Jens Wiebensohn

Farm management information systems

Farm management information systems centered around the farm manager in open-field crop production.Prevailing differences between academic and commercial farm management information systems.Grouping of farm management information systems based on cluster analysis. Farm Management Information Systems (FMIS) in agriculture have evolved from simple farm recordkeeping into sophisticated and complex systems to support production management. The purpose of current FMIS is to meet the increased demands to reduce production costs, comply with agricultural standards, and maintain high product quality and safety. This paper presents current advancements in the functionality of academic and commercial FMIS. The study focuses on open-field crop production and centeres on farm managers as the primary users and decision makers. Core system architectures and application domains, adoption and profitability, and FMIS solutions for precision agriculture as the most information-intensive application area were analyzed. Our review of commercial solutions involved the analysis of 141 international software packages, categorized into 11 functions. Cluster analysis was used to group current commercial FMIS as well as examine possible avenues for further development. Academic FMIS involved more sophisticated systems covering compliance to standards applications, automated data capture as well as interoperability between different software packages. Conversely, commercial FMIS applications targeted everyday farm office tasks related to budgeting and finance, such as recordkeeping, machinery management, and documentation, with emerging trends showing new functions related to traceability, quality assurance and sales.

Spatial inference with an interchangeable rule format

Rule interchange between information systems is expanding as new interoperable rule formats are emerging from research. However, existing spatial inference systems generally operate on locally stored data with an internal rule format. Consequently, their design offers little support or facilities for rule interchange. This article presents the requirements, components and design for a spatial inference system with rule interchange. Computational efficiency and overall functionality of the design are considered separately, with the latter demonstrated using encoded agricultural legislation and data. A spatial inference system with rule interchange is based on three primary components: rule representation, spatial functionality and data integration. Of these, the interoperable rule representation and data integration distinctly differ from existing spatial inference systems. The presented inference system combines a spatial superset of the W3C Rule Interchange Format (RIF) with full Open Geospatial Consortium simple feature access (OGC SFA) functionality and on-demand data integration utilising Resource Deception Framework (RDF). The design was found to be effective with a computational efficiency depending predominantly on the spatial operations. This design could be further adapted to implement spatial extensions for existing inference systems. Considerable benefits were also discovered when RIF was used as the native language for the inference engine, thereby removing the need for rule transformations and facilitating on-demand data integration with the GML.

Prediction of Soil Nitrogen from Spectral Features Using Supervised Self Organising Maps

Soil Total Nitrogen (TN) can be measured with on-line visible and near infrared spectroscopy (vis-NIRS), whose calibration method may considerably affect the measurement accuracy. The aim of this study was to compare Principal Component Regression (PCR) with Supervised Self organizing Maps (SSOM) for the calibration of a visible and near infrared (vis-NIR) spectrophotometer for the on-line measurement of TN in a field in a German farm. A mobile, fiber type, vis-NIR spectrophotometer (AgroSpec from tec5 Technology for Spectroscopy, Germany) mounted in an on-line sensor platform, comprising of measurement range of 305–2200 nm was utilized so as to obtain soil spectra in diffuse reflectance mode. Both PCR and SSOM calibration models of TN were validated with independent validation sets. The obtain root mean square error (rmse) was equal to 0.0313.The component maps of SSOM allow for a visualization of different correlations between spectral components and nitrogen content.