Graduate Theses and Dissertations Iowa State University Capstones, Theses andDissertations

2009

Fossil energy use in conventional and low-external-input cropping systemsMichael James CruseIowa State University

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Recommended CitationCruse, Michael James, "Fossil energy use in conventional and low-external-input cropping systems" (2009). Graduate Theses andDissertations. 10664.https://lib.dr.iastate.edu/etd/10664

Fossil energy use in conventional and low-external-input cropping systems

by

Michael James Cruse

A thesis submitted to the graduate faculty

in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

Major: Crop Production and Physiology

Program of Study Committee:

Matthew Liebman, Major Professor Raj Raman

Mary Wiedenhoeft

Iowa State University

Ames, Iowa

2009

Copyright © Michael James Cruse, 2009. All rights reserved.

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TABLE OF CONTENTS

LIST OF FIGURES

LIST OF TABLES

ABSTRACT

CHAPTER 1. Energy Inputs in Agriculture

CHAPTER 2. Energy Analysis of LEI and Conventional Agriculture

CHAPTER 3. Study Conclusions

APPENDIX

BIBLIOGRAPHY

ACKNOWLEDGEMENTS

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LIST OF FIGURES

Fig. 1 Boundary System

Fig. 2 Average Energy Inputs

Fig. 3 Distribution of Energy Inputs in Cropping Rotations

Fig. 4 Energy Input per Crop for Individual Rotations

Fig. 5 Monetary Return to Land Management per MJ of Input Across Years

Fig. 6 Potential Energy Outputs

Fig. 7 Energy Gain Ratio

Fig. 8 Weight Output

Fig. 9 Weight/E Ratio

Fig. 10 Harvest Weight per Crop per Energy Investment

Fig. 11 Average Hourly Inputs

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LIST OF TABLES

Table 1 Analyses of Economic Performance of Three Contrasting

Cropping Systems

Table 2 Higher Heating Values of Harvested Materials

Table A.1 Grain Prices

Table A.2 Forage Prices

Table A.3 Commercial Nutrient Values

Table A.4 Economic Analysis Data

Table A.5 Field Operation Fuel Usage

Table A.6 Input Energy Values

Table A.7 Crop identities, planting and harvest dates, seeding rates, and

row spacings used in 2007-2008

Table A.8 Synthetic and organic soil fertility amendments for crops grown

in contrasting rotation systems in 2007-2008

Table A.9 Mechanical and chemical weed management practices for crops

grown in contrasting rotation systems in 2007-2008

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ABSTRACT

The production of fossil fuels will crest within the next decade and with reliance of modern

conventional agriculture on fossil fuel energy inputs, food production will be stressed.

Modern production practices also have negative impacts on the environment, but it is well

established that the ecological impact of conventional agriculture can be reduced by utilizing

alternative agricultural practices. Conservation tillage practices and diverse crop rotations

reduce the need for fossil fuel energy inputs while maintaining high crop yields. An ongoing

field experiment in the Central Corn Belt compares viable alternative systems to

conventional corn-soybean rotations. The alternative systems, referred to as Low-External-

Input (LEI) systems, incorporate diverse crop rotations (of 3 or 4 years) and alternative input

sources with conventional techniques. The goal of the present study was to compare the

energy use efficiencies of the LEI systems versus conventional agriculture. An energy

analysis performed on 6 years of field log data from the experiment. The 3- and 4-year LEI

systems reduced fossil fuel energy use by 41% and 56%, respectively, compared to the 2-

year conventional system. The primary energy input for all rotations was grain drying. The

monetary return, harvested weight, and potential energy of the 2-year and 4-year systems

were similar. Efficiency ratios, calculated as a ratio between the outputs and input energy

levels, were significantly improved in the LEI systems, with most of the variability due to

differences in energy inputs. According to the output measurements selected, LEI systems

are more energy efficient than conventional corn-soybean production systems, but the

increased management time required in the LEI rotations coupled with high priced labor and

cheap fossil fuels retards the adoption of these cropping systems.

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CHAPTER 1. Energy Inputs in Agriculture

Inputs of external energy are essential to production agriculture, and with ever

increasing demand for harvest production, and the potential for global energy crises, it is

critical to assess this input. A thorough analysis of energy input management requires two

things: an historical perspective of how current practices evolved, and a critical assessment of

current energy use impacts. To assess the energy efficiency of production systems, the next

step is to identify alternative management practices that may reduce energy inputs while

maintaining output levels and then test the use of those alternatives in comparison to

conventional agriculture practices.

History of Agricultural Energy Inputs

Hunter/gatherer societies and swidden agriculture systems were the first organized

food production systems and primarily relied on human energy inputs. Due to the natural

dispersal of food in environmental systems and the loss of fertility under early crop

production methods, a large amount of human energy was required to move between areas of

production. This large energy investment, coupled with low levels of production, led to low

energy use efficiency and small sustainable population densities (Pimentel and Pimentel,

1979). The primary limitation of these two production systems was found in the physical

limitations of man, which restricted both power output during field operations and the

duration of those field operations. The use of donkeys around 3000 B.C. and cattle in 2500

B.C. represent the first alternatives to human energy inputs in agriculture (Pimentel and

Pimentel, 1979). The use of animals increased the power available for field operations from

50-100 W to 400-800 W (Smil, 2008); this significant increase in power allowed the

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utilization of more advanced farming techniques, such as deep tillage, which increased crop

production. Animal agriculture energy use efficiencies were subject to region and species

variability, dependent upon many factors including, but not limited to: energy intake

requirements, the ability to digest certain types of forage at differing amounts, and the ability

to be managed effectively by humans. With the widespread use of animal power in

agriculture, energy use efficiencies increased and human populations were able to also

increase, but the increases were limited by the large amounts of energy required to sustain the

animals. The incorporation of fossil fuels into agriculture supplemented animal energy inputs

with non-renewable off-farm sources. Fossil fuel energy was more consistent over time and

easier to use in diverse tasks. The introduction of fossil fuels correlates well with significant

increases in global population levels (DESA, 2009), indicating that fossil fuels allowed for

larger and more consistent production. Fossil fuel energy can now be found in almost every

aspect of modern agriculture from direct to indirect inputs (Fluck, 1992).

Problems with Fossil Fuel Based Agriculture

While the use of fossil fuels is responsible for improvements in quantity and

consistency of agricultural production, their widespread use has created many fundamental

problems. The overarching negative impact of fossil fuel use in agriculture, independent of

input category, is that burning fossil fuels releases greenhouse gasses that are currently

causing significant changes in our global climate patterns (Matthes, 2008), but each category

of fossil fuel input has specific impacts beyond the release of greenhouse gases. For example,

the control of pest species under fossil fuel based agriculture typically involves the use of

broadband spraying chemicals, but this system can have detrimental effects both within and

3

outside the farm system. In Silent Spring, Carson (1962) showed how DDT negatively

impacts bird populations, over application and incorrect management has led to pest

resistance (as of 1986, 440 insects were known to be resistant to some pesticides (Council,

1989)), and more recent research has shown that the use of a number of pesticides still has

potential health hazards for humans (Merchant et al., 2006). The use of fossil fuels has

allowed for the development of complex equipment that has increased power outputs and has

the potential to destroy soil resources through intensive tillage operations. Intensive tillage

operations have been correlated with reduced soil carbon pools (Reicosky et al., 2002), thus

reducing soil structure and resiliency and increasing vulnerability to erosion processes. The

subsequent loss in topsoil has negative impacts on potential crop growth (Fenton et al., 2005;

Larney et al., 2000) through reductions in three soil quality measurements: resistance to

erosion, the ability to provide plants with nutrients, and the availability of a root environment

positive for root growth (Hussain et al., 1999). The use of fossil fuels has also allowed the

specialization of regional crop production through increased reliance on off-farm synthetic

fertilizers (Smil, 2008); for example, by relying on fossil fuel based fertilizers Iowa

agriculture has reduced crop diversity primarily to two row crops that contribute significantly

to average Iowa soil losses of 12 tonnes ha-1 yr-1 (NRCS, 2007). Furthermore, the over-

application of nutrient forms that can easily leak from the soil profile has been connected to

such problems as the Hypoxic Zone in the Gulf of Mexico (Burkart and James, 1999) and the

need to spend increased amounts of capital to clean drinking water supplies (Lewandowski et

al., 2008). Reliance on fossil fuels also makes agricultural production vulnerable to off-farm

energy market fluctuations, and coincidental evidence suggest that crises in supply regions

lead to economic stress in countries and industries dependent upon those fossil fuels (Barsky

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and Kilian, 2004). The possibility of peak global oil production occurring in the near future

(Asif and Muneer, 2007) suggest fossil fuel supplies and processes dependent upon those

supplies could be stressed and as urban populations increase over the next 30 years (DESA,

2009) fossil fuel use for agricultural production will be required to increase, especially in

developing regions of the world.

