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FARM PROGRAMS AND CLIMATE CHANGE J. K. LEWANDROWSKI 1 and R. J. BRAZEE 2 1Resources and Technology Division, Economic Research Service, United States Department of Agriculture, 1301 New York Avenue NW, Washington, DC 20005, US.A. 2Department of Forestry, University of Illinois at Urbana-Champaign, 1301 W. Gregory Drive, Urbana, IL 61820, U.S.A. Abstract. The view that the agricultural sector could largely offset any negative impacts of climate change by altering production practices assumes the govern- ment will not create disincentives for farmers to adapt. U.S. farm programs, how- ever, often discourage such obvious adaptations as switching crops, investing in water conserving technologies, and entry or exit. We outline a simple portfolio model describing producer decision making: we then use this framework to assess how specific U.S. farm programs might affect adaption to climate change. Three future climate scenarios are considered and in each the present structure of U.S. farm programs discourages adaptation. Farm Programs and Adapting to Climate Change The announcement by NASA that 1991 was the second hottest year in over a century should intensify the climate change debate. Se, ven of the 8 warmest years on record have now occurred since 1981.1 Also motivating the climate change debate are several recent dry weather events. Much of the Western United States has experienced a persistent drought for over 5 years (since 1987); the northern hemisphere had its lowest recorded snow fall in 1990; and in 1988, drought condi- tions in the West, Southeast, Northern Plains, and Midwest combined to cause the worst U.S. agricultural losses in decades. While few scientists are willing to conclude that these historically high tempera- tures and extended dry periods are related to the greenhouse effect, the possibility of pending climate change should be of concern to agriculture. Because agriculture is largely defined by the climate, farmers' choices of inputs, outputs, and methods of production reflect their expectations of upcoming temperature, precipitation, growing season, and soil moisture patterns. These are also the variables whose means and variances may change as the levels of greenhouse gases in the atmos- phere increase. A changing climate then, could rearrange the map of U.S. agri- culture by altering regional (and international) patterns of comparative advantage in the production of commercially important crops and livestock. Recently, economists have started to consider the potential costs and benefits relating to agriculture from possible climate change (Dudek, 1989; Adams et al., i In descending order, the hottest years on record are 1990, 1991, 1988, 1983, 1987, 1944, 1989, and 1981. Climatic Change 23: 1-20, 1993. © 1993 Kluwer Academic Publishers. Printed in the Netherlands.

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FARM P R O G R A M S AND CLIMATE CHANGE

J. K. L E W A N D R O W S K I 1 and R. J. B R A Z E E 2

1Resources and Technology Division, Economic Research Service, United States Department of Agriculture, 1301 New York Avenue NW, Washington, DC 20005, US.A. 2Department of Forestry, University of Illinois at Urbana-Champaign, 1301 W. Gregory Drive, Urbana, IL 61820, U.S.A.

Abstract. The view that the agricultural sector could largely offset any negative impacts of climate change by altering production practices assumes the govern- ment will not create disincentives for farmers to adapt. U.S. farm programs, how- ever, often discourage such obvious adaptations as switching crops, investing in water conserving technologies, and entry or exit. We outline a simple portfolio model describing producer decision making: we then use this framework to assess how specific U.S. farm programs might affect adaption to climate change. Three future climate scenarios are considered and in each the present structure of U.S. farm programs discourages adaptation.

Farm Programs and Adapting to Climate Change

The announcement by NASA that 1991 was the second hottest year in over a century should intensify the climate change debate. Se, ven of the 8 warmest years on record have now occurred since 1981.1 Also motivating the climate change debate are several recent dry weather events. Much of the Western United States has experienced a persistent drought for over 5 years (since 1987); the northern hemisphere had its lowest recorded snow fall in 1990; and in 1988, drought condi- tions in the West, Southeast, Northern Plains, and Midwest combined to cause the worst U.S. agricultural losses in decades.

While few scientists are willing to conclude that these historically high tempera- tures and extended dry periods are related to the greenhouse effect, the possibility of pending climate change should be of concern to agriculture. Because agriculture is largely defined by the climate, farmers' choices of inputs, outputs, and methods of production reflect their expectations of upcoming temperature, precipitation, growing season, and soil moisture patterns. These are also the variables whose means and variances may change as the levels of greenhouse gases in the atmos- phere increase. A changing climate then, could rearrange the map of U.S. agri- culture by altering regional (and international) patterns of comparative advantage in the production of commercially important crops and livestock.

Recently, economists have started to consider the potential costs and benefits relating to agriculture from possible climate change (Dudek, 1989; Adams et al.,

i In descending order, the hottest years on record are 1990, 1991, 1988, 1983, 1987, 1944, 1989, and 1981.

Climatic Change 23: 1-20, 1993. © 1993 Kluwer Academic Publishers. Printed in the Netherlands.

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2 J.K. Lewandrowski and R. J. Brazee

1988; Arthur and Abizadeh, 1988; Kane et al., 1992). For U.S. agriculture, the general conclusion has been that there may be important regional impacts but, in aggregate, the effects will probably be quite small. To date, however, adequate con- sideration has not been given to the role farm programs might play in shaping agri- culture's response to climate change (Lewandrowski and Brazee, 1991).

Farm programs affect agricultural markets by increasing the expected returns to and/or lowering the risks associated with a subset of the farmer's production deci- sions. By encouraging or discouraging farm sector adaptations to new environ- mental conditions these programs could greatly impact the economics of climate change. For example, in much of the United States hotter and/or drier summers might favor investments in water conserving technologies (e.g., more water efficient methods of irrigation and minimum tillage equipment) and/or shifts to crops with more resistance to heat and drought. Farmers, however, would have little incentive to make these adaptations if government programs subsidize irrigation water (as now happens on 10 million acres in the western United States), support the prices of crops less suited to the new environment, and/or provide disaster assistance when crops fail. The potential exists then, for farm programs to influence both the nature and the cost of agriculture's response to climate change.

In this paper we discuss how farm programs might affect adaptation to climate change. The topic is important for two reasons. First, the view that the agricultural sector could largely offset any negative impacts of climate change by altering pro- duction practices assumes the government will not create significant disincentives for farmers to adapt. Current programs, however, often discourage such obvious adaptations as switching crops and investing in water conserving technologies. Second, farm programs have proven to be very durable. Many of today's programs are descendants of the Agricultural Adjustment Acts of 1933 and 1938, and the Agricultural Act of 1949. It is thus possible that programs similar to today's will exist when the effects of climate change become apparent (2 to 3 decades by most estimates).

