Survey and Experimental Techniques as an Approach for Agricultural Risk Analysis

The purpose of this session is to discuss various procedures for modeling agricultural price and yield distributions. The model results are then used to provide improved analyses for agricultural risk management with the focus primarily upon the agricultural production sector. Each of the papers presented here contributes to that end. The discussion of these papers will be organized as follows: First, comments on each paper beginning with Richardson, Klose and Gray (RKG), then some general observations and concerns, along with a suggestion of issues for further discussion.

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Risk analysis model and agricultural derivative market use

Independent Journal of Management & Production ◽

10.14807/ijmp.v12i8.1499 ◽

2021 ◽

Vol 12 (8) ◽

pp. 2508-2534

Author(s):

João Batista Ferreira ◽

Luiz Gonzaga Castro Junior

Keyword(s):

Risk Management ◽

Risk Analysis ◽

Value At Risk ◽

Price Risk ◽

Analysis Model ◽

Agricultural Risk ◽

Market Risks ◽

Price Risks ◽

Derivative Markets ◽

Price Risk Management

This research aims to build conceptual guidelines regarding price risk management through the agricultural derivatives market. Specifically, to identify the common price risk management methods and strategies employed, the risk analysis models of derivative markets, and the barriers to agricultural risk management. This is an integrative review, the search for literature on the models of risk management analysis of agricultural derivatives started by listing the largest possible number of keywords on the topic, in the Scopus and Web of Science. Forty-five publications were found meeting the pre-established criteria that served as the basis for this research. Based on the literature review, we list the main information on the subject and we also propose a theoretical model for analyzing the market risks of agricultural derivatives. Still, it was possible to notice that among the methodologies for measuring market risk, Value at Risk (VaR) stands out. We exemplify and demonstrate the existence of several statistical analyzes and mathematical models, as well as software available for the management of price risks. It is concluded that strategies with the futures and options market, even though they are the most efficient for risk management, lack incentives to become practical.

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Influence of environment and climate on occurrence of the cixiid planthopperHyalesthes obsoletus, the vector of the grapevine disease ‘bois noir’

Bulletin of Entomological Research ◽

10.1017/s0007485313000163 ◽

2013 ◽

Vol 103 (6) ◽

pp. 621-633 ◽

Cited By ~ 10

Author(s):

Bernd Panassiti ◽

Michael Breuer ◽

Stacey Marquardt ◽

Robert Biedermann

Keyword(s):

Environmental Factors ◽

Risk Analysis ◽

Biological Research ◽

Habitat Requirements ◽

Agricultural Risk ◽

Distribution Models ◽

Bois Noir ◽

Fine Soil ◽

Modelling Framework ◽

Information Theoretic Method

AbstractSpecies distribution models (SDMs), which are well established in many fields of biological research, are still uncommon in the agricultural risk analysis of pest insects. To exemplify the use of SDMs, we investigated the influence of environmental factors on the occurrence ofHyalesthes obsoletusSignoret (Hemiptera: Cixiidae). The planthopper is the only known vector of the grapevine yellows disease ‘bois noir’. The study was conducted in 145 locations in the Baden region of southwest Germany. The planthopper was surveyed on host plant patches, consisting of stinging nettle and/or bindweeds. We used a stratified modelling framework where (1) species presence–absence data were related to an extensive environmental dataset using logistic regressions; and (2) different types of average models were developed based on an information theoretic method. The results show that the incidence ofH. obsoletusis associated to above- as well as below-ground environmental factors, particularly to the amount of fine soil and average annual precipitation. This result was consistent across all average models. The relative importance of other environmental variables was dependent upon the average model under consideration and thus may vary according to their intended use, either the explanation of habitat requirements or the prediction and mapping of occurrence risks. The study showed that SDMs offer a quantification of species’ habitat requirements and thus, could represent a valuable tool for pest management purposes. By providing examples of current issues of grapevine pests in viticulture, we discuss the use of SDMs in agricultural risk analysis and highlight their advantages and caveats.

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