-1152/paper39.pdf
|volume=Vol-1152
|dblpUrl=https://dblp.org/rec/conf/haicta/AggelopoulouG11
}}
==Delineation of Management Zones In an Apple Orchard: Correlations Between Yield and Soil Properties==
https://ceur-ws.org/Vol-1152/paper39.pdf
Delineation of management zones in an apple orchard:
correlations between yield and soil properties
Katerina Aggelopoulou and Theofanis Gemtos
University of Thessaly, Department of Crop Production and Rural Environment, Fytoko Str.,
N. Ionia, Magnesia 38446, Greece
E-mail: aggelop@agr.uth.gr
Abstract: In the present paper the yield and soil spatial variability in an apple
orchard was studied. Apples were collected manually and placed in plastic
bins along the tree rows. Yield per ten trees was weighted and the
geographical position in the centre of the ten trees was recorded, using a GPS,
in order to create the yield map. The orchard was divided in management
zones with the Management Zone Analyst (MZA) software, based on the yield
map. In each zone soil samples were taken and analysed for the following
characteristics: soil texture (% sand, % silt and % clay), pH, nitrogen (N),
phosphorus (P), potassium (K), calcium carbonate (CaCO3) and organic
matter (OM) content. The correlation between yield and soil properties was
performed in all zones. The results showed significant variability in yield and
some soil properties. Yield was negatively correlated with pH, clay, organic
matter, and CaCO3.
Keywords: precision agriculture, management zones, apples
1 Introduction
Precision Agriculture is the management of crop and soil variability in order to
increase profitability and reduce adverse environmental impact (Earl et al., 1996).
Precision Agriculture has mainly focused in arable crops like cereals (Blackmore et
al, 2003; Godwin et al, 2003), soybean (Dobermann and Ping, 2004) and cotton
(Velidis et al., 2003; Gemtos et al, 2004). However, the opportunities to apply
Precision Agriculture in high value crops, such as fruits, are very promising due to
the fact that it is easier to pay the investment. Moreover field patterns (spatial and
temporal trends) tend to be more stable in perennial than annual crops, which can
facilitate the management of fields according to fixed management zones over time.
Some of the applications in high value crops are in citrus (Zaman and Schuman,
2006, Mann et al, 2011), in olives (Granados et al., 2004, , Fountas et al, 2011), in
apples (Aggelopoulou et al, 2010, Aggelopoulou et al, 2011a, Aggelopoulou et al,
2011b ), in grapes (Bramley and Hamilton 2004; Tagarakis et al, 2006), in pears
(Perry et al, 2010), in palm trees (Mazloumzadeh et al, 2009), in berries (Zaman et
al, 2008) and in peaches (Ampatzidis et al, 2009).
The most common method to manage field variability is the use of management
zones. Management zones are regions or areas of the field which have been
_________________________________
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In: M. Salampasis, A. Matopoulos (eds.): Proceedings of the International Conference on Information
and Communication Technologies
for Sustainable Agri-production and Environment (HAICTA 2011), Skiathos, 8-11 September, 2011.
443
differentiated from the rest of the field for the purpose of receiving individual
management attention. Each zone gets the appropriate level of inputs (seed,
fertilizers, pesticides, water) according to the plant requirements. Management zones
are usually defined on the basis of soil (Fraisse et al, 2001; Taylor et al, 2003;
Vrindts et al, 2005) and yield (Diker et al, 2004; Ping & Dobermann, 2005)
information over several years. Other researchers have used aerial images,
vegetation indices and combination of yield soil and remote sensing data to create
management zones. Boydell and McBratney, (2002) used aerial images of the
developing crop to delineate management zones in cotton. In citrus crop, Zaman and
Schumann (2006) created nutrient management zones based on variation of soil
properties and tree performance, while Mann et al (2010), produced productivity
zones using fruit yield, ultrasonically measured tree canopy volume, normalized
difference vegetation index (NDVI), elevation and electrical conductivity. In olive
trees, Granados et al. (2004), created site-specific fertilization maps based on leaf
nutrient spatial variability and Fountas et al. (2011), created management zones for
fertilizers, using soil chemical properties. Aggelopoulou et al. (2011a), produced
fertilization maps for apple trees based on the amount of nutrients that were removed
from the soil with the previous year’s yield.
