TECHNOLOGICAL AND ECONOMIC EVALUATION OF DISTRICT COOLING WITH
EVALUATION OF THE IMPACT OF TECHNOLOGICAL JINGWEI …
Transcript of EVALUATION OF THE IMPACT OF TECHNOLOGICAL JINGWEI …
EVALUATION OF THE IMPACT OF TECHNOLOGICAL
PROGRESS ON CROPLAND VALUES
by
JINGWEI WEI, B.Eco.
A THESIS
IN
AGRICULTURAL AND APPLIED ECONOMICS
Submitted to the Graduate Faculty of Texas Tech University in
Partial Fulfillment of the Requirements for
the Degree of
MASTER OF SCIENCE
Approved
/ ^ Accepted / ^
December, 2000
sOo ACKNOWLEDGEMENTS
. ^ ^ I would like to express my deep appreciation and sincere gratitude to Dr. Eduardo
Oi^-p.'T^ Segarra, my major professor. His guidance and patience on this project helped me gain a
better understanding of what I have learned and what I need to learn. Without his
assistance, none of this would have been possible.
I would also like to thank Dr. Octavio Ramirez for his support and
encouragement. His generosity of time and dedication to my work were enlightening.
I would also like to thank Dr. Phillip Johnson for his advice and suggestions. A
special thanks to all the former and current faculty and staff in the Agricultural and
Applied Economics Department for their continuous support.
Thanks also go to Sherry Andrews, Dr.Graves, Jay Youngblood and Marty
Middleton for your support, help and fiiendship. I will always cherish those good times
and timely help.
Finally, my deepest gratitude and appreciation are given to my family for their
unconditional love, understanding and confidence in me. I appreciate their love and
encouragement that allow me to follow my dreams.
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS ii
LIST OF TABLES v
LIST OF FIGURES vi
CHAPTER
I. INTRODUCTION 1
1.1 Biotechnology in Agriculture 2
1.2 Land in Agriculture 8
1.3 General Problem 8
1.4 Specific Problem 11
1.5 Objectives 11
IL LITERATURE REVIEW 13
2.1 Research on the Impacts of Biotechnology in Crop Production 13
2.2 Research on the Impacts of Technological Advancement
on Land Values 17
2.3 Cropland Value Analysis 19
m. CONCEPTUAL FRAMEWORK 22 3.1 Crop Production under Given Technology 22
3.2 Cropland Value Theory 30
rV. METHODS AND PROCEDURES 31
4.1 Study Area 31
4.2 Variables Included in the Models 34
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V. RESULTS 39
5.1 Results for the Estimations 39
5.2 Implications of Results 44
VI. SUMMARY AND CONCLUSIONS 52
6.1 Summary and Conclusions 52
6.2 Limitations and Further Research 54
REFERENCES 56
APPENDIX
A. PRODUCTIVITY INDEX (PI) 60
B. LP FORECASTS WITH DIFFERENT RATES OF PI 64
IV
LIST OF TABLES
5-1. Initial Estimates of the Models 40
5-2. Estimation of Final Models 41
5-3 Joint Model for LMA2 and LMA3 45
A-1. Productivity Index (PI) in LMA2 60
A-2. Productivity Index (PI) in LMA3 61
A-3. Productivity Index (PI) in LMA4 62
B-1. LP forecasts with different rates of PI in LMA2 64
B-2. LP forecasts v^th different rates of PI in LMA3 65
LIST OF FIGURES
3-1 Technological Progress and MPP does not Change 26
3-2 Technological Progress and MPP Increases at an Increasing Rate 28
3-3 Technological Progress and MPP Increases at a Decreasing Rate 29
4-1 Study Area 33
4-2 LP in LMA2, LMA3 and LMA4 35
5-1 LP in LMA2 with No PI growth 47
5-2 LP in LMA3 with No PI growth 47
5-3 LP in LMA2 with PI Increasing at 1%, 2 % and 3% Rates 48
5-4 LPinLMA3 with PI Increasing at 1%, 2 % and 3% Rates 48
5-5 LP in LMA2 with PI Decreasing at 0.5% and 1% Rates 49
5-6 LP in LMA3 with PI Decreasing at 0.5% and 1% Rates 49
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CHAPTER I
INTRODUCTION
In the 20 ^ century, U.S. agriculture became increasingly dependent upon science
for technological advances to increase productivity and ensure a safe and competitive
food supply. Until the close of the land fi-ontier in the early part of the 1900s, most
agricultural production increases came from expanding the area devoted to crops. Today,
growth in U.S. agricultural production comes almost entirely fi'om increases in crop
yields. The basis of this growth has been the application of modern science and
technology to agricultural production.
Technological innovation in production agriculture has caused far-reaching
changes in the techniques farmers use to produce agricultural commodities in the U.S.
The transition fi-om horsepower to mechanical power, the widespread use of chemicals,
and the development of new and improved seed varieties have resulted in substantial and
continuing increases in agricultural productivity. Innovations in agricultural production
such as these have significantly increased the quantities of many agricultural
commodities produced in the U.S. and around the world. These increased quantities have
generally led to significant shifts in total supplies of many commodities. Because of the
impact on prices and availability of consumer products derived from agricultural
commodities, shifting supplies have had meaningful social and economic impacts.
Common in most American industries, widespread expectations for technological
progress are to continue to play a fundamental role in the production of agricultural
commodities.
1.1 Biotechnology in Agriculture
Biotechnology is a rapidly developing and continuously changing area of
technological progress, the accumulating knowledge of which is generally expected to
result in positive impacts on agricultural productivity. Broadly defined, biotechnology
includes " any technique that uses living organisms or processes to make or modify
products, to improve plants or animals, or to develop microorganisms for specific uses"
(Office of Technology Assessment, p.4). The era of modem biotechnology began in the
early nineteen fifties when the molecular structure of DNA was discovered. Since then,
participants at every stage of this ambitious field have sought to apply the ever-expanding
scientific achievements toward improving agricultural crop production. Modification of
the genetic scheme of crop plants has been the focus of a long and growing hst of crop
production research strategies.
Advances in science and technological breakthroughs in the understanding of the
biology of plants, animals, humans, and organisms at the molecular level, combined with
the power of new information technologies, are creating a new technology platform, that
is, biotechnology. This powerful technological base, combined with the development of
enhancing technologies, such as genomics, bioinformatics, and proteomics, is speeding
up the identification of genes that control valuable traits, shrinking the timelines to
commercialize new products, and expanding the commercial potential of biotechnology
across a growing number of market sectors, including agriculture.
Agriculture is no stranger to biotechnology, innovation and change. Looking
back through history, technological innovation has been a key to the growth of
production agriculture. Production agriculture has been shaped by technological
breakthroughs that have brought to the farm crop inputs, such as hybrid seed varieties and
crop protection chemicals. In addition, technological innovation has moved farm
mechanization from the early days of the plow to today's four wheel drive tractors
outfitted with precision farming technology. These technologies have been geared
toward boosting farm productivity, which has improved farmers' profits and made their
lives easier. These technologies have helped shape the business structures that form the
foundation of today's agribusiness infrastructure.
It has been a long-held vision that agricultural biotechnology would transform and
usher in a dynamic new era of growth for agriculture. In fact, the potential appears to be
even greater than it had been thought to be just a few years ago. It is believed that the
potential economic and business impacts of agricultural biotechnology remain severely
underestimated. This new developing technological-base has the potential for not only
creating products, but more importantly, it could dramatically expand the value creation
potential of agriculture and enhance the linkage of agriculture with the industrially-based
economy.
Research in agricultural biotechnology continues to be generally guided toward
four areas of crop production: reducing input use, improving crop yields, improving crop
plant quality, and improving traditional crop plant breeding programs. Crop yields are
the most generally used measure of crop performance. Agriculturists have long sought to
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improve crop yields, usually by increasing them. Prior to the beginning of the 20
century, almost all increases in crop production were achieved by expanding cultivated
area. Farmer selection had led to the development of landraces suited to particular
agroclimatic environment. But grain yields, even in favorable environments, rarely
averaged above 2.0 metric tons per hectare (30 bushels per acre). Efforts to improve
yields through farmer seed selection and improved cultivation practices had a relatively
modest impact on yields prior to the application of the principles of Mendelian genetics
to crop improvement. In the U.S, for example, com yields remained essentially
unchanged, at below 30 bushels per acre, until the 1930's and, it was not until the
introduction of hybrid varieties that the com yield ceiling was broken (Duvick, 1996).
Similar yield increases have occurred in other crops. These increases occurred
first in the U. S., then in Westem Europe and Japan. Since the early 1960's, dramatic
yield increases, heralded as a " green revolution" took place in many developing
countries, primarily in Asia and Latin America. By the 1990's, several countries in
Africa were beginning to experience substantial gains in maize and rice yields (Eicher,
1995).
The development of in vitro tissue and cell culture techniques, which were
occurring parallelly with monoclonal antibody and rDNA (recombinant deoxyribonucleic
acid) techniques, would make possible the regeneration of whole plants from a single cell
or a small piece of tissue. It was anticipated that the next series of advances would be in
plant protection through introduction or manipulation of genes that confer resistance to
pests and pathogens. Many leading participants in the development of the new
biotechnologies expected that these advances would lead to measurable increases in crop
yields by the early 1990s (Sundquist, Mens, and Neumeyer, 1982).
