economically optimal management of huanglongbing...
TRANSCRIPT
1
ECONOMICALLY OPTIMAL MANAGEMENT OF HUANGLONGBING IN FLORIDA CITRUS
By
ABDUL WAHAB SALIFU
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2013
2
© 2013 Abdul Wahab Salifu
3
To Mma Memunatu, M’pa’a Abiba, M’bihi Nasara mini Tehsuma
4
ACKNOWLEDGMENTS
All thanks and praises are due to God. On this note I would like to first
acknowledge that this research was supported by the Citrus Initiative Grant.
I wish to express my heart-felt appreciation and gratitude to the chair of my
committee, Dr. Thomas Spreen, for his role as a professional father throughout my
career as a Ph.D. student. I especially want to thank him for offering me this research
opportunity in spite of my shortcomings in recognizing his generosity from the onset of
my Ph.D. career.
I also wish to extend my deepest gratitude to my advisory committee: Dr. Jerome
Hogsette, Dr. Kelly Grogan, Dr. Fritz Roka, and Dr. Diego Valderrama. I have been very
fortunate to have their expertise available to me. Their kindness is very much
appreciated. Dr. Grogan has been especially instrumental in the model development for
this research, and I am so grateful to her and Dr. Roka for the timely intervention in
sourcing for the much needed funding for the completion of this research and degree.
I would like to acknowledge Drs. Eunice Bonsi, Conrad Bonsi, and Robert
Zabawa for providing me the opportunity to pursue my master’s degree at Tuskegee
University in 2007. I would also like to acknowledge Dr. Nii Tackie for his guidance at
some point in my master’s student career.
The collective and individual contributions and support of all my class mates,
FRED and UF staff and faculty, and the entire gator nation towards this noble
achievement is herein acknowledged and much appreciated. Long live the spirit of the
gator nation. I especially wish to thank Jessica Herman for all the administrative support
and encouragement I got from her at FRED.
5
I am also very blessed to have a superb family. To my wife, Abiba Wumbei, who
is selfless in supporting me and our two equally gracious daughters, Thalma and Thaida
Wahab. I am most gracious to my mom, Memunatu Sumani, whom, in spite of her
illiteracy insisted and ensured that I get circular education. May God bless her for me.
My late dad, Salifu Alidu has also been equally inspirational and supportive to me
throughout his life, may God have mercy on his soul.
6
TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 8
LIST OF FIGURES ........................................................................................................ 10
ABSTRACT ................................................................................................................... 11
CHAPTER
1 INTRODUCTION .................................................................................................... 13
Background ............................................................................................................. 13 Problem Statement ................................................................................................. 14
Strategies of Control ............................................................................................... 16 Objectives ............................................................................................................... 20 Scope of Research ................................................................................................. 20
2 LITERATURE REVIEW .......................................................................................... 22
HLB Disease Incidence, Latency, and Spread ........................................................ 22
The Impact of HLB .................................................................................................. 25
HLB Control ............................................................................................................ 26
Social Consequences of HLB Persistence .............................................................. 29 Effects of HLB on Yield and Cost of Production ...................................................... 30 Economics of Disease Control Strategies ............................................................... 33
Bioeconomic Models of Disease Control (with Incorporated Discount Rates) ........ 36
3 BIOECONOMIC ESTIMATION ............................................................................... 38
Optimal Investment Theory ..................................................................................... 38 Overview .......................................................................................................... 38 Optimal Capital Investment Model .................................................................... 39
The Economic Model .............................................................................................. 40
The Biological Model............................................................................................... 40
4 MODEL RESULTS ................................................................................................. 44
Model Estimation Assumptions ............................................................................... 44
Empirical Results of Model ............................................................................... 45 Conclusions ...................................................................................................... 49
5 SENSITIVITY ANALYSIS ....................................................................................... 63
7
The Effects of a Price Decline ................................................................................. 63
The Effects of a Price Increase ............................................................................... 64 The Effects of a Lower Annual Rate of Spread ....................................................... 65
The Effects of an Increased Annual Rate of Spread ............................................... 66 The Effects of a Lowered Latency Period ............................................................... 67
6 CONCLUSIONS, RECOMMENDATIONS AND LIMITATIONS .............................. 86
LIST OF REFERENCES ............................................................................................... 90
BIOGRAPHICAL SKETCH ............................................................................................ 99
8
LIST OF TABLES
Table page 4-1 Baseline Parameter Values ................................................................................ 50
4-2 Non-Valencia Orange Yield Estimated Boxes per Tree, by Age Group in Florida, 2004-2005 through 2008-2009 .............................................................. 51
4-3 NPV1 for Strategy 1 (Do Nothing) ....................................................................... 52
4-4 NPV1 for Strategy 2 (Symptomatic Tree Removal) ............................................. 53
4-5 NPV1 for Strategy 3 (Enhanced Foliar Nutritional Program) ............................... 54
4-6 NPV1 for the Three Strategies for Age Classes 0 and 3 ..................................... 55
4-7 NPV1 for the Three Strategies for Age Classes 6 and 10 ................................... 56
4-8 NPV1 for the Three Strategies for Age Classes 14 and 17 ................................. 57
4-9 NPV1 for the Three Strategies for Age Classes 0 and 3 at Different Yield Penalty2 Levels for Strategy 3 ............................................................................ 58
4-10 NPV1 for the Three Strategies for Age Classes 6 and 10 at Different Yield Penalty2 Levels for Strategy 3 ............................................................................ 59
4-11 NPV1 for the Three Strategies for Age Classes 14 and 17 at Different Yield Penalty2 Levels for Strategy 3 ............................................................................ 60
5-1 NPV1 for the Three Strategies for Age Classes 0 and 3 from a Price Decline2 ... 69
5-2 NPV1 for the Three Strategies for Age Classes 6 and 10 from a Price Decline2 .............................................................................................................. 70
5-3 NPV1 for the Three Strategies for Age Classes 14 and 17 from a Price Decline2 .............................................................................................................. 71
5-4 NPV1 for the Three Strategies for Age Classes 0 and 3 from a Price Increase2 ............................................................................................................ 72
5-5 NPV1 for the Three Strategies for Age Classes 6 and 10 from a Price Increase2 ............................................................................................................ 73
5-6 NPV1 for the Three Strategies for Age Classes 14 and 17 from a Price Increase2 ............................................................................................................ 74
5-7 NPV1 for the Three Strategies for Age Classes 0 and 3 from a Decline in Beta2 ................................................................................................................... 75
9
5-8 NPV1 for the Three Strategies for Age Classes 6 and 10 from a Decline in Beta2 ................................................................................................................... 76
5-9 NPV1 for the Three Strategies for Age Classes 14 and 17 from a Decline in Beta2 ................................................................................................................... 77
5-10 NPV1 for the Three Strategies for Age Classes 0 and 3 from an Increase in Beta2 ................................................................................................................... 78
5-11 NPV1 for the Three Strategies for Age Classes 6 and 10 from an Increase in Beta2 ................................................................................................................... 79
5-12 NPV1 for the Three Strategies for Age Classes 14 and 17 from an Increase in Beta2 ................................................................................................................... 80
5-13 NPV1 for the Three Strategies for Age Classes 0 and 3 from a Lowered Latency Period2 .................................................................................................. 81
5-14 NPV1 for the Three Strategies for Age Classes 6 and 10 from a Lowered Latency Period2 .................................................................................................. 82
5-15 NPV1 for the Three Strategies for Age Classes 14 and 17 from a Shortened Latency Period2 .................................................................................................. 83
10
LIST OF FIGURES
Figure page 4-1 Net Present Value per Acre as a Function of Disease Incidence and Average
Age (Years) of Trees at First Detection with Contour Lines for the Do Nothing Strategy .............................................................................................................. 61
4-2 Net Present Value per Acre as a Function of Disease Incidence and Average Age (Years) of Trees at First Detection with Contour Lines for Strategy 2 ......... 61
4-3 Net Present Value per Acre as a Function of Disease Incidence and Average Age (Years) of Trees at First Detection with Contour Lines for Strategy 3 (30% Yield Penalty) ............................................................................................ 62
4-4 Dominant Strategy Given Disease Incidence at First Detection and Average Grove Age (Price = $1.50/pound solid, 30% yield penalty for strategy 3) ........... 62
5-1 Dominant Strategy Given Disease Incidence at First Detection and Average Grove Age from a Change in Price: Top Subplot is Baseline, Middle and Bottom Subplots Shows Price Decline (from $1.50 to $1.20) and Increase (from $1.50 to $1.80), respectively ..................................................................... 84
5-2 Dominant Strategy Given Disease Incidence at First Detection and Average Grove Age from a Change in Beta: Top Subplot is Baseline, Middle and Bottom Subplots Shows Beta Decline and Increase, respectively ...................... 84
5-3 Dominant Strategy Given Disease Incidence at First Detection and Average Grove Age from a Change in Latency: Top Subplot is Baseline, Bottom Subplot Shows Decline in Latency from 1 year to 6 Months for Groves with Average Age of 0 and 3 while the Latency for Groves 6 Years or Larger Remain at 2 Years .............................................................................................. 85
11
Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
ECONOMICALLY OPTIMAL MANAGEMENT OF HUANGLONGBING IN FLORIDA
CITRUS
By
Abdul Wahab Salifu
May 2013
Chair: Thomas H. Spreen Major: Food and Resource Economics
Following the declaration of the endemic status of Huanglongbing (HLB) in
Florida in 2005 with no formal control policy for the disease, it is natural that an
empirical examination and justification of the management protocols implemented at the
farm-level to control HLB be made. We develop farm level decision rules to judge when
it is economically justified to implement a particular control strategy. Models are
developed that allow economic assessment of each strategy and determine the
scenarios for which each strategy is optimal or yield a positive net present value,
considering average grove age at first detection, and rates of infection at first detection.
Our results justify the heterogeneous decisions of growers regarding their choice among
control strategies, in a way that optimizes each grower’s utility. As hypothesized, the
superiority of either strategy depends upon the level of infection at the time when the
disease is first found in a particular block, the rate of spread of the disease, the average
age of the grove at first infection, expectations of future fruit prices, and the latency
period. Our research identifies important efficacy targets that must be achieved for the
long-term economic viability of a citrus grove. Our results provide a recommendation of
12
the optimal control strategy for a given set of conditions such as the age of the planting
and initial rate of infection.
13
CHAPTER 1 INTRODUCTION
Background
Huanglongbing (HLB) is a bacterial disease that affects all varieties of citrus. It is
commonly referred to as citrus greening. HLB was first discovered in Florida in 2005
and is now found in all counties where commercial citrus is produced (Manjunath et al.
2008). It is spread by a small leaf-feeding insect, the Asiatic citrus psyllid (ACP). The
ACP was first found in June 1998 in Delray Beach, and it is noted for its short-range
maneuverability and long range drift by wind, which facilitates its ability to spread HLB
far and wide. HLB acts to disrupt the phloem of the tree thereby limiting its ability to
uptake nutrients. Initially this leads to yellowing of leaves, promotion of premature fruit
drop, and production of small, misshapen fruit that contain bitter juice with no economic
value. As the disease spreads through the tree, the amount of usable fruit produced
diminishes until eventually the tree is of no economic value (Brlansky et al. 2011).
Worldwide, three different bacteria are known to cause HLB: Candidatus
Liberibacter asiaticus (LAS), Candidatus Liberibacter africanus (LAF), and Candidatus
Liberibacter americanus (LAM). The most prevalent of these is LAS, which is found
worldwide, including the United States. Asiatic HLB is caused by LAS, and it is
transmitted by the Asian citrus psyllid (ACP), Diaphorina citri. While LAM is found to be
prevalent in Brazil and China, the African HLB caused by LAF, can be found in Africa,
Saudi Arabia, and the South Asia, and is spread by its vector, the African citrus psyllid
(Trioza erytreae) (Gottwald 2010).
HLB is the single most vicious and debilitating citrus disease responsible for the
destruction of almost 100 million trees in major citrus growing areas of the world where
14
the disease has become endemic (Aubert et al. 1985, Bové 1986). This is partly due to
its elusiveness to various regionally specific management prescriptions. At the present
time, the only known way to effectively combat the disease is through early detection
and a strict eradication program of infected trees. The standard control strategy adopted
by HLB affected regions of the world is an integrated control program that involves
psyllid control, symptomatic tree removal, restricted movement of citrus propagation
materials, and distribution of disease-free seedlings and budwood (Gottwald et al. 2012,
Aubert 1990).
Problem Statement
Florida is the leading citrus-producing state in the United States, with nearly
600,000 acres devoted to commercial production. HLB poses as the most serious
obstacle faced by the state’s $9.3 billion citrus industry (National Research Council
2010), which supports almost 80,000 jobs. In its eight-year presence in Florida, it is
estimated that over 10 million of the 60 million orange trees are currently infected with
HLB (Irey et al. 2011), and $1.3 billion in citrus revenue have been lost (Hodges and
Spreen 2012; Bolton 2012). To appreciate the devastating impact of HLB on Florida
citrus, it is said to cause far worse tree damage than citrus canker, which was
responsible for the destruction of over 4 million trees. Tree removal due to HLB infection
has resulted in the reduction of approximately 10 percent of Florida’s commercial citrus
production, and a 40 percent increase in production costs (Irey et al. 2008). HLB has
already been implicated for loss in land acres allocated to citrus in the state since 2006,
and soaring grower costs in terms of tree eradication, psyllid control, inspections, and
replanting costs (TBO 2008). Hodges and Spreen (2012) estimated that within the last
five years, Florida has lost 8,257 jobs, total revenue of $4.541 billion comprised of
15
indirect revenue of $2.717 billion, due to HLB. A more important longer-term
consequence has been the fact that HLB has created huge uncertainty among Florida
citrus growers with respect to future investment/planting.
HLB is a disease with two important characteristics. First, the rate of spread is
strongly affected by tree age because psyllids prefer new growth (Brlansky et al. 2008).
Young trees, which are more vigorous as compared to mature trees, produce more
flushes and thereby are more susceptible to psyllid feeding and disease transmission. In
the case of mature trees, the disease spreads more slowly (Gottwald 2010).
Consequently, an infected mature tree is capable of producing usable fruit for several
years while at the same time serving as a source of infection for other healthy trees.
Other factors that affect the rate of spread of HLB are the ACP population and initial
level of infection at first find of the disease. The density of the ACP population is the
single most important factor because theoretically, if the ACP population is reduced to
zero, spread of HLB will stop with immediate effect. Second, control through tree
eradication is complicated by a latency period between the time a tree first becomes
infected and when it expresses visual symptoms. Once a mature tree is infected, it may
not begin to exhibit symptoms of the disease for up to two years (Gottwald 2010). If the
rate of infection in a particular grove is relatively high at the time the disease is first
discovered, a policy of eradication of symptomatic trees may result in destruction of the
entire grove.
Just a few months after the discovery of HLB in Florida, the citrus canker
eradication program was terminated following the sweeping spread of canker over most
southern Florida groves by a series of hurricanes that blew over the citrus belt in 2004
16
and 2005. Later in 2005, an interdisciplinary team of USDA HLB experts declared HLB
endemic to Florida, with no chances of eradication (Gottwald and Dixon, 2006). So far, it
is even more troubling to note that neither the citrus industry nor the state or USDA has
put in place a clear cut and decisive procedure for control of HLB, unlike in the case of
the aborted citrus canker control program.
Strategies of Control
At this time, there are three distinct strategies being employed to deal with
greening. Strategy 1, referred to as “do nothing”, allows the disease to spread and takes
no measures to slow its spread including controlling psyllid populations or mitigating
HLB’s impact on tree health. Strategy 1 has no effect on per acre costs as management
tactics are not modified. Per acre revenues, however, are gradually affected as the
disease spreads and the number of healthy fruit that can be harvested and utilized
gradually declines. At some point, per acre revenues will not cover per acre grove
maintenance costs and at that point, the grove is no longer economically viable. The
disease spreads faster in younger groves, so younger groves cease to be economically
viable at a faster rate compared to an older grove with the same initial level of infection.
Strategy 2 follows the standard plant pathology disease control model and is the
only internationally accepted control strategy for HLB (Aubert 1990). Under Strategy 2,
an aggressive psyllid control program is also put into place to suppress psyllid
populations. In addition, between four and twelve inspections are conducted annually to
identify symptomatic trees. Once found, symptomatic trees are immediately eradicated
(Brlansky et al. 2008). The logic behind Strategy 2 is that by eradicating symptomatic
trees, the level of inoculum in a particular citrus grove gradually will be reduced.
Eventually the incidence of the disease will be reduced to a point where it can be
17
economically tolerated. Muraro (2010) has estimated that in Florida, Strategy 2
increased pesticides costs by about $450 per acre. Overall production cost have
increased from $800 (2004, pre HLB) to $1,500 (2009, post HLB + canker). There are
five problems associated with Strategy 2. First, plant pathologists have yet to
characterize the key parameters that would significantly define the timeline by which to
control HLB through eradication of symptomatic trees. These parameters include a
controllable base level of HLB infection, the number of years it would take to achieve
that base level, and the probability that young tree resets will survive to productive
maturity. Second, the latency period of the disease implies that not all diseased trees
will be removed in a timely manner, and these asymptomatic trees will serve as a
reservoir of the disease inoculum. Third, if a grove is already at a high level of known
infection and given that more trees are infected but not yet symptomatic, it may not be
possible to effectively reduce inoculum levels in a particular grove without eradicating
the entire grove. The probability of this outcome is related to the age of the grove and
the level of infection when the first positive tree is found. Fourth, eradication or
suppression of the disease to a tolerable level in one grove may not be possible if
neighboring growers are not adequately suppressing the disease in their groves.
Neighboring groves will serve as sources of the inoculum, and the disease may be
continually re-introduced into the groves of the grower following Strategy 2. Fifth, relying
on visual detection of HLB-infected trees by scouting is estimated to be about 50%–
60% effective in finding all the symptomatic trees in a single survey (Futch et al. 2009;
Spann et al. 2010). One other factor that also impacts the effectiveness of this strategy
18
is the neighbor’s HLB management behavior. If psyllid control or tree removal is not
coordinated with neighbors of a grove, inoculum builds up in the local vicinity.