Discouraging farm sector adaptation to climate change could be costly. Table I details support program expenditures (in 1985 dollars) for the period 1982-1990. 2 The high outlays for commodity purchases and storage in 1983, 1984, 1987, and 1988, reflect the costs of removing large quantities of the previous crop years' pro- duction from the market. Very poor harvests can also be expensive. In fiscal year 1989, the government spent over $3.1 billion in disaster payments to help farmers cover losses related to the 1988 drought. Under several future climate scenarios popular in the current literature, U.S. agricultural production is likely to be subject

2 Except where noted, the monetary values presented in this paper reflect constant 1985 dollars. Dollar figures obtained from other sources were converted to 1985 equivalents using the U.S. pro- ducer price index given in the February 1992 issue of the International Monetary Fund's International Financial Statistics (Vol. XLV, No. 2). This index is reproduced for the years 1982-90 at the bottom of Table I.

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Farm Programs and Climate Change 3

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4 J.K. Lewandrowski and R. J. Brazee

to more frequent booms and/or busts. Climate change then, has the potential to greatly affect the costs of farm programs, particularly if their present structure is maintained.

Our paper focuses on the existing set of U.S. farm programs. As evidenced by a number of market-oriented changes implemented in 1990, some modifications in the farm programs are to be expected over the next 20 to 30 years. Here, we do not attempt to predict the form of any such changes. By focusing on how today's pro- grams might affect adaptation to climate change we suggest areas where changes in the farm programs might be worth considering. As background, we first provide a brief overview of farm programs and a summary of the uncertainties that make it impossible to predict how climate change will affect agriculture. Next we outline a simple portfolio model describing producer decision-making; we then use this framework to illustrate how specific farm programs might affect adaption to climate change. In the summary we discuss program modifications that might help promote adaptation.

U.S. Farm Programs -- An Overview

The primary goal of U.S. farm policy is to maintain farm incomes and stabilize commodity prices. Other important goals include ensuring adequate supplies of agricultural products, promoting U.S. farm exports, and reducing the negative environmental impacts of agriculture. In terms of government expenditures and costs to society, the most important elements of farm policy are the commodity programs. These programs emphasize price and/or income supporting interven- tions in specific commodity markets. The Department of Agriculture (USDA) must, by law, operate support programs for wheat, corn, sorghum, oats, barley, rye, cotton, rice, peanuts, tobacco, sugar, soybeans, milk, wool, mohair, and honey. The primary means of providing support are nonrecourse loans and commodity pur- chases; target prices with deficiency payments and acreage restrictions are also common? Production quotas, import quotas, and tariffs are used less often but are important to specific programs. A more complete description of the support pro- grams is given in Table II which lists the required and optional intervention tools available for selected commodities. Typically, upper and lower limits on the use of the required tools are legislated. Within these guidelines, however, the degree of intervention exercised is largely up to the Secretary of Agriculture.

The usual justification for the commodity programs is that society receives large

3 NorLrecourse loans enable farmers to hold crops for later sale. The commodities act as collateral and farmers can repay the loans in cash or by forfeiting the crops to the Government. The 'loan rate' is the price per unit (bushel, bale, etc.) at which the Government provides the loan. Currently, the loan rate is calculated as 85% of the average market price over the last five years, (usually) dropping the high and low years. A target price is a mark set by law. When the market price falls below the target, program participants receive a deficiency payment equal to the difference between the target and the market price or the loan rate (whichever is less).

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Farm Programs and Climate Change

TABLE II: Required (R) and Optional (O) policy tools of selected farm programs

Dairy: purchases and government held stocks of processed milk products (R), export incen- tive program (R), dairy indemnity plan (insures producers against losses due to contamination) (O)

Wheat: nonrecourse loans and government held stocks (R), target prices and deficiency pay- ments (R), national and farm program acreage limits (R), acreage reduction programs (O), marketing loans (O), disaster payments (O)

Feed Grains (Corn, Barley, Oats, Sorghum, and Rye):

nonrecourse loans and government held stocks (R), target prices and deficiency payments (required for corn, oats, barley, and sorghum; not available for rye), national and farm program acreage limits (R), marketing loans (O), acreage reduc- tion programs (O), disaster payments (o)

Tobacco: production quotas (R)

Rice: nonrecourse loans and government held stocks (R), target prices and deficiency pay- ments (R), marketing loans (R), national and farm program acreage limits (R), acreage reduction programs (O), disaster payments (o)

Soybeans: nonrecourse loans and government held stocks (R), marketing loans (O) disaster pay- ments (O)

Sugar (beet and cane): nonrecourse loans and government held stocks (R), import quotas (R), processor allotments (R), disaster payments (O)

Cotton (upland): nonrecourse loans and government held stocks (R), target prices and deficiency pay- ments (R), national and farm program acre- age limits (R), marketing loans (R), market- ing certificates (R), acreage reduction pro- grams (O), import quotas (O), disaster pay- ments (O)

Honey: nonrecourse loans and government held stocks (R), Marketing loans (O)

Source: Pollack, S. L. and Lynch, L. (1991), 'Provisions of the Food, Agriculture, Conservation, and Trade Act of 1990', USDA, ERS, AIB No. 624.

non-market benefits from a stable supply of high quality agricultural products. The opportunity cost of these benefits, however, is also large. Table ! shows that between 1982 and 1990, annual support program and related expenditures ranged between $15.2 and $41.3 billion; the average was $28.1 billion. Not included in Table I are the consumer costs of the quota on imported sugar and the quota/tariff system on imported peanuts. Existing estimates suggest these costs are on the order of S1.8 to $2.7 billion a year for sugar (Babcock and Schmitz, 1986; Cotmcil of Economic Advisors, 1986; Borrel et al., 1987) and S200 million a year for peanuts (Crowder et al., 1990).

In addition to the commodity programs, agriculture is assisted by myriad other government programs. The USDA extends or guarantees operating loans to many farmers who cannot obtain private financing, offers crop insurance and other forms of disaster assistance at below market rates, provides numerous marketing services (e.g. inspection, grading, and commodity promotion programs), funds a national network of agricultural research and extension, and guarantees loans to some credit risky foreign countries to buy U.S. farm products. The combined

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6 J.K. Lewandrowski and R. J. Brazee

annual cost of these programs generally ranges between S 5 and S 7 billion (U.S. OMB, 1983-1992).

