This paper discusses the relationship between weather and rice productivity modeled using the Cobb–Douglas production function principle, with the hypothesis that rice production will increase in line with the increase in average rainfall, wind speed, and temperature every month and then decrease if the weather conditions exceed the threshold. As a result, farmers have the risk of losing rice production. To overcome this problem, we try to estimate the value of the risk. The purpose of this study is to estimate the risk of losses that occurred in rice plants due to weather changes. The method used in this study is risk estimation with the Tail Value at Risk (TVaR) approach. In addition to TVaR, it is estimated simultaneously with Value at Risk (VaR) and Conditional Value at Risk (CVaR). This study uses weather data consisting of rainfall data, wind speed, and air temperature collected from geophysical and meteorological data. Meanwhile, yield data were obtained and processed from the Central Statistics Agency and the West Java Agricultural Service. The data used are data from 2008 to 2021. There are two main parts of the results in this study, namely mathematical analysis and data analysis. The mathematical analysis is a risk model derivation process, which includes TVaR risk measurement. The data analysis process is a simulation of the estimated risk of rice production loss. The results obtained from this study are the value of opportunity risk of loss based on the VaR, CVaR, and TVaR approaches. The conclusion of this study is that the rice plants have a risk of loss in the form of reduced yields caused by weather changes. Farmers can plan to overcome this loss problem, by setting up a reserve fund. Risk of loss can be managed through the rice agricultural insurance program. This is in line with the Indonesian government’s program through the ministry of agriculture. Thus, farmers, insurance companies, and the government can manage the risk of losing rice yields.
As the most contributed sectors in agriculture, rice farming is facing various risks, namely uncertainty such as crop failure caused by climate change, including air temperature, weather, rainfall and others. Indonesia is categorised as an agricultural country with a tropical climate. By this season, the farmers can plant the rice. Rice farming is currently an inseparable part of most agricultural societies in Indonesia, especially in West Java. However, changes in air temperature, weather and annual rainfall, can increase the uncertainty and upward the risk of crop failure. Thus, the current study seeks to investigate the decision making for agricultural risk assessment (climate variable) through the formulation of a risk model for agricultural insurance in Indonesia. This study utilised the climate variables, which consist of air temperature, wind speed, maximum and minimum temperatures, and rainfall. For determining the magnitude of risk, we applied the Block Maxima method and Peak Over Threshold. The results of this study found that the highest risk of losses occurred in November, December, January, February and March with a value of 0.17485.
Paddy farming is a source of livelihood for most rural communities in Indonesia. Indonesia as an agrarian country with a tropical climate, where the sun shines all the time and farmers can grow crops throughout the season. However, changes in air temperature, weather, and annual rainfall which sometimes change uncertainly cause changes in cropping patterns, which are caused by minimal water supplies in the dry season and flooding in the rainy season. This uncertainty will certainly increase the risk of crop failure. In addition to the risk of weather, other disturbances from plant pests and plant pests also cause the risk of crop failure, which results in greater losses to farmers. One way to transfer this risk is through a rice agricultural insurance program. This study aims to calculate the premium price using the Extreme Value Theory (EVT) method with the Operational Value at Risk (OpVaR) approach based on weather, pests and plant diseases. In this study, the data used are weather data Pests and plant diseases disturbance in West Java in 2009-2019. The stages in this research, the first step is to determine the estimated threshold value to obtain extreme data, then estimate the parameters using the likelihood method. Data suitability test with Generalized Pareto Distribution (GPD) was performed using the QQplot test. Risk is determined by the Operational Value at Risk (OpVaR) approach, the results of which are used to calculate the appropriate premium for paddy agricultural insurance.
Bibliometric analysis is the quantitative study of bibliographic material. In this paper, a systematic review of papers, authors, and journals is carried out. This is necessary to determine and set targets to be achieved in further research. The main objective of this study is to identify some of the most relevant research and the latest trends according to the information found in the Google Scholar, Publish or Perish, Science Direct, and Dimension databases. The methods used are classification, analysis of the most cited journals of all time, and the most prolific and influential authors. The results are information on the number of papers, citations, researchers, h-index, g-index, major reference journals, and visualization of research roadmaps for the topic of agricultural insurance mathematical models. The research findings identify the core themes, which are used as research gaps for future research.
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