LIFE+ HydroSense
Action 4: Deliverable 4.1
Energy use efficiency of cotton production in the Hydrosense project
TABLE OF CONTENTS
Executive summary ................................................................................................................... 2
Introduction ............................................................................................................................... 2
Methods..................................................................................................................................... 3
Results and Discussion ............................................................................................................. 5
References ............................................................................................................................... 14
1
Executive summary
An energy input-output analysis was performed for cotton production under conventional
management as practiced by producers in the area and under site-specific management as
practiced by variable-rate application in 6 demonstration sites of the HydroSense project.
Site-specific management was superior to conventional management because on average it
consumed 19% less total energy and less energy per kg product (9.1 versus 10.9 MJ kg-1).
Non- renewable energy from diesel, chemicals, fertilizers and machinery represented the
bigger source of energy consumption in cotton production. Compared to conventional
management, site-specific management was a more sustainable production system because it
decreased non-renewable energy consumption by 20%. However, site-specific management
required more human labor and machinery for installation of equipment and monitoring of
crop needs within the growing season.
Introduction
The aim of this report is to determine the energy-use efficiency of crop production in the
HydroSense project under two management systems tested, conventional and precision
agriculture (site-specific management). On-farm energy efficiency is becoming increasingly
important in the context of rising energy costs and concern over greenhouse gas emissions.
Energy inputs represent a major cost and one of the fastest growing cost inputs to primary
producers. The Greek cotton growing industry is highly mechanized and heavily reliant on
fossil fuels (electricity and diesel). Within highly mechanized farming systems such as those
used within the cotton industry, machinery inputs are significant and can represent 40–50%
of the cotton farm input costs. Direct energy use is a major component of these costs. Given
the major dependence on direct energy inputs and rising energy costs, energy use efficiency
is an emerging issue for the Greek cotton industry. The energy input considers obvious
factors such as the amount of fuel used by agricultural equipment, but also includes the
energy associated with the manufacture of inputs into the system such as fertilizers and crop
protection products. Understanding energy usage in agricultural production is very
important. The main problems facing energy usage are insufficient resources, high
production costs, wrong resource allocation and increasing national and international
competition in agricultural trade (Dagistan et al., 2009). The excessive and unconscious use
of input in the production of cotton causes increasingly negative effects to both the
environment and farmers. Thus, to increase energy usage efficiency, the input balance should
2
be improved (Signh et al., 1997). Precision agriculture is an emerging, highly promising
technology, that is conceptualized by a system approach to re-organize the total system of
agriculture towards a low-input, high-efficiency, and sustainable agriculture (Shibusana
2002). In this context, precision agriculture was developed with the purpose to rationalize
inputs and reduce environmental impacts (Zhang et al., 2002). In this report, an energy input
output balance is calculated for the conventional management system of cotton cultivation
used in Thessaly plain and the precision agriculture system applied by the HydroSense
project.
Methods
The calculation of energy sequestered in the crop was based on the farmers work schedule,
time needed for each operation, the number of workers and the machinery and inputs used
(seeds, fertilizers, insecticides and pesticides). Appropriate adjustments of energy
calculations were performed for the HydroSense pilot areas. Although the HydroSense
experiment was based on delineation of management zones, analysis of the energy inputs
outputs was estimated as the average values of management zones in each pilot area and
expressed in MJha-1. The only energy output of cotton cultivation was considered to be
cotton yield (kg). Unfortunately there is a methodological problem of energy sequestered in
agricultural practices regards the conversion factors used for the energy equivalent
determinations, as there are different methods to assign energy values to practices in the
literature. The conversion factors used in this report to calculate input and output energies
are given in Table 1 and the source of information is referenced. These coefficients were
obtained from a number of different studies about relevant subjects.
