**[Modeling the impact of loss in U.S. soybean production]{.smallcaps}**

**[resulting from soy rust disease]{.smallcaps}**

+-----------------------+-----------------------+-----------------------+
| **Aldo A. Zagonel**   | **Stephen H. Conrad** | **Paul G. Kaplan**    |
|                       |                       |                       |
| aazagon@sandia.gov    | shconra@sandia.gov    | pgkapla@sandia.gov    |
+-----------------------+-----------------------+-----------------------+

Sandia National Laboratories[^1]

Critical Infrastructure Modeling and Simulation Department

P.O. Box 5800 MS 1138 -- Albuquerque, NM 87185-1138 -- USA

Proceedings of the 23^rd^ International Conference of the System
Dynamics Society

Boston, MA, USA, July 17-21, 2005 (Draft dated June 1^st^, 2005)

Our objective is to examine the consequences of soy rust to the U.S.
agriculture in the next 2-5 years. In 2000, the U.S. harvested
approximately 2.8 billion bushels of soybeans from almost 73 million
acres of cropland, accounting for more than 50 percent of the world\'s
production. The crop generated \$12.5 billion dollars, \$6.66 billion in
exports. Soy rust established itself in the south last November and is
expected to disseminate and deposit in the crops during this year's
planting season. The extent of outbreaks depends upon climatic
conditions. Early detection is crucial since soy rust is deadly to the
soy plant within 48 hours. Monitoring systems will warn farmers of the
presence of the spores and farmers are instructed on how to identify and
treat it. There is uncertainty regarding the sufficient and timely
availability of fungicide. In addition to historical data, we
incorporate observations of on going planting and harvesting. Parameter
ranges in the model are narrowed as more information becomes available
and existing uncertainties dissipate. The impact of soy rust is analyzed
in aggregate, looking at overall production and market share contrasted
against natural noise in the yields.

#### **Key words:** Soybean, grain, soy rust, plant disease, corn blight, agriculture

####### Introduction

This paper reports on-going efforts to model the U.S. agricultural
infrastructure, in particular examining the consequences of a soy rust
outbreak starting in the 2005 harvest. Soybean is the second largest
crop in U.S. agriculture. In 2000, approximately 2.8 billion bushels of
soybeans were harvested from almost 73 million acres of cropland,
accounting for more than 50 percent of the world's production. The crop
generated \$12.5 billion dollars in cash receipts from sales; \$6.66
billion in the form of exports.

Soy rust established itself in the south past November and over
wintered. Now it is expected to disseminate and deposit in the crops
during this year's planting season. The extent of an outbreak is heavily
dependent on climatic conditions, as illustrated in Figure 1.

**Fig. 1 -- USDA map of soy rust susceptibility across the United
States**

**(Overlapped with soybean production)**

There are two varieties of rust, and one is deadly to the soy plant
within 48 hours. Early detection is crucial for adequate treatment using
fungicides, but it is unclear whether the two forms can be distinguished
by farmers. Monitoring systems are being put in place to warn farmers of
the presence of the spore in their regions, and farmers are being
instructed on how to proceed to identify the disease and treat it
effectively. But, there is uncertainty regarding the availability of
fungicide and equipment to timely respond to outbreaks.

The objective of this study is to examine the consequences of soy rust
to U.S. agriculture. An important opportunity presents itself, which is
to have the modeling effort take place at the same time that soybeans
are being planted and harvested in North America, for the first time
subject to this disease. In addition to historical data, we can
incorporate observations of on going planting and harvesting. Parameter
ranges in the model can be narrowed as more information becomes
available and existing uncertainties dissipate. Every week there will be
new information available, both to parameterize the model, and to
contrast real data with model behavior. This means that this modeling
work will not be helpful in terms of informing this year's soy
production. But, a good model that skillfully represents what's going on
in the fields and in the grain market will potentially be useful for
medium and long-term analyses of soy rust impact.

