The main objective of this research is to find out if quantitative factors describing agriculture have a statistically significant role on the parliamentary election results in Latvia. If this statistical relationship can be proven, then it is important to interpret the causality behind it. 18 agricultural indicators from the 2001 Census of Agriculture were selected to be analyzed with the 8th Saeima political party election results by using multiple linear regression analysis. These 18 parameters are different in their character, and they describe the size of farms, level of education for farmers, land usage statistics, proportion and productivity of certain crops, livestock and the usage of farm machinery. The main hypothesis of this research was that the rural civil parishes with a high intensity of agriculture have a statistically significant difference in election results when compared to the rest of the election results in Latvia. Initial results showed a strong correlation between election results and agricultural indicators, but when the ethnic factor was taken into account in the linear regression model the role of these agricultural indicators was greatly diminished.

Kriging is a method of interpolation, which predicts unknown values from data observed at known locations. This method uses a variogram to express spatial variation, and it minimizes the error of predicted values which are estimated by spatial distribution. The objective of the current work was to investigate how the accuracy of predicted runoff average depth values ranges by applying ordinary Kriging interpolation method when parameters of the method are altered. The simulation was tested by the following method parameters on: fitted semivariogram model, lag size, neighbouring points; the number of sectors of the circle. The best results of the spatial distribution of runoff average depth were received applying the ordinary Kriging method when the exponential variogram is used, the lag size being 27 km while 6 contiguous points and the orbicular scheme divided into sectors in 45° angle are taken. Applying the Ordinary Kriging method having selected all parameters with probability p = 0.95 the model described about 74% of all investigated values.

In Lithuania, forests are managed by Lithuanian State Forest Enterprise, municipalities, ministries, etc. and private forest owners. About 50% of all forest land is State importance, privately owned forests cover 40% of forest land, and about 10% of forest land belongs to forests reserved for restitution. Forest management of private ownership force many challenges, because private forest owners are people, who have purchased or received the property after restitution, and often lacks knowledge about forest resources, its dynamics and sustainable forest management. As remote sensing is a valuable source for forest monitoring, because it provides periodic data on forest resource and condition status, these methods are gaining increased attention worldwide. In this context, more scientific efforts are made at developing remote sensing derived geo-spatial data services for sustainable forest management through a web service platform, which would integrate geo-information into daily decision making processes and operation for private forest owners. This article presents a review of privately owned forests’ statistics, questionnaire-based survey about GIS usage and demand for forest owners in Lithuania and links available sources of open geo-spatial data useful for sustainable forest management.