Categories of fossil fuel inputs

In a system-wide analysis, the distribution of direct fossil fuel energy inputs (fossil

fuels consumed on-farm) in United States agriculture consists of: 30% for field operations,

25% for transportation, 20% for irrigation, 12% for livestock, 8% for crop drying and 5%

miscellaneous; while indirect energy inputs (fossil fuel energy used off-farm to create other

inputs) are heavily weighted towards the creation of fertilizers (Fluck, 1992). Recent results

from Rathke et al. (2007) indicate that fertilizer and fuel are the primary energy inputs for a

corn, soybean production system; with the largest percentage in corn production being

fertilizer (40-60%) and then fuel consumption (17-36%). Other recent studies iterate that fuel

consumption is more important than fertilizer inputs in both soybean (Rathke et al., 2007)

and alfalfa production (Duffy, 2008; Meyer-Aurich et al., 2006). Other significant fossil fuel

inputs can be found in the use of pesticides and inputs from seed production. Understanding

these categories and the management decisions that involve them is essential to analyzing the

energy use efficiency of farm production today.

5

Fertilizers

In rain fed agriculture, chemical fertilizers are the largest energy input (Smil, 2008),

especially for grain crops such as corn (Karlen et al., 1995; Pimentel and Pimentel, 1979;

Rathke et al., 2007). Of the three macro nutrients, nitrogen fertilizer application tends to be

higher than the amounts of either potassium or phosphorus, especially in areas such as the

Central United States (NASS, 2008). Furthermore, the amount of energy needed to produce

potassium and phosphorus is nine times less than the amount of energy needed to produce

nitrogen fertilizer on a weight basis (Smil, 2001), decreasing their relative percentage in

fertilizer energy inputs. Historical sources of nitrogen include guano, sodium nitrate, and

cyanamide synthesis, and the first artificial production system developed used electric arcs,

reaching production energy efficiencies between 180-270 GJ/t N (Smil, 2001). Most modern

production systems rely on designs derived from the Haber-Bosch process and create

ammonia using pure substrate and high pressure. Although ammonia is a dangerous and

difficult-to-handle material, much of it is used as the primary substrate to create other forms

of fertilizer that allow for more wide use of nitrogen fertilizers (Smil, 2001). A shift to

natural gas as substrate and energy source for plant operation, coupled with plant design

improvements, has enhanced production energy use efficiencies, but many developing

regions still use coal as the primary energy source for nitrogen production, decreasing

average global efficiencies. Nitrogen fertilizers represent a proportionally large energy

investment for agriculture, as shown in Khakbazan (2009) increasing nitrogen application

from 20 kg ha−1 to 80 kg ha−1 produces a 40% increase in whole-farm energy requirements,

but many benefits have justified their application. For example, yields increase with

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increasing fertilizer rates up to certain levels of fertilization (Havlin, 2005; Jokela, 1992) and

even if global production of nitrogen used natural gas exclusively, less than 5% of annual

global consumption of natural gas would be used in this one process (Smil, 2001).

Three alternative sources of nutrients provide the opportunity to significantly

contribute to soil nutrient levels and reduce energy inputs from synthetic fertilizers: manure,

legume production and incorporation of crop residue. Manure nutrient supply is variable due

to factors such as the species of animal producing the manure, the diet of the animal, and the

subsequent processing of the manure (Troeh and Thompson, 1993). Additionally, nutrient

concentrations in manure may not match well with crop needs, and the low nutrient

concentrations require increased application rates, increasing energy required for application

procedures. Even with these challenges, manure has the potential to increase long term

nutrient levels in production systems by providing nutrients in more stable forms than

synthetic sources (Council, 1989; Wilkins, 2008), and the integration of crop and livestock

farming has been advantageous for reducing nitrate leaching and improving fossil fuel energy

use (Loges et al., 2008).

Legumes fix nitrogen through a symbiotic relationship with different species of

Rhizobium (Hopkins and Hüner, 2004). The consequence of this relationship is increased

nitrogen availability in the soil, making legume growth advantageous before crops that

demand high levels of nitrogen for optimum growth. Both cereals and legumes show

increased energy gains when grown in rotation with each other rather than continuously by

themselves (Meyer-Aurich et al., 2006; Rathke et al., 2007); furthermore, rotations that

include forage legume species have the lowest use of fertilizer energy (Noble and Christmas,

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2008). Disadvantages of legume production include decreased grain production in

comparison to cereal crops grown in similar areas. Also legume grain is usually less

digestible to humans than other species of grain crops (Smil, 2001). Historically different

geographical areas have been better able to use legumes – dependent upon growing season

length and available varieties – and their use has led to increased human carrying capacities

based upon area cropped (Smil, 2001).

Crop residues have lower nutrient concentrations than grain because nutrient content

of vegetative matter decreases upon the filling of grain. Sufficient levels of nutrients are still

present within crop residues after the harvest season, thus their incorporation into the soil

matrix has positive impacts on nutrient availability to the following crop (Stewart, 2008).

The positive impacts of crop residue incorporation can be attributed to maintaining surface

cover, stabilizing soil structure to prevent nutrient loss during erosion (Torbert et al., 1999),

and providing a long-term source of nutrients (Larson et al., 1972), the majority of which are

typically released within one year of incorporation (Alberts and Shrader, 1980). New

production systems advocate the removal of crop residue as another marketable product, but

while this removal may be offset by fertilizer inputs in certain areas, the loss of nutrients is

substantial enough that reliance on synthetic, fossil fuel derived inputs will have to increase

in order to achieve long-term desired production levels (Karlen et al., 1984).

Pesticides

The use of chemicals to control pests has significantly changed the profile of energy

inputs into conventional agriculture systems. Since the mid-1940s, 50,000 different

pesticides have been legally registered (Smil, 2008), with a disproportionate amount applied

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in United States agriculture (Fluck, 1992). Furthermore, application rates are unequal among

cropping systems. For example, vegetable crops require increased amounts of insecticides

and any crop that reduces other energy inputs, such as reduced fertilizer use in soybeans

compared to corn, will increase the energy inputs from chemicals on a percentage basis

(Pimentel and Pimentel, 1979). Smil (2008) places energy requirements for global pesticide

production at 500 PJ annually, which puts active ingredient production at 100-200 MJ/kg; but

as most active ingredients are applied at low levels per farmed area, production energy inputs

from this resource are considered to be relatively small when compared to other categories of

fossil fuel energy input. Pesticide use does have benefits to management. For example, by

using glyphosate-resistant crops, US farm production has saved $1.2 billion when compared

with other means of weed control (Gianessi, 2005), but alternative methods to control pests

could work in tandem with the use of pesticide application. Kempenaar and Lotz (2004)

published a list that includes preparing a false seed bed, utilizing a weed preventative soil

cover (ASOLFIL), applications of hot water, and use of a minimum lethal herbicide dose

(MLHD) on integrated systems. Integrated pest management (IPM) techniques have been

shown to be effective in many different cropping species such as apples (Blommers, 1994),

corn (Johnson et al., 1998), soybean (Kogan and Turnipseed, 1987), and wheat (Verreet et

al., 2000); IPM strategies have dramatic impact on improving energy use efficiency, as

natural control of pests reduces the amount of action required by the farm manager (Kogan,

1998). Other studies considering IPM strategies have shown that systems that reduce tillage

or herbicide inputs are more efficient at converting energy to yield than high-input systems

(Clements et al., 1995) and crop rotations that include forage species that disrupt pest’s life

cycles have the lowest use of herbicide energy (Noble and Christmas, 2008).

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Fuel Use

Fossil fuels are consumed directly on-farm in activities such as field operations, the

drying of harvest material, operation of irrigation pumps, and transportation of materials.

Early adoption of fossil fuels relied heavily on gasoline engines, but starting in the 1950s

there was a shift to diesel fuel and electricity. Diesel engines became the primary engines in

agriculture by the 1990s, due to multiple factors: diesel fuel contains more energy per liter –

36.4 MJ for diesel vs. 35 MJ for gasoline (Oak Ridge National Laboratory, 2009), diesel

engines deliver more mechanical energy per MJ of input (Cleveland, 1995), and diesel

engines have fewer moving parts, which increases the durability of the engines and their

individual life spans. This shift in fuel source significantly improved energy use efficiency of

cropping systems.

Energy use in field operations is directly related to equipment design, type of field

operation, and soil conditions. Early tractors were heavy and had smaller power outputs than

even horses, around 2.00-2.22 W/kg, but by the 1920s power output had surpassed that of

horses and has continued to improve to today’s complex machines, which have power

outputs per mass around 12.5-14.3 W/kg (Smil, 2008). A modern 175 kW tractor will on

average use 55 L/h of diesel fuel (Smil, 2008), an improvement over time that has increased

energy use efficiency of production systems dependent on tractor use. Energy use by tractors

during field operations is constantly tested, and recent publications from Iowa State

University Extension give estimated energy use values for these operations (Hanna, 2001).

This extension publication and other research show that management decisions such as

tillage method have significant impacts on both energy use and economic viability of

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production systems (Sijtsma et al., 1998). For example, conservation tillage decreased energy

inputs of both corn and soybean in Nebraska (Rathke et al., 2007) and no-till soybean has the

potential to maximize output to input energy ratios when compared to tillage operations

(Singh et al., 2008).

Artificial drying of grains is an essential component of crop production in regions

were natural dry down within the field is not possible. Forced heat drying of grain is

advantageous because it increases grain storage times, allows for decreased losses during

harvest due to cracking, and reduces exposure of the crop to adverse weather conditions in

the field (Anderson, 2009). The availability of cheap fossil fuel sources to operate drying

systems has led to a 110 fold increase in fuel used for drying purposes between 1945 and

1992 (Fluck, 1992). When crop drying is included in agricultural energy analysis, it has been

found to be a significant proportion of energy inputs (Leach, 1975). Production systems that

rely upon artificial grain drying are typically found in northern latitudes that still support long

season grain production, like the Central Corn Belt of the United States.

