Current challenges in natural resource management have created demand for integrated, flexible, and easily parameterized hydrologic models. Most of these monolithic models are not modular, thus modifications (e.g., changes in process representation) require considerable time, effort, and expense. In this paper, the feasibility and challenges of using the Object Modeling System (OMS) for natural resource model development will be explored. The OMS is a Java-based modeling framework that facilitates simulation model development, evaluation, and deployment. In general, the OMS consists of a library of science, control, and database modules and a means to assemble the selected modules into an application-specific modeling package. The framework is supported by data dictionary, data retrieval, GIS, graphical visualization, and statistical analysis utility modules. Specific features of the OMS that will be discussed include: 1) how to reduce duplication of effort in natural resource modeling; 2) how to make natural resource models easier to build, apply, and evaluate; 3) how to facilitate long-term maintainability of existing and new natural resource models; and 4) how to improve the quality of natural resource model code and ensure credibility of model implementations. Examples of integrating a simple water balance model and a large monolithic model into the OMS will be presented.

An improved ant colony optimization (ACO) formulation for the allocation of crops and water to different irrigation areas is developed. The formulation enables dynamic decision variable option (DDVO) adjustment and makes use of domain knowledge through visibility factors (VFs) to bias the search towards selecting crops that maximize net returns and water allocations that result in the largest net return for the selected crop, given a fixed total volume of water. The performance of this formulation is compared with that of other ACO algorithm variants (without and with domain knowledge) for two case studies, including one from the literature and one introduced in this paper for different water-availability scenarios within an irrigation district located in Loxton, South Australia near the River Murray. The results for both case studies indicate that the use of VFs (1) increases the ability to identify better solutions at all stages of the search; and (2) reduces the computational time to identify n...

SUMMARY: There are many reasons why agricultural researchers carefully evaluate approaches to experimental data analysis. Agricultural experiments are typically highly complex, with many types of variables often collected at a wide range of temporal and spatial scales. Furthermore, research in the developing world is often conducted on-farm where simple and conventional experimental designs are often unsuitable. Recently, a variant of stochastic dominance called stochastic efficiency with respect to a function (SERF) has been developed and used to analyse long-term experimental data. Unlike traditional stochastic dominance approaches, SERF uses the concept of certainty equivalents (CEs) to rank a set of risk-efficient alternatives instead of finding a subset of dominated alternatives. This study evaluates the efficacy of the SERF methodology for analysing conventional and conservation tillage systems using 14 years (1990–2003) of economic budget data collected from 36 experimental plots at the Iowa State University Northeast Research Station near Nashua, IA, USA. Specifically, the SERF approach is used to examine which of two different tillage systems (chisel plough and no-till) on continuous corn (Zea mays) and corn/soyabean (Glycine max) rotation cropping systems are the most risk-efficient in terms of maximizing economic profitability (gross margin and net return) by crop across a range of risk aversion preferences. In addition to the SERF analysis, we also conduct an economic analysis of the tillage system alternatives using mean-standard deviation and coefficient of variation for ranking purposes. Decision criteria analysis of the economic measures alone provided somewhat contradictive and non-conclusive rankings, e.g. examination of the decision criteria results for gross margin and net return showed that different tillage system alternatives were the highest ranked depending on the criterion and the cropping system (e.g. individual or rotation). SERF analysis results for the tillage systems were also dependent on the cropping system (individual, rotation or whole-farm combined) and economic outcome of interest (gross margin or net return) but only marginally on the level of risk aversion. For the individual cropping systems (continuous corn, rotation corn and rotation soyabean), the no-till tillage and rotation soyabean system was the most preferred and the chisel plough tillage and continuous corn system the least preferred across the entire range of risk aversion for both gross margin and net return. The no-till tillage system was preferred to the chisel plough tillage system when ranking within the continuous corn and the corn-soyabean rotation cropping systems for both gross margin and net return. Finally, when analysing the tillage system alternatives on a whole-farm basis (i.e. combined continuous corn and corn-soybean rotation), the no-till tillage system was clearly preferred to the chisel plough tillage system for both gross margin and net return. This study indicates that the SERF method appears to be a useful and easily understood tool to assist farm managers, experimental researchers and, potentially, policy makers and advisers on problems involving agricultural risk.

This study applied a broad continuum of risk analysis methods including mean-variance and coefficient of variation (CV) statistical criteria, second-degree stochastic dominance (SSD), stochastic dominance with respect to a function (SDRF), and stochastic efficiency with respect to a function (SERF) for comparing income-risk efficiency sustainability of conventional and reduced tillage systems. Fourteen years (1990–2003) of economic budget data derived from 35 treatments on 36 experimental plots under corn (Zea mays L.) and soybean (Glycine max L.) at the Iowa State University Northeast Research Station near Nashua, IA, USA were used. In addition to the other analyses, a visually-based Stoplight or “probability of target value” procedure was employed for displaying gross margin and net return probability distribution information. Mean-variance and CV analysis of the economic measures alone provided somewhat contradictive and inconclusive sustainability rankings, i.e., corn/soybean gross margin and net return showed that different tillage system alternatives were the highest ranked depending on the criterion and type of crop. Stochastic dominance analysis results were similar for SSD and SDRF in that both the conventional and reduced tillage system alternatives were highly ranked depending on the type of crop and tillage system. For the SERF analysis, results were dependent on the type of crop and level of risk aversion. The conventional tillage system was preferred for both corn and soybean for the Stoplight analysis. The results of this study are unique in that they highlight the potential of both traditional stochastic dominance and SERF methods for distinguishing economically sustainable choices between different tillage systems across a range of risk aversion. This study also indicates that the SERF risk analysis method appears to be a useful and easily understood tool to assist farm managers, experimental researchers, and potentially policy makers and advisers on problems involving agricultural risk and sustainability.

The fishing industry in Abu-Dhabi, United Arab Emirates (UAE), plays an important role in diversifying food sources in order to enhance national food security. The fishing industry is facing an increasing risk that may impact the sustainability (i.e., quantity and quality) of the fish caught and consumed in the UAE. Therefore, the main objective of this study is to analyze common Abu-Dhabi fishing management alternatives using various stochastic dominance techniques (i.e., first/second degree stochastic dominance, stochastic dominance with respect to a function and stochastic efficiency with respect to a function) to assess the risk facing UAE fishermen. The techniques represent a risk assessment continuum, which can provide a ranking of management alternatives to improve decision making outcomes and help maintain long-term UAE fishing sustainability. Data for the stochastic dominance analyses were obtained from a cross-sectional survey conducted through face-to-face interviews of Abu Dhabi, UAE, fishermen. Analysis of fishing methods, trap sizes and trap numbers using stochastic efficiency with respect to a function (SERF) showed that fishermen efficient practices were not the same for risk-neutral fishermen compared to risk averse fishermen. Overall, the stochastic dominance results illustrated the importance of considering both attitude towards risk and economic inefficiencies in managing UAE fishery practices and designing successful fishery policies, as well as improving decision-making at the fishermen level.