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100 (2008) 198– 205
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/issn/15375110
Research Paper: PM—Power and Machinery
Influence of the physical parameters of fields and of crop
yield on the effective field capacity of a self-propelled
forage harvester
C. Amiama, J. Bueno, C.J. Álvarez
Department of Agroforestry Engineering, University of Santiago de Compostela, Escola Politécnica Superior, Campus Universitario, 27002
Lugo, Spain
ar t ic l e i n f o
Data for the effective field capacity of two self-propelled forage harvesters were collected
from 163 fields located in Galicia, Northwest Spain, during the silage maize-harvesting
Article history:
season by using a telemetry system. The values obtained from the study were related to
Received 23 August 2007
crop yield, field area, field longitudinal slope and cross slope, and to attributes of field
Received in revised form
shape: number of total vertices, number of acute vertices and shape index. Because of the
25 February 2008
lack of a shape index that reliably reflects the impact of field shape on the effective field
Accepted 14 March 2008
capacity of harvesters, two new shape indices are proposed in this study. The results
obtained using these two indices are analysed and compared with the results obtained
using the conventional shape index (area/perimeter squared).
The results of the analysis suggest a strong correlation between effective field capacity
and both crop yield and field area. In addition, significant but weak correlations were found
between effective field capacity and both longitudinal slope and shape index. The results
obtained by considering all fields and just the largest fields (of more than 1 ha) were similar.
Fields smaller than 1 ha showed a worse fit of the regression line to the data, which
suggests the need to include new variables in the model. The results of the shape indices
developed in this study were slightly better than the results obtained using the
conventional index.
& 2008 IAgrE. Published by Elsevier Ltd. All rights reserved.
1.
Introduction
The widening of the European Union (EU) to include new
countries, the greater scarcity of inputs used on farms and
the development of European legislation, which progresses
towards achieving sustainable systems, are some of the
reasons that compel farmers to optimise resources with the
aim of minimising costs.
The introduction of techniques such as precision agriculture seeks to reduce costs. However, such techniques have
mainly focused on reducing fertiliser and pesticide costs,
ignoring that one major element is machinery cost, and that a
more efficient use of machinery could offer significant
savings for the farmer (Yule et al., 1999).
In agriculture, the increasing use of the positioning systems
that use satellite signals (GPS, GLONASS, GALILEO) is a reality.
However, these systems have mainly focused on precision
agriculture techniques (Linseisen, 2001; Renschler et al., 2002;
Zhang et al., 2002). This, together with the appearance of
General Packet Radio Service (GPRS) communication systems
Corresponding author. Tel.: +34 982 252 231; fax: +34 982 285 926.
E-mail address: amiama@lugo.usc.es (C. Amiama).
1537-5110/$ - see front matter & 2008 IAgrE. Published by Elsevier Ltd. All rights reserved.
doi:10.1016/j.biosystemseng.2008.03.004
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(with wide coverage) and Universal Mobile Telecommunications System (UMTS) (in expansion), as well as with the
affordable cost of computing systems, has led to a quick
development of telemetry and vehicle location systems in
sectors in which they had hardly been applied until now. This
is the case for fleet management in agricultural machinery
co-operatives. The implementation of these new technologies
will allow great amounts of georeferenced information to be
obtained, with considerable benefits for traceability.
Moreover, the implementation of technologies that allow
for data acquisition from many sensors installed on agricultural equipment (Grenier, 2001; Krallmann and Foelster, 2002;
Craessaerts et al., 2005) contributes to a wider use of
telemetry systems. The combination of automatic data
acquisition systems installed on agricultural machinery
(GPS, sensors, etc.) and analysis software that allows for
correction between fields in terms of working times, stops
and turns enables common farming of adjoining fields
(transborder-farming). In tests conducted by Deiglmayr et al.
(cited by Rothmund et al., 2002), the use of transborderfarming has resulted in savings of more than 30% in working
time and labour costs and up to 11% in variable machine
costs, as compared with the values obtained by working each
field individually.