In the Western United States, the Department of Interior provides many farmers with subsidized irrigation water and low cost use of public lands for crops and livestock. The true costs of these subsidies are hidden because they are incurred largely as foregone government revenues. For example, the government generally charges farmers less than $40.53 per 1000 m 3 ($50 per acre foot) for water from federal projects; often the prices are as low as S 1.62-$4.05 per 1000 m 3 ($2-$5 per a c r e foo t ) . 4 Recent studies suggest that much of this water is priced well below its market value (Moore, 1991). This is particularly true near large urban areas (e.g., Southern California, Denver, Salt Lake City, and Sparks-Reno), where water rights have recently traded at prices between $40.53 and $251.31 per 1000 m 3 (between S 50 and S 310 per acre foot) per year.

By altering the expected returns to and/or risks associated with specific produc- tion decisions, government farm programs greatly affect the behavior of U.S. agri- cultural producers. One implication is that farm programs could either promote or hinder farm sector adaptations to shifts in regional climate patterns. Before con- sidering how farm programs might affect such adaptations, we first discuss the uncertainties facing U.S. agriculture related to climate change.

Climate Change Uncertainties

At present, it is impossible to say what form climate change will take or what effects it will have on U.S. agriculture. In order to better understand the range of potential farm sector effects, we review the sources of climate change uncertainty facing U.S. agriculture. 5

The most important source of uncertainty concerns the types of climate effects that will result from the accumulating levels of greenhouse gases in the atmosphere. These effects can only be guessed at and today's predictions are not expected to be very accurate. Recent attempts to estimate the potential impacts of climate change on U.S. agriculture have, for the most part, been based on future climate scenarios produced by general circulation models (GCMs) (Adams et al., 1988; Dudek,1989; Rosenzweig, 1985; Wilks, 1988; U.S. EPA, 1989). 6 At present, however, GCMs are

4 The prices paid by farmers for water from federal projects in the Western United States are set by long-term contracts and do not change from year to year. The prices described here refer to these contract prices. 5 We do not discuss uncertainties related to the timing and speed of climate change. Most studies con- clude, or assume, that the effects of climate change will appear gradually starting in 20 to 50 years. Another possibility is that the environment has some threshold for greenhouse gases and once this level is passed the effects will show up quite rapidly. 6 Commonly cited GCMs include the GISS model (Goddard Institute for Space Studies), the GFDL model (Geophysical Fluid Dynamics Laboratory), the OSU model (Oregon State University), the NCAR model (National Center for Atmospheric Research), and the UKMO model (United Kingdom Meteorological Office).

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Farm Programs and Climate Change 7

not considered to be reliable. One problem is that the roles of several key climate shaping forces (specifically ocean currents, evapotranspiration, regional cloud cover, and various hydrological processes) must be greatly simplified in GCM simulations. Additionally, GCM simulation results often disagree with reality. Globally, the models tend to over-predict mean surface temperatures (suggesting a warming trend should already be observable); regionally, the simulated weather patterns often disagree with those that are observed. Finally, regional climate change forecasts often vary widely among the different GCMs (Hansen et al., 1988; Hansen, 1989; Hillel and Rosenzweig, 1989).

Their shortcomings aside, GCMs have been popular for two reasons. First, there are few alternatives for modeling the longrun climate effects of today's greenhouse gas emissions. Second, the predictions of the different GCM's do become more consistent over larger areas and longer time horizons. In the middle latitudes, which include the contiguous United States, the different models tend to agree that within 30-50 years mean surface temperatures will be hotter, annual precipitation levels will be higher, summers will be drier, and droughts will be more frequent (Hansen, 1989).

The well documented increase of greenhouse gases in the atmopshere, the logic of some warming given current emissions trends, and disagreement concerning the validity of GCM simulations has resulted in three benchmark scenarios concerning future temperature and precipitation levels. With respect to current climate, these are: (1) no change; (2) moderate increases in the means; and (3) moderate increases in both the means and variances.

Inaccurate predictions concerning what effects today's greenhouse gas emissions will have on future climate conditions is but one source of climate change uncer- tainty facing U.S. agriculture. Even assuming one of the benchmark scenarios is basically correct, the farm sector impacts are unclear. Several of the more popular predictions appear to have both positive and negative implications for agricultural production. Higher levels of atmospheric CO2, for example, would promote photo- synthesis in some plants and increase water use efficiency in others. Hence, many crops would either grow quicker or need less irrigation. On the other hand, the higher CO2 levels might also promote weed growth. Increased precipitation would be a benefit if the extra water is delivered evenly throughout the growing season; again, irrigation requirements might be reduced and dryland farming expanded. But if the extra rainfall comes in the form of severe spring storms it could kill seed- lings and/or delay in planting dates. Similarly, the aggregate impact of higher tem- peratures is ambiguous. In the northern United States, longer growing seasons would expand production possibilities and, in some areas, might allow for multiple plantings. In the South the effect would probably be negative as there would be more days with temperatures above critical plant tolerance levels. Hotter summers might also increase irrigation requirements.

More important for agriculture than the individual components of climate change are their interactions with each other, and with a host of related environ-

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8 J.K. Lewandrowski and R. J. Brazee

mental processes. Again, the net impacts are unclear. For example, water increases the heat tolerance of many crops. Hence, in areas of the South that have ample water, the negative impacts of hotter temperatures could be offset with more irriga- tion. Higher temperatures would probably expand the ranges of many insect and disease pests; likely candidates include the potato leafhopper, the corn earworm, rift valley fever and African swine fever (U.S. EPA, 1989). Higher levels of atmos- pheric CO2 combined with more precipitation during the growing season would improve conditions for many crops. The same factors, however, could also promote weed growth and increase the demand for soil nutrients. The interactions described above suggest commercial farming could become more dependent on chemical pesticides and fertilizers.

Finally, U.S. agriculture faces uncertainties about the impacts of climate change on farm output worldwide. Several GCMs predict that the effects of cfimate change will be minimal in Argentina, Brazil, and Australia; in Canada the impacts are less clear but some research suggests that the net effects could be favorable to agri- culture (Rosenzweig, 1985; Arthur and Abizadeh, 1988). These countries are major suppliers in important world commodity markets. If crop production costs in the United States rise relative to these countries, U.S. farmers will become less competitive. If so, the conclusion that U.S. farmers will be little affected by climate change may hinge on an expanded use of production subsidies and/or restrictions on foreign agricultural products.

