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A report commissioned by Australia's rural research and development corporations, Land and Water Resources Research and Development Corporation. Wright, V. 1983, Some bounds to the relevance of decision theory, Australian Journal of Agricultural Economics, vol. 27, no 3, pp. 221-230. Wynne, B. 1989, 'Sheepfarming After Chernobyl', Environment, vol. 31, no. 2, pp. 10-39. - A1 - Appendix A Kelly�s Role Repertory Grid The original version of Kelly’s repertory grid was called the ‘Role Construct Repertory Test’ (Rep Test), where ‘role’ refers to role titles of important or influential people in the client’s life (relevant to the clinical setting). The role titles are design to cover six groupings, including self, situational, values, family, valencies, intimates and authorities (Fransella et al. 2004). Kelly’s Rep Test originally, comprised 24 role title, including: a. A teacher you liked (or the teacher of a subject you liked); b. A teacher you disliked (or the teacher of a subject you disliked); c. Your wife or present girlfriend; d. (for women) your husband or present boyfriend; e. an employer, supervisor or officer under whom you worked or served and whom you liked (or someone under whom you worked in a situation you liked); f. your mother (or the person who has played the part of a mother in you life); g. your father (or the person who has played the part of a father in your life); h. your brother who is nearest your age (or the person who has been most like a brother); i. your sister who is nearest your age (or the person who has been most like a sister); - A2 - j. a person with whom you have worked who was easy to get along with; k. a person with whom you worked who was hard to understand; l. a neighbour with whom you get along well; m. a neighbour whom you find hard to understand; n. a boy you got along well with when you were in high school (or when you were 16 years old); o. a girl you got along well with when you were in high school (or when you were 16 years old); p. a boy you did not like when you were in high school (or when you were 16 years old); q. a girl you did not like when you were in high school (or when you were 16 years old); r. a person of your own sex whom you would enjoy having as a companion on a trip; s. a person of your own sex whom you would dislike having as a companion on a trip; t. a person with whom you have been closely associated recently who appears to dislike you; - A3 - u. the person whom you would most like to be of help to (or whom you feel most sorry for); v. the most intelligent person whom you know personally; w. the most successful person whom you know personally; x. the most interesting person whom you know personally. - A4 - - A5 - Appendix B Focus Group Reports Winchelsea Focus Group Report 7 June 2006, 10:00am, Winchelsea Hotel, Winchelsea, Victoria This focus group was a formal Best Wool group that had only been together for about one year, so most participants knew each other. The focus group followed a group session they had had that morning on monitoring. There were 12 people present. The area was formerly a dairy area, but was now predominantly sheep. There were, according to one participant, now “only about 90 cows left in the area.” The group was friendly, but a little uncomfortable about the focus group setting as indicated by a lack of commenting and verbal participation. Often comments were not said directly, but more in an offhand manner. This made recording difficult at times. Q. What tools and methods do you use to control parasites on your farm? This was a written exercise of about five minutes duration where people could quietly write down methods of parasite control, either by themselves or in consultation with a friend. No questions were asked about the types of information required at this stage. This exercise resulted in about 55 responses representing 20 different categories of control relating to worms, flies and lice. A summary list is provided below: Knowledge/Skill/Practice Times mentioned Grass management 2 Monitoring of stock movements off farm purchases etc 1 Feeding 1 Grazing management (stubbles) 1 Time of drenching (1-2 summer) 1 Vets 1 Rotational graze/paddock rotation/rotate mobs 3 "Safe" paddock 1 Fly traps 1 - A6 - Crutching 5 Mulesing 8 Back Line 2 Jetting (Click) 5 Dipping 1 Maintaining boundary fences 1 Running a closed flock 1 Drenching & capsuling 1 Drenching 7 FEC/Worm test/WEC 9 Drench resistance tests (2-3 years) 3 Q. How should we categorise the list of parasite control methods? Response sheets were collected and placed up on a wall for all to see so that the group could run through a categorisation exercise of placing like things together. The items were broken down into lists under parasite type, including worms, flies and lice. Participants were asked during the process to verify the categories for accuracy. Q. Which items on your list are also on the researchers’ list? Participants were haded a list containing 20 items nominated by the IPM-s researchers as being most important to integrated parasite management and parasite management in general. Participants were asked to look at the list in their own time and to independently mark off things on the list that they believed they had mentioned. The group then ran through the list and probed about items that may not have been mentioned or which required more clarification to assess whether items mentioned by the group were similar to the researchers’ list despite typically being less descriptively written. For instance, Researcher list item number 18: Only drench when monitoring or planning indicates a real need. The group was asked whether this was an important part of drenching and at least three participants indicated that monitoring helped them determine when to drench. When asked about farm management plan, one respondent stated: “We do have a plan, you don’t want to put another mob in the paddock if it is not clean” - A7 - This was indicative of the general feeling that written or formal plans did not exist for most people and that management was more intuitive or based on knowing in their head what has happened on the farm. This was indicated also for maintaining written paddock histories. Scouring was a confusing issue, with some participants unsure and mocking how this could be done. One participant however indicated: ”you can stop it by keeping them healthy” when another person questioned how you would stop scouring. The use of cattle was not mentioned by the group and this is indicative of the lack of and dislike for cattle in the region. A list of the items not mentioned by the group is provided below: Management tool not mentioned by group Comments 19. Make sure parasite management activities are part of overall Farm Management Plan 1 & 2 closely related 20. Understand how and when parasites develop etc 16. Use insecticide appropriately etc 10. Monitor ewe condition scores etc 7. Use breeding strategy to produce worm resistant sheep etc 5. Keep history of worm egg counts etc 14. Management to minimise scouring 9. Use sheep/cattle interchange Upon seeing the list of items on the Researchers’ list, one participant felt the need to defend his and the group’s knowledge by stating: “all the things you have listed up hers, I was thinking physical things, such as mulesing...I wasn’t thinking about pasture management. I would say that the majority of the things up there, we all do.” Q. How would you prioritise the list of parasite management tools? This exercise gave the participants a chance to indicate what was important to them in terms of parasite management, using both their own and the Researchers’ list. This went a way to alleviating somewhat the feeling that the participants were not knowledgeable sheep managers because they had not mentioned all the things on the Researchers’ list in the same way. - A8 - Worms There was some debate about whether, with regards to worm management, monitoring came first or drenching. Grazing management was placed high on the list after theser two, despite one participant asking the person who nominated “how do you use it? What do you actually do?”. The participants also used this section to ask each other and their group leader about some of the management techniques, such as “Once you drench the mob, should they go on a different paddock every time?” Participants repeatedly indicated that season was a big impact on management practices for worms. All participants indicated that they had conducted a drench resistance test, though it was not asked whether these were formal or informal.Supplementary feeding was indicated as being very important by one person, and was related to ‘nutritional needs’ of the sheep rather than part of a grazing management plan. There was a mix of how important vets were to decision making, with some people indicating they always used an advisor and others stating they made decisions themselves. The list form worm control was prioritised in the following order and was verified with the group at the time: Priority Worm management practice or tool 1 FEC testing & Vet advice 2 Drench resistance tests & Rotation grazing 3 Drenching 4 Grazing management & Mob rotation (clean paddocks) 5 Nutrition - supplements Flies There was unanimous agreement that mulesing was the main strategy for control of flies, despite issues relating to animal welfare activists. Genetics was proposed as being important by one participant was not placed on the list at the time. Discussion of genetics was taken up again at the end of the prioritisation for flies, with one participant very keen to have it on - A9 - the list specifically for flies, stating: “it’s something that always comes to mind when you are buying sheep.” This same participant indicated that they use EBVs when purchasing sheep – typically EBVs are related to the heritability of worm resistance, not flies. Inclusion here indicates a potential misunderstanding about what EBVs are used for, however it could also indicate the persons concern that we hadn’t talked about genetics in the context of worms earlier. Another participant stated: “there’s more so in the bloodline rather than EBVs”, indicating a perception that EBVs do not capture all elements of genetics for some producers as well as the understanding that bloodlines, including traits such as wrinkly or smooth skin, can be an important consideration for fly control, since sheep with wrinkly skin seem to be more likely to get flyblown due to the moisture getting trapped in the skin folds. Crutching was a key practice, and one participant wondered if drenching should also be on the list (though this practice typically relates to worms). Back-lining was considered a ‘costly’ control method, with most indicating their preference for jetting instead. One person indicated that they also use flytraps. The list was verified during the session. The priority list for fly control was: Priority Fly management practice or tool 1 Mulesing 2 Crutching 3 Jetting 4 Click 5 Back-lining 6 Drenching 7 Fly traps - monitoring 8 Genetics (back of mind consideration) Lice Lice was a fairly easy category, with the methods seemingly cut and dry. The discussion lasted about five minutes in total. Fencing was seen as important and related to this were neighbours, with one participant stating “Choosing your neighbours” as being an advantage to lice management. The resulting prioritised list was: - A10 - Priority Lice management practice or tool 1 Back-line 2 Dipping 3 Fencing management - boundaries 4 Quarantine 5 Closed flock Q. What are the positive and negative aspects of these management practices? At this point participants were again asked to write down on different coloured sheets of paper the positive and negative aspects of each management practice. Each practice was run through sequentially, starting with FEC testing. When all practices were completed, these were complied and participants given the opportunity to comment. The most common factors affecting management practices for all tools were cost and time. Although mulesing attracted a comment also about negative perceptions from animal activists. The social perception of this practice was also acknowledged however as being positive as it has led to the development of new technologies for combating fly strike. Q. How would you prioritise the leftover researchers’ list? Participants ranked “Parasite management as an overall farm management plan’ as the most important on the researcher list. The producers in the group did not mention having a farm management approach on their list, but as pointed out by one participant earlier, the group was more likely thinking of practical tools and ideas. However, IF a management plan was a frequently used tool, I suspect someone would have mentioned it. Second ranked was ‘Understanding how the parasite works’, which is again something that was not mentioned by producers. 3. Monitor use of insecticide, followed by 4. Using insecticide appropriately. There some some discussion about which of the latter two should come first and a vote was taken in favour of monitoring. Having a breeding strategy was agreed at number 5, this was something producers had mentioned in their own way, - A11 - Rotation using cattle or other (such as stubble) was nominated as number six (despite one producer indicating earlier on that there were not many cattle in the district), but relegated to last after participants decided that keeping a history of FEC was more important. Management to minimise scouring ranked as number 7. Q. What are the positives and negatives for the leftover items? � In discussing having a FMPlan comments included: ‘most important tool for maintaining animal health’, and ‘prevention rather than cure’, ‘Knowing which drench to use’. The comments indicate a seemingly strategic approach was favoured by some of the participants, even though having a plan was not mentioned in their list. � Comments made about understanding parasite biology and epidemiology included: ‘knowing when to drench’, ‘knowing how long they (worms) stay in the ground’, and ‘grazing management’. So even though this was another piece of management not mentioned by the producers, they did have a good understanding of how such knowledge was useful. � Comments on positives and negatives for Monitoring use of insecticides included: ‘Protecting your markets’ - A12 - ‘making sure that its always there’, ‘Checking to see if it works’. A negative mentioned was time. � Monitoring ewe condition scores etc, received the following positives: ‘Knowing what to feed’, Better lambs’, ‘More lambs’, ‘likely to survive parasites’, ‘better control’. Negatives included: ‘trying to find the sheep’, � Comments on Using Breeding Strategie: ‘Availability of genetic information’. � Comments on Keeping a record of FEC: ‘More book work’. The latter comment was typical of anything involving record keeping that was not ‘in the head’. � Comments on Management to minimise scouring included: ‘Clean sheep, less crutching’ (Positive) - A13 - ‘Need to find cause of scouring’ (Negative) � Comments on Use of sheep/cattle interchange include: ‘Clean paddocks’, ‘Get to feed them grass. If you were rotating crops, you can give them better grass.’ ‘Got to run cattle’ (Negative) ‘Got to feed cattle’ ‘Need bigger fences’ (because of cattle) Someone asked whether other were using wethers instead of cattle. - A14 - Dunkeld Focus Group Report 8 June 2006, Royal Mail Hotel, Dunkeld, Victoria This was a Best Wool group again, with the difference that it had been together for about 10 years in various forms. This group have participated in several courses, such as Risk Assessment and ProGraze. Participants are well known to each other and were very open about some antagonisms amongst members approach to sheep production. Q. What tools and methods do you use to control parasites on your farm? This was a written exercise of about five minutes duration where people could quietly write down methods of parasite control, either by themselves or in consultation with a friend. No questions were asked about the types of information required at this stage. This exercise resulted in about 93 responses representing 74 different categories of control relating to worms, flies and lice. A summary list is provided below: Knowledge/Skill/Practice Times mentioned Drench into clean paddocks if possible 1 New products and advice from Vet 1 stray stock, fencing etc 1 paddock history 2 weather conditions 1 Class of stock - ewes with lambs 1 High copper mineral supplement ad-lib for sheep 1 Contract sheep dipping when the neighbours give us lice 1 Double fenced nearly all boundaries 1 FEC before drenching sheep 1 FEC weaners when required past autumn (June/July) 1 Clean paddocks through capsules 1 Clean paddocks past hay making (weaners) 1 Follow dry stock with weaners or lambing ewes 1 Breeding large % prime lambs so that they don't stay past 6-8 months old 1 Time of drenching 2 Capsules for vulnerable sheep - weaners & lambing ewes in winter - if necessary 1 Capsules pre-lambing in Xbred ewes in April 1 Drench into new paddock 1 Drench merino ewes at weaning 1 Drenching plan - early summer drench and pre-winter drench 1 - A15 - Leave some 'good condition' (CS 4+) sheep in mob undrenched at the summer drench 1 White and clear drenches 1 Rotational grazing to break worm cycle 1 Set paddocks in a rotation used by only one class of sheep 1 Drench/ capsule for worms 2 Use drench found to be suitable after drench resistance test (every 2 years) 3 Personally monitor egg counts in mobs - drench when count above 200 1 Drench based on faecal egg counts 3 Strategically monitor WEC - let some young sheep get exposure to build immunity 1 Worm counts 3 Scouring 1 Loosing condition 1 Worm counts, use during winter to help sheep condition 1 Stock rotation, especially for weaner management 1 Use stubbles over summer 1 Use cattle to help clean up worms 3 Mulesing and click at lamb marking 1 Extinosat for fly strike - not an organophosphate so less dangerous for oeprators 1 Don't put unnecessary pressure on the worm population to develop resistance - only drench when necessary 1 Visual assessment/observation of sheep for drenching (rubby, dirty sheep) 2 Rotate drench types 2 Wean lambs into hay paddocks if possible or else cleanest possible 1 Don't drench all the sheep in a mob 1 Capsules as a last resort when alternating drench types 1 Assess stock condition to measure vulnerability to worms 1 Drench pre-summer 1 Crutch sheep prior to november for fly control 1 Apply Click to mulesed lambs (flys) 1 Mules lambs (flys) 1 Dock tails on sheep (Flys) 1 Run more Xbred sheep (flys) 1 Keep sheep out of long grass (flys) 1 Shear in January (flies) 1 Jet rams in November and February (Flies) 1 Paddock topography 1 Drench resistance tests 1 Graze paddocks heavily pre-summer to reduce shelter for worms 1 Rotate dry stock after ewes to consume worms 1 Drench sheep November and February with option of mid-year 1 Prevent stray sheep from entering property - adequate fencing 3 Prevent stray sheep - close gates 1 prevent stray sheep - capture strays on road 1 Dip with diajina if lice present 1 Purchase lice free sheep 1 Lice control - monitor for rubbing sheep 1 Lice - dip or Backline if required 2 Flies - shear 2 Flies - crutch 2 Flies - lamb marking 1 - A16 - Flies- Dressing (Click) 1 Apply Extinosad to fly blown sheep 2 Dip sheep for lice when required - about every 4 years 1 Extinsoad at lamb marking 1 Q. How should we categorise the list of parasite control methods? This was performed by Lyndal on the computer into Worms, Lice and Flies and participants were asked if they agreed at the time. Q. How would you prioritise the list of your parasite management tools? This exercise gave the participants a chance to indicate what was important to them in terms of parasite management, using both their own and the Researchers’ list. This went a way to alleviating somewhat the feeling that the participants were not knowledgeable sheep managers because they had not mentioned all the things on the Researchers’ list in the same way. Worms There was a lot of chatter in the first few minutes about whether drenching or drench resistance testing or cleaning paddocks was the number one priority. It was eventually decided on ‘just drenching’ because ‘there’s no point doing FEC if you can’t drench’. Everybody then agreed that number two should be FEC. DRT was listed as third behind FEC, followed by ‘something about paddocks’ (Cleaning paddocks). Although most agreed on cleaning paddocks being important, there was one dissenter who stated ‘There’s no such thing as a clean paddock’. Visual assessment was rated at 5. When questioned about rotation grazing, this sparked a conversation about being able to clean paddocks again, with one person indicating they have no spare paddocks for this and another saying they didn’t clean and that it should be rated low. Drench planning was rated next and then finally rotation grazing. - A17 - Some people indicated they rotated sheep and cattle, and this seemed to be more common and accepted within this group as compared to the Winchelsea group. The list form worm control was prioritised in the following order and was verified with the group at the time: Priority Worm management practice or tool 1 Drenching 2 Drench on FEC 3 Drench resistance tests 4 Clean paddocks 5 Rotate drench types 6 Visual assessment before drenching 7 Rotate dry stock before ewes 8 Use capsules 9for cleaning paddocks) 9 Drenching plan (summer) 10 Worm control paddock history 11 Rotation graze (spell paddocks) 12 Wean lambs into hay/Use of crop & sheep/cattle rotation (clean paddocks) 13 High copper mineral supplement 14 Don’t drench all sheep at once Lice Dipping was suggested as number one, isolation fencing was suggested as number 2, but following some discussion this was swapped. There was some dissent about the benefit of fencing if you already have lice. Shearing was thought to be very important by some, but ended up at the bottom of the list. Priority Lice management practice or tool 1 Fencing 2 Dipping 3 Purchasing lice-free sheep 4 Jetting and backliner 5 Shearing Flies Mulesing was suggested as top for flies, followed by crutching and lamb marking and breeding. Someone suggested paddock selection, and in particular having long grass that ‘the breeze can’t get through’, as being important to flies. Effective chemical control was also mentioned for this group. - A18 - The priority list for fly control was: Priority Fly management practice or tool 1 Mulesing/Shearing/Crutching 2 Lamb marking 3 Jetting 4 Breeding/Genetics 5 Foot health 6 Paddock selection 7 Drenching (daggy sheep) 8 Effective chemicals Q. Which items on your list are also on the researchers’ list? There was some discussion about the use of plans, some people indicated that ‘the vet does it for you’, while others said the drench plan was ‘on the drench’ and another stated ‘we know what works.’ So there are a variety of approaches to planning. When questioned about worm life cyles, initially someone indicated they had a good idea and in terms of drenching ‘that’s why we say January’, however upon further explanation of knowing about the worm lifecycle, and then asked are they conscious of these thing, the same person answered: ‘I think not so conscious of it.’ Another however stated ‘The eggs that are dropped in August/September are the ones that will hit you in April/May.’ It was not specifically mentioned, and most did not obviously use such knowledge in management (as indicated by the comments relating to worm management planning), so understanding of the worm lifecycle was left on the researchers’ list. Number Knowledge/skill/practice 1 Use various methods to maintain the effectiveness of drenches 6 Minismise sheep deaths and weight loss through acceptable drenching program e.g. consult Drench Decision Aid, WormBoss etc. 8 Use breeding strategy to produce worm resistance sheep and reduce scouring 10 Monitor ewe condition scores at lambing as well as weaner body weights. Have set targets for these 11 Have a plan for using feed supplements to maintain sheep health and bodyweight when needed 12 Quarantine new or sick sheep 14 Management to minimise scouring 20 Understand how and when parasites develop and when they are most vulnerable and most active. Use this information actively to make parasite management decisions - A19 - Q. What are the positive and negative aspects of these management practices? Below is a list of the positives and negatives about the management practices listed by the producers: List of things they do or need to know to control parasites Positive Negative Drenching kills worms handling stock assess condition through handling labour intensive expensive drench resistance tests not drenching unnecessarily getting samples, complicated - understanding resistance issues, effective drenches save money save work long term prolonging use of drenches drench based on FEC reduce need to drench getting samples saving money time consuming prolong effectiveness of drenches cost of sending off and analysis labour saving waiting time for results learn about whats going on - effects of clean and dirty paddocks vulnerability awareness increases rotate drench types prolonging use price increases for new type and combination rotate sheep and cattle cheap need to have cattle - or have a lot of cattle complimentary pugging the ground destroy dams dirty the water end up feeding cattle as well as sheep (e.g. grain) need facilities to handle cattle visual assessment before drenching (experience - based???) more accurate could be other causes pre-warning foe need to FEC only see part of the story most important tool don't drench all sheep at once leave non-resistant worms in mob not getting total kill rotation grazing (spell paddocks) grow more grass infrastructure Cleaner paddocks sheep become disoriented use capsules (for cleaning) 100-day worm control, cleaner paddocks worms go mad after 100-days Cleaner paddocks expensive peace of mind may promote resistance through trialing - A20 - less labour application is time consuming and frustrating less crutching long-term sheep health more lambs capsule remains inside sheep more wool better return save drenching better prime lambs and weaning weights later drenching of lambs drenching plan (summer) it works requires flexibility don't have to make decision yourself action may not be necessary use of crop rotation to clean paddocks reduces need to drench expensive reduces exposure to drenches ploughing the paddock cash crop must be part of a pasture management plan wean lambs into hay (clean paddocks) Lamb health - better growth rate - reach target weights could still be wormy reduces need to drench need to test to see if wormy reduces exposure to drenches cash crop high copper mineral supplement maintain health another job! Benefits not proven worm control paddock history saves drenching record keeping better knowledge of what’s happening with stock & paddocks put sheep in 'right' paddock rotate dry stock before ewes saves drenching maintaining quarantining periods better knowledge of what’s happening with stock & paddocks dry does not mean clean - need FEC to tell put sheep in 'right' paddock feed consumption when needed for ewes clean paddocks (some disagree) saves drenching (all of above) achieving it! better knowledge of what’s happening with stock & paddocks put sheep in 'right' paddock There was a discussion about the benefit of visual assessment, with one of the older group members stating that he thought it was more accurate. This was a brave statement as he was concerned that other group members were going to ‘jump on him’ for saying it. He did however think that FEC was inaccurate. The wife of a family who did the FEC at home indicated that she thought FEC testing was ‘deceptive’ but did not wish to further this statement. The general gist was however that there were issues with low egg counts saying the sheep were OK, but then the same mob having trouble not too long afterwards. - A21 - Eventually most people did agree that there was not much negative about visual assessment, but that it could be that the sheep sometimes looked ‘crook’ due to something other than worms. There was debate about the practice of not drenching all the sheep at once and leaving some ‘well conditioned’ sheep un-drenched. Some did not understand the argument for this and it was agreed that the ‘jury is still out’ on this practice. The use of capsules also rated a mention, with one producer stating that they were good for cleaning paddocks because of a 100 day control period, while another stated that the 100 days was negative because ‘the worms go mad’ after 100 days. Another found many good points, but yet another stated that the ‘application time is consuming and frustrating’. Another negative related to concern over the sheep ingesting the capsule and it being plastic and not breaking down. An interesting comment made about the positives of drench planning was ‘it means you don’t have to make the decision yourself’, which was not an uncommon feeling about some of the more prescriptive practices. Others though however that a plan meant you might be drenching when in fact it was ‘not necessary’. Crop rotation also produced polar comments, with one supporter stating the benefits were: ‘reducing the necessity to drench, therefore reducing exposure to drench, reducing resistance’. While another countered with: - A22 - ‘its irrelevant. It doesn’t reduce worms’. This is typical of the polarised opinion as to whether cleaning paddocks is something you can actually do. There was also debate about the usefulness of using supplements. People saw paddock rotation as expensive (due to infrastructure and having to keep sheep off some paddocks). This also related back to the cleaning paddocks debate, with the group members quite comfortable with the level of dissention within the group. Q. How would you prioritise the leftover researchers’ list? Q. What are the positives and negatives for the leftover items? These last two were not completed due to the length of previous discussions. - A23 - Glen Innes Focus Group Report 11 July 2006, 10:00am, Glen Innes Ex-Serviceman’s Club, Glen Innes, NSW This focus group was very small, but producers present held a wide variety of views and had a variety of experience. There was a father-son team in this group. One of the participant had been in farming a very long time and had recently sold-up but felt he had much to offer in the way of perspective. Q. What tools and methods do you use to control parasites on your farm? This was a written exercise of about five minutes duration where people could quietly write down methods of parasite control, either by themselves or in consultation with a friend. No questions were asked about the types of information required at this stage. This exercise resulted in about responses representing different categories of control relating to worms, flies and lice. A summary list is provided below: Times mentioned Knowledge/Skills/Practice 3 Rotational grazing - clean paddocks 3 Rotation with cattle - clean paddocks 1 Capsules 1 Drench strategy 1 Adequate and good feed 1 Good nutrition 2 drench resistance testing 2 Worm monitoring (FEC) 1 Quarantine new sheep 2 smaller paddocks 1 Adequate supply of good quality water 2 Ram selection (EBV, natural resistance) 1 Mob size 1 Length of joining 1 Keep sheep in medium condition (not below score 3) 2 mulesing 1 crutching 1 Fly treatment as lambs 2 culling 1 Only use chemicals as last resort (flys) (except at mulesing) - A24 - 1 Treat only as required (cattle lice) 1 Dipping (only when lice imported) Paddock history - planning 1 Have a Quarantine strategy - 1 Appropriate and strategic use of insecticide – monitoring, observe witholding periods, resistance management etc 1 Visual Assessment 1 Wean early (?) 