Another way of improving the efficiency of field operations
is to pre-plan a pattern that the machine must follow,
minimising the turns and distances travelled in working a
field- (Palmer et al., 2003). Grisso et al. (2004) related traffic
patterns to effective field capacity.
In 2005, a group of researchers at the University of Santiago
de Compostela developed a telemetry and vehicle location
system for self-propelled forage harvesters (Amiama et al.,
2008) that allowed for the acquisition of data of working time,
idle time or location for different harvesters. Because the field
data collected were position and time-related, the use of a
Geographic Information System (GIS) provided a method for
integrating data collected from different sources and for
processing data for a further decision-making process (Earl et
al., 2000). By implementing such a system, the effective field
capacity of the harvester could be accurately determined.
Buckmaster (2006) identified power, throughput capacity,
speed and drive as factors that determine the field capacity of
a harvester. However, the author pointed out that speed and
soil trafficability are not limiting factors for the operation of
the harvester. In Northwest Spain, the small size of the fields
and the relief of the area, sometimes abrupt with steep
slopes, bring about the need to consider new variables that
better characterise effective field capacity. Under these
conditions, the effective field capacity of machinery operations is affected by field area and shape (González et al., 2007).
The negative effects of the irregular shape of a field are
mainly due to the extra time required for turning and the
preservation of field borders. Landers (2000) carried out a
study in fields of 10 ha each and different in shape. The
author used a 3 m-wide machine and observed that the best
efficiencies were obtained in rectangular fields when field
operations were conducted parallel to the longest sides of the
field. The differences became more marked when field size
decreased and tended to disappear in large fields. Witney
(1995) assigned the highest value of efficiency to a rectangle
100 (2008) 198 – 205
199
with a 4:1 ratio between the lengths of its sides. To determine
the impact of field shape on harvester efficiency, González
(2002) used a shape index (SI) based on the field area-toperimeter squared ratio. By using such an index, it was
possible to perform morphological characterisations independent of the area of each field. However, this index tended
to give priority to the fields with the most compact shapes
(circular, triangular, square-shaped, etc.).
Taylor et al. (2002) monitored the performance of a harvester using a system that was fairly similar to the system used in
our tests, and observed that harvest efficiency was highly
conditioned by turning times. Turning time is the one nonproductive element which is built into every field operation
(Witney, 1995). The number of turns can be reduced by using
wider implements or creating longer fields. However, turns
cannot be fully suppressed. With an increase in the field area,
the number of turns decreases; the field efficiency of
machinery increases and higher operational speeds can be
achieved. Witney (1995) proved that the proportion of time
spent in productive operations considerably increased with
an increase in the field size. Furthermore, this increase
became more significant with an increase in the working
width of the machine. However, Taylor et al. (2001) conducted
tests with different maize planters and suggested that field
efficiency was not affected by field size. Yet, the smallest
fields were larger than 0.5 ha, and most test fields had an area
between 2 and 20 ha.
The objective of this paper is to analyse the influence of
crop yield values and of the relief and morphological
characteristics of fields on the effective field capacity of
self-propelled forage harvesters. In addition, different shape
indices are assessed and the results obtained with each index
are compared.
2.
Material and methods
Data were collected in September and October 2006, during
the silage maize harvest. Data were collected from 163 fields
located in the municipalities of Ribadeo and Xermade, in the
province of Lugo (Galicia, Spain). The area of the analysed
fields ranged from 0.2 to 7.8 ha. Data were acquired using two
self-propelled forage harvesters of the same brand and model
(New Holland FX58 with Racine 2025 bunker).
To select the variables that should be considered in the
analysis of the effective field capacity of the harvester, the
literature review conducted during the research was taken as
a reference and complemented with the conclusions drawn
from assessing the performance of the dependent variable in
the field. Based on these two elements, the following
explanatory variables were chosen for the analysis: crop
yield, field area, longitudinal slope, cross slope and field
shape attributes (shape index, total vertices number, acute
vertices number). The harvester pattern observed for each
field also affected the field efficiency. However, this factor was
not taken into consideration because of the problems of
integrating this variable into a regression analysis.