The inability to accurately predict the effects of climate change on U.S. agri- culture greatly limits the usefulness of assuming one future scenario or one set of hypothetical effects. Instead, we focus on how the tools of today's farm programs affect producer decision-making. This allows us to outline how government pro- grams might affect farm sector response to each of the benchmark climate change scenarios.

Farm Programs and Adaption to Climate Change

It is widely believed that the U.S. farm sector could do much to adapt to all but the most pessimistic climate change scenarios (Hillel and Rosenzweig, 1989; Easter- ling et al., 1989; and Rawlins, 1989). 7 Much of this optimism assumes shifts to alternative crops where new climates no longer favor traditional outputs, invest- ments in water conserving technologies where summers become hotter and/or drier, and entry to (exit from) agriculture where growing conditions significantly improve (deteriorate). Whether or not farmers view such adaptations as economi- cally rational, however, could well depend on the structure of farm programs.

7 Ward et al. (1989) present the opposing view. These authors stress the poor quality of soils where agriculture is expected to expand relative to where it is expected to contract. They also question opti- mistic assumptions about future rates of technological change and the availabihty of irrigation water. Finally, they note the huge outlays that would be required for water efficient irrigation equipment and infrastructure relocation.

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Farm Programs and Climate Change 9

To analyze how farm programs might affect the degree and speed of adaptation to climate change, we use an investment portfolio model to describe farm produc- tion decisions. Farmers start with a set of resources (or level of wealth) which they invest among available assets to maximize utility. If farmers are risk averse, utility increases with expected returns and decreases with the riskiness of expected returns. This implies a trade-off between higher returning assets with more risk, and lower returning assets with less risk. More important than any one asset's expected return and risk are those associated with the entire portfolio. Hence, a risk averse investor may choose an asset that is itself very risky, if it reduces overall portfolio risk. Insurance, for example, generally returns nothing but returns big exactly when other investments in the portfolio fail. Buyers of insurance accept a lower expected return on their portfolio in exchange for a reduction in the proba- bility of a very poor return. Whether an investor chooses a risky portfolio with a high expected return or a relatively safe portfolio with a lower expected return will depend on his/her attitude toward risk.

We represent the risk associated with any given asset by the variance of its return. Farm related investments include various crops, durable equipment, and insurance against crop failure. A financial asset is assumed to represent the best non-farm investment. For simplicity, we assume that farmers know the expected returns and the variance of returns for each asset.

The mechanics of optimizing portfolio models are well known and can be found elsewhere (Fama, 1976; Robison and Barry, 1987). We summarize the process here in only enough detail to illustrate how farm programs might affect the resource allocation decisions of farmers given a changing climate. Letting E (R) denote the expected return from the entire portfolio, E ( r i ) the expected return from asset i, x i

the wealth allocated to asset i, o the variance of the entire portfolio, oi, / the vari- ance/covariance of returns between assets i and j, and N the number of assets in the portfolio, the expected return to and variance of the portfolio are, respectively:

N

E ( R ) = ~, x i E ( r i ) , (1) i=1

and N N

02 = E ~ XiX]~Tij. ( 2 ) i - 1 j = l

A simple way to incorporate risk into the farmer's utility maximization problem is to make both the expected return of the portfolio, and the risk of the expected return, explicit arguments of the utility function. A tractable form for the utility function is one in which the weights for expected return and risk remain constant:S

s Although this is an important restriction for some classes of problems, it is probably unimportant here. That is, for the range of most farm decisions in the U.S., constant marginal utilities is a reasonable assumption.

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10 J.K. Lewandrowski and R. J. Brazee

V ( E ( R ) , = a E ( R ) - f t .2 . (3)

The utility function is maximized with respect to the xi's subject to a wealth con- straint:

N

Z x i - W = O , (4) i=1

where Wis initial wealth. The Kuhn-Tucker Theorem can be used to determine the optimal levels of the

xi's. The decision rule that emerges is the marginal utility of the expected return from investment i should equal the marginal disutility of the risk from investment plus the marginal value of a dollar in alternative investments. Standard comparative statics methodology (Samuelson, 1947; Silberberg, 1978) can be used to evaluate the impacts of changing a parameter such as the E(ri)'s and %'s on the choice variables, x[s. Details of the maximization and comparative statics procedures are illustrated in the appendix.

The analysis suggests three decision rules for the mix of assets in a farmer's port- folio. More resources will be allocated to producing crop i when: (1) the expected returns to crop or investment i (E (ri)) increase relative to other investments; (2) the risks associated with producing crop or investment i (o~, i) decrease relative to other investments; and/or (3) the covariance between the returns to crop or invest- ment i and the returns to other assets in the portfolio (oi, j) decrease, i.e. if positive the covariance becomes smaller, or if negative the covariance becomes larger. This last result for the covariance also requires that less than one-half of the farmer's resources are devoted to crop or investment i. Changes in both the environment and farm programs can impact farm decisions. One way to assess how government programs might influence farm sector adaptation to climate change (i.e., changes in the xi's ) is to consider how these programs and a changing climate would jointly impact the E (ri)'s, oi, i's, and o~,/s of the model.

Table III groups the tools of farm policy on the basis of how they would affect Equations (1) and (2). The largest group of tools focus on keeping the expected returns to various agricultural investments above their free market levels. This is done through interventions that either reduce the costs of production (e.g., irriga- tion subsidies or marketing loans) or restrict market supply (e.g., acreage limits, import restrictions, and production quota).

A second group of tools targets both the expected returns and the variance of returns to producing certain commodities. Nonrecourse loans with government commodity purchases, and, target prices with deficiency payments, guarantee pro- ducers a minimum price; their effect then, is to put a lower limit on E(r~) and decrease o~, ~. Of these tools, target prices with deficiency payments have more potential for affecting farm sector response to climate change. Target prices are generally set above the levels that evolve in the market while nonrecourse loan rates are usually less than market prices. Hence, participants in programs with target

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Farm Programs and Climate Change

TABLE III: Grouping farm policy tools by primary effect

11

A. Increase expected returns, E(ri), to agricultural investments: By increasing output price:

Acreage restrictions (program and farm) Acreage reduction programs Production/marketing quotas Import restrictions Export programs

By reducing production costs: Subsidized water sales Agricultural research and extension Marketing loans Subsidized credit (other than crop insurance)

B. Reduce variance (risk) of returns, oi, ~, to agricultural investments: Nonrecourse loans and government purchases of surplus production Target prices and deficiency payments

C. Provide farmers with assets whose covariance of returns with agricultural investments, a~,j, is strongly negative: Disaster payments Crop insurance Milk indemnity program

prices and deficiency payments would have less incentive to switch to new crops than participants in programs where nonrecourse loans are the principal method of support.

