11.1 A broad comparison with the counterfactual 105 11.2 Impacts at the farm scale 106 11.3 Impacts at the system scale 106 11.4 Impacts in geographic industry centres 117 12 Environmental outcomes and benefits 118 12.1 The method used to determine SDLs 118 12.2 Expected environmental outcomes 120 12.3 Measured environmental outcomes 122 13 Conclusions 124 Appendices 126 1 Socio-economic data 127 1.1 Metrics and indicators used in this report 127 1.2 Socio-economic context information 131 1.3 Regional Wellbeing Survey 144 2 Basin Plan Sustainable Diversion Limit (SDL) water recovery scenarios 149 2.1 Purpose 149 2.2 Discussion 149 3 Effects of the different approaches to water recovery 160 3.1 Buyback 161 3.2 Delivery system water savings 163 3.3 On farm water savings 164 4 Water price effects of water recovery 168 5 Expected future horticultural demands for water 176 6 References 181
between ‘those who trade water as a large part of farm water use’ and ‘those who pay higher amount for the water’ probability level There is an association between ‘those who have implemented on-farm irrigation upgrades’ and ‘those who have long term plan to use allocation trade’ Chi-square test 8.204 Significant at 0.004 probability level There is an association between ‘those who have implemented on-farm irrigation upgrades’ and ‘those who are reliant on allocation trade’ Chi-square test 6.597 Significant at 0.04 probability level For there is an association between ‘growing perennial pasture’ and Chi-square test 5.914 Significant at 0.05 probability level
‘having sufficient amount of water entitlement’ For dairy, there is a correlation between ‘herd size’ and ‘amount of High Reliability Water Share ownership’ Correlations r value = 0.70 Significant at 0.01 probability level For there is a correlation between ‘the size of the property’ and ‘herd size’ Correlations r = 0.80 Significant at 0.01 probability level There is no correlation between ‘size of irrigated land owned’ and ‘amount of High Reliability Water Share owned’ Correlations R = 0.226 Correlation value low There is no association between ‘growing perennial pasture’ and ‘allocation trade form a large part of farm water use’ Chi-square test 2.78 Not significant at 0.10 level
context ABS census data: on population, employment, income trends ABS census data: Employment on industry level ‘Agriculture, Forestry and Fisheries’. ABS census data: Employment at 3-digit ANZSIC categorisation in ‘Dairy Cattle Farming’, ‘Dairy Product Manufacturing’, ‘Fruit and Tree Nut Growing’ and ‘Fruit and Vegetable Processing’ and also 4-digit level. Regional Wellbeing survey: “People and communities” and “Farmers and agriculture” reports available. The “Environment and natural resource management” report should provide insights when released. Community relative vulnerability EBC 2011: provides classification The groups of metrics presented in the are consistent with the groups used in One exception is ‘Measure of rate of return per unit of water used’. The use of such metrics is not recommended. ABS discontinued use of the metric of gross farm budget per ML based on advice from the Productivity Commission, since such measures do not take into account the differing capital requirements and other fixed costs that vary between industries. Caution is advised if using these partial metrics. There are a number of metrics that will provide useful information in the future — notably when 2016 ABS Census data becomes available, when the Regional Wellbeing “Environment and natural resource management” report becomes available, and when the impact of a dry sequence can be observed given water recovery and changes to the mix of irrigation industries. As above, when using this set of metrics care is required in the attribution of change to water recovery. This section presents a set of data that provides the socio-economic context for the regions of northern Victoria. Information is included from 1991 to 2011 ABS Census on population, income, unemployment and participation rates, and the disaggregation of employment between the Agriculture/Forestry/Fisheries sector and other sectors. A common trend is the declining role of agricultural jobs in total employment.
Table 35: An initial assessment of mature irrigation requirements of existing crops in the Victorian Mallee and some projections of future possible demand Category 2009 area (ha) 2015 area (ha) % change Maximum application rate ML/ha 2009 Mature irrigation requirement ML 2015 mature irrigation requirement ML 2015 irrigation requirement plus 75% restoration of vacant land ML 2015 mature irrigation requirement plus 75% restoration of vacant land plus NID proposals ML 2015 mature irrigation requirement plus 75% restoration of vacant land plus NID proposals plus due diligence ML Almond 19,905 20,620 4% 14 278,670 288,680 288,680 316,400 365,640 Other nut tree 285 12 0 3,420 3,420 3,420 6,255 Wine grape 13,050 10,130 -22% 8 104,400 81,040 81,040 81,040 95,740 Table grape 5,730 7,295 27% 12 68,760 87,540 87,540 87,540 88,260 Dried grape 2,905 3,040 5% 8 23,240 24,320 24,320 24,320 57,320 Other grape 35 45 29% 8 280 360 360 360 360 Citrus 3,655 3,720 2% 12 43,860 44,640 44,640 44,640 45,840 Olive 3,625 12 0 43,500 43,500 43,500 43,500 Avocado 560 13 0 7,280 7,280 7,280 7,280 Stone fruit 580 12 0 6,960 6,960 6,960 6,960 fruit tree 565 155 12 6,780 1,860 1,860 1,860 1,860 unspecified tree 4,980 60 12 59,760 720 720 720 720 Nursery 180 12 0 2,160 2,160 2,160 2,160 Woodlot 250 0 0 0 0 0 0
Misc. 20 0 0 0 0 0 0 Vacant Perennial horticultural land 6,680 7,215 8% 0 0 0 48,701 48,701 48,701 Vacant > 10 years perennial horticulutral land 475 Total area perennial crop land 57,505 58,255 1% Maximum perennial horticultural irrigation requirements 585,750 592,480 641,181 668,901 770,596 Long-term perennial horticulture irrigation requirement allowing for continuous renewal (90% of maximum) 527,175 533,232 577,063 602,011 693,537