While the early projections were overly enthusiastic, significant applications were
beginning to occur by the mid-1990s. The first commerically successful vims resistant
crop, a vims resistant tobacco, was introduced in China in the early 1990s. The Calgene
Flavr Savr tomato, the first genetically altered whole food product to be commercially
marketed, was introduced (unsuccessfully) in 1994. Important progress was made in
transgenic approaches to the development of herbicide resistance, insect resistance, and
pest and pathogen resistance in a number of crops. DNA (deoxyribonucleic acid) marker
technology was being employed to locate important chromosomal regions affecting a
given trait in order to track and manipulate desirable gene linkages with greater speed and
precision. By the 1998 crop year, approximately 70 million acres (28 million hectares)
had been planted worldwide to transgenic crops, primarily herbicide or vims resistant
soybeans, maize, tobacco and cotton (James, 1998).
Crop yields are significantly affected by environmental factors, pests, diseases,
soil characteristics, and stmctural design of the plants themselves. Biotechnological
approaches can lead to the creation of transgenic crop plants that can optimize the
exploitation of specific environments. Complete understanding of the genetic basis of
crop yields has generally eluded researchers. However, considerable progress has
continued toward that end.
Several crop plant species have been genetically altered to tolerate powerful
herbicides, such as glyphosate, that eliminate most weeds. Crop productivity may
increase where weeds have been effectively eliminated by herbicides applied to resistant
crops that otherwise would suffer from competition for valuable water and soil nutrients.
With continued development in major crops, herbicide tolerance could enjoy widespread
acceptance.
The physiological stmcture of crop plants can be changed to take better advantage
of specific environmental characteristics. For example, crop plants may be designed to
respond more favorably to short winter day lengths that impare productivity in much of
the world. Crop plants genetically altered to withstand stresses could have profound
effects on crop productivity.
Biotechnology may affect crop yields through techniques that equip crop plants
with improved and more efficient nutrient use. Legumes may be designed to fix their
own nitrogen allowing for more efficient use of this nutrient. Non-legume crop plants
may also be developed to use nitrogen more efficiently, reducing nitrogen application and
potential negative environmental extemalities. Microorganisms can provide useful
compounds in the soil to increase crop plant growth.
Use of biotechnology to promote insect resistance in crop plants has produced
some effective results. Crop plants may be designed to produce proteins that are toxic to
insects. For example, plants with the genetic coding sequence of the bacteria Bacillus
thuringiensis (Bt) produce a protein toxic to the larvae of many insects. Bt genes have
been incorporated into crop plants, such as cotton, tobacco, potato, and tomato. The
isolation of genes that can be signaled to cause a resistance mechanism to activate only
when pests attack may prove useful in the long term. The introduction into a crop plant
of several mechanisms giving resistance to a single pest may also be possible. Such a
crop plant might then be effectively resistant to the pest because of the difficulty for the
pest to mutate to overcome all of the resistance mechanisms.
The flexibility of genetic approaches and techniques permits researchers to
address many varied problems in agricultural production. Biotechnology can affect crop
productivity by influencing crop yields, quality characteristics, or costs of production.
While the technology being brought to the marketplace over the next few years
will largely continue to focus on input traits, its long-term potential lies in the
development of value-added output traits that address a wide range of needs. The needs
of conventional markets for food and feed, and new markets created through the
development of novel biotechnology-based value-added traits, is likely to be addressed in
the future. The ability to create proprietary value-added traits can tum many agricultural
commodities into premium-priced specialty and quasi-specialty products. This could
dramatically change what farmers produce, redefine what markets demand, re-engineer
the agricultural production and distribution infrastmcture to meet the new demands of the
market place, and redefine the linkage between the farmer and the end-user.
1.2 Land in Agriculture
Land supports all human endeavor. Unlike other factors of production, land
simultaneously performs many public and private functions. It is a factor of production
used to produce income streams for owners and tenants; it is space that produces amenity
values for owners, renters, and passersby; it is a thing of value that is identifiable and
fixed in place, so it can easily be taxed to help finance public functions in nearby areas.
Because land is essential to humans, it is a central component of the modem economic
stmcture.
The total land area of the contiguous 48 states in the U.S. is approximately 1.9
billion acres, with an additional 365 million acres in Alaska and slightly over 4 million
acres in Hawaii. Cropland use comprised 25 percent of total land use in 1987, this was
the third largest user of total land. Total cropland (used for crops, pasture, and idled)
declined over 8 million acres or about 2 percent from 1969 to 1987 (Kupa and
Daugherty,1990). Cropland used for crops has fluctuated more than total cropland
available, as the amount of land idled by Federal programs has varied (Vesterby, 1994).
1.3 General Problem
Land is one of the most important factors of production. Land's potential uses
and its location determine its economic value. Land use can affect the sustainability of
production. Because land used in one way often prevents or reduces other uses, there
occurs great competition and conflicts among uses of land. Land is different from other
commodities in that land is a non-reproducible resource and its supply is limited. Yet
the demands for its services have increased dramatically.
Population growth has been the primary long-term force driving up the demand
for land. Moreover, the movement of population away from central cities into adjacent
mral countries hastens the loss of prime farmland as agricultural land is converted to
residential and commercial use. In response to expanding population, land in urban uses,
commercial and industrial uses increased 285 percent from 15 million acres in 1945 to an
estimated 58 million acres in 1992. Over the same period, the amount of land urbanized
almost quadmpled. Land used for recreation and wildlife areas also expanded 285
percent from 1945 to 1992 (Daugherty, 1997). The increased use of land on non-
agricultural activities has reduced the effective amount of available cropland.
Pests and diseases, soil characteristics, the stmctural design of plants, and
environmental factors significantly affect crop yields. Various environmental factors
cause stress to crop production including: insects, weeds, pathogens, excessively high and
low temperatures, water deficit and excess, physical and chemical properties of the soil,
electromagnetic energy, growth regulators and pesticides, air pollution, and damage
resulting from among other things wind, hail, and dust. Each year plant stress prompts a
significant reduction in crop yields on cropland all around the world. For total world
crop production, from planting to harvest, Cramer estimated that production is lowered
by a third because of insect, disease, and weed pests.
Because of adverse weather or soil conditions at planting time, low crop prices,
holding for eventual conversion to nonagricultural uses, or government programs.
cropland is idled every year, further reducing acreage used for crop production. The
amount of cropland used for crop production in the U.S. has ranged from 383 million
acres in 1949 to 331 million acres in 1987 (Vesterby, 1994). Overall, the growth in
population and increased urbanization have decreased the "effective" supply of cropland
and have placed upward pressure on land values.
Given the rapid stmctural changes occurring in U.S. land utilization, it appears
reasonable to argue that commercial farmers expect to rely on the continued development
of biotechnological progress in agriculture to enhance, or at least maintain their
productivity and thus cropland values. There are many potentially beneficial applications
of biotechnology in agriculture. New technology enables farmers to substitute new and
cheaper inputs for more expensive less productive ones, thereby lowering unitary
production costs, and also enables them to produce higher valued products. Higher crop
yielding varieties, improved livestock breeds, improved methods of pest and disease
control, and increased mechanization are examples of new technologies that have
increased agricultural productivity. Many technologies now being developed have the
potential to increase cropland productivity
Since cropland is in fixed supply, there is an upper limit on crop production given
a level of technological progress. Given this, technological innovations could be devoted
to save cropland and lessen the restrictions imposed by the limited supply of land.
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1.4 Specific Problem
The development and adoption of biotechnological advances is becoming
increasingly important to the agricultural industry. However, little is known about the
relationship between current scientific advances and expected crop yields and costs
associated with the adoption of these advances. A critical threshold of expected benefits
from the application of biotechnology would be an important aid to focus research needs
and identify potential impacts of biotechnological innovation. Knowledge regarding the
relationships between technology and profitability of cropland is helpful in evaluating
changes on cropland value.
Research on cropland values has expanded in recent years. However, few
economists have complemented these efforts by conceptually and empirically exploring
the linkages between the profitability of cropland, biotechnology- induced productivity
changes, and the value of cropland. A better understanding of these relationships is
essential to policy makers, researchers and producers. With reduced govemment support
in the agricultural sector, the fixture profitability and viability of agriculture is becoming
more important as individual farmers remain the central decision-makers with respect to
biotechnology adoption and cropland use in the U.S.
1.5 Objectives
The general objective of this study is to evaluate the impacts of technological
progress on cropland values. Specifically, this research evaluates possible cropland value
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changes due to technological progress adoption, stemming from expected
biotechnological advances in agriculture in the Northern Plains Region of Texas (NPRT).
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CHAPTER II
LITERATURE REVIEW
Literature related to this study relies upon prior work in three areas of agricultural
research. The areas of research are: the impacts of biotechnology on crop production, the
impacts of technological advances on land values, and cropland value analysis.
2.1 Research on the Impacts of Biotechnology in Crop Production
Biotechnology, used here interchangeably with the term "genetic engineering,"
focuses on improving a plant's ability to withstand stress through genetically modifying
plants to have new or enhanced characteristics. Modification of economically important
crops could have significant implications on the way agricultural producers operate.
Genetically engineered crop varieties, such as Bt cotton and greenbug tolerant wheat,
could increase agricultural productivity.
Productivity improvements resulting from biotechnological advances in plant
genetics could have broad stmctural implications in production agriculture. In general,
like in many other economic activities, agricultural producers seek to optimize an
objective or set of objectives subject to a set of constraints. These objectives may include
maximization of utility, profit, or revenue, and/or minimization of cost of production.
Biotechnologically developed innovations are likely to be adopted by producers only if
these innovations have the potential to improve their current situation.