Although present and dangerous, soy rust is not expected to be
devastating to U.S. soy production. Overall it only marginally affected
productivity in South America; moreover, production in these countries
has actually continued to increase. The U.S. will benefit from existing
experience dealing and controlling the disease in other countries. The
EPA already granted approval to use fungicides to treat the disease. A
strong market has flourished to provide farmers with product and
equipment to treat it. Therefore, it is likely that the disease will be
controlled adequately.

However, soy rust is bound to have some impact in U.S. agriculture. It
will increase the cost of producing soybeans (due to increased labor,
purchase of fungicide and new equipment, and the cost of application)
and its risk might induce farmers to substitute other crops for soybeans
or even avoid planting. Therefore, it is possible that soy rust will
cause a reduction in the U.S. market share in the global market due to
comparative disadvantage, since soybean production will become more
labor intensive, and labor in this country is more expensive than for
main competitors in developing countries, primarily Brazil and
Argentina.

The increased use of fungicide is likely to raise concerns related with
human health and environmental degradation. Also, some inequities
vis-à-vis other bean crops may come into play since special EPA
authorization applies only to soybeans, while other types of beans are
equally susceptible to the disease.

Yet another significant issue may result from an imbalance between the
demand and supply of fungicide in one season, potentially leading to
market overreaction in the following season --similarly to this winter's
shortage of the flu vaccine due to overproduction and underutilization
last winter. Therefore, there may be a supply management problem here,
resulting in oscillations due to imbalances between the supply and
demand of fungicide. This would indicate market failure --a problem that
could be aggravated to the extent that the fungicide produced for this
year may not be useful anymore next year, as the fungus builds-up
resistance to the fungicides used in the treatment of the crops--
resulting in economic losses either to fungicide producers or consumers,
whoever bears the burden of the unused product. In the case of the flu
vaccine it was the manufacturers that were stuck with the bill, and some
went bankrupt as a result. In the case of fungicide, it is more likely
that those toward the end of the distribution chain will bear the
burden, since fungicide is currently being sold with a nonrefundable,
no-return policy.

A definitive solution to the soy rust problem may take five to ten
years, and it will involve developing a genetically resistant soybean.
This hinges on the assumption that the needed strain is available in a
gene bank. Otherwise, it has to be genetically modified, raising yet new
problems since genetic modification of food is a highly controversial
issue.

Taken together, these issues suggest there is value in building a system
dynamics model to look at the consequences of soy rust, and to examine
problems that may unfold within a two to five years time horizon.

####### Building toward a "generic" crop model

Our soybean model builds upon the corn model reported in Conrad (2004),
but it incorporates international competition from southern hemisphere
countries already exposed to and experienced in this disease. It treats
the disease itself exogenously, and relies upon expert estimation of the
overall seasonal crop losses. For now, foreign supply and demand are
treated exogenously too, but foreign production is accounted for in the
computation of the price received by U.S. soybean farmers. In this
estimation we use an econometric model developed by Plato and Chambers
(2004). But, the system dynamics model will endogenously balance the
supply and demand loops, giving shape to key dynamic indicators such as:
relative coverage (ratio of supply to demand), soybean price, seasonal
crop planted and on-going demand. The impact of soy rust is analyzed in
aggregate, looking at overall production and market share contrasted
against natural noise in the yields.

This year's crop production will be monitored carefully to parameterize
and calibrate the model. By August most of the existing short-term
uncertainties will have been clarified (changes in production due to
risk perception, incidence of the disease, timely diagnosis,
availability of fungicide and equipment, timely treatment, and overall
seasonal crop losses). In addition, we intend to test the model
simulating the consequences of the 1970 corn blight --which affected in
average 25-30 percent of the national harvest, completely destroying as
much as 80-100 percent of the crop in some areas of the country. Thus,
behavioral reproduction tests --contrasting model output with real data,
both historical and for this harvest-- will help us refine, calibrate,
and build confidence in the model.

