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A
thesis submitted for the degree of
Master of
Agricultural
Economics
in
the
University of Queensland
Department
of
Agriculture
August,
1991
iii
Abstract
The
economic issues involved in arid rangeland degradation have become
an
increasing focus
in agricultural economics research world-wide. The economic and social factors which
contribute to degradm'on in Queensland's arid mulga rangelandr are explored in this thesis.
Many
of the region's problems, including the small property size structure, grdng
management practices and land tenure have their origins in the historical development of the
region. These and other factors are idenn3ed using both a regression analysis of cross-
sectional
dm and a stochastic dynamic programming model of the rangeland.
Regressions pelformed on data j?om parallel economic and land condition surveys of
46
graziers in the south-west Queensland mulga rangelandr are used to establish
a
link
between degradation and land utilisation policy. Land degradation is shown to be more
severe on propem'es with higher stocking rates. The importance of property size,
financial
and domestic cost pressures, land condition
and
proportion of residual land types in land use
decisions are explored. De analysis supports the hypotheses that smaller property sizes and
higher interest cost commitments are associated with higher stocking rates.
As
expected,
propem'es with a greater proportion ofpoorer quality land types tend to adopt lower stocking
rates. Kangaroo numbers, an effective regional proxy variable, is positively related to
stocking rates indicating the tendency for native grazer populations to be higher on more
.
productive land types. The regression analysis also provides some evidence that incomes are
higher on properties with a greater degree of
land
degradation.
While the cross-sectional regression approach is emtive in idennning some of the
economic issues,
it
has distinct shortcomings. Regional biases are diflcult to isolate from the
relationships between stocking rates and the various economic factors and much of the
variation in stocking rate may in fact be due to regional
dzrerences. Moreover, the
technique does not adequately consider the intertemporal nature of the rangeland resource
problem.
iv
A
stochastic dynamic programming model, amalgamating the cross-sectional swwy
data with historical field trial data is developed to address these shortcomings.
Ihe
@nmic
programming model describes a Markov decision process, with stocking rate
as
the
sole land
use decision and pasture biomass as the indicator of rangeland condition. For a given
pasture biomass and stocking rate, the transition to a'following stare
is
described
by
a
probability dism'bution derived porn simulated climaric data.
By
individually varying
economic parameters in the model, including property size, wool prices, discount rates and
risk, the response in optimal stocking rates and
returns can be assessed.
The
dynamic
programming model also generates optimal net present values and shadow prices associated
with the marginal usage of pasture biomass corresponding to each optimal decision set.
Pasture condition is imputed from the
long-term probability distribution of pasture stares
generated
by
the model.
_
The dynamic programming model reveals that the small property size structure in the
mulga rangelands, largely a legacy of early land a.inistra.tion policy, is a majorpotential
factor in land degradation. Graziers with small holdingsjind
it
economically optimal to
stock
at
higher rates in an efon to achieve economies offlock size.
The
costs of degradatr'on
incurred by higher stocking strategies are balanced
by
savings porn more encient
management.
l2e
model shows that
at
the average wool prices assumed,
an
area of at least
35,000
hectares is required to produce positive net present values
at
all levels of pasture
biomass.
I
The
effect of wool prices on optimal land use rates reflects the non-linear relationship
between wool quality and price, which makes finer wools relatively more attractive during
periods of high prices. Graziers can gain
by
opportunistically stocking at higher rates to
induce finer wool. The so-called lfine wool effect' can be achieved by felling mulga for
supplementary feed.
The
individual grazier's discount rate is expected to vary according to financial
circumstances, planning horizon and arn'n.de to risk.
Sensitivity analysis of the model
revealed that optimal stocking rates increased
as
the grazier's discount rate rose, reflecting
a decreasing concern for resource conservation. The amtude of graziers to risk is firther
This thesis reports the original work of
the
author, except
as
otherwise stated.