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Chiou et al.(1993) developed a stmctural cotton quality/quantity choice model to
evaluate the impacts of improved fiber quality and the resulting impacts on economic
surplus under biotechnological scenarios. Two biotechnological scenarios, trend
biotechnology and rapid biotechnological advancement were used for comparative
analysis of fiber quality improvement across four U.S. cotton production regions. The
trend scenario refers to a steady growth rate of fiber quality improvement for two five-
year periods, 1990-94 and 1995-99. The rapid scenario assumes a higher growth rate of
fiber quality improvement for the last five-year period, 1995-99. Assuming accelerated
technological advances occurring in the second five-year period, the rapid scenario
implies break-throughs in fiber quality improvements. The quality change in the rapid
scenario is assumed to be twice the ones in the trend scenario. Given a stmctural model
framework, Chiou et al.'s study (1993) captures the interrelationships between supply
and demand factors that affect quality and quantity choices for cotton. This stmctural
quality choice model is then incorporated into a stmctural quantity choice model to
analyze impact simulations. Their results indicate that improvements in the major fiber
characteristics of cotton could have a significant impact on cotton prices. The stmctural
implicit market for fiber characteristics allows for the simulation of the impacts on
biotechnology of fiber characteristics and their implicit prices. Chiou et al.'s study
relates specially to methods and procedures. Hedonic techniques have attracted the
interest of economists as a means of measuring values of nonmarket goods. Hedonic
analyses could be used to estimate the values of important land characteristics. Yields or
prices of crops produced on certain land could be introduced into two scenarios to
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evaluate possible changes of land values before and after the adoption of biotechnological
advances.
Tauer and Love (1989) measured the potential economic impact of using
herbicide-resistant com varieties in the U.S. The economic impacts were estimated using
an econometric- simulation model called AGSLM. They evaluated the new technology
by changing per acre yields and production costs. This study evaluated six adoption
scenarios for the period 1991 to 1996. Three scenarios assumed simultaneous availability
of the herbicide-resisting technology in all regions, but varied costs and adoption rates.
The three other scenarios assumed initial availability in Illinois, the Delta region, and the
Northeast. This study examined the impacts on production, acreage, costs, prices, and
producers and consumer surplus. They estimated that a $13 per acre technology fee,
substituting for the current chemical herbicide costs, would increase aggregate com
production by as much as 4 percent, decrease prices by as much as 30 cents per bushel,
and increase social welfare. Overall, they found that expected yield increases would be
small, and would lead to a small change in regional acreage and incomes. The results of
this study suggest that the gains from herbicide resistance in com will probably result
from technologies that are cost reducing rather than yield enhancing.
Cassandra et al. (1999) estimated cotton yields and pesticide use for farmers
growing Genetically Modified (GM) cotton using yearly data from the nationwide
Agricultural Resource Management Study (ARMS) survey developed by the Economic
Research Service (ERS) and the National Agricultural Stafistics Service (NASS) of
USD A. The ARS survey data Hnks the adoption of genetically modified crops with
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yields, management techniques, chemical use, and profits. The survey indicated that
expected profitability positively influenced the adoption of GM cotton. Factors expected
to increase profitability by increasing revenues per acre or reducing costs are generally
expected to positively influence adoption. Percent differences between yields and
pesticide use of adopters and non-adopters of GM cotton varieties were compared
statistically using a difference of means test. Difference between the mean estimates for
yields and pesticides use derived from survey results cannot necessarily be attributed to
the use of genetically engineered seed. This is because yield and pesticide use are
influenced by other factors not controlled for in the analysis. In general, yield differences
between adopters and non-adopters of herbicide-tolerant cotton are not statistically
significant. Alternatively there are many cases where adopters of Bt cotton appear to
have statistically significantly higher yields than nonadopters. Cassandra, et al.developed
an econometric model to estimate the impacts from farmer adoption of GM cotton on
profits, yields, and pesticide use. The model was estimated using data from the 1997
ARMS survey for cotton. The results of the farm-level impact model show that the
effects of herbicide-tolerant and Bt cotton adoption on yields and profits were
significantly positive. Herbicide use was not significantly affected by the adoption of
herbicide-tolerant cotton when controlling for other factors. On the other hand, Bt cotton
did significantly decrease the use of other synthetic insecticides, although the use of
organophosphate insecticides was not significantly associated with the adoption of Bt
cotton.
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2.2 Research on the Impacts of Technological Advancement
on Land Values
The studies reviewed in this section relate specifically to the objectives of the
research problem. Only one study was found that addressed the impact of a specific
biotechnology on land values. A second study addressed the general relationship
between adoption of technological progress and farmland prices.
Jafileksono and Otsuka (1993) estimated the effects of adoption of improved
modern rice varieties (MVs) on land prices by estimating land price equations. Their
study was based on farmers' recall data of land transactions in Lampung, Indonesia.
Their findings demonstrated that the adoption of a series of improved MVs increased
land prices by increasing land productivity. This study emphasized the environmental
specificity of MVs, and took into account the production environment (measured by
irrigated, lowland rainfed, and upland conditions). Their observations of differential
adoption of MVs, and the widening productivity differential between favorable and
unfavorable production environments highlights that in developing Asian countries, the
regional difference in labor income may not be as large as the regional productivity
differential suggests. The large regional difference in returns to land created by the
differential adoption and productivity impact of modem rice varieties results from the
fact that the adoption and profitability of a specific biotechnology can be constrained by
the production environment.
Herdt and Cochrane identified various forces influencing the agricultural land
market in the U.S. and synthesized an aggregate supply-demand model of this market.
The authors explained how technological advances take place and analyzed whether in
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fact it has occurred. The first section of the article uses the theory of the firm to describe
how, with widespread adoption of technological advances and stable prices due to farm
price supports, the expectation of higher incomes as a result of individual farm
technological adopfion is bid into land values. The second section discusses the concept
of a market for farmland with price determined by the interaction of supply and demand
for farmland. The authors used a simultaneous- equation model with price and quantity
endogenously determined to approximate the complex relationship prevailing in the land
market. Both the theoretical and empirical evidence indicate that the expectation of
rising incomes from technological advance adoption in conjunction with supported farm
prices (and from increasing urban demand as well) have been important in contributing to
the rise in farmland prices. Expected income increases, because technological advance
lowers unit cost of production and increases individual farm incomes with supported
prices, thus providing an incentive to expand farm size, which in tum creates an upward
pressure on land prices. The authors concluded that farmland prices rise as farmers bid
for land to capture the gains of technological advance adoption. The expected income
gain from technological advance adoption on individual farms vanish as the competitive
process of acquiring land forces land prices up and absorbs the gains from technological
advance adoption. In this study, price support is cmcial to the explanation of rising land
prices, since its elimination from the model makes the outcome less decisive. Thus, farm
price support provides an important link in the explanation of rising land prices. This
study provides a well-developed deduction leading from the theory of the firm to market
model results.
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2.3 Cropland Value Analysis
This section is closely related to both the conceptual framework and methods and
procedures. Perhaps no economic good has been the subject of so many inquiries about
its "tme market value" as land. The inquiries began in medieval times when a number of
scholars attempted to show the tme and fair value of all the land in England, Great
Britain, France, and a number of other nations (Marshall, 1948). 'Tractical" work
regarding land value was present in writings of many Classical economists, but most
stopped short of actually calculating or measuring value. Instead they listed the sources
of value to include such things as the income producing capacity, location, and public
value.
With the tum of the twentieth century, studies of land value began to take a form
that is easily recognizable today. Galton's (1889) and Pearson's (1948) early work with
correlation analysis paved the way for numerous studies in which investigators attempted
to ascertain the importance of various attributes (such as the presence of buildings, yields
of common crops and distance to town) in explaining the value of land. Haas used
regression techniques to determine the contribution that buildings, land classes, land
productivity, and distance to markets made of land value. Others followed with scores of
studies appearing in the 1930s and in every decade since.
Advances in multivariate analysis and economic theory, especially the hedonic
methods pioneered by Rosen, allowed additional progress (e.g., Chicione; King and
Sinden; Miranowski and Hammes; Pope and Goodwin; Palmquist and Danielson). When
applied to the land market, hedonic techniques assume a given parcel can be identified by
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a unique set of attribute levels, and that the value of a land parcel is an aggregation of the
values of its individual attributes.
Barnard et al. (1997) used two approaches to examine the effect of govemment
program payments on crop land values: the first was a standard parametric analysis that
estimates the effect of govemment payments on cropland values, and the second was a
nonparametric regression. Both approaches used the value of cropland data from the
USDA's June Agricultural Survey (JAS) for the analysis.
The first approach used ordinary least squares to estimate the percentage of
cropland value that is accounted for by farm program payments. Linear equations were
estimated for twenty U.S. Land Resource Regions. The second approach used a non-
parametric estimator. The non-parametric approach has the advantage of providing
estimates of the effects of govemment payments that have much greater spatial detail and
enables display of those estimates on spatially continuous maps. The tract-specific data
on cropland values permit the analysis to be conducted for small geographic areas, but in
the context of multiple program crops.
Falk and Lee (1998) decomposed farmland price movement into a component
driven by fundamental force and a component driven by fad forces to provide measures
that help resolve the issue of the relative importance of these two components in
explaining overall farmland price movements. They also estimated and compared the
dynamic responses of farmland prices to non-fundamental shocks and two types of
fundamental shocks. A trivariate vector autoregression was formulated in terms of
farmland price, farmland rent, and a time-varying discount rate. The decomposition
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approach is useful for studying the importance of fundamental versus nonfundamental
factors in explaining farmland behavior and the dynamic response of farmland prices to
shocks to each of these components.