The effective field capacity of the harvester was calculated
by dividing the area of the field by the readings of harvesting
times collected from the location and telemetry system
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installed on the harvester (Amiama et al., 2008). Because of
some errors made by the driver in using the software, it was
necessary to check for, and correct if necessary, atypical
values of effective field capacities (values below 0.5 or above
2.5 ha h1) by using GPS position data. The stops and
operation time obtained for the fields with anomalous readings were thoroughly analysed.
Crop yield was calculated by multiplying the number of
crop discharges by a mean value of maize weight per
discharge.
Discharges
were
automatically
recorded
using a position sensor that detected the raising of the
harvester bunker. The mean value of maize weight was
set at 8500 kg of green matter, based on the observations
of the weight display installed on the harvester cab. The total
value obtained was divided by the area of the field. The area
of the fields was obtained directly from the GIS. Previously,
the harvester patterns collected by the GPS receiver were
checked in the field in order to detect potential map
errors that distorted the results obtained. The main
errors detected were due to: errors in the configuration of
the field; occurrence of areas that were not cropped (excessive
slope, reduced fertility, etc.); and common farming of adjoining fields.
Generally, errors were rectified with the aid of GPS data, but
a topographical survey of the cropped area was sometimes
required. The longitudinal and cross slopes of the fields were
determined by using a clisimeter.
In order to define a parameter that included the effect of
field shape, the rectangular field was considered as the
field with the most efficient shape from the harvesting
perspective (Landers, 2000; Witney, 1995). The first shape
index (SI1) was obtained by developing a software application
on MapObjectss 2.3 (Environmental Systems Research
Institute, Inc.), which was used to determine the quadrilateral
with orthogonal sides that best circumscribed the analysed
field. The quadrilateral with the highest ratio between
the area of the circumscribed field and the total area
of the quadrilateral was considered as the most efficient
quadrilateral. The value of this ratio was taken as the shape
index (SI1).
The validity of the results might be conditioned by the
hypothetical occurrence of fields that have a large number of
100 (2008) 198– 205
vertices but conform fairly well to a rectangular field
(a situation exaggerated in Fig. 1). Therefore, the number of
total vertices of the field was included in the model. The
number of total vertices was determined by developing an
application on MapObjectss 2.3 that analysed the polyline
that determined the field perimeter and counted the number
of bends. To avoid the occurrence of high values in fields with
curved boundaries, the minimum distance between vertices
was set at 5 m. In addition, the acute vertices number was
computed for each field because it was considered that the
effective field capacity of the harvester would be most
unfavourably affected by acute vertices.
The second index proposed in this study (SI2) was developed based on SI1, applying a correction factor obtained by
comparing the field perimeter (FP) with the perimeter of the
quadrilateral that circumscribed the field (QP). In order to
include all the values in the range 0–1, the following equation
was used:
SI2 ¼ 1 ABSð1 FP=QPÞ
This index would penalise fields like the one shown in
Fig. 1.
The third shape index (SI3) analysed in this study was the
shape index used by Witney (1995) and González et al. (2007),
obtained from the field area-to-perimeter squared ratio.
Table 1 shows the characteristics of the analysed fields,
grouped by range.
The results obtained were analysed as a whole and by field
size, differentiating fields larger than 1 ha and fields smaller
than 1 ha, with the aim of improving the interpretability of
results. A multiple linear regression model was used for the
statistical analysis. In this model, the dependent variable was
effective field capacity (ha h1) and the independent
variables were crop yield, field area, longitudinal slope,
cross slope, shape index, total vertices and acute vertices
(different analyses were carried out for each shape index).