12-14 weeks requires planning for clean paddocks and feed etc 1 Have a lice biosecurity plan – regular monitoring, quarantining, fence maintenance etc. � Rotational grazing was mentioned as a ‘very effective weapon’, while another producer indicated that keeping sheep in medium condition mean ‘they’re not being challenged, that’s a minimum.’ Chris noted that the categories were very broad and attempted to ‘extract a bit more information’ to narrow them down somewhat. � There was some conversation about monitoring, with one producers indicating he did it all the time, and another stating: “We have in the past, but it sort of fell by the wayside due to other pressures.” One producer indicated that the RLPB had been doing FEC tests on their sheep for a few years, And in terms of frequency, another, younger producer, indicated that they FEC tested about 6 weeks after every drench treatment. And that this was done six weeks regardless. This producer did his own testing and only sent the samples away if there was a problem. � On the issue of drench resistance, one producer indicated that they had resistance on their property and therefore tested every year, the fellow who had the RLPB board doing FEC indicated that they also did DRT. The older producer in the group, Paul, indicated that he had never done a DRT and that he believed rotation grazing: “was the most effective method of beating drench resistance” His full speech is below: - A25 - “I have never tested for drench resistance. I have done some monitoring. Personally I think that the most effective method of beating drench resistance worms is by a planned system of rotation. Drench you sheep with the most effective drench, put them in a paddock that has been filled for six weeks to two months. Depending on rainfall. We might hammer it hard to knock the feed down. Like put 200 cows or calves in a paddock for two weeks, just to knock it down. I think rotation grazing is the most effective long-term method that we will have. It requires a little bit of planning, you have to get out your pencil and paper and get down your paddock and work out what you have. What you can do, what you think you can carry. And for people that can do that, they will be surprised that they have more feed than what they thought they had. Because if you put sheep in a paddock and there’s heaps of feed, they will sit in the corner and eat that out. But if you put a thousand sheep in a paddock you are forcing them to eat it. We have to change the way we run stock. We have to change our mindset. That’s far more effective than any other thing that I can think of, that includes monitoring them.” Mark, who used to farm in southern Queensland until 5 years ago, indicated that paddock rotation was vital due to the seasonal conditions out west where flooding meant there was a need for clean paddocks where the sheep wouldn’t fill-up with worms. In drought years it also meant you had some green pick if you had spelled paddocks. � One producer thought ram selection and, in particular, where they come from is very important. He stated: “P10 - I think the ram selection is an important one. I think where the ram comes from is very important. We basically won’t buy a ram that is a big negative ram, but any improvement we can get without comprising the other selection criteria. Lyndal - What are some of the other selection criteria. - A26 - P10 - Basically, density, nourishment, Lyndal - So nutrition is very important. Do people see that nutrition is a good worm management control. P11 - That’s number 1. A healthy sheep won’t attract parasites.” There was strong support for genetics being a key issue with more research needed, such as they believed had occurred in the cattle industry. There was general consensus that resistance was heritable, although one producer indicated his confusion with it: “That’s beyond us, quite frankly. You go to a bull sale and you get a print out that is the size of a book. A lot of it, we are confused now. You used to look at a bull and think that was a good looking bull.” � Given the good understanding and general acceptance of the benefits of testing, the group was asked about visual assessment as a technique for worm control. One producer stated: “once the sheep are showing signs, it’s past. If the sheep are healthy, that doesn’t say to me that they are not carrying worms” This was a comment that occurred several times in focus groups and later in interviews. Q. How should we categorise the list of parasite control methods? Response sheets were collected and placed up on a wall for all to see so that the group could run through a categorisation exercise of placing like things together. The items were broken down into lists under parasite type, including worms, flies and lice. Participants were asked during the process to verify the categories for accuracy. - A27 - Q. Which items on your list are also on the researchers’ list? No. Practices/Skills/Knowledge 1 Use various methods to maintain the effectiveness of drenches 6 Minismise sheep deaths and weight loss through acceptable drenching program e.g. consult Drench Decision Aid, WormBoss etc. 8 Use breeding strategy to produce worm resistance sheep and reduce scouring 10 Monitor ewe condition scores at lambing as well as weaner body weights. Have set targets for these 11 Have a plan for using feed supplements to maintain sheep health and bodyweight when needed 12 Quarantine new or sick sheep 14 Management to minimise scouring 20 Understand how and when parasites develop and when they are most vulnerable and most active. Use this information actively to make parasite management decisions The following comments were also recorded. Paul was quite happy with the researchers’ list, stating: “I couldn’t object to any of those, quite frankly. But as I’ve said before, I think that rotational grazing as the most effective long-term measure. And the fringe benefit is that you have a better control over your feed. You know how much feed you have several months ahead.” He also talked about the need to wean lambs early as they pick up worms from mum. The other benefit that the mother’s resistance builds up more quickly since: “everyone knows that when a ewe is looking after a lamb their worm resistance drops to zero.” There was general agreement about the early weaning, with one other producer noting that they had noticed this at Cobar. � None of the producers had a worm management plan, though one producer had a vet advising him. Most indicated that they had an understanding of the worm lifecycle, but still used heuristics such as ‘there is a rule out west that you drench within three weeks of rain’ etc. Most did not agree with using a plan such as wormkill, due to seasonal conditions, reliance on rotation grazing (Paul), with Paul stating: “I rely on my own judgement. I’ve been around for a while.” - A28 - � Participants were asked about weighing, and one indicated that he didn’t need to because he could assess condition visually. He also indicated he scanned ewes for twinning, and separated out any such ewes. This practice was also carried out by at least one other participant. � Participants were also in favour of rotating sheep with cattle. � Every participant indicated that they quarantine new sheep, or used cydectin and a follow-up to kill anything new sheep had inside. � In conversation about fly management, nobody had a set strategy (except one had a set plan for lambs). Another indicated that they bought only Merryville stock since they are a plainer skinned sheep. � Shearing was viewed as an important strategy for blowfly control. � The need to know about withholding periods was answered with respect to having to fill in forms when selling sheep at the saleyards and the need to keep chemicals out of the wool. � In conversation about lice, one producer stated: “Getting rid of lice is not an issue, it’s about being reinfested. We only treat for lice if we import them. We’re pretty lucky here because there aren’t too many people running sheep. The resistance to lice is just as great as resistance to worms.” Another producer agreed that neighbours’ stray infested sheep were a big problem. There was some discussion about whether parasite management or nutrition was the number one priority in sheep management. The conversation ran as follows: “P58 - Parasite management is number 1. You don’t go along and put these sheep here. You say that you are going to do this. P59 - I tend to disagree. I say number one is nutrition, and concentrate on pasture improvement and winter cropping. And that tends to correct your nutrition problems and that will bring about parasite control. And other parasite control will follow on from that.” - A29 - � When questioned about understanding of the worm life cycle, participants generally believed everyone would know about it because of access to information. Some comments included: “Chris - Do you have a reasonable understanding of the life cycle of the parasite. P64 - The department has promoted it over the years and I believe that there is a good understanding out there. Chris - That’s were you need a good understanding for your rotational grazing. P65 - There is a lot of access of information to that. The departments, the Unis have a lot of information.” Q. How important is are cost and time to management choices? A general question was asked at the end about how important time and cost were to management. The following comments were received, indicating that market return in particular was the issue rather than costs in one sense. That is, they weren’t necessarily concerned that some of the things they needed to do were expensive, more that there was not enough return in the industry to be able to afford some of these things: “P68 - I could answer that by saying that if we were getting more out of the industry we could afford to do it. The ways things have been, economic pressures on us we can only do so much. We can’t do all the things the greenies think we should be doing because there is not a sufficient return on the industry. “ Lyndal - With testing, I know that you said you had the Board doing it for you. If the Board didn’t do it would you say that time and cost has stopped you fro doing it. - A30 - P69 - Yes, we did the best we could. We knew that we couldn’t afford more people to do these things. P70- I’ve got to go, but my parting shot is to say that I agree with everything said in that regard, but because of worms, what you essentially do is you fence up while it will cost you money, it can also produce a greater return simply because you have to manage your properties better. Technically, it should put more money in your pocket. P71 - I’ve got two years wool in my shearing shed. We’ve got cows and calves and that pays my grocery bill. Everyone who runs sheep will tell you the same thing, you can never leave the bastards alone. If you’re not shearing them, you’re drenching them, if you’re not drenching them, you’re marking them, if you’re not marking them, you’re treating them for fly strike, the cycle is never ending. We put cows and calves in the paddock and we brand them and that’s it.” - A31 - Walcha Focus Group Report 14 July 2006, 10:00am, Walcha Ex-Serviceman’s Club, Walcha, NSW This focus group was comprised mainly of younger farmers, many of whom were very focussed on genetics and new technology. There was an older famer there who seemed quite interested in what the younger group had to say, and also offered a different perspective to theirs on the situation, past and present. Q. What tools and methods do you use to control parasites on your farm? This was a written exercise of about five minutes duration where people could quietly write down methods of parasite control, either by themselves or in consultation with a friend. No questions were asked about the types of information required at this stage. This exercise resulted in 47 responses representing 25 different categories of control relating to worms, flies and lice. A summary list is provided below: Times mentioned Practices 1 Drench for fluke in April and spring 1 Check for lice at shearing 6 Worm egg counts (FEC) 2 Drench resistance testing 4 Rotate drenches 2 Cull sheep with body strike 1 Mules as lambs 4 Paddock rotation/rotational grazing 4 Rotate sheep and cattle 1 spell paddocks 2 genetics/breeding for resistance 1 mix drenches 1 pasture length 2 crutching 1 short wool thru summer 1 click 2 jet lambs 1 capsules 1 injectible drenches 1 Drench young sheep into spelled paddock - A32 - 3 dipping 1 breeding away from flies 2 nutrition management 1 cell grazing 1 Backline after shearing � While discussing FEC testing one producer indicated that there was no set routine, the strategy was mainly seasonal and impacted by visual assessment to some degree. � One person indicated that they had not done a DRT for 5 or 6 years. There was some confusion between FEC testing and DRT, with a couple of producers using them interchangeable and one producer indicating that they did their DRT at home – indicating the use of informal DRT rather than a formal test such as drench rite. On average it seemed that the producers checked their drenches in some way every 4-6 years. � There was a discussion about the efficacy of the different drench types, with Levamisole considered useless and Rametin highly regarded. One producer indicated that he believed that if you did not use a drench for a while “they’ll go back up again” in terms of percentage efficacy. One of the younger producers indicated in a discussion on grazing management systems, that stock management was definitely an issue and indicated that “you have to break the worm lifecycle.” His comments included the following: “You need to break the worm cycle. Worms can’t live on the ground forever and a day, they need to be inside the sheep. So if you can spell the paddock for a certain time frame then it’s a good idea. The younger sheep are more susceptible than the older sheep, so if you can rotate from lambs, to lambing ewes, to wethers so not having a set pattern. Using cattle on the place for a while.” Some producers indicated they used cattle in their rotations. But mainly for spelling the paddock, with cattle on one year and sheep the next. Their rotation was based on their own experience with no consultant advice. One older producer commented about consultants in the following way: - A33 - “I agree with the boys. You can talk about consultants if you want to, but I find that we’ve used one or two, but an old friend said that the best consultant is to find someone who is doing well and look over the fence.” � During a discussion about selective breeding and genetics, some producers indicated that they were considering it, but one producer stated: “I don’t think there’s enough information to say that one breed is better than another.” Another indicated that he was selectively breeding in a ‘roundabout fashion’ by putting rams that did not perform as well ‘in the cool room.’ Genetics and selective breeding was also considered important to flystrike, with one producer stating: “With all our sheep anything that gets body struck is automatically culled.” Another indicated that there was a focus on bad shoulders and breeches, stating: “...then you’re breeding away from an area that is going to attract flies.” � With regards to nutrition and supplementary feeding, one producer commented: “I know if you want to feed them extra protein you’re going to kill out a lot of the worms. It’s not something that we aim for.” Others indicated that they aimed to keep sheep in a certain condition, with another specifically indicating a weight range of about 45-50kgs. Producers indicated that they saw a relation between sheep in good condition and parasite problems, with one producer stating: “Apparently there’s a bit of research coming out that meatier sheep, those carrying more meat, actually have a better ability to resist worms as well. I think it might flow through by default. If you - A34 - grow sheep for better wool, then they are meatier and then they will be more resistant to worms. If they have more protein in the system, they will be able to fight worms. It’s only just come out.” Q. How should we categorise the list of parasite control methods? Response sheets were collected and placed up on a wall for all to see so that the group could run through a categorisation exercise of placing like things together. The items were broken down into lists under parasite type, including worms, flies and lice. Participants were asked during the process to verify the categories for accuracy. Q. Which items on your list are also on the researchers’ list? The list of items remaining included the following: List No. IPM-s Practices 1 Have a planned strategy for maintaining drench efficacy on farm using various techniques (manage drench resistance) 2 Test regularly for drench resistance (every 3 years) 4 Use WEC to determine drench strategy 5 Use WEC history of paddocks for setting weaning and lambing paddocks 6 Minismise sheep deaths and weight loss through acceptable drenching program e.g. consult Drench Decision Aid, WormBoss etc. 7 Have breeding strategy for worm resistant sheep and less scouring 10 Set targets and monitor for ewe condition scores at lambing and weaner bodyweights 11 Nutrition - Able to identify supplementation strategies 14 Management to minimise scouring 16 Appropriate and strategic use of insecticide – monitoring, observe witholding periods, resistance management etc 17 Have a lice biosecurity plan – regular monitoring, quarantining, fence maintenance etc. 18 Treat for parasites only when monitoring and/or planning indicates a genuine need 19 Ability to integrate parasite control into farm management program 20 Sufficient knowledge of parasite biology to make considered management decisions/choices � In talking about use of a drench plan, all producers indicated that they did not. One producer stated that he used to, another was concerned about how well they worked for his region stating: “One of the biggest problems is that if you do use a program that’s been designed in Western Australia and it’s too good you build the resistance up a lot faster. So you don’t want one that’s too good.” - A35 - This indicates an issue with knowledge about what drench plans are available for NSW through the Department of Agriculture, since there are plans designed specifically for each sheep growing region in NSW. � On the topic of keeping written paddock histories, most producers present indicated that they ‘have a mental note’ about what has occurred in their paddocks. One producer stated: “I’m quite sure that if you had a hell of a lot of country, you’d tend to write it down.” � With regards to having a breeding strategy, this came back to culling poorly performing animals, with one producer stating: “No. …. Those sheep that have shares in the Ivomec factory, you’d just cull them. That’s what you mean.” And other stated: “P23- “We don’t have a …. We’re not actually going out and sourcing rams. P24 - But Ross, you’re not breeding from the ewe …. You will have one that is never any good, you don’t breed from them. P23 - No that’s right. But as far as going the other way and actually looking for rams…” � Producers indicated that they did not have set targets for weights and condition scores. � Producers indicated that they tended to quarantine drench new sheep with cydectin, while another indicated that they quarantined by not buying in any stock. With regards to sheep quarantine and lice, one producer brought up the issue of neighbours having infested sheep. � Culling was used for blowfly strike. While another indicated that they used to mules for flystrike but try not to any more. No one used fly traps. Shearing was used to help with flystrike also. � One person mentioned they did pregnancy scanning. � One the topic of opportunity drenching, producers indicated that they “did a bit of both” (ie visual assessment , FEC testing and opportunity drenching). - A36 - � One producer indicated that he used capsules every year on lambs only. There was non concern about sheep health due to the capsules not breaking down as there was in Dunkeld. � When asked about integrating parasite management into farm management, one producer stated: “Aren’t we already doing that with putting cattle in with the sheep to try and clean up a paddock?” Another said: “In a good season you can do a lot of things. These couple of seasons we’ve had you’ve just got to get through it the best you can.” Another, older producer indicated that they had moved from more piecemeal management to strategic management: “...we came here in ’72, those days you got your sheep, you drenched them and then you sheared them. If you didn’t do it, they died. Now, we are doing things a little more strategically. We know how the parasites work, we’ve talked about Ivomec drenches, your clears and your whites. We all know how they work.” � On the topic of doing FEC, producers indicated that it was “Better doing that, then drenching a mob of sheep.” This comment was to indicate that cost was not an issue. Though the producer making this comment did his FEC at home and was one of the younger producers who had been to university. The older farmer in the group questioned him about being able to do cultures at home and the cost compared to using a vet. The older producer indicated his testing cost him $65 a throw, and when the younger indicated it cost him only 20 cents, the older producer stated: - A37 - “We might have to look into that. We’ve done that, we’ve drenched with Cydectin and put them back in the paddock and got a faecal egg count and it’s come back zero, zero, zero. So you know that that drench is still working.” When asked about whether they found FEC results deceptive, the younger, university- educated producer stated: “The only time that will happen is that in the middle of summer and barber’s pole is about to take off, and you’re picking up a lot of immature worms that are just being picked up, but they are not producing eggs, but if you relate all your egg counts to what the seasons like, what the sheep are like and what the conditions are like, once you get a figure, if it’s potentially slightly higher, but they’ve got lots of feed in front of them, you might say you will let them go a little longer. But if it’s a little lower than when you normally drench them, but coming into winter you might be inclined to drench them. You don’t just take them on the reading.” � There was some discussion about the benefit of breeding resistance and EBVs, with some concerned about the tradeoffs between the resistance trait and other traits, with one producer stating “Depends on what the trade off is. Generally those sheep with a lower egg count, generally have a trade off with lower production because they divert protein from going into production to fighting worms. There can be a trade off for the two. I’m hoping that there’s a couple of new drenches on the horizon being developed for cattle and what not that will eventually come back to sheep. The drenches we’ve got at the moment if they’re used wisely with rotation will last us quite OK. Even if they don’t we’ll go back to copper sulphate.” � When asked about how long they think drenches might last, producers indicated that they had no idea, but maybe about ten years. Another indicated that the issue was ‘always at the back of his mind’. One producer was not willing to say he believed that - A38 - the industry was losing drenches, but indicated he thought that you could prolong them. Q. How would you prioritise the list of parasite management tools? This exercise gave the participants a chance to indicate what was important to them in terms of parasite management, using both their own and the Researchers’ list. This went a way to alleviating somewhat the feeling that the participants were not knowledgeable sheep managers because they had not mentioned all the things on the Researchers’ list in the same way. Worms Priority Worm management practice or tool 1 FEC testing & Vet advice 2 Drench resistance tests & Rotation grazing 3 Drenching 4 Grazing management & Mob rotation (clean paddocks) 5 Nutrition - supplements Flies The priority list for fly control was: Priority Fly management practice or tool 1 Mulesing 2 Crutching 3 Jetting 4 Click 5 Back-lining 6 Drenching 7 Fly traps - monitoring 8 Genetics (back of mind consideration) Lice Priority Lice management practice or tool 1 Back-line 2 Dipping 3 Fencing management - boundaries 4 Quarantine 5 Closed flock The final 3 questions were not addressed due to time constraints. - A39 - Appendix C Full list of 86 Delphi Responses 1 A basic understanding of the parasite life cycle 2 A basic understanding of the parasite epidemiology 3 Ability to interpret information sources on parasite control 4 Understanding of farm worm history - property specifics 5 Understanding of when a sheep is susceptible to parasite infection 6 Understanding of seasonal patterns of worm infection in the region 7 Good knowledge of the core or basic worm control program appropriate to the region 8 Knowledge of clinical signs of worm parasitism 9 Knowledge of clinical signs that may be confused with those indicating anaemia 10 Knowledge of the drench groups 11 Knowledge of current effectiveness of drenches on farm 12 Knowledge of drench capsules and newer long-acting products 13 Knowledge of correct drench technique for different drenches (oral liquid, capsule, injection) 14 Understand