Figure 2 is a systems-level diagram of our generic crop model. The
description of the diagram and the order in which we are building the
layers of complexity is more or less as follows:

**Fig. 2 -- Systems diagram of generic crop model**

1.  The stock-and-flow structure captures the processes of planting,
    growing, harvesting, storing and selling the grains. Some of the key
    inputs to this production chain are: farmer's seasonal planting
    commitment, period of the planting season (start and duration), time
    for the crop to mature (LOS in fields), duration of the harvest,
    yield under normal conditions, and demand for grains

2.  We are interested in studying and contrasting two exogenous effects
    upon this production chain, soy rust disease vs. noise in the system
    (mostly how the weather affects the yields)

    a.  A key parameter in the model is the net *fraction of crop loss*.
        It depends upon a number of things, such as fraction of the crop
        vulnerable, disease education, crop monitoring, timely
        diagnosis, treatment training, availability of fungicide and
        equipment, and timely treatment. The better we measure this
        risk, the narrower its range of variance. Thus, the validity of
        our conclusions in the comparison between noise vs. impact of
        the disease relies heavily on this parameter

    b.  Another important exogenous element is the magnitude of the
        impact of weather on the yields, captured as historical variance
        in the yield, controlled for advances in productivity

3.  The seasonal planting commitment by farmers depends on a number of
    things: the forecasted price of grains provided by the USDA, the
    price of grains in the futures market, elasticity of grain supply,
    government subsidies, risk perception regarding soy rust disease,
    availability/cost of insurance, return on alternative investments
    (including other crops), and availability of land to plant. Some of
    these inputs to farmers' decisions are easier to comprehend and
    synthesize than others. We'll do our best to capture as many as
    possible. Note that the subsidized price of grains, if greater than
    the break-even price, constitutes a floor in terms of production,
    and land availability constitutes a ceiling. The broader the range
    between the floor and the ceiling, the more important it is to
    capture accurately these things that shape farmers' planting
    decisions

4.  To close the production loop, we need to capture the processes
    through which the future price of grains (and/or the price of grains
    in the futures market) is forecasted. This involves examining both
    the USDA and Chicago Board of Trade (CBT) forecasting procedures,
    and perhaps reconciling them. We assume these forecasts are based
    upon a number of things, such as: expected demand for grains
    (domestic and foreign), adequacy of the physical inventory (grain in
    storage vis-à-vis grains needed to meet demand until the next
    harvest, or for the following "N" months), adequacy of the upcoming
    harvest (what will be the yield coming into the inventory?), and the
    impact of inventory coverage upon grain prices (coverage elasticity)

5.  Crop loss and subsequent reduction in harvested yields is likely to
    trigger a number of compensating mechanisms:

    a.  More production, provided the shortage makes prices rise, and
        provided there is additional land to plant

    b.  More imports to accommodate existing demand

    c.  Rationing if the shortage is serious and imports are not
        available

    d.  Adjustments in demand due to rise in the price of grains. This,
        in turn, depends upon the elasticities of demand (for animal
        feed and for other usages)

6.  We will consider expanding the model boundary to treat endogenously
    some of the exogenous parameters or time series:

    a.  Building the interdependencies between the demand and supply of
        fungicides (interdependency with chemical industry) and
        equipment

    b.  Building the interdependencies between irrigation (energy and
        water infrastructures) and crop land availability

    c.  Including corn as a separate sector and examining the
        interactions between these two commodities (both in terms of
        production and consumption)

    d.  Endogenizing foreign production to address global grain
        interdependencies (both in production and consumption)

    e.  Coupling the soybean and corn sectors with the beef and dairy
        sectors; adding poultry and hogs

This is an ambitious scope of work. For this paper, we would like to be
able to conclude item 4 and as many as possible of the compensating
mechanisms mentioned in item 5.