Table. 1. Energy content of cotton production inputs and outputs
Energy
Item
Content
Reference
-1
(MJunit )
Human labour (h)
Tractor 50 KW (h)
Plough (h)
Sprayer (h)
Wagon (h)
Pump (h)
Fertilizer N (kg)
1.96
41.4
22.8
23.8
71.3
2.4
60.60
Sing 2002, Sing and Chandra 2001,Mani et al. 2007
Tsatsarelis 1993, Fluck 1985, Loewer at al. 1977
Tsatsarelis 1993, Fluck 1985, Loewer at al. 1977
Tsatsarelis 1993, Fluck 1985, Loewer at al. 1977
Tsatsarelis 1993, Fluck 1985, Loewer at al. 1977
Tsatsarelis 1993, Fluck 1985, Loewer at al. 1977
Sing 2002, Sing and Chandra 2001, Mandal at al.
3
Fertilizer P (kg)
11.1
Fertilizer K (kg)
6.7
Insecticides (kg)
278
Fungicides (kg)
276
Herbicides (kg)
Seed (kg)
288
25
Diesel (1)
56.31
Water for irrigation (m3)
Cotton (kg)
0.63
11.8
2002, Mani at al. 2007, Shrestha 1998
Sing 2002, Sing and Chandra 2001, Mandal
2002, Mani at al. 2007, Shrestha 1998
Sing 2002, Sing and Chandra 2001, Mandal
2002, Mani at al. 2007, Shrestha 1998
Hülsbergen at al. 2002, Dalgaard at al. 2001,
2001, Meul at al. 2007
Hülsbergen at al. 2002, Dalgaard at al. 2001,
2001, Meul at al. 2007
Hülsbergen at al. 2002
Sing 2002
Sing 2002, Sing and Chandra 2001, Mandal
2002, Mani at al. 2007
Yaldiz at al. 1993
Sign 202
at al.
at al.
Wells
Wells
at al.
The agricultural practices in the research area for cotton are presented in Table 2. The land
is tilled twice between October-November using a plough. Then, after 2 rounds of thinning
in February and March, the cotton seed is sown in April. An average of 22 kg ha-1 cotton
seed is used. The variety of cotton seed used in the experiment was “Celia”. Cotton is dripirrigated about 13 times between June and August. Fertilizer is applied before planting when
needed and approximately 2 times within the growing season between June and July. Plant
protection with pesticide and herbicide application s starts in April and ends in August.
Table 2. Cotton management practices in the pilot areas. Bolt practices are used only in the
precision agriculture system.
Agricultural practices
Period/Frequency
Variety used
Seed (kgha-1)
Land preparation
Average tilling number
Thinning
Average number of thinning
Sowing
Management Zone delineation
Installation of sensors /equipments
Monitoring of sensors
Irrigation border period
Average number of irrigation events
Fertilization period
Average number of fertilization applications
Scanning of canopy for fertilization prediction
Spraying period
Weed seeker
Hoeing period
Harvesting period
Celia
21-23
October-November (using plough)
2
February -March
2
April
April
April-June
June-August
June- August
13
March –July
3.5
July –August (2 times average)
April-August
June-August (1-2 times average)
March-July
September-October
4
On average, the cotton crop is hoed two times by hand and 1-2 times by machinery during
the period of March to July. Cotton is generally harvested by 2 or 4 row harvesters twice,
called the “first and second hand”.
The inputs used in cotton production, their energy equivalents and the energy ratios per
hectare are presented in Tables 5 to 10 for every pilot area in the Hydrosense project. No
energy equivalents were determined for the transfer of equipment and humans to the fields.
For irrigation, except the relevant to irrigation water energy equivalent, diesel consumption
was calculated for the pumping of irrigation water from canals to the fields. This was not the
case for the Gyrtoni fields where irrigation water was supplied by a local irrigation network,
thus, diesel for pumping was not estimated. “Specific energy” was calculated by dividing the
total energy input by yield / ha, in other words, energy consumption per kg cotton produced
(Table 3).