It
has
not
been submitted previously for a degree at
any
university.
analysed in a utility-maximising version of the dynamic programming model. Quadratic,
negative exponential
and
spliced utility Bnctions are used to convert monetary outcomes to
un'lity values. Generally, a more risk-averse
attitude
infers more conservative stocking rates.
Apart from examining the importance of propeny size, wool prices
and
discount rate
in land degradation, the
study
seeks to validate the use of dynamic programming analysis in
policy issues where intertemporal elements are of central importance.
The
problems of
dimensionality,
data
requirements
and
compurational limits which hamper the efectiveness
of dynamic programming
as
a decision-making tool are less resmktive of its usefllness in
examining policy concern. The dynamic programming method
has
firther potential uses in
identBing minimum property sizes for long-tern viability and in the analysis
of
the response
of
land
prices to degradation.
vii
CONTENTS
ABSTRACT
LIST OF TABLES
LIST OF FIGURES
xii
1.
MULGA RANGELAND DEGRADATION
-
AN
INTRODUCTION
1.1.
Defining degradation
1.2.
Degradation of the mulga rangelands
1.2.1.
The degradation cycle
1.2.2.
Extent of mulga rangeland degradation
1.3.
Theory
and analytical issues: A synopsis
e
2.
ECONOMICS
OF
LAND
DEGRADATION
13
A historical perspective
Early grazing management
a;,
Closer settlement policy and property size
A market failure approach to land degradation
Imperfect market for information and research
Divergence between private and social discount
rate
Intergenerational equity issues and market failure
Financial pressures
Ownership intentions and planning horizon
Uncertainty about irreversibility
Property rights and land tenure
Land tenure in the mulga rangelands
.
Land market imperfections
Externalities
Government policy failure
Characteristics of region and production system
Drought management and mulga top-feeding
Native and feral grazing animals
The effect of stocking rate on wool
quality
Summary
C
3.
ANALYSIS OF ECONOMIC FACTORS INFLUENCING
LAND DEGRADATION IN
THE
MULGA RANGELANDS
3
1
3.1.
Hypotheses
3.2.
Survey data
3.2.1.
Survey methods
3.2.2.
Economic data
viii
Land condition
data
Techniques of analysis
The
relationship between stocking rates and degradation
A land degradation variable
Analysis
Economic factors influencing stocking rates
Identification of economic variables
'
Analysis
A two-stage least squares approach
Property incomes and stocking rates
Analysis of land prices and degradation
Land
price analysis for the mulga region
Intra-regional comparison
Inter-regional comparison
C
4.
INTERTEMPORAL OPTIMISATION OF
RESOURCE USE
-
THEORY
AND
APPLICATION
The mulga rangeland resource and its characteristics
A model of renewable resource use
Maximum
sustainable yield
A simple graphical intertemporal model
Dynamic optimisation methods
Optimal control
theory
Dynamic programming
Application of dynamic optimisation methods
Review of dynamic optimisation literature
5.
A STOCHASTIC DYNAMIC PROGRAMMING
MODEL FOR
THE
MULGA RANGELANDS
The model framework
Determination of state variables
Indicators of rangeland productivity
Pasture biomass
Basal
area
Selected state variables and partitions
Determination of decision variables
Selected decision variable
The transition matrix
Methods for deriving the transition matrix
Pasture biomass transition matrix
The stage
return
function
Property sizes
Wool cut per head
Wool quality
Wool price
Stock replacement and values
5.5.5.1.
Mortalities
5.5.5.2.
Normal replacements
5.5.5.3.
Stock adjustment between seasons
5.5.6.
Variable production costs
5.5.7.
Variable marketing costs
5.5.8.
Fixed costs
5.5.9.
Discount rates
5.6.
An
alternative specification
-
A
constrained choice model
C
6.