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CHAPTER m
CONCEPTUAL FRAMEWORK
The analysis of the effects of technological advancement on cropland values
integrates neoclassical production economics theory and present value analysis.
Neoclassical production economics theory provides the framework to analyze production
decisions made by the firm. The present value analysis will take into account cropland
value changes associated with the adoption of technological progress.
Firms engaged in agricultural production, particularly crop production, are
generally regarded as operating in markets deduced from the theory of the competitive
firm. In this regard, the prices of inputs and outputs are known with some degree of
certainty. That is, for example, in the short mn, a change of the quantity of commodity
output by a particular farm will not affect the price of the commodity.
The conceptual framework in this study is organized into three sections. The first
section addresses crop production optimality with a given technology The second
section considers the cropland value with given technology level. The last section
analyzes the impacts of new technology adoption on cropland values.
3.1 Crop Production under Given Technology
In production economics theory, it is assumed that the technical relationship
between a variable factor of production and output can be represented by di production
function, which is mathematically expressed as:
22
Y=f(X,/X2), (3.1)
where: Y denotes output or total physical product (TPP) or crop yield, usually on a per
acre basis; Xi is a vector of variable factors of production, such as seed, fertilizer, labor,
and irrigation water; and X2 represents the fixed factors of production. The production
function in equation (3.1) expresses the maximum amount of output possible from
alternative levels of X given a particular production technology.
The production technology describes how inputs are transformed into outputs.
Marginal physical productivity (MPP) of input X is defined as: MPP = d(TPP)/dX =
dY/dX = df(X)/dX =/'(X). The marginal productivity function gives the exact rate of
change of the total product function for an infinitesimal change in the level of input use.
That is, marginal productivity is the slope of the total product function evaluated at a
particular level of input use.
In the crop production process, the objective of the firm can be broadly
considered to be that of profit maximization. The profit function can be expressed as the
difference between total revenue and total cost:
7C = TR-TC, (3.2)
where T: is the profit from crop production, TR is total revenue to crop production, TC is
total cost associated with crop production. Assuming perfect competition in the output
and input markets, we have:
TR = P *Y or TR = P *f(X), and (3.3)
TC = R * X + F , (3.4)
23
where P is the market price of the crop produced, X represents the bundle of inputs in
crop production, and Y is the quantity of the output on a per acre of crop land basis, R is
a vector of the prices of the inputs, R*X is variable cost, and F is fixed cost.
Substituting equations (3.3) and (3.4) into equation (3.2), the firm's profit
function can be defined as:
71 = P* f(X)-R*X-F. (3.5)
The first-order condition of profit maximization is given by:
d7r/dX = 0, (3.6)
which gives: d7t/dX = Pdf(X)/dx-R = 0, which implies:
VMP = R or MPP*P = R. (3.7)
Condition (3.7) implies that the profit-maximizing level of input X is obtained by
equating VMP with factor price.
The value of an asset is determined by the asset's earnings over time discounted
by some interest rate back to the present value. Cropland, as a form of wealth, is
expected to earn profit over a long period of time. Cropland values correspond to the
profits generated on land because a person would not pay more for a piece of land than
what this land could earn. Therefore cropland values are largely determined by the
expected level of profits derived from it. Cropland values can be expressed as the
present value of the expected future profit from the land (Shoemaker, 1990): PVo = Z Ttt
/ ( l+ i ) \ where Vo is the present value of land, iTttis the sum of profits over time t (from
o to oo) and i is the discount rate. From the above equation, we can see that land values
24
are highest when producers maximize profits. The cropland values associated with
profit maximization can be expressed as:
PVo* = l7t*t/(l+i)\ (3.8)
where 7t*t represents the optimal level of TT at every time period.
Given the rapid stmctural changes taking place in U.S. agriculture, it is reasonable
to argue that commercial farmers expect to rely on the continued development of
technological progress in agriculture to enhance or at least maintain their cropland values.
Many technologies being developed have the potential to increase cropland productivity.
These include technology that conserves cropland by increasing crop yields with the
same or lower level of inputs. Productivity is used as a measure of technological advance
on cropland value.
Productivity captures the relationship between outputs and inputs in production.
It is most commonly expressed as total factor productivity (TFP), which can be measured
as the ratio of total outputs, measured in an index form, to total inputs, also measured as
an index (Ahearn et al.,1998). If the ratio of total outputs to total inputs is increasing,
then the ratio can be interpreted to mean that more outputs can be obtained for a given
input level. Productivity, or TFP, captures the growth in outputs not accounted for by the
growth in production inputs. The notion of TFP is based on the concept of the production
function. When output is expanded, differences in agricultural TFP over time can result
from technological change.
In the simplest case, consider a single output (Y) is produced with a bundle of
inputs (X). In Figure 3-1(a), any point along the curve Yw/o indicates the maximum
25
profit
0
profit of Yw
profit of Yw/o
(b)
Figure 3-1. Technological Progress and MPP does not Change.
26
amount of Y that can be obtained for a given level of X. Additional units of Y could be
produced for a given level of inputs, such as XO, through technical innovation. Clearly,
the technology of U.S. agriculture in the 1990s differs significantly from that of the
1940s. For example, with agricultural biotechnology, crops are being developed to resist
pests, disease, and herbicides or tolerate adverse environmental conditions, such as
drought and frost. There are three possible scenarios that can result from production
technology innovation.
In the first scenario, technological progress could result in a parallel shift of the
production function, that is, the marginal physical productivity does not change. This is
depicted in Figure 3-l(a) by the shift of the production surface from Yw/o to Yw. If the
input level remains at XO, output increases from Yw/o to Yw In Figure 3-1(b), the profit
curve shifts from profit of Yw/o to profit of Yw.
In the second scenario, technological progress increases marginal physical
productivity (MPP) of input usage at an increasing rate. With the new technology, for an
infinitesimal change in the factor X, the rate of change of output Y increases. Therefore,
in Figure 3-2(a), the production surface Yw/o would rotate to the left to Yw. In Figure
3-2(b), the associated profit curve would rotate from profit of Yw/o to profit of Yw.
The last scenario is one in which a new technology increases output at a
decreasing rate. That is, MPP increases at a decreasing rate with the new technology.
Hence, in Figure 3-3 (a), the production surface rotates from Yw/o to Yw, but at a
decreasing rate. In Figure 3-3(b), the associated profit curve will be profit of Yw instead
of profit of Yw/o.
27
Yw/o
(a)
profit
p rofit of Yw
profit of Yw/o
Y
(b)
Figure 3-2. Technological Progress and MPP Increases at an Increasing Rate.
28
Yw
Yw/o
XO X
(a)
profit
profit of Yw
profit of Yw/o
(b)
Figure 3-3. Technological Progress and MPP Increases at a Decreasing Rate.
29
2 Cropland Value Theorv
Agricultural land values are determined by the supply and demand for land. Land
is different from other commodities in that there is limited supply of land. Since the
supply of land is fixed, the value of cropland is determined by the demand. The demand
for land and the resulting land values are largely determined by the expected earnings
from the land.
Under the assumption of perfect competition, the prices of outputs and inputs are
assumed to be constant regardless of the quantity of inputs and outputs. The value of
cropland under the assumption of technological progress adoption can be obtained as:
PVi = Z 7rit/(l+iy, where TTU is the profit level brought by adopting technological
progress and PVi is the net present value for cropland assuming technological progress
adoption. When producers seek to maximize profit, cropland value under technological
progress is: PVi* = Z 7C*it/(l+i)\ where the " 1 " is used to denote variables assuming
technological progress adoption.
Land values correspond to the potential gains that can be made from land because
a person would not pay more for a piece of land than what the land could earn. Land, as
a form of wealth, is expected to cam income. Except for some intrinsic qualities that a
specific parcel of land may have, the demand for land is based on an expectation of
earning income in the same way a return is expected from owning a bond or any other
income- earning asset. The durability of the land parcel resuhs in land values being
formed within the context of a long-term planning horizon.
30
CHAPTER IV
METHODS AND PROCEDURES
The general approach followed in this study consists of two phases. First, data
used in the modeling were collected from various sources, and then econometric models
of mral land values were developed. Parameters of the econometric models were
estimated using ordinary least squares (OLS) procedures. These models were evaluated
using the signs and magnitudes of the coefficients, F and t-tests, the coefficients of
determination and R-square. Finally, the value of mral land was predicted using the
estimated models under various scenarios of technological progress.
4.1 Study Area
Texas farmers produce a significant portion of the total production of many major
field crops in the United States. In 1998, Texas led the nation in production of cotton,
with approximately one third of the nation's total cotton crop produced in the state.
Likewise, Texas produced more grain sorghum than any other state, about a third of the
national production. In 1998's national rankings, Texas was second in all hay and fourth
in winter wheat production, resulting in 8 percent and 7 percent shares of national
production, respectively.
