Non-Parametric Analysis of Yield Risk in Lithuanian Crop Farming

Abstract

Socioeconomic development requires meeting the goals of food security. Yield risk constitutes an important factor of farming business viability. As the Central and Eastern European countries have been affected by both economic and environmental transformations, there is a need to develop a robust methodology for assessment of yield risks in order to propose convincing guidelines for both farmers and government institutions in regards to risk management and viability of agricultural business in general. This paper attempts to devise non-parametric measures of yield risk for Lithuanian crop farming. The research covers the period of 2000–2015. County-level data from Statistics Lithuania are employed for the analysis. The non-parametric analysis of yield risk relies on information diffusion theory and linear moving average. The results indicate that there exist differences in yield trends, yield loss rates and yield risk among crops and regions. Maize, buckwheat and winter rape exhibited the highest yield risk. These results shed light on the extent of yield risks underlying crop farming in Lithuania and, to a certain extent, can be contrasted to situation in Central and Eastern European countries. Indeed, the obtained results can be applied in decision making at different levels of management.

Farmers' awareness of eco‐efficiency and cleaner production as environmental responsibility: Lithuanian case

“…The question of food security in the context of socioeconomic development in scientific discussions had been also raised from yield risk and farming business viability (Baležentis, Štreimikienė, Melnikienė, & Yu, 2017). Research results suggest that there exist meaningful differences among crops and regions in Lithuania regarding yield trends, yield loss rates and yield risk, which contrasts to the situation in Central and Eastern European countries.…”

Improving mariculture insurance premium rate calculation using an information diffusion model

“…Some scholars used the IDM to assess the risk of Alzheimer’s disease [ 38 ]. The IDM has also been introduced in the assessment of crop yield risk [ 39 , 40 ]. Furthermore, researchers have frequently applied this approach to risk analysis of natural hazards, such as algal bloom outbreaks [ 41 ], hurricane [ 42 ], flood [ 43 , 44 ], drought [ 45 , 46 ], and grassland biological disasters [ 47 , 48 ].…”