Because different independent variables were considered,
the analysis first determined the extent to which these
variables were correlated. The presence of multicollinearity
would affect the study of the model because the parameters
of the model would be very unstable, with large variances. To
detect the presence of multicollinearity, the correlation
matrix for the independent variables (R) was analysed by
using the Condition Index (CI), as obtained from the ratio
shown below
CI ¼ ðmax eigenvalue RÞ=ðmin eigenvalue RÞ
Fig. 1 – Field with a high SI but with a highly irregular shape.
(1)
(2)
A common rule of thumb is that a condition index over 15
indicates a possible multicollinearity problem and a condition
index over 30 suggests a serious multicollinearity problem
(Allison, 1999). When high values of the condition index were
detected, some independent variables were gradually
dropped from the model by using the variance inflation
factor.
Then, Backward Stepwise Regression was used, and the
least influential variables found according to the t-test were
gradually eliminated until all the variables included in the
model were significant (p ¼ 0.05). The SPSS computer package
was used to solve the statistical tests.
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Table 1 – Number of fields grouped by range
FC (ha h1)
Fields (No)
o1.00
23
1.00–1.25
55
1.26–1.50
55
1.51–1.75
21
1.76–2.00
8
42.00
1
FA (ha)
Fields (No)
o0.50
22
0.50–1.00
55
1.01–1.50
30
1.51–2.01
16
2.01–4.00
31
44.00
9
LS (%)
Fields (No)
p2
29
3–5
49
6–10
63
11–15
14
16–20
6
420
2
CS (%)
Fields (No)
p2
48
3–5
66
6–8
24
9–11
9
12–14
8
414
8
SI1
Fields (No)
o0.50
3
0.50–0.60
24
0.61–0.70
37
0.71–0.80
38
0.81–0.90
33
0.91–1.0
28
SI2
Fields (No)
o0.50
17
0.50–0.60
44
0.61–0.70
34
0.71–0.80
26
0.81–0.90
26
0.91–1.0
16
SI3
Fields (No)
o0.02
3
0.02–0.03
8
0.031–0.04
25
0.041–0.05
47
0.051–0.06
51
40.060
29
CY (t ha1)
Fields (No)
o20
14
20–30
69
31–40
67
41–50
12
450
1
TV (No)
Fields (No)
o5
30
5–10
85
11–15
31
16–20
13
420
4
AV (No)
Fields (No)
p2
68
3–4
80
5–6
13
7–8
1
48
1
FC: effective field capacity, FA: field area, LS: longitudinal slope, CS: cross slope, SI1: shape index 1, SI2 shape index 2, SI3: shape index 3, CY:
crop yield, TV: total vertices, AV: acute vertices number.
3.
Results and discussion
The first multiple regression analysis integrated all the study
fields, using SI1 as the shape index. As the condition index
value exceeded 15, suggesting a multicollinearity problem,
variables with the highest variance inflation factor were
removed, starting with the variable ‘total vertices’ first. Where
variables had similar variance inflation factor values, those
with the highest p-value, according to the t-test, were
eliminated first. The variable with the highest p-value was
‘acute vertices’. Because the multicollinearity problem persisted after having removed this variable, the next variable
with the highest p-value, ‘cross slope’, was removed. Considering only the remaining four variables, the condition
index value equals 19.3, which can be considered acceptable,
particularly because all the remaining variables are significant (p ¼ 0.05), as shown in Table 2. The variables removed
from the model provided little information, as evidenced by
the r2 values obtained: the value of r2 obtained for the model
that integrates all the variables was 0.640, while the value of r2
for the model that considers only the variables that were not
discarded was 0.638.