correct choice of anthelmintics for specific treatment situations 15 Basic understanding of how to rotate drenches 16 Know what is an acceptable number of annual drenches 17 Understand witholding periods & ESIs for drenches and lice/ fly chemicals used (meat and wool) 18 Minismise sheep deaths and weight loss through acceptable drenching program 19 Understand basics of drench resistance (genetic selection of resistant worms, risks of frequent drenching) 20 More advanced knowledge of drench resistance - principles of "refugia" & risks of low refugia, when this is likely to occur 21 Knowledge of the methods available to test for drench resistance 22 Knowledge of the role of worm egg count monitoring 23 Basic interpretation of WECs 24 Nutrition - know target condition scores for breeding ewes 25 Understand the concept of estimated breeding values (EBV) particularly as applied to worm resistance 26 Understand difference between Nemesis FEC EBVs and other EBVs 27 Principles of weaner management - time of weaning, preparation of weaning paddocks, target weights, monitoring weight & FEC of weaners 28 Understanding of parasite control strategies other than drenching 29 Understand principles of Grazing management -(role of)- for worm control 30 Understand how to do Smart Grazing (Vic) or rotation grazing (NSW) 31 Grazing management - understand principles of sheep/ cattle interchange for worm control 32 Breeding for resistance - principles of how to go about it (ram breeder) 33 Breeding for resistance- understand relationship/ balance with production traits 34 Breeding for resistance- interpret EBVs for resistance purposes (commercial producers) 35 Ram breeders- how parasite control fits in with breeding objectives 36 Knowledge of groups of chemicals available (lice and blowflies), advantages & disadvantages of these 37 Knowledge of chemical products available - pros and cons 38 Know chemcial application techniques and suitability for different chemicals (lice - A40 - and blowflies) 39 Undertand the main OH&S issues associated with parasiticide use 40 Knowledge of effectiveness of backliners 41 Know lice status of sheep on their property 42 Understanding of when appropriate to apply chemicals (e.g. timing, withholding periods) 43 Flystrike management: understanding of strategic and non-strategic jetting 44 Awareness of emerging backliner resistance issue 45 Consider implications of residues in meat and wool from using chemicals 46 Understand principles of blowfly strike prevention - making sheep less susceptible (mulesing, selecting against fleece rot etc), 47 How to prevent infestation (care with purchased and neighbouring sheep & Management of stray sheep) 48 How to control/ eradicate lice infestations 49 Understand the key IPMs strategies available for each of the parasites of concern (eg. grazing management, use of alternative hosts, use of host nutrition, biological control methods etc) 50 Understanding of how time of lambing dictates feed demand of flock, how this interacts with parasitism 51 Good working knowledge of practical sheep nutrition 52 Ability to undertake strategic drenching for worm control 53 Ability to determine timing of non-strategic drenching 54 Able to use drench equipment correctly (for all types) 55 Able to calibrate drench-gun 56 Able to weigh sheep correctly 57 Able to assess sheep condition scores 58 Able to record sheep weights and condition scores 59 Maintain records of annual drench program and sheep deaths and weight losses 60 Carry out regular WEC 61 Monitor WEC results 62 Know how to collect samples for WEC monitoring and how to appropriately package and transport samples 63 Breeding for resistance - Able to source resistant rams 64 Ability to carry out pasture assessments (availability/Quality) 65 Management of introduced sheep - able to quarantine/or quarantine drench 66 Able to apply chemicals/insecticides using correct method 68 Be able to tell if sheep is anaemic 69 Ability to recognise struck sheep 70 Maintain records of flystrike 71 Cull animals with flystrike 72 Able to detect fleece rot 73 Use of suppression methods e.g. Luci traps 74 Ability to properly mules sheep 75 Ability and knowledge to dock sheeps tails to correct length 76 Ability to recognise lice 77 Ability to recognise infested sheep 78 Regular monitoring/inspection of sheep for lice 79 Maintain fences 80 Good record keeping - management and financial 81 Computer/spreadsheeting/internet skills (help but not essential) 82 Pregnancy scanning if multiple births exceed 10% 83 Have a single shearing time (NSW) - A41 - 84 Sheep selection/classing skills 85 Avoid summer drenching (WA) 86 Organise for drench resistance testing - A42 - - A43 - Appendix D Interview information sheet and invitation Invitation for New England producers - A44 - Invitation for Victorian interviews - A45 - Personal interview cover letter - A46 - - A47 - Appendix E Full survey report Copies of the IPM-s benchmark survey, the full report and its associated appendices can be found on the attached CD-ROM. The following tables have been extracted from the 2004 Benchmark Survey (Reeve and Thompson 2005) for convenience as they are referred to in Chapter 7. Table 7.1 Proportion of respondents who use supplementary feeds - A48 - Table 7.2 Proportion of survey respondents feeding ewes and lambs by region - A49 - Table 7.3 Grazing strategies used in 2003 Table 7.4 Key objectives in using grazing strategies - A50 - Table 7.5 Proportion of respondents drenching newly introduced sheep Table 7.6 Respondents using WEC - A51 - Table 7.7 Number of times worm egg counts typically monitored – wethers Table 7.8 Number of times worm egg counts typically monitored – adult ewes - A52 - Table 7.9 Number of times worm egg counts typically monitored – weaners Table 7.10 Proportion of respondent indicating they had conducted a drench resistance test - A53 - Table 7.11 Year of Most Recent DRT Table 7.12 Type of drench resistance test used - A54 - Table 7.13 Proportion of respondents using particular worm control techniques Table 7.14 Reasons for using ‘Other’ Grazing techniques - A55 - Table 7.15 Reasons for using any grazing strategy – All regions Table 7.16 Reasons for using treatments and techniques other than grazing (part (a)) - A56 - Table 7.17 Other techniques and treatments (part (b)) Table 7.18 Other Factors regarded as important by respondents when deciding to drench ewes Table 7.19 Other factors regarded as important by respondents when deciding to drench weaners - A57 - Table 7.20 Main advisor for worm control In te gr at ed P ar as ite M an ag em en t i n th e Sh ee p In du st ry A N at io na l S ur ve y D ea r S he ep P ro du ce r, W e ar e se ek in g yo ur h el p w ith a n at io na l s ur ve y in ve st ig at in g in te gr at ed p ar as it e m an ag em en t i n sh ee p (I P M -s ). M an y pa ra si te s ha ve d ev el op ed r es is ta nc e to ch em ic al c on tr ol s an d it is v it al f or a p ro fi ta bl e sh ee p in du st ry th at w e ha ve o th er m et ho ds to w or k al on gs id e ch em ic al s. T he re is a ls o a ne ed to e ns ur e lo w c he m ic al re si du es in w oo l a nd m ea t i n or de r to m ai nt ai n ou r su cc es s in o ve rs ea s m ar ke ts a s w el l a s to r ed uc e st af f ex po su re to c he m ic al s in o ur in du st ry . T hi s re se ar ch is s up po rt ed b y A us tr al ia n W oo l I nn ov at io n (A W I) a nd r es ea rc h pa rt ne rs li st ed b el ow . T he I ns ti tu te f or R ur al F ut ur es h as b ee n em pl oy ed to c ar ry ou t t hi s su rv ey .o n be ha lf o f th es e or ga ni sa ti on s. T he p ro je ct is lo ok in g at n ew pa ra si te c on tr ol m et ho ds to r ed uc e re li an ce o n ch em ic al s, a nd p ar as it e re si st an ce to ch em ic al s, w hi le m ai nt ai ni ng o r im pr ov in g pr od uc ti on . T he a im o f th is s ur ve y is to fi nd o ut w ha t p ar as it e co nt ro l m et ho ds a re c ur re nt ly b ei ng u se d fo r th e co nt ro l o f in te rn al a nd e xt er na l p ar as it es in s he ep to m ak e su re th e re se ar ch m ee ts th e ne ed s of s he ep p ro du ce rs s uc h as y ou . Y ou r ad dr es s is o ne o f a sm al l s am pl e of A W I le vy -p ay in g w oo l p ro du ce rs s el ec te d w ho m ay w is h to a ss is t i n ou r su rv ey . T he n um be r ap pe ar in g at th e to p ri gh t- ha nd co rn er o f th is p ag e is f or m ai li ng p ur po se s on ly – th is w il l e ns ur e th at y ou w il l n ot be s en t a ny u nn ec es sa ry r em in de rs . Y ou r na m e is n ot r eq ui re d on th e su rv ey , s o yo ur r es po ns es w il l b e st ri ct ly c on fi de nt ia l. Y ou d o no t h av e to f il l o ut th is s ur ve y; h ow ev er y ou r as si st an ce in th is im po rt an t re se ar ch w il l b e ve ry m uc h ap pr ec ia te d. I k no w it is h ar d fo r pr od uc er s to f in d ti m e to f il l i n su rv ey s an d I ha ve m ad e ev er y ef fo rt to m ak e th e qu es ti on s as s ho rt a nd ea sy to a ns w er a s po ss ib le . I w ou ld b e gr at ef ul if y ou c ou ld ta ke th e tw en ty m in ut es ne ed ed to f il l i n th e fo rm . A r ep ly -p ai d en ve lo pe is p ro vi de d fo r th e re tu rn o f yo ur qu es ti on na ir e. I f yo u w ou ld li ke to k no w m or e ab ou t t he p ro je ct , p le as e ca ll m e on (0 2) 6 77 3 51 44 . M an y T ha nk s, L yn da lR ed m an -T ho m ps on ,I ns ti tu te fo r R ur al F ut ur es , P le as e fe el f re e to a dd c om m en ts in th e sp ac e be lo w o r on a s ep ar at e pi ec e of p ap er if y ou r eq ui re m or e ro om . T H A N K Y O U F O R Y O U R P A R T IC IP A T IO N . If yo u w ou ld lik e to be co nt ac te d ab ou t fu rt he r de ve lo pm en ts in IP M -s , in cl ud in g w or ks h op s or f ie ld d ay s, p le as e in di ca te y ou r pr ef er en ce s ab ov e an d se nd th is su rv ey fo rm ba ck or e- m ai l L yn da l R ed m an -T h om ps on lr ed m an @ po bo x. un e. ed u. au . If y ou l os e th e re tu rn e nv el op e, p le as e se nd t he s ur ve y to U .N .E . R ep ly P ai d 61 88 3, N SW 2 35 1. T hi s pr oj ec t h as b ee n ap pr ov ed b y th e H um an R es ea rc h E th ic s C om m itt ee o f th e U ni ve rs it y of N ew E ng la nd ( A pp r. N o. H E 04 /0 15 , v al id to 3 1/ 12 /0 6. S ho ul d yo u ha ve a ny c om pl ai nt s co nc er ni ng th e m an ne r in w hi ch th is r es ea rc h is c on du ct ed , p le as e co nt ac t t he R es ea rc h E th ic s O ff ic er a t t he f ol lo w in g ad dr es s: R es ea rc h S er vi ce s, U ni ve rs ity o f N ew E ng la nd , A rm id al e, N S W 2 35 1. P ho ne : ( 02 ) 67 73 3 44 9; F ax ( 02 ) 67 73 3 54 3; E m ai l: E th ic s@ m et z. un e. ed u. au . - 1 - - 10 - PA R AS IT E M AN AG EM EN T IN T H E SH EE P IN D U ST R Y Su rv ey In st ru ct io ns : 1. D o yo u ow n or m an ag e 50 0 or m or e sh ee p? � Y E S � N O If N O , p le as e se nd th is b la nk s ur ve y ba ck in th e en ve lo pe p ro vi de d. 2. T he b es t p er so n to c om pl et e th is s ur ve y is th e pe rs on w ho m ak es th e m aj or de ci si on s ab ou t t he m an ag em en t o f liv es to ck o n th e pr op er ty . 3. Pl ea se c om pl et e th e qu es tio ns f or th e pr op er ty o n w hi ch y ou r es id e or s pe nd th e m os t t im e. 4. M os t o f th e in fo rm at io n re qu es te d fo r th is s ur ve y is f or 2 00 3. H ow ev er , f or s om e di st ri ct s th is w as a ti m e of d ro ug ht a nd y ou m ay h av e ad op te d pr ac ti ce s di ff er en t to y ou r us ua l p ro gr am . I f th is is th e ca se f or y ou , p le as e pr ov id e in fo rm at io n ab ou t t he u su al m an ag em en t p ra ct ic es o n yo ur p ro pe rt y w he re a sk ed . SE C TI O N A : P R O PE R TY A N D O PE R AT IO N D ET AI LS P le as e pr ov id e in fo rm at io n fr om 2 00 3, u nl es s ot h er w is e sp ec if ie d. Q 1. W ha t r ai nf al l d id y ou r ec ei ve in 2 00 3? … … … … … m m O R … … … … … in ch es ( 1 in ch = 2 5. 4 m m ) Q 2. W ha t i s t he a ve ra ge a nn ua l r ai nf al l f or y ou r ar ea ? … … … … … m m O R … … … … … i nc he s (1 in ch = 2 5. 4 m m ) Q 3. W ha t w as th e es tim at ed p er ce nt ag e of in co m e fr om e ac h en te rp ri se ? In co m e (% ) W oo l s al es W oo l s he ep s al es (s to re s, c ul ls & c as t f or a ge , b oa t w et he rs ) F ir st c ro ss e w e sa le s fo r br ee di ng M ea t s he ep (1 st o r 2n d cr os s pr im e or s to re la m bs ) B ee f ca tt le C ro pp in g O th er ( Pl ea se sp ec ify ) T O T A L 10 0% Q 29 . Pl ea se in di ca te b el ow w hi ch o f t he fo llo w in g lic e co nt ro l t ec hn iq ue s yo u ha ve u se d in th e pa st th re e (3 ) y ea rs (2 00 1- 20 03 ) a nd sh ow a ls o w ha t p ro du ct (s ) y ou u se d. Q 30 . H av e yo u ev er su sp ec te d re si st an ce to a li ce p ro du ct o n yo ur fa rm ? Pl ea se ti ck o ne o nl y Y E S G o to 3 0A b el ow . N O SK IP to la st se ct io n Ab ou t Y ou . Q 30 A . P le as e in di ca te to w hi ch p ro du ct y ou th in k th e re si st an ce m ig ht h av e oc cu rr ed . ` … … … … … … … … … … … … … … … F in al ly , w e ju st n ee d a lit tle in fo rm at io n ab ou t y ou rs el f Q 31 . W ha t i s t he p os tc od e fo r th e ar ea in w hi ch y ou r pr op er ty is lo ca te d? … … … … … … … … … Q 32 . A re y ou M al e? F em al e? Q 33 . In w ha t y ea r w er e yo u bo rn ? 19 … … … … .. Y ea r/ s U se d Pr od uc t/s U se d O ff - sh ea rs M ob il e di pp in g co nt ra ct or P lu ng e di p S ho w er d ip Po ur -o n ‘b ac kl in er ’ O th er ( Pl ea se sp ec ify ) … … … … … … … … … L on g w oo l H an d je tt in g P ou r- on ‘ ba ck lin er ’ O th er ( Pl ea se sp ec ify ) … … … … … … … … … N o li ce tr ea tm en ts u se d - 9 - - 2- Q 24 . A t w ha t l en gt h do y ou d oc k la m bs ’ t ai ls ? Pl ea se ti ck o ne o nl y Q 25 . H ow o ft en d o yo u ty pi ca ll y tr ea t s he ep fo r lic e on y ou r pr op er ty ? … … … … … … … … … … … … … … … … … … … … … … … … … … … ... Q 26 . W er e an y of y ou r sh ee p lo us y w he n th ey w er e la st sh or n? Y es N o N ot S ur e Q 27 . H ow m an y ye ar s i n th e la st te n ye ar s h av e yo ur sh ee p be en in fe st ed w ith li ce ? … … … … … … .. ye ar s Q 28 . If y ou r sh ee p ha d lic e, w ha t d o yo u be lie ve w as th e ca us e/ s? Pl ea se ti ck a ny a pp lic ab le P oo r m us te r In fe ct ed n ei gh bo ur s S tr ay s he ep P ur ch as ed s he ep T re at m en t f ai lu re O th er ( Pl ea se sp ec ify ) … … … … … … … … … … … … … … … … … . Q 4. W ha t a re as o f y ou r pr op er ty a re u se d fo r th e fo llo w in g? Q 5. H ow m an y ca tt le d id y ou h av e in 2 00 3? P le as e in di ca te th e nu m be r yo u ty pi ca lly r un if th is is d iff er en t t ha n th e nu m be r fo r 20 03 , a s w el l a s t he u su al m on th o f c al vi ng . Q 6. H ow m an y sh ee p di d yo u ha ve a t t he m ai n w ea ni ng ti m e in 2 00 3, o r N ov em be r 20 03 if y ou h av e an a ll w et he r flo ck ? In di ca te th e nu m be r yo u ty pi ca lly r un if d iff er en t t ha n th e nu m be r yo u ha d in 2 00 3. M uc h sh or te r th an ti p of v ul va in e w es ( ‘b ut te d ta il ’) Ju st s ho rt er th an ti p of v ul va ( ‘s ho rt ta il ’) E qu al to th e ti p of th e vu lv a L on ge r th an th e ti p of th e vu lv a O th er (P le as e sp ec ify ) … … … … … … … … … … … … … … … … … … … … . SE C TI O N D : LI C E C O N TR O L A re a H a A c A re a gr az ed A re a cr op pe d C ro pp in g ar ea g ra ze d as s tu bb le C ro pp in g ar ea g ra ze d as g re en O th er (P le as e sp ec ify ) T ot al p ro pe rt y ar ea N um be r of p ad do ck s Pe rc en ta ge G ra ze d Im pr ov ed % Pe rc en ta ge G ra ze d un im pr ov ed % N um be r 20 03 N um be r ty pi ca lly r un M on th (s ) of ca lv in g C ow s H ei fe rs ( w ea ni ng – 2 y ea rs ) St ee rs ( w ea ni ng – s al e) B ul ls O th er ( Pl ea se sp ec ify ) B re ed N um be r in 20 03 N um be r ty pi ca lly r un M er in o ew es M er in o O th er e w es ( Pl ea se sp ec ify b re ed ) W et he rs M er in o w ea ne rs M er in o O th er w ea ne rs ( Pl ea se sp ec ify ) R am s - 7 - - 4 - Q 7. In w hi ch m on th (s ) d o yo u sh ea r an d cr ut ch ? M on th (s ) sh or n M on th (s ) cr ut ch ed E w es ( ol de r th an 1 2 m on th s) W et he rs ( ol de r th an 1 2 m on th s) W ea ne rs ( le ss th an 1 2 m on th s) Q 8. Pl ea se g iv e de ta ils o f t he 2 00 3 w oo l c lip fo r yo ur m ai n br ee d of sh ee p: B re ed ? W oo l s ho rn f ro m : … … ..… … … … … … … … .. A du lt br ee di ng ew es A du lts - dr y ew es & w et he rs W ea ne rs (L es s th an 12 m on th s) N um be r of s he ep s ho rn T ot al q ua nt it y of w oo l s ho rn or w oo l c ut p er h ea d (k g) … … … … … … (k g) … .… … … … (k g) … ..… … ..… (k g) F ib re d ia m et er o f m ai n li ne (m ic ro ns ) Q 9. If y ou h av e ew es , p le as e pr ov id e de ta ils a bo ut th ei r br ee di ng p ro gr am : T yp e of e w e M er in o m at ed to M er in o ra m s M er in o m at ed to M ea t- br ee d ra m s C ro ss -b re d ew es O th er … … … … … ... M on th r am s pu t i n w it h ew es 2 00 3 M on th r am s ta ke n ou t i n 20 03 M ar ki ng % in 2 00 3 (a du lt s on ly ) … … … … … (% ) … … … … … (% ) … … … … … (% ) … … … … (% ) T yp ic al m ar ki ng % (a du lt s on ly ) … … … … … (% ) … … … … … (% ) … … … … … (% ) … … … … (% ) M on th la m bs w ea ne d in 2 00 3 Q 19 . W ho is th e m ai n ad vi so r fo r w or m c on tr ol o n yo ur p ro pe rt y? Pl ea se ti ck o ne o nl y SE C TI O N C : B LO W FL Y C O N TR O L Q 21 . If y ou h ad b lo w fly st ri ke o n yo ur p ro pe rt y du ri ng 2 00 3, p le as e pr ov id e de ta ils b el ow . Q 22 . H ow d o yo u ty pi ca lly tr ea t f or b lo w fly st ri ke ? P le as e tic k an y th at a pp ly Q 23 . W ho p er fo rm s t he m ul es o pe ra tio n on y ou r sh ee p? … … … … … … … … … … … … … … … … … … … … … … … … … … … … .. M e or m em be r of m y st af f L oc al v et P ri va te v et er in ar y co ns ul ta nt A g co ns ul ta nt A g D ep ar tm en t o ff ic er R ur al M er ch an di se r ep re se nt at iv e D ru g co m pa ny r ep re se nt at iv e O th er (P le as e sp ec ify ) … … … … … … … … … … … … … … … … … … … .. T yp e of S tr ik e Pe rc en ta ge E w es af fe ct ed Pe rc en ta ge W he th er s af fe ct ed Pe rc en ta ge W ea ne rs af fe ct ed B re ec h S tr ik e % % % B od y St ri ke % % % T re at r ou tin el y fo r pr ev en ti on a t a bo ut th e sa m e tim e ea ch y ea r T re at w he n th e w ea th er s ug ge st s a fl yw av e m ig ht o cc ur T re at th e w ho le m ob o nc e st ri ke s ta rt s T re at in di vi du al s he ep w hi ch b ec om e st ru ck O th er (P le as e sp ec ify ) … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … - 7 - - 4 - Q 18 . Pl ea se r an k ho w im po rt an t t he fo llo w in g fa ct or s a re w he n de ci di ng w he th er to d re nc h w ea ne r sh ee p. T ic k on e pe r l in e. Q 19 . W hi ch o f t he fo llo w in g te ch ni qu es tr ea tm en ts o r te ch ni qu es d o yo u us e fo r sh ee p w or m c on tr ol ? Pl ea se ti ck st ra te gi es u se d. Q 10 . W ha t t yp e of g ra zi ng st ra te gi es d id y ou u se in 2 00 3? Pl ea se ti ck a ny th at a pp ly Q 11 . W ha t a re y ou r ke y ob je ct iv es fo r us in g th e ty pe (s ) o f g ra zi ng st ra te gi es in di ca te d ab ov e? … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … Q 12 . D es cr ib e th e su pp le m en ta ry fe ed in g pr og ra m fo r yo ur p ro pe rt y in a ty pi ca l y ea r: T yp e of f ee d (O at s, w he at , l up in s, m ea l, bl oc ks or li ck s et c) A m ou nt f ed M on th s fe d (e .g . J an -A pr il ) E w es (o ld er th an 12 m on th s) W ea ne rs (l es s th an 1 2 m on th s) V er y Im po rt an t Im po rt an t So m ew ha t Im po rt an t N ot Im po rt an t R es ul ts f ro m f ae ca l w or m eg g co un t C on di ti on s co re o f sh ee p T im e of y ea r S ea so na l w ea th er c on di ti on s A va il ab il it y of p as tu re Q ua li ty o f pa st ur e P re se nc e of d ag gy s he ep in m ob O th er ( Pl ea se sp ec ify ) … … … … … … … … … … … .. S et s to ck ed S et s to ck ed la m bi ng o nl y A lt er na ti ng b et w ee n sh ee p & c at tl e A lte rn at in g be tw ee n sh ee p & c ro p st ub bl e A lt er na ti ng b et w ee n sh ee p an d fo ra ge c ro p C el l g ra zi ng ( la rg e m ob s, s m al l p ad do ck s fo r pe ri od s of 1 -4 d ay s) R ot at io na l g ra zi ng ( sm al le r m ob s, la rg e pa dd oc ks f or . 2 -6 w ee ks ) O th er (P le as e sp ec ify ) … … … … … … … … … … … … … … … … … … … … … … … … … … … … D es cr ip tio n P re pa re p as tu re s by ‘ S m ar t g ra zi ng ’ P re pa re p as tu re s by o th er g ra zi ng te ch ni qu es L ea ve s om e sh ee p un -d re nc he d at s um m er tr ea tm en ts S ho w % le ft u n- dr en ch ed : … … … … ..% F ee di ng s tr at eg y U se r am s se le ct ed f or r es is ta nc e to w or m s A re th es e E B V te st ed ? … … … … … … O rg an ic m et ho ds D re nc hi ng O th er ( Pl ea se sp ec ify ) … … … … … … … … … … … … … … … … … - 5 - - 6- Q 13 . Sh ow th e nu m be r an d ty pe o f d re nc he s a nd c on tr ol le d- re le as e ca ps ul es gi ve n to e ac h cl as s o f s he ep fr om S ep te m be r 20 02 to D ec em be r 20 03 . T ic k ap pr op ri at e tr ea tm en t. Q 14 . D o yo u dr en ch n ew ly in tr od uc ed sh ee p on th ei r ar ri va l t o th e pr op er ty ? Pl ea se ti ck o ne o nl y Q 14 A . Pl ea se in di ca te w ha t d re nc h yo u ty pi ca lly u se fo r ne w ly in tr od uc ed sh ee p. … … … … … … … … … … … … … … … … … … … … … … … … … … … … … .. Q 15 . H ow m an y tim es d id y ou m on ito r w or m e gg c ou nt s i n ea ch c la ss o f sh ee p in 2 00 3? Q 16 . H av e yo u ev er te st ed fo r dr en ch r es is ta nc e in y ou r flo ck ? Y E S G o to Q 16 A be lo w N O SK IP to Q 17 . Q 16 A . P le as e in di ca te th e ye ar o f y ou r m os t r ec en t d re nc h re si st an ce te st a nd th e te st y ou u se d: Y ea r? … … … … … … … . T es t? … … … … … … … … … … … … … … … … . Q 17 . Pl ea se r an k ho w im po rt an t t he fo llo w in g fa ct or s a re w he n de ci di ng w he th er to d re nc h ew es . T ic k on e pe r l in e. SE C TI O N B : W O R M C O N TR O L M on th /s o f T re at m en t M et ho d T re at m en t D re nc h C ap su le P ro du ct u se d U nw ea ne d la m bs 1. 2. W ea ne rs 1. (l es s th an 1 2 m on th s) 2. 3. 4. M ai de n ew es 1. (1 2 – 24 m on th s) 2. 3. A du lt ew es 1. (o ld er th an 2 4 m on th s) 2. 3. W et he rs 1. (o ve r 12 m on th s) 2. 3. Y E S G o to Q 14 A be lo w D on 't bu y sh ee p Sk ip to Q .1 5 N O Sk ip to Q .1 5 N um be r of ti m es m on it or ed W ea ne rs (L es s th an 1 2 m on th s) W et he rs (O ld er th an 1 2 m on th s) A du lt e w es ( O ld er th an 2 4 m on th s) V er y Im po rt an t Im po rt an t So m ew ha t Im po rt an t N ot Im po rt an t R es ul ts f ro m f ae ca l w or m e gg co un t C on di ti on s co re o f sh ee p T im e of y ea r S ea so na l w ea th er c on di ti on s A va il ab il it y of p as tu re Q ua li ty o f pa st ur e Pr es en ce o f da gg y sh ee p in m ob O th er (P le as e sp ec ify ) 2004 BENCHMARK SURVEY 2004 BENCHMARK SURVEY Ian Reeve Lyndall Thompson REPORT TO IPM-SHEEP July 2005 i CONTENTS 1 INTRODUCTION.......................................................................................................................1 2 METHODS.................................................................................................................................3 2.1 SURVEY................................................................................................................................3 2.2 ANALYSIS .............................................................................................................................3 3 RESULTS..................................................................................................................................5 3.1 LOCATION OF RESPONDENTS ...............................................................................................5 3.1.1 Regional frequency of responses ..............................................................................6 3.2 RESPONDENT AGE AND GENDER...........................................................................................8 3.3 PROPERTY DETAILS .............................................................................................................8 3.3.1 Rainfall .........................................................................................................................8 3.3.1.1 Mean annual rainfall in district (mm) .................................................................................8 3.3.2 Income sources ...........................................................................................................9 3.3.2.1 Proportion of income derived from sheep and wool (%) ..................................................9 3.3.2.2 Other sources of income ....................................................................................................9 3.3.3 Types of sheep and wool income ............................................................................10 3.3.4 Property size and land use .......................................................................................11 3.3.4.1 Total area of property (ha) ...............................................................................................11 3.3.4.2 Proportion of total property area grazed (incl. cropping areas grazed (%) ...................12 3.3.4.3 Proportion of total property area cropped (%) ................................................................12 3.3.4.4 Proportion of cropping area grazed as stubble (%)........................................................13 3.3.4.5 Proportion of cropping area grazed as green (%) ..........................................................13 3.3.4.6 Proportion of pastures improved (%)...............................................................................14 3.3.4.7 Number of paddocks ........................................................................................................14 3.3.4.8 Average paddock size (ha) ..............................................................................................15 3.3.5 Cattle..........................................................................................................................15 3.3.5.1 Proportion of respondents with cattle in a typical year...................................................15 3.3.5.2 Cattle DSEs in a typical year ...........................................................................................16 3.3.5.3 2003 compared to a typical year .....................................................................................16 3.3.5.4 Calving ..............................................................................................................................17 3.3.6 Sheep.........................................................................................................................17 3.3.6.1 Sheep DSEs in a typical year ..........................................................................................17 3.3.6.2 2003 compared to a typical year .....................................................................................17 3.3.6.3 Flock composition in a typical year – ewes as a proportion of the total flock ...............18 3.3.6.4 Flock composition in a typical year – wethers as a proportion of the total flock ...........18 3.3.6.5 Flock composition in a typical year – weaners as a proportion of the total flock ..........19 3.4 WOOL CUT AND FIBRE DIAMETER.......................................................................................19 3.4.1.1 Adult breeding ewe wool cut and fibre diameter by breed – all regions........................19 3.4.1.2 Sheep other than ewes ....................................................................................................19 3.4.1.3 Wool cut (kg/head), 2003 clip, adult sheep (breeding ewes, dry ewes and wethers) by region 20 3.4.1.4 Fibre diameter (�), 2003 clip, adult sheep (breeding ewes, dry ewes and wethers) by region 20 3.5 ANIMAL HUSBANDRY (OTHER THAN PARASITE MANAGEMENT)...........................................21 3.5.1 Shearing and crutching.............................................................................................21 3.5.1.1 Proportion of respondents shearing and crutching ewes in each month of the year....21 3.5.1.2 Proportion of respondents shearing and crutching wethers in each month of the year22 3.5.1.3 Proportion of respondents shearing and crutching weaners in each month of the year 23 3.5.2 Breeding program .....................................................................................................24 3.5.2.1 Proportion of respondents putting rams with ewes each month of the year in 2003....24 3.5.2.2 Number of weeks Merino rams left with Merino ewes....................................................25 3.5.2.3 Number of weeks meat breed rams