Paul L. Fackler

CONSERVING SPECIES IN A WORKING LANDSCAPE: LAND USE WITH BIOLOGICAL AND ECONOMIC OBJECTIVES

Habitat loss and fragmentation are major threats to biodiversity. Establishing formal protected areas is one means of conserving habitat, but socio-economic and political constraints limit the amount of land in such status. Addressing conservation issues on lands outside of formal protected areas is also necessary. In this paper we develop a spatially explicit model for analyzing the consequences of alternative land-use patterns on the per- sistence of various species and on market-oriented economic returns. The biological model uses habitat preferences, habitat area requirements, and dispersal ability for each species to predict the probability of persistence of that species given a land-use pattern. The eco- nomic model uses characteristics of the land unit and location to predict the value of commodity production given a land-use pattern. We use the combined biological and eco- nomic model to search for efficient land-use patterns in which the conservation outcome cannot be improved without lowering the value of commodity production. We illustrate our methods with an example that includes three alternative land uses, managed forestry, agriculture, and biological reserve (protected area), for a modeled landscape whose physical, biological, and economic characteristics are based on conditions found in the Willamette Basin in Oregon (USA). We find that a large fraction of conservation objectives can be achieved at little cost to the economic bottom line with thoughtful land-use planning. The degree of conflict between conservation and economic returns appears much less using our joint biological and economic modeling approach than using a reserve-site selection ap- proach, which assumes that species survive only inside of reserves and economic activity occurs only outside of reserves.

Identifying Monetary Impacts on Agricultural Prices in VAR Models

Since the mid-1970s, a number of studies have used vector autoregressive (VAR) models to evaluate the magnitude and timing of macroeconomic impacts on agriculture. Use of the VAR method has been viewed as a way to obtain empirical evidence about these impacts that might not emerge from more traditional overidentified and less dynamic econometric models. The hope has been that by placing minimal restrictions on VAR models, the true structure of the economic relationships under investigation can be observed. While the aim is laudable, critics of VAR models have argued that it has had the unfortunate consequence of obscuring some important structural identification issues and seeming to promise that something can be obtained for nothing (Cooley and Leroy, Leamer). They point out that identifying restrictions are required to give economically interpretable meaning to the results of any simultaneous equation model (SEM). In standard practice, identification of VAR models is obtained implicitly by choice of a Choleski decomposition of the covariance matrix of one-step-ahead forecast errors, which is equivalent to imposing a recursive structure for the economy. Of concern is that the recursive structure may not be believable. Further, as Bernanke has observed, examination of ad hoc variants of the recursive order implies a strange set of prior beliefs in which the analyst holds strongly that the system is recursive but is not sure in what order the variables should be arranged. In response to these concerns, several recent papers have pointed out that identification of VAR models does not have to rely on the assumption that the contemporaneous structure is recursive (Bernanke, Sims). Rather, the distinguishing features of a VAR model are, first, that identification rests on the assumption that distinct, mutually orthogonal behavior shocks drive the economy, and, second, that lagged relationships among the endogenous and exogenous variables (if any) are left entirely unrestricted. Given these features, a VAR model must be identified solely by restrictions placed on the contemporaneous interactions among the variables, but the identifying restrictions do not have to preclude simultaneity. In this paper, we elaborate on the structural interpretation of VAR models and use this analysis to examine monetary impacts on agricultural prices. We discuss the identification problem and the implications of a recursive structure. We illustrate our points with respect to the three-variable model of money, industrial prices, and agricultural prices that has been used in several articles to evaluate the effects of monetary shocks on price dynamics (Bessler, Devadoss and Meyers). We then introduce a somewhat richer behavioral model of the macroeconomy and its effects. A simultaneous model is shown to offer a more believable identification of monetary policy and other macroeconomic shocks than a recursive model, and monetary impacts on agricultural prices are assessed.

Calibration of Option-Based Probability Assessments in Agricultural Commodity Markets

A method for evaluating the reliability of option-based price probability assessments is developed based on the calibration concept. Empirical tests using goodness-of-fit criteria are applied to four agricultural commodities. Results suggest that assessments in the corn and live cattle markets are reliable, but such assessments overstate the volatility of soybean prices and understate the location of hog prices.

Estimating the Degree of Market Integration

Existing tests of spatial market integration are commonly based on statistical criteria without an explicit link to an economic model of price determination. This article proposes new measures of market integration defined directly in terms of a well-known spatial price determination model and develops an econometric methodology for estimating these measures. Due to the intractability of the conditional density function of prices, we use indirect inference to estimate the model parameters and market integration measures. The methodology is illustrated with simulated data and is applied to soybean price data for the United States, Brazil, and the EU. Copyright 2008, Oxford University Press.