Strategy 3 is an approach first developed in southwest Florida and is, in part, a
response to the Achilles heel of Strategy 2, namely if Strategy 2 is initiated too late, the
entire grove may be eradicated before the disease can be suppressed. While an initial
high rate of disease incidence is one possible motivation to adopt Strategy 3, it is also
possible that under some conditions, Strategy 3 may yield a higher return than Strategy
2 even though Strategy 2 could successfully reduce HLB inoculums to a manageable
level. Strategy 3 proposes to treat the symptoms of HLB through foliar application of
micro and macro nutrients. The tree’s defense response to an HLB infection is to
produce compounds that block phloem vessels of the tree’s vascular system. This
resulting damage to the root system inhibits the ability of the tree to uptake nutrients
from the ground. In the foliar feeding method, a portion of the nutritional needs of the
tree is applied through foliar sprays including both macro and micro nutrients (Spann et
al. 2010). Formulation of the enhanced nutritional program depends on the program, but
generally the active ingredients include standard essential micronutrients, and
phosphite, and salicylate salts (Gottwald et al. 2012). Symptomatic trees are not
removed and scouting for the disease is discontinued. As with Strategy 2, a strong
psyllid control program is practiced. Roka, et al. (2010) have estimated that the
additional nutrient applications increase production costs between $200 to $600 per
acre, depending on the type and amount of foliar nutritionals a grower decides to apply.
The primary concern among plant pathologists with Strategy 3 is that HLB
inoculum is left unchecked. The economic implications of Strategy 3 include whether it
19
is feasible for young trees (ages 3-8) to reach their productive maturity, whether planting
the next generation of citrus trees is economically viable, and whether the presence of a
grove following Strategy 3 while other growers follow Strategy 2 will cause increased
damage on the latter growers’ fields. Spatial analysis of disease spread in south Florida
suggests that spread between citrus blocks is a more significant portion of disease
spread than the spread of the disease within a citrus block (Gottwald et al. 2008). This
suggests that heterogeneous control methods may reduce the viability of Strategy 2.
This study addresses the economic consequences of the three strategies. In
other words, how does a grower determine which strategy is in her/his best interests
(given average grove age and initial infection rate)? Strategy 1 needs to be considered
as a baseline to reference Strategies 2 and 3. Growers make heterogeneous decisions
regarding their choice among control strategies. Models are developed that allow
economic assessment of each strategy and determine the scenarios for which each
strategy is optimal or yield a positive net present value, considering tree age at first
detection, and rates of infection at first detection. Since the optimal strategy may vary
due to tree age at first detection and the rate of infection at first detection, the optimal
strategy may vary across growers located nearby. Currently, the long term net present
value of the control strategies is unknown because of uncertainty in the efficacy of the
strategies. Our research identifies important efficacy targets that must be achieved for
the long-term economic viability of a citrus grove.
Our results provide a recommendation of the optimal control strategy for a given
set of conditions. It is hypothesized that the superiority of any one strategy depends
upon the level of infection at the time when the disease is first found in a particular
20
block, the rate of spread of the disease, the average age of the grove at first infection,
expectations of future fruit prices, and the latency period. The rate of spread is a
function of psyllid populations and the efficacy of psyllid control measures.
Objectives
The primary objective of this study is to determine the optimal economic
management strategies of citrus greening in Florida. This is accomplished through the
following specific objectives:
1. Identify grove age and level of initial disease incidence at which each strategy yields positive economic returns.
2. Determine the ranges of initial grove age and initial disease incidence for which a given control method is economically preferred over other available methods.
Scope of Research
The study implements a net present value analysis of the control strategies
adapted by Florida citrus growers following the advent of HLB in the state. This is
essential to the determination of which strategy is economically superior, from the
grower’s point of view. It is of more importance to consider the private benefits/cost of
the tree eradication policy to the grower, as no compensation is paid for removed trees.
The impact of HLB on citrus yield is first modeled through a disease spread function; a
discrete logistic function approximated from a Gompertz function. Since the spread rate
of HLB is dependent on the average grove age, the logistic function is approximated for
three average age classes of 0, 3, and 6 or older. Due to lack of available data for the
estimation of model parameters for Florida, we obtain parameter estimates from a
corresponding region of HLB spread. Given this logistic function, disease spread in an
infected grove with a tree density of 150 per acre is simulated for given parameter
values for each age class, while varying the initial level of infection. The logistic curves
21
thus incorporate both asymptomatic and symptomatic trees expressed in a ratio
involving total diseased trees. Total diseased is the sum of the asymptomatic (latently
infected trees without visual symptoms) and symptomatic tree categories. From this, a
spread function is generated and fed into HLB tree and grove severity functions for the
calculation of the relative yield due to HLB presence in the affected grove. The net
present value is then estimated from the corresponding relative yield estimates given
the yield from a healthy grove unaffected by HLB, obtained as estimated boxes of fruit
per tree by age group for non-Valencia oranges from the Florida agricultural statistics
service (Florida citrus statistics 2008-2009). Fruit prices are expressed as delivered-in
(to the processing plant) $/pound solids ($1.50/pound solid is the baseline price) with
pound solids per box values dependent on tree age. The model described above is the
baseline model for the ‘do-nothing’ policy. Hence two other models are developed: the
infected tree eradication model and the enhanced foliar nutrition model. These models
are unique in the sense that they include a latency period of HLB infection, as well as
take into account the average grove age, the natural variation in disease incidence at
first detection across groves in a region, and periodic removal of symptomatic trees
(specific to the tree eradication model). The robustness of each model is tested by a
sensitivity analysis conducted for the main model parameters.
22
CHAPTER 2 LITERATURE REVIEW
Responses to stem the devastating effects of HLB or plant diseases in general
especially in academia have been enormous. This chapter reviews relevant literature in
all aspects of related disciplines including HLB epidemiology, the variety of control
methods experimented to date, the impact of HLB across the globe and in Florida as
well as its social ramifications if left unchecked. In addition, the review includes work on
HLB effects on production and yield costs, and general economic and bio-economic
models of disease control.
HLB Disease Incidence, Latency, and Spread
Disease incidence has been estimated using a variety of approaches. Gottwald
et al. (2010) determined disease incidence via a logistic spread rate per year calculated
by linear regression of transformed1 disease incidence in Florida. HLB incidence in
Florida has also been found in similar studies to increase within 10 months from 0.2 %
to as much as 39 % (Gottwald et al. 2007b, 2008; Irey et al. 2008). Spatiotemporal
spread models have also been used to characterize HLB in Florida where simultaneous
within and across grove spread were common (Gottwald et al. 2008). Other studies
have been conducted such as in Vietnam where HLB incidence is found to vary
depending on the management strategy employed (Gatineau et al. 2006) or in Brazil
where incidence has been shown to depend on proximity to HLB-infected citrus groves
and/or on neighbors’ behavior (Bassanezi et al. 2006; 2005, Gatineau et al. 2006;
1 The disease incidence data was first transformed via a logistic linear function given
by )1/ln()(logit yyy .
23
Gottwald et al. 2007a; 2007b). Albrecht et al. (2012) showed in a Florida study that
HLB disease incidence is unaffected by the type of rootstock used in propagation.
Disease latency refers to the time between when infection by a pathogen occurs
and the onset of symptoms. HLB latency has also been demonstrated in some studies
where for every symptomatic tree in a given grove, 13 (range 2 to 56) HLB-positive but
asymptomatic trees existed in its neighborhood, which expressed symptoms in
subsequent assessments (Bassanezi et al. 2006). Irey et al. (2006) use PCR
techniques to test for the presence of the bacteria that causes HLB (Candidatus
Liberibacter asiaticus) in plots of about 190 trees and found that 60 percent more
asymptomatic trees existed in addition to the symptomatic trees that were found (Irey et
al. 2006). High correlation (R2 = 0.89) between infected trees and total number of
infected trees among the plots suggests natural disease transition from asymptomatic
trees to symptomatic trees. In some instances, high bacteria titer was found with PCR in
some asymptomatic trees, suggesting the need for roguing asymptomatic trees as well
(National Research Council 2010; Irey et al. 2006). The presence of a high percent
(80%) of infected trees within 25 m of a symptomatic tree also signifies short distance
spread of HLB (Irey et al. 2006).
HLB progression in a grove has also been determined to depend on the vector
population and inoculum levels as well as average grove age at first detection. HLB
progression in Reunion Island, China, and the Philippines is reported to follow a sigmoid
curve, with clustering of diseased trees (Gottwald and Aubert 1991; Gottwald et al.
1989, 1991). In Reunion Island more aggregation towards the direction of prevailing
wind was observed, suggesting that psyllids are dispersed by the wind. Aggregation in
24
China was facilitated by closer tree spacing. Logistic growth rates are more plausible for
both growth of an infested area in space and population density growth than constant
growth rates (Kompas and Che 2009). This suggests that an infected area initially
grows exponentially, slows down and finally stops as the potential range of the species
is attained. Disease progression can reach asymptotic levels faster in young groves
than older groves (Gottwald et al. 2007, 2007a). The dispersal distance for HLB-infected
psyllids have been estimated to range from 0.88 to 1.61 km with a median of 1.58,
which may imply that groves more than 2km apart are unlikely to directly affect each
other with HLB (Gottwald et al. 2007b, Gottwald et al. 2008). Thus HLB spread is
spatially continuous and simultaneous, primarily via psyllid feeding behavior between
groves and secondarily through within grove feeding of the psyllids, necessitating the
need for landscape management practices (neighbors HLB management practices
should be compatible) for effective control. Manjunath et al. (2008) in a study to detect
HLB bacteria from a sample of over 1,200 psyllid adults and nymphs in Florida found
that the bacteria spread in an area may be detected one to several years before
symptom development in plants. Raphael et al. (2012) developed a deterministic
mathematical model that involve susceptible citrus, infectious but asymptomatic citrus,
symptomatic citrus, non-infective adult ACP, and infective adult ACP that acquired HLB
in the adult and nymph stages to study the dynamics of HLB in a citrus grove. Results
show that all trees in the grove are infected after 5 years even after removal of
symptomatic trees with 47% detection efficiency. They concluded that the best control
strategy is the reduction of the vector populations. Chiyaka et al. (2012) used a
mathematical model of HLB transmission to indicate the importance of ACP for initial
25
HLB infection. Their work also underscores the importance of flush production and
latency period in influencing HLB development.
The Impact of HLB
HLB, which in Chinese means “yellow dragon disease”, was first described in
southern China in 1919 and spread widely and devastated citrus establishments in the
Philippines, Indonesia, Thailand, and South Africa between 1960 – 1980. Until recently
(2004/5), symptoms of HLB were found in two countries in the Americas; specifically in
São Paulo State in Brazil, in which nearly three million HLB infected trees were
removed in subsequent years, and in Florida, USA (Bové 2006; National Research
Council, 2010). HLB now occur in other North American areas, such as Cuba, Georgia,
Louisiana, South Carolina, Nayarit (Mexico), California, Texas, Costa Rica, and Belize.
HLB is a very serious, debilitating disease that affects all varieties of citrus. HLB’s
destructive abilities are unwavering no matter the mode of propagation; reducing yield
significantly through fruit drop, dieback and stunted growth, in addition to causing poor
quality of un-harvested fruits (National Research Council, 2010). Depending on the
psyllid vector population, bacteria titer, and age cohort of the grove at first detection,
HLB can take over an entire grove in 3 – 13 years following the expression of first
symptoms (Catling and Atkinson 1974; Aubert et al. 1984; Gottwald et al. 1991;
Gatineau et al. 2006; Gottwald et al. 2007a; Gottwald et al. 2009). Symptoms can
become very severe within one to five years from onset of the disease, depending upon
tree age at time of infection and the range of infection (Lin 1963; Schwarz et al. 1973;
Aubert 1992). The progression of HLB severity in a grove results in yield reduction,
rendering the grove uneconomical within 7 – 10 years after planting. (Aubert et al. 1984;
Aubert 1990; Gottwald et al. 1991; Roistacher 1996).
26
Worldwide, nearly 100 million trees are estimated to be affected by HLB. In some
parts of Thailand in 1981, close to 100 percent of trees were affected. Between 1961
and 1970 in the Philippines, citrus acreage was reduced by 60 percent, which
represents the fallout from the infection of an estimated seven million trees in 1962
(Altamirano et al. 1976; Martinez and Wallace 1969). Three million trees were removed
in Java and Sumatra within that same period, and a loss of 3.6 million trees were
reported in Bali within four years from 1984 to 1987. The HLB havoc extended to
southwestern Saudi Arabia, where most sweet orange and mandarin trees were killed
by 1983. In the 1960s, the entire citrus industry in Reunion Island was devoured by HLB
(Altamirano et al. 1976; Martinez and Wallace 1969).
Since its arrival in São Paul State, Brazil in early 2004, three million HLB affected
sweet orange trees have been removed as part of measures taken to control HLB
(National Research Council 2010). In the wake of the panic from the first reports of HLB
in Florida citrus in 2005, no public policy has emerged to handle HLB, as a result of
which growers evolved their own private stop-gap management strategies, rendering
the citrus industry to be labeled as an endangered industry. Before effective control of
the African psyllid with systemic insecticides was discovered in the late 1980s, HLB
devastated the South African citrus industry across the length and breadth of the
country, affecting four million out of the 11 million trees in South Africa, during the mid-
1970s (National Research Council 2010).
HLB Control
This section outlines the various recommended control measures for a pre- and
post-HLB presence in a given region. These include quarantine, roguing, psyllid control,
27
and use of healthy nursery propagation materials. The effectiveness of some of these
measures as gleaned from the literature is also presented.
So far, the first line of control of HLB is by adoption of quarantine measures to
prevent disease introduction. If however HLB is found in a hitherto HLB-free region, a
series of coordinated actions known as preventative control measures could be taken to
control the disease. Affected areas are mapped through surveys to identify infected
trees, which are later removed to prevent re-infection. A rigorous psyllid control program
should also be put in place. To avoid infection through plant propagation practices,
production of healthy citrus seedlings should be ensured especially if resetting is
required after symptomatic trees are removed. This is because without control, it takes
on average eight years for a grove to reach 100% infection (Bové 2006).
Control by roguing is effective through well-timed and carefully repeated surveys
to identify all affected trees as much as possible. The latency period of HLB, which can
be up to two or more years (Gottwald 2010), reduces the effectiveness of roguing as a
control measure; hence the need for repeated surveys. The quality of roguing is also
affected by the presence of uncontrolled psyllids in the grove in which infected tree
removal is practiced. Roguing must therefore be accompanied with a rigorous psyllid
control regime. Detection of HLB in Florida and São Paulo is done by mounting
platforms that allow for inspection of the tops of mature trees as it is reported that many
affected trees start showing symptoms first on the upper part of the canopy (National
Research Council 2010). Brlansky et al. (2009) recommend four inspections per year,
even though some growers carry out two to three inspections per year. Futch et al.
(2009) indicated that no scouting method is 100% accurate in detecting HLB
28
symptomatic trees. This re-emphasizes the need for multiple inspections within the
year. Irrespective of age or severity of infection, all symptomatic trees should be
removed (Ayres et al. 2005), and prior to removal these symptomatic trees should be
sprayed with a contact insecticide (Rogers et al. 2010). Unlike citrus canker in which
infected trees as well as all surrounding trees at 15 m radii are removed, this practice is
not feasible for HLB (Bassanezi 2005), as the psyllid vector feeds randomly across a
given grove, and can disperse farther to other groves by wind, hurricanes or storms.
The proportion of the infected trees removed depends on the initial disease incidence
and hence the entire grove can be eradicated at very high rates of initial disease
incidence. For instance, a grove with 10% symptomatic trees implies 20% infected
trees, and groves with 20%, 30% and 50% symptomatic trees give rise to 36%, 50%
and 70% infected trees respectively, due to latency and hence the presence of
asymptomatic trees (Bové 2006). Recently, Bové (2012) has been discounting the
latency period saying that it is just incomplete inspections, while Futch et al. (2009)
indicates that the latency period of HLB is unknown within a tree. Resetting can be done
with healthy seedlings, after infected trees are removed.
Application of contact and systemic insecticides as well as use of biological
agents reduces psyllid populations and HLB spread, depending on the species of the
psyllid. Biological control is reported to have been successful in Reunion Island (Aubert
and Bové 1980; Aubert et al. 1980), mainly due to the fact that there were no
hyperparasitoids on the introduced Tamarixia radiata and T. dryi parasitoids to hamper
their effectiveness (Aubert and Quilici 1984). Predators such as spiders, lacewings,
ladybugs, minute pirate bugs, and some wasp parasitoids attack the Asian citrus psyllid.
29
However, the most effective natural enemy is reported to be the coccinellid lady beetles
Olla v-nigram, and Harmonia axyridis (Michaud, 2004). In Florida, attempts have been
made to establish the biological agent T. radiata to control psyllids (Bové 2006) with
little effect on the citrus psyllid population. Major reasons for this failure include
presence of hyperparasites and inadequate number of alternative hosts for the
parasitoids (Halbert and Manjunath 2004, National Research Council, 2010).
In Brazil, encouraging results were obtained in the use of tree removal and
insecticides against psyllids to control HLB. HLB incidence decreased from 7% to
0.03% in the 10th survey of a grove with 71,000 trees (Ayres et al. 2005). The African
version of HLB in South Africa was effectively managed for some period by the adoption
of disease-free nursery stock, intensive psyllid control coupled with rouging of
symptomatic trees. In China, however, the Asian HLB has proven more difficult to
handle with preventative control measures. Aggressive implementation of similar
measures brought some success in Brazil, and gave rise to the identification of factors
that affect HLB preventative control effectiveness. These factors include farm size, age
cohort of grove, HLB incidence frequency in the area of the grove, neighbors HLB
management behavior, HLB incidence at first inspection, date of first scouting, number
of scouting for affected trees, and frequency of insecticide application (Belasque, Jr. et
al. 2009). In Florida, it is also been observed that large grove size with well-maintained
groves in the same area that have low bacteria titer lowers HLB incidence by reducing
the spread across groves.
Social Consequences of HLB Persistence
The $9.3 billion citrus industry in Florida supports almost 80,000 jobs (grove
employees, seasonal pickers, haulers, processors, and packers). With total annual
30
wages of $2.7 billion, these workers earn roughly 1.5 percent of Florida’s wage income
(Norberg 2008). Inefficiency in managing HLB in the state will affect not only these full-
time equivalent job employees of the industry, but also the general public will be
deprived of health benefits derived from citrus products, albeit still enjoyed at a higher
cost from imported juice and fruit. Growers who cherish citrus production as a way of life
will be affected. The worldwide recognition of Florida as a citrus producing state whose
brand name has contributed to the state’s attractiveness to tourists, retirees, and
consumers will also be compromised. To augment shortfalls in both fresh and
processed citrus demand domestically, imports have to rise, putting further strains on
the economy.