In areas where rain does not provide adequate water supplies, irrigation is required for

growth of crops that demand high amounts of water. Ancient irrigation systems utilized

human and animal powered machines in order to provide water supplies (Smil, 2008);

modern irrigation projects are powered by a variety of sources including gasoline and solar

power. Energy use in irrigation is dependent upon the energy source used to drive motors,

crop being irrigated, source of water, and timing of irrigation; significant energy savings can

be made in utilizing the optimal amount of irrigation at the correct time (Fluck, 1992). Even

with optimization of irrigation time in non-rain fed agriculture, it is the greatest energy input;

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this is important to American agriculture as irrigated corn acreage has increased from 1% in

1945 to 18% in 1990 (Fluck, 1992).

Over time many modes of transporting materials have been used – barges, carts – but

the use of gasoline, in an internal combustion engine, was the most significant improvement

in transportation history (Smil, 2008). The internal combustion engine allowed a more

diverse system of material distribution, and as engines became more powerful and lighter in

weight, 270 g/W in 1880 to 1 g/W today (Smil, 2008), these vehicles were used at increasing

rates. Other forms of transportation are still used because of their more efficient use of

energy; for example, rail uses 1/7 of the energy required by trucks to move the same amount

of material (Pimentel and Pimentel, 1979). As these alternative systems are restricted by

infrastructure availability however, trucks remain a dominant form of transportation.

Alternative System of Interest

Increased use of alternative management methods may provide an opportunity to

improve energy use efficiencies of modern production agriculture; Lockeretz et al. (1981)

studied organic and conventional systems in the Midwest United States, investigating the use

of alternative methods versus conventional methods and concluded that organic systems

require less fossil fuel input and are less vulnerable to energy market fluctuations. More

importantly, though, is the fact that the authors state, “there may be intermediate systems

that, from the combined viewpoints of productivity, profitability, and resource use, would

prove more attractive than either of the two systems we studied” (conventional or organic). A

recent study considering intermediate management practices, Liebman et al. (2008),

investigated alternative crop rotation systems in central Iowa to gain insight into how to

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increase agricultural food output without continuing to degrade the ecosystem. The

conventional system included in the Liebman study was composed of a corn, soybean

rotation that used only synthetic fertilizer sources and primarily chemical pest control, as to

be a representative of typical farming practices in the Central Corn Belt. The two alternative

systems included in the experiment were described as low-external-input (LEI) systems,

where the goal was to minimize off-farm inputs by using on-farm cycling and natural

processes to reduce the demands of crop production. The two LEI systems used longer crop

rotations (three and four years, respectively) and diverse crop species that provide the

benefits of disruption of pests’ life cycles and decreased soil degradation potential. The third

year of both LEI systems incorporated a legume forage with small grain production, giving

erosion protection through dense population structure, establishment advantages for legumes

through the use of the small grain nurse crop, and the N-related advantages that legumes

provide for subsequent crops. The legume in the 3-year rotation is considered a green manure

and plowed under at the end of the season, while the 4-year rotation uses the legume alfalfa

to provide another source of revenue in the fourth year of production. In the fall at the end of

both 3- and 4-year rotations, manure was applied and incorporated as a soil amendment.

Synthetic forms of fertilizer were also used, but only after field sampling indicated a need for

increased nutrient application; chemical control of pests was also reduced but not removed

completely in the LEI systems with the use of inter-row tillage in corn and soybean crops.

With the close relation between the ecological goals of the Liebman et al. (2008)

study and the concerns presented in this paper, it is critical to consider their findings to

ascertain feasibility of using LEI systems for further analysis. Corn and soybean production

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levels of the LEI systems were able to match levels produced in the conventional system, and

even with reduced production acres of both corn and soybean in the LEI systems, the

replacement crops provided marketable raw materials and their economic results showed the

4-year system had higher net returns than the 2-year system, with the 3-year system returning

slightly lower values than the 2-year rotation. Synthetic nitrogen application was reduced in

the LEI systems in comparison to the conventional production, and increased grain outputs

were attributed to manure and rotation impacts on soil quality and plant health. Weed control

was also accomplished in the two LEI systems without the use of large amounts of herbicide,

reducing possible ecological impacts of pesticide use. The information provided justifies the

use of this study to consider fossil fuel inputs in a conventional system vs. an alternative

system that has been shown to be more ecologically sound.

Energy Analysis

In order to assess energy use in the LEI vs. conventional systems, an energy analysis

must be completed. Energy analyses are a useful means through which to quantifiably

compare different production systems. According to Fluck (1992), the goal of an energy

analysis is to present an “objective analysis of the physical quantities of energy involved in a

process, system, etc.” or in other words, to analyze the quantifiable data of a system in terms

of energy to better understand the fluxes of energy present within the given system. Fluck

goes on in chapter 5 of Energy in Farm Production to lay out a methodology that clearly

describes the steps in completing an energy analysis:

1. Choose a boundary: An energy analysis is highly dependent upon where one begins

and ends measuring variables, so it is important for the investigator to first clearly

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understand the question that is desired to be answered and then design the boundary

system around these goals. Typical boundaries selected for use in agriculture systems

include the firm, ‘farm gate’, field and buildings.

2. Identify all inputs: Once boundaries are drawn, then the next step is to identify the

inputs of interest into the system and acquire quantifiable data for each of the

individual inputs. Care must be given to this step as the investigator must understand

what inputs are of interest. For example, if a study desires to consider only fossil fuel

energy use, then the removal of solar energy from the analysis is critical.

3. Assign energy values: This step is where inputs, and possibly outputs, are assigned

energy values.

4. Identify all outputs: Similar to step 2, the identification of outputs is dependent upon

the boundary system created in step 1. If a comparison is desired between different

systems within the boundary, it is imperative that outputs are selected that can be

assessed for all systems of interest. For example, mass per area of production can be

computed for all cropping systems, but analyzing protein output of two crops that

have different end users is not an effective output if boundaries are delineated at the

field scale.

5. Relate inputs to outputs: The final step is to compare energy inputs into the system

with the outputs of interest. This can be accomplished through utilizing ratios or

differences.

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As with any analysis system, flaws exist in doing an energy analysis. Fluck (1992)

makes mention of these weaknesses of energy analysis. First, one source of energy may be

presented using one unit of measurement and a second source may be reported as another,

and these two different units may not readily interchange with one another. This requires

multiple conversions that can reduce the accuracy of energy inputs and remove some power

behind a study. Second, multiple sources of material are used and each must be given some

value for energy input. This typically requires reliance upon multiple reporting sources that

may or may not use similar system analyses when acquiring their individual numbers,

possibly reducing accuracy. Their reporting structure may also differ, making data

acquisition difficult as well. Finally, multiple crops produce multiple forms of output; it is

difficult to measure across crop species in order to understand their individual efficiencies.

The energy analysis presented hereafter uses the framework presented above; drawing

system boundaries, identifying inputs and their values, identifying outputs and their values,

and comparing the inputs to the outputs. The experiment was designed to test two

hypotheses:

1. The LEI systems will reduce reliance on off-farm fossil fuel energy inputs compared

to conventional row crop production.

2. The output for the LEI systems will be able to effectively compete with conventional

agriculture based on energy use efficiency.

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CHAPTER 2. Energy Analysis of LEI and Conventional Agriculture

Introduction

With the need to increase energy use efficiency in agricultural production and an

understanding that many alternative practices by themselves have the potential to improve

the use of energy in comparison to conventional practices (Khakbazan et al., 2009; Lockeretz

et al., 1981; Noble and Christmas, 2008), it is important to analyze production systems that

incorporate multiple alternative practices over time to understand their impact on efficiencies

of energy use. A recent study presented by Liebman et al. (2008) compares production

systems that incorporate multiple alternative practices with conventional agriculture systems.

In this study, three rotations are run concurrently, a 2-year conventional system and 3- and 4-

year low-external-input (LEI) systems. The 2-year rotation of corn and soybean is managed

in a style comparable to average crop management in central Iowa. The 3- and 4-year

systems differ in their use of manure as a soil amendment and their use of mechanical

controls along with herbicides to control weeds. LEI systems, such as the 3- and 4-year

rotations evaluated by Liebman et al. (2008), also attempt to reduce the use of off-farm

inputs and increase ecologically beneficial diversity.

An energy analysis is required to understand the differences between fossil fuel use in

the conventional and LEI systems, but energy analyses are limited in their comparability

across studies by the fact that boundaries of each individual study rarely match perfectly with

the boundaries of another. So the fact that “many analyses stop at the farm gate, others add

just the leading indirect subsidies, and yet others make systematic efforts to account for

energies used to make and maintain field machinery” (Smil, 2008) makes comparing

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conventional and alternative practices from different studies essentially impossible. The

presence of both conventional and alternative practices within the same field experiment

allows for the use of similar energy values and the same system boundary design, giving

power to comparisons made across the production schemes. The boundary for this study

takes a reductionist point of view in that it only considers the on-field management of the

cropping systems, which is a departure from past studies of agriculture energy use that have

utilized wide system boundaries to attempt to quantify more holistically the energy use in

agriculture (Hoeppner et al., 2006; Karlen et al., 1995). While this does reduce the scope of

energy inputs, it focuses the research on key aspects of the systems that are of interest,

providing a clearer picture of specific system differences.