####### Modeling foundation

This modeling work builds upon Meadows' (1970) hogs' model, addressing
commodity production cycles. The basic feedback structure for production
cycles proposed by Meadows is shown in Figure 3. Inventory coverage is
at the center of a pair of negative feedback loops which act to
eliminate imbalances between demand and supply; the resulting price acts
as its signal in promoting the efficient allocation of resources
(production and consumption). However, due to delays in capacity
acquisition and bounded decision making by producers, market reactions
of demand and supply to price are very slow, resulting in oscillations
(Sterman, 2000; Meadows, 1970). Additional instabilities and delays are
introduced to the extent that commodities are interdependent or act as
substitutes. For example, grains (such as corn and soybeans) are used in
animal feed, and the price of feed influences decisions regarding animal
heard sizes, which in turn affects the consumption of feed, thus closing
the loop through its influence in grain prices. Moreover, different
grains (corn vs. soybeans) can be used in the production of animal feed,
depending upon their relative prices, thus changing relative demand and
serving to balance grain prices.

**Fig. 3 -- Feedback loop structure of production cycles (copied from
Meadows, p. 19)**

Conrad (2004) described an initial crop model capturing the production
cycle for corn, and how it interacted with beef and dairy production.
Figure 4 shows the corn sector. The negative feedback loop for
production is identified by the orange arrows. The total corn inventory
and the corn sales together determine the corn coverage time, which in
turn determines the price of corn. Since corn production is so strongly
seasonal (planted in the spring and harvested in the fall), seasonal
effects are explicitly captured in the model. Although in reality
farmers can respond to price signals during the growing season by
varying their applications of fertilizer and pesticide, the foremost way
they respond to price is in their decision about how much corn to plant
in the spring. In the model this is the only way for corn producers to
respond to price. Corn production responds to relative coverage (through
price) but is confined within a range characterized by a floor (the
subsidized corn price or a break even price) and a ceiling (the maximum
acres of land available for production). Harvested corn accumulates in
the fall and is depleted over the course of months until the next
harvest. Harvested corn is distributed primarily as animal feed (\~ 58
percent), but it also goes to dry and wet mills and exports. Consumption
depends upon demand from the various types of buyers.

**Fig. 4 -- Corn sector diagram (copied from Conrad, p. 5)**

Rasmussen and Becker (2004) did an initial stability and sensitivity
analysis of Conrad's three-commodities model. Their assessment focused
upon behavioral stability of the agricultural sub-sectors (corn, beef
and dairy) given parameter changes, particularly changes in assumptions
regarding aggregate agent reactions to prices and sector stresses,
captured in the model as *elasticities*[^2]. They found model behavior
to be highly sensitive to these elasticities. For corn, they
demonstrated that dramatic oscillations occur in *acres of corn
(planted)*, with small increases in *corn planting elasticity* (the
elasticity of supply), as illustrated in Figure 5.

**Fig. 5 -- *Acres of corn* under different assumptions for *corn
planting elasticity*,**

**0.5, 0.625 and 0.75 (copied from Rasmussen and Becker, p. 6)**

Rasmussen and Becker concluded that two key elements dominate the sector
dynamics: (1) the manner in which agents react to prices and sector
stresses, and (2) where production delays occur and their nature. In
order to advance the modeling effort, they recommended (i) adopting
model simplifications to clarify model functionalities, (ii) further
investigation of the human decisions models/curves to limit the family
of resulting behaviors to the range of realistic dynamics, and (iii)
using historical time series to gain better insight into the
agricultural sub-sector dynamics. In spite of the concerns raised, they
concluded that this version of the model provided "good initial systems
approximations and, in particular, a wealth of information about how to
model the detailed sub-sector price formation processes." (p. 1)

We believe this modeling extension follows suggestions (ii) and (iii).
This work aims towards careful estimation and calibration of model
parameters and table functions, within empirically derived ranges
whenever available, using both "snapshots" and time series comparisons
to refine and build confidence in the model and simulations. A better
understanding of the physical and behavioral processes captured in the
crop model will allow us to narrow the range of feasible real-world
model behaviors. Hopefully this will provide not only better insights
into structural-behavioral links, but also more robust forecasts of soy
rust impacts on U.S. agriculture and economy. (Appendix 1 illustrates
the use of parameterization and calibration spreadsheets to substantiate
and document parameter ranges, as well as available data, for the piece
of the model dealing with planting and harvesting with disease.)