Table 3. Specific energy calculations for the cotton cultivation tested
Pilots
Total energy input
(MJha-1)
Eleftherio 2010
Gentiki 2010
Gyrtoni 2010
Control
33079,03
30993,4
22669
Gyrtoni 2011
Omor/ori 2011
Gentiki 2011
24726,8
36783,5
35650,9
HydroSense
26482,59
26286,7
16164
21210,2
30158,1
30103,9
Yield (kgha-1)
Control
HydroSense
Specific energy (MJkg-1)
2666
2180
3122
2326
2064
2886
Control
12,41
14,22
7,26
4409
3139
2495
3767
3701
2700
5,61
11,72
14,29
HydroSense
11,39
12,74
5,60
5,63
8,15
11,15
Results and Discussion
The results revealed that in most all cases the specific energy was lower with site-specific
management applied in the pilot areas in comparison to the conventional system (Table 3).
Based on the energy equivalents of the inputs and the outputs presented in Table 4, the total
energy consumed by conventional management ranged from 22669 MJha-1 in Gyrtoni
(2010) to 36783 MJha-1 in Omorfochori (2011) with an average energy consumption of
30650 MJha-1. The total energy consumed by site-specific management was estimated to
range from 16164 MJha-1 in Gyrtoni (2010) to 30158 MJha-1 in Omorfochori (2011) with an
average energy consumption of 25067 MJha-1. The differences in energy consumption
5
between conventional and site-specific management ranged from 15% to 28% and were
explained by the energy savings in diesel, fertilizer chemicals and irrigation water in sitespecific management (Table 4).
Table 4. Comparison of total energy input output for the cultivation systems tested
Differenc
Total energy output
Pilots
Total energy input (MJha-1)
e (%)
(MJha-1)
Eleftherio 2010
Gentiki 2010
Gyrtoni 2010
Control
33079,03
30993,4
22669
HydroSense
26482,59
26286,7
16164
Gyrtoni 2011
Omor/ori 2011
Gentiki 2011
24726,8
36783,5
35650,9
21210,2
30158,1
30103,9
Difference
(%)
19,94
15,19
28,70
Control
31458,80
25724,00
36839,60
HydroSense
27446,80
24355,20
34054,80
12,75
5,32
7,56
14,22
18,01
15,56
52026,20
37040,20
29441,00
44450,60
43671,80
31860,00
14,56
-17,90
-8,22
In this study, the energy inputs of nitrogen fertilizer and diesel consumption represent the
biggest shares of the total energy inputs in cotton production. Nitrogen fertilizers represent
21% to 38% of the total energy inputs in the conventional system and 11% to 31% in the
variable-rate system. Greatest energy savings from nitrogen fertilizers were recorded in the
Gyrtoni pilot area in 2010 (Table 4). Diesel consumption was slightly greater in site-specific
management because of mapping operations for management zone delineation and crop N
requirement. However, the overall diesel consumption was less in the pilot areas.
Energy equivalent for irrigation water (pumping and irrigation) was substantially less in the
pilot areas compared to the conventional control due to lower water consumption in the
pilots (Tables 5-10). Energy equivalent of chemicals (insecticides, fungicides herbicides)
was also considerably less in the pilot areas. The energy savings from reduced chemical
applications ranged from 26% (Gentiki 2010) to 66% ( Omorfochori 2011). However,
equivalent energy consumptions of human labor and machinery were greater in the pilot
areas mainly because of sensors installations, monitoring and mapping operations. Although
equivalent energy for human labor and machinery was 40-45% and 14-21% higher in the
pilots than in the controls, the overall contribution of those two categories is much reduced
relative to the total energy consumption (0,04-0,1% of total energy for human labor and
0,5%-0,8% for machinery).