Effects of HLB on Yield and Cost of Production
Effective management of HLB implies a dramatic increase in production costs
through adoption of various control measures such as use of disease-free nursery
stock, scouting and roguing symptomatic trees, and psyllid vector control. Other
reasons for reduced profit include declining yield and fruit quality of affected trees,
production of healthy nursery trees, costs of tree replacement and care, and value of
income/production losses from replaced trees. Yield effects of HLB depend on
tree/grove age and severity of infection. Young trees/groves become unproductive
faster than mature trees/groves. Mature trees/groves remain productive for several
years with less severe infection, and productive life could be reduced to as low as two
years with severe infections on a tree/grove (National Research Council 2010).Yield
reduction is high (19%) for younger infected groves (1-5 years olds) 2-4 years after the
onset of infection compared to older groves (over 5 years olds) where high yield
reduction occurs only after 5-10 years of first symptomatic tree onset (Bassanezi et al.
31
2011; Bassanezi and Bassanezi 2008). Optimal control policy for a pest has been
shown to depend significantly on the costs of pest damage per unit of infected area
(Carrasco et al. 2009; Sharov 2004).
Stringent requirements for raising disease free nursery trees in screened houses
have resulted in an increase from $4.50 to $9.00 in the cost per nursery tree. Given the
recommended four inspections per year for symptomatic trees and an estimated cost of
between $25–30/acre per inspection, annual inspection costs could add as much as
$100 to $120 to production costs (Morris et al. 2008). Assuming six trees are detected
for removal each year, tree removal costs add another $34 per acre per year to
production expenses (Muraro 2008b). Morris et al. (2008) suggested little economic
difference between controlling HLB with roguing and doing nothing to ameliorate the
impact of the disease until the grove becomes economically useless.
Psyllid control is accomplished either with soil application of recommended
insecticides or application of foliar insecticides such as zeta-cypermethrin , carbaryl,
dimethoate, imidacloprid, chlorpyrifos, malathion, phosmet, spinetoram, spirotetramat,
fenpropathrin, and petroleum oil. A combination of soil insecticide and three foliar
insecticide applications is required for psyllid control of a mature grove at an estimated
cost of $288/acre/year (Morris et al. 2008).
Citrus production costs and returns depend on the variety, intended use (fresh
fruit market or processed juice market), and yield quantities. Fruits for the processed
juice market are sold on pounds-solids basis in Florida, which relates to the juice
content of the fruit. This is estimated to be about 6.5 pounds of solids/90-pound box of
fruit, on average. Given production costs for Valencia oranges without HLB or citrus
32
canker at $1,657 per acre, harvesting, delivery and assessment costs at $1,226 per
acre, yield of between 300 to 600 90-lb boxes per acre, and assuming no resetting of
removed trees, the break-even price has been calculated to range from $0.80 to $1.19
per pound solids and with HLB or canker, the break-even price would be $0.89 to $1.38
(Muraro 2008a).
Fresh fruit is sold on a per box basis and for white grapefruit in the Indian River
area; total production cost is estimated at $3,195 per acre without canker or HLB and
$3,600 when both canker and HLB are present. This results in break-even prices of
between $8.37 to $5.82 per box for yields of between 350 to 650 boxes per acre without
HLB and canker and $10.03 to $6.71 with both HLB and canker (Muraro 2008a).
Assuming prices of pound solids range from $1.25 to $1.50, Morris et al. (2008) deduce
that processed citrus production will remain profitable in spite of the 41% increase in
production cost due to the presence of HLB, which can even be offset with a suggested
increase in planting densities.
Growers have been shown to benefit significantly in terms of the yield increase,
improved quality of produce and labor productivity, as well as the reduced control costs,
following their participation in a landscape management program against fruit flies in
Hawaii (Mau et al. 2007). Expected expenses needed for the control of an established
invasion of pests such as pesticides, labor, and equipment have been shown to depend
on the distribution, numbers, and rate of spread of the pests (Stohlgren and Schnase
2006). Likewise, a credible economic assessment of pest control requires an
understanding of the biology of the pest, the region of invasion, and temporal data of the
pest in the region under investigation (Stohlgren and Schnase 2006). Further, grower
33
losses must include not only reduced yield following a pest infestation, but also the
adjusted price effects if the infestation has the potential of affecting the supply/demand
mechanisms for the commodity in the locality, depending on its price elasticity (Myers et
al., 1998).
Economics of Disease Control Strategies
Economic models have been developed to determine the optimal control
strategies for some plant diseases. Such models range from barrier effectiveness
(Brown et al. 2002; Sharov 2004; Huffaker et al. 1992), infection risk and insurance
premium rates assessments (Goodwin and Piggott 2009) to models of optimal control of
invasive species such as effects of the environment, discount rate, marginal damages of
invasion and marginal costs of control on optimal control choices (Olson and Roy 2002).
Some models study the effects of imperfect information about the degree of infestation
from an invasive species on optimal control policy (Haight and Polasky 2010). Models
on optimal control of invasive species management using a logistic growth function to
express the growth of the invasive species have also been demonstrated (Eiswerth and
Johnson 2002). It has been shown that the optimal control strategy involving pest
eradication, reducing pest spread rate or doing nothing is a function of the size of the
area infested, the pest damage per unit area and the rate of discount used in the net
benefit calculation (Sharov and Liebhold 1998; Sharov 2004). Yet, others have
demonstrated the effectiveness of using disease-free plants and biological control of
psyllid vectors in survey studies (Aubert et al. 1996). Chan and Jeger (1994) developed
a dynamic mathematical model to assess among other things, the effects of disease
control by tree removal and planting resets. They found that at low infection rates,
symptomatic tree removal alone is sufficient to eradicate the disease. At high infection
34
rates, removal of asymptomatic but infected as well as symptomatic plants is advisable.
Jeger and Chan (1995) examined the relevance of theoretical models to strategic
disease management decisions and concluded that it is the interplay of theory, relevant
epidemiological data and predicting the likely effects of control that offers a useful
means of refining tactical disease control decisions.
Fishman et al. (1983) developed bioeconomic models for citrus tristeza virus
(CTV) infection and spread to assess the cost-effectiveness of roguing as a disease
eradication policy in Israel. They simulated the model to estimate the net present values
to reflect private and social gains from the two policies of eradication or do nothing and
found that roguing is more cost effective than do nothing. Kobori et al. (2011) developed
an Individual-Based Model (IBM) that simulated the disease spread dynamics of HLB
and suggested that delaying the latency period and roguing are two effective ways of
reducing the spread of HLB in a grove. Their preliminary results showed that regional
control may be effective in reducing HLB bacteria titer in the field.
Fishman and Marcus (1984) provide a deterministic model of infectious disease
spread within and across rows of plants with periodic roguing. Improving the detection
method with other parameters and conditions held constant results in time of infection
reduced in some rows, or the number of infected plants increases initially, attains a
maximum and declines afterwards. Pierre et al. (2006) considers the optimal
combination of monitoring (minimum cost of establishing, maintaining and monitoring
traps for fruit flies) and the cost of control once fruit flies are detected. They applied a
Bayesian decision process to solve the optimization problem of choosing between
detection expenses (cost of traps set around entry points) and eradication expenses
35
(cost of spraying with insecticides, release of sterile male flies, or quarantine measures).
They found optimal trapping density for two entry locations (Miami and Tampa) to be
higher than the actual number of traps deployed in practice, suggesting the need for
additional modifications to the model. Batabyal and Nijkamp (2008) use renewal theory
to construct and analyze a dynamic and stochastic model of optimal control for invasive
species in an orchard. They also derive the long run expected cost (LREC) for the
orchard per unit time and show that the optimal roguing and resetting densities solves
the derived LREC minimization problem. Their model treated commercial orchards as
entities whose growth and output are by nature dynamic and stochastic under constant
threats from a variety of invasive plant or animal species. Lominac and Batabyal (2009)
focused on a representative tree in a grove and used discrete time Markov chains to
model the tree’s management under attack of an invasive species. The specified model
is used to define the trees one step transition probabilities, determine the time the tree
is affected in the long run and the long run schedule of replacements for the
representative tree when it dies.
Morris and Muraro (2008) perform an economic analysis of greening
management of tree removal with/without different densities of resetting, versus do
nothing. They concluded that resetting is preferable to do nothing if resets reach
maturity in a mature grove. Replanting at higher density is best for a grove that is
unproductive due to HLB. Oleś et al. (2012) present a bioeconomic model for
optimization of disease control with latency, using the network/individual – based
methodology. Depending on total cost of disease incidence (consisting of costs of
treating infected individuals and prevention of infection), three optimal strategies are
36
identified. These include total population preventive treatment, local treatment within a
neighborhood of certain size, and only treatment of diagnosed cases with no prevention.
Some epidemiological factors do affect the optimal strategy of local treatment.
Bioeconomic Models of Disease Control (with Incorporated Discount Rates)
Bioeconomic approaches to optimal management of perennial biological
resources involving complex patterns or transitioning processes such as pests/disease
incidence require economic assessment that incorporates discount rates into cost–
benefit estimates involving present value analysis. The net present value of an asset is
the weighted exponential function of the time at which net expected revenues of the
asset are obtained; T
r tetN0
)( ,)( where N(t) is the net revenue at time t, r is the discount
rate, and T is the time period (Clark 1976). Only Sharov and Liebhold (1998) use
present value for optimization of long-term pest management options involving barrier
zones. Their theoretical model shows that pest control mechanisms that slow pest
population spread is a feasible strategy unlike strategies that stop population spread,
which require natural barriers to be optimal. Enkerlin and Mumford (1997) estimated the
net present value for three improved management options to control the Mediterranean
fruit fly across three countries (Israel, Palestinian Territories, and Jordan). Given the
nine-year analysis, the predominant control method is the sterile male suppression
option whereas over a longer time period, the sterile male eradication option
predominates. Odom et al. (2003) developed and applied a deterministic dynamic
programming model in a case study to derive optimal control rules for the management
of an environmental weed (scotch broom) in a national park. Model results show the
need for biological control as a viable option. In a similar study, Chalak‐Haghighi et al.
37
(2008) utilized ecological and economic information to construct a dynamic bio-
economic optimization model to evaluate the net benefits of a range of possible control
options for Californian thistle (Cirsium arvense) weed in New Zealand pasture. Factors
considered in the maximization of the net benefit include the costs and effectiveness of
control options, and the revenue from animal production. Their results suggest that the
optimal strategy is a mix of a bio-control agent with one or more integrated weed
management strategy, especially when the initial density of the thistle population
exceeds 1.0 shoot m-2.
38
CHAPTER 3 BIOECONOMIC ESTIMATION
This chapter contains an overview of optimal investment theory, which provides
the theoretical framework for the use of net present value analysis in the study. Next is
an expatiation on the income method for asset valuation and its appropriateness for this
type of study, herein known as our economic model. Finally, a biological model is
presented, and it spans the Gompertz and logistic functions, the tree and grove severity
functions, and the negative exponential function for relative yield.
Optimal Investment Theory
Overview
Two possibilities exist in designing a framework for the theory of optimal
investment, namely the neoclassical theory of optimal capital accumulation and utility
maximization theory. We will adopt the neoclassical theory of the firm for our theoretical
framework since it is a more powerful theory than the utility-maximizing theory
(Jorgenson 1967). Neoclassical theory assumes that capital growth depends on utility
maximization of a consumption stream. Essentially, a given firm maximizes
consumption utility subject to a given production function at fixed current and future
prices and interest rates for both input and output flows. A production plan is then
chosen to maximize the present value of the returns from the investment. Maximizing
the present value of the firm is the only criterion consistent with utility maximization
theory. The resulting theory of optimal capital accumulation broadly includes special
cases of econometric models of investment actions (Jorgenson 1967).
When an investment involves expense and income flows into the future, such as
investments in perennial crop production, it is necessary to estimate the present value
39
of the series of cash flows projected from the proposed investment. Net present value
(NPV) calculation attempts to do just that, and gives a measure of how much the
investor gains today for investing in the project. In NPV calculation, future cash flows
are discounted at a specified discount rate that depends on the time value of money,
the interest from an alternative guaranteed investment, and the degree of risk
compensation that is being accepted in the project.
Optimal Capital Investment Model
The present value of a firm that is assumed to produce a single output from a
single variable and capital input is given by the expression:
)()()( ,)(0 tRtqtw(t)Cp(t)Q(t)I(t) dttIrteNPV
0),,F ),()((t)K :Subject to KC(QtKtR
where r is the discount rate, I is net income, Q, C, and R represent output, variable input
and fixed inputs respectively, and p, w, and q are their corresponding prices
(Jorgenson, 1967). Net present value (NPV) is maximized subject to the constraints that
the rate of change of the flow of capital services ( (t)K ) is related to the flow of net
investment ( )()( tKtR ), where is a constant of proportionality and denotes
depreciation of capital and K is capital services. The second constraint states that the
production function is constrained by output (Q) and input (C, K) levels. The
maximization problem is solved for the optimal variable input (C*), output (Q*), and
capital services (R*) and the marginal productivity for variable input and capital as well
as the shadow price for capital services. Thus the complete optimal capital
accumulation model consists of the production function, the two marginal productivity
conditions, and the function for the shadow price for capital services (Jorgenson 1967).
40
The Economic Model
A citrus grove is an asset. We estimate the economic impact of HLB through its
effect on the value of a particular citrus grove. There are a variety of approaches in
asset valuation, but the most appropriate approach in this application is the income
method. In the income method, future costs and revenues are estimated to give per
annum net revenue. Future net revenue is discounted to the present to give net present
value (NPV) using the formula,
T
ttr
tQ
tC
tQ
tP
NPV
11)1(
))((
where tP is price in time period t, tQ is yield in time period t, tC are costs in time period t,
and r is the discount rate. HLB affects the NPV of an infected grove by increasing costs
if control is implemented, and decreasing future fruit production, thereby reducing future
revenues. Since the rate of spread depends in part upon the tree age at first infection,
we compute NPV as a function of tree age as well as the level of infection at first
detection. Since the NPV of a particular grove depends upon several factors, which are
subject to random variation, stochastic dominance is an appropriate method to identify
the superior strategy. At this time, however, knowledge of the underlying probability
distributions of those random factors is not available, so our economic assessment is
done in a deterministic framework.
The Biological Model
Our original idea to depict HLB spread was motivated by a Gompertz function as
proposed by Bassanezi and Bassanezi (2008). This function specifies that the disease
incidence, y, at time t is:
41
1)-(3 )
0ln(
tey
eGt
y
where y0 is the disease incidence at first detection and is the annual rate of spread of
the disease. However, the Gompertz function always converges to 100% infection,
which does not allow us to analyze control strategies that prevent 100% disease
infection. A logistic function has the advantage of being more flexible and allows for a
steady state level of disease infection that is less than 100%. In this case we estimate
the parameters of the logistic function that approximate the Gompertz function, and use
those parameters to estimate the impact of Strategies 2 and 3. To do this, we use
parameter values for y0 and for each age class from Bassanezi and Bassanezi
(2008) to simulate Gompertz spread from low to high incidence until field incidence
reaches 100%. Using nonlinear regression, the simulated Gompertz data for each age
class are used to estimate the corresponding logistic . Our logistic function is derived
from the deterministic differential equation:
2)-(3 1
),1( Gt
yY,Gt
yGt
yYYYYt
Y
where Y is the proportion of diseased trees at time t, Y is the change in the proportion of
diseased trees and is the annual rate of spread of the disease. The result of this
procedure yielded our logistic estimates to be 1.5148125, 0.8450625, and 0.4440625
of their Gompertz counterparts of 1.3, 0.65, and 0.325 obtained from Bassanezi and
Bassanezi (2008), for each corresponding age class consisting of average grove age of
0, 3, and 6 (Table 4-1). The logistic curves are then generated according to Equation 3-
3:
42
3)-(3 )1
1(1
ˆ1
t
Yt
Yt
Yt
Y
For strategy 1, tY includes both symptomatic disease incidence, s
tY as well as
asymptomatic disease incidence, a
tY . The assumption on latency period in the baseline
model (Strategy 1) is 1 year for groves of average age of 0 and 3, and 2 years for
groves with average age of 6 or larger (Gottwald 2010). In the sensitivity analysis,
latency period is one of the parameters we alter to check model robustness. For
Strategy 2, if the assumption on latency is 1 year for instance, then trees remain
asymptomatic for one year, implying that a
t
s
t YY 1 . Further, we assume that all
symptomatic trees are immediately removed once the tree exhibits symptoms, implying
that 1tY in Equation 3-3 equals a
tY 1 . Since the disease moves both across trees in the
grove and across canopy in a given infected tree, we need to model the spread of the
disease in canopy area as well to determine the yield effect of HLB for Strategies 1 and
3. It is worthwhile to mention here that HLB is spread only by psyllids hence vector
control will have significant effects on disease spread. As a result, one of the factors for
sensitivity analysis addressed in Chapter 5 is latency period, which we assume to be
the proxy for psyllid control. We estimate the yield impact of HLB ( tr ) as a function of
symptomatic grove canopy area or disease severity tX and yield of a healthy grove (Rt,
average boxes per tree) for Strategy 1 using the negative exponential model:
4)-(3 1,2,....t;i ,)1
ˆ
1
ˆ( ),(1
it
xLt
yt
t
Li
yt
XtbX
et
Rt
r
)))()1)
0/1(1/(1
texx
43
where Rt equals 1, denotes the full yield of a healthy grove (average boxes per tree), 1
tr
is the percent of healthy yield obtained for a given level of disease severity for strategy
1, b is the rate of yield reduction as a function of HLB severity, Xt is total grove severity
at time t, x is the fraction of HLB symptomatic tree canopy area at time t, x0 is the
fraction of HLB symptomatic tree canopy area at first detection, and θ is the annual rate
of disease severity progress in an affected tree. For Strategy 2, all symptomatic trees
are removed, so the spread of yield losses through the canopy does not occur.
For Strategy 3, the yield effect is assumed to be in-between the yield effect for
strategy 1 and a healthy grove. Since the reduction in yield relative to a healthy grove is
unknown, we use averages between healthy yield and strategy 1’s yield given by:
5)-(3 ..3,........0.1,0.2,0. where ),1(13 t
rt
r
With all three strategies modeled, we determine the scenarios for which each
strategy would be optimal, considering all possible strategy efficacies and tree age and
rates of infection at first detection.
44
CHAPTER 4 MODEL RESULTS
In this chapter, the baseline model results are presented. Key parameters such
as the annual rate of spread of HLB ( ), price per pound solids, length of latency, and
yield penalties are fixed at specified values according to relevant literature and
secondary data sources (Table 4-1). The chapter begins with an exposition of the
assumptions of the model, after which empirical results of the model are presented
followed with some concluding remarks.