####### On-going model refinements

Introspection and feedback from several reviewers led to the following
list of model refinements and tests:

-   Reformulation of the *planting* and *harvesting* processes

-   Reformulation of the *disease scenario* and *crop losses*

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-   Reformulation of *relative coverage*

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-   Logistic growth issue involving formation of the *unsubsidized corn
    price* (goal-seeking vs. S-shaped adjustment)

-   Revisit resource allocation formulation (*sales*)

-   Parameterization and calibration of the *production* and
    *consumption* loops (including coverage, production and consumption
    elasticities)

-   "Snapshot" behavioral reproduction tests to examine model output
    against cross-sectional data

-   "Longitudinal" behavioral reproduction test, replicating the 1970
    Corn Blight, to contrast model behavior with actual time-series data

The first two items in this list were addressed and are reviewed in the
next section. We are currently working on the third item, the
reformulation of *relative coverage*, also discussed below.

*Planting and harvesting with disease*

The motivation to reformulate the planting and harvesting with disease
processes in the model was due to:

-   Desire to parameterize these processes drawing upon *length of
    planting season*, *length of stay of crop in the fields*, and
    *length of harvesting season*, in addition to *start of planting*,
    using auxiliaries to capture *end of planting*, *start of
    harvesting* and *end of harvesting*, previously treated as
    parameters

-   Elaborate the disease scenario to include *fraction of crop
    vulnerable*, as well as make the formulation more flexible and
    robust in terms of variations in the *fraction of crop loss
    scenario* and the timing of the disease (*onset of disease* and
    *disease duration*)

-   Capture both physical *crop loss* (acres/month) and *yield loss*
    (tons/month), using a co-flow structure

-   Generate an indicator of *fractional yield loss* while the crop is
    in the field, as an early signal about the yield of the future
    harvest

The resulting model diagram is depicted in Figure 6.

**Fig. 6 -- Planting & harvesting with disease model diagram**

The results of a five year simulation --assuming a *net fraction of crop
loss* of 25 percent during the second season-- are captured in Figure 7.
Every year the crop is planted and harvested, as illustrated in terms of
*crop in fields* (line 1). As a function of the disease in year 2, the
*average seasonal yield* (line 2) falls during the time the crop is
growing and maturing. When the crop is harvested, an equivalent loss in
terms of acreage is captured as *crop loss* (line 3). The *fractional
yield loss* (line 4) normalizes the *average seasonal yield* and
constitutes the signal that there is going to be a problem with the
future harvest. This signal is perceived in the market as soon as word
is out about the effects of the disease on the crop, and it precedes the
physical accounting of soybeans that happens months later, during the
harvest itself.

**Fig. 7 -- Disease scenario (*net fraction of crop loss* = 0.25 during
the second season)**

*Relative coverage*

The motivation to revisit the formulation of relative coverage was due
to the issues discussed earlier and highlighted in the systems diagram,
involving USDA price of grains forecasts and the CBT futures market.
Both these forecasts take the perceived adequacy of inventory (plus
harvest) vs. expected demand (i.e. relative coverage) as the signal to
adjust future prices. Thus, we replaced the previously used table
function capturing *desired coverage* with formulation that computes the
*desired stock to use ratio* from *coverage needed until harvest* (a
combination of *demand* and *time remaining until the next harvest*) and
*desired reserves* (a minimum inventory coverage level just before the
new harvest begins to refill the inventory). We also incorporated *time
to deplete the remaining inventory* and we wish to determine if there is
(or could be) a rationing policy in the case of shortages.

As of yet, the revised model does not include the demand feedback loops.
*On going demand* is treated for now as constant. The revised model
diagram is depicted in Figure 8.

**Fig. 8 -- Relative coverage model diagram**

Figure 9 portrays the results of the same simulation for *harvesting
yield* (line 1), *on going [sales]{.underline}* (line 2), *harvested
crop* (line 3), *adequacy of physical inventory* (line 4), and
*perceived adequacy of future inventory* (line 5). During the
simulation, *on going [demand]{.underline}* remains constant and equal
to 20 million tons per month. The saw-shaped behavior of *harvested
crop* (inventory level) matches that of the *desired coverage*, except
when there is a shortage in the harvest due to disease (during the
second season). This shortage unfolds into the following seasons.[^3]
Note that the *inadequacy of the physical inventory* is not perceivable
until the new harvest comes in. However, the *inadequacy of future
inventory*, which takes into account the early signal from the health of
the crop in the field, is perceivable as soon as word gets out about the
disease.^3^ *On-going sales* are constrained by the availability of
inventory.