Materials and Methods

Research Site

The analysis was conducted on a long term rotational study run on the Iowa State

University Marsden Farm, in Boone County, Iowa. The study was designed as a randomized

complete block design with 4 blocks and a total of 36 plots. With 9 plots per block, each

stage of each of three rotations is represented once in each block, every year of the study.

Specific management strategies such as crop varieties, weed management techniques, soil

testing, and soil amendments are described by Liebman et al. (2008) and in the appendix of

this paper.

During operation of the experimental plots, a log was kept of all field activities,

including inputs and outputs of the three systems. Reported harvest weight and moisture data

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allowed for the calculation of drying cost and listing of field operations with equipment used

allowed for connection between actual field activities and estimated fuel usage.

System Analysis and Energy Inputs

In doing a system analysis it is first most important to understand the subject of study

and determine the boundaries of that subject (Fluck, 1992). To accomplish this, a boundary

system was created that depicts the inputs and outputs that describe the crop production

aspect of an integrated farming practice (Figure 1). The system is separated into three major

sections – off-farm, on-farm, and on-field – and the energy measurements are made on

components that flow across the on-field boundary.

Off-Farm

On-Farm

On-Field

Crop Production

Manure, Money

Feed, Bedding, Money

Inputs: Chemicals, Fertilizer, Fuel, Seed

Outputs: Harvest

Return: Money

Livestock Production

Fig. 1 Boundary System: Depiction of the system of interest. Inputs and outputs that cross the on-field boundary are of interest to this analysis.

19

When considering which energy components to measure, it was decided to look at

what Gopalakrishnan (1994) considered to be “economic energy,” or “forms of energy that

command a price.” This reduces the spectrum of energy sources down to those that can be

directly controlled by an individual’s management decisions; thus, sources like the sun are

not included in that analysis while fuel usage is. Other assumptions within the boundary

system are discussed in the Assumptions section below.

The components in the field log were divided into separate years, then individual

crops, and finally into 5 categories of energy input. The five categories were selected to help

better understand the difference between energy input dependency between rotations and the

five categories selected were: field operations, seed, fertilizer, pesticides, and grain handling.

Grain handling involves hauling harvested material out of the field and drying the material to

standard storage conditions. A list of field operations and fuel usage can be found in Table

A.5. Published literature was then used to provide energy values (Table A.4) for the inputs

into the system and total rotational inputs were computed and compared across the three

rotations.

Economics

Two different analyses were completed using the model set forth by Liebman et al.

(2008). Data sources for economic inputs included Estimated Costs of Crop Production in

Iowa (Duffy, 2008) and USDA statistics (USDA-NASS, 2009). In one economic analysis,

manure is considered a waste product of the livestock operation and thus is regarded as being

free except for the application costs, which is consistent with the analysis pattern of Liebman

et al. (2008). According to Karlen et al. (1995), treating manure as a free input is the critical

20

assumption for increasing the profitability of alternative systems that utilize manure, so a

second economic analysis was done giving commercial prices to the nutrients within the

manure as to present the highest potential value of manure cost. Crop prices for 2003-2007

are from USDA statistics and 2008 prices are projected estimates by university extension

specialist. Crop subsidies are not included in this analysis.

Energy Content

A random sample of all harvested material was collected in 2008 from individual

plots at each crops harvest date. The samples were then dried at 60°C for 48 hours and

stored until all samples were collected. Samples were then ground using a Willey Mill

Model 2 and collected after passing through a 2 mm sieve. The ground samples were

processed to find their calorific values at The Central Analytical Laboratory, University of

Arkansas Division of Agriculture using the protocol found in the “Standard test method for

gross calorific value of coal and coke by the adiabatic bomb calorimeter, in gaseous fuels;

coal and coke” (ASTM, 1989). A single year sample was accepted as representative across

all years because similar crop varieties were grown throughout the study and fertilizer rates

did not vary widely throughout the study, which has been shown to influence corn seed

composition (Miao et al., 2006).

Weight Output

Weight output is based on the dry weight of harvested material. Totals were averaged

across years for each rotation and then converted to common units.

21

Assumptions

1. Energy inputs included in the analysis are comprised of purchased forms of energy

because that is within the control of the farm manager

2. In the first economic analysis, manure is not considered to be a purchased energy

input because manure is considered by many to be a waste product on most Iowa

farms, but the energy used to apply the manure is included in the field operations

category

3. In the second economic analysis, manure nutrients are valued at commercial nutrient

prices to present the highest range of manure nutrient prices

4. Farm infrastructure is not included in the calculations as it is outside the boundaries

of the field system and available data is out of date

5. 2002 data was not used as this was the first year of the experiment and no rotational

effect had been established

6. 2004 lime application was not included in the analysis as it was placed on a limited

number of plots and the application was considered a way of equalizing plot

characteristics based on management before the rotation began

7. Drying grain is included in the analysis but storage of grain is not as it is a part of

marketing the grain, not producing it

22

8. All field operations have been idealized to match normal farm scenarios (i.e. alfalfa

was always considered to be cut, raked, and baled even if plots were chopped due to

weather and time complications)

9. All drying numbers are based from a high temperature system with air circulation

10. Field operations that were not directly listed in “PM 709: Fuel required for field

operations” were associated with the closest similar field operation for fuel efficiency

11. Crop prices were from the USDA data bank and represent the average price for the

entire year after harvest in the state of Iowa

12. Labor is accounted for in the economic analysis but since actual energy input from

labor is so small, the energy input is omitted

13. Hauling distances are limited to ½ mile one way for the following reasons:

information on traveling distances for crops, especially in Iowa, is not available and

beyond removing the crop from the field, the distance traveled is largely dependent

on marketing choices which are outside of the focus of this project

14. All seeds are considered to be produced at 1.5 times the production energy of regular

production grain except for corn, in which a factor of 4.7 is used (Shapouri and

Duffield, 2003)

Statistics

All statistics were done using SAS version 9.1.3 and the PROC MIXED function with

a RCBD design. Energy in values were tested using rotation as the fixed effect and year as a

random effect. All other data was tested by adding blocks within years and year by rotation

to the random statement. Significance

rotations and assessing the corresponding p

graphs are the standard errors

Results

Energy Inputs and Distribution

0

4

8

12

16

20

2 Yr 3 Yr 4 Yr

GJha-1

yr-1

Rotation System

16

0

4

8

12

16

20

2 Yr 3 Yr

GJha-1

yr-1

Rotation System

Fig. 3 Distribution of Energy Inputs in Cropping Rotations: Average annual energy inputs depicted in the five selected categories. ‡ G.H. = Grain Handling; F.O. = Field Operations; P = Pesticides; F = Fertilizers

Fig. 2 Average Energy Inputs: Fossil fuel energy inputs averaged across all years for each rotation.

23

random effect. All other data was tested by adding blocks within years and year by rotation

to the random statement. Significance was found by estimating the difference between

rotations and assessing the corresponding p-values. Error bars presented in the following

graphs are the standard errors of treatments.

Energy Inputs and Distribution

The 2-year rotation had the largest

energy investment (Figure 2), while the 3

4-year rotation showed a reduction in energy

investment when compared to the 2

rotation of 41% and 56%, respectively. All

rotational energy inputs were significantly

different from each other.

4 Yr

G.H. ‡

Seed

F.O. ‡

P ‡

F ‡0

5

10

15

20

25

30

2 Yr 3 Yr 4 Yr

GJha-1

yr-1

Rotation System

9

7

4 Yr

Rotation System

Fig. 4 Energy Input per Crop for Individual Rotations: Average energy inputs across all years for individual rotations and crops. ⁰ C = Corn; SB = Soybean; SG = Small Grain; A = Alfalfa

Fig. 3 Distribution of Energy Inputs in Cropping Rotations: Average annual energy inputs depicted

‡ G.H. = Grain Handling; F.O. = Field = Fertilizers

y Inputs: Fossil fuel energy averaged across all years for each rotation.

random effect. All other data was tested by adding blocks within years and year by rotation

was found by estimating the difference between

values. Error bars presented in the following

year rotation had the largest

energy investment (Figure 2), while the 3- and

year rotation showed a reduction in energy

investment when compared to the 2-year

of 41% and 56%, respectively. All

rotational energy inputs were significantly

4 Yr

C ⁰

SB ⁰

SG ⁰

A ⁰

Fig. 4 Energy Input per Crop for Individual Rotations: Average energy inputs across all years

C = Corn; SB = Soybean; SG = Small Grain; A

24

The distribution of energy inputs is shown here in two different ways. In Figure 3, each

individual rotation is divided into its 5 categories: fertilizer, pesticides, field operations, seed,

and grain handling. This shows that grain handling is the most demanding energy category

in all three rotations, requiring input percentages of 51% in the 2-year rotation, 58% in the 3-

year, and 49% in the 4-year rotation. Other trends include an increase in the field operation

category percentage from the 2-year to the 3-year and finally to the 4-year rotation. Fertilizer

input percentages did the reverse, dropping in percentage when moving from the 2-year to

the 3- and 4-year rotations. The second breakdown of the energy inputs is in Figure 4, where

the average inputs are shown for each crop in each rotation. This shows a reduction in the

energy inputs for corn production in the 3- and 4-year rotations when compared to the 2-year

rotation (18% and 31% respectively). The soybean crop also shows a reduction of 19% for

the 3-year rotation 11% for the 4-year rotation, when compared to the conventional system.