The third group of policy tools provides farmers with assets that are negatively correlated with agricultural production and so focus on the cri,/s (? ¢ j). This group of tools has been the least expensive to operate in aggregate but their cost could increase significantly if climate change increases the frequency of poor harvests. The magnitude of the possible cost increase is suggested by the S 3.1 bil- lion in disaster payments related to the 1988 drought and a recent rise in the use of crop insurance. For crop years 1981 through 1989, the USDA generally had between $6 and $7 billion of crop insurance in force; for the 1989 and 1990 crop years there were, respectively, S 12.2 and S 11.4 billion in force (US OMB, 1983- 92).

To examine how farm programs might affect agriculture's response to climate change, we consider the three benchmark scenarios, and the adaptations that would be encouraged with and without today's programs. Scenario 1 simply in- creases the level of atmospheric carbon. The additional carbon would enhance the growth rates of crops in the C 3 plant group (e.g., wheat, rice, soybeans, legumes, and root crops) and might reduce the water requirements of crops in the C4 plant group (e.g., corn, sorghum, sugarcane) (Hillel and Rosenzweig, 1989; Rosenberg, 1989). Scenario 1, then, would favor farm production with the most likely result being an increase in the frequency of very good harvests (at least for C3 crops).

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12 J.K. Lewandrowski and R. J. Brazee

More frequent bumper crops would increase the occurrence of years with low commodity prices and could, over time, lower the expected returns to farm invest- ments. The market response would be to force higher cost producers to exit agri- culture. Today's farm programs would work against much of this adjustment. Acreage reductions programs could ease the downward pressures on prices for wheat, feed grains (corn, barley, sorghum, oats, and rye), cotton and rice. Tighter import quotas on sugar and smaller production quotas for tobacco and peanuts could have similar effects on the prices of these crops. Nonrecourse loans would establish a minimum output price for participants in the rye, soybean, peanut, tobacco, sugar, and honey programs; in the framework of our model, the effects would be to put a lower limit on E (ri) and reduce ~i, i. For qualifying acreage in the wheat, corn, cotton, rice, oats, barley, and sorghum programs, minimum output prices would be set by target prices with deficiency payments (because target prices are greater than loan rates).

Given benchmark scenario 1, today's commodity programs would allow many producers to stay in agriculture who would otherwise be forced to exit. The reduced exit rate combined with the better growing conditions would increase the probability of the government acquiring large stocks of commodities. The cost implications are significant. Table I shows that for the period 1982-90, support program expenditures were 1.5 to 2 times higher in years with large bumper crops (compare FY1983, FY1986, FY1987, and FY1988 with the other fiscal years listed).

In addition to raising the concentration of atmospheric carbon, benchmark scenario 2 increases the mean temperature and precipitation levels. Crop responses are likely to vary from one region to another. In the northern states, warmer aver- age temperatures will lengthen growing seasons and expand cropping choices. Producers of orchard crops might also benefit from a reduction in the occurrence of spring frosts. In the central and southern states, production possibilities will probably decrease as there would be more days with temperatures above the criti- cal tolerance levels of crops in the present output mix.

In many areas of the United States, a small increase in average summer tempera- tures could significantly increase the variability of returns for important commer- cial crops. Mearns et al. (1984) have estimated that a 1.7 °C rise in the mean July temperature (variability held constant) in Des Moines, Iowa, would raise the proba- bility of observing 5 consecutive days with temperatures above 35 °C (95 °F) from a current level of 6% to 21%. Heat waves can greatly reduce the yields of some crops if they occur at key stages of plant development (e.g., the tasseling period for corn and during grain filling for wheat). Hence, even a modest increase in average summer temperatures would encourage some northward shifts in the production of heat sensitive crops (e.g., wheat and corn) and more intense production of heat resistant crops (e.g., sorghum and soybeans) in the South.

In addition to regional shifts in crop production, scenario 2 would favor invest- ments in irrigation equipment. Water increases the heat tolerance of many crops so

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farmers could offset some of the effects of warmer temperatures by expanding irri- gation. At the same time, however, irrigation water is likely to become dearer. Warmer temperatures could promote summer drying (through higher evaporation rates) and boost nonfarm water demand (e.g., for additional lawn watering, land- scaping, water recreation, and personal hygiene). Where irrigation water becomes scarcer, the market would encourage investments in water conserving technologies. Allowing for the above adaptations, and other adjustments such as shifting growing seasons and using different cultivars, scenario 2 seems likely to favor agricultural production. Probable effects include more frequent bumper crops, an expansion of agricultural land in the North, and some exit from farming in the South.

As in scenario 1, imposing today's farm programs on scenario 2 reduces the incentives facing farmers to adapt to climate change. The structure of the com- modity programs strongly discourages participants from switching to new crops. For most program crops, a farm's acreage allotment is based on the average acreage it has planted in the crop over the last few years. Since it takes time to build pro- gram acreage in new crops, and because there is a multiyear penalty for reducing any one year's program acreage, switching crops generally lowers a farmer's expect- ed returns to agricultural investments.

Also diminishing the incentives of some producers to switch crops, are the different levels of support provided by the various commodity programs. Note that most of the tools in Table III are commodity specific. That is, they affect the expect- ed returns to and/or risks associated with producing specific commodities. The dairy and 'basic' crop (wheat, corn, rice, sugar, peanuts, and tobacco) programs have the highest levels of support. For participants in these programs, much of any climate change related decrease in the expected returns to agricultural investments would be mitigated. They would thus have less incentive to switch to alternative crops than nonprogram participants or producers of less supported crops.

The combination of current output mixes, more frequent heat waves, and pos- sibly drier summers, would increase the probability of incurring large crop losses for farmers in some regions, particularly the South. Today's farm programs, how- ever, would allow producers to pass much of this risk on to society. Through subsi- dized crop insurance and disaster payments, the government provides farmers with assets that pay when their crops fail; in the framework of our model, the returns on these assets are very negatively correlated with the returns on crops. If farmers believe government programs will provide a minimal level of income when their crops are lost, they will have little incentive to switch to less profitable but more heat resistant crops.

