Simple tools for spatial analysis - key enabling technologies for Precision and Digital Viticulture
Abstract
Precision Agriculture (PA) is an approach to production in which an understanding of field and vineyard variability is used to enhance the certainty and efficiency of production. Thus, inputs are applied only where they are needed, and importantly for winegrowing, grapes may be selectively harvested and ‘streamed’ to particular target end uses (wines of different styles or price points). Key enabling technologies include geographical information systems (GIS) and methods of spatial analysis. Drawing on past (mainly viticultural) research, this project produced ‘PAT – Precision Agriculture Tools’ - a set of accessible ‘freeware’ tools that promote and facilitate low-cost adoption of PA.
Summary
Precision Agriculture (PA) is a management strategy that gathers, processes and analyses spatial, temporal and other data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production. Precision Viticulture (PV) is the application of PA in winegrowing.
Over the last 20 years, vineyard variability and the development of PV has been a growing area of research worldwide, to the extent that, in Australia, winegrowing is arguably as much to the fore in the sophistication of its use of digital technologies as is the case in other cropping systems that have explored the use of PA. Thus, it has been demonstrated that vineyards are variable and that managing them as though they are homogenous through the use of uniform management is a sub-optimal strategy. Accordingly, PV approaches to grape and wine production, which employ high resolution spatial data to inform strategies such as selective harvesting, may be highly profitable.
Whilst the sophistication and potential profitability of PV is suggestive of advanced application of digital technology, recently surveys in both the wine and grains sectors suggest that adoption of PV/PA, including new methods of on-farm experimentation which may support the targeting of management, will be greatly facilitated by factors which simplify the adoption process. Access to assistance, low cost and process simplification were identified as particularly critical. This project sought to address an opportunity in terms of both the cost and simplification of the spatial data analysis which is critical to PV/PA, by producing a suite of open source tools, accessible through a freeware platform, which may enable a non-expert to implement the core data analysis tasks which PV/PA require. This project was supported by Wine Australia, through funding from the Australian Government Department of Agriculture, Water and the Environment as part of its Rural R&D for Profit program, and CSIRO.
Drawing on CSIRO-led research in PV/PA conducted over the last 20 years, the project produced a suite of tools for spatial analysis that are freely available and accessible. pyprecag is a Python library containing these tools which is accessed using ‘PAT’ (Precision Agriculture Tools), a Plugin for QGIS, a freeware geographical information system. This is supplemented by tools for the analysis of so-called ‘whole of block’ experiments, coded in R, a freeware statistical programming language, which is also accessible via PAT. Users do not need to have expertise in spatial analysis and geostatistics to make use of PAT and it is therefore hoped that the availability of these tools will greatly facilitate adoption of PV/PA.
Instructions for setting up QGIS for PAT are accessible from https://github.com/CSIRO-Precision-Agriculture/PAT_QGIS_Plugin#precision-agriculture-tools-pat-plugin-for-qgis. Users are encouraged to install PAT from the QGIS Plugin repository having downloaded QGIS. Enquiries regarding PAT can be directed to PAT@csiro.au.
It is recommended that as a follow-up to this work, additional investments be made in user training, and also in software maintenance and upgrading to maintain compatibility with QGIS and Python as these systems continue to evolve.
Funding partner
This project is supported by funding from the Australian Government Department of Agriculture, Water and the Environment as part of its Rural R&D for Profit programme.