The model shows a good fit between crop yield and the
effective field capacity of the harvester, in agreement with
Buckmaster (2006). The clear correlation found between field
area and the effective field capacity of the harvester is
consistent with the findings reported by Witney (1995), and
appears to support the implementation of techniques that
increase the average area of the fields, such as land
consolidation or transborder-farming. A far less evident
Table 2 – Coefficients of regression using SI1
Constant
CY
(t ha1)
FA (ha)
LS (%)
SI1
Unstandardised
coefficients
Significance
level (%)
Partial
correlation
1.740
0.024
1
1
0.72
0.082
0.009
0.242
1
1
5
0.53
0.27
0.19
Dependent variable: FC (ha h1)
CY: crop yield, FA: field area, LS: longitudinal slope, SI1: shape
index 1, FC: effective field capacity.
relationship is observed between the effective field capacity
of the harvester and variables ‘longitudinal slope’ and ‘SI1’,
although the method reveals significant values for both
coefficients.
Fig. 2 shows the regression lines that relate ‘effective field
capacity’ to ‘crop yield’ and ‘field area’. As shown in the figure,
the effective field capacity of the harvester tends to increase
with an increase in the field area. This means that field size
had a significant effect on field efficiency, in disagreement
with the results obtained by Taylor et al. (2001) with maize
planters. Conversely, effective field capacity tends to decrease
with an increase in crop yield. The figures that correlate the
dependent variable with the longitudinal slope and the shape
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Table 3 – Coefficients of regression using SI2
FC, ha h–1
2.0
1.5
Constant
CY
(t ha1)
FA (ha)
LS (%)
SI2
1.0
0.5
0.0
0
1
2
3
4
5
FA, ha
6
7
8
9
Significance
level (%)
Partial
correlation
1.759
0.023
1
1
0.72
0.083
0.009
0.230
1
1
1
0.54
0.27
0.20
Dependent variable: FC (ha h1)
CY: crop yield, FA: field area, LS: longitudinal slope, SI2: shape
index 2, FC: effective field capacity.
2.5
FC, ha h–1
Unstandardised
coefficients
2.0
1.5
1.0
Table 4 – Coefficients of regression using SI3
0.5
0.0
0
10
20
30
40
50
60
CY, t ha–1
Fig. 2 – Relationships between FC–FA (top) and FC–CY
(bottom). FC: effective field capacity, FA: field area, CY: crop
yield.
Constant
CY
(t ha1)
FA (ha)
LS (%)
Unstandardised
coefficients
Significance
level (%)
Partial
correlation
1.935
0.023
1
1
0.71
0.076
0.010
1
1
0.50
0.29
Dependent variable: FC (ha h1)
index of the field are not shown because the correlation
coefficients were low. However, the coefficients suggest that
effective field capacity decreases with an increase in the field
slope. Conversely, the values obtained for the shape index
suggest that effective field capacity increases when the shape
of the field approximates a rectangle, which is consistent with
the results reported by Landers (2000). SI1 is the parameter
with the weakest correlation. However, by jointly analysing
the range of values of the different variables (see Table 1) and
the values of the coefficients, it can be observed that SI1,
together with crop yield, has the highest weight in the value
of the dependent variable.
The value of the coefficient of determination obtained using
SI2 instead of SI1 is 0.639, which is almost identical (Table 3).
However, in this case, variable SI2 is highly significant, which
suggests that the conditions shown in Fig. 1 were not
common among the study fields. Despite the high significance of this variable, the fits between the dependent variable
and both SI2 and longitudinal slope are not good, contrary to
the fits observed for crop yield and field area.
Table 4 shows the results obtained by considering SI3. The
value of the coefficient of determination obtained using SI3 is
0.624, which is similar to the value obtained with the other
shape indices. However, variable SI3 is no longer significant in
this model, and the number of significant variables amounts
to three, with a poor fit of longitudinal slope.
The results obtained using SI1 and SI2 are very similar, and
slightly better than the results obtained with SI3. However,
none of these variables shows a strong correlation with the
dependent variable.
As shown in Table 1, the distribution of fields according to
field area is not homogeneous: 48.2% of the fields have an
CY: crop yield, FA: field area, LS: longitudinal slope, SI3: shape
index 3, FC: effective field capacity.