Within Texas, the most agriculturally intensive region is the Northern Plains
Region of Texas (NPRT), which consists of fifty-five counties in the northwestern
portion of the state. Upland cotton, grain sorghum, wheat, and com are the primary field
crops produced. Most of the production of these crops in Texas takes place in the NPRT
31
Cotton production for 1998 in the NPRT was 1.03 million bales, making up 23 percent of
total national cotton production. The NPRT's production of grain sorghum was 56
million bushels, representing 10 percent of grain sorghum production in the United
States. Of the 2.4 billion bushels of all wheat produced in the nation, the NPRT produced
70 million bushels. NPRT production of com in 1998 was 131 million bushels. This was
approximately 2 percent of com production in the United States. As suggested above, the
NPRT is an area important to agricultural production in the United States. Production of
cotton in the region makes up more than 20 percent of national production of this
commodity. About 10 percent of the national grain sorghum production take place in the
NPRT. Though not as large, a significant share of national production of both wheat and
com output is also produced in the region.
The NPRT's land market area 2 (LMA2), LMA3 and LMA4 were used to
examine the relationship between productivity and cropland values. The counties
included within the land market areas studied are shown in Figure 4-1. LMA2 includes
the following counties: Armstrong, Briscoe, Carson, Castro, Deaf Smith, Gray, Parmer,
Randall, and Swisher. LMA3 includes the following counties: Borden, Crosby, Dawson,
Floyd, Garza, Hale, Lubbock, and Lynn. LMA4 includes the following counties:
Andrews, Bailey, Cochran, Ector, Gaines, Hockley, Howard, Lamb, Martin, Midland,
Terry, and Yoakum.
32
Figure 4-1. Study Area.
33
4.2 Variables Included in the Models
Regression equations for land prices were estimated by following three steps.
First, each regression equation started with an initial model, which included several
variables thought to affect land prices. Next, a selection process was followed to choose
the appropriate functional form of the equation and the variables included in the equation,
i.e, the function with those variables that had a reasonable relationship with land prices.
Finally, forecasted values of the independent variables were used in the selected
regression equations to derive annual expected cropland values.
The initial regression equations estimated for the three land market areas were of
the following general form:
LPt = f(PIt, D85, RCTPt, RCNPt, RWHPt, RSOPt ,NRt, PINCOMEt, RIRt), (4.1)
where LP is cropland price ($/Ac) in year t and represents annual data of the real median
mral land prices. The real median prices were derived by deflating the nominal prices (as
reported by the real estate center of Texas A&M University) using the GDP implicit price
deflator (using 1977 as base year). Pit is the productivity index for year t. A positive
relationship between PI and LP is expected, i.e. the higher PI is in the area, the higher LP
in the area and vice versa.
Figure 4-2 depicts real LP in the three land market areas from 1969 to 1996. As
can be seen in that figure, there was a dramatic drop in LP around 1985. Thus, a dummy
variable, D85, was used to explain the stmctural land price change around 1985 in the
three areas. That is, D85 took the value of zero from 1969 to 1984, and the value of one
from 1985 on. A negative relationship is expected between D85 and LP, which would
34
a a
s
Year
Figure 4-2. LP in LMA2, LMA3 and LMA4.
indicate a decrease in land prices since 1984. RCTPt, RCNPt, RWHPt and RSOPt are the
real prices for cotton ($/lb), com ($^u), wheat ($^u), and sorghum ($/lb). These four
crops prices were selected because they represent the major crops produced in the three
areas.
NRt is the realized net agricultural income ($/Ac), which besides farm income
also accounts for the total production expenses. PINCOMEt is personal income per
capita ($) each area in year t. It is expected that the higher the personal income per
capita, the larger the demand for cropland. With a fixed supply of cropland, it could be
expected that cropland prices will be driven up by increases in the demand. Finally, RIRt
is the real interest rate in year t. It is expected that there is a negative relationship
between RTR and LP
The productivity index variable used was not readily available. For this reason, it
was derived from a transformation of existing data. Specifically, from 1969 to 1996, for
each land market area, weighted average crop yields were calculated as the sum of crop
yield in each county within the land market area multiplied by the proportional weight of
the number of acres in that county (WACY). Then, the weighted average crop yields of
the three areas for each crop was calculated to derive the specific crop index. The
weighted average crop yield across land market areas was defined as the weighted
average crop yield across the three areas (WACYT). The crop indexes were derived as
WACY divided by WACYT. Once these crop indexes were derived for all areas and all
crops, they were added up on a land market area basis, and an overall relative production
index for the land market area was derived. This production index was then divided by
36
the cost of input index for Texas (using 1977 as base year) to get a relative productivity
index in each area for each year.
The results of the calculations of productivity indices for LMA2, LMA3 and
LMA4 are listed in Appendix A in Tables A-1, A-2 and A-3. It is expected that there is
lagged effect of PI on LP That is, the Pit variable used in the model was modified to
account for the fact that PI in year t has the largest impact on LP, and the impact of PI of
previous years diminishes through time. Thus, the PI index variable was modified using
the following equation:
Pit = 3/4 Pit +3/4*l/4*PIt-l +3/4*(l/4)^PIt-2+3/4*(l/4)^PIt-3. (4.2)
In practice, the current year land price is not only affected by the current year crop
prices, but it is also affected by the crop prices of past years. To capture the effect that
the lagged values of crop prices have on cropland values, the above four crop price
variables were also modified using the following geometric lag model specification:
RCTPt = 3/4 RCTPt +3/4*l/4*RCTR-l +3/4*(l/4)^RCTPt-2+3/4*(l/4)^RCTPt-3,
(4.3) RSOPt = VA RSOPt +3/4*l/4*RSOPt-l +3/4*(l/4)^RSOPt-2+3/4*(l/4)^RSOPt-3,
(4.4)
RWHPt = y4RWHPt +3/4*l/4*RWFIPt-l +3/4*(l/4)^RWHPt-2+3/4*(l/4)^RWHPt-3,
(4.5)
RCNPt = VA RCNPt +3/4*l/4*RCNPt-l +3/4*(l/4)^RCNPt-2+3/4*(l/4)^RCNPt-3.
(4.6)
This implies that the real cotton price in year t has the largest effect on LP, and the effect
of cotton prices of previous years diminishes through time. The same stmcture was
37
assumed for the prices of com, wheat, and sorghum. Usually, when the prices of major
crops increase, which implies that more income is expected from the area, cropland
prices would be expected to increase. Thus, a positive relationship between the prices of
the crops and cropland prices is expected.
Realized net income is also expected to have lagged effects on land prices.
Therefore, it was also modified as shown below:
NRt=3/4*NRt +3/4* %*NRt-l+3/4*(l/4)^NRt-2+3/4*(l/4)^NRt-3. (4.7)
It is expected that the higher the realized net income, the higher the cropland prices will
be. Therefore, a positive relationship is expected to exist between realized net income
and cropland price.
38
CHAPTER V
RESULTS
5.1. Results of the Model Estimation
The resuhs of the initial regression models estimated are presented in Table 5-1
In the initial model for LMA2, PI had a negative effect on LP although insignificant,
which is contradictory to the expected relationship between PI and LP. NR, RIR and
RWHP had the appropriate signs, however, the parameters were not significant, which
implies that these three variables do not affect cropland prices significantly. RCTP,
RCNP, RSOP and PINCOME indicate contradictory relationships with cropland prices.
D85 was the only variable that had the expected relationship with cropland prices and
also was statistically significant.
The model selection process for LMA2 started by dropping the variables that had a
contradictory relationship with LP and also were statistically significant as compared to
other variables, like PINCOME in this case, and followed by the variables that had
contradictory effect on LP most significantly in the smaller model. This selection process
was repeated until a final model including variables with appropriate signs was derived.
The estimation results of the final model for LMA2 are listed in Table 5-2.
In the final model of LMA2, PI had a positive relationship with LP, but was not
significant. D85 had a significant negafive relationship with LP, which implies that after
1985, LP in LMA2 decreased dramatically. RCNP had a positive yet insignificant effect on
LP. RCTP had a negative yet insignificant effect on LP.
39
Table 5-1.
Variable INT
PI
D85
RCTP
RCNP
RWFIP
RSOP
NR
PINCOME
RIR
R^
Initial Estimates of the Models. Parameter Estimates
LMA2 746.12 (2.17)* -15.52 (-0.28)
-119.84 (-2.61)* -132.83 (-0.76)
-7.25 (-0.07)
20.99 (0.43)
-360.66 (-0.05)
0.13 (0.67) -0.04
(-2.17)* -2.80
(-0.70)
0.90
LMA3 -122.77 (-0.25) 150.82 (1.46)
-243.32 (-4.73)*
-2.69 (-0.01)
5.08 (0.04) 70.70 (1.24)
-4570.38 (-0.48)
-0.75 (-2.17)*
0.01 (0.22)
1.41 (0.26)
0.91
LMA4 240.84 (1.43) -21.99 (-0.76)
-153.11 (-6.96)*
-7.02 (-0.06) -25.24 (-0.36)
1.90 (0.06)
3659.91 (0.80)
0.13 (1.08)
0.01 (1.34)
2.05 (0.71)
0.92 *: variables with 95% statistical significance
t value included within () below the estimates
40
Table 5-2. Estimations of Final Models
Variable INT
PI
D85
RCTP
RCNP
R^
Parameter Estimates LMA2 122.42 (0.49) 45.05 (1.11)
-171.73 (-6.38)* -123.81 (-0.75)
41.13 (0.96)
0.85
LMA3 208.42 (0.73) 44.28 (0.67)
-252.65 (-7.02)*
191.89 (1.00) 16.22
(0.30)
0.88
LMA4 431.31 (3.16)* -53.65 (-1.98)
-119.16 (-6.28)*
37.29 (0.32) 28.60 (1.01)
0.88
*: variables with 95% statistical significance t value included within () below the estimates
41
The initial model for LMA3 included the same independent variables as the ones for
LMA2 and LMA4, the results for this model are listed in Table 5-1. In the initial model, PI,
D85, RCNP, RWHP, and PINCOME had the expected effects on LP D85 was statistically
significant at the 95% level, which indicates that after 1985, LP in LMA3 decreased
significantly. NR was also statistically significant at the 95% level, but had a negative
effect on LP, which is contradictory to expectations about the relationship between NR and
LP.
