Dynamic Feedback between Surface and Groundwater Systems: Implications for Conjunctive Management

A key feature of hydrologically connected surface and groundwater stocks is the two-way exchange of water between the systems. Increasing water scarcity, particularly in arid environments, has spurred debate on how to coordinate management of the two resources. In this paper, I present a model that describes the dynamic feedback loop between surface and groundwater systems when economic agents withdraw water from both for use in production. I use the model to describe optimal water extraction from both stocks and to evaluate how a conjunctive management policy shifts welfare between surface and groundwater user groups. Finally, I explore the importance of accounting for two-way feedback between the two stocks, when it exists, in estimating the benefits to a conjunctive management system. I estimate that the returns to conjunctive management in a closed system are greater than 6.5 times that in a system with an open feedback loop between water stocks.groundwater, conjunctive management, dynamic optimization, Gisser-Sanchez effect, Resource /Energy Economics and Policy,

Environmental Conservation on Agricultural Working Land: Assessing Policy Alternatives Using a Spatially Heterogeneous Land Allocation Model

Multifunctionality refers to the ability of agricultural systems to produce an array of non-market goods and services in addition to market commodities. This thesis focuses explicitly on the provision of environmental benefits, through reduced soil erosion and fertilizer applications, by agricultural producers. Soil erosion and nutrient contamination from agricultural production are the foremost contributors to ground and surface water degradation in the United States. Reducing their production implies gains in social welfare, but may generate significant private losses to producers. The objective of this analysis is to quantify the tradeoff between environmental improvements and producer welfare and to examine the extent to which public policy can influence that tradeoff. To address this objective, a land use allocation model is constructed using slope to reflect terrain heterogeneity. The model is formulated as a mathematical programming problem, with the objective of maximizing producer welfare subject to an exogenous land endowment and a series of production constraints. The model developed in this thesis differs from previous empirical models in several substantive ways. First, crop and livestock production activities are explicitly modeled as either separable or non-separable activities. The advantage to doing so is that it gives the model the flexibility to choose the optimal degree of integration between the two. The model also diverges from previous studies by incorporating a common set of variables that affect the economic and environmental aspects of commodity production. Specifically, the spatial allocation of land use practices impacts economic and environmental outcomes via a yield damage function and differentiated rates of soil erosion. These two aspects are expected to improve the model’s predictive ability. One of the primary benefits of the model is that it can be used to identify the economic factors driving landscape-level production patterns. The analysis demonstrates that the land use allocation is relatively insensitive to changes in commodity prices. Therefore, altering the level of commodity-based income support payments is insufficient to attain environmental improvements. Several hypothetical “green” policy instruments are simulated to estimate the cost to producers of reducing environmental damages. The results indicate that limiting soil erosion to an environmentally acceptable level with either a regulatory standard or a tax reduces the average return to land by ten percent. A program of green subsidy payments for less erosive land management practices cannot attain the same standard with less cost to producers. Overall, the inelastic response of land use change to commodity prices indicates that targeting the use of productive inputs, as opposed to commodity outputs, may be a more efficient means of encouraging agricultural producers to provide environmental benefits

The Role of Harvest Timing in Pest Management: Grower Response to Infestation by the California Olive Fruit Fly

Crop Production/Industries,

Pests and Agricultural Commodity Losses: Evaluating Alternative Approaches to Damage Function Estimation

Estimating the economic impact of a pest requires linking biological and economic systems via a damage function. The most common damage function approach links exogenous pest populations to cumulative commodity yield losses at harvest. This type of representation is a reduced form because is not pest population levels per se that drive damage, but the underlying factors that affect pest populations and the susceptibility of the host. We specify and estimate a structural damage function and compare the results with those of the reduced form. We do so using two alternative models, one that explains the level of crop damage from a pest, and one that explains the timing of that damage during the host’s growing season. We address our objectives within an empirical application to the olive fruit fly in California. In formulating the structural damage function, we draw from current scientific literature on olive fly and olive fruit phenology. The structural damage function takes into account the feedback between climate, host susceptibility, and pest populations. Moreover, the structural approach disaggregates damage rates across space and time, unlike the typical reduced form. The estimation results indicate that endogeneity is a salient concern in both the timing of initial crop damage, and in the levels of damage evidenced in some cultivars. The structural damage function dominates the trapping-based reduced form in terms of explanatory power in every model estimated.Crop Production/Industries,