left with Merino ewes ............................................26 3.5.2.4 Number of weeks rams left with Cross-bred ewes .........................................................27 3.5.2.5 Typical marking percentage – Merino ewes mated to Merino rams..............................28 3.5.2.6 Typical marking percentage – Merino ewes mated to meat breed rams ......................28 3.5.2.7 Typical marking percentage – Cross-bred ewes ............................................................29 3.5.2.8 Marking percentages in 2003 compared to typical years...............................................29 3.5.3 Supplementary Feeding............................................................................................30 3.5.3.1 Proportion of respondents who use supplementary feeds.............................................30 ii 3.5.3.2 Feed types in supplementary feeding of ewes ...............................................................30 3.5.3.3 Feed types in supplementary feeding of weaners ..........................................................31 3.5.3.4 Duration of supplementary feeding .................................................................................31 3.5.3.5 Proportion of respondents feeding ewes and weaners in each month of the year.......32 3.6 GRAZING MANAGEMENT.....................................................................................................33 3.6.1 Grazing strategies used in 2003 ..............................................................................33 3.6.2 Key objectives in using grazing strategies ..............................................................33 3.7 WORM CONTROL................................................................................................................34 3.7.1 Number, timing and type of treatment – September 2002 to December 2003.....34 3.7.1.1 Unweaned lambs..............................................................................................................34 3.7.1.2 Weaners............................................................................................................................34 3.7.1.3 Maiden ewes.....................................................................................................................35 3.7.1.4 Adult ewes ........................................................................................................................35 3.7.1.5 Wethers.............................................................................................................................36 3.7.2 Drenching of newly introduced sheep .....................................................................36 3.7.3 Monitoring worm egg counts ....................................................................................37 3.7.3.1 Frequency of monitoring worm egg counts.....................................................................37 3.7.3.2 Frequency of monitoring worm egg counts in 2003 compared to typical frequency ....37 3.7.4 Drench resistance testing.........................................................................................38 3.7.4.1 Year of most recent drench resistance test – all regions ...............................................38 3.7.4.2 Type of drench resistance test.........................................................................................39 3.7.5 Treatments and techniques for worm control .........................................................39 LIST OF APPENDICES Appendix 1 Methodology Appendix 2 Additional Results Appendix 3 Copies of Questionnaires iii EXECUTIVE SUMMARY v ACKNOWLEDGMENTS The benchmark survey was funded by Australian Wool Innovations Ltd as part of the IPM-sheep project. The assistance of the Board of Management of the IPM-sheep project in developing the content of the questionnaires and in designing the analysis approach is greatly appreciated. We are indebted to the farmers who kindly gave their time to fill in the questionnairess and without whom the benchmark study would not have been possible. Those who took the trouble to supply additional information and comment are thanked for the valuable insights they provided Survey logistics and data entry was managed by Ruth McGregor. Institute for Rural Futures 1 1 INTRODUCTION The IPM-sheep (Integrated Parasite Management – sheep) project, funded by Australian Wool Innovations Ltd, is devising and demonstrating integrated parasite control programs for the major sheep parasite areas within Australia. The primary focus of the project is mainstream wool producers with a lesser emphasis on organic producers. The institutions involved are University of New England, Department of Primary Industries, Queensland, Western Australian Department of Agriculture, University of Melbourne and Chr. Hansen group (commercial partner). Adoption of principles being developed in IPM-sheep across the wool industry will require producers to make incremental, but nevertheless significant, changes in their management approach. Integrated parasite management may involve changes in grazing management, animal husbandry operations and the timing of various management operations. These changes may require producers to entertain a broader range of practices for parasite control than that to which they are accustomed. There may also be production and business risks associated with changes in parasite management which will play an important role in the adoption of integrated parasite management practices and the ultimate success of the project. For these reasons, an understanding of current practices and the views of producers about parasite control are an important aspect of the design of technology transfer programs later in the project. Information for this aspect of the technology transfer is being supplied by the socio-economic component of the IPM-sheep project. AIMS OF THE SOCIO-ECONOMIC COMPONENT OF IPM-SHEEP To quantify regional key performance indicators. To determine regional parasite control practices. To investigate and solve on-farm and industry barriers to adoption To achieve the above aims, two benchmark surveys of wool producers are to be conducted, one close to project commencement, and a second one after several years of the project have elapsed. In addition, a program of focus groups and interviews with producers is to be undertaken. This report presents the findings of the first benchmark survey. Institute for Rural Futures2 Institute for Rural Futures 3 2 METHODS 2.1 Survey The methods are described in full in Appendix 1. The results presented in this report are based on a random sample of wool producers drawn from a list of levy-payer addresses supplied by Australian Wool Innovations Ltd. The list covers postcode areas in the regions identified by regional IPM-sheep project managers as being within the ‘sphere of influence’ of the programs they intended to run. The content of the questionnaire was pilot tested in a mail out to 300 addresses from this list. On the basis of a satisfactory number of correctly filled out responses received in the first two weeks after mailing, the main survey was proceeded with. A copy of the questionnaire is provided in Appendix 2. This questionnaire was mailed out to 6362 addresses during September 2004, with a reminder and second copy of the questionnaire mailed out to non-responders a month later. A short one-page questionnaire containing a small number of key questions was mailed to remaining non-responders several weeks after the reminder. The survey data to be analysed for this report was taken as all questionnaires received by 10 February 2005. The final response rates are shown in Table 2.1. Further details of the final response rates are provided in Appendix 1. Table 2.1 Survey response rates for the main questionnaire and the short one-page questionnaire. Region Response rate – full questionnaire (%) Response rate – full questionnaire together with short questionnaire (%) QLD 33.5 51.3 New England 35.7 56.5 NSW(remainder) 31.0 54.9 VIC 34.3 55.6 SA 37.3 56.5 WA 20.3 42.1 TOTAL 30.4 52.3 2.2 Analysis A number of quality control procedures were carried out with the survey data, including testing for non-response bias, caused when those responding to the survey are systematically different in particular respects to those not responding. These procedures are fully described in Appendix 1. A range of analysis techniques were used according to the information that was required from the data. A brief description of analysis techniques is provided where necessary in the presentation of results in section 3, below. A full description of analysis techniques is given in Appendix 1. As described in sections A1.8 to A1.10 in Appendix 1, a comparative analysis of the data from those who filled in the full survey and those who did not respond to the full survey, but responded to the short survey, suggested that there is some minor non-response bias present in the responses to the full survey. This includes under-representation of producers with greater numbers of cattle and under- representation of producers who had tested their sheep flock for drench resistance (for a full listing of significant differences between those responding to the full and short surveys, see Tables A1.2 to A1.11 in section A1.8 of Appendix 1). It was concluded from the analysis that the level of non- response bias was not sufficient to warrant adjusting all the findings from the full survey. However, the importance of the small set of questions chosen for the short survey (and common with the full survey) to the aims of the IPM-sheep project was considered as sufficient grounds for adjusting the findings from these questions to compensate for any non-response bias and provide the best possible Institute for Rural Futures4 estimates for generalising to the overall sheep producer population. A full account of the reasoning and supporting data for this decision is given in sections A1.9 and A1.10 in Appendix 1. Tables with adjusted figures include those relating to:  total cattle and sheep numbers,  testing for drench resistance,  factors considered to be important in deciding when to drench ewes,  grazing strategies, and  treatments for blowfly strike. Tables with adjusted figures are noted as such where they occur in the report. Institute for Rural Futures 5 3 RESULTS 3.1 Location of Respondents The regions from which responses were received are shown in Figure 3.1, below. The figure also shows the regions into which respondents have been grouped for the reporting of results in the ensuing sections. The number of responses from each postcodes area within these regions is shown in Figure 3.2, below. Figure 3.1 Regions in which respondents were located. Abbreviation Region SW & S Qld South western and southern Queensland GB & DD Queensland Granite Belt and Darling Downs New England New England region of New South Wales C & S Tablelands Central and southern tablelands of New South Wales S NSW & N Vic Southern New South Wales and northern Victoria Gippsland Gippsland region of Victoria W Vic & SE SA Western Victoria and south eastern South Australia S SA Southern region of South Australia KI Kangaroo Island WA South western region of Western Australia Institute for Rural Futures6 3.1.1 Regional frequency of responses The geographical distribution of responses is shown in Figure 3.2, below, together with the total number of usable responses to the full and short surveys from each of the regions in Figure 3.1 on the previous page. Figure 3.2 Frequency of responses in each postcode area from which responses were received. Region Usable responses to full survey Usable responses to short survey Total SW & S Qld 63 40 103 GB & DD 23 8 31 New England 180 105 285 C & S Tablelands 186 133 319 S NSW & N Vic 163 139 302 Gippsland 12 9 21 W Vic & SE SA 389 236 625 S SA 71 39 110 KI 42 13 55 WA 208 235 443 All regions 1337 957 2294 Institute for Rural Futures 7 EXPLANATION OF TABLES The tables presented in the ensuing sections show the results for each of the regions in Figure 3.1, above, as well as the results for all regions combined. The tables are of two types, depending on the type of data each question generated. For continuous data, such as property size or flock size, the sample size (n), the minimum, median and maximum values, the mean and the 95% confidence interval on the estimate of the mean are provided. A small histogram of the frequency distribution is also provided. Within any one table, the histograms have the same range on the horizontal axis, so that visual comparisons can be made between regions. However, the histograms are scaled to be of the same height, so that the histograms for regions with a small number of responses are not unduly small and difficult to discern. The class limits for the histogram bars are provided under each table. Histogram counts are the number of respondents with values greater than the low class limit and less than or equal to the upper class limit. For example, for the class limits 100-260-420-580-740-900-1060- 1220-1380-1540-1700, the count of respondents represented by the left-most histogram bar is the number of respondents with values greater than 100 and less than or equal to 260. The count for the next histogram bar is the number of respondents with values greater than 260 and less than or equal to 420, and so on. Below the histogram class limits at the base of each table, basic statistics are provided for an analysis of variance to test whether there are significance differences in the mean between regions. Some care should be exercised in interpreting the analysis of variance statistics when the histograms show a strongly bi-modal or skewed distribution, i.e. the tallest bars are at each end, or all up one end (see Appendix A1.11). A number of questions provided ordinal data, such as ranking of importance of factors used in deciding whether to drench ewes. As the number of categories used in these questions was four or less, which is below the threshold at which ordinal data can be treated as continuous data, the findings are presented as proportions of respondents in each category. The sample size (n) is also provided. For nominal data, such as type of grazing strategy used, the findings are presented as proportions of respondents in each category, together with the sample size (n). For tables reporting proportions for ordinal and nominal data, and where space permits, the upper and lower 95% confidence limits on the estimates of proportions are provided in greyed italicised text to the left and right of the proportion. Details of the significance of regional differences, if any, in the table are provided immediately below the table. Significance values are calculated by Monte Carlo simulation when the number of cells with expected frequencies less than 5 exceeds 12.5 per cent of the total number of cells in the table, otherwise significance values are calculated from the chi squared distribution with the number of degrees of freedom shown. Where there are significant regional differences, individual proportions that are significantly higher than the national average are bolded and underlined, and those that are significantly lower than the national average are bolded. Where questions are such that respondents could tick more than one choice, or give multiple answers, it is not possible to use a chi square test for significant regional differences. The tables of results for these questions carry a footnote explaining that the percentages for any one region sum to more than 100, due to the multiple choice or answers. Respondents who omitted to complete particular questions are omitted from the tables that report on those questions. For this reason, the sample size reported in the table column headed “n” will vary from table to table and will generally be less than the number of usable responses listed on the previous page. Institute for Rural Futures8 3.2 Respondent age and gender There were no significant differences between the regions in the age or gender composition of respondents. Across all regions, the mean age of respondents was 51 years, and 95 per cent of respondents were males. Further details of the age and gender composition of respondents are provided in Appendices A2.1 and A2.2. 3.3 Property Details Respondents were asked to provide a range of details about their property, including the average annual rainfall, the rainfall in 2003, the proportion of their income derived from various sources and the areas under various land uses. 3.3.1 Rainfall 3.3.1.1 Mean annual rainfall in district (mm) Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 62 229 508 800 517 35 GB & DD 20 635 693 750 686 14 New England 175 620 813 1250 828 13 C & S Tablelands 183 178 650 1628 637 20 S NSW & N Vic 160 250 594 950 591 19 Gippsland 12 600 633 712 640 23 W Vic & SE SA 382 203 610 914 612 10 S SA 68 330 488 660 512 22 KI 42 457 563 825 575 26 WA 201 203 450 1143 473 20 All Regions 1305 178 610 1628 611 8 Histogram class limits: 100-260-420-580-740-900-1060-1220-1380-1540-1700. Anova: F=117.12, d.f.=9, p<0.0005. Comparison of 2003 rainfall with average annual rainfall showed that the northern regions had experienced a drier than average year in 2003, while the southern regions had experienced a wetter 2003. For example, half of respondents in south western and southern Queensland had experienced a deficit of over 101mm in 2003 compared to the annual average for their district. The corresponding figure for the Granite Belt and Darling Downs was 132mm. Regions further south in eastern Australia Institute for Rural Futures 9 also suffered deficits in 2003, although not as great as in Queensland. However, many respondents from the southern region of South Australia and from Kangaroo Island reported higher than average rainfalls for 2003. For example, half of Kangaroo Island respondents reported a 21mm or greater increase in rainfall in 2003 compared to their district average. In Western Australia, half of respondents reported more rainfall in 2003 than their district average and half reported less. 3.3.2 Income sources 3.3.2.1 Proportion of income derived from sheep and wool (%) Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 60 2 72 100 68 7 GB & DD 21 40 90 100 79 11 New England 173 18 72 100 71 3 C & S Tablelands 181 15 80 100 76 3 S NSW & N Vic 162 0 58 100 59 4 Gippsland 12 17 95 100 74 20 W Vic & SE SA 383 10 70 100 70 2 S SA 70 8 50 100 55 6 KI 41 20 90 100 79 8 WA 203 7 55 100 56 4 All Regions 1306 0 70 100 67 1 Histogram class limits: 0-10-20-30-40-50-60-70-80-90-100. Anova: F=13.64, d.f.=9, p<0.0005. 3.3.2.2 Other sources of income Across all regions, the mean proportion of income derived from beef cattle was 12.5 per cent. The mean proportion of income from beef was significantly different across the regions (anova: F=21.24, d.f.=9, p<0.0005). The highest mean proportion of income from beef was in the New England region, with 24.4 per cent, while the lowest proportion was in Western Australia, with 4.1 per cent. The mean proportion of income derived from cropping was 17.1 per cent across all regions, and this was also significantly different across the regions (anova: F=38.24, d.f.=9, p<0.0005). The highest mean proportion occurred in Western Australia (36.8 per cent) and the lowest in the New England region (0.9 per cent). Institute for Rural Futures10 The mean proportion of income derived from sources other than sheep, beef and cropping was 3.5 per cent and there was no significant difference between the regions. Across all regions, 84.0 per cent of respondents had no income derived from sources other than sheep, beef and cropping, while 97.7 per cent derived over half of their income from sheep, beef and/or cropping. Among those with income from sources other than sheep, beef and cropping, 58.5 per cent derived income from some other primary production (such as dairying, goats, pigs, grapes, olives), 17.0 per cent worked off-farm and 13.0 per cent derived income from off-farm investment. 3.3.3 Types of sheep and wool income Considering just income from sheep and wool, respondents could be separated using cluster analysis (see Appendix 1.12) into two groups: those mainly dependent on meat sheep (first and second cross prime lambs or store lambs), and those mainly dependent on income from wool sales. These two groups are labelled “Group 1” and “Group 2” in the two tables below. Mean percentage of income* Income source Group 1 Group 2 Significance of difference between means (t-test) Wool sales 26.6 67.8 p<0.0005 Sheep sales (stores, culls and cast for age, boat wethers 20.1 24.9 p<0.0005 First cross ewe sales for breeding 2.2 1.4 n.s. Meat sheep (1st or 2nd cross prime or store lambs) 61.2 6.0 p<0.0005 * income from each of the categories in the left hand column of the table, as a percentage of total income derived from wool sales, sheep sales, first cross ewe sales and meat sheep. Proportion of respondents in Groups 1 and 2 (%) Region n Group 1 Group 2 SW & S Qld 61 5 13 22 78 87 95 GB & DD 21 0 10 22 78 91 103 New England 178 11 17 22 78 83 89 C & S Tablelands 184 16 22 28 72 78 84 SW NSW & NE Vic 162 34 42 50 50 58 66 Gippsland 12 1 25 50 51 75 100 W Vic & SE SA 388 31 36 41 59 64 69 S SA 70 37 49 60 40 51 63 KI 42 5 17 28 72 83 95 WA 211 11 16 20 80 84 89 All regions 1329 25 28 30 70 73 75 �2 = 87.332, d.f. = 9, p < 0.00005. 1 cell (5.0%) has expected count less than 5. As the table above shows, there were significant differences between regions in the proportions of respondents in Group 1 (sheep and wool income mainly from meat sheep) and Group 2 (sheep and wool income mainly from wool sales). South-western New South Wales and north-eastern Victoria, western Victoria and south-eastern South Australia, and southern Australia have relatively more Institute for Rural Futures 11 producers whose sheep and wool income is mainly from meat sheep, while Western Australia has relatively more producers whose sheep and wool income is mainly from wool sales. 3.3.4 Property size and land use Respondents were asked to provide the areas of their properties under various grazing, cropping and other land uses, as well the total property area. For 52.3 per cent of respondents, the areas under the various grazing, cropping and other land uses were equal to the total property area. For the remainder, there were minor to very large disparities between the sum of areas and the total property areas, due mainly to either the omission of areas or the double counting of part or all of the four land uses: “Area grazed”, “Area cropped”, “Cropping area grazed as stubble” and “Cropping area grazed as green”. The procedures followed to provide the best estimates of land use areas and total property area are described in Appendix 1.7.1. 3.3.4.1 Total area of property (ha) Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 62 140 7,285 161,880 18,909 8,027 GB & DD 22 350 2,410 7,285 2,861 860 New England 178 51 874 5,261 1,119 138 C & S Tablelands 183 86 700 8,903 987 159 S NSW & N Vic 161 108 760 40,470 1,365 552 Gippsland 12 255 443 3,830 1,051 714 W Vic & SE SA 385 72 660 64,752 1,210 367 S SA 71 123 1,200 9,308 1,547 333 KI 41 62 672 2,752 692 151 WA 207 95 1,578 11,900 2,030 238 All Regions 1322 51 867 161,880 2,172 440 Histogram class limits: 0-610-1220-1830-2440-3050-3660-4270-4880-5490-6100 Anova: F=38.74, d.f.=9, p<0.0005. Note: respondents with properties larger than 6,000 ha (57) have been excluded from the histograms (and only from the histograms) to prevent the property size distribution being reduced to a single bar, due to the influence of the small number of very large properties. Institute for Rural Futures12 3.3.4.2 Proportion of total property area grazed (incl. cropping areas grazed (%) Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 61 64 100 100 95 61 GB & DD 21 85 100 100 96 21 New England 178 40 100 100 97 178 C & S Tablelands 183 33 100 100 92 183 S NSW & N Vic 161 10 96 100 86 161 Gippsland 12 50 92 100 87 12 W Vic & SE SA 384 15 100 100 90 384 S SA 71 37 96 100 89 71 KI 41 56 89 100 85 41 WA 207 18 90 100 83 207 All Regions 1319 10 100 100 90 1319 Histogram class limits:10-19-28-37-46-55-64-73-82-91-100 Anova: F=11.59, d.f.=9, p<0.0005. 3.3.4.3 Proportion of total property area cropped (%) Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 61 0 0 40 6 3 GB & DD 21 0 0 18 3 3 New England 178 0 0 98 3 2 C & S Tablelands 183 0 5 75 14 3 S NSW & N Vic 161 0 24 100 28 4 Gippsland 12 0 0 49 8 10 W Vic & SE SA 384 0 7 89 16 2 S SA 71 0 8 89 21 6 KI 41 0 7 58 12 5 WA 207 0 32 96 33 3 All Regions 1319 0 7 100 17 1 Histogram class limits: 0-10-20-30-40-50-60-70-80-90-100. Anova: F=36.39, d.f.=9, p<0.0005. Institute for Rural Futures 13 3.3.4.4 Proportion of cropping area grazed as stubble (%) Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 20 0 0 100 35 20 GB & DD 7 0 0 100 21 36 New England 56 0 0 100 11 8 C & S Tablelands 108 0 46 100 48 9 S NSW & N Vic 125 0 50 100 50 8 Gippsland 6 0 0 0 0 0 W Vic & SE SA 231 0 33 100 47 6 S SA 45 0 62 100 56 14 KI 27 0 18 100 40 18 WA 186 0 100 100 68 6 All Regions 811 0 50 100 50 3 Histogram class limits: 0-10-20-30-40-50-60-70-80-90-100. Anova: F=9.91, d.g.=9, p<0.0005. 3.3.4.5 Proportion of cropping area grazed as green (%) Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 20 0 13 100 25 17 GB & DD 7 0 16 100 28 34 New England 56 0 100 100 59 13 C & S Tablelands 108 0 0 100 22 7 S NSW & N Vic 125 0 0 67 6 2 Gippsland 6 0 25 100 42 52 W Vic & SE SA 231 0 0 100 13 4 S SA 45 0 0 100 8 7 KI 27 0 0 0 0 0 WA 186 0 0 100 6 3 All Regions 811 0 0 100 15 2 Histogram class limits: 0-10-20-30-40-50-60-70-80-90-100. Anova: F=21.85, d.g.=9, p<0.0005. Institute for Rural Futures14 3.3.4.6 Proportion of pastures improved (%) Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 62 0 4 100 25 9 GB & DD 22 0 0 100 11 11 New England 179 0 60 100 54 5 C & S Tablelands 185 0 80 100 69 5 S NSW & N Vic 162 0 85 100 71 5 Gippsland 12 20 68 100 66 20 W Vic & SE SA 386 0 90 100 76 3 S SA 71 0 90 100 78 7 KI 42 0 100 100 82 10 WA 207 0 95 100 75 5 All Regions 1328 0 80 100 68 2 Histogram class limits: 0-10-20-30-40-50-60-70-80-90-100. Anova: F=8.80, d.g.=9, p=0.0005. 3.3.4.7 Number of paddocks Almost one fifth of respondents (18.1 per cent) did not provide the number of paddocks in their response to Question 4. Using the information provided by the remainder, the number of paddocks and its distribution is not substantively different between the regions (although it is still statistically significant: F=3.30, d.f.=9, p=0.001). The mean number of paddocks ranged from 20 in the Granite Belt and Darling Downs to 39 in South Australia, with a national mean of 30. In all regions, the great majority of respondents had less than 36 paddocks. Institute for Rural Futures 15 3.3.4.8 Average paddock size (ha) Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 55 19 283 5153 773 284 GB & DD 16 29 173 438 173 63 New England 148 6 33 111 37 4 C & S Tablelands 150 6 28 144 33 3 S NSW & N Vic 131 7 31 1349 50 21 Gippsland 12 9 29 157 47 30 W Vic & SE SA 320 5 27 1294 44 11 S SA 60 5 35 354 44 12 KI 32 9 24 61 27 5 WA 169 5 59 231 66 6 All Regions 1093 5 33 5153 84 17 Histogram class limits: 0-46-92-138-184-230-276-322-368-414-460. Anova: F=50.98, d.g.=9, p<0.0005. Note: respondents with average paddock sizes larger than 500 ha (27) have been excluded from the histograms (and only from the histograms) to prevent the average paddock size distribution being reduced to a single bar, due to the influence of the small number of very large average paddock sizes. 3.3.5 Cattle 3.3.5.1 Proportion of respondents with cattle in a typical year Region n Proportion with cattle (%) SW & S Qld 102 79 86 92 GB & DD 30 57 73 89 New England 280 85 89 92 C & S Tablelands 313 47 53 58 SW NSW & NE Vic 312 47 52 58 Gippsland 21 44 65 85 W Vic & SE SA 600 51 55 59 S SA 110 56 65 74 KI 53 33 47 60 WA 444 19 23 27 All regions 2265 51 53 55 �2 = 360.66, d.f. = 9, p < 0.00005. Note: percentages are adjusted for non-response bias as described in Appendix A1.10. Institute for Rural Futures16 3.3.5.2 Cattle DSEs in a typical year Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 54 180 3203 18400 4395 1184 GB & DD 12 48 1427 8236 2313 1634 New England 152 19 2096 32210 2926 573 C & S Tablelands 91 42 946 17650 1916 614 S NSW & N Vic 90 24 1102 11032 1956 483 Gippsland 7 131 359 9055 1775 3012 W Vic & SE SA 215 12 1437 43300 2443 522 S SA 43 259 1924 8790 2692 698 KI 19 206 770 2866 1029 423 WA 43 24 1750 14450 2412 866 All Regions 726 12 1449 43300 2530 249 Histogram class limits: 0-1000-2000-3000-4000-5000-6000-7000-8000-9000-10000. Anova: F=3.20, d.f.