6
Table 5. Energy consumption and energy input output relationship for cotton production at Eleftherio 2010
Input
Quantity per unit area
Energy Equivalent
Total Energy
(ha)
(MJunit-1)
equivalent(MJ)
Human labour(h)
-Land preparations
-Sowing
-Canopy scanning
-Weed seeker
-Harvesting
-Sensors monitoring
-Sensors installation
-Manual Herbicide
-Other practices
Machinery (h)
-Land preparation
-Sowing
-Canopy scanning
-Harvesting
-Weed seeker
-Other practices
Chemical Fertilizer kg)
-Nitrogen
-Phosphorus
-Potassium
Seed (kg)
Chemicals (kg)a
-Insecticides
-Fungicides
-Herbicides
Diesel-oil (l)
Water for irrigation (m3ha-1)
Diesel for irrigation (l)
Total energy input (MJha-1)
Yield (kgha-1)
Energy output-input ratio
Conve/nal
HydroSense
2,83
1,3
0
0
1,5
0
1,6
1,6
2
2,83
1,3
1,4
0,7
1,5
4
0
0
2
2,83
0,66
0
1
0
1,5
Percentage of total
energy input (%)
Conve/nal
HydroSense
Conve/nal
HydroSense
1,96
1,96
1,96
1,96
1,96
1,96
1,96
1.96
1,96
5,55
2,55
0,00
0,00
2,94
0,00
0,00
3,14
3,92
5,55
2,55
2,74
1,37
2,94
7,84
5,88
0,00
3,92
0,017
0,008
0,000
0,000
0,009
0,000
0,000
0,009
0,012
0,021
0,010
0,010
0,005
0,011
0,030
0,022
0,000
0,015
2,83
0,66
1,4
1
0,7
1,5
41,4
22,8
23,8
22,8
23,8
22,8
117,16
15,05
0,00
22,80
0,00
34,20
117,16
15,05
33,32
22,80
16,66
34,20
0,354
0,045
0,000
0,069
0,000
0,103
0,44
0,05
0,12
0,086
0,06
0,12
130
50
50
22
58
50
50
22
60,6
11,1
6,7
25
7878,00
555,00
335,00
550,00
3514,80
555,00
335,00
550,00
13,27
2,09
1,26
2,07
2,12
0
5,48
169
5970
144
2,12
0
2,35
179,5
4960
120
278
276
288
56,31
0,63
56.31
2666
2326
11,8
589,36
0
1578,24
9516,39
3761,10
8108,64
33079,03
31458,8
0,95
589,36
0
676,80
10107,65
3124,80
6757,20
26482,59
27446,8
1,04
23,816
1,678
1,013
1,663
0,000
1,782
0
4,77
28,76
11,30
24,51
100
7
2,25
0
2,55
38,16
11,79
25,51
100
Table 6. Energy consumption and energy input output relationship for cotton production at Gyrtoni 2010
Input
Quantity per unit area
Energy Equivalent
Total Energy
(ha)
(MJunit-1)
equivalent(MJ)
Human labour(h)
-Land preparations
-Sowing
-Canopy scanning
-Weed seeker
-Harvesting
-Sensors monitoring
-Sensors installation
-Manual Herbicide
-Other practices
Machinery (h)
-Land preparation
-Sowing
-Canopy scanning
-Harvesting
-Weed seeker
-Other practices
Chemical Fertilizer kg)
-Nitrogen
-Phosphorus
-Potassium
Seed (kg)
Chemicals (kg)a
-Insecticides
-Fungicides
-Herbicides
Diesel-oil (l)
Water for irrigation (m3ha-1)
Total energy input (MJha-1)
Yield (kgha-1)
Energy output-input ratio
Conve/nal
HydroSense
2,83
1,3
0
0
1,5
0
0
1,6
2
2,83
1,3
1,4
0
1,5
3
4
0
2
2,83
0,66
0
1
0
1,5
Percentage of total
energy input (%)
Conve/nal
HydroSense
Conve/nal
HydroSense
1,96
1,96
1,96
1,96
1,96
1,96
1,96
1.96
1,96
5,54
2,548
0
0
2,94
0
0
3,136
3,92
5,54
2,548
2,744
0
2,94
5,88
7,84
0
3,92
2,83
