Risk Management Tools for Sustainable Agriculture: A Model for Calculating the Average Price for the Season in Revenue Insurance for Citrus Fruit

Ronco, Alicia Mateos; Izquierdo, Ricardo

doi:10.3390/agronomy10020198

Cited by 8 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is also important for the industry seeking to provide inputs, and their knowledge of risk situations enables them to provide more appropriate services to farmers (28). The search for factors that constitute risk aversion is an essential issue that helps in risk management, which should take a special place in agricultural policy (31). Because risk aversion leads to a reluctance to take a decision surrounded by risks that can not be avoided in any way in agriculture, which is associated with risk and uncertainty (12).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Estimating Off –Farm Labor Supply and Analysing Relationship Between Risk and Farm Size

Awf A. A.,

E. H. Ali,

A. S. Shukr

2024

Iraqi J. Agric. Sci.

View full text Add to dashboard Cite

This study was aimed to estimate off-farm labor supply model. Data were collected randomly from 267 wheat producers in Salah El-Din province for the year 2020, 67.4% of them are produced using pivot sprinklers for irrigation and with three tenure categories (60,80,12) dunums. Furthermore. The KS coefficient was used to analyze the producers' risk-taking behavior after estimating the production function and determining the area variable that has the most influence on the production process. If it increased by 1%, production would increase by 0.802%. The farmer's decision to adopt the technology was based on economic, social, and institutional factors. It turns out that 40% of farmers make their decision based on financing. When analyzing the decisions of farmers under risk, it was found that 35% of them make the decision in relation to the price, which is the main incentive for production. In order to understand the relationship between the risk, the return, and the size of the farm, it was found that when the area is increased, the return increases, and the revenue of a dunum at the tenure size of 10 dunams amounted to 481,600 dinars and at a higher KS level, what is known as risk-haters, the revenue was the highest possible and the risk also increased with it, and the farmers became among the large holdings they prefer. In any case, some farmers believe that when you want to get a higher return, it is important to keep in mind that there is a greater risk. Risk is affected by a number of factors, including economic, divided into price and productivity, social ones, and others related to the farmer himself in terms of efficiency, management, skill, and experience. . The research recommended a review of tenure laws and the development of risk management strategies by providing adequate funding that ensures ,Providing factories within rural areas that contribute to absorbing surplus production, creating price stability, as well as creating a labor market that reduces poverty in the rural area.

show abstract

Section: Theoretical Frameworkmentioning

confidence: 99%

Estimating Off –Farm Labor Supply and Analysing Relationship Between Risk and Farm Size

Awf A. A.,

E. H. Ali,

A. S. Shukr

2024

Iraqi J. Agric. Sci.

View full text Add to dashboard Cite

show abstract

“…It also promotes production and economic growth [2]. The agriculture section confronts a capital escape due to an incapability to compete with the industry and the service sector and have a higher risk activity than other fields [3]. Generally, the agricultural sector includes a more risk level than the other sectors [4].…”

Section: Introductionmentioning

confidence: 99%

Agricultural Risk Management Using Fuzzy TOPSIS Analytical Hierarchy Process (AHP) and Failure Mode and Effects Analysis (FMEA)

et al. 2020

View full text Add to dashboard Cite

Failure mode and effects analysis (FMEA) is a popular technique in reliability analyses. In a typical FMEA, there are three risk factors for each failure modes: Severity (S), occurrence (O), and detectability (D). These will be included in calculating a risk priority number (RPN) multiplying the three aforementioned factors. The literature review reveals some noticeable efforts to overcome the shortcomings of the traditional FMEA. The objective of this paper is to extend the application of FMEA to risk management for agricultural projects. For this aim, the factor of severity in traditional FMEA is broken down into three sub-factors that include severity on cost, the severity on time, and severity on the quality of the project. Moreover, in this study, a fuzzy technique for order preference by similarity to ideal solution (TOPSIS) integrated with a fuzzy analytical hierarchy process (AHP) was used to address the limitations of the traditional FMEA. A sensitivity analysis was done by weighing the risk assessment factors. The results confirm the capability of this Hybrid-FMEA in addressing several drawbacks of the traditional FMEA application. The risk assessment factors changed the risk priority between the different projects by affecting the weights. The risk of water and energy supplies and climate fluctuations and pests were the most critical risk in agricultural projects. Risk control measures should be applied according to the severity of each risk. Some of this research’s contributions can be abstracted as identifying and classifying the risks of investment in agricultural projects and implementing the extended FMEA and multicriteria decision-making methods for analyzing the risks in the agriculture domain for the first time. As a management tool, the proposed model can be used in similar fields for risk management of various investment projects.

show abstract

Sustainability assessment of the agriculture sector using best worst method: Case study of Baltic states

Streimikis,

Bathaei,

Štreimikienė

2024

Sustainable Development

View full text Add to dashboard Cite

Sustainable agriculture development holds significant global and regional importance, particularly within the Baltic countries. On a global scale, it is a critical strategy for meeting the escalating demand for food while simultaneously mitigating the adverse environmental and social consequences associated with agricultural practices. In the context of the Baltic nations, where agriculture constitutes a substantial portion of the economy, the adoption of sustainable farming practices is imperative for ensuring the sector's long‐term viability, safeguarding the integrity of the region's distinct ecosystems, and guaranteeing food security for their populations. A comprehensive understanding of the opportunities and challenges facing Baltic agriculture is impeded by a notable research deficiency concerning the intricate and distinct problems facing these nations. The use of indicators to assess economic sustainable agriculture indicators plays a pivotal role in guiding agricultural practices. By taking social and environmental variables into account, these metrics quantify the economic viability of farming. Consequently, these indicators empower policymakers and farmers alike to make well‐informed decisions, striking a balance between profitability and resource conservation, thereby contributing to the enduring sustainability of agriculture in the Baltic countries and beyond. Notably, a comprehensive assessment of economic sustainability in agriculture identified 31 indicators, which were refined to 9 through expert consensus using the Delphi method. Subsequently, the best worst method was applied to rank these indicators. The results indicate that investment intensity, diversification of income, agricultural labor productivity, and market access emerge as the most crucial indicators for ensuring economic sustainability in agriculture.

show abstract

Risk Management Tools for Sustainable Agriculture: A Model for Calculating the Average Price for the Season in Revenue Insurance for Citrus Fruit

Cited by 8 publications

References 19 publications

Estimating Off –Farm Labor Supply and Analysing Relationship Between Risk and Farm Size

Estimating Off –Farm Labor Supply and Analysing Relationship Between Risk and Farm Size

Agricultural Risk Management Using Fuzzy TOPSIS Analytical Hierarchy Process (AHP) and Failure Mode and Effects Analysis (FMEA)

Sustainability assessment of the agriculture sector using best worst method: Case study of Baltic states

Contact Info

Product

Resources

About