=9, p=0.001. Note: respondents with average cattle DSEs greater than 10,000 (23) have been excluded from the histograms (and only from the histograms) to prevent the average cattle DSE distribution being reduced to a single bar, due to the influence of the small number of very large average cattle DSEs. 3.3.5.3 2003 compared to a typical year Respondents with cattle who were carrying the same number of cattle DSEs in 2003 as in a typical year comprised 47.5 per cent of the sample. Those who were carrying less cattle in 2003 than in a typical year comprised 37.9 per cent of the sample, while the remaining 14.6 per cent of respondents were carrying more cattle DSEs in 2003, compared to a typical year. There was a significant different between the regions in the proportions of respondents who were carrying more, less or the same cattle DSEs in 2003, compared to a typical year (�2=66.63, d.f.=18, p<0.0005). Across the southern Australian regions, over 50 per cent of respondents were carrying the same number of cattle DSEs as in a typical year, with as many as 30 per cent carrying more in 2003 than in a typical year. The proportion who were carrying the same number of cattle DSEs declined northwards, to 25 per cent in the Granite Belt and Darling Downs. In south western and southern Queensland, 57 per cent of respondents were carrying fewer cattle DSEs in 2003 than in a typical year. Further details are provided in Appendix A2.3. Institute for Rural Futures 17 3.3.5.4 Calving There were significant differences between the regions in the length of the calving period for cows (anova: F=4.89, d.f.=9, p<0.0005), with relatively longer mean calving periods around four months in duration in south western and southern Queensland, Granite Belt and Darling Downs, and southern South Australia. The mean length of calving period in the other regions was around 2.5 months. The mean length of calving period for heifers across all regions was 2.3 months, and there was no significant difference between the regions in the length of the calving period for heifers. Further details on calving periods are provided in Appendix A2.4 – A2.5. Time of calving tended to be later in the calendar year in northern regions – around August to October – and earlier in the southern regions – around March to April. Further details on the time of calving are provided in Appendix A2.6 – A2.7. 3.3.6 Sheep 3.3.6.1 Sheep DSEs in a typical year Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 63 600 6000 72585 8773 2747 GB & DD 24 500 3234 8846 3373 941 New England 180 50 3553 28670 4845 646 C & S Tablelands 186 22 2971 50750 4794 890 S NSW & N Vic 172 0 2596 21687 3464 491 Gippsland 12 1193 2728 17570 4564 3152 W Vic & SE SA 378 420 3068 39200 4288 408 S SA 71 680 2630 16240 4078 871 KI 42 625 3750 15820 4170 955 WA 209 300 4405 53150 5798 782 All Regions 1337 0 3284 72585 4746 279 Histogram class limits: 0-2000-4000-6000-8000-10000-12000-14000-16000-18000-20000. Anova: F=7.33, d.f.=9, p<0.0005. Note: respondents with average sheep DSEs of 20,000 and over (21) have been excluded from the histograms (and only from the histograms) to prevent the average sheep DSE distribution being reduced to a single bar, due to the influence of the small number of very large average sheep DSEs. 3.3.6.2 2003 compared to a typical year The figures for all regions and the regional pattern of differences between sheep DSEs in 2003 and in a typical year was very similar to that for cattle. Across all regions, 47.5 per cent of respondents carried the same number of sheep DSEs in 2003 as they did in a typical year, while 38.5 per cent carried less and 14.0 per cent carried more. The proportion of respondents carrying less DSEs in 2003 than in a typical year increased from 20-30 per cent in southern Australia to 70 per cent in south western and southern Queensland. Further details are provided in Appendix A2.8. Institute for Rural Futures18 3.3.6.3 Flock composition in a typical year – ewes as a proportion of the total flock Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 63 0 46 100 44 7 GB & DD 24 0 1 99 18 12 New England 180 0 44 100 45 3 C & S Tablelands 186 0 49 100 54 4 S NSW & N Vic 171 0 52 100 57 4 Gippsland 12 28 42 98 55 17 W Vic & SE SA 378 0 50 100 56 3 S SA 71 0 62 100 65 5 KI 42 29 51 99 53 5 WA 209 0 53 100 55 2 All Regions 1336 0 50 100 53 1 Histogram class limits: 0-10-20-30-40-50-60-70-80-90-100. Anova: F=12.40, d.f.=9, p<0.0005. 3.3.6.4 Flock composition in a typical year – wethers as a proportion of the total flock Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 63 0 23 100 32 9 GB & DD 24 0 99 100 75 15 New England 180 0 27 100 27 3 C & S Tablelands 186 0 21 100 22 3 S NSW & N Vic 171 0 0 100 16 3 Gippsland 12 0 31 47 25 11 W Vic & SE SA 378 0 17 100 21 3 S SA 71 0 0 100 8 4 KI 42 0 24 49 23 4 WA 209 0 8 100 12 2 All Regions 1336 0 16 100 21 1 Histogram class limits: 0-10-20-30-40-50-60-70-80-90-100. Anova: F=24.96, d.f.=9, p<0.0005. Institute for Rural Futures 19 3.3.6.5 Flock composition in a typical year – weaners as a proportion of the total flock Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 63 0 22 100 23 5 GB & DD 24 0 0 39 6 5 New England 180 0 26 100 26 2 C & S Tablelands 186 0 25 100 23 2 S NSW & N Vic 171 0 26 100 26 3 Gippsland 12 0 22 33 19 8 W Vic & SE SA 378 0 23 100 22 2 S SA 71 0 25 54 26 4 KI 42 0 23 50 23 3 WA 209 0 33 89 31 2 All Regions 1336 0 25 100 25 1 Histogram class limits: 0-10-20-30-40-50-60-70-80-90-100. Anova: F=8.55, d.f.=9, p<0.0005. 3.4 Wool Cut and Fibre Diameter 3.4.1.1 Adult breeding ewe wool cut and fibre diameter by breed – all regions Merino Merino crosses Dual purpose breeds Meat breed Data given for several breeds n 668 78 17 5 44 Average cut per head - kg 5.03 4.52 5.26 3.90 4.48 n 766 72 23 5 50 Average fibre diameter - � 19.92 28.35 26.64 28.60 26.02 Cut per head - anova: F=6.22, d.f.=4, p<0.0005; fibre diameter – anova: F=424.96, d.f.=4, p<0.0005. 3.4.1.2 Sheep other than ewes For wethers and weaners, there were insufficient data supplied by respondents to warrant reporting wool cut and fibre diameters for any breed other than Merino. Across all regions, Merino dry ewes and wethers averaged 5.28kg per head wool cut and 19.64� fibre diameter. The corresponding figures for Merino weaners were 2.62kg per head wool cut and 18.14� fibre diameter. Institute for Rural Futures20 3.4.1.3 Wool cut (kg/head), 2003 clip, adult sheep (breeding ewes, dry ewes and wethers) by region Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 47 2.5 4.6 7.0 4.8 0.3 GB & DD 17 3.0 4.0 7.0 4.3 0.5 New England 130 2.3 4.2 8.0 4.3 0.2 C & S Tablelands 137 2.7 4.8 9.0 5.0 0.2 S NSW & N Vic 129 2.0 5.0 8.5 5.3 0.2 Gippsland 8 3.3 5.6 7.4 5.2 1.1 W Vic & SE SA 286 2.3 5.0 9.3 5.2 0.1 S SA 58 2.8 6.0 8.0 5.9 0.3 KI 28 3.0 5.6 7.4 5.6 0.4 WA 151 3.0 5.2 7.5 5.3 0.1 All Regions 991 2.0 5.0 9.3 5.1 0.1 Histogram class limits: 2.00-2.73-3.46-4.19-4.92-5.65-6.38-7.11-7.84-8.57-9.30. Anova: F=15.42, d.f.=9, p<0.0005. 3.4.1.4 Fibre diameter (�), 2003 clip, adult sheep (breeding ewes, dry ewes and wethers) by region Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 51 15.1 20.4 32.0 20.5 0.6 GB & DD 17 16.4 18.0 27.0 18.9 1.3 New England 151 15.5 18.4 35.0 19.0 0.4 C & S Tablelands 142 16.7 19.4 32.0 20.5 0.6 S NSW & N Vic 140 15.6 20.6 31.5 21.8 0.6 Gippsland 10 17.6 20.0 29.0 22.0 3.1 W Vic & SE SA 322 16.5 20.5 33.0 21.9 0.4 S SA 64 18.5 22.2 30.0 22.8 0.7 KI 30 20.0 21.9 23.8 21.8 0.3 WA 174 17.5 20.6 23.2 20.6 0.2 All Regions 1101 15.1 20.2 35.0 21.0 0.2 Histogram class limits: 15-17-19-21-23-25-27-29-31-33-35. Anova: F=15.52, d.f.=9, p<0.0005. Separate figures for wool cut and fibre diameter for breeding ewes, dry ewes and wethers, and weaners are provided in Appendix A2.9-A2.14 Institute for Rural Futures 21 3.5 Animal Husbandry (Other Than Parasite Management) 3.5.1 Shearing and crutching 3.5.1.1 Proportion of respondents shearing and crutching ewes in each month of the year Region n Proportion of respondents shearing in month n Proportion of respondents crutching in month SW & S Qld 53 52 GB & DD 13 14 New England 171 169 C & S Tablelands 176 173 S NSW & N Vic 158 160 Gippsland 12 12 W Vic & SE SA 352 347 S SA 69 69 KI 38 39 WA 197 186 All Regions 1239 1221 Figures for the histograms above are provided in Appendix A2.15. Institute for Rural Futures22 3.5.1.2 Proportion of respondents shearing and crutching wethers in each month of the year Region n Proportion of respondents shearing in month n Proportion of respondents crutching in month SW & S Qld 48 44 GB & DD 20 20 New England 148 136 C & S Tablelands 131 129 S NSW & N Vic 88 87 Gippsland 9 9 W Vic & SE SA 253 245 S SA 38 36 KI 34 33 WA 141 130 All Regions 910 869 Figures for the histograms above are provided in Appendix A2.16. Institute for Rural Futures 23 3.5.1.3 Proportion of respondents shearing and crutching weaners in each month of the year Region n Proportion of respondents shearing in month n Proportion of respondents crutching in month SW & S Qld 50 43 GB & DD 6 4 New England 147 145 C & S Tablelands 144 126 S NSW & N Vic 120 103 Gippsland 9 9 W Vic & SE SA 270 236 S SA 52 38 KI 37 29 WA 181 118 All Regions 1016 851 Figures for the histograms above are provided in Appendix A2.17. Institute for Rural Futures24 3.5.2 Breeding program 3.5.2.1 Proportion of respondents putting rams with ewes each month of the year in 2003 Region Merino ewes mated to Merino rams Merino ewes mated to meat breed rams Cross-bred ewes SW & S Qld GB & DD New England C & S Tablelands S NSW & N Vic Gippsland W Vic & SE SA S SA KI WA All Regions Figures for the histograms above are provided in Appendix A2.18. Institute for Rural Futures 25 3.5.2.2 Number of weeks Merino rams left with Merino ewes Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 42 6.0 8.1 16.0 9.4 0.8 GB & DD 7 6.0 6.0 10.0 7.1 1.5 New England 151 3.0 6.0 20.0 6.9 0.4 C & S Tablelands 134 1.4 6.0 20.0 7.3 0.5 S NSW & N Vic 99 1.1 7.0 20.0 8.0 0.7 Gippsland 8 5.0 6.3 13.0 7.0 2.1 W Vic & SE SA 230 2.0 7.0 28.0 7.8 0.5 S SA 47 5.0 8.0 32.0 9.2 1.3 KI 35 5.0 7.0 28.0 8.4 1.5 WA 189 1.4 7.0 52.1 7.9 0.6 All Regions 942 1.1 7.0 52.1 7.8 0.2 Histogram class limits: 1.0-3.1-5.2-7.3-9.4-11.5-13.6-15.7-17.8-19.9-22.0 Anova: F=3.52, d.f.=9, p<0.0005. Note: respondents who left rams with ewes from six months or more (6) have been excluded from the histograms (and only from the histograms) to prevent the distribution being reduced to a single bar, due to the influence of the small number of relatively long time periods. Institute for Rural Futures26 3.5.2.3 Number of weeks meat breed rams left with Merino ewes Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 12 6.0 8.0 12.9 8.7 1.4 GB & DD 4 6.0 8.3 12.0 8.6 4.0 New England 46 5.0 6.5 16.0 7.7 0.9 C & S Tablelands 65 1.4 7.0 21.7 8.3 0.9 S NSW & N Vic 70 5.0 8.0 28.0 9.6 1.0 Gippsland 4 5.7 6.3 20.0 9.6 11.1 W Vic & SE SA 174 2.0 8.0 52.1 8.9 0.7 S SA 45 6.0 8.0 32.0 10.0 1.4 KI 26 5.0 8.0 32.0 10.1 2.6 WA 93 4.6 8.0 52.1 9.3 1.5 All Regions 539 1.4 8.0 52.1 9.0 0.4 Histogram class limits: 1.0-3.1-5.2-7.3-9.4-11.5-13.6-15.7-17.8-19.9-22.0 Anova: F=0.96, d.f.=9, p=0.476. Note: respondents who left rams with ewes from six months or more (10) have been excluded from the histograms (and only from the histograms) to prevent the distribution being reduced to a single bar, due to the influence of the small number of relatively long time periods. Institute for Rural Futures 27 3.5.2.4 Number of weeks rams left with Cross-bred ewes Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 4 4.0 10.0 12.9 9.2 6.5 GB & DD New England 36 5.0 8.0 16.0 8.6 1.0 C & S Tablelands 43 1.4 8.0 20.0 9.9 1.4 S NSW & N Vic 50 5.0 10.0 30.3 12.0 1.8 Gippsland 5 6.0 8.0 26.0 11.4 10.3 W Vic & SE SA 140 1.1 8.0 26.0 10.0 0.7 S SA 20 5.0 10.0 20.0 10.6 1.9 KI 9 5.0 10.0 26.1 11.7 5.5 WA 13 4.6 8.0 52.1 16.0 10.2 All Regions 320 1.1 8.0 52.1 10.5 0.6 Histogram class limits: 1.0-3.5-6-8.5-11-13.5-16-18.5-21-23.5-26.0 Anova: F=2.78, d.f.=9, p=0.006. Note: respondents who left rams with ewes from six months or more (9) have been excluded from the histograms (and only from the histograms) to prevent the distribution being reduced to a single bar, due to the influence of the small number of relatively long time periods. Institute for Rural Futures28 3.5.2.5 Typical marking percentage – Merino ewes mated to Merino rams Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 40 65 89 115 87 4 GB & DD 5 75 80 90 81 7 New England 139 40 85 110 86 2 C & S Tablelands 118 60 85 110 83 2 S NSW & N Vic 90 65 85 108 86 2 Gippsland 7 70 80 100 84 9 W Vic & SE SA 201 60 85 120 85 1 S SA 41 70 93 120 92 4 KI 28 50 84 100 82 5 WA 168 60 85 120 86 1 All Regions 837 40 85 120 86 1 Histogram class limits: 40-48-56-64-72-80-88-96-104-112-120. Anova: F=3.72, d.f.=9, p<0.0005. 3.5.2.6 Typical marking percentage – Merino ewes mated to meat breed rams Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 8 80 93 100 92 6 GB & DD 4 80 80 85 81 4 New England 41 60 92 110 92 3 C & S Tablelands 53 60 90 110 90 3 S NSW & N Vic 59 60 90 115 90 3 Gippsland 3 90 90 110 97 29 W Vic & SE SA 145 65 90 120 91 2 S SA 37 70 100 120 98 4 KI 22 65 90 120 89 6 WA 76 60 90 110 88 2 All Regions 448 60 90 120 91 1 Histogram class limits: 60-66-72-78-84-90-96-102-108-114-120. Anova: F=3.04, d.f.=9, p=0.002. Institute for Rural Futures 29 3.5.2.7 Typical marking percentage – Cross-bred ewes Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 2 90 93 95 93 32 GB & DD New England 31 90 130 145 123 6 C & S Tablelands 39 70 110 145 108 5 S NSW & N Vic 43 75 100 130 107 4 Gippsland 5 98 120 120 115 12 W Vic & SE SA 121 80 118 150 116 3 S SA 16 90 123 150 119 9 KI 6 85 108 180 115 37 WA 8 80 100 140 104 20 All Regions 271 70 115 180 114 2 Histogram class limits: 70-81-92-103-114-125-136-147-158-169-180. Anova: F=3.76, d.f.=9, p<0.0005. 3.5.2.8 Marking percentages in 2003 compared to typical years Across all regions, and for Merino ewes mated to Merino rams, Merino ewes mated to meat-breed rams, and cross-bred ewes, there were more respondents reporting lower marking percentages in 2003 compared to a typical year, than respondents who reported either the same marking percentage in 2003, or a higher percentage in 2003. For Merino ewes mated to Merino rams, there were sufficient responses in each of the regions to indicate a significant difference between the regions, with greater proportions of respondents in some Queensland and New South Wales regions experiencing lower marking percentages in 2003 and lower proportions of respondents in Western Australia experiencing lower marking percentages. Detailed figures on the differences between 2003 marking percentages and those for a typical year are given in Appendix A2.19. Institute for Rural Futures30 3.5.3 Supplementary Feeding 3.5.3.1 Proportion of respondents who use supplementary feeds Region n Proportion using feeds (%) SW & S Qld 57 34 47 60 GB & DD 16 32 56 81 New England 171 67 74 80 C & S Tablelands 179 75 81 87 SW NSW & NE Vic 169 83 88 93 Gippsland 12 40 67 93 W Vic & SE SA 369 75 79 84 S SA 69 62 72 83 KI 41 68 80 93 WA 204 93 96 99 All regions 1287 78 80 83 �2 =92.16, d.f. = 9, p < 0.00005. 2 cells (10.0%) have expected counts less than 5. 3.5.3.2 Feed types in supplementary feeding of ewes Proportion of feeds mentioned in categories below (%) Region n* Barley, oats, wheat, triticale, corn Lupins, beans, lentils, peas Cottonseed, cottonseed meal Straw, hay, silage, forage Pellets, nuts Bypass meal Blocks, licks SW & S Qld 34 15 3 18 3 0 9 53 GB & DD 14 14 0 14 0 21 0 50 New England 200 25 22 2 3 16 4 30 C & S Tablelands 237 50 13 0 12 10 0 14 S NSW & N Vic 269 57 10 0 16 3 0 13 Gippsland 8 63 0 0 13 25 0 0 W Vic & SE SA 501 49 14 0 23 2 0 12 S SA 71 20 38 0 34 0 0 8 KI 62 47 24 0 27 0 0 2 WA 376 39 35 0 18 1 0 7 All Regions 1772 43 20 1 17 5 1 14 * n in this table is the number of feed types mentioned by respondents. Respondents were able to indicate more than one feed type. For example, pellets and nuts comprised 5 per cent of the feed types mentioned across all regions. Institute for Rural Futures 31 3.5.3.3 Feed types in supplementary feeding of weaners Proportion of feeds mentioned in categories below (%) Region n* Barley, oats, wheat, triticale, corn Lupins, beans, lentils, peas Cottonseed, cottonseed meal Straw, hay, silage, forage Pellets, nuts Bypass meal Blocks, licks SW & S Qld 18 17 17 17 6 6 0 39 GB & DD 9 11 0 11 0 33 0 44 New England 134 28 18 1 5 15 6 26 C & S Tablelands 188 47 23 0 16 7 1 5 S NSW & N Vic 197 51 19 1 19 2 0 9 Gippsland 13 46 8 0 23 15 0 8 W Vic & SE SA 392 45 17 1 26 2 0 9 S SA 46 26 35 0 30 0 0 9 KI 62 40 31 0 27 0 2 0 WA 335 36 37 0 18 2 0 7 All Regions 1394 41 24 1 19 4 1 10 * n in this table is the number of feed types mentioned by respondents. Respondents were able to indicate more than one feed type. For example, pellets and nuts comprised 4 per cent of the feeds mentioned across all regions. 3.5.3.4 Duration of supplementary feeding The duration of the period over which ewes received supplementary feeding varied from one month to 12 months, with a mean of five months across all regions. The mean duration of the supplementary feeding period ranged from four months in south western and southern Queensland, in New England, in southern South Australia and Kangaroo Island, to six months in Gippsland. The figures for the supplementary feeding of weaners were very similar, with the same range of durations, and mean duration, as for the feeding of ewes. The differences in the mean duration between regions were also very similar to those for the feeding of ewes. Further details on the duration of supplementary feeding for ewes and weaners are in Appendix A2.21. Institute for Rural Futures32 3.5.3.5 Proportion of respondents feeding ewes and weaners in each month of the year Region n Proportion of respondents feeding ewes n Proportion of respondents feeding weaners SW & S Qld 20 12 GB & DD 8 5 New England 112 70 C & S Tablelands 124 104 S NSW & N Vic 133 95 Gippsland 5 6 W Vic & SE SA 252 187 S SA 43 20 KI 32 27 WA 190 163 All Regions 919 689 Figures for the histograms above are provided in Appendix A2.20. Institute for Rural Futures 33 3.6 Grazing Management 3.6.1 Grazing strategies used in 2003 Proportion with grazing strategy below (%) Region n Set stocked Set stocked at lambing only Alternating between sheep and cattle Alternating between sheep and crop stubble Alternating between sheep and forage crop Cell grazing Rotational grazing SW & S Qld 94 52 17 42 16 15 4 41 GB & DD 30 70 10 45 1 10 10 18 New England 281 57 33 32 3 8 11 33 C & S Tablelands 310 54 30 18 34 11 4 38 S NSW & N Vic 294 39 40 26 47 11 3 44 Gippsland 21 48 41 33 10 2 10 39 W Vic & SE SA 615 55 31 23 31 11 6 40 S SA 110 17 46 27 31 5 4 71 KI 55 44 49 23 39 0 4 40 WA 435 41 26 7 60 6 6 30 All Regions 2245 48 31 22 36 9 6 38 Note 1: percentages may sum to more than 100 as respondents could give more than one strategy. Note 2: percentages are adjusted for non-response bias as described in Appendix 1.10. 3.6.2 Key objectives in using grazing strategies Proportion with key objective below (% Region n P ar as it e co nt ro l P as tu re m gt A ni m al m gt S us ta in - ab ili ty E as e U se o f cr op s an d st ub bl es M ax im is e or in cr ea se pr od u ct iv it y or pr od u ct io n O th er SW & S Qld 55 27 31 27 7 7 4 5 35 GB & DD 19 16 47 21 5 16 0 11 21 New England 146 36 47 33 5 13 1 9 17 C & S Tablelands 150 21 47 44 2 13 7 3 15 S NSW & N Vic 129 18 38 46 2 11 15 8 18 Gippsland 8 38 38 63 0 13 0 0 13 W Vic & SE SA 307 21 40 37 2 14 5 5 19 S SA 63 32 35 35 0 13 27 5 13 KI 36 22 44 31 3 19 0 11 14 WA 159 14 48 36 3 13 15 11 17 All Regions 1072 23 42 37 3 13 8 7 18 Note: percentages may sum to more than 100 as respondents could give more than one strategy. Institute for Rural Futures34 3.7 Worm Control 3.7.1 Number, timing and type of treatment – September 2002 to December 2003 3.7.1.1 Unweaned lambs Region n* Prop’n treating unweaned lambs (%) Mean number of times treated Prop’n using capsules (%)** Month with highest prop’n of treatments** Prop’n which were the most popular product - ML Moxidectin (%)** SW & S Qld 45 36 1.2 0 Dec 29 GB & DD 24 29 1.4 0 Feb 25 New England 166 65 1.4 0.7 Dec 29 C & S Tablelands 181 45 1.3 0 Oct 24 S NSW & N Vic 157 34 1.2 0 Jul, Sep 21 Gippsland 11 46 1.3 0 Aug 17 W Vic & SE SA 360 40 1.3 0 Sep 37 S SA 70 30 1.2 0 Jul 30 KI 39 51 1.3 0 Jul 80 WA 194 16 1.3 2.6 Aug, Sep 29 All Regions 1247 39 1.3 0.3 Sep 32 Chi-squared test for proportion treating unweaned lambs: �2=101.27, d.f.=9, p<0.0005. Kruskal-Wallis test for number of times treated: �2=6.83, d.f.=9, p=0.655 * the sample size given is for the proportion treating unweaned lambs. For the remaining figures in the table, the sample size will be equal to the sample size given, multiplied by the proportion treating unweaned lambs. ** proportion of treatments. Further details for the treatments for worm control in unweaned lambs are provided in Appendix A2.22.1 and Appendix A2.22.3. 3.7.1.2 Weaners Region n* Prop’n treating weaners (%) Mean number of times treated Prop’n using capsules (%)** Month with highest prop’n of treatments** Prop’n which were the most popular product - ML Moxidectin (%)** SW & S Qld 45 73 1.9 0 Feb 39 GB & DD 24 38 2.9 0 Aug 42 New England 166 90 2.9 2.8 Apr 32 C & S Tablelands 181 86 2.3 2.3 Dec 27 S NSW & N Vic 157 83 2.1 3.3 Nov 29 Gippsland 11 91 2.4 4.0 Nov 27 W Vic & SE SA 360 82 2.2 4.2 Dec 36 S SA 70 79 1.8 3.9 Jul, Sep, Nov 41 KI 39 87 2.4 2.4 Feb, Sep 63 WA 194 94 1.6 1.1 Dec 24 All Regions 1247 84 2.2 2.9 Dec 33 Chi-squared test for proportion treating weaners: �2=67.33, d.f.=9, p<0.0005. Kruskal-Wallis test for number of times treated: �2=128.14, d.f.=9, p<0.0005 * the sample size given is for the proportion treating weaners. For the remaining figures in the table, the sample size will be equal to the sample size given, multiplied by the proportion treating weaners. ** proportion of treatments. Further details for the treatments for worm control in weaners are provided in Appendix A2.22.2 and Appendix A2.22.4. Institute for Rural Futures 35 3.7.1.3 Maiden ewes Region n* Prop’n treating maiden ewes (%) Mean number of times treated Prop’n using capsules (%)** Month with highest prop’n of treatments** Prop’n which were the most popular product - ML Moxidectin (%)** SW & S Qld 45 53 2.2 0 Feb, Nov 43 GB & DD 24 29 3.2 0 Aug 39 New England 166 78 2.9 2.3 Sep 27 C & S Tablelands 181 75 2.4 2.6 Nov 26 S NSW & N Vic 157 69 1.9 4.8 Nov 25 Gippsland 11 64 2.6 0 Nov 24 W Vic & SE SA 360 72 2.1 4.1 Dec 40 S SA 70 73 1.6 4.8 Dec 33 KI 39 87 2.2 1.3 Jan 64 WA 194 77 1.4 1.9 Dec 23 All Regions 1247 73 2.1 3.1 Dec 32 Chi-squared test for proportion treating maiden lambs: �2=41.93, d.f.=9, p<0.0005. Kruskal-Wallis test for number of times treated: �2=198.15, d.f.=9, p<0.0005. * the sample size given is for the proportion treating maiden ewes. For the remaining figures in the table, the sample size will be equal to the sample size given, multiplied by the proportion treating maiden ewes. ** proportion of treatments. Further details for the treatments for worm control in maiden ewes are provided in Appendix A2.22.1 and Appendix A2.22.3. 3.7.1.4 Adult ewes Region n* Prop’n treating adult ewes (%) Mean number of times treated Prop’n using capsules (%)** Month with highest prop’n of treatments** Prop’n which were the most popular product - ML Moxidectin (%)** SW & S Qld 45 76 2.3 0 Dec 44 GB & DD 24 50 3.0 0 Aug 39 New England 166 92 3.2 2.2 Sep 27 C & S Tablelands 181 91 2.4 3.0 Dec 27 S NSW & N Vic 157 89 1.9 3.5 Nov 25 Gippsland 11 100 2.6 0 Nov 27 W Vic & SE SA 360 91 2.2 4.9 Dec 41 S SA 70 96 1.7 5.1 Jan 32 KI 39 97 2.3 1.1 Jan 64 WA 194 85 1.4 0 Dec 23 All Regions 1247 89 2.2 3.0 Dec 33 Chi-squared test for proportion treating adult ewes: �2=61.73, d.f.=9, p<0.0005. 4 cells (20.0%) have expected counts less than 5. Kruskal-Wallis test for number of times treated: �2=249.81, d.f.=9, p<0.0005 * the sample size given is for the proportion treating adult ewes. For the remaining figures in the table, the sample size will be equal to the sample size given, multiplied by the proportion treating adult ewes. ** proportion of treatments. Further details for the treatments for worm control in adult ewes are provided in Appendix A2.22.6 and Appendix A2.22.8. Institute for Rural Futures36 3.7.1.5 Wethers Region n* Prop’n treating wethers (%) Mean number of times treated Prop’n using capsules (%)** Month with highest prop’n of treatments** Prop’n which were the most popular product - ML Moxidectin (%)** SW & S Qld 45 62 2.0 0 Feb 50 GB & DD 24 88 3.0 0 Jan, Mar, Aug, Sep, Nov 33 New England 166 74 2.6 0.3 Sep 25 C & S Tablelands 181 66 2.0 0.4 Dec 27 S NSW & N Vic 157 47 1.7 3.5 Dec 26 Gippsland 11 73 2.0 0 Nov 17 W Vic & SE SA 360 58 1.7 4.9 Dec 39 S SA 70 34 1.2 5.1 Dec 36 KI 39 77 1.8 1.1 Jan 56 WA 194 44 1.3 0 Dec 24 All Regions 1247 58 1.9 3.0 Dec 32 Chi-squared test for proportion treating wethers: �2=76.07, d.f.=9, p<0.0005. Kruskal-Wallis test for number of times treated: �2=153.05, d.f.=9, p<0.0005 * the sample size given is for the proportion treating wethers. For the remaining figures in the table, the sample size will be equal to the sample size given, multiplied by the proportion treating wethers. ** proportion of treatments. Further details for the treatments for worm control in wethers are provided in Appendix A2.22.9 and Appendix A2.22.10. 3.7.2 Drenching of newly introduced sheep Across all regions, 59 per cent of respondents reported that they purchased sheep and brought them on to their property. The proportion ranged from 49 per cent in Western Australia to 91 per cent in the Granite Belt and Darling Downs. Further details are provided in Appendix A2.22.11. The proportions of those who purchased sheep who also drenched them on their arrival to their property are shown below. Region n Proportion drenching sheep on arrival (%) SW & S Qld 46 54 67 81 GB & DD 20 85 95 105 New England 100 89 94 99 C & S Tablelands 102 84 90 96 SW NSW & NE Vic 105 72 80 88 Gippsland 7 60 86 112 W Vic & SE SA 227 82 86 91 S SA 38 63 76 90 KI 26 71 85 98 WA 97 60 69 78 All regions 768 81 83 86 �2 =39.96, d.f. = 9, p <0.0005. Across all regions, the drench most commonly used was ML Moxidectin, which was used by 41 per cent of respondents. Further details are provided in Appendix A2.22.12. Institute for Rural Futures 37 3.7.3 Monitoring worm egg counts Region n Proportion of respondents monitoring worm egg counts (%) SW & S Qld 63 42 54 66 GB & DD 24 43 63 82 New England 174 51 59 66 C & S Tablelands 179 41 49 56 SW NSW & NE Vic 169 32 39 46 Gippsland 12 40 67 93 W Vic & SE SA 368 35 40 45 S SA 70 26 37 48 KI 42 23 38 53 WA 206 26 33 39 All regions 1307 41 44 46 �2 =41.75, d.f. = 9, p <0.0005. 3.7.3.1 Frequency of monitoring worm egg counts Across all regions, the frequency with which respondents typically monitored worm egg counts ranged from an average of 3.0 times per year for weaners to 2.6 times per year for adult ewes. The typical frequency of monitoring was significantly different between regions for weaners, adult ewes and wethers, with higher frequencies being reported in the Granite Belt and Darling Downs, and in the New England region, and generally lower frequencies in the southern Australian regions. Additional information on the typical frequency of monitoring worm egg counts is provided in Appendices A2.22.13–A2.22.15. 3.7.3.2 Frequency of monitoring worm egg counts in 2003 compared to typical frequency Across all regions, and for all three classes of sheep, 95 per cent or more of respondents had the same frequency of monitoring in 2003 as they did in a typical year. Additional information on the comparison between the frequency of monitoring of worm egg counts in 2003 and in a typical year is provided in Appendices A2.22.16 – A2.22.18. Institute for Rural Futures38 3.7.4 Drench resistance testing Region n Proportion of respondents who have tested for drench resistance (%) SW & S Qld 101 19 28 37 GB & DD 33 42 59 76 New England 277 51 57 63 C & S Tablelands 314 39 44 50 SW NSW & NE Vic 311 40 45 51 Gippsland 21 44 65 85 W Vic & SE SA 606 44 48 52 S SA 108 38 47 57 KI 54 44 57 70 WA 438 46 51 56 All regions 2263 46 48 50 �2 =35.78, d.f. = 9, p <0.0005. Note: percentages are adjusted for non-response bias as described in Appendix A1.10. 