The objectives of this paper were (i) to study spatial variability of yield and soil
properties in an apple orchard, (ii) to delineate management zones in the orchard,
based on yield variability and (iii) to correlate yield and soil properties in each
management zone.
2 Materials and Methods
The present study was carried out in a 5 ha commercial apple orchard for the year
2005. The orchard was located in Agia area, Central Greece (22o 35΄41΄΄ E, 39o
40΄28΄΄ N and 160m elevation). The main cultivar was Red Chief and the pollinator
was Golden Delicious. The tree spacing was 3.5m between the rows and 2m in the
row. Trees were trained as free palmette.
For yield mapping apples were collected manually in September and placed in
plastic bins. Yield per ten trees was weighted and the geographical position in the
centre of the ten trees was recorded using a hand-held computer with GPS (Trimble
pathfinder). The yield map was created only for the main cultivar (Red Chief).
In December twenty soil samples were taken before winter crop fertilization to a
sampling depth of 0-30 cm in order to explore the variability of some soil physical
and chemical properties. The samples were air-dried and passed through a 2mm
sieve and analysed for the following properties: soil texture (% sand, % silt and %
clay), pH, nitrogen (N), phosphorus (P), potassium (K), CaCO 3 and organic matter
(OM) concentration. The sampling positions were geo-referenced using a hand-held
computer with GPS.
The delineation of management zones in the orchard was performed using
Management Zone Analyst (MZA) software with the fuzzy clustering method
(Fridgen et al., 2004). Management Zone Analyst calculates descriptive statistics,
performs the unsupervised fuzzy classification procedure for a range of cluster
numbers, and provides the user with two performance indices [fuzziness
444
performance index (FPI) and normalized classification entropy (NCE)] to aid in
deciding how many clusters are most appropriate for creating management zones.
The optimum number of zones is when FPI and NCE have the lower values (Fridgen
et al, 2004).
3 Results and Discussion
From the yield map (Fig 1) it can be seen that yield ranged from 0-91.2 ton/ha. Yield
spatial variability was significant (the coefficient of variation was about 51%). The
coefficient of variation, which is the ratio of the standard deviation divided by the
mean, is a measure of the spatial variability. In this orchard there was a high yielding
zone in the central part of the field while there were two zones with lower yield in
the left and the right of the high yielding zone.
Yield Agia 2005
4393450
4393400
(ton/ha)
4393350
Northing (m)
4393300 80
4393250 60
40
4393200
20
4393150
0
650600 650700 650800
Easting (m)
Figure 1. Yield map of the orchard for year 2005 (Red Chief cultivar)
The descriptive statistics (average, minimum, maximum and coefficient of
variation (CV)) of the soil properties are presented in Table 1. Soil texture was
sandy clay loam. Soil pH ranged from 6.9-8.1. The optimum for apple trees is from
6.5 to 6.8 (Vasilakakis, 2004). The organic matter content was from 1.1 to 3.2 and
it was moderate (Koukoulakis, 1995). Soil variability was small for texture, pH,
CaCO3 (CVs from 3.7-21.9%) , moderate for organic matter, N, K (CVs from 28.6-
52.7%) and high for P(CV=76.9%).
The calculation of the FPI and NCE indices was performed for a number of zones
form 2-8 (Table 2). The results showed that the lower values of the FPI and NCE
were for 6-7 zones, and therefore the optimum number of zones for this orchard was
six to seven. The orchard was divided in six management zones, which is a number
that the farmer can handle (Fig 3). In each zone the average values of yield and soil
445
properties were calculated (Table3). Linear correlation between yield and the soil
properties was performed in each zone. It was assumed that liner correlation was
suitable for yield and soil data as many researchers use this method for this kind of
data in the orchards (e.g. Zaman and Schuman, 2006).