What Causes Commodity Price Backwardation

A recently proposed explanation for futures price backwardation is examined. An equilibrium model with spatial heterogeneity leads to the interpretation of backwardations as mismeasurement by the analyst. However, the model predicts that backwardations are more affected by the location of stocks than by their aggregate level. Empirical examination of the United States corn market fails to support this result. Copyright 1999, Oxford University Press.

An adaptive-management framework for optimal control of hiking near golden eagle nests in Denali National Park.

Unintended effects of recreational activities in protected areas are of growing concern. We used an adaptive-management framework to develop guidelines for optimally managing hiking activities to maintain desired levels of territory occupancy and reproductive success of Golden Eagles (Aquila chrysaetos) in Denali National Park (Alaska, U.S.A.). The management decision was to restrict human access (hikers) to particular nesting territories to reduce disturbance. The management objective was to minimize restrictions on hikers while maintaining reproductive performance of eagles above some specified level. We based our decision analysis on predictive models of site occupancy of eagles developed using a combination of expert opinion and data collected from 93 eagle territories over 20 years. The best predictive model showed that restricting human access to eagle territories had little effect on occupancy dynamics. However, when considering important sources of uncertainty in the models, including environmental stochasticity, imperfect detection of hares on which eagles prey, and model uncertainty, restricting access of territories to hikers improved eagle reproduction substantially. An adaptive management framework such as ours may help reduce uncertainty of the effects of hiking activities on Golden Eagles.

Optimal Storage by Crop Producers

When post-harvest marketing strategies are restricted by disallowing speculative purchases, sales out of storage becomes an irreversible decision and the dynamic marketing problem becomes analogous to the optimal exercise of a financial option. The optimal marketing strategy is to hold at low prices and to sell at high prices with a cutoff price function marking the boundary between low and high prices. A method for estimating the cut-off price function is developed and applied to Illinois soybean prices. The decision rule is demonstrated to result in substantial gains from storage. Copyright 2002, Oxford University Press.

Long-Run Identifying Restrictions for an Error-Correction Model of New Zealand Money, Prices and Output

Abstract Dynamic interactions among money, prices and output are modeled for New Zealand. We find evidence of one cointegrating relationship and two stochastic trends among these series and base structural inferences on long-run identifying restrictions of the type proposed for VEC models by King, Plosser, Stock and Watson (KPSW). Under the assumption that a monetary shock has no effect on output in the long-run, a productivity shock for which there is partial monetary accommodation explains most of the variability of output at all forecast horizons, whereas the monetary shock is the most important determinant of levels of money and prices. An alternative identifying restriction that imposes a long-run impact of a monetary shock on output results in less plausible dynamic effects.

Complex decisions made simple: a primer on stochastic dynamic programming

Summary Under increasing environmental and financial constraints, ecologists are faced with making decisions about dynamic and uncertain biological systems. To do so, stochastic dynamic programming (SDP) is the most relevant tool for determining an optimal sequence of decisions over time. Despite an increasing number of applications in ecology, SDP still suffers from a lack of widespread understanding. The required mathematical and programming knowledge as well as the absence of introductory material provide plausible explanations for this. Here, we fill this gap by explaining the main concepts of SDP and providing useful guidelines to implement this technique, including R code. We illustrate each step of SDP required to derive an optimal strategy using a wildlife management problem of the French wolf population. Stochastic dynamic programming is a powerful technique to make decisions in presence of uncertainty about biological stochastic systems changing through time. We hope this review will provide an entry point into the technical literature about SDP and will improve its application in ecology.

Addressing structural and observational uncertainty in resource management

Most natural resource management and conservation problems are plagued with high levels of uncertainties, which make good decision making difficult. Although some kinds of uncertainties are easily incorporated into decision making, two types of uncertainty present more formidable difficulties. The first, structural uncertainty, represents our imperfect knowledge about how a managed system behaves. The second, observational uncertainty, arises because the state of the system must be inferred from imperfect monitoring systems. The former type of uncertainty has been addressed in ecology using Adaptive Management (AM) and the latter using the Partially Observable Markov Decision Processes (POMDP) framework. Here we present a unifying framework that extends standard POMDPs and encompasses both standard POMDPs and AM. The approach allows any system variable to be observed or not observed and uses any relevant observed variable to update beliefs about unknown variables and parameters. This extends standard AM, which only uses realizations of the state variable to update beliefs and extends standard POMDP by allowing more general stochastic dependence among the observable variables and the state variables. This framework enables both structural and observational uncertainty to be simultaneously modeled. We illustrate the features of the extended POMDP framework with an example.