3.7.4.1 Year of most recent drench resistance test – all regions Year of most recent drench resistance test Proportion of respondents (%) 1980 0.4 1982 0.2 1986 0.2 1989 0.6 1990 3.9 1991 0.4 1992 1.3 1993 0.7 1994 2.6 1995 4.4 1996 2 1997 3 1998 7.2 1999 7.2 2000 13.3 2001 11.9 2002 17.8 2003 14.6 2004 8.3 n=540 Respondents were grouped into those whose most recent drench resistance test was previous to the year 2000 and those whose most recent test was in 2000 or more recently, as a measure of the recency of adoption of drench resistance testing. There was no significant difference between the regions in this measure. Further information is provided in Appendix A2.22.19. Institute for Rural Futures 39 3.7.4.2 Type of drench resistance test Proportion of respondents using tests below (%) Region n DrenchRite FECR DrenchRite or FECR* Other** SW & S Qld 7 0 0 0 6 43 80 0 14 40 6 43 80 GB & DD 6 0 0 0 0 0 0 0 17 46 54 83 113 New England 55 0 4 9 22 35 47 0 7 14 41 55 68 C & S Tablelands 45 5 16 26 2 11 20 1 9 17 50 64 78 SW NSW & NE Vic 30 0 7 16 0 3 10 0 10 21 66 80 94 Gippsland 5 0 40 83 0 20 55 0 0 0 0 40 83 W Vic & SE SA 88 3 9 15 6 14 21 2 7 12 61 70 80 S SA 17 0 6 17 0 12 27 0 6 17 56 76 97 KI 11 0 0 0 0 18 41 0 18 41 35 64 92 WA 53 0 4 9 4 13 22 4 13 22 57 70 82 All regions 317 5 8 10 12 16 20 6 9 12 62 67 72 �2 =42.84, d.f. = 27, p = 0.027. 26 cells (65.0%) have expected counts less than 5. * Sufficient information given to identify test as DrenchRite or FECR test, but not sufficient to determine which of the two. ** Tests other than DrenchRite and FECR tests, or cases where information given was only sufficient to identify that some form of drench resistance testing had been carried out by the respondent. 3.7.5 Treatments and techniques for worm control Proportion of respondents using technique below (%) Region n Smart grazing Other grazing Sheep un- drenched Feeding Rams Organic Drenching Other SW & S Qld 54 19 28 2 11 13 4 80 15 GB & DD 24 4 21 0 8 8 0 96 8 New England 177 29 46 2 13 24 1 89 10 C & S Tablelands 184 34 33 5 21 8 3 89 11 S NSW & N Vic 165 30 35 3 21 5 2 84 15 Gippsland 12 25 25 0 33 17 8 100 17 W Vic & SE SA 363 33 34 2 23 10 3 91 10 S SA 70 40 30 3 26 19 1 84 16 KI 42 31 48 14 36 21 5 86 5 WA 200 23 23 18 19 21 1 89 18 All regions 1291 29 33 5 20 14 2 89 12 Note: percentages may sum to more than 100 as respondents could give more than one strategy. ”Sheep un-drenched” = Leave some sheep un-drenched at summer treatments. “Feeding” = Feeding strategy. “Rams” = Use rams selected for resistance to worms. “Organic” = Organic methods. A small number of respondents gave explanatory descriptions of the treatments or techniques they were using. Further information about these is provided in Appendix A2.22.20. Institute for Rural Futures40 Institute for Rural Futures 1 APPENDIX 1 Institute for Rural Futures2 Institute for Rural Futures 3 A1 METHODS A1.1 Survey content A first draft of the benchmark survey questionnaire was prepared in consultation with the participating institutions in the IPM-sheep project. A1.2 First pilot survey A pilot questionnaire of 300 was sent out in May 2004 to four regions, including New England, Southern Queensland, Victoria and Western Australia. Addresses were chosen from a database of rural addresses selected randomly from Australian Federal Electoral Rolls. Addresses within this database were selected according to areas within each region identified as being within a ‘sphere of influence’ of the programs being run by regional IPM-sheep project managers. Postcodes deemed to fall within these areas provided the basis for the random selection of addresses from the Electoral Rolls. A response rate of 24.5% (85 surveys) was achieved - this figure includes those who were ineligible (i.e. they had less than 500 sheep), as well as those who completed the survey. Eight completed surveys were received in total (response rate from 300 of 2.6% or 10% of those returned). After four weeks a short form was sent out on 4 June to all addresses from which no response had been received. Those who had responded as either ineligible or RTS were not included in the mail-out. This abbreviated one-page survey aimed to provide information as to whether the low response rate was due to a low proportion of wool producers in the sampling frame, or to factors specific to the questionnaire content and format that were discouraging responses. In addition, a number of non–respondents in WA and Victoria were phoned shortly after the short survey was sent out. This revealed some issues that may have affected response rate. In particular, respondents in WA indicated that they were finalising their seeding operations and non-vital mail had not been looked at for several weeks. A similar situation occurred in Victoria, and it was also noted that several Victorian addresses had received two surveys from IRF in error - the other being one on foot-and-mouth preparedness, which being smaller was filled out in preference to the IPMS survey. The short survey form achieved a response rate of 22% (48 of 218) by 25th June. Important feedback was received via e-mail from one respondent phoned as part of the pilot follow-up, and his comments were incorporated into the new version of the questionnaire. A1.3 Analysis of first pilot survey The completed surveys were relatively well filled in, with most responses indicating that the questions were easily understood, though some have required reworking (e.g. Q6, Q11). Several of the more detailed questions were frequently skipped or poorly answered (Qs 9, 10, 18, 26 & 34). There was no negative feedback regarding length or format of the survey, however the low response rate to the pilot was taken as an indication of this. The response to the short survey suggested that the length and format of the full questionnaire was reducing response rates. This was indicated by several factors, including:  the more immediate initial response to the short survey;  the response of wool producers with well over 500 sheep to the short survey but not to the full questionnaire used in the pilot;  indication from the same producers that they regarded IPM as being applicable to their property. To reduce the perceived length of the questionnaire, the format was changed back to that originally specified by IRF, an A5 booklet. In consultation with the Board of Management, the survey content Institute for Rural Futures4 was altered with several questions that were too complex and time consuming to answer, removed. Other questions were rearranged to make them easier to read and answer. Further, approval was sought from AWI to use its levy-payers database. A request was placed on 21 June 2004 and the database was received on 23 August. A1.4 Second pilot survey The second pilot using the new questionnaire content and formatting in A5 booklet form was sent out to 300 sheep farmers using the AWI database from 27 August 2004. This second pilot achieved a response of 36 completed surveys in the first two weeks. On the basis of this relatively quick response compared to the first pilot, and without analysis of the results, it was decided to proceed with the main survey. Time was a factor affecting the decision to proceed, as well as the knowledge that the AWI database was being used and it was assumed that the target audience was being achieved. The prompt response indicated that the new format was not a problem. An initial analysis of the first 25 completed surveys confirmed that most respondents were able to understand the questions (by filling them in correctly) and that most questions were not problematic (since a majority were answered by most respondents). A total of 36 completed surveys were eventually received. A1.5 Main survey The addresses provided in the AWI database were from a list of postcodes provided to AWI. These postcodes were selected, as before, on the basis of the regions of influence indicated by the IPM-sheep regional project managers. Addresses were sorted by State and region basis (QLD, New England, NSW, VIC, SA & WA), then assigned random numbers. Due to there being less than 1500 addresses (the target number per state) in QLD (383), SA (751) and New England (728), all addresses provided by AWI were used in these areas. In NSW, VIC and WA the first 1500 addresses were selected from the randomised list (excluding any addresses used in the pilot). A total of 6362 addresses were selected. The first surveys were sent out from late September over a period of several weeks, with surveys being sent to WA addresses later in the period. Reminders were sent out during the week beginning 25 October 2004 to New England, QLD, NSW, VIC and SA, with reminders sent to WA addresses the week after. A short one page letter and questionnaire (short survey) was developed in consultation with the board of management members and sent out from 25 November 2004 to those who had not responded at this time. This was to encourage non-responders to answer just a few key questions from the main questionnaire so that it was possible to analyse the extent to which there was non-response bias in the data from the full questionnaire. Data from the surveys received up until 10 February 2005 was included in the analysis. Surveys received after this date were entered into the survey database and the data will be used in the analysis and report that follows the second report. Figures for responses received up until 10 February 2005 are shown in Table A1.1. The total number of geographically locatable responses from respondents with 500 or more sheep in 2003 or in a typical year was 1342 full surveys and 961 short surveys. A1.6 Coding of text answers The full questionnaire contained 77 questions or parts of questions where the respondent could provide a text answer (rather ticking a box, or providing a numerical answer or numerical rating). In many cases, questions with tick boxes or numerical ratings of a series of items were followed by a space with “Other, please describe”. This provided a check that the series of items had not omitted something that was important to respondents. Where a small number of text answers were provided, and it could be inferred from these answers that no important item had been omitted, the test answers were used as a check on the answers to the items preceding the “Other, please describe” space. Institute for Rural Futures 5 Table A1.1. Survey response rates. Response rate is calculated as follows: the number of producers with 500+ sheep in the original mailout is estimated using the proportion of returned questionnaires with <500 sheep and 500+ sheep. The response rate is given by the number of completed questionnaires with 500+ sheep as a percentage of the estimated number of producers with 500+ sheep in the original mailout (allowing for questionnaires returned as not deliverable by Australia Post due to the addressee having left the address or not being known at the given address). Region No. Mailed Out Mailed Out Less RTS Full surveys returned 500+ sheep Full surveys returned <500 Sheep Short surveys returned 500+ sheep Short surveys returned <500 sheep Estimate of No. in Mail Out with >500 Sheep Response Rate (full survey) (%) Response Rate (full and short surveys) (%) New Eng. 728 719 181 101 105 19 506 35.7 56.5 QLD 383 374 88 49 47 8 263 33.5 51.3 NSW (rem) 1500 1472 319 212 245 32 1027 31.0 54.9 VIC 1500 1472 357 215 222 24 1042 34.3 55.6 SA 751 729 202 95 104 11 541 37.3 56.5 WA 1500 1460 218 122 235 40 1075 20.3 42.1 TOTAL 6362 6226 1365 794 958 134 4456 33.6 52.1 There was only one question where text answers indicated that an item important to respondents had been omitted (question 21, concerning incidence of flystrike). In this case, the text answers were used to create another item in the list of types of strike (pizzle strike) in the survey dataset. The remaining questions with text answers required analysis in their own right and coding schemes for each question were developed in close consultation with the project participants. A1.7 Data quality control Data was analysed using SPSS and R (SPSS Inc, 2001;R Development Core Team, 2004). Frequency distributions of all variables in the dataset were examined (the dataset comprised a rectangular array of numbers with a row for each respondent and a column or columns for each question – each row is termed a case, and each column is termed a variable). Where values outside the expected range of values were encountered, the data was checked against the returned questionnaires for misreading or keystroke errors and corrections made where necessary. Where out-of-range values were not due to either misinterpretation of the question by the respondent or an error by the data entry operator, these were noted as possible outliers and given further consideration as to their inclusion or exclusion at the appropriate stage of the analysis. A number of questions required specific quality control procedures. These are described in the subsections below A1.7.1 Property area The total property area reported by the respondent was compared with the sum of the areas under various land uses, viz. area grazed, area cropped, cropping area grazed as stubble, cropping area grazed as green and ‘Other’. For 52.3 per cent of respondents the sum of areas under various land uses was equal to the area given as total property area. In these cases, it is assumed that respondents provided the land uses on the property at a particular point in time. Consequently, the figures reported under “Area grazed”, “Cropping area grazed as stubble” and “Cropping area grazed as green” were summed to give the overall area grazed on the property. Similarly, the three land uses: “Area cropped”, “Cropping area grazed as stubble” and “Cropping area grazed as green”, were summed to provide a figure for the area cropped. Institute for Rural Futures6 The sum of the areas of the various types of land use was greater than the total property area for 33.8 per cent of respondents. Four of these respondents had obviously made errors in reporting their total property area, possibly leaving off some digits from their answer. In these cases the total property area was set to the sum of areas and the adjustments described in the previous paragraph made. In the remaining cases where the areas of the various types of land use was greater than the total property area, the areas entered under “Area grazed”, “Area cropped”, “Cropping area grazed as stubble” and “Cropping area grazed as green” referred to all or part of the same area of land, i.e. the respondent had provided figures typical of land use over time, such that there was an element of double counting, resulting in the sum of areas exceeding the total property area. Inspection of individual responses suggested that the commonest form of double counting was when “Area cropped”, “Cropping area grazed as stubble” and “Cropping area grazed as green” referred to all or part of the one area of land. Consequently, “Area cropped” was let stand, while the overall area grazed was obtained by adding “Area grazed” to the greater of “Cropping area grazed as stubble” and “Cropping area grazed as green”. The remaining 13.9 per cent of respondents provided a total property area that was greater than the sum of areas. In several cases, this disparity was due to a total property area in acres being written in the space for total property area in hectares and these cases were corrected. For the remaining respondents, it appears that the cause of the disparity was the omission of some land uses from the figures provided. For this reason, the total property area provided by the respondent was taken as the total property area. Similar to the approach taken where the sum of land uses equalled the total property area, the figures reported under “Area grazed”, “Cropping area grazed as stubble” and “Cropping area grazed as green” were summed to give the overall area grazed on the property. The three land uses: “Area cropped”, “Cropping area grazed as stubble” and “Cropping area grazed as green”, were summed to provide a figure for the area cropped. A1.8 Non-response bias The responses to the full and short surveys were compared for the set of questions common to both surveys to assess the extent of non-response bias in the full survey responses. The rationale for this is that, if those who responded to the full survey were systematically different in some way from those who did not respond, then the generalisation of the survey results to the overall producer population will not be valid. For example, if those who do not respond tend to have smaller flocks, then the estimate of flock size calculated from the returned questionnaires will be biased upwards. If it is assumed that those who responded to the short survey are representative of all those who did not respond to the full survey, then comparison of the responses to the full and short surveys provides an indication of the existence of non-response bias. If there are significant differences between the full and short surveys on particular questions, then the magnitude of these differences can be used to calculate weighting factors to adjust the findings from the full survey, so that the influence of non- response bias is reduced as much as possible. The questions for which there was a significant (p<0.01) difference between the full and short survey responses are shown in the tables below. The tables are presented in the order in which the questions appeared in the short survey. As the weighting procedure requires that respondents be grouped according to their responses to the questions that were common to the full and short surveys, sheep numbers were used to divide respondents into quartiles. In the case of cattle numbers, slightly over 50 per cent of respondents had no cattle and the remaining respondents were divided into three approximately equal groups according their cattle numbers. In the tables below, the numbers of respondents varies from table to table as respondents can miss answering particular questions or parts of questions. A1.8.1 Cattle numbers Those who did not fill in the full survey, but responded to the short survey, had significantly more cattle. Table A1.2. Difference in cattle numbers between the full and short surveys. Institute for Rural Futures 7 Proportion of respondents with cattle numbers in the ranges below (%) Responders to ... No cattle Less than 50 50 – 149 150 or more Full survey 62.1 15.0 12.6 10.2 Short survey 48.1 8.6 17.3 26.0 Chi-squared test: �2=128.09, d.f.=3, p<0.00005, n=2274. A1.8.2 Drench resistance test Those who did not fill in the full survey, but responded to the short survey, were more likely to have tested for drench resistance in their flock. Table A1.3. Difference in testing for drench resistance between the full and short surveys. Responders to ... % who had tested for drench resistance Full survey 43.7 Short survey 49.8 Fisher’s Exact Test, p=0.005, n=2272 A1.8.3 Ranking of factors important in deciding when to drench ewes Those who did not fill in the full survey, but responded to the short survey, appear to be less convinced about the importance of faecal egg counts when deciding when to drench ewes. Table A1.4. Difference between the full and short surveys in respondents’ ranking of the importance of faecal egg count results in deciding when to drench ewes. Proportion of respondents indicating the ranks below (%) Responders to ... Very important Important Somewhat important Not important Full survey 59.0 17.9 9.1 14.0 Short survey 48.8 24.8 13.2 13.1 Chi-squared test: �2=24.71, d.f.=3, p<0.00005, n=1723. Institute for Rural Futures8 Those who did not fill in the full survey, but responded to the short survey, also appear to be less convinced about the importance of the time of year when deciding when to drench ewes. Table A1.5. Difference between the full and short surveys in respondents’ ranking of the importance of the time of year in deciding when to drench ewes. Proportion of respondents indicating the ranks below (%) Responders to ... Very important Important Somewhat important Not important Full survey 54.0 32.7 9.2 4.1 Short survey 46.3 40.1 9.9 3.6 Chi-squared test: �2=14.29, d.f.=3, p=0.003, n=2074. A similar pattern of response differences between the full and short survey is evident in the ranking of the importance of seasonal weather conditions in deciding when to drench ewes. Table A1.6. Difference between the full and short surveys in respondents’ ranking of the importance of seasonal weather conditions in deciding when to drench ewes. Proportion of respondents indicating the ranks below (%) Responders to ... Very important Important Somewhat important Not important Full survey 30.5 32.7 23.6 13.2 Short survey 23.8 38.9 24.2 13.2 Chi-squared test: �2=13.01, d.f.=3, p=0.005, n=1934. Those who did not fill in the full survey, but responded to the short survey, appear to rank pasture quality slightly higher than those who responded to the full survey. Table A1.7. Difference between the full and short surveys in respondents’ ranking of the importance of pasture quality in deciding when to drench ewes. Proportion of respondents indicating the ranks below (%) Responders to ... Very important Important Somewhat important Not important Full survey 16.6 34.0 27.0 22.4 Short survey 17.6 38.7 28.3 15.4 Chi-squared test: �2=15.01, d.f.=3, p=0.002, n=1832. Those who did not fill in the full survey, but responded to the short survey, appear to rank the presence of daggy sheep in the mob more highly as a factor in deciding when to drench ewes. Table A1.8. Difference between the full and short surveys in respondents’ ranking of the importance of the presence of daggy sheep in the mob in deciding when to drench ewes. Proportion of respondents indicating the ranks below (%) Responders to ... Very important Important Somewhat important Not important Full survey 23.7 28.3 31.9 16.1 Short survey 27.4 33.7 30.0 8.9 Chi-squared test: �2=27.62, d.f.=3, p<0.00005, n=1957. Institute for Rural Futures 9 A1.8.4 Grazing strategy Those who did not fill in the full survey, but responded to the short survey, were less likely to be following a set stocked grazing strategy. Table A1.9. Difference between the full and short surveys in the proportion of respondents with a set stocked grazing strategy. Responders to ... % with set stocking grazing strategy Full survey 55.4 Short survey 46.0 Fisher’s Exact Test, p<0.00005, n=2223 Those who did not fill in the full survey, but responded to the short survey, were more likely to be following a grazing strategy that involved alternating between sheep and crop stubble. Table A1.10. Difference between the full and short surveys in the proportion of respondents with a grazing strategy that involved alternating between sheep and crop stubble. Responders to ... % with a grazing strategy that involved alternating between sheep and crop stubble Full survey 27.0 Short survey 38.7 Fisher’s Exact Test, p<0.00005, n=2218 A1.8.5 Treatment for blowfly strike Those who did not fill in the full survey, but responded to the short survey, were less likely to indicate that they typically treated blowfly strike by treating individual sheep that become struck. Table A1.11. Difference between the full and short surveys in the proportion of respondents who indicated that they typically treated blowfly strike by treating individual sheep that become struck. Responders to ... % treating individual sheep Full survey 75.6 Short survey 65.9 Fisher’s Exact Test, p<0.00005, n=2241 A1.9 Derivation of weights for non-response bias The preceding tables show that there are some significant differences between those who filled in the full survey and those who filled in the short survey, suggesting that estimates of the characteristics of the population of sheep producers derived from the full survey sample may be affected by non- response bias. This bias may be corrected by weighting procedures based on the differences in the tables above. However, where there are differences across a relatively large number of survey questions, the numbers of full survey respondents in the groups to which particular weighting factors are applied may become unduly small. Large weighting factors applied to small groups of respondents may introduce other biases that are not apparent from the subset of questions common to the full and short surveys. For this reason, it is necessary to rank the tables listed in the preceding section according to the magnitude of the differences exhibited and examine the size of respondent groups and weighting factors as the number of tables included in the calculation is increased to include tables with smaller differences (Table A1.12). Institute for Rural Futures10 Table A1.12. Table of respondent groups and calculated weighting factors based on including one, two or three questions in the calculation. Cattle numbers show the greatest difference between the full and short surveys, followed by a grazing strategy that involves alternating between sheep and crop stubble, followed by blowfly treatment that typically involves treating individuals in the mob that become struck. No of tables in weighting calculation Cattle numbers Alternating between sheep and crop stubble Typically treat individuals that become struck Number of respondents to full survey Calculated weighting factor 1 No cattle 816 0.84 Less than 50 197 0.70 50-149 166 1.26 150 or more 134 2.08 2 No cattle No 532 0.68 Less than 50 No 145 0.65 50-149 No 128 1.13 150 or more No 107 1.94 No cattle Yes 238 1.21 Less than 50 Yes 49 0.77 50-149 Yes 31 1.76 150 or more Yes 21 3.00 3 No cattle No No 123 0.84 Less than 50 No No 39 0.76 50-149 No No 37 1.15 150 or more No No 19 3.72 No cattle Yes No 50 1.64 Less than 50 Yes No 9 1.75 50-149 Yes No 6 2.65 150 or more Yes No 6 3.59 No cattle No Yes 392 0.62 Less than 50 No Yes 101 0.61 50-149 No Yes 86 1.16 150 or more No Yes 82 1.59 No cattle Yes Yes 182 1.07 Less than 50 Yes Yes 40 0.54 50-149 Yes Yes 25 1.53 150 or more Yes Yes 15 2.61 It can be seen from Table A1.12, that as the number of questions included in the calculation of weighting factors increases, there is also an increase in the incidence of small respondent groups with relatively large weighting factors. As might be expected, the small respondent groups are those with relatively larger cattle numbers who are pursuing a grazing strategy that involves alternating between sheep and crop stubbles. With two questions included in the calculation of weighing factors, there are only 21 respondents with 150 or more cattle and pursuing the above grazing strategy. These 21 would be multiplied by a weighting factor of 3 if the full survey data was to be adjusted for non-response bias using cattle numbers and the grazing strategy of alternating between sheep and crop stubbles. This was judged as attributing too much weight to a relatively small group of respondents. Accordingly, non- response weights were based solely on cattle numbers. Institute for Rural Futures 11 A1.10 Significance of weighted distributions Using the weighting factors in the top four rows of Table A1.12, above, i.e. those based solely on cattle numbers, weighted frequency distributions were calculated for a selection of the questions common to the full and short surveys. The weighted and unweighted frequency distributions are shown in the tables below. Table A1.13. Difference in sheep number (typical year) estimates with and without weighting for non-response bias. Proportion of respondents with sheep numbers in the ranges below (%) Basis 500-1499 1500-2999 3000-4999 5000 or more Unweighted 24.4 28.2 22.5 24.9 Weighted 21.4 26.9 22.7 28.9 Chi-squared goodness-of-fit test: �2=13.97, d.f.=3, p=0.003, n=1342. Table A1.14. Difference in cattle number estimates with and without weighting for non-response bias. Proportion of respondents with cattle numbers in the ranges below (%) Basis No cattle Less than 50 50 – 149 150 or more Unweighted 62.2 15.0 12.6 10.2 Weighted 52.3 10.6 15.9 21.2 Chi-squared goodness-of-fit test: �2=133.65, d.f.=3, p<0.00005, n=1313. Table A1.15. Difference between unweighted and weighted estimates of the proportion of sheep producers testing for drench resistance. Basis % who had tested for drench resistance Unweighted 43.7 Weighted 45.9 Binomial test, p=0.116, n=1326. Table A1.16. Difference between the unweighted and weighted estimates of respondents’ ranking of the importance of faecal egg count results in deciding when to drench ewes. Proportion of respondents indicating the ranks below (%) Basis Very important Important Somewhat important Not important Unweighted 59.0 17.9 9.1 14.0 Weighted 60.7 17.4 8.7 13.3 Chi-squared goodness-of-fit test: �2=1.08, d.f.=3, p=0.782, n=900. Table A1.17. Difference between the unweighted and weighted estimates of respondents’ ranking of the importance of the time of year in deciding when to drench ewes. Proportion of respondents indicating the ranks below (%) Basis Very important Important Somewhat important Not important Unweighted 54.0 32.7 9.2 4.1 Weighted 52.6 34.2 9.1 4.1 Chi-squared goodness-of-fit test: �2=1.23, d.f.