Model Estimation Assumptions
We create disease spread curves using β values described in Chapter 3 and use
those parameters to estimate the NPV of Strategies 1, 2, and 3. Historical data on
boxes of fruit per tree by age group for non-Valencia oranges from the Florida
Agricultural Statistics Service (Florida Citrus Statistics 2008-2009) are used to establish
yield curves by variety. Next logistic curves of disease spread are interacted with the
investment or NPV model as specified above to estimate the impact of HLB on grower
earnings based on tree age and first detection of the disease. Fruit prices are expressed
in $/pound solids delivered-in ($1.50/pound solid) with pound solids per box values
dependent on tree age. The estimates are made on a per acre basis for a grower with
150 trees per acre and 100% original tree acreage remaining. We use a 10% discount
rate for calculation of net present values. Operating and production costs for a mature
grove include herbicide, pesticide, and fertilizer applications, irrigation, and pruning, but
do not include HLB foliar nutritional sprays or pesticide applications in the baseline
calculations. Since we assume no resetting (replacing trees lost in the citrus grove), the
45
adjusted reset grove costs by tree age are assumed to be zero2, as well as the
establishment costs/acre for new solid set, the cost of tree removal and planting reset-
replacement trees, reset frequency, and reset yield adjustments. Yield loss due to
freeze or other diseases is assumed to be zero.
We calculate net present value of a stylized citrus grove using a 15-year time
horizon. Beyond 15 years, the net present value per year approaches zero. We
calculate the net present value for groves with an initial average age ranging from 0 to
17. Beyond 17 years of age, tree yields no longer increase, so calculations for groves
of this age represent our net present value upper bound.
Empirical Results of Model
Under Strategy 1 (do nothing) all groves with an average tree age of 0 and 3
years yield a negative net present value at any initial disease incidence rate. Groves
that contain younger trees at first detection also experience a faster spread of the
disease. Consequently, young groves that become infected with HLB are unable to
produce a sufficient volume of fruit to recover investment costs. Irrespective of the
disease incidence rate at first detection, all groves with an average age of 6 years and
over yield a positive net present value under Strategy 1. In Table 4-3 the net present
values for groves with initial rates of disease incidence varying from 0.1% to 50% and
for average initial grove ages of 0, 3, 6, 10, 14, and 17 years are reported under
Strategy 1. A plot of the net present values as a function of disease incidence and
average age at first detection is shown in Figure 4-1. Also shown are contour lines, with
2 The assumption of no resetting greatly simplifies the calculation of disease spread and the
accompanying reduction in fruit production per acre. The assumption clearly is a limitation on the derived results.
46
the green contour line marking the ages and disease rates at which the net present
value is $0.00.
Under tree removal (Strategy 2), groves with average age of 0 display negative
net present values whereas groves with an average age of 3 years show negative net
present value when the initial disease incidence is 20% and larger. Groves with an
average age of 6 show positive net present value for initial disease incidence ranging
from 0.1% to 30%, but shows negative net present value for initial disease incidence of
40% and 50%. All other age categories show a positive net present value, no matter the
initial rate of disease incidence (Table 4-4). In Figure 4-2, the green contour line marks
the ages and disease rates at which the net present value is $0.00 for strategy 2.
An enhanced foliar nutritional program (Strategy 3) is expected to boost yield of
an HLB affected grove, but will be lower compared to a disease free grove. This
analysis assumes a yield penalty of 30% compared to a healthy grove under Strategy 3.
The estimated NPVs associated Strategy 3 is presented in Table 4-5. As before,
groves with average age of 0 show negative net present value at all levels of initial
disease incidence. For this strategy, the ages and disease rates at which the net
present value is $0.00 are indicated by the green contour line of Figure 4-3.
For ease of comparison, Tables 4-6 through 4-8 juxtapose the net present value
for the three strategies for each age class. Bolded values indicate the superior strategy
at a particular age of first detection and initial rate of infection. For groves whose
average age is 0 at first detection, the net present values are all negative. For trees with
average age of 3 years, Strategy 2 is better than Strategies 1 and 3 when disease
incidence ranges from 0.1% to 7.0%, and thereafter (incidence of 8.0% to 50%),
47
Strategy 3 is better than both Strategies 1 and 2. For trees with average age of 6 and
10, Strategy 1 is better than Strategies 2 and 3 at lower rates of initial disease incidence
(0.1% to 2.0%), after which Strategy 2 becomes superior to Strategies 1 and 3 when the
disease incidence ranges between 3.0% and 10.0%. At the highest initial disease
incidence of between 20% and 50%, Strategy 3 is superior to Strategy 2 and 1 in net
present value. For trees with average age of 14 and 17, Strategy 1 outperforms the
other two strategies at the low rates of disease incidence (0.1% to 2.0%), and for the
middle rates of disease incidence of between 3.0% and 8.0%, Strategy 2 is better than
the other two strategies. At the highest rates of initial disease incidence (10% to 50%),
Strategy 3 becomes superior to Strategies 1 and 2.
The results presented in Table 4-6 through 4-8 provide several interesting
implications. While it may be surprising that Strategy 1 is ever identified as the superior
strategy, the results suggest that at very low levels of initial infection, the costs
associated with both Strategy 2 and Strategy 3 exceed gains to be realized in the future
by mitigating the effects of the disease. One could describe this result as the
“temptation of waiting.” At higher levels of initial infection, Strategy 3 emerges as the
superior strategy because of the large number of trees that must be removed if Strategy
2 is followed. The results herein support that argument that once the disease becomes
well-established, the economic rational approach is to attempt to “live with the disease”
via Strategy 3.
These results also point out a major limitation of the methodology employed
here. The neighbor effects are ignored. Adoption of any of the three strategies implies
neighbor effects. Since both Strategy 1 and 3 entail non-removal of symptomatic trees,
48
the level of disease inoculum in a particular grove is not being diminished. Strategy 2
calls for the removal of symptomatic trees and its primary intent is to reduce the level of
inoculum. If one grower pursues Strategy 2 and his/her neighbor pursues Strategy 3,
the actions of the second neighbor will adversely affect the first neighbor because the
neighboring grove will continue to serve as a source of inoculum.
Figure 4-4 delineates the ranges of initial grove age and initial disease incidence
for which each strategy maximizes net present value, after adjusting for age classes in
which all strategies post negative net present values (age class of 0 and sometimes 3).
Strategy 3 dominates at incidence levels of 8% - 50% for groves of almost all ages.
Strategy 2 dominates for groves with average age of 0 and 3 years at low initial disease
incidence of 0.1% to 2% and also at disease incidence levels of 3% to 8% for all groves
with average age of 6, 10, 14, and 17. Strategy 1 dominates for all groves with average
age of 6, 10, 14, and 17 only when disease incidence is 0.1% to 2%. Therefore, for
almost all groves at almost all initial HLB disease incidence, there is the likelihood that
the enhanced nutritional program will generate a higher NPV for the grower than if one
were to follow a do nothing or tree eradication management strategy. For groves with
average age of 6 - 17 years at initial HLB disease incidence of 3% to 8%, tree
eradication program management strategy will yield higher returns to the grower than
do nothing or implementation of the enhanced nutritional program. For groves at 6 - 17
years at very low HLB incidence (0.1% to 2%), the grower will be better off by doing
nothing than either implementing the tree eradication or enhanced nutritional program.
For a new solid set grove at any level of initial disease incidence, the enhanced
nutritional program is likely to give the grower the best earnings on his/her investment
49
than any other strategy. No matter how high the initial rate of disease incidence, each
strategy remains positive in net present value for mature groves (groves with average
age of 6 or larger). Strategy 3 performs even better especially for mature trees at almost
all rates of disease incidence when the assumption on yield penalty of a healthy grove
is 5%, 10%, or even 20% instead of the 30% yield penalty (Tables 4-9 to 4-11) used in
the comparison. For all age classes, cost eventually exceeds revenue, especially for
mature groves at high rates of initial disease incidence.
Conclusions
Which strategy is superior to the other(s) depends on the age of trees at first
detection and the initial rate of disease incidence at first detection. Each strategy has its
range of relevance region within which it maximizes a grower’s net returns given the
initial level of infection and the average age of the grove. Growers with groves of all
ages at 20% or more initial incidence may be better off implementing the enhanced
nutritional program (Strategy 3). For growers whose groves are three years or older in
average age with initial HLB infection rate at 3% to 8%, (or growers with newly
established groves at 0.1% - 2% HLB incidence), the best strategy is Strategy 2
(infected tree removal). Strategy 1 is the least optimal strategy and it is only optimal
when incidence is very low (0.1% to 2%) for groves with average age of 6 or larger.
50
Table 4-1. Baseline Parameter Values
Parameter Trees Age Class
0 3 6
Annual HLB Spread Rate Gompertz (β) 1.3000000 0.65000000 0.32500000
Annual HLB Spread Rate Logistic (β) 1.5148125 0.84506250 0.44406250
Price/pound solid ($) 1.5000000 1.50000000 1.50000000
Latency Period (years) 1.0000000 1.00000000 2.00000000
Tree Severity Rate of HLB (θ) 3.6800000 1.84000000 0.92000000
Yield Reduction Rate of HLB (b) 1.8000000 1.80000000 1.80000000
Initial Severity (x0) 0.2000000 0.10000000 0.05000000
Source: Bassanezi and Bassanezi (2008); Bassanezi et al. (2011)
51
Table 4-2. Non-Valencia Orange Yield Estimated Boxes per Tree, by Age Group in Florida, 2004-2005 through 2008-2009
Tree Age Average1 Yield (2004/5 - 2008/9)
Yield2 (boxes/tree)
1
1.2
0
2 0
3 1
4 1.2
5 1.4
6
1.8
1.7
7 1.8
8 1.9
9
2.26
2
10 2.1
11 2.3
12 2.4
13 2.5
14
3.05
2.6
15 2.7
16 2.8
17 2.9
18 3
19 3.1
20 3.2
21 3.3
22 3.4
23 3.5
Sources: Florida Citrus Statistics 2008-2009. FASS 1Average yields of 1.2, 1.8, 1.26, and 3.05 boxes/tree are for groves of ages 3 – 5, 6 – 8, 9 -13, and 14 – 23 years respectively 2 Yield for each tree age is derived from the 5-year average yield in column two
52
Table 4-3. NPV1 for Strategy 1 (Do Nothing)
Disease Incidence at First Detection
Average Age (Years) of Trees at First Detection
0 3 6 10 14 17
0.001 -2,614 3,843 11,463 14,551 16,487 17,101
0.010 -4,142 927 9,539 12,562 14,488 15,102
0.020 -4,532 -17 8,442 11,407 13,322 13,935
0.030 -4,696 -662 7,686 10,601 12,505 13,118
0.040 -4,779 -961 7,213 10,084 11,978 12,591
0.050 -4,942 -1,182 6,673 9,505 11,389 12,002
0.060 -5,004 -1,599 6,360 9,157 11,032 11,644
0.070 -5,052 -1,754 5,893 8,656 10,521 11,133
0.080 -5,089 -1,886 5,659 8,393 10,250 10,861
0.100 -5,140 -2,097 5,265 7,947 9,786 10,396
0.200 -5,338 -2,960 3,555 6,032 7,799 8,405
0.300 -5,369 -3,531 2,563 4,897 6,604 7,207
0.400 -5,462 -3,988 1,463 3,634 5,278 5,877
0.500 -5,482 -4,164 1,077 3,176 4,779 5,375 1 Cumulative 15-year NPV ($/ac).
Beta (1) = 1.5148125 for the 0 Age Class; Beta (2) = 0.8450625 for age Class of 3; Beta (3) = 0.4440625 for Age Classes of 6 or Larger.
53
Table 4-4. NPV1 for Strategy 2 (Symptomatic Tree Removal)
Disease Incidence at First Detection
Average Age (Years) of Trees at First Detection
0 3 6 10 14 17
0.001 -645 4,830 8,441 11,534 13,470 14,084
0.010 -4,050 4,322 8,207 11,276 13,204 13,818
0.020 -5,478 3,790 7,949 10,993 12,910 13,525
0.030 -6,302 3,287 7,694 10,712 12,620 13,235
0.040 -6,871 2,813 7,442 10,435 12,333 12,947
0.050 -7,297 2,363 7,193 10,160 12,049 12,663
0.060 -7,639 1,936 6,946 9,888 11,768 12,382
0.070 -7,916 1,531 6,701 9,619 11,489 12,103
0.080 -8,152 1,144 6,460 9,353 11,213 11,828
0.100 -8,529 423 5,983 8,828 10,670 11,284
0.200 -9,569 -2,411 3,745 6,359 8,111 8,725
0.300 -10,043 -4,421 1,721 4,124 5,790 6,404
0.400 -10,295 -5,937 -106 2,101 3,686 4,300
0.500 -10,433 -7,114 -1,752 276 1,784 2,399 1 Cumulative 15-year NPV ($/ac).
Beta (1) = 1.5148125 for the 0 Age Class; Beta (2) = 0.8450625 for age Class of 3; Beta (3) = 0.4440625 for Age Classes of 6 or Larger.
54
Table 4-5. NPV1 for Strategy 3 (Enhanced Foliar Nutritional Program)
Disease Incidence at First Detection
Average Age (Years) of Trees at First Detection
0 3 6 10 14 17
0.001 -2,170 3,030 7,822 10,915 12,852 13,466
0.010 -2,610 2,211 7,245 10,318 12,252 12,866
0.020 -2,727 1,872 6,916 9,972 11,902 12,516
0.030 -2,776 1,679 6,689 9,730 11,657 12,271
0.040 -2,826 1,589 6,547 9,575 11,499 12,113
0.050 -2,850 1,523 6,385 9,401 11,322 11,936
0.060 -2,868 1,398 6,291 9,297 11,215 11,829
0.070 -2,883 1,351 6,151 9,146 11,062 11,675
0.080 -2,894 1,312 6,081 9,067 10,981 11,594
0.100 -2,909 1,248 5,962 8,933 10,841 11,454
0.200 -2,968 989 5,449 8,359 10,245 10,857
0.300 -2,978 818 5,152 8,019 9,887 10,498
0.400 -3,006 681 4,822 7,640 9,489 10,099
0.500 -3,011 628 4,706 7,502 9,339 9,948 1 Cumulative 15-year NPV ($/ac).
Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3. Beta (1) = 1.5148125 for the 0 Age Class; Beta (2) = 0.8450625 for age Class of 3; Beta (3) = 0.4440625 for Age Classes of 6 or Larger.
55
Table 4-6. NPV1 for the Three Strategies for Age Classes 0 and 3
Disease Incidence at First Detection
Average Age (Years) of Trees at First Detection
0 3
Strategy Strategy
1 2 3 1 2 3
0.001 -2,614 -645 -2,170 3,843 4,830 3,030
0.010 -4,142 -4,050 -2,610 927 4,322 2,211
0.020 -4,532 -5,478 -2,727 -17 3,790 1,872
0.030 -4,696 -6,302 -2,776 -662 3,287 1,679
0.040 -4,779 -6,871 -2,826 -961 2,813 1,589
0.050 -4,942 -7,297 -2,850 -1,182 2,363 1,523
0.060 -5,004 -7,639 -2,868 -1,599 1,936 1,398
0.070 -5,052 -7,916 -2,883 -1,754 1,531 1,351
0.080 -5,089 -8,152 -2,894 -1,886 1,144 1,312
0.100 -5,140 -8,529 -2,909 -2,097 423 1,248
0.200 -5,338 -9,569 -2,968 -2,960 -2,411 989
0.300 -5,369 -10,043 -2,978 -3,531 -4,421 818
0.400 -5,462 -10,295 -3,006 -3,988 -5,937 681
0.500 -5,482 -10,433 -3,011 -4,164 -7,114 628 1 Cumulative 15-year NPV ($/ac).
Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3. Beta (1) = 1.5148125 for the 0 Age Class; Beta (2) = 0.8450625 for age Class of 3; Beta (3) = 0.4440625 for Age Classes of 6 or Larger.
56
Table 4-7. NPV1 for the Three Strategies for Age Classes 6 and 10
Disease Incidence at First Detection
Average Age (Years) of Trees at First Detection
6 10
Strategy Strategy
1 2 3 1 2 3
0.001 11,463 8,441 7,822 14,551 11,534 10,915
0.010 9,539 8,207 7,245 12,562 11,276 10,318
0.020 8,442 7,949 6,916 11,407 10,993 9,972
0.030 7,686 7,694 6,689 10,601 10,712 9,730
0.040 7,213 7,442 6,547 10,084 10,435 9,575
0.050 6,673 7,193 6,385 9,505 10,160 9,401
0.060 6,360 6,946 6,291 9,157 9,888 9,297
0.070 5,893 6,701 6,151 8,656 9,619 9,146
0.080 5,659 6,460 6,081 8,393 9,353 9,067
0.100 5,265 5,983 5,962 7,947 8,828 8,933
0.200 3,555 3,745 5,449 6,032 6,359 8,359
0.300 2,563 1,721 5,152 4,897 4,124 8,019
0.400 1,463 -106 4,822 3,634 2,101 7,640
0.500 1,077 -1,752 4,706 3,176 276 7,502 1 Cumulative 15-year NPV ($/ac).
Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3. Beta (1) = 1.5148125 for the 0 Age Class; Beta (2) = 0.8450625 for age Class of 3; Beta (3) = 0.4440625 for Age Classes of 6 or Larger.
57
Table 4-8. NPV1 for the Three Strategies for Age Classes 14 and 17
Disease Incidence at First Detection
Average Age (Years) of Trees at First Detection
14 17
Strategy Strategy
1 2 3 1 2 3
0.001 16,487 13,470 12,852 17,101 14,084 13,466
0.010 14,488 13,204 12,252 15,102 13,818 12,866
0.020 13,322 12,910 11,902 13,935 13,525 12,516
0.030 12,505 12,620 11,657 13,118 13,235 12,271
0.040 11,978 12,333 11,499 12,591 12,947 12,113
0.050 11,389 12,049 11,322 12,002 12,663 11,936
0.060 11,032 11,768 11,215 11,644 12,382 11,829
0.070 10,521 11,489 11,062 11,133 12,103 11,675
0.080 10,250 11,213 10,981 10,861 11,828 11,594
0.100 9,786 10,670 10,841 10,396 11,284 11,454
0.200 7,799 8,111 10,245 8,405 8,725 10,857
0.300 6,604 5,790 9,887 7,207 6,404 10,498
0.400 5,278 3,686 9,489 5,877 4,300 10,099
0.500 4,779 1,784 9,339 5,375 2,399 9,948 1 Cumulative 15-year NPV ($/ac).
Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3. Beta (1) = 1.5148125 for the 0 Age Class; Beta (2) = 0.8450625 for age Class of 3; Beta (3) = 0.4440625 for Age Classes of 6 or Larger.
58
Table 4-9. NPV1 for the Three Strategies for Age Classes 0 and 3 at Different Yield Penalty2 Levels for Strategy 3
Disease Incidence at First Detection
Average Age (Years) of Trees at First Detection
0 3
Strategy Strategy
1 2 3 1 2 3
20% 10% 5% 20% 10% 5%
0.001 -2,614 -645 -1,535 -900 -582 3,843 4,830 3,477 3,924 4,147
0.010 -4,142 -4,050 -1,828 -1,046 -655 927 4,322 2,894 3,632 4,002
0.020 -4,532 -5,478 -1,906 -1,085 -675 -17 3,790 2,705 3,538 3,954
0.030 -4,696 -6,302 -1,939 -1,101 -683 -662 3,287 2,576 3,473 3,922
0.040 -4,779 -6,871 -1,955 -1,110 -687 -961 2,813 2,516 3,444 3,907
0.050 -4,942 -7,297 -1,988 -1,126 -695 -1,182 2,363 2,513 3,421 3,896
0.060 -5,004 -7,639 -2,000 -1,132 -698 -1,599 1,936 2,389 3,380 3,875
0.070 -5,052 -7,916 -2,010 -1,137 -701 -1,754 1,531 2,358 3,364 3,868
0.080 -5,089 -8,152 -2,017 -1,141 -702 -1,886 1,144 2,331 3,351 3,861
0.100 -5,140 -8,529 -2,027 -1,146 -705 -2,097 423 2,289 3,330 3,850
0.200 -5,338 -9,569 -2,067 -1,160 -715 -2,960 -2,411 2,117 3,244 3,807
0.300 -5,369 -10,043 -2,073 -1,169 -716 -3,531 -4,421 2,002 3,187 3,779
0.400 -5,462 -10,295 -2,092 -1,178 -721 -3,988 -5,937 1,911 3,141 3,756
0.500 -5,482 -10,433 -2,096 -1,180 -722 -4,164 -7,114 1,876 3,123 3,747 1 Cumulative 15-year NPV ($/ac). 2Yield from HLB infected trees reduced 20%, 10% and 5% on “normal” yield for strategy 3. Beta (1) = 1.5148125 for the 0 Age Class; Beta (2) = 0.8450625 for age Class of 3; Beta (3) = 0.4440625 for Age Classes of 6 or Larger.
59
Table 4-10. NPV1 for the Three Strategies for Age Classes 6 and 10 at Different Yield Penalty2 Levels for Strategy 3
Disease Incidence at First Detection
Average Age (Years) of Trees at First Detection
6 10
Strategy Strategy
1 2 3 1 2 3
20% 10% 5% 20% 10% 5%
0.001 11,463 8,441 7,865 7,907 7,929 14,551 11,534 10,958 11,002 11,023
0.010 9,539 8,207 7,480 7,715 7,832 12,562 11,276 10,560 10,803 10,924
0.020 8,442 7,949 7,260 7,605 7,778 11,407 10,993 10,329 10,687 10,866
0.030 7,686 7,694 7,109 7,529 7,740 10,601 10,712 10,168 10,607 10,826
0.040 7,213 7,442 7,036 7,482 7,716 10,084 10,435 10,087 10,555 10,800
0.050 6,673 7,193 6,906 7,428 7,689 9,505 10,160 9,949 10,497 10,771
0.060 6,360 6,946 6,844 7,397 7,673 9,157 9,888 9,879 10,462 10,754
0.070 5,893 6,701 6,750 7,350 7,660 8,656 9,619 9,779 10,412 10,739
0.080 5,659 6,460 6,704 7,327 7,638 8,393 9,353 9,727 10,386 10,715
0.100 5,265 5,983 6,625 7,287 7,619 7,947 8,828 9,637 10,341 10,693
0.200 3,555 3,745 6,283 7,116 7,533 6,032 6,359 9,254 10,150 10,597
0.300 2,563 1,721 6,085 7,017 7,484 4,897 4,124 9,027 10,036 10,541
0.400 1,463 -106 5,864 6,907 7,429 3,634 2,101 8,775 9,910 10,478
0.500 1,077 -1,752 5,787 6,869 7,409 3,176 276 8,683 9,864 10,455 1 Cumulative 15-year NPV ($/ac). 2Yield from HLB infected trees reduced 20%, 10% and 5% on “normal” yield for strategy 3. Beta (1) = 1.5148125 for the 0 Age Class; Beta (2) = 0.8450625 for age Class of 3; Beta (3) = 0.4440625 for Age Classes of 6 or Larger.
60
Table 4-11. NPV1 for the Three Strategies for Age Classes 14 and 17 at Different Yield Penalty2 Levels for Strategy 3
Disease Incidence at First Detection
Average Age (Years) of Trees at First Detection
14 17
Strategy Strategy
1 2 3 1 2 3
20% 10% 5% 20% 10% 5%
0.001 16,487 13,470 12,895 12,939 12,961 17,101 14,084 13,509 13,553 13,575
0.010 14,488 13,204 12,495 12,739 12,861 15,102 13,818 13,110 13,353 13,475
0.020 13,322 12,910 12,262 12,622 12,802 13,935 13,525 12,876 13,236 13,417
0.030 12,505 12,620 12,099 12,541 12,762 13,118 13,235 12,713 13,155 13,376
0.040 11,978 12,333 12,015 12,488 12,735 12,591 12,947 12,629 13,102 13,349
0.050 11,389 12,049 11,876 12,429 12,706 12,002 12,663 12,490 13,043 13,320
0.060 11,032 11,768 11,804 12,393 12,688 11,644 12,382 12,418 13,007 13,302
0.070 10,521 11,489 11,702 12,342 12,672 11,133 12,103 12,316 12,956 13,287
0.080 10,250 11,213 11,648 12,315 12,649 10,861 11,828 12,261 12,929 13,263
0.100 9,786 10,670 11,555 12,269 12,626 10,396 11,284 12,168 12,883 13,240
0.200 7,799 8,111 11,158 12,070 12,526 8,405 8,725 11,770 12,683 13,140
0.300 6,604 5,790 10,919 11,951 12,466 7,207 6,404 11,531 12,564 13,080
0.400 5,278 3,686 10,653 11,818 12,400 5,877 4,300 11,265 12,431 13,014
0.500 4,779 1,784 10,554 11,768 12,375 5,375 2,399 11,164 12,380 12,988 1 Cumulative 15-year NPV ($/ac). 2Yield from HLB infected trees reduced 20%, 10% and 5% on “normal” yield for strategy 3. Beta (1) = 1.5148125 for the 0 Age Class; Beta (2) = 0.8450625 for age Class of 3; Beta (3) = 0.4440625 for Age Classes of 6 or Larger.
61
05
1015
0.10.2
0.30.4
0.5-1
-0.5
0
0.5
1
1.5
x 104
Age of Trees at First detectionHLB Incidence at First Detection
Ne
t P
rese
nt
Va
lue
-5000
0
5000
10000
15000
Figure 4-1. Net Present Value per Acre as a Function of Disease Incidence and
Average Age (Years) of Trees at First Detection with Contour Lines for the Do Nothing Strategy
05
1015
0.10.2
0.30.4
0.5-1.5
-1
-0.5
0
0.5
1
x 104
Age of Trees at First detectionHLB Incidence at First Detection
Ne
t P
rese
nt
Va
lue
-1
-0.5
0
0.5
1
x 104
Figure 4-2. Net Present Value per Acre as a Function of Disease Incidence and
Average Age (Years) of Trees at First Detection with Contour Lines for Strategy 2
62
05
1015
0.10.2
0.30.4
0.5-5000
0
5000
10000
Age of Trees at First detectionHLB Incidence at First Detection
Ne
t P
rese
nt
Va
lue
-2000
0
2000
4000
6000
8000
10000
12000
Figure 4-3. Net Present Value per Acre as a Function of Disease Incidence and
Average Age (Years) of Trees at First Detection with Contour Lines for Strategy 3 (30% Yield Penalty)
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50
2
4
6
8
10
12
14
16
Disease Incidence at First detection
Cu
mm
ula
tive
Ave
rag
e G
rove
Ag
e
Strategy 3
Strategy 2
Strategy 1
Figure 4-4. Dominant Strategy Given Disease Incidence at First Detection and Average
Grove Age (Price = $1.50/pound solid, 30% yield penalty for strategy 3)
63
CHAPTER 5 SENSITIVITY ANALYSIS
In this chapter, the robustness of the model’s conclusions is tested by performing
sensitivity analysis to determine how changes the main parameters of the model affect
the optimal strategy mix. Changes in the age-dependent rate of spread (β) affect the
disease spread, which in turn alter fruit yield and net returns with a resulting impact on
the optimal strategy. Changes in the price per pound solids directly impacts the net
present value estimates. Other parameters that affect the optimal choice of the model
include the period of latency and fruit yield. This chapter considers the effects of
changing prices, betas (rate of spread) and period of latency on optimal strategy. First,
the impact of a price decrease from $1.50/pound solids to $1.20, followed by a price
increase from $1.50 to $1.80/pound solids are examined for each of the three age
cohorts. Next, the age dependent rate of spread and the latency periods are also
adjusted to observe their effect on model results.
The Effects of a Price Decline
Tables 5-1 through 5-3 presents the net present values for the three strategies
for a delivered-in price from $1.50 to $1.20 per pound solid for each of the three age
categories. In Table 5-1, irrespective of the strategy or disease incidence at first
detection, the age cohorts of 0 and 3 produces negative net present values when price
falls. This trend is reversed for the mature groves with average ages of 6, 10, 14, and
17 where net present values are positive, except at high incidence values of 30% to
50%, where some strategies still yield negative net present values. In Tables 5-2 and 5-
3, at low disease incidence of 0.1% - 10.0% (at high incidence of 20% - 50%), Strategy
1 (Strategy 3) is the superior strategy for groves with average ages of 6, 10, 14, and 17.
64
Overall, the lowered price results in lower net present value for all groves at all levels of
disease incidence. A fall in price favors strategy 1 as it completely replaces strategies 2
and 3 at the lower levels of incidence of 0.1% - 10%.
The Effects of a Price Increase
When price is increase from $1.50 to $1.80 per pound solid, the net present
value is still negative for almost all levels of incidence for groves with average age of 0,
but now positive for groves with average ages of 3 or more except at high incidence of
8% to 50% (30% to 50%) in which Strategy 1 (Strategy 2) posts negative net present
values (Table 5-4). In Table 5-4, at low initial disease incidence (0.1% to 7%), Strategy
2 is better than Strategies 1 and 3 for groves with average age of 3. Thereafter, at initial
disease incidence of 8% to 50%, Strategy 3 overtakes Strategy 2 as the best strategy in
net present value. This result again confirms that at higher rates of infection, Strategy 3
is preferred over Strategy 2 because of the high tree removal rates associated with
Strategy 2. For groves with average age of 6 or more, Strategy 1 is only dominant at
0.1% to 1% level of initial incidence, whereas Strategy 2 is dominant for all initial
incidence rates ranging from 2% to 8%. When the initial incidence rate exceeds 10%,
Strategy 3 takes over from Strategy 2 (Tables 5-5 and 5-6). Increased price results in
higher net present value for all groves at all levels of disease incidence. The switch
point (7%) between Strategy 2 and 3 for groves of average age 3 do not change when
price increase. This may be attributed to the fact that as price increase; net present
value of existing fruits increases making it more expensive to remove trees.
In Figure 5-1 (middle subplot), the ranges of initial grove age and initial disease
incidence for which each strategy maximizes net present value shows that when price
falls, Strategy 1 (Strategy 3) is the optimal strategy for all groves, when disease
65
incidence ranges between 0.1% to 10% (20% to 50%). Therefore, when price is
lowered, Strategy 1 replaces Strategy 2 (and some part of Strategy 3) as the optimal
strategy for all groves when disease incidence ranges from 0.1% to 10%. In Figure 5-1
(bottom subplot), when price increase, the optimal strategy is Strategy 2 for initial
incidence of 2% to 8% for all groves and for groves of 6 years or larger, Strategy 1 is
optimal at incidence of 0.1% to 1%. For all groves at 10% to 50% incidence, Strategy 3
is the best strategy. Strategy 1’s area at 2% initial disease incidence for groves over 6
years is taken over by Strategy 2, and Strategy 2’s area at 1.0% initial disease
incidence for groves older than 14 years is taken over by Strategy 3.
The Effects of a Lower Annual Rate of Spread
Tables 5-7 through 5-9 presents results for a lower annual rate of spread of HLB
from Beta (1) = 1.5148125– 0.3= 1.2148125; Beta (2) = 0.8450625 – 0.3 = 0.5450625;
Beta (3) = 0.4440625– 0.3 = 0.1440625, for the respective age cohorts. A lower rate of
spread could be attributed to several factors, but most notably, if a psyllid control
program proves effective, its primary consequence would be a reduction in the rate of
spread. When the betas are lowered, groves with average age of 0 show negative net
present values irrespective of the initial disease incidence (Table 5-7). For groves with
average age of 3, Strategy 2 is superior from disease incidence of 1% to 10%, after
which Strategy 3 becomes superior from disease incidence of 20% to 50%. In Tables
5-8 and 5-9, Strategy 1 dominates from 0.1% to 10% initial disease incidence, whereas
Strategy 3 dominates thereafter from 20% to 50% initial disease incidence, for all
matured groves with average ages of 6 or more. The decreased betas have resulted in
higher net present value for all groves at all levels of disease incidence.
66
The Effects of an Increased Annual Rate of Spread
An increase in the annual rates of spread from Beta (1) = 1.5148125+ 0.3 =
1.8148125; Beta (2) = 0.8450625 + 0.3 = 1.1450625; Beta (3) = 0.4440625+ 0.3 =
0.7440625; for the respective average age category, gives similar results for groves with
average age of 0 and 3, in which the former presents negative net present values, and
the later shows dominance for Strategy 2 at initial incidence of between 0.1% to 2.0%,
and thereafter from 3% to 50%, Strategy 3 is the best strategy. However, for groves with
average age of 6 or larger, Strategy 1 does best for incidences of 0.1% only, Strategy 2
does best for incidences of 1.0% to 6%, and Strategy 3 does best for incidences 7% to
50%. The increased betas have resulted in smaller net present value for all groves at
all levels of disease incidence.
A reduction in the betas also affects the optimal strategy mix (Figure 5-2).
Strategy 1 (Strategy 3) is the optimal strategy for groves 6 years or larger when initial
disease incidence is 0.1% to 10% (20% to 50%). For groves with average age of 0 and
3 (0 or larger, i.e. all groves), Strategy 2 (Strategy 3) is optimal at incidence of 1% to
10% (20% to 50%). As a result of the reduction in the betas, Strategy 1 replaces
Strategy 2 as the dominant strategy for groves older than 6 years at disease incidence
of 3% to 10%. For an increase in the betas (Figure 5-2, bottom subplot), Strategy 1 has
the smallest area of optimality, which occurs only at the lowest level of initial incidence
of 0.1% for groves 6 years or larger. For all groves at incidence of 1% - 6% (7% - 50%),
Strategy 2 (Strategy 3) is the best strategy. Strategy 2 replaces strategy 1 as dominant
strategy for groves older than 6 years when disease incidence is between 1.0% and
2.0%, whereas Strategy 3 replaces Strategy 2 for groves older than 6 years when
disease incidence is from 6% to 8%.
67
The primary consequence of decreased rate of spread is to make Strategy 2
more attractive. This result makes sense as the goal of Strategy 2 is to suppress the
level of disease inoculum. A lower rate of disease spread gives a grower more time to
initiate Strategy 2 and thereby enjoy its benefits. Lower disease spread rate also
means that fewer trees are being removed early in the treatment period. This results in
a smaller decrease in fruit revenue. The contrary effect emerges when the rate of
spread is increased. Faster spread of the disease means the growers have a shorter
window of opportunity to implement Strategy 2; Strategy 3 is preferred at younger ages
of first detection and smaller levels of initial incidences at first detection.
The Effects of a Shortened Latency Period
The latency period refers to the interval between the time a tree first becomes
infected and when is expresses symptoms. The existence of the latency period is one
of the most vexing dimensions of the disease in that a tree removal policy fails to
eliminate all diseased trees. Bové (2012) has recently argued that the latency period
may be shorter than that suggested in earlier literature on HLB. In this section we
investigate the impact of a shorter latency period on the optimal strategy.
Another dimension to this analysis is the efficacy of scouting in detecting the
disease. Futch et al. (2009) argues that one pass through a grove where HLB is present
will result in 50% of symptomatic trees being detected. Bové’s (2012) argument
regarding latency is based upon the observation that symptomatic trees may be
present, but scouts are unable to detect them. Therefore, improved detection
techniques could reduce the latency period.
Tables 5-13 through 5-15 presents results for the scenario when the latency
period is reduced such that groves with ages of 0 and 3 now are assumed to have a
68
latency period of 6 months instead of 1 year, while the latency period of groves 6 years
or larger remain unchanged at 2 years. Results show that all cases in which age of first
detection is 0 display negative net present values. Groves with average age of 3
displays net present values in which Strategy 2 is best when disease incidence is 0.1%
to 10.0%; Strategy 3 is best when disease incidence is 20.0% to 50.0%. For groves 6
years or larger, Strategy 1 does best from disease incidence of 0.1% to 1.0%, after
which Strategy 2 is best at 2.0% to 10.0% disease incidence, followed by Strategy 3,
which is optimal at incidence of 20.0% to 50.0%. In Figure 5-3, the reduction in latency
period favors Strategy 2 more compared to Strategy 1 (and 3), for groves older than 6
years when disease incidence is 2% (10%). Strategy 3 is dominant at disease incidence
of 20% to 50% for all groves. The change in latency has resulted in lower net present
value for all groves at all levels of disease incidence.
Comparison of the results in Table 4-7 and Table 5-13, however, suggest that
shortening the latency period does impact the optimal strategy. Under the baseline
latency period, for groves of three years of age at first detection, Strategy 3 is superior
for initial infection rates of 9% and higher, but with a shortened latency period,
superiority of Strategy 3 shifts to initial infections rates of 10% and higher. While this is a
small change, it does indicate that superior detection methods that could reduce the
period of latency would benefit Strategy 2.