Economic Return

Analysis #1 Analysis #2 Rotation US $ Return ha-1 yr-1 US $ Return MJ-1 US $ Return ha-1 yr-1 US $ Return MJ-1

2 Year $614 $0.047 $614 $0.047 3 Year $605 $0.084 $512 $0.073 4 Year $624 $0.107 $555 $0.097

Using the assumption that manure is a free input to cropping systems, Liebman et al.

(2008) found that during 2003-2006, the economic return to land and management was

greatest in the 4-year rotation, lowest in the 3-year rotation, and intermediate for the 2-year

rotation. Analysis of this experiment for 2003-2008, indicates a similar pattern, with no

Table 1 Analyses of Economic Performance of Three Contrasting Cropping Systems: Monetary return to land and management and monetary return to land and management per MJ of energy investment averaged across all years. Analysis #1 considers manure to be a free input and Analysis #2 values manure nutrient inputs at commercial nutrient prices.

25

significant differences between the rotations (Table 1, Analysis #1). By combining this

information with the energy inputs for each rotation, the revenue returned to land and

management per megajoule of input can be calculated (Table 1); here the 2-year rotation is

significantly different than both the 3- and 4-year rotations with the 3- and 4-year rotations

being similar. Using the 2-year rotation as the control group and comparing the other two

rotations to it, an increase in economic return per megajoule of input of 80% and 130% can

be found in the 3- and 4-year rotations, respectively. Considering the nutrients found in

manure to be valued at commercial nutrient costs reduced annual return to the 3- and 4-year

per hectare by $93 and $69, respectively. In the second analysis a significant difference was

found between the 2-year and 3-year returns, but the 2-year and 4-year were not significantly

different. The ratio of return to land and management to energy inputs was only significantly

different for the 4-year rotation in relation to the 2-year system, for this analysis.

The data over the six years of the study are very consistent (Figure 5). Deviations

from the general pattern observed in 2007 can be explained by several different factors.

First, the numerators in the ratio of money returned to energy invested increased for all three

$-

$0.05

$0.10

$0.15

$0.20

$0.25

$0.30

2003 2004 2005 2006 2007 2008

US $ MJ-1

ha-1

Year

2 Yr

3 Yr

4 Yr

Fig. 5 Monetary Return to Land and Management per MJ of Input Across Years: Average monetary return per MJ of energy investment on a hectare basis.

26

rotations due to higher prices for agricultural commodities. Second, the denominators in

2007 were lower than the first four years of the experiment. In years 2003, 2004, and 2006,

it was necessary to incorporate added fertilizer due to soil test results, which increased the

quantity of energy inputs. In 2005, the corn was harvested at a moisture content of 22%,

which required added energy for drying the corn to 15.5%. This again increased the amount

of energy input, which drove down the ratio in 2005. When comparing the three production

systems from 2006 to 2007, all increased at a relatively equal rate (by a factor of 3.57, 4.16,

and 4.33 for the 4-year, 3-year, and 2-year rotations respectively).

Energy Output

Crop kJ/g Corn Grain 17.57 ± 0.21

Soybean Grain 22.25 ± 0.13 Oat Grain 18.18 ± 0.06 Oat Straw 16.60 ± 0.25

Triticale (grain and straw) 16.47 ± 0.24 Alfalfa 15.46 ± 0.39

When utilizing the energy content values presented in Table 2, the average potential

energy from each rotation can be computed on an average annual basis. The data (Figure 6)

show that the conventional system produces the most energy, the 3-year the least, and the 4-

year rotation is intermediate; with significant differences between the 2-year and 3-year

rotations and the 3-year and 4-year rotations. When comparing this output with energy input,

the ratios show an increase in energy out for energy input of 67% and 135% for the 3- and 4-

Table 2 Higher Heating Values of Harvested Materials: Energy content per mass of 2008 harvested material.

year systems, respectively, when compared to the conventional 2

all differences are significant.

Weight Output

Compared to the 2-year rotation, weight out

4% and up 7% for the 4-year rotation

the 3-year and 4-year rotations

efficiencies increased by 70% in the 3

both were compared to the conventional system and all differences are signif

The weight output per MJ input on a crop basis

significantly higher weight return on energy invested than the conventional system and that

the increased field operations required for the management i

system reduces the efficiency of the small grain production in comparison to the 3

system (Figure 10).

124

0

20

40

60

80

100

120

140

2 Yr 3 Yr

GJ ha-1

yr-1

Rotation System

Fig. 6 Potential Energy Outputs: Systemoutputs averaged across all years for each rotation.

27

when compared to the conventional 2-year system (Figure

all differences are significant.

year rotation, weight output for the 3-year rotation wa

year rotation and the only significant difference was found

year rotations (Figure 8). When compared with energy input values,

% in the 3-year system and 149% in the 4-year system

both were compared to the conventional system and all differences are signif

per MJ input on a crop basis shows that the longer rotations had a

higher weight return on energy invested than the conventional system and that

the increased field operations required for the management in the third year of the 4

reduces the efficiency of the small grain production in comparison to the 3

116 126

4 Yr

Rotation System

8.39

14.01

0.00

6.00

12.00

18.00

24.00

2 Yr 3 Yr

Rotation System

uts: System energy averaged across all years for each

Fig. 7 Energy Gain Ratio: Energy output per energy invested, averaged across all experiment years.

year system (Figure 7) and

year rotation wa down by

and the only significant difference was found between

. When compared with energy input values,

year system, when

both were compared to the conventional system and all differences are significant (Figure 9).

shows that the longer rotations had a

higher weight return on energy invested than the conventional system and that

third year of the 4-year

reduces the efficiency of the small grain production in comparison to the 3-year

14.01

19.75

4 Yr

Fig. 7 Energy Gain Ratio: Energy output per energy invested, averaged across all experiment

6675

0

2000

4000

6000

8000

2 Yr 3 Yr

kg ha-1

yr-1

Rotation System

0

1

2

3

4

5

Corn SB Small Grains

kg MJ-1

Fig. 8 Weight Output: System dry weightaveraged across all years for each rotation.

Fig. 10 Harvest Weight per Energy Investment: Weight output per energy invested, averaged across all experiment years. Shown for each individual crop.

28

6377 7165

3 Yr 4 YrRotation System

0.450

0.767

0.000

0.300

0.600

0.900

1.200

1.500

2 Yr 3 Yr

kg MJ-1

Rotation System

Small Grains

Alfalfa

2 Yr

3 Yr

4 Yr

dry weight outputs averaged across all years for each rotation.

Fig. 9 Weight/E Ratio: Weight output per energy invested, averaged across all experiment years.

Fig. 10 Harvest Weight per Energy Investment: Weight output per energy invested, averaged across all experiment years. Shown for each

0.767

1.122

4 YrRotation System

Fig. 9 Weight/E Ratio: Weight output per energy invested, averaged across all experiment years.

29

Discussion

It is understood that the measurements selected in this analysis do not show the

entirety of the agriculture system as inputs in farm infrastructure are critical components of

energy use and many of the harvested materials are produced for something other than their

weight, energy content, or monetary value. The concept behind this analysis was to focus

directly on the cropping systems and their management, thus requiring the drawing of distinct

boundaries around the field system. The first boundary removed machinery and buildings as

these factors can differ even when multiple managers utilize the same production systems.

The second boundary required using measurements that were consistent across the three

systems at the time that the product left the field, eliminating the need to consider other

output measurements like nutrients, protein, and oil contents.

On an energy input basis, the 3- and 4-year LEI rotations required lower inputs than

the 2-year conventional rotational system. Most system outputs were not found to be

significantly different, meaning that most variability in energy use efficiency ratios was due

to energy input values. When considering manure as a free input in the first economic

analysis the return to land and management for all systems was similar, while the use of

commercial nutrient costs for manure nutrients in the second analysis reduced returns for the

LEI systems. In both analyses the 4-year rotation was still significantly more efficient in

energy use than the 2-year rotation (Table 2). Both economic analyses do not completely

capture the true impact of fertilizer use, as the true economic value of manure probably lies

between the free and commercial costs due to handling difficulties and individual

agreements. Most of the energy savings between the 2-year system and the LEI systems is

30

due to the reduced use of nitrogen fertilizer, which is consistent with the results of past

research (Hoeppner et al., 2006). The importance of crop management at harvest time of corn

is the most obvious outcome of this study, as grain handling accounts for the largest input

percentage for all three rotations. This will be difficult to manage for in northern latitudes

when both size of operation and length of season reduce the amount of time farmers can

leave corn in the field to naturally dry down. Management improvements could lead to

increases in energy use efficiency of the 2-year system in regards to fertilizer, while the 3-

and 4-year systems possibly have room for improvement in their use of fuel in field

operations.

