The objective of the FIGARO project is to significantly reduce the use of fresh water on farm level through developing a cost-effective, precision irrigation management platform. The platform will be structured for data acquisition from monitoring devices and forecasting tools, data interpretation, system control, and evaluation mechanisms enabling full decision support for end users at farm scale. These tools will be integrated with multiple state-of-the-art irrigation technologies and strategies as well as newly adapted devices leading to further increased water productivity.

This project is funded by the innovation program of the Federal Ministry of Food and Agriculture (BMEL), funding agency is the Federal Office for Agriculture and Food (BLE). Collaborative project: Development of a low-priced miniaturized mid-infrared (MIR)-Sensor for integrated area-wide slurry management According to the guideline of the BMEL about promoting innovations in agricultural engineering, the project aims to develop low-priced miniaturized mid-infrared (MIR)-Sensors as well as the accompanying control technology and a data infrastructure for the realization of an area-wide slu

Development of two Decision Support Systems for calculation of the utilisation of nutrients in slurry from animal production. The systems will lead to reduction of ammonium emmission and economical benefits for farmers.

The truly innovative idea of this partnership is to integrate data from distributed information sources to provide new technologies, solutions and cultivation techniques for modern high yielding and low emis-sion precision farming. The com-mercial output targets optimization of key operations in the cropping cycle by balancing profitability for the farmer with risk of unintended emissions from fields and subsur-face.

Summary This project builds on the current RIRDC project: PRJ007477, Rice NIR and Remote Sensing. It involves maintaining the NIR instrument at Yanco and updating the panicle initiation (PI) tissue, grain and straw nitrogen calibrations. The instrument and calibrations are used to determine the PI tissue nitrogen (N) content of the samples submitted to the NIR Tissue Test Service. The NIR, using numerous calibrations, is also used to analyse grain, straw and tissue samples in several other RIRDC research projects.

This project uses UAV technologies to look at urine patches on farm. Funded by Lincoln Agritech Ltd. Case Study: Linking nitrogen application to nitrogen requirements INDUSTRY: AGRICULTURE The Need: Smarter and more responsible application of nitrogenous fertilisers to NZ's pastoral farms - reducing environmental damage while maintaining or improving productivity.

This project uses hyperspectral sensors to look at nutrients in the landscape. The ultimate goal is to be able to evaluate fertiliser requirements in hill country without having to do traditional soil and pasture testing. This project is under a "Primary Growth Partnership" (PGP) with Ravensdown and Ministry for Primary Industries (MPI) New Zealand. The Pioneering to Precision programme, led by Ravensdown, seeks to improve fertiliser practice on hill country farms through remote sensing of the nutrient status of the farms and precision application of fertiliser.

The objective of this project is to propose to wine growers and to oenologists an integrated approach from vineyard to winery, to enhance nitrogen management according to the wine aimed profile. Indeed, the nitrogen content of the grape must is an essential criterion to control and regulate the aromatic quality of the obtained wine and so for their export competitiveness.

The ability to optimize inputs at spatial scale has been enabled with the use of Variable Rate Applications (VRA). Despite the fact that there are numerous VRA systems aimed at arable crops, specific systems for orchard management utilizing precision monitoring and application of the orchards are still lacking. These cropping systems face increasing market pressure to produce quality products, and provide a detailed traceable system for the origin of the product including, the treatments and the conditions that have occurred during the production.

This proposal aims to develop an automated system for precise application of nitrogen (N) fertiliser and plant growth regulators (PGRs). Algorithms and software will be developed for integrating diverse forms of data from crop sensing instruments, yield maps, soil maps and soil N measurements. This will enable more accurate N fertiliser and PGR management through real-time decision making in the field, both on a field-by-field basis and on a metre-by-metre basis.

Abstract The project brings together agronomy research, on rapid protein assays for milling wheat, with engineering of photonic sensors, image recognition & mechatronic systems. The ultimate goal is to deliver a tractor-mount scanning unit for autonomous mapping of protein content across wheat fields, to a spatial resolution better than 2 square metres at full field application speeds (17km/hr) for precision application of nitrogen (N). N is the primary input cost and 80% of the carbon footprint, in milling wheat production, however it is over applied in 3 out of 4 cases.

Abstract In current practice, a tractor mounted sensor to calculate Normalized Difference Vegetation Index (NDVI) detects live, green vegetation from a target area and can be used to analyse crop nutritional requirements. By adding high-resolution satellite data it is possible to achieve a variable rate (VR) fertiliser recommendation. Current practice lacks two key factors in the determination of optimum N supply to growing crops: availability of high-resolution data to inform on soil fertility status; and technologies that ensure accurate and consistent placement of nutrient.

This project is funded by the innovation program of the Federal Ministry of Food and Agriculture (BMEL), funding agency is the Federal Office for Agriculture and Food (BLE) . mobile soil-sensor-module and data fusion for resource efficient plant production The small-scale knowledge of soil properties combined with further information is an essential basis for crop production and largely determines the use of resources. Recent methods - with laboratory analyses of soil samples - do not offer options for online verification of the measured results and require transporting of soil.