The selection process for the final model for LMA3 was similar to that for LMA2. It
started with the variables that had a contradictory relationship with LP and also more
significant compared to other variables, such as NR in this case. Then, the most
insignificant variable among the remaining variables was dropped. The final model for
LMA3 is depicted in Table 5-2.
In the final model for LMA3, PI, RCNP and RCTP had a positive relationship with
LP, yet were insignificant. Only D85 was statistically significant, which indicates that after
1985, LP in LMA3 decreased significantly.
The initial model for LMA4 started with the same variables as the ones for LMA2
and LMA3. The initial estimation indicated that PI had a negative effect on LP, although
statistically insignificant, which is contradictory to the relationship expected between PI and
LP. D85, RWHP, RSOP, NR and PINCOME had appropriate signs, but they were not
statistically significant, which implied that these variables do not affect cropland prices
significantly. PI, RCTP, RCNP and RIR indicated a contradictory relationship with
42
\
cropland prices. D85 was the only variable that had the expected relationship with cropland
prices and also was statisticallv significant.
Next, in the selection process for the initial model for LMA4, the variables that had
contradictory relationships were first dropped. Then, the most insignificant variable among
the remaining variables were dropped. After such repeated process, the results of the final
model for LMA4 are depicted in Table 5-2.
In the final model for LMA4, PI still exhibited a negative relationship with cropland
prices. All other variables in the final model had the expected relationship with cropland
values. Among the variables, D85 was found to affect cropland values significantly at the
95% level. The other variables included were found to be statistically insignificant at the
95% level.
Because the separate models for LMA2 and LMA3 resulted in somewhat similar
results, to increase the number of observations, these two separate models were combined
into one model. An F test was carried out to test whether the independent variables affected
LP in LMA2 and LMA3 in the same manner. That is, it was desired to find out if it would
be justifiable to join the separate models for LMA2 and LMA3 into a single model. The F
test conducted was performed by using the following equation:
Fq.n-k = (ESSr-ESSur)/q , (5.1)
ESSur/n-k
where: q is the degrees of freedom in the numerator; n-k is the degrees of freedom in the
denominator; ESSr is the sum of the square of the residuals of the restricted models (the
joint model); and ESSur is the sum of the square of the residuals of the unrestricted models (the final models in LMA2 and LMA3). After estimating the restricted and unrestricted
43
models, the following results were found: ESSur = ESS2 +ESS3 =
43818.744+69591.717=113410.461, ESSr =130610.937, and given that q= 12-7=5 and n-
k=56-12=44,then:
Fq,n-k = (ESSr-ESSurVg = (130610.937-113410.461 V5 =1.33. ESSur/n-k 113410.461/44
According to the F table, at the certainty level of 95%, with F5 44=2.37, which is larger than
1.33. Thus, the F test results indicate that the restricted model would be equivalent to the
unrestricted model.
In the joint model, there was one additional variable included: an intercept shifter
(ISH). ISH takes on the value of zero for LMA3 and the value of one for LMA2. The joint
model was therefore adopted. The joint model of LMA2 and LMA3 was estimated and the
resuhs obtained are presented in the Table 5-3.
In the joint model, PI had a significant positive effect on LP, with a certainty level of
95%. That is, for each unit increase of PI, LP would be expected to increase by $82.87.
D85 was also significant, which implies that LP decreases significantly in both LMA2 and
LMA3 after 1984. All the other variables had the expected effects on LP, but were not
statistically significant. The parameter associated with ISH was significant and negative,
indicating that other factors being constant, LP in LMA2 is lower than LP in LMA3 by
$147.83.
5.2.Implications of Resuhs
Based on the estimation results of the joint model, a series of forecasts were made to
determine how LP for LMA2 and LMA3 would change from 1997 to 2016 under different
44
^
Table 5-3. Joint Model for LMA2 and LMA3. Variable Parameter Estimates
INT
PI
ISH
D85
RCTP
RCNP
66.39 (0.38) 82.87
(2.47)* -147.83 (-7.24)* -207.94 (-9.22)*
58.83 (0.46) 30.63 (0.88)
R 0.87 *: variables with 95% statistical significance
t value included within 0 below the estimates
45
\
assumed changes of PI. Given that the results for LMA4 were not as anticipated, LMA4
was not included in the forecasts. D85 was fixed at 1, since all of these years are after 1984
Ramirez (2000) estimated the joint probability distribution function (pdf) for West Texas
irrigated cotton, com, sorghum, and wheat production and prices. The estimated pdf was
applied to evaluate the changes in the risk and returns of agricultural production in the
region resuhing from observed and predicted price and production trends. In his study,
RCNP is expected to decrease at a constant rate of $ 0.0171^u per year, and RCTP is
expected to decrease at a constant rate of $ 0.0018/ lb per year (Ramirez, 2000).
In the forecast, PI was assumed to increase at annual rates of 1%, 2%) and 3%, and
decrease at the rates of 0.5% and 1%. Also, a scenario in which PI was assumed to remain
unchanged from the level of 1996 was derived. The purpose of this last scenario, the
baseline scenario, was to find out how LP would change given the expected changes in crop
prices only. The LP forecasts are listed in Appendix B in Tables B-1 and B-2. The
graphical representations of the forecasts are presented in Figures 5-1 to 5-6.
Discussion of the resuhs from each growth rates of PI in LMA2 and LMA3 follows
the ensuing general pattem. First, the results of each scenario are presented and a
comparison is made between the baseline scenario and the scenarios in which PI are
expected to decrease or increase each year from its 1996 level. The baseline model
describes the expected value of LP when PI remains unchanged from 1996. That is, from
1997 to 2016, PI is assumed to stay at the same as the level of 1996. RPCT and RPCN are
assumed to decrease each year. Therefore, the expected changes in LP under this scenario
reflect only the effects of RPCN and RPCT. In the scenarios of increasing or decreasing PI,
46
164 -162 -160 -158 -156 -154 -152 -150 1 148 -
1995 2000 2005 2010 2015
Year
2020
Figure 5-1 LP in LMA2 with No PI growth
240 -238 -
^ 236 -< 234-^ 232 -
230 -228 -226 n 224 -
1995 2000 2005 2010 2015 2020
Year
Figure 5-2. LP in LMA3 with No PI growth
47
o <
v^ D-J
500 -
400 -
300 -
200 n
100 1
0
1995 2000 2005 2010 2015 2020
Year
Figure 5-3. LP in LMA2 with PI Increasing at 1%, 2% and 3% Rates.
;/Ac;
LP
(3
500 -
400 -
300 H
200 H
100
0
-A-1%
-•—2%
-•—3%
1995 2000 2005 2010 2015 2020
Year
Figure 5-4. LP in LMA3 with PI Increasing at 1%, 2% and 3% Rates.
48
: \
200 -
^ 150^ <
^ 100 -
" 50 -
0
-•--0.50%
1995 2000 2005 2010 2015
Year
2020
Figure 5-5. LP in LMA2 with PI Decreasing at 0.5% and 1%> Rates.
<^ C # CN ^ C??' CS "" CN"" ^ " ^ \ " ^<^ ^^^
Year
Figure 5-6. LP in LMA3 with PI Decreasing at 0.5% and 1% Rates.
49
r>v
the expected changes in LP can be divided into the impact of PI and the effects of RCTP and
RCNP A comparison of the baseline scenario and the scenarios of increases and decreases
of PI can help explain the change of LP due to the impact of PI.
In the baseline scenario, in which PI is assumed to remain the same as in 1996. LP in
LMA2 and LMA3 is expected to decrease from 1997 to 2016. Specifically, in LMA2,
LP decreases from $161.89/Ac in 1997 to $149.97 /Ac in 2016. LMA3, LP decreases
from $237.90/Ac in 1997 to $225.98/Ac in 2016. This decrease of $11.92/Ac in LP
comes from the effect of the decrease in both RCTP and RCNP.
In the scenarios that assumed increases in PI, LP in LMA2 and LMA3 increase from
1997 to 2016. Figure 5-3 and Figure 5-4 show that the larger the rate of increase in PI, the
higher the expected value of LP each year. When PI was assumed to increase 1% each year
from 1996 on, LP in LMA2 increases from $162.95/Ac in 1997 to $227.28/Ac in 2016.