A
DYNAMIC ANALYSIS OF ECONOMIC
INFLUENCES ON OPTIMAL STOCKING
RATES
Effects of property size
Optimal stocking rates
Optimal net present value function
Shadow prices
Long-run probabilities of
pasture
states
Effects of changing model parameters
Wool
price variations
Wool quality differentials
Discount rates
A
utility maximising model
Constrained choice model
Optimal net present values
and
shadow prices
Sensitivity to property size
and
discount rate
Summary
CH
7.
CONCLUSIONS
7.1.
Economic factors
7.2.
The role of dynamic optimisation techniques
7.3.
Policy issues
.
ACKNO
S
RE
CES
APPENDIX
I
-
APPENDIX
I1
-
APPENDIX
m
-
APPENDIX
IV
-
APPENDIX V
-
APPENDIX
VI
-
APPENDIX
VII
-
APPENDIX
VIII
-
Survey Data Collection Techniques
Pasture Biomass Transition Matrix
Arabella Trial Data
Wool Price Series
Matrix of Stage Return Components
Sheep Mortality Rates
Wool Price Sensitivity Analysis
The GPDP Computer Package
APPENDIX REFERENCES
LIST
OF
TABLES
Table
3.1.
Summary of Economic Data from a Survey of Mulga
Rangeland Graziers, Average per Property.
Table
3.2.
Summary of Land Condition Data from
a
Survey of
Mulga Rangeland Graziers, (per cent of total random points).
Table
3.3.
OLS
Regressions of Land Degradation Variables on Stocking
Rate.
Table
3.4.
Regressions of Economic and Physical Variables on Stocking
Rate.
Table
3.5.
OLS
Regressions of Economic and Physical Variables on
Stocking Rates, excluding Residual Land Types.
Table
3.6.
Land Degradation and Stocking
Rate
-
A
Two-Stage
Least
Squares Approach.
Table
3.7.
Significance of Property Size and Land Condition
in
Explaining Property Incomes.
Table
5.1.
Average Pasture Biomass for Land Classes, 1982 Land
Condition Survey.
Table
5.2.
Pasture Biomass Partitions and Midpoints
Table
5.3.
Stocking
Rate
Decisions and Partitions
Table
5.4.
Pasture Biomass Regressions for Deriving the Transition Matrix,
Double-Log Functional Form.
,
Table
5.5.
Economic Characteristics of Property Size Groups.
Table
5.6.
Wool Cut Per Head for Different Rates of
Pasture Utilisation (Arabella Trial)
Table
5.7.
Average Wool Prices for Fibre Diameter, 1973174 to 1989190,
in
1987188 Dollars.
Table
5.8.
Costs of Mulga Feeding.
Table
5.9.
Average Saleyard Prices for Young and Aged Wethers
at
Dalby
(1987188 Dollars).
Table
5.10.
Penalty Costs for Stock Adjustment over Time.
xi
Table
5.11.
Labour and Materials Costs per DSE,
1987188
Dollars.
Table
5.12.
Variable Production Costs used in Estimating Stage Returns.
Table
5.13.
Wool Marketing Charges.
Table
5.14.
Fixed Costs.
Table
6.1.
Effect of Property Size on Optimal Stocking Rates.
Table
6.2.
Effect of Property Size on Optimal Net Present
Values at Full Equity.
Table
6.3.
Effect of Property Size on Optimal Net Present
Values with Average Interest Costs Included.
Table
6.4.
Effect of
Wool
Prices on Optimal Stocking Rates.
Table
6.5.
Effect of Wool Prices on Net Present Values per
Hectare.
Table
6.6.
Effect of
Wool
Quality Differentials on Optimal
Stocking Rates.
Table
6.7.
Effect of Discount Rates on Optimal Stocking Rates.
Table
6.8.
Optimal Stocking Rates using Quadratic &d Negative
Exponential Utility Functions.
Table
6.9.
Optimal Stocking Rates for a Risk-Averse Grazier at
Four Equity Levels, Spliced Utility Function.
Table
6.10.
Effect of Property Size on Optimal Stocking Rate ~djukrnents.
Table
6.11.