Finally, the structure of federal irrigation subsidies discourages many farmers in the western United States from investing in water extending technologies. Water is a very scarce resource in much of the West; some urban areas are already willing to pay S162.14-$243.20 per 1000 m 3 ($200-$300 per acre foot) above what the government charges agriculture. This price would certainly increase if summers in the West become longer, hotter, and/or drier. At the same time, agriculture's water

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14 J.K. Lewandrowski and R. J. Brazee

needs could be greatly reduced by the use of more water efficient irrigation tech- nologies. Most recipients of irrigation water from federal projects, however, have little incentive to consider the value of the water in other uses. In general, farmers pay less than $40.53 per 1000 m 3 for their water allotments but institutions bar them from selling the water for a profit or transferring it to nonagricultural users. Hence, only when water allotments are not adequate for crop production given existing irrigation methods do most farmers have an incentive to invest in more water efficient systems.

The above discussion suggests that agriculture's response to benchmark scenario 2, would look very different with and without today's farm programs. Without pro- grams, we might expect some regional shifts in crop production, some regional entry and exit, investments in water conserving technologies, and harvests that would probably be better, on average, than at present. Imposing today's farm pro- grams on scenario 2 implies much less change in regional production patterns, allows more high cost producers to stay in agriculture, and decreases investments in water conserving technologies. Depending largely on the timing of summer heat waves, harvests are likely to be either very good or very poor. Again, the implica- tions for program costs are significant. Relative to today there would be a greater probability of incurring the program costs associated with both large surpluses and large crop losses.

Benchmark scenario 3 adds more variability in both temperature and precipita- tion to scenario 2. Relative to scenario 2, much of the United States becomes more prone to severe weather events (e.g., droughts, heat waves, and severe storms). As a result, scenario 3 has the greatest probability of observing years with large crop losses. Still, in years when severe weather events do not occur, growing conditions would be very good. Relative to today, but not scenarios 1 or 2, there could also be an increase in the frequency of very good harvests.

Without today's farm programs, agriculture's response to scenario 3 would be similar to its response to scenario 2, but more pronounced. Relative to scenario 2, the market would encourage larger shifts of heat sensitive crops to northern regions, increased plantings of heat resistant crops in the South, additional invest- ments in water efficient irrigation systems and other water extending technologies, and more exit from agriculture in the South.

In a manner similar to scenario 2, today's farm programs reduce the attractive- ness of altering current production practices in response to changing climate condi- tions. The increased probability of incurring large crop losses (due to both the more variable weather patterns and the program disincentives to adapt production practices), however, would encourage actions that ensure some minimal level of income. Hence, we might expect to see more use of government crop insurance and disaster payments as farmers act to protect themselves from very poor harvests. Because disaster assistance would be needed more frequently and would cover more producers, farm program costs would likely be much higher in poor years compared with the past.

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Summary and Conclusions

We have presented an overview of how farm programs might affect farm sector adaptation to climate change. In each of the three benchmark scenarios considered, today's programs offer substantial opportunities for transferring much of the potential farm sector costs to society. By supporting the expected returns to specific agricultural investments and shielding producers from the risks associated with large surpluses and/or large crop losses, today's farm programs would dis- courage farm sector adaptation to climate change. The high costs of farm programs in previous bumper crop years, the availability of government crop insurance, and S 3.1 billion in disaster assistance related to the 1988 drought, suggest society could pay a high price for reducing farm incentives to adapt to climate change.

At the same time, the numerous and important uncertainties concerning the impacts of climate change on U.S. agriculture argue against pursuing expensive mitigation or adaptation strategies at present. Most published studies estimate that it will be 2 to 3 decades before the effects of climate change are apparent; almost any farm sector adaptation one can imagine could be accomplished within five years. Still, there are modifications to today's farm programs that might be worth considering which would facilitate adjustments to climate change. These modifica- tions either have more immediate justifications or would be inexpensive to imple- ment.

One program modification with immediate benefits would be to expand the flexibility provisions implemented in 1990. Currently, producers of wheat, feed grains, (upland) cotton, and rice are paid deficiency payments based on 85% of their program (or base) acreage. Output from the remaining 15% is not eligible for deficiency payments but this land may be planted to other crops. The effect is to reduce the level of program support without affecting the calculation of a farmer's program acreage. Producers may choose to plant an additional 10% of their pro- gram acreage (for a total of 25%) in alternative crops with the same consequences. Flexibility, as proposed here, would remove all the output restrictions of the com- modity programs and allow farmers to choose from a set of crops without directly affecting their level of program support. The immediate benefits include possible groundwater protection and promoting crop rotations (McCormick and Algozin, 1989). Should climate change alter regional comparative advantages in crop pro- duction, flexibility would encourage farmers to choose the program crop(s) best suited to their new climates.

Another program adjustment worth considering is the implementation of poli- cies that promote water conservation in agriculture. Two policies that have the potential to produce immediate social benefits are the removal of institutional barriers to the development of water markets in the West, and promoting farmer adoption of water efficient production technologies in general. Removing the insti- tutional barriers to the development of markets for water from federal projects in the West would greatly facilitate the flow of this water to its highest value use. By

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16 J.K. Lewandrowski and R. J. Brazee

allowing farmers to sell their allocations to higher value bidders, water markets would also provide farmers with the resources and the incentive to invest in more water efficient methods of irrigation. In addition to urban areas in the West, there may be other regions where promoting water conservation in agriculture would be socially desirable (e.g., reducing withdrawals from the Ogallala aquifer in the Southern Plains). In these cases public policies, such as tax breaks or cost share programs, could be used to encourage investments in more water efficient irriga- tion technologies.

The high cost of water efficient irrigation technologies make these systems unlikely investments for farmers who have access to adequate water supplies. Where irrigation water is publicly subsidized, where withdrawals exceed natural replacement, or where water has alternative recreational or environmental uses, the social benefits of reducing farm water use could justify programs that help farmers acquire more water efficient irrigation equipment. Additionally, farmers would be more able to adjust to climate change should it take the form of hotter and/or drier growing seasons.