Based on the ratios presented, both LEI systems are more efficient energy users than

the conventional system. Furthermore, the incorporation of alfalfa in the 4-year rotation is

considered important as it is the only LEI system that matched the two year production

economically in both analyses. The 2007 data also reinforce the importance of the late spring

nitrate test in Iowa, as this test can help lower fertilizer nitrogen applications, and harvesting

at the correct crop moisture levels. With both of these two activities done correctly and the

influx of higher prices for agronomic raw materials, the 2007 data show that efficiency ratios

can be improved greatly.

Energy is not the only variable considered when choosing a management system and

another of great interest is the increase in management time required for the LEI systems in

relation to the conventional systems, and the current analysis provides an indication of the

impact of time on management decisions.

Hourly inputs in the three rotations

seen in energy inputs, with the 4

rotation having the smallest input. As compared to the 2

increased by a factor of 1.54 and the 4

differences were found to be significant.

fact that the incorporation of small grains and alfalfa into the 3

offset some of the extra time investment

soybean and corn production

agriculture practices is based upon expensive labor prices and availability of cheap fossil

fuels. The concept of time and labor investment requires more investigation, but

whole give a good indication of the

energy consumption patterns in agriculture.

1.7

0.000

1.000

2.000

3.000

4.000

2 Yr

Hr ha-1 yr-

1

Rotation System

Fig. 11 Average Hourly Inputs: Systeminputs averaged across all years for each rotation.

31

Hourly inputs in the three rotations (Figure 11) showed the opposite trend that was

seen in energy inputs, with the 4-year rotation having the largest inputs and the 2

tation having the smallest input. As compared to the 2-year rotation, the 3

increased by a factor of 1.54 and the 4-year rotation increased by a factor of 1.91. All

differences were found to be significant. While this analysis does not take into account the

he incorporation of small grains and alfalfa into the 3- and 4-year systems will

offset some of the extra time investment into parts of the year that do not overlap with peak

soybean and corn production, it does give an indication that the use of conventional

agriculture practices is based upon expensive labor prices and availability of cheap fossil

fuels. The concept of time and labor investment requires more investigation, but

a good indication of the positive impacts that using LEI rotations can have on

energy consumption patterns in agriculture.

2.7

3.4

3 Yr 4 YrRotation System

Hourly Inputs: System hour averaged across all years for each rotation.

showed the opposite trend that was

year rotation having the largest inputs and the 2-year

year rotation, the 3-year rotation

year rotation increased by a factor of 1.91. All

e into account the

year systems will

into parts of the year that do not overlap with peak

tion that the use of conventional

agriculture practices is based upon expensive labor prices and availability of cheap fossil

fuels. The concept of time and labor investment requires more investigation, but the data as a

positive impacts that using LEI rotations can have on

32

CHAPTER 3. Study Conclusions

Study Objectives

With increased reliance of modern agriculture on fossil fuel inputs (Smil, 2008) and the

negative impacts those inputs have given rise to, it is imperative that alternative production

practices that reduce reliance on fossil fuels are better understood. Many studies have

considered individual alternative practices such as conservation tillage, reduced use of

pesticides, and reduced use of nitrogen fertilizers, but an analysis of systems that incorporate

multiple alternative practices is critical to understanding the potential alternative systems

have in reducing fossil fuel inputs in agriculture. To that effect, the low-external-input

systems presented by Liebman et al. (2008) offered an opportunity to study management

systems that incorporate multiple alternative energy use practices with conventional

agriculture, and more specifically provided the chance to test the hypotheses:

1. The LEI systems will reduce reliance on off-farm fossil fuel energy inputs compared

to conventional row crop production.

2. The output for the LEI systems will be able to effectively compete with conventional

agriculture based on energy use efficiency.

Study Findings

Overall energy use in the 3- and 4-year rotations was significantly reduced in

comparison to the 2-year conventional rotation. The distribution of energy inputs showed a

clear reduction in energy inputs for both fertilizer and grain handling in the integrated

systems, while proportionally the conventional system was less reliant on fuel use than the

33

LEI systems. The LEI systems also showed a reduction in the amount of energy required to

produce a corn crop. All output analysis were similar for the 2-year conventional and 4-year

LEI system, while the 3-year LEI system had statistically lower values for weight output,

energy output, and economic return when manure is given commercial nutrient value. The 4-

year rotation did not have the reduced outputs seen in the 3-year rotation, illustrating the

importance of including alfalfa production in the rotational systems to reach production

levels seen in conventional systems. The ratios derived from comparing outputs to energy

inputs all showed significant differences between the three rotations with the LEI rotations

having larger returns per unit of energy invested than the convention production system and

other than the small differences described in the 3-year system output, the variability among

systems in the energy use efficiency ratios stems from the variability in energy inputs. Over

six years of operating the experiment, the data were very consistent even while there were

large fluctuations in weather conditions, input costs, and crop prices; the data show that even

during these variance events, relative performance of the three systems to one another

remains constant.

Study Conclusions

Quantitatively, the LEI systems used less energy than the conventional systems in six

years of production, and of the LEI rotations, the 4-year rotation used less energy annually

than the 3-year rotation. This lower amount of energy input coupled with fairly similar output

amounts of all three outputs measured led to increased energy use efficiency in the LEI

rotations, again with the 4-year system outperforming the 3-year system, with most of the

variability due to differences in energy inputs. Considering manure as a free input is a critical

34

assumption, but even when giving manure nutrients a value comparable to commercial

fertilizers, the 4-year rotation still produced economically at relatively similar levels as the 2-

year system and was significantly more efficient than the 2-year system in energy use;

considering manure at full commercial costs is not likely a true assessment as manure prices

would likely be reduced because of handling difficulties and increased management

requirements, which indicates a reduced costs for nutrient content and increased revenue for

the LEI rotations. This study thus indicates that based upon energy input, the alternative

production methods used in the LEI systems will outperform conventional Iowa production

systems on a fossil fuel input basis; so as fossil fuel energy becomes more expensive, LEI

systems should become more attractive as they use input energy more efficiently to produce

relatively the same amount of output.

Future Analysis

Until economics are directly correlated with energy use, energy studies will not

influence cropping practices, because as Smil stated, “studies of embodied energy have not

displaced standard economic analyses because neither individuals nor corporations base their

decisions on the minimization or optimization of energy costs” (Smil, 2008), and as shown in

Figure 11, the amount of time required to operate the 3- and 4-year systems is significantly

larger than the 2-year system. This means that even though the 2-year system operates on

tighter profit margins, managers are able to utilize more ground and actually increase

economic return to the farm as a unit. The time factor is further compounded if the 3- and 4-

year cropping systems are considered to be integrated with livestock production, which

would require increased year-round management time.

35

Further analysis of the economic returns for the different systems should include this

time factor and would be of great interest if they could point to incentives that would

encourage the use of the integrated systems. Some topics related to this subject that I believe

should be analyzed include: the impact of incorporating the livestock system with the crop

system and how that may potentially reduce the amount of available management time for

crop operation, the effect of both land value and production level as these are not consistent

over any relative amount of land area and provide further complication to the time issue, and

how policy could provide incentives for the use of the LEI or similar systems.

36

APPENDIX

Table A.1 Grain Prices (Barnhart, 2009; USDA-NASS, 2009; Wisner, 2009) Crop Year U.S. $/kg Corn 2003 $0.09 Corn 2004 $0.08 Corn 2005 $0.08 Corn 2006 $0.12 Corn 2007 $0.17 Corn 2008 $0.14

Soybeans 2003 $0.28 Soybeans 2004 $0.21 Soybeans 2005 $0.20 Soybeans 2006 $0.24 Soybeans 2007 $0.39 Soybeans 2008 $0.32

Small Grains 2003 $0.09 Small Grains 2004 $0.08 Small Grains 2005 $0.08 Small Grains 2006 $0.13 Small Grains 2007 $0.18 Small Grains 2008 $0.19

37

Table A.2 Forage Prices (Barnhart, 2009; USDA-NASS, 2009)) Crop Year U.S. $/Tonne Straw 2003 $66.14 Straw 2004 $66.14 Straw 2005 $66.14 Straw 2006 $66.14 Straw 2007 $88.18 Straw 2008 $88.18 Alfalfa 2003 $90.38 Alfalfa 2004 $93.69 Alfalfa 2005 $89.29 Alfalfa 2006 $97.00 Alfalfa 2007 $126.76 Alfalfa 2008 $150.64

Table A.3 Commercial Nutrient Values (Duffy, 2008) Year N ($ kg-1) P2O5 ($ kg-1) K2O ($ kg-1) 2003 $0.44 $0.55 $0.26 2004 $0.55 $0.62 $0.33 2005 $0.66 $0.73 $0.40 2006 $0.77 $0.82 $0.51 2007 $0.68 $0.82 $0.51 2008 $1.01 $1.10 $0.60

38

Table A.4 Economic Analysis Data Analysis #1: Manure Value: Free Analysis #2 - Manure Value: Commercial Nutrient Value

Year Rotation Crop

Gross Revenue ($ ha-1) Costs ($ ha-1)

Return to Land &

Management ($ ha-1) Year Rotation Crop

Gross Revenue ($ ha-1) Costs ($ ha-1)

Return to Land &

Management ($ ha-1)