0,66
1,4
1
0
1,5
41,4
22,8
23,8
22,8
23,8
22,8
117,162
15,048
0
22,8
0
34,2
117,162
15,048
33,32
22,8
0
34,2
129
50
50
21
32
50
50
21
60,6
11,1
6,7
25
7817,4
555
335
525
1939,2
555
335
525
0,96
0
2,96
158
5100
0
0
1,21
166
4540
278
276
288
56,31
0,63
2064
11,8
0
0
348,48
9347,46
2860,2
16164
24355,2
1,50
0,034
0,015
0,016
0
0,0181
0,0363
0,0484
0
0,024
0
0,723
0,092
0,205
0,140
0
0,211
0
11,97
3,42
2,067
3,24
0
0
0
2,15
57,73
17,66
100
2180
266,88
0
852,48
8896,98
3213
22669
25724
1,13
0,024
0,011
0
0
0,0129
0
0
0,013
0,017
0
0,516
0,066
0
0,100
0
0,150
0
34,44
2,44
1,47
2,31
0
1,17
0
3,75
39,19
14,15
100
8
Table 7. Energy consumption and energy input output relationship for cotton production at Gentiki 2010
Input
Quantity per unit area
Energy Equivalent
Total Energy
(ha)
(MJunit-1)
equivalent(MJ)
Human labour(h)
-Land preparations
-Sowing
- Canopy scanning
- Harvesting
- Sensors installation
-Sensors monitoring
- Weed seeker
-Manual Herbicide
-Other practices
Machinery (h)
-Land preparation
-Sowing
- Harvesting
- Canopy scanning
-Weed seeker
-Other practices
Chemical Fertilizer kg)
-Nitrogen
-Phosphorus
-Potassium
Seed (kg)
Chemicals (kg)a
-Insecticides
-Fungicides
-Herbicides
Diesel-oil (l)
Water for irrigation (m3ha-1)
Diesel for irrigation (l)
Total energy input (MJha-1)
Yield (kgha-1)
Energy output-input ratio
Conve/nal
HydroSense
2,83
1,3
0
1,5
0
0
0
1,6
2
2,83
1,3
1,4
1,5
3
4
0
0
2
2,83
0,66
1
0
0
1,5
Percentage of total
energy input (%)
Conve/nal
HydroSense
Conve/nal
HydroSense
1,96
1,96
1,96
1,96
1,96
1,96
1,96
1.96
1,96
5,547
2,548
0,000
2,940
0,000
0,000
0,000
3,136
3,920
5,547
2,548
2,744
2,940
5,880
7,840
0,000
0,000
3,920
0,018
0,008
0,000
0,009
0,000
0,000
0,000
0,010
0,013
0,021
0,010
0,010
0,011
0,022
0,030
0,000
0,000
0,015
2,83
0,66
1
1,4
0
1,5
41,4
22,8
23,8
22,8
23,8
22,8
115,920
15,048
23,800
0,000
0,000
62,100
115,920
15,048
23,800
31,920
0,000
62,100
0,374
0,049
0,077
0,000
0,000
0,200
0,441
0,057
0,091
0,121
0,000
0,236
110
50
50
23
69
50
50
23
60,6
11,1
6,7
25
6666,000
555,000
335,000
575,000
4060,200
555,000
335,000
575,000
21,508
1,791
1,081
1,855
15,446
2,111
1,274
2,187
2,17
0
2,02
170
5970
144
2,17
0
0,93
180
4760
115
278
276
288
56,31
0,63
56.31
2,295
0
1,01
38,55
11,40
24,63
100
2886
11,8
603,260
0
267,840
10135,800
2998,800
6475,650
26286,757
34054,8
1,296
1,946
0
1,87
30,88
12,13
26,16
100
3122
603,260
0
581,7
9572,7
3761,1
8108,6
30993,4
36839,6
1,189
9
Table 8. Energy consumption and energy input output relationship for cotton production at Gyrtoni 2011
Input
Quantity per unit area
Energy Equivalent
Total Energy
(ha)
(MJunit-1)
equivalent(MJ)
Human labour(h)
-Land preparations
-Sowing
-Canopy scanning
- Harvesting
- Sensors installation
-Sensors monitoring
- Weed seeker
-Manual Herbicide