69
Table 5-1. NPV1 for the Three Strategies for Age Classes 0 and 3 from a Price Decline2
Disease Incidence at First Detection
Average Age of Trees at First Detection
0 3
Strategy Strategy
1 2 3 1 2 3
0.001 -3,986 -3,397 -4,655 521 347 -1,115
0.010 -5,043 -5,902 -4,972 -1,626 -26 -1,759
0.020 -5,321 -6,950 -5,056 -2,320 -418 -1,967
0.030 -5,436 -7,553 -5,090 -2,794 -788 -2,109
0.040 -5,494 -7,969 -5,108 -3,013 -1,137 -2,175
0.050 -5,610 -8,280 -5,142 -3,174 -1,468 -2,223
0.060 -5,653 -8,529 -5,155 -3,480 -1,782 -2,315
0.070 -5,686 -8,730 -5,165 -3,594 -2,080 -2,349
0.080 -5,712 -8,901 -5,173 -3,690 -2,365 -2,378
0.100 -5,747 -9,174 -5,183 -3,843 -2,895 -2,424
0.200 -5,884 -9,922 -5,225 -4,472 -4,979 -2,613
0.300 -5,905 -10,259 -5,231 -4,887 -6,456 -2,737
0.400 -5,970 -10,436 -5,250 -5,218 -7,568 -2,837
0.500 -5,983 -10,532 -5,254 -5,345 -8,433 -2,875 1 Cumulative 15-year NPV ($/ac). 2Price per pound solid is reduced from $1.50 to $1.20. Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3. Beta (1) = 1.5148125 for the 0 Age Class; Beta (2) = 0.8450625 for age Class of 3; Beta (3) = 0.4440625 for Age Classes of 6 or Larger.
70
Table 5-2. NPV1 for the Three Strategies for Age Classes 6 and 10 from a Price Decline2
Disease Incidence at First Detection
Average Age of Trees at First Detection
6 10
Strategy Strategy
1 2 3 1 2 3
0.001 5,980 2,852 2,260 8,149 5,025 4,433
0.010 4,562 2,680 1,834 6,684 4,835 3,993
0.020 3,754 2,490 1,592 5,832 4,626 3,738
0.030 3,196 2,302 1,425 5,238 4,420 3,560
0.040 2,848 2,116 1,320 4,858 4,215 3,445
0.050 2,450 1,932 1,201 4,431 4,013 3,317
0.060 2,219 1,750 1,131 4,175 3,812 3,240
0.070 1,875 1,570 1,028 3,805 3,614 3,130
0.080 1,703 1,392 977 3,612 3,418 3,072
0.100 1,412 1,041 889 3,283 3,031 2,973
0.200 152 -609 511 1,872 1,212 2,550
0.300 -578 -2,099 292 1,036 -435 2,299
0.400 -1,389 -3,446 49 105 -1,925 2,020
0.500 -1,673 -4,659 -36 -232 -3,270 1,918 1 Cumulative 15-year NPV ($/ac). 2Price per pound solid is reduced from $1.50 to $1.20. Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3. Beta (1) = 1.5148125 for the 0 Age Class; Beta (2) = 0.8450625 for age Class of 3; Beta (3) = 0.4440625 for Age Classes of 6 or Larger.
71
Table 5-3. NPV1 for the Three Strategies for Age Classes 14 and 17 from a Price Decline2
Disease Incidence at First Detection
Average Age of Trees at First Detection
14 17
Strategy Strategy
1 2 3 1 2 3
0.001 9,575 6,452 5,860 10,028 6,904 6,312
0.010 8,103 6,256 5,418 8,555 6,708 5,871
0.020 7,243 6,039 5,160 7,695 6,492 5,613
0.030 6,642 5,826 4,980 7,093 6,278 5,432
0.040 6,253 5,614 4,863 6,705 6,067 5,316
0.050 5,819 5,405 4,733 6,271 5,857 5,185
0.060 5,556 5,197 4,654 6,007 5,650 5,106
0.070 5,180 4,992 4,541 5,630 5,445 4,993
0.080 4,980 4,789 4,481 5,430 5,242 4,933
0.100 4,638 4,389 4,379 5,088 4,841 4,830
0.200 3,174 2,503 3,939 3,620 2,955 4,390
0.300 2,293 793 3,675 2,738 1,245 4,125
0.400 1,316 -757 3,382 1,758 -305 3,831
0.500 948 -2,159 3,272 1,387 -1,706 3,720 1 Cumulative 15-year NPV ($/ac). 2Price per pound solid is reduced from $1.50 to $1.20. Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3. Beta (1) = 1.5148125 for the 0 Age Class; Beta (2) = 0.8450625 for age Class of 3; Beta (3) = 0.4440625 for Age Classes of 6 or Larger.
72
Table 5-4. NPV1 for the Three Strategies for Age Classes 0 and 3 from a Price Increase2
Disease Incidence at First Detection
Average Age of Trees at First Detection
0 3
Strategy Strategy
1 2 3 1 2 3
0.001 -1,371 2,108 314 7,165 9,313 7,176
0.010 -3,241 -2,197 -247 3,480 8,671 6,140
0.020 -3,744 -4,006 -398 2,286 7,998 5,712
0.030 -3,956 -5,050 -461 1,469 7,363 5,467
0.040 -4,065 -5,773 -526 1,090 6,762 5,353
0.050 -4,275 -6,315 -557 810 6,194 5,269
0.060 -4,355 -6,749 -581 282 5,655 5,111
0.070 -4,418 -7,102 -600 85 5,142 5,052
0.080 -4,467 -7,402 -615 -83 4,653 5,001
0.100 -4,533 -7,883 -635 -351 3,741 4,921
0.200 -4,791 -9,216 -712 -1,449 157 4,592
0.300 -4,832 -9,828 -724 -2,175 -2,386 4,374
0.400 -4,954 -10,155 -761 -2,758 -4,305 4,199
0.500 -4,980 -10,334 -769 -2,983 -5,796 4,131 1 Cumulative 15-year NPV ($/ac). 2Price per pound solid is increased from $1.50 to $1.80. Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3. Beta (1) = 1.5148125 for the 0 Age Class; Beta (2) = 0.8450625 for age Class of 3; Beta (3) = 0.4440625 for Age Classes of 6 or Larger.
73
Table 5-5. NPV1 for the Three Strategies for Age Classes 6 and 10 from a Price Increase2
Disease Incidence at First Detection
Average Age of Trees at First Detection
6 10
Strategy Strategy
1 2 3 1 2 3
0.001 16,947 14,030 13,384 20,952 18,043 17,397
0.010 14,516 13,734 12,655 18,441 17,717 16,643
0.020 13,131 13,409 12,239 16,982 17,359 16,205
0.030 12,176 13,087 11,953 15,964 17,005 15,900
0.040 11,578 12,768 11,773 15,311 16,654 15,704
0.050 10,896 12,453 11,569 14,579 16,307 15,485
0.060 10,500 12,141 11,450 14,140 15,964 15,353
0.070 9,911 11,832 11,273 13,507 15,624 15,163
0.080 9,615 11,527 11,185 13,175 15,287 15,063
0.100 9,117 10,926 11,035 12,610 14,625 14,894
0.200 6,957 8,098 10,387 10,192 11,506 14,169
0.300 5,705 5,542 10,011 8,759 8,683 13,739
0.400 4,314 3,234 9,594 7,163 6,128 13,260
0.500 3,827 1,154 9,448 6,585 3,822 13,086 1 Cumulative 15-year NPV ($/ac). 2Price per pound solid is increased from $1.50 to $1.80. Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3. Beta (1) = 1.5148125 for the 0 Age Class; Beta (2) = 0.8450625 for age Class of 3; Beta (3) = 0.4440625 for Age Classes of 6 or Larger.
74
Table 5-6. NPV1 for the Three Strategies for Age Classes 14 and 17 from a Price Increase2
Disease Incidence at First Detection
Average Age of Trees at First Detection
14 17
Strategy Strategy
1 2 3 1 2 3
0.001 23,398 20,488 19,843 24,174 21,264 20,619
0.010 20,873 20,152 19,086 21,649 20,928 19,862
0.020 19,401 19,781 18,644 20,175 20,557 19,420
0.030 18,369 19,415 18,335 19,143 20,191 19,110
0.040 17,703 19,052 18,135 18,477 19,828 18,910
0.050 16,959 18,693 17,912 17,733 19,469 18,687
0.060 16,508 18,338 17,776 17,281 19,114 18,551
0.070 15,863 17,986 17,583 16,635 18,762 18,358
0.080 15,520 17,638 17,480 16,292 18,414 18,254
0.100 14,934 16,952 17,304 15,705 17,728 18,078
0.200 12,424 13,719 16,551 13,190 14,494 17,324
0.300 10,915 10,787 16,098 11,676 11,563 16,870
0.400 9,239 8,130 15,596 9,996 8,906 16,366
0.500 8,609 5,727 15,407 9,362 6,503 16,175 1 Cumulative 15-year NPV ($/ac). 2Price per pound solid is increased from $1.50 to $1.80. Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3. Beta (1) = 1.5148125 for the 0 Age Class; Beta (2) = 0.8450625 for age Class of 3; Beta (3) = 0.4440625 for Age Classes of 6 or Larger.
75
Table 5-7. NPV1 for the Three Strategies for Age Classes 0 and 3 from a Decline in Beta2
Disease Incidence at First Detection
Average Age of Trees at First Detection
0 3
Strategy Strategy
1 2 3 1 2 3
0.001 -2,065 46 -1,986 5,872 4,859 3,639
0.010 -3,872 -1,462 -2,529 2,498 4,595 2,627
0.020 -4,228 -2,650 -2,635 1,394 4,308 2,296
0.030 -4,526 -3,540 -2,725 637 4,026 2,068
0.040 -4,649 -4,243 -2,762 274 3,750 1,960
0.050 -4,731 -4,817 -2,786 -262 3,479 1,799
0.060 -4,893 -5,298 -2,802 -489 3,213 1,731
0.070 -4,946 -5,710 -2,851 -677 2,951 1,674
0.080 -4,991 -6,067 -2,864 -1,107 2,695 1,546
0.100 -5,059 -6,663 -2,885 -1,373 2,195 1,465
0.200 -5,294 -8,417 -2,955 -2,422 -62 1,151
0.300 -5,346 -9,330 -2,971 -3,106 -1,987 946
0.400 -5,449 -9,909 -3,002 -3,655 -3,649 781
0.500 -5,470 -10,303 -3,008 -4,112 -5,095 644 1 Cumulative 15-year NPV ($/acre). 2 Beta (1) =1.5148125 – 0.3 = 1.2148125 for the 0 Age Class; Beta (2) = 0.8450625 – 0.3= 0.5450625 for age Class of 3; Beta (3) = 0.4440625 – 0.3= 0.1440625 for Age Classes of 6 or Larger. Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3.
76
Table 5-8. NPV1 for the Three Strategies for Age Classes 6 and 10 from a Decline in Beta2
Disease Incidence at First Detection
Average Age of Trees at First Detection
6 10
Strategy Strategy
1 2 3 1 2 3
0.001 11,826 8,449 7,931 14,918 11,542 11,025
0.010 11,288 8,285 7,769 14,351 11,361 10,855
0.020 10,759 8,104 7,611 13,791 11,160 10,687
0.030 10,289 7,922 7,469 13,291 10,959 10,537
0.040 9,866 7,742 7,343 12,840 10,759 10,402
0.050 9,483 7,561 7,228 12,430 10,560 10,278
0.060 9,132 7,381 7,123 12,053 10,361 10,165
0.070 8,810 7,202 7,026 11,706 10,162 10,061
0.080 8,513 7,023 6,937 11,383 9,964 9,965
0.100 7,978 6,667 6,776 10,802 9,570 9,790
0.200 6,081 4,914 6,207 8,710 7,629 9,163
0.300 4,827 3,209 5,842 7,306 5,740 8,752
0.400 3,106 1,550 5,393 5,454 3,901 8,266
0.500 1,303 -63 4,774 3,441 2,113 7,582 1 Cumulative 15-year NPV ($/acre). 2 Beta (1) =1.5148125 – 0.3 = 1.2148125 for the 0 Age Class; Beta (2) = 0.8450625 – 0.3= 0.5450625 for age Class of 3; Beta (3) = 0.4440625 – 0.3= 0.1440625 for Age Classes of 6 or Larger. Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3.
77
Table 5-9. NPV1 for the Three Strategies for Age Classes 14 and 17 from a Decline in Beta2
Disease Incidence at First Detection
Average Age of Trees at First Detection
14 17
Strategy Strategy
1 2 3 1 2 3
0.001 16,854 13,479 12,962 17,469 14,093 13,576
0.010 16,280 13,290 12,790 16,894 13,904 13,404
0.020 15,712 13,081 12,619 16,326 13,695 13,233
0.030 15,205 12,872 12,467 15,818 13,487 13,081
0.040 14,746 12,664 12,329 15,358 13,278 12,943
0.050 14,327 12,457 12,204 14,940 13,071 12,817
0.060 13,943 12,249 12,089 14,555 12,864 12,702
0.070 13,588 12,043 11,982 14,200 12,657 12,596
0.080 13,259 11,837 11,883 13,870 12,451 12,497
0.100 12,663 11,427 11,705 13,274 12,041 12,318
0.200 10,505 9,407 11,057 11,112 10,021 11,669
0.300 9,043 7,440 10,629 9,646 8,054 11,240
0.400 7,138 5,525 10,127 7,738 6,139 10,737
0.500 5,062 3,661 9,424 5,658 4,276 10,033 1 Cumulative 15-year NPV ($/acre). 2 Beta (1) =1.5148125 – 0.3 = 1.2148125 for the 0 Age Class; Beta (2) = 0.8450625 – 0.3= 0.5450625 for age Class of 3; Beta (3) = 0.4440625 – 0.3= 0.1440625 for Age Classes of 6 or Larger. Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3.
78
Table 5-10. NPV1 for the Three Strategies for Age Classes 0 and 3 from an Increase in Beta2
Disease Incidence at First Detection
Average Age of Trees at First Detection
0 3
Strategy Strategy
1 2 3 1 2 3
0.001 -3,104 -2,466 -2,298 2,514 4,712 2,632
0.010 -4,388 -6,016 -2,683 60 3,327 1,896
0.020 -4,683 -7,247 -2,772 -784 2,110 1,642
0.030 -4,789 -7,836 -2,804 -1,337 1,123 1,476
0.040 -4,966 -8,237 -2,857 -1,444 297 1,395
0.050 -5,039 -8,535 -2,879 -1,810 -409 1,335
0.060 -5,093 -8,771 -2,895 -1,963 -1,023 1,289
0.070 -5,131 -8,964 -2,906 -2,081 -1,564 1,253
0.080 -5,157 -9,126 -2,914 -2,405 -2,047 1,156
0.100 -5,181 -9,387 -2,921 -2,602 -2,877 1,097
0.200 -5,374 -10,110 -2,979 -3,341 -5,532 875
0.300 -5,378 -10,468 -2,980 -3,640 -7,058 786
0.400 -5,473 -10,691 -3,009 -4,042 -8,090 665
0.500 -5,492 -10,479 -3,015 -4,214 -8,832 613 1 Cumulative 15-year NPV ($/acre). 2 Beta (1) = 1.5148125 + 0.3 = 1.8148125 for age class of 0; Beta (2) = 0.8450625 + 0.3= 1.1450625 for age class of 3; Beta (3) = 0.4440625 + 0.3= 0.7440625 for age classes 6 or larger. Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3.
79
Table 5-11. NPV1 for the Three Strategies for Age Classes 6 and 10 from an Increase in Beta2
Disease Incidence at First Detection
Average Age of Trees at First Detection
6 10
Strategy Strategy
1 2 3 1 2 3
0.001 10,474 8,423 7,525 13,551 11,514 10,615
0.010 7,698 8,025 6,692 10,658 11,082 9,747
0.020 6,637 7,597 6,374 9,514 10,617 9,404
0.030 6,085 7,183 6,208 8,901 10,166 9,220
0.040 5,505 6,781 6,034 8,267 9,728 9,030
0.050 5,187 6,391 5,939 7,909 9,303 8,922
0.060 4,929 6,012 5,862 7,615 8,890 8,834
0.070 4,512 5,644 5,737 7,156 8,489 8,696
0.080 4,317 5,286 5,678 6,932 8,098 8,629
0.100 3,992 4,600 5,580 6,558 7,347 8,517
0.200 2,767 1,641 5,213 5,148 4,100 8,094
0.300 1,926 -718 4,961 4,174 1,498 7,802
0.400 1,237 -2,635 4,754 3,370 -629 7,560
0.500 875 -4,197 4,645 2,940 -2,369 7,432 1 Cumulative 15-year NPV ($/acre). 2 Beta (1) = 1.5148125 + 0.3 = 1.8148125 for age class of 0; Beta (2) = 0.8450625 + 0.3= 1.1450625 for age class of 3; Beta (3) = 0.4440625 + 0.3= 0.7440625 for age classes 6 or larger. Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3.
80
Table 5-12. NPV1 for the Three Strategies for Age Classes 14 and 17 from an Increase in Beta2
Disease Incidence at First Detection
Average Age of Trees at First Detection
14 17
Strategy Strategy
1 2 3 1 2 3
0.001 15,487 13,450 12,552 16,101 14,064 13,166
0.010 12,580 13,008 11,679 13,193 13,622 12,294
0.020 11,421 12,531 11,332 12,034 13,145 11,946
0.030 10,794 12,068 11,144 11,407 12,682 11,758
0.040 10,147 11,619 10,950 10,760 12,233 11,564
0.050 9,777 11,183 10,839 10,389 11,797 11,452
0.060 9,472 10,758 10,747 10,084 11,372 11,361
0.070 9,002 10,345 10,606 9,613 10,960 11,220
0.080 8,769 9,944 10,536 9,379 10,558 11,149
0.100 8,375 9,171 10,418 8,985 9,785 11,031
0.200 6,889 5,820 9,972 7,495 6,435 10,584
0.300 5,854 3,123 9,662 6,456 3,737 10,272
0.400 4,995 911 9,404 5,594 1,525 10,014
0.500 4,526 -907 9,263 5,121 -293 9,872 1 Cumulative 15-year NPV ($/acre). 2 Beta (1) = 1.5148125 + 0.3 = 1.8148125 for age class of 0; Beta (2) = 0.8450625 + 0.3= 1.1450625 for age class of 3; Beta (3) = 0.4440625 + 0.3= 0.7440625 for age classes 6 or larger. Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3.
