No abstract

Landfill leachates are potentially harmful to the environment and to human health. The objective of this study was to characterize leachates in order to analyze whether a relationship exists between the stored waste and the composition of leachates, and to detect possible leakages of pollutants into the environment. To achieve these objectives, field data, Global Positioning System data and physico-chemical data were used. Biological tests are becoming increasingly popular in determining leachate toxicity; therefore, two toxicity tests were performed with the seeds of white mustard (Sinapis alba L.) and duckweed (Lemna minor L.). Leachates were sampled from the leachate pond. Groundwater quality was monitored by using drill holes. The research and analysis carried out are important to determine their potential impact on agricultural areas located near the landfill. Demonstrably increased (P < 0.05) concentrations of heavy metals were detected only in the leachate pond which closes the landfill body, where it links up with the landfill insulation layer. Water sampled from drill holes reaching into groundwater was not contaminated. The results showed that the leachates did not leak outside the landfill. Nevertheless, they were found to be phytotoxic. Both toxicity tests showed that the increasing amount of leachates resulted in the increasing growth inhibition of the tested plants. The proper handling of leachates should have been ensured.

No abstract

Intensive farming on land represents an increased burden on the environment due to, among other reasons, the usage of agrochemicals. Precision farming can reduce the environmental burden by employing site specific crop management practices which implement advanced geospatial technologies for respecting soil heterogeneity. The objectives of this paper are to present the frontier approaches of geospatial (Big) data processing based on satellite and sensor data which both aim at the prevention and mitigation phases of disaster risk reduction in agriculture. Three techniques are presented in order to demonstrate the possibilities of geospatial (Big) data collection in agriculture: (1) farm machinery telemetry for providing data about machinery operations on fields through the developed MapLogAgri application; (2) agrometeorological observation in the form of a wireless sensor network together with the SensLog solution for storing, analysing, and publishing sensor data; and (3) remote sensing for monitoring field spatial variability and crop status by means of freely-available high resolution satellite imagery. The benefits of re-using the techniques in disaster risk reduction processes are discussed. The conducted tests demonstrated the transferability of agricultural techniques to crisis/emergency management domains.

Yield mapping is a subject of research in (precision) agriculture and one of the primary concerns for farmers as it forms the basis of their income and has implications for subsidies and taxes. The presented approach involves deployment of field harvesters equipped with sensors that provide more detailed and spatially localized values than merely a sum of yields for the whole plot. The measurements from such sensors need to be filtered and subject to further processing, including interpolation, to facilitate follow-up interpretation. This paper aims to identify the relative differences between interpolations from (1) (field) measured data, (2) measured data that were globally filtered, and (3) measured data that were globally and locally filtered. All the measured data were obtained at a fully operational farm and are considered to represent a natural experiment. The revealed spatial patterns and recommendations regarding global and local filtering methods are presented at the end of the paper. Time investments into filtering techniques are also taken into account.