sugarcane , matching N supply to crop N requirements can lead to substantial reductions in N losses , and is weighted 60 per cent , the timing of fertiliser application is weighted 30 per cent , compared to application method which is weighted 10 per cent . The Paddock to Reef Sugarcane Water Quality Risk Framework is currently being updated and is provided as Attachment 1 . Note that as at October 2018 , this framework is being updated as part of the review of the Paddock to Reef program and will be checked with the final version as soon as it is available .
The alignment of the framework with related terminology including the previously used ‘ ABCD ’ management practices framework and industry BMP programs are shown in Table 4.1 .
Table 4.1 . Water Quality Risk Frameworks for the Reef 2050 Water Quality Improvement Plan and alignment with the ‘ ABCD ’ terminology and industry BMP programs ( generalised ).
Terminology |
|
|
Practice Standard |
|
ABCD |
A |
B |
C |
D |
Water Quality |
Lowest risk , |
Moderate-low risk |
Moderate risk |
High risk |
Risk Framework |
commercial feasibility may be unproven |
|
|
|
|
Innovative |
Best practice |
Minimum Standard |
Superseded |
Industry BMP ( generalised )
Above industry standard
( typically aligns with Moderate-Low risk but in some instances aligns with Lowest risk state )
Industry Standard
Below Industry Standard
Importantly : �
The suites of practices relevant to each pollutant are described in the frameworks . Not all of the practices in the production system are described , only those practices that pose the greatest potential water quality risk , through movement of sediments , nutrients or pesticides off-farm , are described .
� The majority of these practices also present productivity and / or profitability enhancements . �
Not all practices are equal . The frameworks allocate a percentage weighting to each practice depending upon its relative potential influence on off-farm water quality .
4.2 Principles for improving water quality from sugarcane areas
The following principles have been derived from the Burdekin WQIP 2016 and updated here with new information extracted from the 2017 Scientific Consensus Statement ( Chapter 4 , Eberhard et al ., 2017 ; sections led by Peter Thorburn [ nutrients ] and Mark Silburn [ pesticides ]).
4.2.1 Reducing nutrient losses
The biggest factor driving nitrogen losses in both runoff and deep drainage is fertiliser application rates and the timing of application . Managing irrigation efficiencies in the dry season is critically important .
Optimising fertiliser use in sugarcane is about matching nitrogen supply to match crop nitrogen requirements ( Thorburn et al ., 2013 ), factoring in timing of application in proximity to wet season rainfall events and subsurface application of fertiliser ( see Eberhard et al ., 2017 ). Expanding on this :
�
Lower nitrogen surpluses ( and lower nitrogen application rates ) result in lower nitrogen losses from fields ( Webster et al ., 2012 ; Armour et al ., 2013a ; Armour et al ., 2013b ; Rohde et al ., 2013a , Rohde et al ., 2013b ; Donaldson et al ., 2015 ). This is achieved through the development of nitrogen budgets based on the grower ’ s own yield expectations for specific blocks and ratoon numbers , and consideration of climate predictions , both seasonal ( for rate ) and weekly ( for timing of application ).
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