Table 5 – Coefficients of regression using SI1 (FAp1 ha)
Constant
CY
(t ha1)
Unstandardised
coefficients
Significance
level (%)
Partial
correlation
1.855
0.020
1
1
0.68
Dependent variable: FC (ha h1)
CY: crop yield, FA: field area, SI1: shape index 1, FC: effective field
capacity.
area smaller than or equal to 1 ha and 52.8% of the fields have
an area over 1 ha. These two groups of fields were then
analysed separately, in order to verify whether the results
obtained for the separate groups are the same as those for the
whole sample.
For the fields with an area smaller than or equal to 1 ha
analysed using SI1 as the shape index considered, the
procedure identifies just one explanatory variable (Table 5).
The coefficient of determination obtained for this model is
0.470, a lower value than the r2 obtained by considering all the
fields.
Contrary to what was expected, field shape is not a
significant variable for this range of field areas, and the
effective field capacity of the harvester is conditioned
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exclusively by the volume of forage to harvest. The low value
of the coefficient of determination suggests that there are
other factors not considered in the test that may have a
significant influence on the results obtained. As suggested by
Landers (2000), the harvester pattern might be very relevant
in small fields and might strongly influence the effective field
capacity.
Fig. 3 shows the regression line for effective field capacity of
the harvester and crop yield. The effective field capacity of the
harvester tends to decrease with the increase in crop yield,
and the slope of the line is similar to the slope obtained for
the analysis of the whole dataset. Exactly the same results are
obtained by replacing SI1 with SI2, as shown in Table 6.
The value obtained for the coefficient of determination with
SI3 as the shape index is 0.508 (Table 7), which is similar to the
value obtained for other shape indices. The variables longitudinal slope and SI3 are significant in this model, but show a
poor correlation.
Applying the same procedure to the group of fields over 1 ha
with SI1 as the shape index shows a good fit (Table 8), with r2
equal to 0.715.
As when considering all the fields, the model shows a good
fit between the effective field capacity of the harvester and
both crop yield and field area. This relationship is far less
evident for the variables ‘longitudinal slope’ and ‘shape
index’, but the statistic reveals significant values for both
coefficients. González et al. (2007) working with a potato crop
found that any variation in the shape or the size of plots
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100 (2008) 198 – 205
Table 7 – Coefficients of regression using SI3 (FAp1 ha)
Unstandardised
coefficients
Significance
level
Partial
correlation
2.021
0.019
1
1
0.66
0.009
4.081
5
5
0.25
0.27
Constant
CY
(t ha1)
LS (%)
SI3
Dependent variable: FC (ha h1)
CY: crop yield, LS: longitudinal slope, FA: field area, SI3: shape
index 3, FC: effective field capacity.
Table 8 – Coefficients of regression using SI1 (FA41 ha)
Unstandardised
coefficients
Significance
level (%)
Partial
correlation
1.818
0.028
1
1
0.78
0.060
0.010
0.407
1
1
1
0.49
0.33
0.33
Constant
CY
(t ha1)
FA (ha)
LS (%)
SI1
Dependent variable: FC (ha h1)
CY: crop yield, LS: longitudinal slope, FA: field area, SI1: shape
index 1, FC: effective field capacity.
2.5
2.5
2.0
1.5
FC, ha h–1
FC, ha h–1
2.0
1.0
0.5
0.0
0
10
20
30
40
50
1.5
1.0
0.5
60
CY, t ha–1
0.0
Fig. 3 – Relationship between FC–CY in fields with an area
lower than or equal to 1 ha. FC: effective field capacity, CY:
crop yield.
0
1
2
3
4
5
6
7
8
9
FA, ha
2.5
Constant
CY
(t ha1)
Unstandardised
coefficients
Significance
level (%)
Partial
correlation
1.855
0.020
1
1
0.68
Dependent variable: FC (ha h1)
CY: crop yield, FA: field area, SI2: shape index 2, FC: effective field
capacity.