Implications of Simultaneity in a Physical Damage Function

A modeler must often rely on highly simplified representations of complex physical systems when analyzing associated economic issues. Herein, we consider a management problem in which a bioeconomic system exhibits simultaneity in processes governing productivity and damage. In this case, it may benefit the producer to sacrifice productivity to reduce the costs associated with increased damage. We specify empirically a structural damage relationship that explains the biological process by which an invasive species damages a host and estimate the structural model and its reduced form with an exceptional dataset on infestation of olives by the olive fruit fly. We contrast the results of these models with the approach typically taken in the economic literature, which expresses damage as a function of pest density. The population-based approach introduces significantly greater bias into the individual grower\u27s choice of damage-control inputs than estimates based on the structural model

Identifying Irrigated Areas in the Snake River Plain, Idaho: Evaluating Performance Across Composting Algorithms, Spectral Indices, and Sensors

There are pressing concerns about the interplay between agricultural productivity, water demand, and water availability in semi-arid to arid regions of the world. Currently, irrigated agriculture is the dominant water user in these regions and is estimated to consume approximately 80% of the world’s diverted freshwater resources. We develop an improved irrigated land-use mapping algorithm that uses the seasonal maximum value of a spectral index to distinguish between irrigated and non-irrigated parcels in Idaho’s Snake River Plain. We compare this approach to two alternative algorithms that differentiate between irrigated and non-irrigated parcels using spectral index values at a single date or the area beneath spectral index trajectories for the duration of the agricultural growing season. Using six different pixel and county-scale error metrics, we evaluate the performance of these three algorithms across all possible combinations of two growing seasons (2002 and 2007), two datasets (MODIS and Landsat 5), and three spectral indices, the Normalized Difference Vegetation Index, Enhanced Vegetation Index and Normalized Difference Moisture Index (NDVI, EVI, and NDMI). We demonstrate that, on average, the seasonal-maximum algorithm yields an improvement in classification accuracy over the accepted single-date approach, and that the average improvement under this approach is a 60% reduction in county scale root mean square error (RMSE), and modest improvements of overall accuracy in the pixel scale validation. The greater accuracy of the seasonal-maximum algorithm is primarily due to its ability to correctly classify non-irrigated lands in riparian and developed areas of the study region

A Practical Guide for Managing Interdisciplinary Teams: Lessons Learned from Coupled Natural and Human Systems Research

Interdisciplinary team science is essential to address complex socio-environmental questions, but it also presents unique challenges. The scientific literature identifies best practices for high-level processes in team science, e.g., leadership and team building, but provides less guidance about practical, day-to-day strategies to support teamwork, e.g., translating jargon across disciplines, sharing and transforming data, and coordinating diverse and geographically distributed researchers. This article offers a case study of an interdisciplinary socio-environmental research project to derive insight to support team science implementation. We evaluate the project’s inner workings using a framework derived from the growing body of literature for team science best practices, and derive insights into how best to apply team science principles to interdisciplinary research. We find that two of the most useful areas for proactive planning and coordinated leadership are data management and co-authorship. By providing guidance for project implementation focused on these areas, we contribute a pragmatic, detail-oriented perspective on team science in an effort to support similar projects

Dynamic Feedback between Surface and Groundwater Systems: Implications for Conjunctive Management

A key feature of hydrologically connected surface and groundwater stocks is the two-way exchange of water between the systems. Increasing water scarcity, particularly in arid environments, has spurred debate on how to coordinate management of the two resources. In this paper, I present a model that describes the dynamic feedback loop between surface and groundwater systems when economic agents withdraw water from both for use in production. I use the model to describe optimal water extraction from both stocks and to evaluate how a conjunctive management policy shifts welfare between surface and groundwater user groups. Finally, I explore the importance of accounting for two-way feedback between the two stocks, when it exists, in estimating the benefits to a conjunctive management system. I estimate that the returns to conjunctive management in a closed system are greater than 6.5 times that in a system with an open feedback loop between water stocks