-Other practices
Machinery (h)
-Land preparation
-Sowing
- Harvesting
- Canopy scanning
-Weed seeker
-Other practices
Chemical Fertilizer kg)
-Nitrogen
-Phosphorus
-Potassium
Seed (kg)
Chemicals (kg)a
-Insecticides
-Fungicides
-Herbicides
Diesel-oil (l)
Water for irrigation (m3ha-1)
Total energy input (MJha-1)
Yield (kgha-1)
Energy output-input ratio
Conve/nal
HydroSense
2,83
1,3
0
1,5
0
0
0
1,6
2
2,83
1,3
1,4
1,5
3
4
0
0,8
2
2,83
0,66
1
0
0
1,5
Percentage of total
energy input (%)
Conve/nal
HydroSense
Conve/nal
HydroSense
1,96
1,96
1,96
1,96
1,96
1,96
1,96
1.96
1,96
5,54
2,54
0
2,94
0
0
0
3,136
3,92
5,54
2,54
2,744
2,94
5,88
7,84
0
1,56
3,92
2,83
0,66
1
1,4
0
1,5
41,4
22,8
23,8
22,8
23,8
22,8
117,162
15,048
23,8
0
0
34,2
117,16
15,04
23,8
31,92
0
34,2
158
50
50
21
111
50
50
21
60,6
11,1
6,7
25
9574,8
555
335
525
6726,6
555
335
525
0
0
2,52
158
6200
0
0
1,01
166
5040
278
276
288
56,31
0,63
3767
11,8
0
0
290,88
9347,46
3175,2
21210,2
44450,6
2,09
0,026
0,011
0,012
0,013
0,027
0,036
0
0,007
0,018
0
0,551
0,070
0,112
0,150
0
0,161
0
31,67
2,61
1,57
2,47
0
0
0
1,36
44,01
14,95
100
4409
0
0
725,76
8896,98
3906
24726,8
52026,2
2,10
0,022
0,010
0
0,011
0
0
0
0,012
0,015
0
0,473
0,060
0,096
0
0
0,138
0
38,67
2,24
1,35
2,12
0
0
0
2,93
35,94
15,77
100
10
Table 9. Energy consumption and energy input output relationship for cotton production at Omorfochori 2011
Input
Quantity per unit area
Energy Equivalent
Total Energy
(ha)
(MJunit-1)
equivalent(MJ)
Human labour(h)
-Land preparations
-Sowing
-Canopy scanning
- Harvesting
- Sensors installation
-Sensors monitoring
- Weed seeker
-Manual Herbicide
-Other practices
Machinery (h)
-Land preparation
-Sowing
- Canopy scanning
- Harvesting
-Weed seeker
-Other practices
Chemical Fertilizer kg)
-Nitrogen
-Phosphorus
-Potassium
Seed (kg)
Chemicals (kg)a
-Insecticides
-Fungicides
-Herbicides
Diesel-oil (l)
Water for irrigation (m3ha-1)
Diesel for irrigation
Total energy input (MJha-1)
Yield (kgha-1)
Energy output-input ratio
Conve/nal
HydroSense
2,83
1,3
0
1,5
0
0
0
1,6
2
2,83
1,3
1,4
1,5
3
4
0
0,8
2
2,83
0,66
1
0
0
1,5
Percentage of total
energy input (%)
Conve/nal
HydroSense
Conve/nal
HydroSense
1,96
1,96
1,96
1,96
1,96
1,96
1,96
1.96
1,96
5,547
1,294
0,000
2,940
0,000
0,000
0,000
3,136
3,920
5,547
1,294
2,744
2,940
5,880
7,840
1,372
0,000
3,920
0,015
0,004
0,000
0,008
0,000
0,000
0,000
0,009
0,011
0,018
0,004
0,009
0,010
0,019
0,026
0,005
0,000
0,013
2,83
0,66
1
1,4
0
1,5
41,4
22,8
23,8
22,8
23,8
22,8
115,920
15,048
0,000
22,800
0,000
62,100
115,920
15,048
33,320
22,800
16,660
62,100
0,315
0,041
0,000
0,062
0,000
0,169
0,384
0,050
0,110
0,076
0,055
0,206
158
50
50
21
111
50
50
21
60,6
11,1
6,7
25
10968,600
555,000
335,000
575,000
6666,000
555,000
335,000
575,000
29,819
1,509
0,911