2 $1,154.23 $540.29 $613.94 2 $1,154.23 $540.29 $613.94

3 $1,019.89 $415.86 $604.02 3 $1,019.89 $508.33 $511.56

4 $1,062.39 $438.08 $624.31 4 $1,062.39 $507.43 $554.96

2003 2 $981.90 $491.84 $490.06 2003 2 $981.90 $491.84 $490.06

2004 2 $886.55 $447.62 $438.92 2004 2 $886.55 $447.62 $438.92

2005 2 $880.46 $470.56 $409.90 2005 2 $880.46 $470.56 $409.90

2006 2 $1,118.33 $620.92 $497.41 2006 2 $1,118.33 $620.92 $497.41

2007 2 $1,639.28 $475.65 $1,163.63 2007 2 $1,639.28 $475.65 $1,163.63

2008 2 $1,418.87 $735.16 $683.71 2008 2 $1,418.87 $735.16 $683.71

2003 3 $818.33 $352.22 $466.11 2003 3 $818.33 $390.39 $427.93

2004 3 $722.19 $383.41 $338.78 2004 3 $722.19 $494.44 $227.74

2005 3 $814.94 $390.03 $424.91 2005 3 $814.94 $490.59 $324.35

2006 3 $1,048.12 $470.08 $578.04 2006 3 $1,048.12 $559.74 $488.37

2007 3 $1,450.73 $417.54 $1,033.19 2007 3 $1,450.73 $513.63 $937.10

2008 3 $1,265.02 $481.92 $783.10 2008 3 $1,265.02 $601.17 $663.84

2003 4 $799.58 $315.13 $484.45 2003 4 $799.58 $343.77 $455.82

2004 4 $791.30 $365.85 $425.46 2004 4 $791.30 $449.12 $342.18

2005 4 $889.18 $383.19 $505.99 2005 4 $889.18 $458.62 $430.57

2006 4 $1,121.87 $513.32 $608.55 2006 4 $1,121.87 $580.57 $541.30

2007 4 $1,385.26 $416.60 $968.66 2007 4 $1,385.26 $488.67 $896.59

2008 4 $1,387.15 $634.40 $752.75 2008 4 $1,387.15 $723.84 $663.31

39

Table A.4 Continued * C = Corn; S = Soybean; SG = Small Grain; A = Alfalfa Analysis #1 - Manure Value: Free

Year Rotation Crop

Gross Revenue ($ ha-1)

Costs ($ ha-1)

Return to Land &

Management ($ ha-1)

2003 2 C $1,119.56 $580.80 $538.76 2004 2 C $1,005.18 $523.85 $481.33 2005 2 C $951.17 $630.51 $320.66 2006 2 C $1,521.14 $788.09 $733.05 2007 2 C $1,908.95 $633.21 $1,275.74 2008 2 C $1,697.58 $966.15 $731.43 2003 2 S $844.25 $402.89 $441.36 2004 2 S $767.91 $371.40 $396.51 2005 2 S $809.74 $310.61 $499.13 2006 2 S $715.52 $453.75 $261.77 2007 2 S $1,369.61 $318.09 $1,051.53 2008 2 S $1,140.16 $504.17 $635.99 2003 3 C $1,099.07 $502.36 $596.71 2004 3 C $1,007.64 $541.27 $466.37 2005 3 C $1,091.33 $555.11 $536.22 2006 3 C $1,551.08 $702.91 $848.17 2007 3 C $1,994.61 $611.94 $1,382.66 2008 3 C $1,769.26 $792.39 $976.87 2003 3 S $815.44 $298.41 $517.03 2004 3 S $853.28 $333.76 $519.52 2005 3 S $873.37 $320.66 $552.71 2006 3 S $812.63 $320.86 $491.77 2007 3 S $1,616.14 $384.66 $1,231.48 2008 3 S $1,130.61 $382.78 $747.83 2003 3 SG $540.47 $255.88 $284.59 2004 3 SG $305.64 $275.19 $30.45 2005 3 SG $480.13 $294.32 $185.81

Year Rotation Crop

Gross Revenue ($ ha-1)

Costs ($ ha-1)

Return to Land &

Management ($ ha-1)

2006 3 SG $780.64 $386.47 $394.17 2007 3 SG $741.43 $256.00 $485.42 2008 3 SG $895.18 $270.59 $624.59 2003 4 A $766.11 $161.97 $604.14 2004 4 A $873.25 $240.90 $632.35 2005 4 A $984.34 $263.74 $720.60 2006 4 A $1,092.78 $406.36 $686.41 2007 4 A $821.61 $281.87 $539.74 2008 4 A $1,522.94 $416.53 $1,106.41 2003 4 C $1,074.19 $462.58 $611.61 2004 4 C $1,038.36 $509.85 $528.51 2005 4 C $1,080.55 $539.52 $541.03 2006 4 C $1,597.86 $700.20 $897.65 2007 4 C $2,074.15 $624.23 $1,449.92 2008 4 C $1,737.48 $959.87 $777.61 2003 4 S $833.51 $301.91 $531.59 2004 4 S $842.25 $333.67 $508.58 2005 4 S $878.50 $321.26 $557.24 2006 4 S $806.13 $443.62 $362.51 2007 4 S $1,636.69 $384.77 $1,251.92 2008 4 S $1,259.65 $558.88 $700.77 2003 4 SG $524.52 $334.07 $190.45 2004 4 SG $411.36 $378.97 $32.39 2005 4 SG $613.34 $408.25 $205.09 2006 4 SG $990.72 $503.10 $487.63 2007 4 SG $1,008.59 $375.53 $633.06 2008 4 SG $1,028.54 $602.34 $426.20

40

Table A.4 Continued * C = Corn; S = Soybean; SG = Small Grain; A = Alfalfa Analysis #2 - Manure Value: Commercial Nutrient Value

Year Rotation Crop

Gross Revenue ($ ha-1)

Costs ($ ha-1)

Return to Land &

Management ($ ha-1)

2003 2 C $1,119.56 $580.80 $538.76 2004 2 C $1,005.18 $523.85 $481.33 2005 2 C $951.17 $630.51 $320.66 2006 2 C $1,521.14 $788.09 $733.05 2007 2 C $1,908.95 $633.21 $1,275.74 2008 2 C $1,697.58 $966.15 $731.43 2003 2 S $844.25 $402.89 $441.36 2004 2 S $767.91 $371.40 $396.51 2005 2 S $809.74 $310.61 $499.13 2006 2 S $715.52 $453.75 $261.77 2007 2 S $1,369.61 $318.09 $1,051.53 2008 2 S $1,140.16 $504.17 $635.99 2003 3 C $1,099.07 $616.90 $482.17 2004 3 C $1,007.64 $874.38 $133.26 2005 3 C $1,091.33 $856.79 $234.54 2006 3 C $1,551.08 $971.91 $579.17 2007 3 C $1,994.61 $900.22 $1,094.38 2008 3 C $1,769.26 $1,150.15 $619.11 2003 3 S $815.44 $298.41 $517.03 2004 3 S $853.28 $333.76 $519.52 2005 3 S $873.37 $320.66 $552.71 2006 3 S $812.63 $320.86 $491.77 2007 3 S $1,616.14 $384.66 $1,231.48 2008 3 S $1,130.61 $382.78 $747.83 2003 3 SG $540.47 $255.88 $284.59 2004 3 SG $305.64 $275.19 $30.45 2005 3 SG $480.13 $294.32 $185.81

Year Rotation Crop

Gross Revenue ($ ha-1)

Costs ($ ha-1)

Return to Land &

Management ($ ha-1)

2006 3 SG $780.64 $386.47 $386.47 2007 3 SG $741.43 $256.00 $256.00 2008 3 SG $895.18 $270.59 $270.59 2003 4 A $766.11 $161.97 $161.97 2004 4 A $873.25 $240.90 $240.90 2005 4 A $984.34 $263.74 $263.74 2006 4 A $1,092.78 $406.36 $406.36 2007 4 A $821.61 $281.87 $281.87 2008 4 A $1,522.94 $416.53 $416.53 2003 4 C $1,074.19 $577.11 $577.11 2004 4 C $1,038.36 $842.95 $842.95 2005 4 C $1,080.55 $841.21 $841.21 2006 4 C $1,597.86 $969.20 $969.20 2007 4 C $2,074.15 $912.51 $912.51 2008 4 C $1,737.48 $1,317.63 $1,317.63 2003 4 S $833.51 $301.91 $301.91 2004 4 S $842.25 $333.67 $333.67 2005 4 S $878.50 $321.26 $321.26 2006 4 S $806.13 $443.62 $443.62 2007 4 S $1,636.69 $384.77 $384.77 2008 4 S $1,259.65 $558.88 $558.88 2003 4 SG $524.52 $334.07 $334.07 2004 4 SG $411.36 $378.97 $378.97 2005 4 SG $613.34 $408.25 $408.25 2006 4 SG $990.72 $503.10 $503.10 2007 4 SG $1,008.59 $375.53 $375.53 2008 4 SG $1,028.54 $602.34 $602.34