=3, p=0.745, n=1159. Institute for Rural Futures12 Table A1.18. Difference between the unweighted and weighted estimates of respondents’ ranking of the importance of seasonal weather conditions in deciding when to drench ewes. Proportion of respondents indicating the ranks below (%) Basis Very important Important Somewhat important Not important Unweighted 30.5 32.7 23.6 13.2 Weighted 29.8 33.7 23.6 12.9 Chi-squared goodness-of-fit test: �2=0.53, d.f.=3, p=0.911, n=1054. Table A1.19. Difference between the unweighted and weighted estimates of respondents’ ranking of the importance of pasture quality in deciding when to drench ewes. Proportion of respondents indicating the ranks below (%) Basis Very important Important Somewhat important Not important Unweighted 16.6 34.0 27.0 22.4 Weighted 15.7 33.9 27.3 23.0 Chi-squared goodness-of-fit test: �2=0.67, d.f.=3, p=0.880, n=969. Table A1.20. Difference between the unweighted and weighted estimates of respondents’ ranking of the importance of the presence of daggy sheep in the mob in deciding when to drench ewes. Proportion of respondents indicating the ranks below (%) Basis Very important Important Somewhat important Not important Unweighted 23.7 28.3 31.9 16.1 Weighted 22.5 27.4 33.4 16.6 Chi-squared goodness-of-fit test: �2=1.90, d.f.=3, p=0.594, n=1067. Table A1.21. Difference between the unweighted and weighted estimates of the proportion of respondents with a set stocked grazing strategy. Basis % with set stocking grazing strategy Unweighted 55.4 Weighted 56.2 Binomial test, p=0.573, n=1283 Table A1.22. Difference between the unweighted and weighted estimates of the proportion of respondents with a grazing strategy that involved alternating between sheep and crop stubble. Basis % with a grazing strategy that involved alternating between sheep and crop stubble Unweighted 27.0 Weighted 25.4 Binomial test, p=0.199, n=1279 Institute for Rural Futures 13 Table A1.23. Difference between the unweighted and weighted estimates of the proportion of respondents who indicated that they typically treated blowfly strike by treating individual sheep that become struck. Basis % treating individual sheep Unweighted 75.6 Weighted 75.9 Binomial test, p=0.770, n=1297 The preceding tables show that, apart from the estimates of sheep and cattle numbers, there is no significant difference between unweighted and weighted estimates from a range of questions about grazing and sheep parasite management. It can be be concluded from this that, although sheep producers with larger numbers of cattle are significantly under-represented in the full survey sample, there appears to be little difference in grazing and sheep parasite management between those with relatively more and those with fewer cattle. Consequently, adjustment for the under-representation of sheep producers with larger numbers of cattle has no significant effect on the estimates of characteristics associated with grazing and parasite management. However, these findings then raise the question, if weighting was based on one or more of the questions about grazing and parasite management, whether the adjustment for non-response biases shown by these questions would lead to weighted estimates that were significantly different from unweighted estimates. Table A1.24 shows the size of respondent groups and weighting factors for the three questions about grazing and parasite management that showed the greatest differences between the full and short surveys. The possibility of using a fourth question was investigated, however, because the next question in the sequence had four categories, this resulted in unsatisfactorily small respondent groups. Table A1.24. Table of respondent groups and calculated weighting factors based on including one, two or three questions relating to grazing and sheep parasite management in the calculation. A grazing strategy that involves alternating between sheep and crop stubble shows the greatest difference between the full and short surveys, followed by blowfly treatment that typically involves treating individuals in the mob that become struck, and a set stocked grazing strategy. No of tables in weighting calculation Alternating between sheep and crop stubble Typically treat individuals that become struck Set stocked grazing strategy Number of respondents to full survey Calculated weighting factor 1 No 934 0.89 Yes 345 1.31 2 No No 225 0.68 Yes No 73 0.65 No Yes 676 1.76 Yes Yes 266 3.00 3 No No No 89 1.30 Yes No No 45 2.21 No Yes No 268 0.87 Yes Yes No 154 1.22 No No Yes 136 1.00 Yes No Yes 28 1.38 No Yes Yes 408 0.78 Yes Yes Yes 112 1.00 Institute for Rural Futures14 Table A1.24 shows that three grazing and parasite management questions can be used to calculate weighting factors, without resulting in unduly small respondent groups or unduly large weighting factors. Using the weighting factors in the lower eight rows of Table A1.24, above, i.e. those based on the three grazing and parasite management questions with the greatest difference between the full and short surveys, weighted frequency distributions were calculated for a selection of the questions common to the full and short surveys. The weighted and unweighted frequency distributions are shown in the tables below. Table A1.25. Difference in sheep number (typical year) estimates with and without weighting for non-response bias. Proportion of respondents with sheep numbers in the ranges below (%) Basis 500-1499 1500-2999 3000-4999 5000 or more Unweighted 24.4 28.2 22.5 24.9 Weighted 22.5 26.6 28.3 22.5 Chi-squared goodness-of-fit test: �2=22.86, d.f.=3, p<0.00005, n=1342. Table A1.26. Difference in cattle number estimates with and without weighting for non-response bias. Proportion of respondents with cattle numbers in the ranges below (%) Basis No cattle Less than 50 50 – 149 150 or more Unweighted 62.2 15.0 12.6 10.2 Weighted 62.9 15.3 12.4 9.5 Chi-squared goodness-of-fit test: �2=0.95, d.f.=3, p<0.812, n=1313. Table A1.27. Difference between unweighted and weighted estimates of the proportion of sheep producers testing for drench resistance. Basis % who had tested for drench resistance Unweighted 43.7 Weighted 44.9 Binomial test, p=0.408, n=1326. Table A1.28. Difference between the unweighted and weighted estimates of respondents’ ranking of the importance of faecal egg count results in deciding when to drench ewes. Proportion of respondents indicating the ranks below (%) Basis Very important Important Somewhat important Not important Unweighted 59.0 17.9 9.1 14.0 Weighted 58.0 17.9 10.2 14.0 Chi-squared goodness-of-fit test: �2=1.17, d.f.=3, p=0.761, n=900. Institute for Rural Futures 15 Table A1.29. Difference between the unweighted and weighted estimates of respondents’ ranking of the importance of the time of year in deciding when to drench ewes. Proportion of respondents indicating the ranks below (%) Basis Very important Important Somewhat important Not important Unweighted 54.0 32.7 9.2 4.1 Weighted 54.7 32.8 8.9 3.7 Chi-squared goodness-of-fit test: �2=0.77, d.f.=3, p=0.857, n=1159. Table A1.30. Difference between the unweighted and weighted estimates of respondents’ ranking of the importance of seasonal weather conditions in deciding when to drench ewes. Proportion of respondents indicating the ranks below (%) Basis Very important Important Somewhat important Not important Unweighted 30.5 32.7 23.6 13.2 Weighted 29.3 32.8 24.4 13.6 Chi-squared goodness-of-fit test: �2=0.88, d.f.=3, p=0.831, n=1054. Table A1.31. Difference between the unweighted and weighted estimates of respondents’ ranking of the importance of pasture quality in deciding when to drench ewes. Proportion of respondents indicating the ranks below (%) Basis Very important Important Somewhat important Not important Unweighted 16.6 34.0 27.0 22.4 Weighted 15.9 34.1 27.4 22.6 Chi-squared goodness-of-fit test: �2=0.40, d.f.=3, p=0.941, n=969. Table A1.32. Difference between the unweighted and weighted estimates of respondents’ ranking of the importance of the presence of daggy sheep in the mob in deciding when to drench ewes. Proportion of respondents indicating the ranks below (%) Basis Very important Important Somewhat important Not important Unweighted 23.7 28.3 31.9 16.1 Weighted 23.4 27.8 32.8 16.0 Chi-squared goodness-of-fit test: �2=0.40, d.f.=3, p=0.939, n=1067. Table A1.33. Difference between the unweighted and weighted estimates of the proportion of respondents with a set stocked grazing strategy. Basis % with set stocking grazing strategy Unweighted 55.4 Weighted 48.7 Binomial test, p<0.00005, n=1283 Institute for Rural Futures16 Table A1.34. Difference between the unweighted and weighted estimates of the proportion of respondents with a grazing strategy that involved alternating between sheep and crop stubble. Basis % with a grazing strategy that involved alternating between sheep and crop stubble Unweighted 27.0 Weighted 35.3 Binomial test, p<0.00005, n=1279 Table A1.35. Difference between the unweighted and weighted estimates of the proportion of respondents who indicated that they typically treated blowfly strike by treating individual sheep that become struck. Basis % treating individual sheep Unweighted 75.6 Weighted 68.6 Binomial test, p<0.00005, n=1297 Tables A1.25 – A1.35 show weighting based on the three grazing and parasite management questions with the greatest difference between the full and short surveys results in four frequency distributions that are significantly different from the unweighted distributions, viz., the distribution of flock size (in a typical year) and the three questions on which the weighting was based: whether or not producers used a grazing strategy involving alternation between sheep and crop stubbles, whether or not producers typically treated individual sheep that become struck, and whether or not producers used a set stocked grazing strategy. For other aspects of parasite management, such as the ranking of the importance of various factors to be considered when deciding when to drench ewes and testing for drench resistance, there was no significant difference between the weighted and unweighted distributions. Overall, the investigation of non-response bias suggests that there are not major and systematic differences between the full and short surveys that extend across the full range of questions common to both surveys. There appears to be some minor non-response biases with respect to particular respondent characteristics, however there are not sufficiently strong relationships between these and other characteristics to warrant universal weighting of the findings based on these biases. For example, producers with 150 or more cattle are under-represented in the full survey by a factor of around 2.5 (Table A1.2). Examination of the relationship between cattle numbers and drench resistance testing shows that 59.3 per cent of producers with 150 or more cattle had tested for drench resistance in their sheep flock, compared to 44.1 per cent of producers who had no cattle. However, producers with 150 or more cattle comprise only 16.8 per cent of producers, so that weighting of the data from the full survey to compensate for the under-representation of producers with 150 or more cattle results in only a small and non-significant increase in the estimate of the proportion of producers who have tested for drench resistance, from 43.7 per cent to 45.9 per cent (Table A1.15). While universal weighting of the findings appears not to be warranted, there may be grounds for simple adjustment of the findings for each of the small number of questions for which there were significant differences between the full and short surveys. Given that the questions common to the full and short surveys were chosen for their central relevance to informing the extension phase of the IPM-sheep project, it is worth using the data from the short survey to provide the best possible estimates of the producer characteristics which these questions are concerned. It was also decided that, for reasons of consistency, the findings from the remaining questions common to both surveys (those for which there was not a significant difference between the two surveys) would also be presented as estimates adjusted to take account of the data from both full and short surveys. For example, suppose a question has a proportion of x per cent giving a certain answer in the full survey and y per cent giving the same answer in the short survey. If N respondents answered the question in the full survey and M answered the question in the short survey and P did not respond to either, then the adjusted estimate of the percentage giving the particular answer to the question, xadj is: Institute for Rural Futures 17 xadj = (x � N) + (y � (M + P)) (N + M + P) This assumes that y per cent of those who did not respond to either survey would have given the particular answer if they had responded. A1.11 Analysis of Variance Analysis of variance was used to indicate the significance of the differences between the regional means of continuous variables. In a number of cases, these variables are strongly bi-modal, with the bulk of responses at the minimum and maximum values of the range. In these cases, the distributions are departing substantially from that assumed in the analysis of variance procedure, and significance values may be in error. In particular, care should be taken in the interpretation of significance values close to 0.05 when the distributions of the variable of interest in the regions are strongly bi-modal or skewed. A1.12 Cluster Analysis The form of cluster analysis used was “partitioning around medoids” (“pam”), as implemented in the R statistical package (R Development Core Team, 2004) This method is similar to the well known k- means iterative re-allocation method (Hartigan and Wong, 1979), but has the advantage of greater robustness and a derived silhouette coefficient which provides guidance as to the number of clusters that best represent the structure in the data (Kaufman and Rousseeuw, 1987). Where “pam” was used, the silhouette coefficient was calculated for 2 to 8 cluster solutions and the solution with the maximum silhouette coefficient accepted. Silouhette coefficients were interpreted following the guidelines provided by Kaufman and Rousseeuw (1987), shown below. Silhouette coefficient Interpretation 0.71 – 1.00 A strong structure 0.51 – 0.70 A reasonable structure 0.26 – 0.50 A weak structure, possibly an artefact. 0.00 – 0.25 No structure Only cluster solutions with a silhouette coefficient greater than 0.50 have been reported. The coefficients obtained for the various cluster analyses are given in the table below. Cluster analysis Section of main report No of clusters with maximum silouhette coefficient Silhouette coefficent Q3 – sheep and wool income 3.2.3 2 0.55 A1.13 Calculation of DSEs Where stock numbers have been converted to DSEs, the conversion factors used were taken from Attwood (1997). Attwood provides conversion factors based on daily energy requirements for a number of classes of livestock at two liveweights and, in some case, at different rates of weight gain. As the survey questionnaire did not collect information on liveweight or weight gain, conversion Institute for Rural Futures18 factors in the middle of the range given by Attwood were used. The conversion factors used are shown in the table below. Livestock type in questionnaire Factor for conversion to DSEs Q5 – Cows 12.0 Q5 – Heifers (weaning – 2 years) 7.0 Q5 Steers (weaning – sale) 7.0 Q5 – Bulls 12.0 Q5 – Other Factor chosen according to description Q6 – Merino ewes 1.2 Q6 Other ewes 1.2 Q6 – Wethers 1.0 Q6 – Merino weaners 1.3 Q6 – Other weaners 1.3 Q6 – Rams 1.0 A1.14 Calculation of Mean Wool Cut and Mean Fibre Diameter for Adult Sheep In Q8 of the survey questionnaire, respondents provided data on the number of sheep shorn, wool cut and fibre diameter for adult breeding ewes and adult dry ewes and wethers. To provided a single figure for adult sheep, a weighted mean was calculated for each respondent by multiplying the wool cut or fibre diameter figure by the number of sheep to which the figure applied, adding the products so obtained, and dividing by the total number of adult sheep shorn. A1.15 References Fidler, F. and Cumming, G. 2005. Teaching confidence intervals: problems and potential solutions. Paper presented at the 55th session of the International Statistics Institute, Sydney, Australia, April 5- 12, International Association for Statistical Education Session 49: Research in Statistical Education. Paper available from: www.stat.auckland.ac.nz/~iase/publications/13/Fidler-Cumming.pdf. Hartigan, J.A. and M.A. Wong 1979. A k-means clustering algorithm, Applied Statistics, 28:100–108. Kaufman, L. and P.J. Rousseeuw 1987. Clustering by means of medoids. In: Dodge, Y. (ed.) Statistical Data Analysis based on the L1 Norm North-Holland, Amsterdam 405– 416. McLaren, C. 1997. Dry Sheep Equivalents for Comparing Different Classes of Livestock. Information Note AG0590. Victorian Department of Primary Industries, Melbourne. R Development Core Team 2004. .R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R- project.org. SPSS Inc. 2001. SPSS 11.0 Syntax Reference Guide. SPSS Inc., Chicago.. ISBN 0-13-034842-2. Institute for Rural Futures 19 APPENDIX 2 Institute for Rural Futures20 Institute for Rural Futures 21 A2 ADDITIONAL RESULTS A2.1 Age of Respondents Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 61 27 52 75 51 3 GB & DD 24 25 44 69 47 5 New England 169 16 50 76 51 2 C & S Tablelands 180 24 48 78 50 2 S NSW & N Vic 169 19 51 84 51 2 Gippsland 12 20 44 73 47 9 W Vic & SE SA 373 20 51 80 51 1 S SA 69 25 52 83 52 3 KI 42 26 50 81 51 3 WA 201 18 51 81 52 2 All Regions 1300 16 51 84 51 1 Histogram class limits:16-22.8-29.6-36.4-43.2-50-56.8-63.6-70.4-77.2-84 Anova: F=1.01, d.f.=9, p=0.436. A2.2 Gender of Respondents Proportion of respondents (%) Region n Male Female SW & S Qld 63 92 8 GB & DD 24 100 0 New England 174 93 7 C & S Tablelands 180 94 6 S NSW & N Vic 169 96 4 Gippsland 12 100 0 W Vic & SE SA 375 95 5 S SA 71 99 1 KI 42 93 7 WA 201 94 6 All regions 1311 95 5 �2 = 7.79, d.f. = 9, p =0.556. 5 cells (25.0%) have expected counts less than 5. Institute for Rural Futures22 A2.3 Cattle DSEs in 2003 Compared to a Typical Year Proportion of respondents (%) Region n 2003 < typical 2003 = typical 2003 > typicalt SW & S Qld 54 44 57 71 22 35 48 0 7 14 GB & DD 12 22 50 78 1 25 50 1 25 50 New England 152 44 52 60 30 38 45 6 11 15 C & S Tablelands 91 39 49 60 30 40 50 5 11 17 S NSW & N Vic 90 28 38 48 35 46 56 9 17 24 Gippsland 7 0 29 62 20 57 94 0 14 40 W Vic & SE SA 215 21 27 33 51 57 64 11 16 21 S SA 43 9 21 33 34 49 64 17 30 44 KI 19 0 16 32 61 79 97 0 5 15 WA 43 7 19 30 46 60 75 9 21 33 All Regions 726 34 38 41 44 48 51 12 15 17 Chisquare = 66.63, d.f. = 18, p < 0.0005. 6 cells (20.0%) have expected counts less than 5. A2.4 Length of Calving Period - Cows Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 43 1.0 3.0 12.0 4.4 1.1 GB & DD 11 1.0 3.0 12.0 4.2 2.7 New England 123 1.0 2.0 10.0 2.3 0.3 C & S Tablelands 77 1.0 2.0 12.0 2.4 0.6 S NSW & N Vic 70 1.0 2.0 12.0 3.3 0.7 Gippsland 6 1.0 2.0 4.0 2.0 1.1 W Vic & SE SA 175 1.0 2.0 12.0 2.7 0.3 S SA 40 1.0 3.0 12.0 4.0 1.0 KI 14 1.0 2.0 12.0 2.6 1.7 WA 36 1.0 2.0 6.0 2.5 0.5 All Regions 595 1.0 2.0 12.0 2.9 0.2 Histogram class limits:1-2.1-3.2-4.3-5.4-6.5-7.6-8.7-9.8-10.9-12 Anova: F=4.89, d.f.=9, p <0.0005. Institute for Rural Futures 23 A2.5 Length of Calving Period - Heifers Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 2 1.0 1.5 2.0 1.5 6.4 GB & DD 0 New England 19 1.0 2.0 4.0 2.2 0.5 C & S Tablelands 10 1.0 2.0 10.0 2.8 2.0 S NSW & N Vic 10 1.0 2.0 7.0 2.5 1.2 Gippsland 0 W Vic & SE SA 16 1.0 1.0 9.0 1.9 1.1 S SA 5 1.0 3.0 6.0 3.4 2.6 KI 4 1.0 2.0 3.0 2.0 1.3 WA 3 1.0 1.0 1.0 1.0 0.0 All Regions 69 1.0 2.0 10.0 2.3 0.4 Histogram class limits:1-1.9-2.8-3.7-4.6-5.5-6.4-7.3-8.2-9.1-10 Anova: F=0.80, d.f.=7, p = 0.593. A2.6 Cow Calving Months with Highest Proportion(s) of Respondents Region n Months in which the highest proportion(s) of respondents report cows calving SW & S Qld 43 September, October GB & DD 11 October New England 123 August C & S Tablelands 77 August S NSW & N Vic 70 August Gippsland 6 August, October W Vic & SE SA 175 May S SA 40 March KI 14 March WA 36 April All Regions 595 August Institute for Rural Futures24 A2.7 Heifer Calving Months with Highest Proportion(s) of Respondents Region n Months in which the highest proportion(s) of respondents report heifers calving SW & S Qld 2 August GB & DD 0 New England 19 August C & S Tablelands 10 September S NSW & N Vic 70 February, March Gippsland 0 W Vic & SE SA 16 March S SA 5 February - May KI 4 March WA 3 March All Regions 69 August A2.8 Sheep DSEs in 2003 Compared to a Typical Year Proportion of respondents (%) Region n 2003 < typical 2003 = typical 2003 > typicalt SW & S Qld 63 59 70 81 9 19 29 3 11 19 GB & DD 24 34 54 74 8 25 42 5 21 37 New England 180 45 52 60 31 38 45 5 9 14 C & S Tablelands 186 39 46 53 29 36 43 12 18 23 S NSW & N Vic 172 34 42 49 39 47 54 7 12 16 Gippsland 12 40 67 93 1 25 50 0 8 24 W Vic & SE SA 378 28 33 37 50 55 60 9 13 16 S SA 71 18 28 39 52 63 75 2 8 15 KI 42 9 21 34 52 67 81 2 12 22 WA 209 16 22 27 50 56 63 16 22 28 All Regions 1337 36 38 41 45 47 50 12 14 16 Chisquare = 112.64, d.f. = 18, p < 0.0005. 3 cells (10.0%) have expected counts less than 5. Institute for Rural Futures 25 A2.9 Wool Cut from Breeding Ewes, 2003 Clip (kg/head) Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 39 2.5 4.5 6.8 4.6 0.3 GB & DD 8 3.0 4.0 4.8 3.9 0.7 New England 126 2.0 4.0 8.5 4.1 0.2 C & S Tablelands 134 2.5 4.5 9.0 4.8 0.2 S NSW & N Vic 127 2.0 5.0 8.5 5.2 0.2 Gippsland 8 3.0 4.7 7.4 4.9 1.2 W Vic & SE SA 269 2.3 5.0 9.3 5.0 0.1 S SA 60 2.5 6.0 8.0 5.9 0.3 KI 30 3.0 5.5 7.4 5.5 0.4 WA 153 3.0 5.1 8.6 5.3 0.2 All Regions 954 2.0 5.0 9.3 5.0 0.1 Histogram class limits:2.00-2.73-3.46-4.19-4.92-5.65-6.38-7.11-7.84-8.57-9.30 Anova: F=16.61, d.f.=9, p<0.0005. A2.10 Fibre Diameter, Breeding Ewes, 2003 Clip (�) Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 41 17.5 20.2 32.0 20.7 0.7 GB & DD 8 16.8 18.3 27.0 19.6 2.7 New England 150 15.0 18.4 35.0 19.0 0.4 C & S Tablelands 143 16.6 19.5 32.0 20.5 0.6 S NSW & N Vic 139 15.6 20.6 31.5 21.8 0.6 Gippsland 11 17.4 19.5 29.0 21.4 2.9 W Vic & SE SA 309 16.5 20.5 33.0 22.0 0.4 S SA 66 18.2 22.2 30.0 22.8 0.6 KI 34 20.0 22.0 23.8 21.7 0.3 WA 179 17.5 20.5 23.2 20.7 0.2 All Regions 1080 15.0 20.1 35.0 21.1 0.2 Histogram class limits:15-17-19-21-23-25-27-29-31-33-35 Anova: F=14.79, d.f.=9, p<0.0005. Institute for Rural Futures26 A2.11 Wool Cut from Adult Dry Ewes and Wethers, 2003 Clip (kg/head) Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 34 3.0 4.8 7.5 4.9 0.4 GB & DD 16 3.2 4.2 7.0 4.4 0.5 New England 104 2.5 4.3 7.0 4.3 0.2 C & S Tablelands 94 3.0 5.0 8.0 5.1 0.2 S NSW & N Vic 65 2.0 5.8 9.0 5.7 0.3 Gippsland 6 4.0 5.6 7.5 5.5 1.4 W Vic & SE SA 188 2.7 5.3 9.0 5.5 0.2 S SA 26 3.0 5.7 8.0 5.7 0.5 KI 25 3.0 6.0 8.0 6.0 0.5 WA 98 1.2 5.5 8.3 5.6 0.2 All Regions 656 1.2 5.0 9.0 5.3 0.1 Histogram class limits:1.10-1.89-2.68-3.47-4.26-5.05-5.84-6.63-7.42-8.21-9.00 Anova: F=12.76, d.f.=9, p<0.0005. A2.12 Fibre Diameter, Adult Dry Ewes and Wethers, 2003 Clip (�) Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 40 15.1 20.5 22.0 20.1 0.5 GB & DD 17 16.4 18.0 21.0 18.2 0.7 New England 122 15.5 18.1 28.0 18.3 0.2 C & S Tablelands 109 16.0 19.0 30.0 19.1 0.3 S NSW & N Vic 75 15.6 20.0 30.0 20.1 0.4 Gippsland 9 17.8 20.0 28.0 20.3 2.4 W Vic & SE SA 220 16.0 20.0 32.0 20.1 0.3 S SA 27 17.8 21.0 30.0 21.4 1.1 KI 28 20.0 22.0 23.0 21.8 0.3 WA 113 17.0 20.6 23.0 20.5 0.3 All Regions 760 15.1 19.5 32.0 19.8 0.1 Histogram class limits:15-16.7-18.4-20.1-21.8-23.5-25.2-26.9-28.6-30.3-32 Anova: F=22.73, d.f.=9, p<0.0005. Institute for Rural Futures 27 A2.13 Wool Cut from Weaners Less than 12 Months, 2003 Clip (kg/head) Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 31 1.0 2.0 4.3 2.4 0.3 GB & DD 6 1.4 2.7 3.5 2.6 0.8 New England 112 0.4 2.5 8.7 2.6 0.2 C & S Tablelands 111 0.7 2.5 5.3 2.6 0.2 S NSW & N Vic 94 1.0 2.2 6.0 2.5 0.2 Gippsland 7 1.2 2.3 3.9 2.5 0.9 W Vic & SE SA 208 0.5 2.3 7.0 2.6 0.2 S SA 45 0.8 2.0 7.0 2.6 0.5 KI 27 1.0 2.5 5.1 2.8 0.5 WA 139 0.6 2.0 6.0 2.4 0.2 All Regions 780 0.4 2.3 8.7 2.5 0.1 Histogram class limits:0.40-1.23-2.06-2.89-3.72-4.55-5.38-6.21-7.04-7.87-8.70 Anova: F=0.56, d.f.=9, p = 0.840. A2.14 Fibre Diameter, Weaners Less than 12 Months, 2003 Clip (�) Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 32 16.4 18.6 21.5 18.8 0.4 GB & DD 3 15.3 16.0 18.0 16.4 3.5 New England 125 13.7 17.0 24.0 16.9 0.2 C & S Tablelands 116 15.0 17.8 28.0 18.3 0.5 S NSW & N Vic 89 15.8 18.0 27.0 18.9 0.5 Gippsland 9 16.0 17.8 26.0 18.6 2.3 W Vic & SE SA 233 14.5 18.5 29.0 19.5 0.4 S SA 44 15.5 19.0 28.0 19.9 0.8 KI 30 17.0 19.4 21.5 19.5 0.4 WA 153 15.4 18.6 24.0 18.8 0.2 All Regions 834 13.7 18.0 29.0 18.7 0.2 Histogram class limits:13.0-14.6-16.2-17.8-19.4-21-22.6-24.2-25.8-27.4-29.0 Anova: F=12.85, d.f.=9, p<0.0005. Institute for Rural Futures28 A2.15 Proportion of Respondents (%) Shearing and Crutching Ewes Each Month of the Year A2.15.1 Shearing Ewes Region n J F M A M J J A S O N D SW & S Qld 53 8 4 8 9 11 15 17 17 6 11 4 8 GB & DD 13 23 0 15 8 0 8 8 15 8 15 15 0 New England 171 2 1 4 1 2 8 15 39 34 9 5 2 C & S Tablelands 176 5 9 10 8 10 9 14 17 18 15 18 9 S NSW & N Vic 158 6 15 15 13 6 7 5 18 22 11 10 4 Gippsland 12 17 0 8 0 0 0 8 17 17 0 33 0 W Vic & SE SA 352 8 11 10 9 6 7 11 13 19 19 20 13 S SA 69 0 4 6 7 0 1 3 19 26 22 17 6 KI 38 5 16 13 13 5 0 0 0 26 24 13 11 WA 197 15 16 15 11 3 5 8 11 20 12 8 7 All regions 1239 7 10 10 9 5 7 10 18 21 15 14 8 Note: percentages may sum to more than 100 as respondents could give more than one month. A2.15.2 Crutching Ewes Region n J F M A M J J A S O N D SW & S Qld 52 12 23 19 17 15 8 13 6 8 6 10 10 GB & DD 14 7 0 0 7 21 14 14 14 21 21 0 0 New England 169 12 27 41 44 34 25 28 14 4 2 3 4 C & S Tablelands 173 14 20 26 21 21 18 18 14 12 16 16 13 S NSW & N Vic 160 12 26 23 18 15 13 14 14 15 21 18 12 Gippsland 12 8 8 25 25 33 33 33 33 25 25 17 8 W Vic & SE SA 347 10 18 28 25 20 19 21 18 20 20 16 10 S SA 69 10 20 41 35 28 35 33 35 30 14 6 9 KI 39 3 5 13 33 21 21 21 21 41 23 15 5 WA 186 3 10 16 19 11 9 8 16 33 18 6 3 All regions 1221 10 19 26 26 20 18 19 17 19 16 12 8 Note: percentages may sum to more than 100 as respondents could give more than one month. Institute for Rural Futures 29 A2.16 Proportion of Respondents (%) Shearing and Crutching Wethers Each Month of the Year A2.16.1 Shearing Wethers Region n J F M A M J J A S O N D SW & S Qld 48 8 8 8 13 19 19 21 17 19 10 2 8 GB & DD 20 35 15 20 15 10 10 15 25 35 35 35 10 New England 148 1 2 1 0 0 4 8 16 31 32 19 3 C & S Tablelands 131 6 6 7 8 8 8 8 13 22 18 18 7 S NSW & N Vic 88 6 15 14 13 8 8 10 18 18 7 13 7 Gippsland 9 0 0 0 0 0 0 11 22 11 11 44 0 W Vic & SE SA 253 6 13 9 7 7 11 9 16 22 17 19 9 S SA 38 3 5 5 8 3 5 0 18 13 18 21 5 KI 34 6 15 12 15 6 0 0 3 24 24 12 9 WA 141 8 11 8 8 3 4 11 20 23 13 9 6 All regions 910 6 9 8 7 6 8 9 16 23 18 16 7 Note: percentages may sum to more than 100 as respondents could give more than one month. A2.16.2 Crutching Wethers Region n J F M A M J J A S O N D SW & S Qld 44 20 25 25 18 16 9 9 7 9 7 14 16 GB & DD 20 15 15 15 20 50 40 40 25 20 30 10 5 New England 136 4 13 22 29 30 25 24 13 9 3 3 2 C & S Tablelands 129 11 16 19 19 15 19 16 18 14 16 12 12 S NSW & N Vic 87 13 28 22 16 15 13 16 13 10 20 22 15 Gippsland 9 11 11 22 22 22 33 22 0 11 0 0 0 W Vic & SE SA 245 10 16 21 22 20 20 20 21 17 18 13 10 S SA 36 14 11 28 28 19 22 19 17 31 19 11 19 KI 33 3 6 15 27 12 9 15 12 36 24 21 6 WA 130 1 7 18 25 8 8 8 12 24 12 4 1 All regions 869 9 15 21 23 19 18 17 16 17 14 11 9 Note: percentages may sum to more than 100 as respondents could give more than one month. Institute for Rural Futures30 A2.17 Proportion of Respondents (%) Shearing and Crutching Weaners (Less than 12 Months) Each Month of the Year A2.17.1 Shearing Weaners Region n J F M A M J J A S O N D SW & S Qld 48 8 8 8 13 19 19 21 17 19 10 2 8 GB & DD 20 35 15 20 15 10 10 15 25 35 35 35 10 New England 148 1 2 1 0 0 4 8 16 31 32 19 3 C & S Tablelands 131 6 6 7 8 8 8 8 13 22 18 18 7 S NSW & N Vic 88 6 15 14 13 8 8 10 18 18 7 13 7 Gippsland 9 0 0 0 0 0 0 11 22 11 11 44 0 W Vic & SE SA 253 6 13 9 7 7 11 9 16 22 17 19 9 S SA 38 3 5 5 8 3 5 0 18 13 18 21 5 KI 34 6 15 12 15 6 0 0 3 24 24 12 9 WA 141 8 11 8 8 3 4 11 20 23 13 9 6 All regions 910 6 9 8 7 6 8 9 16 23 18 16 7 Note: percentages may sum to more than 100 as respondents could give more than one month. A2.17.2 Crutching Weaners Region n J F M A M J J A S O N D SW & S Qld 44 20 25 25 18 16 9 9 7 9 7 14 16 GB & DD 20 15 15 15 20 50 40 40 25 20 30 10 5 New England 136 4 13 22 29 30 25 24 13 9 3 3 2 C & S Tablelands 129 11 16 19 19 15 19 16 18 14 16 12 12 S NSW & N Vic 87 13 28 22 16 15 13 16 13 10 20 22 15 Gippsland 9 11 11 22 22 22 33 22 0 11 0 0 0 W Vic & SE SA 245 10 16 21 22 20 20 20 21 17 18 13 10 S SA 36 14 11 28 28 19 22 19 17 31 19 11 19 KI 33 3 6 15 27 12 9 15 12 36 24 21 6 WA 130 1 7 18 25 8 8 8 12 24 12 4 1 All regions 869 9 15 21 23 19 18 17 16 17 14 11 9 Note: percentages may sum to more than 100 as respondents could give more than one month. Institute for Rural Futures 31 A2.18 Proportion of respondents (%) putting rams with ewes each month of the year in 2003 A2.18.1 Merino mated to Merino rams Region n J F M A M J J A S O N D SW & S Qld 39 13 15 21 23 18 3 3 0 0 3 3 0 GB & DD 7 0 0 14 14 43 29 0 0 0 0 0 0 New England 140 0 2 6 53 35 4 1 0 0 0 0 0 C & S Tablelands 113 6 10 34 21 2 0 0 0 0 2 12 14 S NSW & N Vic 93 13 15 27 8 1 0 0 0 0 4 15 17 Gippsland 7 0 14 29 14 14 0 0 0 0 0 0 29 W Vic & SE SA 215 8 12 20 15 1 0 0 1 0 3 17 20 S SA 45 18 18 2 2 0 0 0 0 0 9 29 22 KI 32 25 31 3 0 0 0 0 0 0 0 13 28 WA 173 26 20 2 0 1 0 0 0 0 2 16 34 All regions 864 12 13 15 17 8 1 0 0 0 2 13 18 Chisquare =704.20, d.f. = 90, p<0.0005. 