1,563
22,104
1,840
1,111
1,907
0
0
2,52
158
6200
157
0
0
1,01
166
5040
134
278
276
288
56,31
0,63
56,31
0,115
0,000
1,652
33,609
11,657
25,020
100
3767
11,8
34,750
0,000
498,2
10135,8
3515,4
7545,5
30158,1
43671,8
1,448
0,094
0,000
4,228
26,024
11,184
24,034
100
4409
34,750
0,000
1555,2
9572,7
4113,9
8840,6
36783,5
37040,2
1,007
11
Table 10. Energy consumption and energy input output relationship for cotton production at Gentiki 2011
Input
Quantity per unit area
Energy Equivalent
Total Energy
(ha)
(MJunit-1)
equivalent(MJ)
Human labour(h)
-Land preparations
-Sowing
-Canopy scanning
- Harvesting
- Sensors installation
-Sensors monitoring
- Weed seeker
-Manual Herbicide
-Other practices
Machinery (h)
-Land preparation
-Sowing
- Canopy scanning
- Harvesting
-Weed seeker
-Other practices
Chemical Fertilizer kg)
-Nitrogen
-Phosphorus
-Potassium
Seed (kg)
Chemicals (kg)a
-Insecticides
-Fungicides
-Herbicides
Diesel-oil (l)
Water for irrigation (m3ha-1)
Diesel for irrigation
Total energy input (MJha-1)
Yield (kgha-1)
Energy output-input ratio
Conve/nal
Conve/nal
HydroSense
2,83
0,66
0
1,5
0
0
0
1,6
2
2,83
0,66
1,4
1,5
3
4
0
0
2
1,96
1,96
1,96
1,96
1,96
1,96
1,96
1,96
1,96
5,547
1,294
0,000
2,940
0,000
0,000
0,000
3,136
3,920
5,547
1,294
2,744
2,940
5,880
7,840
0,000
0,000
3,920
0,016
0,004
0,000
0,008
0,000
0,000
0,000
0,009
0,011
0,018
0,004
0,009
0,010
0,020
0,026
0,000
0,000
0,013
2,8
0,66
0
1
0
1,5
2,8
0,66
1,4
1
0
1,5
41,4
22,8
23,8
22,8
23,8
41,4
115,920
15,048
0,000
22,800
0,000
62,100
115,920
15,048
33,320
22,800
0,000
62,100
0,325
0,042
0,000
0,064
0,000
0,174
0,385
0,050
0,111
0,076
0,000
0,206
179
50
50
23
121
50
50
23
60,6
11,1
6,7
25
10847,400
555,000
335,000
575,000
7332,600
555,000
335,000
575,000
30,427
1,557
0,940
1,613
24,358
1,844
1,113
1,910
0,125
0
2,52
170
6420
155
0,125
0
1,01
180
5330
128
278
276
288
56,31
0,63
56,31
0,115
0,000
0,966
33,669
11,154
23,943
100
2700
11,8
34,750
0,000
290,8
10135,8
3357,9
7207,6
30103,96
31860
1,05
0,097
0,000
2,036
26,851
11,345
24,482
100
2495
34,750
0,000
725,7
9572,7
4044,6
8728,0
35650,9
29441
0,82
12
HydroSense
Percentage of total
energy input (%)
Conve/nal
HydroSense
The forms of energy inputs used in cotton production are given in Tables 11 and 12. Energy
input is considered in two different forms: direct and indirect energy or renewable and nonrenewable energy. Site-specific management of the pilot areas consumes significantly less
indirect energy due to fertilizers and chemicals in comparison to the conventional system of
the control areas. This means that cotton production under the conventional management is
more intensive and could lead to environmental problems or waste of capital. In contrast,
site-specific management seems to be more environmental friendly.