**Fig. 9 -- Relative coverage (*Perceived adequacy of future
inventory*)**

For now, we used a weighted formula to establish the *adequacy of future
inventory* combining both the *adequacy of physical inventory* and the
*fractional yield loss* in the field. The weight changes dynamically
depending upon where the soy is found --whether in fields or silos. As
we can observe in Figure 10, this formula discounts both the disease in
the field (between months 15-19) and the shortage in the silos (between
months 27-33 and, later, 39-45 and 51-57). It may not be the ideal
solution to represent how real people, in this case brokers in the grain
market, combine information regarding what's going on in the fields vs.
silos.[^4] Thus, some empirical research is required to build confidence
in this formula.

Figure 10 helps to highlight some issues that come up due to delays in
this system:

**Fig. 10 -- Types of (in)adequacies**

**(How to combine *fractional yield loss* with *adequacy of physical
inventory*?)**

1.  The *fractional yield loss* (line 1) resulting from a disease in the
    field between months 15-19 does not translate into a problem in the
    inventory (*inadequacy of physical inventory*, line 2) until the
    crop is harvested. Therefore, if folk in the silos did not have
    information about what's going on in the fields, they would be
    clueless that there's going to be a problem keeping their
    inventories at the levels they would like them to be;

2.  Worse, the *inadequacy of supply* (line 5) is not truly observed
    until more than a year later, between months 29-32, just before the
    next harvest comes through. This is because *on going demand* can be
    met all the way up until the inventory falls below *desired
    reserves*;

3.  In addition, the shortage caused due to the disease in the second
    year propagates throughout future years unless more grains are
    planted in the subsequent season, to compensate for this season's
    loss. Essentially, this becomes an inventory management problem. In
    fact what corrects for this future problem is exactly the upward
    fluctuation in price due to the shortage. But, as the argued above,
    this information (of a physical shortage) is not going to be
    available until more than a year after the problem occurred. Thus,
    it is the perception that there will be a shortage (or surplus) that
    drives prices, and not actual shortages and surpluses.

This "market failure" due to inherent delays in the system is
compensated by the existence of a futures market, which acts as a proxy
for the market's "invisible hand," attempting to forecast future supply
and demand for grains, and provide early signals of future prices.
Therefore, it is important to capture in the model this decision rule
that real decision makers use to govern grain prices. This calls for
examining how the USDA produces its forecasts, and checking with futures
brokers how the CBT establishes futures grain prices. It might be worth
representing this forecasting process in a policy structure diagram
(Morecroft 1982), capturing the causal structure and time delays
involved in this decision.

We continue to work on the relative coverage issue, and other items of
the outline of refinements. We are also planning on a number of model
additions.

####### Model additions to adapt the crop model from corn to soybeans

-   Introduce specific soy rust issues to disease scenario: timely
    diagnosis, availability of fungicide and equipment, and timely
    treatment. These issues help shape the *fraction of crop loss
    scenario* which, combined with *fraction of crop vulnerable*,
    determines overall seasonal crop losses (*net fraction of crop
    loss*)

-   Incorporate Plato and Chambers's econometric estimation of the price
    received by U.S. soybean farmers (accounting for foreign production)
    in the formulation of *unsubsidized soybeans price*

-   Incorporate changes in production due to risk perception (including
    availability of alternative crops, break-even prices, etc.) to the
    *production* loop

-   Incorporate commodity substitution in animal feed (and maybe other
    areas of consumption) to the *consumption* loop

-   On-going examination of model output vis-à-vis available data as
    planting and harvesting unfolds

-   Revisit parameterization and calibration of *production* and
    *consumption* loops (including disease scenario and coverage,
    production and consumption elasticities), given availability of new
    data from this year's production

The next section addresses where we are headed in terms of model-based
analysis and sought-out insights.