Table1. Descriptive statistics of the soil properties
Soil property Average Min Max CV(%)
Sand(%) 59.5 51.7 65.6 6.9
Clay (%) 23.5 15.1 31.3 19.4
Silt (%) 17 13.3 21.8 13.8
pH 7.6 6.9 8.1 3.7
CaCO3(%) 15.1 11.8 24 21.9
NO3(mg kg-1) 4 0 8.9 70
P(mg kg-1) 2.9 0.5 9.2 76.9
K(mg kg-1) 179.6 60.4 354.8 52.7
Ca(mg kg-1) 295.1 125 492 31.8
Mg(mg kg-1) 212.2 104 298 28.2
Fe(mg kg-1) 9.8 2.8 35.3 84.1
Na(mg kg-1) 124.7 18 323 54.4
Zn(mg kg-1) 1.3 0.81 2.71 43.3
Mn(mg kg-1) 4.5 1.4 9 50.2
Cu(mg kg-1) 0.6 0.1 2.9 103
OM(%) 1.9 1.1 3.2 28.6
Table 2. FPI and NCE indices for a number of classes 2 to 8.
Classes FPI NCE
2 0.0399 0.0152
3 0.0319 0.017
4 0.0268 0.0159
5 0.0251 0.0154
6 0.0219 0.136
7 0.0181 0.0127
8 0.0276 0.0173
446
4393400
Northing (m) 4393350
4393300
4393250
1 to 2
4393200 2 to 3
3 to 4
4 to 5
5 to 6
6 to 8
4393150
650650 650725 650800
Easting (m)
Figure 3. Map of the orchard showing six management zones based on yield
Table 3. Average values for yield and soil properties in each of the six management
zones
Ζone 1 2 3 4 5 6
Yield 1.1 2.24 3.28 4.34 5.52 7.3
pH 7.67 7.82 7.75 7.47 7.56 7.57
K 211 136 204 214 168 121
Na 106 144 119 137 121 109
Mg 249 197 199 242 208 145
Ca 284 318 366 361 215 156
Cu 0.37 1.11 0.35 0.70 0.73 0.34
Zn 1.5 0.89 1.08 1.63 1.67 1.2
Mn 5.13 3.78 5.65 4.9 3.8 2.5
Fe 8.9 4.6 9.4 14 12.7 8.9
NO3 2.2 5.5 5.2 3.8 4.4 4.6
P 4.9 4.2 1.9 1.5 2.9 1.1
Clay 27.3 23.4 26.8 21.4 20.9 18.5
Sand 55.9 61.2 57.9 59.4 61.1 60.9
Silt 16.8 15.3 15.3 19.1 18 20.6
OM 2.3 1.9 2.1 1.8 1.7 1.6
CaCO3 16.1 17.5 15 13.8 14.4 12.6
In the six zones that were created with the MZA software, yield was negatively
correlated with pH with a coefficient of correlation r= -0.62 which was not statistical
significant. The negative correlation between yield and soil pH was probably due to the
fact that soil pH ranged from 6.9-8.1 (alkaline region) when the optimum for apples is
447
6.5-6.8 (Vasilakakis, 2004). Yield was also negatively correlated with CaCO3, with r= -
0.88, which was statistical significant at p=0.05, probably for the same reason because
CaCO3 rises soil pH.
Yield was negatively correlated with clay content (r=-089) and organic matter (r=-0.85).
Both the correlation coefficients were statistical significant at p=0.05. The negative
correlation between yield, organic matter and clay content is probably due to the fact
that both organic matter and clay release nitrogen in the soil which enhances vegetative
growth, that is competing yield (Stylianidis, 2002).
4 Conclusions
From the results of the presented experimental data it can be concluded that:
1. The orchard showed significant spatial variability in yield and soil properties, which
indicates the potential of applying site-specific managements in this orchard according
to the needs of the trees.
2. Yield variability was high with a coefficient of variation about 50%. Soil variability was
different depending on the soil property. Soil texture, pH, and CaCO3 showed small
variability, organic matter, N, and K exhibited moderate variability and P showed
high variability
3. Yield was negatively correlated with pH, clay, organic matter, and CaCO3.
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