AUGALININKYSTĖS GAMYBOS RIZIKOS DĖL NEPALANKIŲ METEOROLOGINIŲ REIŠKINIŲ VALDYMAS PASĖLIŲ DRAUDIMO PRIEMONĖMIS LIETUVOJE

Crop production risk - also known as crop-yield risk - is measured by yield variability. Crop-yield variability is well known to depend on the adverse weather phenomena such as drought, excessive/insufficient rainfall, hail, floods, extreme temperatures and others. Factors other than weather (e.g. plant diseases, plant pest infestation, or an environmental incident) influence the crop-yield variability as well. A high degree of yield variation of cereals, leguminous crops, rape and grain maize (their variation coefficient was 25.3, 28, 26.7 and 38.4 percent respectively over a quarter-century) indicates high-risk in crop production in Lithuania. This article focused purely on crop insurance-based risk management of the crop production risk affected by the extreme weather events. The aim of this study is to examine the crop insurance state and trends in Lithuania and to reveal the determinants which encourage or restrict the use of commercial crop insurance product in order to manage crop-yield risk on farms. Spatial analysis, farmers' survey, correlation and regression analysis methods and analytical tool “insured crops mapping” were used. Research results show that only about one tenth of the crop area is insured in Lithuania nowadays. On the other hand, insured area increased by more than threefold between 2008-2015. Simultaneously, empirical studies have found that the average crop insurance rate has dropped from 5.67 percent in 2009 up to 3.33 percent in 2015. Net financial result of crop insurance, expressed as ratio insurance payments to premium, averaged 0.98 during the past eight years (2008-2015). This shows that the commercial crop insurance products has minimized the crop-yield variability in value terms of the insured crops in the same period. The analysis reveals that a direct moderate correlation exists between the insured crop proportions and variables like soil productivity, farm size, insurable crop area proportion in UAA, crops represented proportion of total agricultural production and crop production intensity. The results of the regression analysis suggest that both first two explanatory variables – soil productivity and farm size – determined the proportion of insured crop area in 56.8 percent in LAU1 surveyed areas. JEL Codes: G22, Q14. DOI: https://doi.org/10.15544/ssaf.2017.0