Finally, disaster assistance payments could be tied to a moving average of yields over the past few growing seasons. This is similar to past disaster assistance pro- grams with one important distinction. Disaster payments to date have been based on various measures of 'normal' production (e.g., average yields for the county/ state or program area planted). These programs, however, have been designed to reduce farm sector losses from specific severe weather events (e.g., the 1988 drought). The Disaster Assistance Acts of 1988 and 1989 use pretty much the same definitions of 'normal' production but the measures used in the 1989 Act do not include poor 1988 harvests. The effect then, is to define measures of 'normal' production that are based on recent yields but which omit very poor yields. If the effects of climate change show up gradually then, in any given region, the growing conditions for the present mix of crops could deteriorate slowly. Relative to today, we might observe a series of crop failures before recognizing that the cause was climate change. The suggested modification would act as a check against making a series of disaster payments when, in fact, yields are aver- age given the new environmental conditions. Additionally, the change would be inexpensive to implement and would have no effect if the climate remained constant.

As a parting thought, we offer the possibility that agriculture might respond to climate change by pursuing more government intervention. For example, if climate change alters international patterns of comparative advantage in the production of important commercial crops, U.S. farmers could become less competitive. As with sugar today, we may be technically able to meet domestic demands but not able to do so at a lower cost than foreign producers. Rather than switching to alternative crops, investing in new technologies, and entry or exit, farm groups might pursue restricting agricultural imports as a cost effective adaptation strategy. While this might greatly reduce the farm sector impacts of climate change, it would do so by

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increasing consumer food prices. The gains to farmers then, would come at the expense of imposing much larger losses on society in general.

Acknowledgements

The views expressed are the authors and do not necessarily represent the views of the Economic Research Service, the United States Department of Agriculture or the University of Illinois. The authors thank John Reilly, Margo Anderson, James Tobey, and two anonymous referees for their comments and suggestions on earlier drafts. All remaining errors are our own.

References

Adams, R. M., McCarl, B. A., Dudek, D. J., and Glyer, J. D.: 1988, 'Implications of Global Climate Change for Western Agriculture', Western J. Agricult. Econom. 13 (2), 348-356.

Arthur, L. M. and Abizadeh, E: 1988, 'Potential Effects of Climate Change on Agriculture in the Prairie Region of Canada', Western J. Agricult. Econom. I3, 216-24.

Babcock, B. and Schmitz, A.: 1986, 'Look for Hidden Costs: Why Direct Subsidy Can Cost Us Less (and Benefit Us More) than a "No Cost" Trade Barrier', Choices, Fourth Quarter, 18-21.

Borrell, B., Sturgiss, R., and Wong, G.: 1987, 'US Sugar Policy: Its Effects on the World Sugar Market', Canberra, Bureau of Agricultural Economics.

Council of Economic Advisors: 1986, Economic Report of the President, pp. 137-39. Crowder, B., Davison, C., Schaub, J., and Wendland, B.: 1990, 'Soybeans and Peanuts; Background for

1990 Farm Legislation', Agric. Information Bul. No. 592, Econ. Research Serv. USDA. Dudek, D.J.: 1989, ~ssessing the Implications of Changes in Carbon Dioxide Concentrations and

Climate for Agriculture in the United States', in Agriculture, Forestry, and Global Climate Change - A Reader, Congressional Research Service, Library of Congress, prepared for the Senate Commit- tee on Agriculture, Nutrition, and Forestry, pp. 205-36.

Easterling, W. E., Crosson, E R., and Parry, M. L.: 1989, ~dapting Future Agriculture to Changes in C' in N. J. Rosenburg et al. (eds.), Greenhouse Warming: Abatement and Adaptation, Resources for the Future, Washington, DC.

Fama, E. E: 1976, Foundations of Finance, Basic Books, New York. Hansen, J. E., Fung, I., Rind, D., Lebedeff, S., Ruedy, R., and Russell, G.: 1988, 'Global Climate

Changes as Forecast by Goddard Institute for Space Studies Three-Dimensional Model', J. Geo- phys. Res. 93 (DS), 9341-9364.

Hansen, J. E.: 1989, 'Modeling Greenhouse Climate Effects', Statement to the United States Senate Committee on Commerce, Science, and Transportation, Subcommittee on Science, Technology, and Space, May 8.

Hillel, D. and Rosenzweig, C.: 1989, 'The Greenhouse Effect and Its Implications Regarding Global Agriculture', Research Bull. No. 724, Mass. Agric. Exp. Station, Amherst, 36 pp.

Kane, S., Reilly, J., and Tobey, J.: 1992, 'An Empirical Study of the Effects of Climte Change on World Agriculture', Climatic Change 21, 17-35.

Lewandrowski, J. K. and Brazee, R. J.: 1991, 'Farm Programs and Climate Change: A First Look', Pro- ceedings of Global Change: Economic Issues in Agriculture, Forestry and Natural Resources.

Mearns, L. O., Katz, R. W., and Schneider, S. H.: 1984, 'Extreme High-Temperature Events: Changes in their Probabilities with Changes in Mean Temperature', J. Clim. AppL Meteorol. 23, 1601-13.

McCormick, I., and Algozin, K. A.: 1989, 'Planting Flexibility: Implications for Groundwater Protec- tion', J. Soil Water Conserv. 44 (5), 379-83.

Moore, M. R.: 1991, 'The Bureau of Reclamation's New Mandate for Irrigation Water Conservation: Purposes and Policy Alternatives', Water Resources Res. 27 (2), 145-155.

Pollack, S. L. and Lynch, L.: 1991, 'Provisions of the Food, Agriculture, Conservation, and Trade Act of 1990" USDA, Econ. Res. Serv., AIB No. 624.

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Rawlins, S. L.: 1989, 'Strategies for Adapting Agriculture to Adapt to Climate Change', in Agriculture, Forestry, and Global Climate Change - A Reader, Congressional Research Service, Library of Congress, prepared for the Senate Committee on Agriculture, Nutrition, and Forestry, pp. 228- 236.

Robison, L. J. and Barry, R J.: 1987, The Competitive Firm's Response to Risk, MacMillan Publishing, New York.

Rosenberg, N. J.: 1989, 'Global Climate Change Holds Problems and Uncertainties for Agriculture', in Agriculture, Forestry, and Global Climate Change - A Reader, Congressional Research Service, Library of Congress, prepared for the Senate Committee on Agriculture, Nutrition, and Forestry, pp. 180-95.

Rosenzweig, c.: 1985, 'Potential CO2-Induced Climatic Change Effects on North American Wheat Producing Regions', Climatic Change 7, 367-89.