81
Table 5-13. NPV1 for the Three Strategies for Age Classes 0 and 3 from a Lowered Latency Period2
Disease Incidence at First Detection
Average Age of Trees at First Detection
0 3
Strategy Strategy
1 2 3 1 2 3
0.001 -4,195 192 -2,625 1,623 4,861 2,364
0.010 -4,898 -334 -2,836 -940 4,614 1,595
0.020 -5,152 -871 -2,913 -1,417 4,344 1,452
0.030 -5,213 -1,365 -2,931 -2,014 4,077 1,273
0.040 -5,244 -1,821 -2,940 -2,199 3,815 1,218
0.050 -5,259 -2,243 -2,945 -2,340 3,556 1,176
0.060 -5,380 -2,634 -2,981 -2,451 3,301 1,142
0.070 -5,392 -2,999 -2,985 -2,542 3,050 1,115
0.080 -5,402 -3,340 -2,988 -2,950 2,802 993
0.100 -5,417 -3,958 -2,992 -3,075 2,316 955
0.200 -5,437 -6,188 -2,998 -3,751 71 752
0.300 -5,498 -7,590 -3,016 -3,928 -1,907 699
0.400 -5,500 -8,558 -3,017 -4,353 -3,665 571
0.500 -5,501 -9,258 -3,017 -4,440 -5,234 546 1 Cumulative 15-year NPV ($/ac). 2 Latency is now 6 months for ages 0 and 3, and 2 years for ages of 6. Beta (1) = 1.5148125 for the 0 Age Class; Beta (2) = 0.8450625 for age Class of 3; Beta (3) = 0.4440625 for Age Classes of 6 or Larger. Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3.
82
Table 5-14. NPV1 for the Three Strategies for Age Classes 6 and 10 from a Lowered Latency Period2
Disease Incidence at First Detection
Average Age of Trees at First Detection
6 10
Strategy Strategy
1 2 3 1 2 3
0.001 11,287 8,447 7,769 14,372 11,540 10,861
0.010 9,181 8,264 7,137 12,193 11,337 10,207
0.020 8,045 8,061 6,796 10,994 11,112 9,848
0.030 7,457 7,859 6,620 10,355 10,888 9,656
0.040 6,849 7,658 6,438 9,703 10,665 9,460
0.050 6,504 7,457 6,334 9,320 10,443 9,345
0.060 6,006 7,258 6,185 8,785 10,223 9,185
0.070 5,761 7,060 6,111 8,509 10,003 9,102
0.080 5,547 6,863 6,047 8,267 9,784 9,030
0.100 4,944 6,471 5,866 7,608 9,350 8,832
0.200 3,533 4,567 5,443 6,006 7,240 8,351
0.300 2,254 2,754 5,059 4,554 5,230 7,916
0.400 1,453 1,031 4,819 3,623 3,320 7,636
0.500 1,075 -603 4,705 3,174 1,507 7,502 1 Cumulative 15-year NPV ($/ac). 2 Latency is now 6 months for ages 0 and 3, and 2 years for ages of 6. Beta (1) = 1.5148125 for the 0 Age Class; Beta (2) = 0.8450625 for age Class of 3; Beta (3) = 0.4440625 for Age Classes of 6 or Larger. Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3.
83
Table 5-15. NPV1 for the Three Strategies for Age Classes 14 and 17 from a Shortened Latency Period2
Disease Incidence at First Detection
Average Age of Trees at First Detection
14 17
Strategy Strategy
1 2 3 1 2 3
0.001 16,308 13,476 12,798 16,923 14,091 13,412
0.010 14,118 13,265 12,141 14,732 13,879 12,755
0.020 12,907 13,030 11,778 13,521 13,644 12,392
0.030 12,257 12,797 11,583 12,870 13,411 12,197
0.040 11,594 12,565 11,384 12,207 13,179 11,998
0.050 11,202 12,334 11,266 11,814 12,948 11,880
0.060 10,657 12,104 11,103 11,268 12,718 11,716
0.070 10,372 11,875 11,017 10,983 12,489 11,631
0.080 10,120 11,647 10,942 10,731 12,262 11,555
0.100 9,444 11,195 10,739 10,054 11,809 11,352
0.200 7,770 8,997 10,237 8,376 9,611 10,848
0.300 6,255 6,903 9,782 6,857 7,517 10,393
0.400 5,265 4,912 9,485 5,864 5,526 10,095
0.500 4,777 3,023 9,339 5,372 3,637 9,947 1 Cumulative 15-year NPV ($/ac). 2 Latency is now 6 months for ages 0 and 3, and 2 years for ages of 6. Beta (1) = 1.5148125 for the 0 Age Class; Beta (2) = 0.8450625 for age Class of 3; Beta (3) = 0.4440625 for Age Classes of 6 or Larger. Yield from HLB infected trees reduced 30% on “normal” yield for strategy 3.
84
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50
5
10
15
Strategy 3
Strategy 2
Strategy 1
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50
5
10
15
Cu
mm
ula
tive
Ave
rag
e G
rove
Ag
e
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50
5
10
15
Disease Incidence at First detection
Figure 5-1. Dominant Strategy Given Disease Incidence at First Detection and Average
Grove Age from a Change in Price: Top Subplot is Baseline, Middle and Bottom Subplots Shows Price Decline (from $1.50 to $1.20) and Increase (from $1.50 to $1.80), respectively
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50
5
10
15
Strategy 3
Strategy 2
Strategy 1
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50
5
10
15
Cu
mm
ula
tive
Ave
rag
e G
rove
Ag
e
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50
5
10
15
Disease Incidence at First detection
Figure 5-2. Dominant Strategy Given Disease Incidence at First Detection and Average Grove Age from a Change in Beta: Top Subplot is Baseline, Middle and Bottom Subplots Shows Beta Decline and Increase, respectively
85
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50
5
10
15
Cu
mm
ula
tive
Ave
rag
e G
rove
Ag
e
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50
5
10
15
Disease Incidence at First detection
Strategy 3
Strategy 2
Strategy 1
Figure 5-3. Dominant Strategy Given Disease Incidence at First Detection and Average
Grove Age from a Change in Latency: Top Subplot is Baseline, Bottom Subplot Shows Decline in Latency from 1 year to 6 Months for Groves with Average Age of 0 and 3 while the Latency for Groves 6 Years or Larger Remain at 2 Years
86
CHAPTER 6 CONCLUSIONS, RECOMMENDATIONS AND LIMITATIONS
The preceding chapters have sought to develop a management strategy for HLB
at the grove level for citrus producers in Florida. A baseline model was developed to
simulate the economic consequences for the “Do nothing” control strategy. This served
as a benchmark for the modeling of the infected tree removal strategy as well as the
enhanced foliar nutritional strategy. The adoption of a particular strategy by a grower is
seen to be a function of certain grove characteristics. This research has identified the
various zones of optimality for each strategy given a grove’s average age and initial
HLB infection rate.
This research attempted to integrate the intricate biological realities of HLB into
an economic decision making framework for producers. The basis of the biological
model is provided by the works of Bassanezi and Bassanezi (2008) and Bassanezi et
al. (2011). This research demonstrate that the complex biological features of HLB can
be transformed into an economic decision making process for citrus growers. The effect
of latency on control effectiveness especially when employing Strategy 2 for example is
addressed in this analysis. The most important contribution of this research is the
incorporation of both symptomatic and asymptomatic trees in the logistic spread curves
used in the analysis. This ensures that even if symptomatic trees are removed (as in
Strategy 2); spread through asymptomatic trees is still accounted for in the model. The
rate of spread of HLB is a function of the grove’s age cohort, which has been intricately
knitted into each of the three models of control strategies in this analysis. Additionally, in
varying the level of initial infection in the analysis, this research also demonstrates the
heterogeneous effects of HLB to NPV at the landscape level, whereby optimal control
87
decisions varies across neighbors. The significance of this research lies in its ability to
address these characteristics and formulate optimal control policy for effective decision
making.
In summary, we find that groves that contain younger trees at first detection have
low or negative net present value due to the faster spread of the disease in younger
groves in addition to low production from young groves. For Strategy 1, all groves with
an average age of 6 years and larger will yield a positive net present value, irrespective
of the initial level of infection. For Strategy 2, except when initial incidence is 40% to
50%, all groves with an average age of 6 or larger yields a positive net present value.
For Strategy 3, all groves with an average age of 3 or larger at all initial incidence levels
yields a positive net present value. Whether cost exceeds revenue in production is a
function of disease incidence and average grove age. The higher the initial incidence
level (the larger the average grove age), the more likely (less likely) that cost of
production exceeds revenue from production.
Finally, we find that the optimal strategy to adopt by a grower depends on the
average grove age at first detection and the initial rate of disease incidence at first
detection. Irrespective of the average grove age, once initial incidence is 20% or larger
in a grove, Strategy 3 should be implemented. The intuition of this recommendation is
that it is better to incur the extra costs of nutritional supplements than remove 20% or
more of productive but sick trees or even do nothing. The marginal revenue from this
action is more than its marginal cost. At higher rates of infection, Strategy 3 is preferred
over Strategy 2 because of the high tree removal rates associated with Strategy 2.
Implementation of Strategy 2 requires that initial incidence should be 3% to 8% for
88
groves 6 years or larger or 0.1% - 2% for groves of average age 0 or 3. Here, the
intuition is that removing 3% to 8% of infected mature trees or 0.1% - 2% of newly
established trees is more cost effective compared to spraying such trees with nutritional
supplements or doing nothing. When there is virtually no infection (0.1% to 1%) in a
grove of age 6 or larger, doing nothing is in the best interest of the producer. The
relationship between the net present value and model parameters such as delivered-in
price, the rate of spread of HLB, and latency has been established through the
sensitivity analysis. It is found that net present value is positively related to price but
negatively related to the rate of spread and the latency period. Changes in these
parameters also results in changes in the optimal strategy mix. In particular, results
indicate that superior detection methods that could reduce the period of latency would
benefit Strategy 2. Results also suggest that the primary consequence of
decreased/increased rate of spread (our proxy for psyllid control) is to make Strategy
2/Strategy 3 more attractive.
The rate of spread of HLB is related to three factors including average grove age,
initial incidence at first detection, and the psyllid population. Even though our model did
not directly incorporate psyllid control into the analysis, reduction in the annual rate of
HLB spread investigated via the sensitivity analysis can serve as proxy for psyllid
control because total elimination of psyllids notably terminates HLB spread.
Effective control of plant diseases that involves spread by vectors and other
weather factors such as HLB requires a model that recognizes landscape management
characteristics, which is missing in this analysis for now. Neighbor effects negatively
affects heterogeneous management protocols by adjacent growers since buildup of
89
bacteria titer in a grove practicing Strategy 1 or 3 could diminish the inoculum reduction
objective of a neighbor practicing Strategy 2. One other drawback is the lack of HLB
spread data from Florida required to estimate the model parameters. Although the
parameters used may not be representative of the HLB situation in Florida, the results
derived here do serve as a guide and reference point for growers and policy makers in
the industry. The assumption of no resetting greatly simplifies the calculation of disease
spread and the accompanying reduction in fruit production per acre. However, this
assumption clearly is a limitation on the derived results. The lack of knowledge on the
underlying distribution of the key variables that affect net present value in the presence
of HLB has forced us to proceed with the analysis in a deterministic framework. Cleary,
stochastic dominance would have been the best estimator of superiority. It is hoped that
future research can be greatly enhanced when most of these limitations are addressed.
90
LIST OF REFERENCES
Albrecht, U., T. G. Mccollum, and K.D. Bowman. 2012. “Influence of Rootstock Variety on Huanglongbing Disease Development in Field-grown Sweet Orange (Citrus sinensis L.) Osbeck Trees.” Scientia Horticulturae, 138:210-220.
Altamirano, D. M., C. I. Gonzales, and R. C. Vinas. 1976. “Analysis of the Devastation of Leaf-mottling (Greening) Disease of Citrus and its Control Program in the Philippines.” Proc. Conf. Int. Org. Citrus Viral., 7th, 22-26.
Aubert, B. 1990. “Integrated Activities for the Control of Huanglungbin Greening and its Vector Diaphorina citri Kuwayama in Asia.” Pages 133-144 in: B. Aubert, S. Tontyaporn, D. Buangsuwon,, eds., Rehabilitation of Citrus Industry in the Asia Pacific Region. Proc. of the Asia Pacific Intl. Conf. on Citriculture, Chiang Mai, Thailand , 4-10 February 1990. UNDP-FAO, Rome.
Aubert, B. 1992. “Citrus Greening Disease, a Serious Limiting Factor for Citriculture in Asia and Africa.” Proceedings of the 7th International Citrus Congress, 817–820.
Aubert B., and J. M. Bové. 1980. “Effect of Penicillin or Tetracycline Injections of Citrus Trees Affected by Greening Disease under Field Conditions in Reunion Island.” Proceedings of 8th Conference of IOCV. Riverside 103-108.
Aubert B., J. M. Bové and J. Etienne. 1980. La Lutte Contre la Maladie du «Greening» des Agrumes à l’île de la Réunion. Résultats et Perspectives. Fruits 35: 605-624.
Aubert, B., and X.Y. Hua. 1990. “Rehabilitation of Citrus Industry in the Asia Pacific Region: Monitoring Flight Activity of Diaphorina citri on Citrus and Murraya Canopies. In Proceedings of the 4th Asia Pacific International Conference on Citriculture, eds. A. Bernard, T. Sanchai and B. D., pp. 181–187, February 4–10, at Chiang Mai, Thailand. FAO-UNDP, Rome.
Aubert, B., M. Garnier, D. Guillaumin, B. Herbagyandodo, L. Setiobudi, and F. Nurhadi. 1985. “Greening, a Serious Disease Threat for the Citrus Production of the Indonesian Archipelago.” Future prospects of integrated control. Fruits 40:549-563.
Aubert, B., M. Grisoni, M. Villemin, and G. Rossolin. 1996. “A Case Study of Huanglongbing (Greening) Control in Réunion.” 276-278 In J. V. da Graça, P. Moreno, and R. K. Yokomi [eds.], Proc. 13th Conference of the International Organization of Citrus Virologists (IOCV). University of California, Riverside.
91
Aubert, B., and S. Quilici. 1984. “Biological Control of the African and Asian Citrus Psyllids (Homoptera: Psylloidea), through Eulophid and Encyrtid Parasites (Hymenoptera: Chalcidoidea) in Reunion Island.” In Proceedings of the 9th Conference of International Organization of Citrus Virologists, eds. S.M. Garnsey, L.W. Timmer and J.A. Dodds, pp. 100–108, May 9–13, 1983, at Argentina. Riverside, CA: IOCV. Available online at http://www.ivia.es/ iocv/archivos/proceedingsIX/9th100_108.pdf.
Aubert, B., A. Sabine, P. Geslin, and L. Picardi. 1984. “Epidemiology of the Greening Disease in Reunion Island before and after the Biological Control of the African and Asian Citrus Psyllas.” In Proceedings of the 5th Meeting of the International Society of Citriculture, 2: 440–442, n.d., at São Paulo, Brazil. Gainesville: University of Florida.
Ayres A.J., C. A Massari, S. A. Lopes, R. B. Bassanezi, Junior J. Belasque, P. T. Yamamoto, D. C. Teixeira, N. A. Wulff, N. Gimenes-Fernandes, and O. A. Bergamaschi. 2005. Proceedings of 2nd International Citrus Canker and Huanglongbing Research Workshop, Florida Citrus Mutual, Orlando 2005, H-4.
Bassanezi R. B., L. A. Busato, A. Bergamin-Filho, L. Amorim, and T. R. Gottwald. 2005. “Preliminary Spatial Pattern Analysis of Huanglongbing in S˜ao Paulo, Brazil.” Proc. 16th Conf. Intern. Org. Citrus Virol., 341–55. IOCV, Univ. Calif., Riverside, CA.
Bassanezi R. B., A. Bergamin Filho, L. Amorim, and T. R. Gottwald. 2006. “Epidemiology of Huanglongbing in São Paulo.” Proceedings of Huanglongbing Greening International Workshop, Ribeirão Preto. p.37.
Bassanezi, R. B., and R. C. Bassanezi. 2008. “An Approach to Model the Impact of Huanglongbing on Citrus Yield”. International Research Conference On Huanglongbing (IRCHLB) Proceedings, Orlando, Florida Dec. 2008.
Bassanezi, R. B., L. H. Montesino, M. C. G. Gasparoto, A. Bergamin Filho, and L. Amorim. 2011. “Yield Loss Caused by Huanglongbing in Different Sweet Orange Cultivars in Sa˜o Paulo, Brazil.” European Journal of Plant Pathology, 130:577–86.
Batabyal, A.A., and P. Nijkamp, P. 2008: “Optimal Resource Management in the Presence of a Deleterious Alien Species: a Stochastic Model for an Orchard.” Lett. Spat. Resour. Sci. 1: 107–115.
Belasque, Jr., J., N. G. Fernandes, and C. A. Massari. 2009. “The Success of Eradication Campaign of Citrus Canker in São Paulo State, Brazil.” Summa Phytopathologica 35(2):91–92.
Bolton, J. S. 2012. “Citrus Under Siege: The Relentless Advance of Huanglongbing (HLB).” http://joansbolton.wordpress.com/2012/04/06/citrus-under-siege/
92
Bové, J. M. 1986. “Greening in Arab Peninsula: Towards New Techniques for its Detection and Control.” FAO Plant Prot. Bull. 34:7-14.
Bové, J. M. 2006. “Huanglongbing: A Destructive, Newly Emerging Century-old Disease of Citrus.” Journal of Plant Pathology, 88: 7-37.
Bové, J. M. (2012) “Comments made at a meeting at the Southwest Florida Research and Education Center, Immokalee, FL
Brlansky, R.H., K. R. Chung and M. E. Rogers. “2008 Florida Pest Management Guide: Huanglongbing (Citrus Greening). University of Florida-IFAS. PP-225. http://edis.ifas.ufl.edu
Brlansky, R. H., M. M. Dewdney, and M. E. Rogers. 2011. “2011 Florida Citrus Pest Management Guide: Huanglongbing (Citrus Greening).” Publication #PP-225. Gainesville: Institute of Food and Agricultural Sciences, University of Florida. Available online at http://edis.ifas.ufl.edu/cg086
Brlansky, R.H., M.M. Dewdney, M.E. Rogers, and K.R. Chung. 2009. Huanglongbing (Citrus Greening). 2009 Florida Citrus Pest Management Guide. Publication #PP-225. Gainesville: Institute of Food and Agricultural Sciences, University of Florida. Available online at http://edis.ifas.ufl.edu/CG086
Brown, C., L. Lynch, and D. Zilberman. 2002. “The Economics of Controlling Insect-Transmitted Plant Diseases.” Amer. J. Agr. Econ. 84(2): 279–291.
