Vojtěch Lukas

Disaster Risk Reduction in Agriculture through Geospatial (Big) Data Processing

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.

Open Farm Management Information System Supporting Ecological and Economical Tasks

A Farm Management Information System (FMIS) is a sophisticated tool managing geospatial data and functionalities as it provides answers to two basic questions: what has happened and where. The presented FOODIE (Farm-Oriented Open Data in Europe) and DataBio (Data-Driven Bioeconomy) approach may be recognized as an OpenFMIS, where environmental and reference geospatial data for precision agriculture are provided free of charge. On the other hand, added-value services like yield potential, sensor monitoring, and/or machinery fleet monitoring are provided on a paid basis through standardised Web services due to the costs of hardware and non-trivial computations. Results, i.e. reference, environmental and farm-oriented geospatial data, may be obtained from the FOODIE platform. All such results of whatever kind are used in the European DataBio project in order to minimise the environmental burden while maximising the economic benefits.

Challenges of agricultural monitoring: integration of the Open Farm Management Information System into GEOSS and Digital Earth

From a global perspective, agriculture is the single largest user of freshwater resources, each country using an average of 70% of all its surface water supplies. An essential proportion of agricultural water is recycled back to surface water and/or groundwater. Agriculture and water pollution is therefore the subject of (inter)national legislation, such as the Clean Water Act in the United States of America, the European Water Framework Directive, and the Law of the Peoples Republic of China on the Prevention and Control of Water Pollution. Regular monitoring by means of sensor networks is needed in order to provide evidence of water pollution in agriculture. This paper describes the benefits of, and open issues stemming from, regular sensor monitoring provided by an Open Farm Management Information System. Emphasis is placed on descriptions of the processes and functionalities available to users, the underlying open data model, and definitions of open and lightweight application programming interfaces for the efficient management of collected (spatial) data. The presented Open Farm Management Information System has already been successfully registered under Phase 8 of the Global Earth Observation System of Systems (GEOSS) Architecture Implementation Pilot in order to support the wide variety of demands that are primarily aimed at agriculture pollution monitoring. The final part of the paper deals with the integration of the Open Farm Management Information System into the Digital Earth framework.

Spectral monitoring of wheat canopy under uncontrolled conditions for decision making purposes

High quality of the crop multispectral images taken under uncontrolled conditions.High correspondence between air and ground spectral measurements.Quantifying the effect of lack of knowledge about the bidirectional reflectance distribution function.Considerations for the incorporation of the images in the decision making process. The increase in the supply of devices for acquiring spectral images has enabled its widespread adoption by farmers. These devices combine technical quality and ease of use, but the abandonment of experimental stations has led to the loss of the scientific supervision, which is necessary for the fulfilment of operating conditions. The aim of this study is to quantify the quality of the spectral images taken under uncontrolled conditions. With this objective, workflows in real plots were simulated for three years. Surveys were carried out by a spectral camera mounted on a ground platform without additional systems to control lighting, sun-target-sensor geometry and interferences. Images were processed using the empirical linear method and a set of five references, so that the goodness of fit is the first quality estimator. Over 97% of images reach a coefficient of determination equal to or above 0.75 at all wavelengths. The images were also compared with the spectral signatures obtained by a field radiometer and with images acquired by an airborne hyperspectral sensor. This allows us to quantify the error in determining the crop reflectance and to assess the effect of the uncontrolled conditions. The median error in comparison with data from the field radiometer is 0.01, 0.02 and 0.03 (green, red and near-infrared respectively) and with the airborne hyperspectral sensor the error is 0.01 or lower. The geometry is the key factor but it can be controlled and it is possible to use successfully the current methodologies for the agronomic interpretation. The monitoring quality is comparable, from a practical point of view, with more sophisticated instruments, but there is a need to find a solution to a bad timely operation of the camera for efficient operation of the workflow.

Effectiveness of Chlorophyll Meter Measurement in Winter Wheat at Field Scale Level

Abstract This paper examines the relationship among chlorophyll meter Yara N-Tester readings, nutrition status and growth parameters (leaf area index (LAI), plant height) of the winter wheat plants. Data used in this study were collected in 2010 from two fields located in the Czech Republic (area 52 and 38 ha) from different farms, both with uniform and conventional crop management. The monitoring of crop stands was done at growth stage BBCH 30 in a regular sampling grid with 150 m distance between points (27 and 18 points). At each sampling point, the plant height, LAI (Delta-T SunScan) and the chlorophyll concentration (Yara N-Tester) were recorded. Plant samples were taken to analyse the content of main nutrients (N, P, K, Mg, Ca and S). The results of plant analysis showed that both fields were in different nutrition status: one in a correct status and another had a complex nutritional deficit (K, Ca and N). Linear regressions and ANOVA proved that under a multiple nutritional deficit, N-Tester readings responded to the growth of the crop, while in the adequate nutritional conditions the sensitivity of N-Tester to the variation in the nitrogen concentration is lower. The relationships between crop parameters and chlorophyll meter readings are not generalisable and thus the interpretation of N-Tester results has to be done separately for each field.

Cereal Canopy Structure – Its Assessment and Use in Efficient Crop Management

To attain higher effectiveness of crop management practices, extensive research on cereal stand structure was conducted in the 1980s and 1990s [1-6]. The stand state and structure reflect variability in soil conditions as well as cropping treatments. Results of individual methods used for modification of cropping treatments depend on a level of stand organization which is observed – a stand (plant population), plant, plant part (leaf, tiller) [7].