Samuelson, E: 1947, Foundations of Economic Analysis, Harvard University Press, Cambridge. Silberberg, E.: 1978, The Structure of Economics: A Mathematical Analysis, McGraw-Hill, New York. U.S. Environmental Protection Agency: 1989, 'The Potential Effects of Global Climate Change on the

United States', Report to Congress, EPA- 230 - 05 - 89 - 050. U.S. Office of Management and Budget: 1983-1992, The Budget of the United States Government, U.S.

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(Received 12 August, 1991; in revised form 23 June, 1992)

Appendix

We use a Lagrangian function to apply the Kuhn-Tucker Theorem to Equations (3) and (4):

For simplicity, we assume that all assets under consideration are in the portfolio. Substituting for e(R) and o 2 from Equations (1) and (2) provides:

. . . . 1 i = 1 i = l / = 1 i = 1

Differentiating the Lagrangian with respect to each xl, the first N first-order conditions are: N

a E ( r i ) - 2 f l ~" xjaij=~. ] - 1

The marginal utility of the expected return from investment i must equal the marginal disutility of the risk from investment i plus the marginal value of an alternative investment. The budget constraint is the N + i first-order condition.

To describe the second-order conditions for a maximum, we construct hessian matrix of second- order partial derivatives. The hessian matrix is:

- - 2 f l O ' 1 1 - - 2 f l O ' 1 2 . . . -2flolN - 1

- 2 f l o ' 2 1 - 2 f l o 2 2 . . . - - 2 f i O 2 N - - 1

- - 2 f l O N 1 - - 2 f l O N 2 . . . - - 2 f i O N N - - 1

-1 -1 ... -1 0

Sufficient second-order conditions for a maximum are that the sign of the determinant of all border- preserving principal minors of size m, m = 3 to N + 1, must be of the sign ( -1)" i (Silberberg, 1978). We assume that the second-order conditions are satisfied.

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Comparative statics results demonstrate the impacts of changing a parameter such as the E (ri)'s and o~j's on the choice variables, xi's. We derive comparative statics using standard methodology (Samuelson, 1947; Silberberg, 1978). If the second-order conditions are met, then the determinant of the hessian matrix is non-zero, and the choice variables, x/s may be defined as implicit functions of the parameters. The comparative statics results for the E(ri)'s and oi/'s are found by totally differentiating the first-order conditions with respect to the parameter of interest, and solving for the partial deriva- tive of the choice variables with respect to the parameter. The algebra may be abbreviated by the use of Cramer's Rule. To apply Cramer's Rule, we solve for the impact of changing the j t h parameter on the i th choice variable by partially differentiating the every first-order condition with respect to the j t h parameter, substituting the negative of the partial derivatives into the i th column, and then finding the ratio the determinants of the resulting (numerator) matrix and the hessian matrix. Illustrating this pro- cess with x i and E (r i), the numerator matrix for axi/ere (r~) equals:

[ --C~--2fl012 ... --2fl~71N --1 ] 0! -- 2fiO'22 ... --2fl20"2N! 1!

| O--2flON2...--2fiONN 1 k o -1 ... -1 o

Taking the determinant of this numerator matrix provides - a multiplied by an N by N border pre- serving principal minor of the hessian matrix. From the second-order conditions, we know that the determinants of N by N border preserving principal minors and the hessian matrices are of opposite sign. We also know that a > 0 and - a < 0, which imply Oxi/OE(ri) > 0. That is, if a farmer currently invests in crop i, an increase in the expected return for crop i, should increase the investment in crop i.

The analysis is similar for (~ii, with 2t~x i replacing - a in the numerator matrix. Taking the determi- nant shows that OXi/IO0"ii < O. This implies that if a farmer currently invests in crop i, an increase in the variance of crop i should decrease the investment in crop i.

Although the process is identical for ai/, the algebra and result are more complex. 9 The algebra is more complex because the covariance enters into N first-order conditions rather than only 1 first- order condition as expected returns and the variance do. Although there are numerous combinations of changes in covariances, the simplest and probably the most interesting is the case in which the co- variance of investment i increases or decreases with respect to all other investments in the portfolio. To show this result, we calculate the determinant of the numerator matrix by first expanding along the first column, and then expanding each resulting co-factor matrix along the last column. After simplifying, we have

Z X~ - X i "Aii, k \ j = l , j e i I

where A,~ is the cofactor matrix associated with the element (i, i) of the numerator matrix. Since the A~ cofactor matrices are precisely the N by N border preserving minors, and all N by N border pre- serving minors are of the opposite sign of the hessian (denominator) matrix, the impact is determined by the size of investment i. If less than (greater than) half of the resources of the portfolio are devoted to investment i, then a decrease in the covariance with other assets increases (decreases) the optimal allocation to investment i.

Since formally demonstrating tiffs result is not only tedious but depends on the number of crops and investments; we illustrate this derivation for the case with three crops or investments. If the covariance of investment 1 with investment 2 and 3 decreases, the numerator matrix is:

" - - 2 f l ( X 2 + X 3 ) - - 2 f i 0 " 1 2 - - 2/3oa3 - 1 --2fix I - - 2 f l 0 " 2 2 - - 2 f i 0 2 3 - - 1

-2fix1 - 2fl023 - 2t3033 - 1 0 -1 -1 0

9 We are indebted to an anonymous referee for helping us clarify the covariance result.

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20 J.K. Lewandrowski and R. J. Brazee

Computing the determinant by expanding along the first column provides:

--2fl0"22 -- 2/3023 -- 1 --2fl012 -- 2/3013 -- 1 --2/3(Xz + X3) --2/3023 -- 2/3033-- 1 + 2f lX 1 --2/3023 -- 2/3033 -- 1

-1 -1 0 -1 -1 0

--2/3X 1 -2flo-12 - 2flo13 - ] -2/3o~2 - 2/3023 - 1

-1 -1 0

Expanding along the last column of each matrix provides:

-2fiXl [-2__fl;12 - 271°13 -2/3;22-271°23 ]

which simplifies to

l - 1 _1 _1

Since the sum of the determinant of the bracketed matrices equals the determinant of a border-we- serving principal minor of size N by N, by the second-order conditions it is the opposite sign of the determinant of the hessian matrix. Hence, x i should increase (decrease), when the covariance between it and other investments decreases, and is less than (greater than) the sum of the other investments.

Climatic Change January 1993