Life+ HydroSense Action 4: Deliverable 1 Energy use efficiency of cotton production in the Hydrosense project



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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

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 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

Item

Energy Content (MJunit-1)

Reference

Human labour (h)

1.96

Sing 2002, Sing and Chandra 2001,Mani et al. 2007

Tractor 50 KW (h)

41.4

Tsatsarelis 1993, Fluck 1985, Loewer at al. 1977

Plough (h)

22.8

Tsatsarelis 1993, Fluck 1985, Loewer at al. 1977

Sprayer (h)

23.8

Tsatsarelis 1993, Fluck 1985, Loewer at al. 1977

Wagon (h)

71.3

Tsatsarelis 1993, Fluck 1985, Loewer at al. 1977

Pump (h)

2.4

Tsatsarelis 1993, Fluck 1985, Loewer at al. 1977

Fertilizer N (kg)

60.60

Sing 2002, Sing and Chandra 2001, Mandal at al. 2002, Mani at al. 2007, Shrestha 1998

Fertilizer P (kg)

11.1

Sing 2002, Sing and Chandra 2001, Mandal at al. 2002, Mani at al. 2007, Shrestha 1998

Fertilizer K (kg)

6.7

Sing 2002, Sing and Chandra 2001, Mandal at al. 2002, Mani at al. 2007, Shrestha 1998

Insecticides (kg)

278

Hülsbergen at al. 2002, Dalgaard at al. 2001, Wells 2001, Meul at al. 2007

Fungicides (kg)

276

Hülsbergen at al. 2002, Dalgaard at al. 2001, Wells 2001, Meul at al. 2007

Herbicides (kg)

288

Hülsbergen at al. 2002

Seed (kg)

25

Sing 2002

Diesel (1)

56.31

Sing 2002, Sing and Chandra 2001, Mandal at al. 2002, Mani at al. 2007

Water for irrigation (m3)

0.63

Yaldiz at al. 1993

Cotton (kg)

11.8

Sign 202

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 drip-irrigated 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

Celia

Seed (kgha-1)

21-23

Land preparation

October-November (using plough)

Average tilling number

2

Thinning

February -March

Average number of thinning

2

Sowing

April

Management Zone delineation

April

Installation of sensors /equipments

April-June

Monitoring of sensors

June-August

Irrigation border period

June- August

Average number of irrigation events

13

Fertilization period

March –July

Average number of fertilization applications

3.5

Scanning of canopy for fertilization prediction

July –August (2 times average)

Spraying period

April-August

Weed seeker

June-August (1-2 times average)

Hoeing period

March-July

Harvesting period

September-October

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)

Yield (kgha-1)

Specific energy (MJkg-1)




Control

HydroSense

Control

HydroSense

Control

HydroSense

Eleftherio 2010

33079,03

26482,59

2666

2326

12,41

11,39

Gentiki 2010

30993,4

26286,7

2180

2064

14,22

12,74

Gyrtoni 2010

22669

16164

3122

2886

7,26

5,60






















Gyrtoni 2011

24726,8

21210,2

4409

3767

5,61

5,63

Omor/ori 2011

36783,5

30158,1

3139

3701

11,72

8,15

Gentiki 2011

35650,9

30103,9

2495

2700

14,29

11,15

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