Effect of Discount Rate on Optimal Stocking Rate Adjustments.
[...]... land value, degradation of arid lands is generally irreversible Treatment of the cause rather than the symptom is therefore crucial Economic analysis has a major role in determining the causes and the appropriate policies to prevent or slow the process of degradation The broad objectives of this thesis are: - - to identify the economic and financial constraints in decisions on' land use in Queensland' s... woody weed infestation and gullying The visible evidence of economic or management influence is the 'fence-line effect' along property boundaries The mulga rangelands comprise some 19 million hectares in the south-west corner of Queensland (Figure 1.1) The rangelands support a significant pastoral industry accounting for about one quarter of annual wool production in Queensland and one twelfth of the beef... in a survey by ee Skinner and Kelsey in 1964 They reported evidence of sheet erosion and increased run-off in the western mulga rangelands and concluded that deterioration had occurred during the previous 20 to 30 years The Western Arid Region Land Use Studies (WARLUS), undertaken in the 1960s and 1970s by the Queensland Department of Primary Industries, also identified degradation in the mulga rangelands. .. relevant to the mulga rangelands include imperfect markets for information, divergence between private and social discount rates, property rights and land tenure, and land market imperfections An analysis of market failure arguments and those relevant to the mulga rangelands follows in Chapter 2, together with a historical perspective on the degradation problem The role of stocking rate in land degradation. .. starting point 2.1 A historical ~erspectivq 2.1.1 and The Queensland mulga rangelands were first settled in the 1 8 6 0 ~ ~ by the 1870s, most of the better grazing land had been claimed Due to an over-optimistic assessment of the grazing capability of the rangeland, many of the smaller landholders soon encountered financial difficulties Their problems were exacerbated by dingoes, isolation, drought and... almost half of the past 25 years (Mills et al., 1989; Mills, 1989) The mulga rangelands comprise two broad land zones, the soft mulga and the hard mulga, distinguished by rainfall, soil and vegetation characteristics Generally, the hard mulga lies to the west of Charleville and the Warrego River, stretching west of Quilpie, while the soft mulga lies to the east The soft mulga zone consists of flat or gently... entirely removed from the system Further symptoms of degradation are reduced infiltration and greater water run-off An increase in the number of flows of the Paroo River, the catchment of which lies almost wholly within the western mulga rangelands, is evidence of greater run-off This occumed despite relatively constant rainfall patterns (Mdes, 1990) Ten-year moving averages of the number of annual Paroo River... summarises the significant economic issues in mulga rangeland degradation in the context of the sustainability debate The usefulness of dynamic optimisation techniques in identifymg the focus of rangeland policy is discussed - Chapter 2 DE :,' ATION The degradation of rangeland from the pristine state through stable states of woody weed ;s infestation to eventual irreversible sheet erosion was attributed in. .. in mulga rangeland degradation may fall into the category of market failure, others are best described as government policy failure, or merely characteristics of the rangeland production system Moreover, many of the problems of land degradation have their origins in the historical development of the region and early grazing management A review of the historical background to degradation is therefore... Physical Factors in the Land Degradation Cycle Figure 32 Actual Land Prices in the Eastern and Western Mulga Rangelands Compared to the Prices Paid Index, 1961-89 Figure 33 Actual Land Prices in the Western Mulga Rangelands and the Blackall District Compared to the Prices Paid Index, 1968-88 Figure 41 The Resource Growth - Stock Relationship and Maximum Sustainable Yield re 42 The Intertemporal Optimum .
AN
INTRODUCTION
1.1.
Defining degradation
1.2.
Degradation of the mulga rangelands
1.2.1.
The degradation cycle
1.2.2.
Extent of mulga rangeland.
quality
Summary
C
3.
ANALYSIS OF ECONOMIC FACTORS INFLUENCING
LAND DEGRADATION IN
THE
MULGA RANGELANDS
3
1
3.1.
Hypotheses
3.2.
Survey data
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