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.

n

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

ni

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

M

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

th

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.

10

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

11

changes due to technological progress adoption, stemming from expected

biotechnological advances in agriculture in the Northern Plains Region of Texas (NPRT).

12

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.

13

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

14

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

15

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.

16

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

17

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.

18

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

19

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

20

i

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.

21

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!)

REFERENCES

Ahearn,M., J. Yee, E. Ball, and R. Nehring "Agricultural Productivity in the United States.'" Economic Research Service, USDA, (January 1998): 2-3.

Barnard, C.H., G.Whittaker, D.Westenbarger and M. Ahearn "Evidence of Capitalization of Direct Govemment Payments into U.S. Cropland Values.''American Journal of Agricultural Economics 79 (Number 5,1997): 1642-1650.

Cassandra, K. I., S. Jans, J. F. Comejo and W.McBride 'Tarm-Level Production Effects Related to the Adoption of Genetically Modified Cotton For Pest Management." AgBioForum. No.2 (spring 1999) :1-10.

Chicoine, D.L. "Farmland Values at the Urban Fringe: An Analysis of Sale Prices." Land Economics 57 (Aug 1981): 352-62.

Chiou, G.T., D.T. Chen, and O.Capps, Jr. "A Stmctural Investigation of Biotechnological Impacts on Cotton Quality and Returns." American Journal of Agricultural Economics 75 (May 1993): 467-78.

Cramer, H.H. "Plant Protection and Worid Crop Production." Pflsnzenschutz Nachrichten "Bayer" 20 (19S1): 59-68.

Daugherty, A "Agricultural Resources and Environmental IndicatOTS."Economic Research Service, USDA,(My 1997): 1-21.

Duvick, D.N. "Plant breeding, an evolutionary concept." Crop Science 36 (1996): 359-548.

Eicher, C.K. "Zimbabwe's maize-based green revolution: preconditions for replication." World Development 23 (1995): 805-818.

Falk, B. and B.Lee. "Fads versus Fundamentals in Farmland Prices." American Journal of Agricultural Economics 80 (1998): 696-707.

Gahon, F Natural Inheritance. London: The Macmillian Publishing Company (1889).

Haas, G C. "Sale Prices as a Basis for Farm Land Appraisal." Tech.Bul.9, Minnesota Agriculture Experiment Station, St. Paul (1992).

Herdt, R.W. and W.W.Cochrane " Farm Land Prices and Farm Technological Advance." Journal of Farm Economics 48 (1966): 243-63.

56

James, C "Global status of transgenic crops in 1998" (ISAAA Briefs No.8) Ithaca, NY International Service for the Acquisition of Agri-biotech Applications (ISAAA) (1998).

Jatileksono,T. and K. Otsuka " Impact of Modem Rice Technology on Land Prices: The Case of Lampung in In&onesxa.'" American Journal of Agricultural Economics 75 (August 1993): 652-665.

King,D.A., and J. A. Sinden "Influence of Soil Conservation on Farm Land Values." Land Economics 64 (1988): 42-52.

Kmpa, K.S., and A.B.Daugherty "Major Land Uses: 1945-1987." Electronic Data VTO&ucxm90Q3, Economic Research Service, USDA, (November 1990): 2-11.

Marshall, A. Principles of Economics 8th edition. New York: The Macmillan Company (1948).

Middleton, M. 'The Economics of Plant Stress Reduction through Biotechnology: An Application to the Northem Plains Region of Texas." MS. Thesis, Texas Tech University (December 1996).

Miranowski, J. A., and Brian D.H. "Implicit Prices of Soil Characteristics for Farmland in Iowa." American Journal of Agriculture Economics 66 (1984): 745-49.

Ramirez, O.A. "Parametric Modeling and Simulation of Joint Price-Price -Production distribution under Non-Normality, Autocorrelation and Heteroscedasticity: A Tool for Assessing Risk in Agriculture." Journal of Agriculture and Applied Economics (2000): 283-297.

Office of Technology Assessment, U.S. Congress, Technology, Public Policy, and the Changing Structure of American Agriculture, OTA-F-285, Washington, DC: U.S Govemment Printing Office (March 1986).

Palmquist, R.B., and E.Damelsun "A Hedonic Study of the Effects of Erosion Control and Drainage on Farmland Values." American Journal of Agricultural Economics, 71 (1989): 55-62.

Pearson, E.S. Karl Pearson's Early Statistical Papers. Cambridge University Press (1948).

Pope,C.A.,in, and H.L.Goodwin,Jr. "Impacts of Consumptive Demand on Rural Land Values." American Journal of Agricultural Economics 66(1984): 750-54.

57

Shoemaker,R. "How Technological Progress and Govemment Programs Influence Agricultural Land Values." Economic Research Service, USDA, (January 1990)

Sundquist, W.B., Menz, KM., and Neumeyer, C.F. "A Technology Assessment of Commercial Com Production in the United States" University of Minnesota Agricultural Experiment Station Bulletin 546. St. Paul, MN: University of Minnesota (1982).

Tauer,L.W., and M.J.Love. "The Potential Economic Impact of Herbicide-Resistant Com in the XJ^K." Journal of Production Agriculture (2)1989 : 202-207.

Vesterby, M "Agricultural Resources and Environmental Indictors". Economic Research Service, USDA, (December 1994).

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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