####### Discussion

The purpose of this model is to capture in aggregate level the impact
that soy rust will have in U.S. agriculture and economy. For this
iteration of the modeling effort, we can [define the
problem]{.underline} as an attempt to identify and measure the medium
and long-term consequences of soy rust in terms of acreage planted,
seasonal production, productivity, break-even prices, crop
substitutions, grain substitutions, transient or steady state imbalances
between supply and demand, volume of sales and exports, and global
market share. But the measuring stick used is not one of acres, tons or
dollars. Although the model will necessarily indicate values and units
that will imply that we can measure the metric decrease in production
and dollar increase in price, etc, we are primarily interested in the
magnitude represented in the soy rust problem, as well as possibly
revealing counter-intuitive insights:

-   Is the potential effect of soy rust upon U.S. agriculture, consumers
    and economy, in the next five years, greater or smaller than have
    been the historical natural effects of, for instance, weather?

-   Will *the current trend* in U.S. market share in this industry
    remain the same now that soy rust is present in North America?

-   Could a relatively minor incidence of the disease this year create a
    market failure that could result in being unprepared for a more
    significant manifestation of the disease in future years?

*Our working hypothesis is that the effect of soy rust will be smaller
vis-à-vis normal fluctuations observed due to weather and noise in this
system.* If, in the process of conducting this study, we find evidence
to reject this hypothesis, then, soy rust presents a BIG problem to the
U.S. agriculture and economy. To this end, we will have also shed some
light on the following questions:

-   Which of the issues related to soy rust present themselves as
    significant vulnerability issues to this industry and, subsequently,
    to the U.S. economy?

-   What management issues do we need to consider (leverage or sensitive
    points in the system)?

-   What are the infrastructure intra-dependencies between soybean and
    other agricultural commodities?

-   What are the interdependencies between soybean (and grains in
    general) and other national infrastructures (e.g. chemical
    industry)?

If we fail to reject our working hypothesis, then, in the absence of
better evidence, we must conclude that while the fungus is present in
this country, and constitutes a serious danger to soy production, soy
rust is not expected to inflict severe aggregate harm to the nation
(provided some safeguards are taken!). However, it will likely cause
many changes in the way of doing business. These changes will generate
new costs and benefits that will reflect in specific gains and losses to
specific sub groups within this industry (agriculture), as well as in
other industries (e.g. chemical). In addition, we may be able to
characterize these sectorial gains and losses, and suggest means by
which they might be minimized or compensated provided due cause.

*Limitations and future research*

There are many limitations to this preliminary effort. First and
foremost the fact that soy rust disease is not modeled endogenously, but
considered as a scenario in the simulations. We believe other methods
and tools are better equipped to model the spread and development of the
disease due to spatial considerations that are not well handled in
system dynamics models. A number of other aspects in the production of
this crop and interactions with other commodities (including other
crops) fall outside of the boundary of this study.

####### References

Conrad SH. 2004. The dynamics of agricultural commodities and their
responses to disruptions of considerable magnitude. *Proceedings of the
2004 International Conference of the System Dynamics Society*. Oxford,
England (July 25-29).

Meadows DL. 1970. *Dynamics of Commodity Production Cycles*. Waltham,
MA: Pegasus Communications.

Morecroft JDW. 1982. A critical review of diagramming tools for
conceptualizing feedback system models. *Dynamica* 8(I): 20-29.

Plato G and W Chambers. April 2004. "How does Structural Change in the
Global Soybean Market Affect the U.S. Price?" Economic Research Service.
United States Department of Agriculture (USDA). OCS 04D-01.

Rasmussen S and N Becker. June 2004. "Initial Stability and Sensitivity
Studies of the CIP/DSS Agricultural Sector Models." Project Report. Los
Alamos National Laboratory.

Sterman JD. 2000. *Business Dynamics: System Thinking and Modeling for a
Complex World*. Boston, MA: The McGraw-Hill Companies, Inc.

