Farming systems and production management

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An assessment of the potential of remote sensing based irrigation scheduling for sugarcane in Australia : Final report 2015/082
(Cotton Research and Development Corporation, 2017) Sinha, P; Lamb, DW; Robson, A
There is currently no operational method of managing irrigation in Australia’s sugar industry on the basis of systematic, direct monitoring of sugar plant physiology. Satellite remote sensing systems, having come a long way in the past 10 years now offer the potential to apply the current ground-based ‘FAO’ or ‘crop coefficient (Kc)’ approach in a way that offers a synoptic view of crop water status across fields. In particular, multi-constellation satellite remote sensing, utilising a combination of freely available Landsat and Sentinel 2 imagery, supplemented by paid-for imagery from other existing satellite systems is capable of providing the necessary spatial resolution and spectral bands and revisit frequency. The significant correlations observed between Kc and spectral vegetation indices (VIs), such as the widely used normalised difference vegetation index (NDVI) in numerous other crops bodes well for the detection and quantification of the spatial difference in evapotranspiration (ETc) in sugar which is necessary for irrigation scheduling algorithms.
Developing remote sensing as an industry wide yield forecasting, nitrogen mapping and research aid : Final report 2013/025
(Sugar Research Australia Limited, 2017) Robson, A; Rahman, M; Muir, J
The science of Earth Observation (EO) is a rapidly developing discipline that has seen an unprecedented rise in remote sensing technologies and application development, including those in agriculture. The Australian sugarcane industry has seen a steady increase in the development and adoption of remote sensing applications over the last decade, predominantly as a result of investment by SRDC and then Sugar Research Australia (SRA). SRA project (DPI025), with collaborative support from Australia's sugar mills, grower's, research institutions and extension agencies, has been at the forefront of this evolution, evaluating modern remote sensing technologies and novel analysis methodologies for improved in-season yield forecasting and Nitrogen management, both issues identified as priorities by the industry. Accurate yield forecasting at the regional level is vital for the Australian sugar industry as it supports decision making processes including harvest scheduling, product handling and forward selling. At the farm scale, accurate yield mapping provides growers with a stronger understanding of in-crop variability, both spatially and temporally, thus supporting the adoption of precision agricultural practices to maximize productivity. Currently, yield forecasting within the Australian sugarcane industry is undertaken by visual inspection or destructive sampling by either growers or mill funded productivity officers. Although relatively accurate, these methods are labour intensive and are subject to the influences of varied seasonal climatic conditions, crop age and human error. Remote sensing technologies have evolved across many cropping systems as an accurate 'tool' for measuring in-season performance and for the prediction of yield, pre- harvest. This project, built on the initial findings of DPI021, further developed regional yield prediction algorithms derived from SPOT satellite imagery for 11 growing regions: Broadwater, Harwood, Condong, Isis, Bundaberg, Maryborough, Burdekin, Herbert, Tully, South Johnstone and Mulgrave); investigated novel statistical methods for improving prediction accuracies at the block level; and investigated time-series remote sensing based models for improved forecasting accuracies earlier in the growing season.
Remote sensing-based precision agriculture tools for the sugar industry : SRDC Final report DPI021
(2013) Robson, A; Abbott, C; Bramley, R; Lamb, D
This project aimed to develop remote sensing applications that were both relevant and of commercial benefit to the Australian sugar industry and therefore adoptable. Such applications included the in season mapping of crop vigour so as to guide future management strategies, the identification of specific abiotic and biotic cropping constraints, and the conversion of GNDVI variability maps into yield at the block, farm and regional level. In order to achieve these applications the project team reviewed an array of remote sensing platforms, timing of imagery capture, software and analysis protocols; as well as distribution formats of derived imagery products, to a range of end users. The project developed strong collaborative linkages with all levels of the industry including mills, productivity services, agronomists, growers and researchers and increased its initial coverage from three individual farms in Bundaberg, Burdekin and the Herbert, coinciding with project CSE022, to include over 33,000 crops grown across 6 growing regions (Mulgrave, Herbert, Burdekin, Bundaberg, ISIS and Condong) during the 2011/2012 season.

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