This increase of $64.33/Ac in LP, represents the net impact of the increase in PI and the
effects of the decreases in RCTP and RCNP. The baseline scenario showed that the effect
of RCTP and RCNP was a decrease of $11.92/Ac on LP. Therefore, the increase in PI at the
rate of 1% per year outweighs the effect of the decreases in RCTP and RCNP and would
have increased LP in LMA2 by $76.25/Ac from 1997 to 2016. Likewise, when PI is
assumed to increase 1% per year from 1996 on, LP in LMA3 increases from $238.75/Ac in
1997 to $288.09/Ac in 2016. This increase of $49.34/Ac in LP is due to the net effect of the
increase in PI and decreases in RCTP and RCNP. It was showed in the baseline scenario
that the combined effect of RCTP and RCNP was to decrease LP by $11.92/Ac. The
comparison between the scenario of increases in PI and the baseline scenario shows that the
50
increase in PI at the rate of 1% per year outweighs the effect of the decreases in RCTP and
RCNP and would increase LP in LMA3 by $61.26/Ac from 1997 to 2016. The comparison
between the scenario of increases in PI and the baseline scenario demonstrates, that the
impact of the increase in PI is expected to outweigh the impact of the decreases in RCTP
and RCNP, and LP is expected to increase in both LMA2 and LMA3
In the scenarios that assumed decreases in PI, LP in LMA2 and LMA3 decrease
from 1997 to 2016. Figure 5-5 and Figure 5-6 show that the larger the rate of decrease in
PI, the lower the expected LP each year. When PI was assumed to decrease by \% per
year from 1996 on, LP in LMA2 would be expected to drop from $157.99/Ac in 1997 to
$83.25/Ac in 2016. This decrease of $74.74/Ac in LP represents the aggregate of the
impact of the decrease in PI and the decreases of RCTP and RCNP. The baseline
scenario showed that the effect of the decreases in RCTP and RCNP is $11.92/Ac on LP
Therefore, the decrease in PI at the 1% rate per year decreases LP in LMA2 by $62.82/Ac
from 1997 to 2016. This shows clearly that the impact of the decrease in PI would
account for most of the drop in LP. Likewise, when PI is assumed to decrease 1% per
year from 1996 on, LP in LMA3 decreases from $234.76/Ac in 1997 to $172.38/Ac in
2016. This decrease of $62.3 8/Ac in LP in LMA3 represents the aggregate of the impact
of the decrease in PI and the decreases of RCTP and RCNP Therefore, the decrease in
PI at the 1% rate per year decreases LP in LMA3 by $50.46/Ac from 1997 to 2016. The
comparison between the scenarios of decreases in PI and the baseline scenario
demonstrate that PI would account for most of the expected decrease in LP from 1997 to
2016 in both LMA2 and LMA3.
51
CHAPTER VI
SUMMARY AND CONCLUSIONS
6.1 Summary and Conclusions
The focus of this study was to evaluate the extent at which technological progress
affects cropland values. Technological progress was measured by evaluating the changes
of crop productivity in the study area. The specific objective of this research was to
evaluate possible cropland value changes due to technological progress adoption,
stemming from expected biotechnological advances, in crop production in the Northem
Plains Region of Texas.
In order to gain an understanding of the approaches taken by other researchers to
evaluate how cropland values are affected and also estimate the impact of technological
progress on cropland values, relevant literature was reviewed and summarized in Chapter
II. The theoretical framework underlying the impacts of technology adoption on crop
yield and profit was discussed in Chapter EI. In particular, in Chapter IE, three scenarios
with respect to the impacts of technological progress on marginal physical productivity
(MPP) were analyzed. The first case analyzed, is the one in which technological progress
increases output, but does not have an impact on MPP The second case analyzed, is the
one in which technological progress increases output and MPP increases at an increasing
rate. The last scenario analyzed, is the one in which technological progress increases
output and MPP increases at a decreasing rate.
52
The econometric models used to estimate the impacts of productivity changes on
real cropland values in the study area were presented and described in Chapter IV The
methods and procedures followed in the estimation of the models consisted of three
phases. First, the econometric models for each area were estimated using variables which
were deemed to be the most relevant in explaining cropland values. The variables
included in the initial models were: a relative productivity index (PI), a year dummy
variable for 1985 (D85), real price of cotton (RCTP), real price of com (RCNP), real
price of wheat (RWHP), real price of sorghum (RSOP), real realized net agricultural
income (NR), real personal income per capita (PINCOME), and real interest rate (RIR).
Second, a model selection process that started by dropping the variables that were the
most statistically insignificant resuhed in the final models. These models included the
following variables: PI, D85, RCTP, and RCNP. The final model for land market area 4
(LMA4) was deemed not to be reliable for forecasting purposes, therefore, it was
dropped. The final models for land market areas 2 and 3 (LMA2 and LMA3) were found
to be stmcturally similar, thus, these two models were estimated together as a single
model.
The implications of resuhs of the joint model estimated for LMA2 and LMA3 are
described in Chapter V. In this chapter, the changes in LP associated with PI annual
increases of 1%, 2%), 3%), and decreases of 0.5%, \% were analyzed. A baseline scenario
that assumed no PI growth was also derived.
In the baseline scenario, in which PI was assumed to remain the same as in 1996,
LP in LMA2 and LMA3 was found to decrease from 1997 to 2016. Given that in this
53
scenario, PI is assumed not to change, the decrease in LP is due to the effect of the
assumed decreases in RCTP and RCNP.
In the scenarios that assumed increases in PI, LP in LMA2 and LMA3 was found
to continuously increase from 1997 to 2016, and as expected, the larger the rate of
increase in PI, the higher the expected value of LP each year. The increase in LP
represents the net impact of the increase in PI and the effects of the decreases in RCTP
and RCNP. The comparison between the scenario which assumes increases in PI and the
baseline scenario demonstrates that the increase in PI dominates and is expected to
increase LP from 1997 to 2016 in both areas, in sphe of the expected decreases in RCTP
and RCNP.
In the scenarios that assumed decreases in PI, LP in LMA2 and LMA3 were
found to decrease from 1997 to 2016, and as expected, the larger the rate of decreases in
PI, the lower the expected level of LP each year. This decrease in LP represents the
aggregate of the impact of the decrease in PI and the effects of decreases in RCTP and
RCNP. The comparison between the scenario of decreases in PI and the baseline
scenario demonstrates that the impact of the decreases in PI would account for most of
the expected decrease in LP from 1997 to 2016 in both areas.
6.2 Limitations and Further Research
There are two major limitations to this research study. First, the data for LP are
not specific. As we know, the land base in the NPRT consists of dryland and irrigated
cropland, and range land. Each type of land makes up a different proportion in each of
54
the three land market areas. However, the data for LP represent the median price of mral
land in each area and do not take into account specific factors affecting the price of each
type of land. Second, the availability of data limited the length of land price movement.
If a longer span of data was available, an evaluation of the complex relationships between
land prices and other variables could be better conducted.
D!)
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58
APPENDIX A
PRODUCTIVITY ESIDICES FOR LMA2, LMA3, AND LMA4
59
Table A-1. Productivity Index (PI) in LMA2. Com Cotton Wheat Sorghum Production Input
Year Index Index Index Index Index Index 1969
1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
1.0761 1.1063 1.0359 0.9703 1.0271 1.0553 1.0005 1.0362 0.9600 1.0347 1.0232 1.0171 1.0693 1.0963 1.0415 1.0122 1.0125 1.0679 1.0437 1.0476 1.0923 1.0499 1.0779 1.0745 1.0235 1.0725 1.0794 1.0673
1.0245 1.0783 0.8424 0.9660 0.8640 0.9704 0.8246 0.8686 1.0757 1.1163 0.9109 1.2675 0.9397 1.0472 1.1081 1.2743 1.0793 1.2829 1.1746 1.0004 1.0969 1.2154 1.2799 0.9263 1.1668 1.2254 1.0524 1.2131
0.9372 0.9713 1.1478 1.2333 1.1496 0.9923 1.1149 1.2409 1.1321 1.2322 1.2670 0.9899 0.9862 1.0255 1.0440 1.0431 1.0098 0.9759 1.1927 0.8246 1.0416 0.9532 1.1154 1.1063 1.0421 0.9950 1.0207 1.0405
1.4887 1.4629 1.3111 1.2380 1.2325 1.2723 1.2387 1.1969 1.1728 1.1686 1.1291 1.2368 1.0945 1.2346 1.3866 1.1980 1.3192 1.3040 1.2867 1.3056 1.1000 1.1838 1.2286 1.0861 1.2804 1.0851 1.1628 1.3007
4.5264 4.6187 4.3372 4.4076 4.2732 4.2902 4.1786 4.3426 4.3406 4.5518 4.3301 4.5113 4.0896 4.4037 4.5802 4.5276 4.4208 4.6308 4.6977 4.1782 4.3308 4.4023 4.7018 4.1932 4.5128 4.3780 4.3153 4.6217
0.9524 0.9320 0.9576 1.0287 1.0464 0.9703 0.9625 1.0076 1.0000 1.0784 1.1047 1.0735 1.0425 1.0607 1.0042 1.0339 1.0011 0.9902 0.9882 1.0059 0.9592 1.0083 1.0079 0.9978 1.0247 0.9992 1.0843 1.0484
4.7528 4.9556 4.5292 4.2848 4.0835 4.4218 4.3412 4.3098 4.3406 4.2210 3.9199 4.2023 3.9229 4.1517 4.5608 4.3792 4.4160 4.6765 4.7536 4.1535 4.5151 4.3661 4.6649 4.2023 4.4040 4.3815 3.9798 4.4082
60
Table A-2.