Fig. 1 -- USDA map of soy rust susceptibility across the United States

(Overlapped with soybean production)

![](media/image1.png){width="4.165972222222222in"
height="3.457638888888889in"}

Fig. 2 -- Conceptual systems diagram of the generic crop model,
displaying interactions between grains (corn vs. soybeans), between U.S.
and foreign production, between grains and other agricultural sectors,
between agriculture and chemical industry, energy and water
infrastructures, and portraying the impact of weather on seasonal yields

![](media/image2.wmf){width="8.981944444444444in" height="5.7875in"}

Fig. 3 -- Feedback loop structure of production cycles (copied from
Meadows, p. 19)

![](media/image3.png){width="5.884722222222222in"
height="2.9069444444444446in"}

Fig. 4 -- Corn sector diagram (copied from Conrad, p. 5)

![](media/image4.wmf){width="8.816666666666666in"
height="6.267361111111111in"}

Fig. 5 -- *Acres of corn* under different assumptions for *corn planting
elasticity*,

0.5, 0.625 and 0.75 (copied from Rasmussen and Becker, p. 6)

![](media/image5.wmf){width="9.029861111111112in" height="2.75in"}

Fig. 6 -- Planting & harvesting with disease model diagram

![](media/image6.wmf){width="6.967361111111111in"
height="6.2243055555555555in"}

Fig. 7 -- Disease scenario (*net fraction of crop loss* = 0.25 during
the second season)

![](media/image7.wmf){width="6.496527777777778in"
height="4.442361111111111in"}

Fig. 8 -- Relative coverage model diagram

![](media/image8.wmf){width="8.986805555555556in"
height="4.367361111111111in"}

Fig. 9 -- Relative coverage (*Perceived adequacy of future inventory*)

![](media/image9.wmf){width="6.311111111111111in" height="3.89375in"}

Fig. 10 -- Types of (in)adequacies

(How to combine *fractional yield loss* with *adequacy of physical
inventory*?)

![](media/image10.wmf){width="6.315277777777778in"
height="3.9319444444444445in"}

Table 1 -- Preliminary model boundary chart

####### 

  ------------------- ----------------- ----------------- -----------------
  **Issue type**      **Endogenous      **Exogenous       **Absent
                      variables**       variables**       variables**

  **Pathogen and                                          
  fungicides**                                            

  **Economic**                                            

  **Non-economic                                          
  vulnerabilities**                                       
  ------------------- ----------------- ----------------- -----------------

**Appendix 1. Parameterization and calibration spreadsheet to
substantiate and document parameter ranges, as well as available data
(*planting and harvesting with disease*)**

![](media/image11.wmf){width="7.688194444444444in" height="5.90625in"}

[^1]: ^†^ Sandia is a multi-program laboratory operated by Sandia
    Corporation, a Lockheed Martin Company, for the United States
    Department of Energy's National Nuclear Security Administration
    under Contract DE-AC04-94AL85000. The Department of Homeland
    Security, Science & Technology Directorate, provided funding for
    this work. This draft describes on-going research involving the
    authors, Nancy Brodsky, Sharon Deland, and Vanessa Vargas. We thank
    Andy Ford and four anonymous reviewers for their constructive
    critiques and helpful comments.

[^2]: Coverage, production and consumption elasticities -- i.e., what
    effects will *coverage* have on price? What effects will price have
    on *production* and on *consumption*?

[^3]: In this version of the model soy production is also constant.
    Thus, the part of the harvest lost in the second season is never
    compensated, except for reduced sales just before the 3^rd^, 4^th^
    and 5^th^ harvests, when inventory is below the level of *desired
    reserves*. Normally producers would plant more following the bad
    season motivated by rise in prices resulting from shortage in
    supply.

[^4]: There is at least one more piece of information that we may need
    to incorporate to establish grain prices, which is the commitment
    farmers make prior to actual planting, captured and reported by
    USDA. This information may constitute an earlier signal that needs
    to be considered in the *perception of the adequacy of future
    inventory*.