41

Table A.5 Field Operation Fuel Usage (Hanna, 2001) Operation Diesel L ha-1 Fertilization Spreading dry fertilizer, bulk cart 1.4 Anhydrous ammonia (30-inch spacing) 5.1 Tillage Shredding cornstalks 4.2 Moldboard plow 15.9 Subsoiler/ripper 15.9 Disk-chisel plow 12.2 Chisel plow 10.3 Offset disk 7.9 Tandem disk, plowed field 6.1 Tandem disk, tilled field 5.1 Tandem disk, cornstalks 4.2 Field cultivate, plowed field 6.5 Field cultivate, tilled field 6.1 Seedbed conditioner 8.4 Planting (30-inch rows) Planter, seed only, tilled seedbed 3.7 Plant with fertilizer and pesticide attachments, tilled seedbed 5.1 Till-planter 5.1 No-till planter 4.2 Grain drill 2.8 Broadcast seeder 1.4 Air drill 6.5 Weed Control (30-inch rows) Sprayer, trailer type 0.9 Rotary hoe 1.9 Row-crop cultivator 3.7 Harvesting Mower 2.8 Mower-conditioner, PTO 5.1 Self-propelled windrower 4.2 Rake 2.3 Baler 3.7 Forage harvester Green forage 7.9 Haylage 10.8 Corn Silage 30.4 High-moisture ground ear corn 15.9 Forage blower Green forage 2.8 Haylage 2.3 Corn silage 11.7 High-moisture ground ear corn 3.7 Combine, soybeans 9.3 Combine, corn 13.6 Hauling, field plus 1⁄2 mile on gravel road Green forage 2.8 Haylage 1.9 Corn silage 11.7 Corn grain 1.9 Soybeans 0.7

42

Table A.6 Input Energy Values Input Energy

Value Units Source

Diesel Fuel 36.4 MJ/L (Oak Ridge National Laboratory, 2009)

Nitrogen 56.97 MJ/kg (Shapouri and Duffield, 2003) Phosphorus 9.30 MJ/kg (Shapouri and Duffield, 2003) Potassium 6.97 MJ/kg (Shapouri and Duffield, 2003) Grain drying 4.65 MJ/kg of H2O (Anderson, 2009) Chemical (Active Ingredient) 358.10 MJ/kg (Shapouri and Duffield, 2003)

43

Table A.7 Crop identities, planting and harvest dates, seeding rates, and row spacings used in 2007-2008 Year Crop Rotation

system Hybrid or cultivar

Planting date

Harvest date(s) Seed density Seed mass

Inter-row spacing

seeds ha-1 kg ha-1 cm

2007 Corn All Agrigold 6395 16 May 26 Oct. 79,534 -- 76 2008 Corn All Agrigold 6395 19 May 3 Nov. 79,534 -- 76 2007 Soybean 2-yr Kruger 287RR 18 May 10 Oct. 400,140 -- 76 2007 Soybean 3-yr and 4-

yr Kruger K-2918 18 May 10 Oct. 400,140 -- 76

2008 Soybean 2-yr Kruger 287RR 21 May 6 Oct. 382,850 -- 76 2008 Soybean 3-yr and 4-

yr Kruger K-2918 21 May 6 Oct. 382,850 -- 76

2007 Oat 3-yr and 4-yr

IN09201 9 Apr. 17 Jul. -- 61 20

2008 Oat 3-yr and 4-yr

IN09201 16 Apr. 8 Aug. -- 65 20

2007 Red clover 3-yr Duration 9 Apr. -- -- 13 20 2008 Red clover 3-yr Duration 16 Apr. -- -- 13 20 2006 Alfalfa 4-yr Farm Science

Genetics 400LH

6 & 10 Apr.

2007: 6 Jun., 5 Jul., 9 Aug., 14 Sep.

-- 17 20

2007 Alfalfa 4-yr Farm Science Genetics 400LH

9 Apr. 2007: 14 Sep.; 2008: 6 Jun., 1 Aug., 17 Sep.

-- 17 20

2008 Alfalfa 4-yr Farm Science Genetics 400LH

16 Apr. 2008: 17 Sep. -- 17 20

2003-2006 data can be found in: Liebman, M., L. R. Gibson, et al. (2008). "Agronomic and economic performance characteristics of conventional and low-external-input cropping systems in the central corn belt." Agronomy Journal 100(3): 600-610.

44

Table A.8 Synthetic and organic soil fertility amendments for crops grown in contrasting rotation systems in 2007-2008

Rotation Crop 2007 2008

2-year Corn 112 kg N ha-1 as urea, at planting 78.4 kg P + 103.88 kg K ha-1, before

planting; 112 kg N ha-1 as urea, at planting; 100.8 kg N ha-1 as urea, side-dressed

2-year Soybean None 78.4 kg P + 103.88 kg K ha-1, before

planting

3-year Corn 121 kg N + 70 kg P + 94 kg K ha-1 as composted manure, before planting

123 kg N + 52 kg P + 114 kg K ha-1 as composted manure, before planting; 100.8 kg N ha-1 as urea, side-dressed

3-year Soybean None None

3-year Small Grain None None

4-year Corn 121 kg N + 70 kg P + 94 kg K ha-1 as composted manure, before planting

78.4 kg P + 103.88 kg K ha-1, before

planting; 123 kg N + 52 kg P + 114 kg K ha-

1 as composted manure, before planting; 100.8 kg N ha-1 as urea, side-dressed

4-year Soybean None 78.4 kg P + 103.88 kg K ha-1, before

planting;

4-year Small Grain None 78.4 kg P + 103.88 kg K ha-1, before

planting;

4-year Alfalfa None 78.4 kg P + 103.88 kg K ha-1, before

planting;

2003-2006 data can be found in: Liebman, M., L. R. Gibson, et al. (2008). "Agronomic and economic performance characteristics of conventional and low-external-input cropping systems in the central corn belt." Agronomy Journal 100(3): 600-610.

45

Table A.9 Mechanical and chemical weed management practices for crops grown in contrasting rotation systems in 2007-2008 Dosages of herbicide active ingredients (kg ha-1) are shown in parentheses

Rotation Crop 2007 2008

2-year Corn PRE, broadcast: S-metolachlor (1.14), isoxaflutole (0.088)

PRE, broadcast: S-metolachlor (1.14), isoxaflutole (0.088)

2-year Soybean POST, broadcast: glyphosate as isopropylamine salt§§§ (2.25); lambda-cyhalothrin (0.035)

POST, broadcast: glyphosate as isopropylamine salt§§§ (2.25); lambda-cyhalothrin (0.014)

3-year Corn Interrow cultivation (1x); POST, banded: nicosulfuron (0.013), rimsulfuron (0.007), mesotrione (0.053)

Interrow cultivation (1x); POST, banded: nicosulfuron (0.013), rimsulfuron (0.007), mesotrione (0.053)

3-year Soybean Interrow cultivation (2x); POST, banded: flumiclorac pentyl ester (0.015), clethodim (0.051), lactofen (0.053) ; lambda-cyhalothrin (0.035)

Interrow cultivation (2x); POST, banded: flumiclorac pentyl ester (0.015), clethodim (0.051), lactofen (0.053) ; lambda-cyhalothrin (0.014)

3-year Triticale/clover or oat/clover

Stubble mowing (1x) Stubble mowing (1x)

4-year Corn Interrow cultivation (1x); POST, banded: nicosulfuron (0.013), rimsulfuron (0.007), mesotrione (0.053)

Interrow cultivation (1x); POST, banded: nicosulfuron (0.013), rimsulfuron (0.007), mesotrione (0.053)

4-year Soybean Interrow cultivation (2x); POST, banded: flumiclorac pentyl ester (0.015), clethodim (0.051), lactofen (0.053) ; lambda-cyhalothrin (0.035)

Interrow cultivation (2x); POST, banded: flumiclorac pentyl ester (0.015), clethodim (0.051), lactofen (0.053) ; lambda-cyhalothrin (0.014)

4-year Triticale/alfalfa or oat/alfalfa

Stubble mowing (1x), hay removal (1x) Stubble mowing (1x), hay removal (1x)

4-year Alfalfa Hay removal (4x) Hay removal (3x)

2003-2006 data can be found in: Liebman, M., L. R. Gibson, et al. (2008). "Agronomic and economic performance characteristics of conventional and low-external-input cropping systems in the central corn belt." Agronomy Journal 100(3): 600-610.

46

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ACKNOWLEDGEMENTS

Anyone who has embarked on graduate work understands that you cannot complete the

process by yourself; there are many that support us along the way. The most important group

for me has been my family; in Mom, Dad, Brandon and Natalee I could always find someone

to lean upon and Uncle Rick deserves special recognition for providing me with so many

opportunities to learn and grow. Two groups within the Agronomy Department have played a

special role in my graduate studies: in the Liebman research group I found support and

intellectual challenges that I had not found before and through Dr. Manu and 154 teaching

group I was able to expand my academic horizons and give back when so much has been

given to me. A special nod goes out to my committee members and especially to my major

Professor, Dr. Liebman, who gave me the opportunity to come back to school and guided me

along the process of my graduate studies. Supporting a graduate student is not a cheap

endeavor, so a big thank you must be given to those who supported me financially: Iowa

State University, the Agronomy Department, and the Leopold Center for Sustainable

Agriculture. To all the teachers, friends and family that have not been mentioned, thank you

for the support, the guidance and faith in me and I hope someday I will be able to repay

everything you have given to me.