Carrasco, L. R., A. MacLeod, J. D. Knight, R. Baker, and J. D. Mumford. 2009. “Optimal Control of Spreading Biological Invasions: For how long should We Apply the Brake?” Paper Presented at the 83rd Annual Conference of the Agricultural Economics Society, 30 March - 1 April, Dublin, Ireland.
Catling, H.D., and P.R. Atkinson. 1974. “Spread of Greening by Trioza erytreae (Del Guercio) in Swaziland.” In Proceedings of the 6th Conference of International Organization of Citrus Virologists, eds. L.G. Weathers and M. Cohen, pp. 33–39, August 21–28, at Swaziland. Riverside, CA: IOCV.
Chan, M.S., and M. J. Jeger. 1994. “An Analytical Model of Plant Virus Disease Dynamics with Roguing and Replanting.” Journal of Applied Ecology, 31 (3): 413-427.
Chalak‐Haghighi, M., E. C. Van Ierland, G. W. Bourdôt & D. Leathwick (2008): Management Strategies for an Invasive Weed: A Dynamic Programming Approach for Californian Thistle in New Zealand, New Zealand Journal of Agricultural Research, 51:4, 409-424.
Chen, C.P. 1941. “Citrus Huanglongbing Research: Report IV. J.” New Agric., Citrus Experiment Station of Lingnan University, PRC. China 3:169-191.
93
Chiyaka, C., B. H. Singer, S. E. Halbert, J. G. Morris Jr., and A. H. C. van Bruggen. 2012. “Modeling Huanglongbing Transmission Within a Citrus Tree,” in Proceedings of the National Academy of Sciences of the United States of America, 2012.
Clark, C. W. 1976. “Mathematical Bioeconomics. The optimal Management of Renewable Resources.” Wiley, New York, New York, USA.
Eiswerth, M.E., and W.S. Johnson. 2002. “Managing Nonindigenous Invasive Species: Insights from Dynamic Analysis.” Environmental and Resource Economics 23(3): 319–342.
Enkerlin, W., and J. D. Mumford. 1997. “Economic Evaluation of Three Alternative Control Methods of the Mediterranean Fruit Fly (Diptera: Tephritidae) in Israel, Palestine and Jordan.” Journal of Economic Entomology, 90:1066–1072.
Fishman, S. and R. Marcus. 1984. “A Model for Spread of Plant Disease with Periodic Removals.” Journal of Mathematical Biology, 21:149-158.
Fishman, S., R. Marcus, H. Talpaz, M. Bar-Joseph, Y. Oren, R. Salomon, and M. Zohar. 1983. “Epidemiological and Economic Models for spread and Control of citrus Tristeza Virus Disease.” Phytoparasitica, 11:39-49.
Futch. S., S. Weingarten, and M. Irey. 2009. “Determining HLB Infection Levels Using Multiple Survey Methods in Florida Citrus. In: Proceedings Florida State Horticultural Society (FSHS), 122, pp. 152–158.
Gatineau, F., T. H. Loc, N. D. Tuyen, T. M. Tuan, N. T. Hien, and N. T. N. Truc. 2006. “Effects of Two Insecticide Practices on Population Dynamics of Diaphorina Citri and Huanglongbing Incidence in South Vietnam.” Proceedings of Huanglongbing–Greening International Workshop, Ribeirão Preto, Brazil. p.110.
Goodwin, B. K. and Piggott, N. E. (2009). “Spatiotemporal Modeling of Asian Citrus Canker Risks: Implications For Insurance And Indemnification Fund Models.” Amer. J. Agr. Econ. 91(4) (November 2009): 1038–1055. Copyright 2009 Agricultural and Applied Economics Association DOI: 10.1111/j.1467-8276.2009.01321.x
Gottwald TR, Aubert B, Huang KL. 1991. Spatial Pattern Analysis of Citrus Greening in Shantou, China. Proc. 11th I.O.C.V., 421-427.
Gottwald, T. R., M. S. Irey, T. Gast, S. R. Parnell, E. L. Taylor and M. E. Hilf. 2010. “Spatio-temporal Analysis of an HLB Epidemic in Florida and Implications for Spread.” Proceedings, 17th Conference IOCV, 2010 – Insect-Transmitted Procaryotes
Gottwald, T. R. 2010. “Current Epidemiological Understanding of Citrus Huanglongbing.” Annual Review of Phytopathology. 48:119-39.
94
Gottwald, T., M. Irey, T. Gast, A. Bergamin-Filho, R. Bassanezi, and C. A. Gilligan. 2008. “A Stochastic Spatiotemporal Analysis of the Contribution of Primary Versus Secondary Spread of HLB.” Proc. Int. Res. Conf. Huanglongbing, 285–90.
Gottwald, T. R., M. Irey, T. Gast, S. Parnell, E. Taylor, and M.E. Hilf. 2009. “Spatio-temporal Analysis of an HLB Epidemic in Florida and Implications for Future Spread.” In Proceedings of the 17th Conference of the International Organization of Citrus Virologists, p. 139, October 22–26, 2007, at Adana, Turkey. IOCV, Riverside, CA
Gottwald T. R., J. V. da Graça, and R. B. Bassanezi. 2007a. “Citrus Huanglongbing: The Pathogen and its Impact.” Plant Health Progress, doi: 10.1094/PHP-2007-0906-01-RV.
Gottwald, T.R., M. Irey, T. Gast, S. Parnell, E. Taylor, and M. E. Hilf. 2007b. “Spatio-temporal Analysis of an HLB Epidemic in Florida and Implications for Future Spread.” In: Proceedings of the 17th Conference of the International Organization Citrus Virologists, Univ. California, Riverside (in press).
Gottwald, T.R., B. Aubert, and H.K. Long. 1991. “Spatial pattern analysis of citrus greening in Shantou, China.” In Proceedings of the 11th Conference of the International Organization of Citrus Virologists, eds. R.H. Brlansky, R.F. Lee and L.W. Timmer, pp. 421–427, November 6–10, 1989, at Orlando, FL. Riverside, CA: IOCV. Available online at http://www.ivia.es/iocv/archivos/
Gottwald, T. R., and T. Dixon. 2006.” Florida Actions Toward HLB Control. Proceedings of the International Workshop on Citrus Greening, July 14-21, Ribeiro Preto, Brazil. p.67-68.
Gottwald, T. R., J. H. Graham, M. S. Irey, T. G. McCollum, and B. W. Wood. 2012. “Inconsequential Effect of Nutritional Treatments on Huanglongbing Control, Fruit Quality, Bacterial Titer and Disease Progress.” Crop Protection 36: 73-82.
Gottwald, T. R., B. Aubert, and X. Y. Zhao. 1989. “Preliminary Analysis of Disease Progress of Citrus Greening Epidemics in the People 's Republic of China and French Reunion Island.” Phytopathology, 79:687-93.
Gottwald, T. R., C. I. Gonzales, and B. G. Mercado. 1991 . “Analysis of the Distribution of Citrus Greening in a Heavily Diseased Grove in Philippines.” Proc. Conf. Int. Org. Citrus Virol. , 11th. In press
Halbert, S. E., and K. L. Manjunath. 2004. “Asian citrus psyllids (Sternorrhyncha: Psyllidae) and greening disease in citrus: a literature review and assessment of risk in Florida.” Fla. Entomol. 87:330-354.
Huffaker, R.G., M.G. Bhat, and S.M. Lenhart. 1992. “Optimal Trapping Strategies for Diffusing Nuisance-Beaver Populations.” Natural Resource Modeling 6(1): 71-97.
95
Haight, R. G., and S. Polasky. 2010. “Optimal Control of an Invasive Species with Imperfect Information about the Level of Infestation.” Resource and Energy Economics, 32:519–533.
Hodges, A. W. and T. H. Spreen. 2012. “Economic Impacts of Citrus Greening (HLB) in Florida, 2006/07–2010/11.” EDIS document FE903, a publication of the Food and Resource Economics Department, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida, Gainesville.
Irey, M., T. R. Gottwald, M. Stewart, and H. Chamberlain. 2008. “Is it Possible to Replant Young Groves in an Area with Endemic HLB: a Hierarchical Sampling Approach to Determine Infection?” Proc. Int. Res. Conf. Huanglongbing, pp. 116–17.
Irey, M.S., T. Gast, and T.R. Gottwald. 2006. Comparison of Visual Assessment and Polymerase Chain Reaction Assay Testing to Estimate the Incidence of the Huanglongbing Pathogen in Commercial Florida Citrus. Proceedings of the Florida State Horticultural Society 119(2006):89–93.
Irey, M., R. A. Morris, and M. Estes. 2011. Survey to Estimate the Rate of HLB Infection in Florida Citrus Groves. plantmanagementnetwork.org/proceedings/irchlb/2011/. p. 73.
Jeger, M.J., and M.S. Chan. 1995. “Theoretical Aspects of Epidemics: Uses of Analytical Models to Make Strategic Management Decisions.” Canadian Journal of Plant Pathology, 17(2):109-114.
Jorgenson, D. W. 1967. “The Theory of Investment Behavior.” In Determinants of Investment Behavior, R. Ferber, UMI, ISBN: 0-87014-309-3, pp. 129 – 188.
Kobori, Y., F. Takasu, and Y. Ohto. 2011. “Development of an Individual-Based Simulation Model for the Spread of Citrus Greening Disease by the Vector Insect Diaphorina citri.” JIRCAS Working Report No. 72.
Kompas, T., and T. N. Che. 2009. “A Practical Optimal Surveillance Measure: The Case of Papaya Fruitfly in Australia.” Australian Centre for Biosecurity and Environmental Economics, Crawford School of Economics and Government, Canberra, ACT.
Lin, C.K. 1963. “Notes on Citrus Yellow Shoot Disease.” Acta Phytophylactica Sinica, 2:243–251.
Lominac, C. D., and A. A. Batabyal. 2009. “An Approach to the Management of Orchards that are Vulnerable to Attack by Invasive Species.” Lett Spat Resour Sci, 2:123–131. DOI 10.1007/s12076-009-0029-5
96
Manjunath, K. L., S. E. Halbert, C. Ramadugu, S. Webb, and R. F. Lee. 2008. “Detection of ‘Candidatus Liberibacter Asiaticus’ in Diaphorina Citri and its Importance in the Management of Citrus Huanglongbing in Florida.” Phytopathology, 98:387-396.
Martinez, A. L., and J. M. Wallace. 1969. “Citrus greening disease in the Philippines.” Proc. 1st Int. Citrus Symp, 3: 1427-31.
Mau, R. F. L., E. B. Jang, and R. I. Vargas. 2007. “The Hawaii Area-wide Fruit Fly Pest Management Program: Influence of Partnerships and a Good Education Programme.” In Area-wide Control of Insects: From research to field implementation, J. B. Vreysen, A. S. Robinson and J. Hendrichs, (eds.), IAEA, Vienna, Austria. pp 671-683.
Michaud, J. P. 2002. “Biological Control of Asian Citrus Psyllid, Diaphorina Citri (Hemiptera: Psyllidae) in Florida: A preliminary report.” Entomological News, 113(3):216–222.
Morris, A., and R. Muraro. 2008. “Economic Evaluation of Citrus Greening Management and Control Strategies.” EDIS document FE712, a Publication of the Food and Resource Economics Department, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL.
Morris, R.A., R.P. Muraro, and T.H. Spreen. 2008. “Invasive Diseases and Fruit Tree Production: Economic Tradeoffs of Citrus Greening Control on Florida’s Citrus Industry.” Paper presented at the Southern Agricultural Economics Association Annual Meeting, February 2–6, in Dallas, TX. Available online at http://purl.umn.edu/6309.
Muraro, R.P. 2010. “Costs of Managing HLB and Citrus Black Spot.” Presented at 2010 Citrus Expo. Ft. Meyers, FL. May 19, 2010.
Muraro, R.P. 2008a. “Summary of 2007-2008 Citrus Budget for the Indian River Production Region.” Gainesville: University of Florida, Institute of Food and Agricultural Sciences. Available online at http://www.crec.ifas.ufl.edu/extension/economics/pdf/IR_Budget_Summ_2007-2008.pdf.
Muraro, R. P. 2008b. “Summary of 2007-2008 Citrus Budget for the Central Florida (Ridge) Production Region.” Gainesville: University of Florida, Institute of Food and Agricultural Sciences. http://www.crec.ifas.ufl.edu/extension/economics/pdf/CF_Budget_Summ_2007_2008.pdf.
Myers, J. H., A. Savoie, and E. van Randen. 1998. “Eradication and Pest Management.” Annual Review of Entomology, 43(1): 471–491.
97
National Research Council. “Appendix C: Liaison Committee on Strategic Planning for the Florida Citrus Industry: Addressing Citrus Greening Disease.” Strategic Planning for the Florida Citrus Industry: Addressing Citrus Greening. Washington, DC: The National Academies Press, 2010. ISBN: 0-309-15208-9.
Norberg, R.P. 2008. “Economic Importance of Florida Citrus.” Paper presented at the U.S. Department of Agriculture-ARS ‘SWAT Team’ Workshop, April 22, in Ft. Pierce, FL. Florida Citrus Mutual, Inc. Available online at http://www.flcitrusmutual.com/files/e47fe5d8-ef81-4c15-9.pdf.
Odom, D. I. S., O. J. Cacho, J. A. Sinden, and G. R. Griffith. 2003. Policies for the Management of Weeds in Natural Ecosystems: The Case of Scotch Broom (Cytisus scoparius, L.) in an Australian National Park. Ecological Economics 44: 119-135.
Oleś, K., E. Gudowska-Nowak, and A. Kleczkowski. 2012. “Understanding Disease Control: Influence of Epidemiological and Economic Factors.” PLoS ONE 7(5):e36026. doi:10.1371/journal.pone.0036026.
Olson, L. J., and S. Roy. 2002. “The Economics of Controlling a Stochastic Biological Invasion.” American Journal of Agricultural Economics, 84 (5), Proceedings Issue (Dec.,2002), pp. 1311-1316.
Pierre, R., T. H. Spreen, and C. B. Moss. 2006. “Invasive Species and Biosecurity Cost: Cost of Monitoring and Controlling Mediterranean Fruit Flies in Florida.” J. Agric. Appl. Econ. 38:337–343.
Tampa Bay Online (TBO). 2008. “Citrus Greening.” Tampa Bay Online, a Media General Company. Available at http://www2.tbo.com/content/2008/dec/21/bz-citrus-greening/news-money/ (February 2011).
Raphael G. d'A. Vilamiu, Sonia Ternes, Guilherme A. Braga, and Francisco F. Laranjeira. 2012. “A Model for Huanglongbing Spread Between Citrus Plants Including Delay Times and Human Intervention.” AIP Conference Proceedings 1479, 2315 (2012); doi: 10.1063/1.4756657.
Rogers, M. E., P. A. Stansly, and L. L. Stelinski. 2010. 2010 Florida Citrus Pest Management Guide: Asian Citrus Psyllid and Citrus Leafminer. Florida Citrus Pest Management Guide. November 2009. ENY-734. Gainesville: University of Florida/IFAS. Available online at http://edis.ifas.ufl.edu/IN686.
Roistacher, C. N. 1996. “The economics of living with citrus Diseases: Huanglongbing (greening) in Thailand.” In Proceedings of the 13th Conference of the International Organization of Citrus Virologists, eds. P. Moreno, J.V. da Graça and L.W. Timmer, pp. 279–285, November 16–23, 1995, at Fouzhou, China. Riverside, CA: IOCV.
98
Roka, F. R., and A. M. Muraro. 2010. “Economics of HLB Management: Pull Trees or Spray Nutritionals.” International Citrus Economics Conference, Orlando, FL. Oct. 2010.
Schwarz, R. E., L. C. Knorr, and M. Prommintara. 1973. “Presence of Citrus Greening and its Psylla Vector in Thailand.” FAO Plant Protection Bulletin 21:132–138.
Sharov, A. A. 2004. “Bioeconomics of Managing the Spread of Exotic Pest Species with Barrier Zones.” Risk Analysis, 24(4):879–892.
Sharov, A. A., and A. M. Liebhold. 1998. “Bioeconomics of Managing the Spread of Exotic Pest Species with Barrier Zones.” Ecological Applications, 3(8):833-845.
Spann, T. M., R. A. Atwood, M. M. Dewdney, R. C. Ebel, R. Ehsani, G. England, S. H. Futch, T. Gaver, T. Hurner, C. Oswalt, M. E. Rogers, F. M. Roka, M. A. Ritenour, M. Zekri, B. J. Boman, K. Chung, M. D. Danyluk, R. Goodrich-Schneider, K. T. Morgan, R. A. Morris, R. P. Muraro, P. Roberts, R. E. Rouse, A. W. Schumann, P. A. Stansly, and L. L. Stelinski. 2010. “IFAS Guidance for Huanglongbing (Greening) Management.” Publication #HS1165. Gainesville: Institute of Food and Agricultural Sciences, University of Florida. Available online at http://edis.ifas.ufl.edu/hs1165.
Stohlgren, T. J. and J. L. Schnase. 2006. “Risk Analysis for Biological Hazards: What we Need to Know about Invasive Species.” Risk Analysis, 26(1):163–173.
99
BIOGRAPHICAL SKETCH
By the decree of God, Abdul Wahab Salifu was born to a noble couple in a
polygamous home in the heart of Tamale in the northern region of Ghana on a blessed
day. He started school at the Methodist primary school in Tamale and proceeded after 6
years to the Bagabaga Demonstration junior secondary school also in Tamale. He
obtained his high school certificate at Ghana secondary school in Tamale in 1990. From
here, he proceeded to the Ohawu Agricultural College in the Volta region of Ghana
where he obtained the general certificate of agriculture in 1993. He started his work
career as an agricultural extension agent at Zabzugu district in the northern region of
Ghana. In 1999 and 2003, he obtained his national diploma of agriculture and B.Sc.
degree respectively at the University of Ghana. Having worked as assistant regional
MIS officer at the Ministry of Agriculture in the northern region of Ghana from 2003 to
2004, he started his research career as an assistant research officer at Ghana’s CSIR-
Savanna agricultural research institute in Tamale from 2004 to 2006. In the spring of
2009 he obtained his MS degree from Tuskegee University in Alabama. He received his
Ph.D. from the University of Florida in the spring of 2013.