FC, ha h–1
2.0
Table 6 – Coefficients of regression using SI2 (FAp1 ha)
1.5
1.0
0.5
0.0
0
10
20
30
CY, t
40
50
60
ha–1
Fig. 4 – Relationship between FC–FA (top) and FC–CY
(bottom) in fields with an area greater than 1 ha. FC:
effective field capacity, FA: field area, CY: crop yield.
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Table 9 – Coefficients of regression using SI2 (FA41 ha)
Constant
CY
(t ha1)
FA (ha)
LS (%)
SI2
Unstandardised
coefficients
Significance
level (%)
Partial
correlation
1.857
0.027
1
1
0.78
0.061
0.010
0.380
1
1
1
0.49
0.34
0.33
Dependent variable: FC (ha h1)
CY: crop yield, LS: longitudinal slope, FA: field area, SI2: shape
index 2, FC: effective field capacity.
Table 10 – Coefficients of regression using SI3 (FA41 ha)
Unstandardised
coefficients
Constant
2.115
0.028
CY
(t ha1)
FA (ha)
0.058
LS (%)
0.011
Dependent variable: FC (ha h1)
Significance
level (%)
Partial
correlation
1
1
0.76
1
1
0.46
0.34
CY: crop yield, LS: longitudinal slope, FA: field area, SI3: shape
index 3, FC: effective field capacity.
causes changes in useful area that affect gross receipts and
changes in tillage time that affect working costs. In this study
with forage harvesters the variation in the shape of plots had
a lower effect on harvest time than the variation in the size of
the plots.
Fig. 4 shows the regression lines that relate the effective
field capacity of the harvester with crop yield and field area.
The effective field capacity of the harvester tends to increase
with an increase in field area, and tends to decrease with an
increase in crop yield, as observed in Fig. 2.
The goodness of fit suggests that this model accounts for a
high percentage of the variability observed. If the same
procedure is repeated by replacing SI1 with SI2 (Table 9), a
value of 0.714 is obtained for the coefficient of determination,
which is similar to the value obtained using SI1.
SI2 does not improve the performance of the model for this
range of data. The results obtained for partial correlation are
very similar to the results obtained with SI1.
Considering SI3 as the shape index (Table 10) gave a
coefficient of determination of 0.680, which is a slightly lower
value than the value obtained considering SI1 and SI2. In this
case, SI3 is no longer significant in the model.
4.
Conclusions
From the analysis of the results obtained, it can be concluded
that ‘crop yield’ is the variable that best accounts for the
100 (2008) 198– 205
effective field capacity of the harvester. ‘Field area’ is the
variable with the second best correlation values. The
highest effective field capacities are associated with the
largest fields. Under these conditions, it seems advisable to
implement techniques that enable an increase in the average
area of the fields, such as land consolidation or transborderfarming.
The efforts made to find a variable that appropriately
explains the lower field capacities obtained in irregular fields
did not produce the results that were expected. The variables
considered as shape indices have been significant in most
cases. However, the correlation of these variables with the
dependent variable has been poor in all cases. Yet, the
variable ‘shape index’ generally has a strong influence on
the effective field capacity of the harvester, which improves
when the shape of the field approximates a quadrilateral. The
fits obtained by considering SI1 or SI2 in the model are similar
and are generally better than the fits obtained by using SI3.
The ‘number of total vertices’ and the ‘number of acute
vertices’ were found to be strongly dependent on shape index
in all cases. Hence, these variables were not significant in any
case in this study.
Differential results are obtained by analysing the whole
dataset and by analysing the results obtained for the fields
with an area smaller than or equal to 1 ha and for the fields
over 1 ha separately. The low value of the coefficient of
determination obtained for the smallest fields, together with
the fact that ‘crop yield’ is the only significant variable found,
suggests that there are important factors that have not been
identified in this study. The results for the largest fields are
similar to the results obtained for all the fields considered
together.
In most of the scenarios considered, the ‘longitudinal slope’
of the field is correlated with the effective field capacity of the
harvester in all cases, and the effective field capacity
decreases with an increase in slope. Such a correlation is
not significant for ‘cross slope’ in any case.
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