Table 11. Energy consumption by energy source category for cotton production in 2010
Pilot
Energy forms
Eleftherio
*Direct energy
*Indirect energy
*Renewable energy
*Non-renewable energy
Gyrtoni
Direct energy
Indirect energy
Renewable energy
Non-renewable energy
Total energy input MJha-1
Conventional
17643,1
11647,8
568,1
28749,8
HydroSense
16897,6
6460,1
582,7
22775,0
8915,0
10540,9
543,1
18912,9
Percentage of total energy
input (%)
Conventional
39,91
26,4
1,28
65
HydroSense
52,16
19,9
1,79
70,3
9378,8
3925,2
556,4
12747,6
28,24
32,25
1,66
57,86
48,80
20,06
2,84
65,15
Direct energy
17699,4
16642,8
Indirect energy
9504,9
6617,1
Gentiki 2
Renewable energy
593,1
606,4
Non-renewable energy
26661,3
22653,6
*Direct energy : Human, diesel
*Indirect energy: Fertilizers, chemicals, machinery, seeds
*Renewable energy: Human seeds
*Non-renewable energy: Diesel, chemicals, fertilizers, machinery
44,36
23,82
1,48
66,70
51,52
20,48
1,87
70,13
The renewable energy (including human labor and seed energy) ratio is similar in both
systems. Small differences are attributed to human labor that consumes more energy in sitespecific management. The non-renewable energy (including diesel, chemicals, fertilizer and
machinery) ratio is about 65%-70% of total used energy, slightly greater in site-specific
management, although non-renewable energy in MJha-1 is greater in the conventional system.
The high ratio of non-renewable to total energy inputs causes negative effects on the
sustainability in agricultural production of small-scale farms (Dagistan et al 2009). Since
cotton requires a high amount of capital and input as the farm size increases, the proportion
of non-renewable energy will decrease.
13
The reduction of the total non-renewable energy (specifically in chemicals and fertilizers
usage) would have positive effects on the sustainability of cotton production as well as other
environmental effects.
Table 12. Energy consumption by energy source category for cotton production in 2011
Pilot
MJha-1
Energy forms
Gyrtoni
*Direct energy
*Indirect energy
*Renewable energy
*Non-renewable energy
Conventional
8915,1
11905,7
543,1
20277,7
Omorfo
chori
Direct energy
Indirect energy
Renewable energy
Non-renewable energy
18430,2
14211,5
591,8
32049,8
HydroSense
9380,3
8654,6
557,9
17477,1
Percentage of total energy
input (%)
Conventional
24,69
32,97
1,50
56,15
HydroSense
31,97
29,49
1,90
59,56
17712,8
8901,9
606,5
26008,2
36,57
28,20
1,17
63,60
46,05
23,14
1,57
67,6
Direct energy
18317,5
17373,6
Indirect energy
13260,8
9344,5
Gentiki
Renewable energy
591,8
605,1
Non-renewable energy
30986,6
26113,0
*Direct energy : Human, diesel
*Indirect energy: Fertilizers, chemicals, machinery, seeds
*Renewable energy: Human seeds
*Non-renewable energy:Diesel,chemicals,fertilizers,machinery
37,91
27,45
1,22
64,14
44,72
24,05
1,55
67,22
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