Year
1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Productivity Index Com
Index
0.8581 0.7928 0.9105 1.0244 1.0827 0.9943 0.9681 1.0460 1.0170 0.9965 1.0079 1.0545 0.9688 0.9419 1.0456 1.0551 1.0095 0.9912 0.9936 0.9477 0.9585 0.9888 1.0032 0.9347 0.9541 0.9492 0.9344 0.8853
Cotton
Index
1.0590 1.0523 1.0984 1.1461 1.1839 1.0389 1.1083 1.1213 0.9981 1.0519 1.0939 0.9662 0.8204 0.9321 0.9582 0.9310 1.0212 0.9206 0.9013 0.9879 0.8913 0.8819 0.8316 0.9945 0.8802 0.8730 0.9200 0.8696
(PI)inLMA3. Wheat Sorghum
Index
1.0515 1.0474 0.7895 0.9572 1.0378 0.8839 1.0598 0.9560 0.9210 0.8782 0.9737 1.0909 1.0232 0.9779 0.9903 0.9500 1.0438 0.9980 0.9377 0.9251 0.8486 0.9714 0.8208 0.8015 0.9613 0.8128 0.9254 1.0258
Index
0.9358 0.9563 1.0649 1.0991 1.0954 1.1558 1.0718 1.2174 1.2295 1.2666 1.2540 1.3405 1.2174 0.9529 0.9431 1.0814 0.9533 0.9481 0.9728 1.0345 1.1068 1.2801 1.0481 0.9696 1.1330 1.2726 1.0380 0.9322
Production Index
3.9044 3.8487 3.8634 4.2268 4.3998 4.0730 4.2079 4.3408 4.1656 4.1932 4.3294 4.4521 4.0297 3.8047 3.9372 4.0176 4.0278 3.8578 3.8054 3.8953 3.8051 4.1223 3.7037 3.7003 3.9286 3.9075 3.8178 3.7129
Input
Index
0.9524 0.9320 0.9576 1.0287 1.0464 0.9703 0.9625 1.0076 1.0000 1.0784 1.1047 1.0735 1.0425 1.0607 1.0042 1.0339 1.0011 0.9902 0.9882 1.0059 0.9592 1.0083 1.0079 0.9978 1.0247 0.9992 1.0843 1.0484
PI
4.0996 4.1295 4.0344 4.1091 4.2045 4.1978 4.3717 4.3080 4.1656 3.8885 3.9193 4.1472 3.8655 3.5871 3.9205 3.8858 4.0234 3.8959 3.8507 3.8723 3.9670 4.0883 3.6747 3.7084 3.8339 3.9106 3.5210 3.5414
61
Year
1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Com
Index
1.0659 1.1009 1.0535 1.0053 0.8902 0.9503 1.0314 0.9177 1.0230 0.9688 0.9689 0.9283 0.9619 0.9618 0.9129 0.9327 0.9780 0.9409 0.9626 1.0046 0.9492 0.9613 0.9188 0.9908 1.0224 0.9783 0.9862 1.0474
Cotton Index
0.9166 0.8694 1.0592 0.8878 0.9521 0.9907 1.0672 1.0101 0.9262 0.8318 0.9953 0.7663 1.2399 1.0207 0.9337 0.7947 0.8995 0.7965 0.9241 1.0117 1.0118 0.9026 0.8885 1.0793 0.9530 0.9016 1.0276 0.9172
Wheat Index
1.0113 0.9814 1.0627 0.8094 0.8125 1.1238 0.8253 0.8031 0.9469 0.8897 0.7594 0.9193 0.9906 0.9966 0.9657 1.0069 0.9464 1.0260 0.8696 1.2503 1.1098 1.0753 1.0638 1.0921 0.9966 1.1923 1.0539 0.9337
Sorghum
Index
0.5755 0.5808 0.6241 0.6630 0.6721 0.5719 0.6895 0.5857 0.5977 0.5648 0.6170 0.4228 0.6882 0.8125 0.6704 0.7206 0.7275 0.7479 0.7406 0.6599 0.7933 0.5362 0.7233 0.9444 0.5865 0.6423 0.7992 0.7671
Production
Index
3.5693 3.5326 3.7995 3.3655 3.3270 3.6368 3.6134 3.3166 3.4938 3.2550 3.3405 3.0366 3.8807 3.7916 3.4827 3.4548 3.5514 3.5114 3.4969 3.9266 3.8641 3.4754 3.5945 4.1065 3.5586 3.7145 3.8670 3.6654
Input Index
0.9524 0.9320 0.9576 1.0287 1.0464 0.9703 0.9625 1.0076 1.0000 1.0784 1.1047 1.0735 1.0425 1.0607 1.0042 1.0339 1.0011 0.9902 0.9882 1.0059 0.9592 1.0083 1.0079 0.9978 1.0247 0.9992 1.0843 1.0484
PI
3.7478 3.7903 3.9677 3.2718 3.1793 3.7483 3.7540 3.2916 3.4938 3.0184 3.0240 2.8287 3.7225 3.5747 3.4679 3.3416 3.5475 3.5461 3.5385 3.9034 4.0285 3.4468 3.5663 4.1155 3.4728 3.7174 3.5663 3.4961
62
APPENDIX B
LAND PRICES FORECASTS FOR LMA2 AND LMA3
63
Table B-1.
Year
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
LP foreca!
1%
162.9515
165.9883 169.0587 172.1750 175.3228 178.5053 181.7290 184.9912 188.2892 191.6296 195.0096 198.4266 201.8872 205.3886 208.9283 212.5127 216.1392 219.8111 223.5231 227.2785
5ts with different rates of PI in LMA2. 2%
165.4431
172.1932 179.0878 186.1418 193.3435 200.6986 208.2165 215.8973 223.7412 231.7576 239.9469 248.3094 256.8549 265.5838 274.4967 283.6036 292.9051 302.4080 312.1106 322.0198
3%
167.9402 178.4537 189.2983 200.4960 212.0424 223.9510 236.2388 248.9140 261.9852 275.4704 289.3791 303.7207 318.5146 333.7710 349.5009 365.7247 382.4539 399.7067 417.4929 435.8315
0% 161.8926 161.2659 160.6362 160.0154 159.3888 158.7590 158.1324 157.5057 156.8760 156.2493 155.6226 154.9929 154.3662 153.7396 153.1098 152.4832 151.8565 151.2326 150.6060 149.9793
-0.50%
159.2250 156.7853 154.3515 151.9357 149.5230 147.1161 144.7212 142.3352 139.9548 137.5863 135.2265 132.8723 130.5298 128.1959 125.8674 123.5505 121.2421 118.9449 116.6532 114.3698
-1%
157.9859 153.7454 149.5380 145.3753 141.2421 137.1410 133.0776 129.0486 125.0505 121.0892 117.1613 113.2633 109.4010 105.5711 101.7702 98.0040 94.2692 90.5684 86.8954 83.2530
64
Table B-2. LP forecasts with different rates of PI in LMA3 Year 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
1%
238.7511 241.0676
243.4104 245.7920 248.1976 250.6306 253.0972 255.5947 258.1205 260.6808 263.2730 265.8944 268.5513 271.2411 273.9609 276.7173 279.5076 282.3348 285.1937 288.0875
2 %
240.7528 246.0525 251.4676 257.0126 262.6750 268.4601 274.3766 280.4240 286.6018 292.9188 299.3746 305.9691 312.7111 319.6004 326.6371 333.8301 341.1795 348.6913 356.3629 364.2005
3%
242.7589 251.0820 259.6704 268.5444 277.6973 287.1406 296.8890 306.9488 317.3261 328.0366 339.0873 350.4852 362.2470 374.3805 386.8937 399.8043 413.1209 426.8587 441.0246 455.6342
0%
237.8966 237.2699 236.6402 236.0194 235.3928 234.7630 234.1364 233.5097 232.8800 232.2533 231.6266 230.9969 230.3702 229.7436 229.1138 228.4872 227.8605 227.2367 226.6100 225.9833
-0.50%
235.7574 233.6741 231.5950 229.5322 227.4706 225.4132 223.3660 221.3259 219.2897 217.2637 215.2447 213.2296 211.2244 209.2262 207.2317 205.2472 203.2694 201.3012 199.3369 197.3793
-1%
234.7619 231.2319 227.7280 224.2617 220.8180 217.3994 214.0118 210.6517 207.3160 204.0103 200.7315 197.4761 194.2500 191.0499 187.8725 184.7237 181.6000 178.5042 175.4302 172.3807
65
Table B-2. LP forecasts with different rates of PI in LMA3.
Year 1% 2 % 3% 0% -0.50% -1%
1997 1998
1999 2000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
238.7511 241.0676 243.4104 245.7920 248.1976 250.6306 253.0972 255.5947 258.1205 260.6808 263.2730 265.8944 268.5513 271.2411 273.9609 276.7173 279.5076 282.3348 285.1937 288.0875
240.7528 246.0525 251.4676 257.0126 262.6750 268.4601 274.3766 280.4240 286.6018 292.9188 299.3746 305.9691 312.7111 319.6004 326.6371 333.8301 341.1795 348.6913 356.3629 364.2005
242.7589 251.0820 259.6704 268.5444 277.6973 287 1406 296.8890 306.9488 317.3261 328.0366 339.0873 350.4852 362.2470 374.3805 386.8937 399.8043 413.1209 426.8587 441.0246 455.6342
237.8966 237.2699 236.6402 236.0194 235.3928 234.7630 234.1364 233.5097 232.8800 232.2533 231.6266 230.9969 230.3702 229.7436 229.1138 228.4872 227.8605 227.2367 226.6100 225.9833
235.7574 233.6741 231.5950 229.5322 227.4706 225.4132 223.3660 221.3259 219.2897 217.2637 215.2447 213.2296 211.2244 209.2262 207.2317 205.2472 203.2694 201.3012 199.3369 197.3793
234.7619 231.2319 227.7280 224.2617 220.8180 217.3994 214.0118 210.6517 207.3160 204.0103 200.7315 197.4761 194.2500 191.0499 187.8725 184.7237 181.6000 178.5042 175.4302 172.3807
65
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