62 cells (56.4%) have expected counts less than 5. A2.18.2 Merino mated to Meat breed rams Region n J F M A M J J A S O N D SW & S Qld 11 0 36 27 27 9 0 0 0 0 0 0 0 GB & DD 4 0 0 0 50 0 25 0 0 0 0 0 25 New England 47 0 4 15 62 17 0 0 0 2 0 0 0 C & S Tablelands 54 15 17 20 6 0 0 0 0 0 0 22 20 S NSW & N Vic 62 18 16 8 3 0 0 0 0 0 10 31 15 Gippsland 3 0 0 0 0 33 0 0 0 0 0 33 33 W Vic & SE SA 162 15 7 9 6 1 0 1 0 0 5 22 34 S SA 39 15 5 3 0 0 0 0 0 5 31 8 33 KI 24 25 21 0 0 0 0 0 0 0 0 17 38 WA 82 27 16 0 1 0 0 1 0 0 6 18 30 All regions 488 16 11 8 10 2 0 0 0 1 6 18 25 Chisquare =511.52, d.f. = 90, p<0.0005. 77 cells (70.0%) have expected counts less than 5. Institute for Rural Futures32 A2.18.3 Cross-bred ewes Region n J F M A M J J A S O N D SW & S Qld 4 0 50 0 0 25 0 0 25 0 0 0 0 GB & DD 1 0 100 0 0 0 0 0 0 0 0 0 0 New England 34 0 6 29 59 3 3 0 0 0 0 0 0 C & S Tablelands 47 17 15 13 2 0 2 0 0 0 2 28 21 S NSW & N Vic 47 26 11 4 0 0 0 0 0 0 13 26 21 Gippsland 4 50 0 0 0 0 0 0 0 0 25 25 0 W Vic & SE SA 137 15 13 7 3 1 0 0 0 0 6 18 38 S SA 17 12 6 6 0 0 0 0 0 0 12 24 41 KI 8 38 25 0 0 0 0 0 0 0 0 13 25 WA 7 14 29 0 0 0 0 0 14 0 0 14 29 All regions 306 16 13 9 8 1 1 0 1 0 6 18 27 Chisquare =303.64, d.f. = 81, p<0.0005. 82 cells (82.0%) have expected counts less than 5. A2.19 Marking percentages in 2003 compared to a typical year A2.19.1 Merino ewes mated to Merino rams Proportion of respondents (%) Region n 2003 < typical 2003 = typical 2003 > typicalt SW & S Qld 39 64 77 90 1 10 20 2 13 23 GB & DD 5 0 40 83 0 40 83 0 20 55 New England 136 36 44 52 20 27 35 21 29 36 C & S Tablelands 118 63 71 79 9 16 23 7 13 19 S NSW & N Vic 89 59 69 78 3 9 15 14 22 31 Gippsland 7 20 57 94 0 14 40 0 29 62 W Vic & SE SA 198 48 55 62 19 25 31 14 20 25 S SA 41 26 41 57 11 24 38 20 34 49 KI 28 21 39 57 15 32 49 12 29 45 WA 167 19 26 32 24 31 38 36 44 51 All Regions 828 47 51 54 20 23 26 23 26 29 Chisquare = 100.43, d.f. = 18, p < 0.0005. 6 cells (20.0%) have expected counts less than 5. Institute for Rural Futures 33 A2.19.2 Merino ewes mated to meat breed rams Proportion of respondents (%) Region n 2003 < typical 2003 = typical 2003 > typicalt SW & S Qld 8 29 63 96 0 13 35 0 25 55 GB & DD 4 1 50 99 0 25 67 0 25 67 New England 39 28 44 59 12 26 39 16 31 45 C & S Tablelands 53 55 68 80 10 21 32 3 11 20 S NSW & N Vic 59 42 54 67 13 24 35 11 22 33 Gippsland 3 100 100 100 0 0 0 0 0 0 W Vic & SE SA 145 35 43 52 28 36 44 14 21 27 S SA 36 16 31 46 16 31 46 23 39 55 KI 22 16 36 56 29 50 71 0 14 28 WA 76 24 34 45 28 39 50 16 26 36 All Regions 445 41 46 50 27 32 36 19 23 27 Chisquare =34.43, d.f. = 18, p = 0.011. 10 cells (33.3%) have expected counts less than 5. A2.19.3 Cross-bred ewes Proportion of respondents (%) Region n 2003 < typical 2003 = typical 2003 > typicalt SW & S Qld 2 100 100 100 0 0 0 0 0 0 GB & DD 1 100 100 100 0 0 0 0 0 0 New England 31 28 45 63 13 29 45 10 26 41 C & S Tablelands 39 33 49 64 16 31 45 8 21 33 S NSW & N Vic 41 49 63 78 11 24 38 2 12 22 Gippsland 5 100 100 100 0 0 0 0 0 0 W Vic & SE SA 119 32 40 49 34 43 52 10 17 24 S SA 16 14 38 61 4 25 46 14 38 61 KI 6 0 17 46 0 33 71 10 50 90 WA 8 0 25 55 29 63 96 0 13 35 All Regions 268 40 46 52 29 35 40 14 19 24 Chisquare = 29.44, d.f. = 18, p = 0.043. 16 cells (33.3%) have expected counts less than 5. Institute for Rural Futures34 A2.20 Proportion of Respondents (%) Feeding Ewes and Weaners Each Month of the Year A2.20.1 Ewes Region n J F M A M J J A S O N D SW & S Qld 20 5 5 15 20 30 65 80 90 55 40 20 10 GB & DD 8 0 0 13 13 50 88 100 88 88 75 25 0 New England 112 8 8 13 18 28 54 87 94 65 21 10 8 C & S Tablelands 124 32 50 63 73 73 58 54 43 23 6 4 7 S NSW & N Vic 133 26 63 77 86 78 56 34 20 6 4 3 5 Gippsland 5 20 20 40 80 60 80 80 80 80 20 20 20 W Vic & SE SA 252 35 64 82 88 79 56 34 24 9 2 3 7 S SA 43 19 42 65 79 79 49 21 9 5 5 2 5 KI 32 38 81 84 81 59 13 0 0 0 0 0 9 WA 190 41 61 83 93 86 58 18 5 1 1 2 5 All regions 919 30 52 67 75 71 55 40 31 17 7 4 7 Note: percentages may sum to more than 100 as respondents could give more than one month. A2.20.2 Weaners Region n J F M A M J J A S O N D SW & S Qld 12 8 17 17 17 25 67 75 83 58 25 8 8 GB & DD 5 0 0 0 0 40 80 100 80 80 60 0 0 New England 70 13 14 19 27 39 70 94 96 70 26 11 10 C & S Tablelands 104 53 69 79 82 73 52 39 29 18 6 3 11 S NSW & N Vic 95 51 75 80 79 66 42 25 16 5 5 5 16 Gippsland 6 50 50 67 67 67 67 67 67 67 50 33 33 W Vic & SE SA 187 57 80 89 91 77 51 31 19 7 2 6 17 S SA 20 60 70 70 90 85 40 15 10 5 5 5 10 KI 27 52 89 100 96 70 15 0 0 0 0 4 19 WA 163 56 73 87 90 82 50 13 4 1 2 8 21 All regions 689 49 67 76 79 71 50 34 25 15 7 7 16 Note: percentages may sum to more than 100 as respondents could give more than one month. Institute for Rural Futures 35 A2.21 Duration of feeding period (months) A2.21.1 Ewes Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 20 2 4 7 4 1 GB & DD 8 2 5 9 5 2 New England 110 1 3 12 4 0 C & S Tablelands 123 1 5 12 5 0 S NSW & N Vic 132 1 4 12 5 0 Gippsland 5 2 6 12 6 5 W Vic & SE SA 248 1 5 12 5 0 S SA 43 1 3 12 4 1 KI 32 1 4 6 4 0 WA 186 1 5 12 5 0 All Regions 907 1 4 12 5 0 Histogram class limits: 1.0-2.1-3.2-4.3-5.4-6.5-7.6-8.7-9.8-10.9-12.0. Anova: F=3.41, d.f.=9, p <0.0005. A2.21.2 Weaners Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 12 2 4 7 4 1 GB & DD 5 2 5 6 4 2 New England 69 1 4 12 5 1 C & S Tablelands 103 1 5 12 5 0 S NSW & N Vic 94 1 5 9 5 0 Gippsland 6 4 5 12 7 4 W Vic & SE SA 187 1 5 12 5 0 S SA 20 1 5 12 5 1 KI 27 3 4 7 4 0 WA 159 1 5 12 5 0 All Regions 682 1 5 12 5 0 Histogram class limits: 1.0-2.1-3.2-4.3-5.4-6.5-7.6-8.7-9.8-10.9-12.0. Anova: F=1.98, d.f.=9, p = 0.039. Institute for Rural Futures36 A2.22 Worm Control, September 2002 – December 2003 A2.22.1 Proportion of treatments (%) of unweaned lambs in each month of the year Region n* J F M A M J J A S O N D SW & S Qld 16 0 19 0 6 0 0 0 0 6 6 6 56 GB & DD 10 20 30 0 0 0 0 0 0 0 10 20 20 New England 126 17 13 1 4 0 0 0 1 1 8 24 32 C & S Tablelands 84 5 2 1 2 1 7 11 11 14 20 17 8 S NSW & N Vic 55 2 4 2 4 2 5 20 9 20 9 18 5 Gippsland 5 0 0 0 0 0 0 0 60 0 20 0 20 W Vic & SE SA 167 2 4 1 1 2 9 17 11 20 10 8 15 S SA 22 0 0 0 5 0 14 45 5 27 0 0 5 KI 24 0 0 0 0 4 0 38 13 33 13 0 0 WA 28 4 0 0 4 4 11 14 21 21 4 0 18 All regions 537 6 6 1 2 1 6 13 9 15 10 13 17 * number of treatments. A2.22.2 Proportion of treatments (%) of weaners in each month of the year Region n* J F M A M J J A S O N D SW & S Qld 54 11 19 9 13 4 11 6 7 6 6 7 2 GB & DD 23 9 4 13 13 4 9 4 17 0 9 9 9 New England 360 12 11 11 13 8 8 5 8 8 5 5 6 C & S Tablelands 303 5 12 9 4 4 7 5 5 9 7 15 18 S NSW & N Vic 244 5 15 3 3 7 3 10 7 9 8 15 14 Gippsland 22 9 5 9 5 5 5 9 5 9 5 27 9 W Vic & SE SA 561 8 11 8 6 7 6 5 7 6 9 11 17 S SA 90 9 7 7 2 1 6 16 2 16 6 16 14 KI 78 14 13 4 5 4 6 6 1 14 13 6 13 WA 255 12 5 5 5 2 3 2 5 9 14 16 20 All regions 1990 9 11 8 7 6 6 6 6 8 8 12 14 * number of treatments. Institute for Rural Futures 37 A2.22.3 Products used – unweaned lambs Active constituent(s) Proportion of treatments (%) Drench not specified 1.1 Cobalt 0.2 Selenium 0.2 Broadspectrum 0.2 BZ unspecified 2 BZ Albendazole 5.6 BZ Fenbendazole 0.4 BZ Oxfendazole 0.2 Clear not specified 1.1 Levamisole 5.4 ML non specified 0.7 ML Abamectin 1.7 ML Ivermectin 13.5 ML Moxidectin 31.9 Naphthalophos 0.2 Closantel 2.8 Triclabendazole 0.6 White + tape 0.2 Clear + tape 0.2 Levamisole + BZ 10.9 Firstdrench + tape 0.4 ML Cydectin + tapeworm 0.9 ML Cydectin + selenium 1.1 Cydectin + Levamisole 0.4 Mineral drench + Ivomectin 0.2 Rametin + BZ 0.6 Rametin + Albendazole 0.6 Rametin + Levamisole 0.4 Closantel + Albendazole 1.3 Closantel + Oxfendazole 0.4 Praziquantel + Abamectin 6.3 Praziquantel + Levamisole 6.1 Ivermectin + Levamisole + BZ 1.5 Triton + Cydectin 0.2 Rametin + Levamisole + BZ 0.4 Ivomec + Praziquantel + Levamisole 0.2 Triton + Closantel 0.4 Institute for Rural Futures38 A2.22.4 Products used – weaners Active constituent(s) Proportion of treatments (%) Drench not specified 1.1 Alternative 0.3 Selenium 0.1 Broadspectrum 0.1 BZ unspecified 1.7 BZ unspecified capsule 0.2 BZ Albendazole 2.5 BZ Albendazole –capsules 0.4 BZ Fenbendazole 0.4 BZ Mebendazole 0.1 BZ Oxfendazole 0.1 Clear not specified 1.1 Levamisole 5.8 ML non specified 1.3 ML Abamectin 2.4 ML Ivermectin 19.6 ML Ivermectin – capsule 0.8 ML Moxidectin 32.8 Naphthalophos 1.9 Closantel 2.2 Triclabendazole 1.2 Combination unspecified 0.3 Oxyclosanide + Levamisole 0.1 Levamisole + unspecified 0.1 Levamisole + BZ 9.0 Levamisole + Albendazole 0.1 Levamisole + Fenbendazole 0.6 Firstdrench + tape 0.1 Ivermectin + white 0.1 ML Cydectin + tapeworm 0.2 ML Cydectin + selenium 0.6 Cydectin + combination 0.2 Cydectin + Fasinex 0.3 Cydectin + Rametin 0.1 Cydectin + Levamisole 0.2 Mineral drench + Ivomectin 0.1 ivermectin + Fasinex 0.1 Cydectin + Closantel 0.1 Rametin + combination unspecified 0.5 Rametin + BZ 1.6 Rametin + Albendazole 0.9 Rametin + Levamisole 1.9 Rametin + Oxfenendazole 0.1 Closantel + Albendazole 0.6 Closantel + Oxfendazole 0.1 Closantal + Levamisole 0.1 Praziquantel + Abamectin 2.0 Praziquantel + Levamisole 1.0 Ivermectin + Levamisole + BZ 2.2 Ivermectin + Ramatin + white 0.2 table continued on next page Institute for Rural Futures 39 Products used – weaners (contd) Active constituent(s) Proportion of treatments (%) Cydectin + BZ +Levamisole 0.2 Rametin + Cydectin + Levamisole 0.1 Rametin + Levamisole + BZ 0.8 Closantel + Levamisole + BZ 0.1 Praziquantel + Levamisole + Febendazole 0.1 Abamectin + Albendazole + Levamisole + Closantel 0.2 A2.22.5 Proportion of treatments (%) of maiden ewes in each month of the year Region n* J F M A M J J A S O N D SW & S Qld 48 4 13 8 6 2 10 6 10 10 8 13 8 GB & DD 19 16 0 11 16 0 0 5 21 5 5 11 11 New England 299 13 12 8 7 6 3 2 8 16 7 8 9 C & S Tablelands 280 6 13 9 4 4 5 8 6 6 9 15 14 S NSW & N Vic 180 11 15 7 6 4 3 6 7 3 2 21 17 Gippsland 18 0 6 11 6 6 6 6 0 11 6 28 17 W Vic & SE SA 479 11 11 8 6 4 8 7 7 5 4 14 16 S SA 72 15 15 11 6 3 6 4 3 7 0 8 22 KI 70 23 11 4 4 6 9 16 6 10 3 3 6 WA 179 20 9 6 8 4 5 2 3 6 5 10 22 All regions 1644 12 12 8 6 4 5 6 6 8 5 13 15 * number of treatments. A2.22.6 Proportion of treatments (%) of adult ewes in each month of the year Region n* J F M A M J J A S O N D SW & S Qld 64 6 13 8 6 2 8 8 11 9 9 6 14 GB & DD 27 15 4 7 15 7 0 4 19 4 7 11 7 New England 399 11 11 7 10 6 3 2 9 16 6 9 10 C & S Tablelands 323 4 12 9 4 3 6 9 7 7 9 14 16 S NSW & N Vic 220 11 15 10 5 4 2 4 6 5 2 19 18 Gippsland 29 3 7 10 3 3 7 7 3 7 3 34 10 W Vic & SE SA 627 10 10 8 6 5 7 9 7 4 5 13 16 S SA 101 18 11 11 7 4 7 6 0 9 2 9 17 KI 84 20 12 4 5 6 8 14 5 13 4 4 6 WA 199 19 8 9 10 4 4 4 4 7 4 6 24 All regions 2073 11 11 8 7 5 5 6 7 8 5 12 15 * number of treatments. Institute for Rural Futures40 A2.22.7 Products used – maiden ewes Active constituent(s) Proportion of treatments (%) Drench not specified 0.7 Alternative 0.3 Cobalt 0.1 Broadspectrum 0.1 BZ unspecified 1.4 BZ unspecified capsule 0.2 BZ Albendazole 1.9 BZ Albendazole –capsules 0.2 BZ Fenbendazole 0.2 BZ Oxfendazole 0.1 Clear not specified 1.2 Levamisole 6.6 ML non specified 1.1 ML Abamectin 1.8 ML Ivermectin 17.9 ML Ivermectin – capsule 1.1 ML Moxidectin 31.9 Naphthalophos 2.0 Closantel 3.4 Triclabendazole 1.6 Combination unspecified 0.1 Oxyclosanide + Levamisole 0.1 Levamisole + BZ 10.7 Levamisole + Albendazole 0.3 Levamisole + Fenbendazole 0.7 ML + Closantel 0.1 ML Cydectin + tapeworm 0.2 ML Cydectin + selenium 0.4 Cydectin + combination 0.2 Cydectin + Fasinex 0.1 Cydectin + Rametin 0.2 Cydectin + Levamisole 0.9 Cydectin + Closantel 0.1 ivermectin + combination unspecified 0.1 Rametin + combination unspecified 0.4 Rametin + BZ 1.3 Rametin + Albendazole 0.7 Rametin + Levamisole 2.2 Rametin + Oxfenendazole 0.1 Rametin + Closantel 0.1 Closantel + Albendazole 0.7 Closantel + Oxfendazole 0.3 Closantal + Levamisole 0.1 Closantel + Triclabendazole 0.1 Closantal + Abamectin 0.1 Closantal + Fasinex 0.1 Praziquantel + Abamectin 1.1 Ivermectin + Levamisole + BZ 2.6 Ivermectin + Ramatin + white 0.3 Cydectin + BZ +Levamisole 0.1 table continued on next page Institute for Rural Futures 41 Products used –maiden ewes (contd) Active constituent(s) Proportion of treatments (%) Rametin + Cydectin + Levamisole 0.1 Rametin + Levamisole + BZ 1.1 Closantel + Levamisole + BZ 0.1 Rametin + BZ + Closantel 0.1 Rametin + Levamisole + Closantel 0.1 Praziquantel + Abamectin + Levamisole 0.1 Abamectin + Albendazole + Levamisole + Closantel 0.2 A2.22.8 Products used – adult ewes Active constituent(s) Proportion of treatments (%) Drench not specified 1.0 Alternative 0.3 Cobalt 0.0 Broadspectrum 0.0 BZ unspecified 1.3 BZ unspecified capsule 0.1 BZ Albendazole 2.5 BZ Albendazole –capsules 0.3 BZ Fenbendazole 0.2 BZ Oxfendazole 0.0 Clear not specified 1.2 Levamisole 6.3 ML non specified 0.9 ML Abamectin 2.0 ML Ivermectin 17.6 ML Ivermectin – capsule 1.0 ML Moxidectin 32.9 Naphthalophos 2.0 Closantel 3.4 Triclabendazole 2.1 Combination unspecified 0.1 Oxyclosanide + Levamisole 0.3 Levamisole + BZ 9.9 Levamisole + Albendazole 0.2 Levamisole + Fenbendazole 0.6 Levamisole + Fasinex 0.1 ML + Closantel 0.0 ML Cydectin + tapeworm 0.0 ML Cydectin + selenium 0.7 Cydectin + combination 0.3 Cydectin + Fasinex 0.1 Cydectin + Rametin 0.1 Cydectin + Levamisole 0.5 Mineral drench + Ivomectin 0.1 Ivermectin + combination unspecified 0.1 Ivermectin + Levamisole 0.1 Rametin + combination unspecified 0.3 Rametin + BZ 0.9 table continued on next page Institute for Rural Futures42 Products used – adult ewes (contd) Active constituent(s) Proportion of treatments (%) Rametin + Albendazole 1.0 Rametin + Levamisole 1.8 Rametin + Oxfenendazole 0.0 Rametin + Closantel 0.0 Closantel + BZ 0.0 Closantel + Albendazole 0.6 Closantel + Oxfendazole 0.3 Closantal + Levamisole 0.1 Closantal + Abamectin 0.0 Praziquantel + Abamectin 1.6 Praziquantel + Levamisole 0.0 Ivermectin + Levamisole + BZ 2.6 Triton + Cydectin 0.0 Ivermectin + Ramatin + white 0.2 Cydectin + BZ +Levamisole 0.0 Rametin + Levamisole + BZ 0.8 Rametin + Levamisole + Vasinex 0.0 Closantel + Levamisole + BZ 0.0 Rametin + BZ + Closantel 0.1 Rametin + Levamisole + Closantel 0.0 Praziquantel + Abamectin + Levamisole 0.1 Abamectin + Albendazole + Levamisole + Closantel 0.1 Note: due to rounding some percentages may show as zero that are actually non-zero percentages less than 0.05 per cent. A2.22.9 Proportion of treatments (%) of wethers in each month of the year Region n* J F M A M J J A S O N D SW & S Qld 49 4 16 4 8 6 10 4 12 14 6 8 6 GB & DD 39 13 8 13 10 5 3 3 13 13 5 13 3 New England 270 11 11 8 8 8 5 2 7 13 8 11 9 C & S Tablelands 202 4 15 11 4 3 4 5 2 6 9 15 19 S NSW & N Vic 115 11 19 6 7 3 2 5 3 3 1 19 20 Gippsland 16 0 0 13 6 6 6 0 6 13 0 38 13 W Vic & SE SA 310 10 12 11 4 4 3 4 4 5 4 18 21 S SA 26 19 8 0 4 4 4 4 4 8 0 0 46 KI 50 30 14 4 2 4 6 8 10 6 6 2 8 WA 96 18 8 7 6 4 3 2 4 7 3 8 28 All regions 1173 11 13 9 6 5 4 4 5 8 5 14 17 * number of treatments. Institute for Rural Futures 43 A2.22.10 Products used – wethers Active constituent(s) Proportion of treatments (%) Drench not specified 1.60 Alternative 0.20 Broadspectrum 0.10 BZ unspecified 1.00 BZ unspecified capsule 0.10 BZ Albendazole 2.20 BZ Fenbendazole 0.10 BZ Oxfendazole 0.10 Clear not specified 1.40 Levamisole 9.20 ML non specified 1.30 ML Abamectin 2.10 ML Ivermectin 15.60 ML Moxidectin 32.00 Naphthalophos 2.50 Closantel 4.60 Triclabendazole 2.00 Combination unspecified 0.20 Oxyclosanide + Levamisole 0.30 Levamisole + BZ 9.50 Levamisole + Albendazole 0.20 Levamisole + Fenbendazole 0.50 Levamisole + Fasinex 0.10 ML Cydectin + selenium 0.40 Cydectin + combination 0.30 Cydectin + Closantel 0.50 Cydectin + Fasinex 0.30 Cydectin + Rametin 0.20 Cydectin + Ivermectin 0.10 Cydectin + Levamisole 0.50 Cydectin + Closantel 0.10 Ivermectin + combination unspecified 0.20 Rametin + combination unspecified 0.30 Rametin + BZ 1.20 Rametin + Albendazole 1.00 Rametin + Levamisole 1.70 Rametin + Oxfenendazole 0.20 Closantel + Albendazole 0.50 Closantel + Oxfendazole 0.30 Closantal + Levamisole 0.10 Closantal + Fasinex 0.10 Praziquantel + Abamectin 1.50 Ivermectin + Levamisole + BZ 2.10 Ivermectin + Ramatin + white 0.30 Cydectin + BZ +Levamisole 0.10 Cydectin + Closantel +Ivomectin 0.10 Rametin + Levamisole + BZ 1.20 Closantel + Levamisole + BZ 0.10 Rametin + BZ + Closantel 0.10 Abamectin + Albendazole + Levamisole + Closantel 0.10 Institute for Rural Futures44 A2.22.11 Drenching of newly introduced sheep Region n Proportion buying sheep (%) SW & S Qld 62 63 74 85 GB & DD 22 79 91 103 New England 173 50 58 65 C & S Tablelands 177 50 58 65 SW NSW & NE Vic 167 56 63 70 Gippsland 12 30 58 86 W Vic & SE SA 369 57 62 66 S SA 69 43 55 67 KI 41 49 63 78 WA 200 42 49 55 All regions 1292 57 59 62 Chisquare =27.30, d.f. = 9, p = 0.001. A2.22.12 Products used to drench newly arrived sheep Active constituent(s) Proportion of respondents (%) Drench not specified 5.00 Alternative 0.20 Broadspectrum 0.70 BZ unspecified 0.30 BZ Albendazole 0.80 BZ Thiabendazole 0.20 Clear not specified 0.70 Levamisole 1.30 ML non specified 3.20 ML Abamectin 0.70 ML Ivermectin 23.80 ML Moxidectin 40.60 Naphthalophos 0.50 Closantel 0.80 Triclabendazole 0.20 Fasinex + Oxyclosanide + Levamisole 0.20 Combination unspecified 1.30 Oxyclosanide + Levamisole 0.20 Levamisole + BZ 1.20 Levamisole + Fenbendazole 0.20 ML + Fasinex 0.20 ML + BZ 0.20 ML Cydectin + selenium 0.70 Cydectin + combination 1.20 Cydectin + Closantel 0.50 Cydectin + Fasinex 0.30 Cydectin + mineral 0.20 Cydectin + Rametin 0.80 Cydectin + Ivermectin 1.70 Institute for Rural Futures 45 Products used to drench newly arrived sheep (contd) Active constituent(s) Proportion of respondents (%) Cydectin + Levamisole 1.50 Mineral drench + Ivomectin 0.20 Ivermectin + Fasinex 0.50 Ivermectin + combination unspecified 0.20 Ivermectin + Levamisole 0.20 Ivermectin + Closantel 0.50 Rametin + BZ 0.80 Rametin + Levamisole 0.30 Rametin + Oxfenendazole 0.20 Closantel + Albendazole 0.50 Closantal + Fasinex 0.20 Praziquantel + Abamectin 1.20 Ivermectin + Levamisole + BZ 5.70 Triton + Rametin 0.20 Triton + Fasinex 0.20 Triton + Q drench 0.20 Cydectin + Rametin + BZ (eg Valbazen) 0.30 Cydectin + Rametin + BZ (eg Valbazen) +SE 0.20 Cydectin + BZ +Levamisole 1.00 Cydectin + Closantel +Ivomectin 0.20 Rametin + BZ + Closantel 0.20 Abamectin + Albendazole + Levamisole + Closantel 1.50 Ivermectin + Rametin + BZ + Fasinez 0.20 Cydectin + Triton 0.20 Cydectin + Ramatin + BZ + Levamisole 0.70 Note: due to rounding some percentages may show as zero that are actually non-zero percentages less than 0.05 per cent. Institute for Rural Futures46 A2.22.13 Number of times worm egg counts typically monitored – weaners Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 22 1 2 7 2.3 0.7 GB & DD 3 2 3 6 3.7 5.2 New England 87 1 3 43 4.3 1.1 C & S Tablelands 77 1 2 26 3.4 0.8 S NSW & N Vic 53 1 2 12 2.2 0.5 Gippsland 6 1 2 3 2.0 0.7 W Vic & SE SA 127 1 2 12 2.8 0.3 S SA 21 1 2 12 3.1 1.5 KI 14 1 3 10 3.4 1.4 WA 61 1 2 8 2.1 0.4 All Regions 471 1 2 43 3.0 0.3 Histogram class limits: 1.0-1.9-2.8-3.7-4.6-5.5-6.4-7.3-8.2-9.1-10.0. Kruskal-Wallis: �2=37.29, d.f.=9, p<0.0005. Note: respondents monitoring more than 10 times (12) have been excluded from the histograms (and only from the histograms) to prevent the size distribution being reduced to a single bar, due to the influence of the small number of respondents monitoring very frequently. A2.22.14 Number of times worm egg counts typically monitored – wethers Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 19 1 2 12 3.1 1.3 GB & DD 13 1 5 25 5.7 3.7 New England 61 1 3 12 3.0 0.6 C & S Tablelands 42 1 2 13 2.4 0.7 S NSW & N Vic 27 1 1 24 2.4 1.8 Gippsland 5 1 1 3 1.6 1.1 W Vic & SE SA 73 1 2 12 2.4 0.5 S SA 5 1 2 5 2.4 2.1 KI 11 1 2 5 2.5 0.8 WA 23 1 2 4 2.0 0.4 All Regions 279 1 2 25 2.7 0.3 Histogram class limits: 1.0-1.9-2.8-3.7-4.6-5.5-6.4-7.3-8.2-9.1-10.0. Kruskal-Wallis: �2=28.34, d.f.=9, p=0.001. Note: respondents monitoring more than 10 times (6) have been excluded from the histograms (and only from the histograms) to prevent the size distribution being reduced to a single bar, due to the influence of the small number of respondents monitoring very frequently. Institute for Rural Futures 47 A2.22.15 Number of times worm egg counts typically monitored – adult ewes Region n Minimum Median Maximum Mean 95% CI Histogram SW & S Qld 24 1 2 12 3.1 1.0 GB & DD 7 2 3 7 3.7 1.9 New England 94 1 3 15 3.4 0.6 C & S Tablelands 76 1 2 17 2.7 0.5 S NSW & N Vic 51 1 1 6 1.6 0.3 Gippsland 8 1 2 3 1.8 0.6 W Vic & SE SA 131 1 2 12 2.5 0.3 S SA 24 1 2 7 1.9 0.6 KI 14 1 3 5 2.9 0.8 WA 57 1 1 6 1.8 0.3 All Regions 486 1 2 17 2.6 0.2 Histogram class limits: 1.0-1.9-2.8-3.7-4.6-5.5-6.4-7.3-8.2-9.1-10.0. Kruskal-Wallis: �2=56.07, d.f.=9, p<0.0005. Note: respondents monitoring more than 10 times (6) have been excluded from the histograms (and only from the histograms) to prevent the size distribution being reduced to a single bar, due to the influence of the small number of respondents monitoring very frequently. A2.22.16 Monitoring frequency in 2003 compared to typical frequency – weaners Proportion of respondents (%) Region n 2003 < typical 2003 = typical 2003 > typicalt SW & S Qld 19 0 5 15 76 89 103 0 5 15 GB & DD 1 0 0 0 100 100 100 0 0 0 New England 85 0 1 3 97 99 101 0 0 0 C & S Tablelands 76 0 0 0 94 97 101 0 3 6 S NSW & N Vic 49 0 2 6 94 98 102 0 0 0 Gippsland 6 0 17 46 54 83 113 0 0 0 W Vic & SE SA 125 4 10 15 85 90 96 0 0 0 S SA 20 0 0 0 85 95 105 0 5 15 KI 14 0 0 0 100 100 100 0 0 0 WA 57 0 2 5 92 96 101 0 2 5 All Regions 452 2 4 6 93 95 97 0 1 2 �2 = 32.10, d.f. = 18, p =0.021. 21 cells (70.0%) have expected counts less than 5. Institute for Rural Futures48 A2.22.17 Monitoring frequency in 2003 compared to typical frequency – wethers Proportion of respondents (%) Region n 2003 < typical 2003 = typical 2003 > typicalt SW & S Qld 17 0 6 17 83 94 105 0 0 0 GB & DD 11 0 9 26 74 91 108 0 0 0 New England 60 0 2 5 92 97 101 0 2 5 C & S Tablelands 41 0 2 7 93 98 102 0 0 0 S NSW & N Vic 27 0 0 0 100 100 100 0 0 0 Gippsland 5 0 0 0 100 100 100 0 0 0 W Vic & SE SA 73 0 4 9 89 95 100 0 1 4 S SA 4 0 0 0 100 100 100 0 0 0 KI 11 0 0 0 100 100 100 0 0 0 WA 22 0 9 21 79 91 103 0 0 0 All Regions 271 1 3 5 94 96 98 0 1 2 �2 = 8.25, d.f. = 18, p =0.975. 22 cells (73.3%) have expected counts less than 5. A2.22.18 Monitoring frequency in 2003 compared to typical frequency – adult ewes Proportion of respondents (%) Region n 2003 < typical 2003 = typical 2003 > typicalt SW & S Qld 23 0 13 27 67 83 98 0 4 13 GB & DD 5 0 20 55 45 80 115 0 0 0 New England 93 0 1 3 97 99 101 0 0 0 C & S Tablelands 75 0 3 6 92 96 100 0 1 4 S NSW & N Vic 50 0 0 0 100 100 100 0 0 0 Gippsland 8 0 0 0 100 100 100 0 0 0 W Vic & SE SA 128 2 5 9 91 95 98 0 0 0 S SA 23 0 0 0 87 96 104 0 4 13 KI 14 0 0 0 100 100 100 0 0 0 WA 53 0 2 6 94 98 102 0 0 0 All Regions 472 2 3 5 94 96 98 0 1 1 �2 = 31.86, d.f. = 18, p =0.023. 21 cells (70.0%) have expected counts less than 5. Institute for Rural Futures 49 A2.22.19 Recency of adoption of drench resistance testing Region n Proportion of respondents who have tested for drench resistance in 2000 or more recently (%) SW & S Qld 11 74 91 108 GB & DD 8 15 50 85 New England 79 64 73 83 C & S Tablelands 73 56 67 78 SW NSW & NE Vic 60 49 62 74 Gippsland 8 45 75 105 W Vic & SE SA 153 59 67 74 S SA 32 36 53 70 KI 16 46 69 91 WA 100 52 62 72 All regions 540 62 66 70 �2 =9.87, d.f. = 9, p = 0.361. A2.22.20 Explanatory descriptions of worm control treatments and techniques Prepare pastures by ‘Smart grazing’ – all regions Explanatory description Proportion of respondents (%) Provide or move treated sheep to clean/ low risk pastures 14 Graze first/ in rotation/ alternate with cattle 23.3 Graze first with cattle & provide crop stubbles 4.7 Graze first with attle &/ or dry sheep 4.7 Graze first with dry sheep 7 Paddocks grazed by sheep given a capsule 4.7 Use rotational grazing incl. cell grazing 9.3 Spell pasture/ paddock 11.6 Shift after treatment onto crop stubbles 11.6 Can't use any grazing techniques 2.3 Nutrition/ grazing management/ good quality pasture 4.7 Give pre-lambing drench 2.3 n=43 Institute for Rural Futures50 Prepare pastures by other grazing techniques – all regions Explanatory description Proportion of respondents (%) Provide or move treated sheep to clean/ low risk pastures 9.8 Graze high risk pastures with dry sheep 2.0 Graze first/ in rotation/ alternate with cattle 25.5 Graze first with cattle & provide clean pastures 2.0 Graze first with cattle & provide crop stubbles 2.0 Graze first with cattle &/ or dry sheep 9.8 Graze cattle & sheep together 2.0 Graze first with dry sheep 13.7 Paddocks grazed by sheep given a capsule 2.0 Use rotational grazing incl. cell grazing 5.9 Spell pasture/ paddock 9.8 Change pasture/ paddock after treatment 2.0 Shift after treatment onto crop stubbles 3.9 Use/ shift after treatment onto fodder or standing crop 2.0 Avoid drenching onto crop stubbles 2.0 Use hay paddock 2.0 Avoid high stocking rate/ use low stocking rate 2.0 Use native pasture 2.0 n=51 Proportion of sheep left un-drenched at summer treatments – all regions n=66, mean=21.29% Feeding strategy – all regions Explanatory description Proportion of respondents (%) Graze first/ in rotation/ alternate with cattle 7.7 Change pasture/ paddock after treatment 7.7 Shift after treatment onto crop stubbles 7.7 Keep feed availability high 7.7 Maintain condition score 38.5 Supplementary feed/ start feeding early 15.4 Feed in troughs 7.7 Nutrition/ grazing management/ good quality pasture 7.7 Graze first/ in rotation/ alternate with cattle 7.7 Change pasture/ paddock after treatment 7.7 n=13 Institute for Rural Futures 51 Proportion respondents who used rams selected for worm resistance and rams were EBV tested Across all regions, and among those respondents who used rams selected for worm resistance, 72.5 per cent indicated that the rams were EBV tested (n=120). There was no significant difference between regions. Drenching – all regions Explanatory description Proportion of respondents (%) Provide or move treated sheep to clean/ low risk pastures 4.7 Shift after treatment onto crop stubbles 4.7 Use minerals 2.3 Use strategic/ summer drenches 18.6 1-summer drench 7.0 2-summer drenches 7.0 Don't summer drench 2.3 Drench frequently 4.7 Drench as needed 9.3 Use correct dose rates 2.3 Use higher dose rates 2.3 Rotate chemicals 9.3 Monitor egg counts before drench 7.0 Assess when to drench visually (appearance of the sheep) 7.0 Don't drench much/ worms not a problem 4.7 Only drench weaners or lambs/ don't drench adult sheep 4.7 Give quarantine drench 2.3 n=43 Other treatments and techniques - all regions Explanatory description Proportion of respondents (%) Provide or move treated sheep to clean/ low risk pastures 7.7 Graze first/ in rotation/ alternate with cattle 4.5 Graze first with cattle & provide crop stubbles 1.3 Graze first with dry sheep 0.6 Paddocks grazed by sheep given a capsule 1.9 Use rotational grazing incl. cell grazing 1.9 Spell pasture/ paddock 8.4 Spell lambing paddock 0.6 Change pasture/ paddock after treatment 0.6 Shift after treatment onto crop stubbles 13.5 Use/ shift after treatment onto fodder or standing crop 0.6 Use hay paddock 0.6 Graze crop stubbles 9.0 Avoid high stocking rate/ use low stocking rate 3.9 Use high stocking rate 0.6 Nutrition/ grazing management/ good quality pasture 5.2 Use minerals 8.4 Nutrition - especially vitamins 0.6 continued on next page Institute for Rural Futures52 Other treatments and techniques - all regions (contd) Explanatory description Proportion of respondents (%) Nutrition - organic 1.9 Monitor BWt 0.6 Use strategic/ summer drenches 2.6 Leave some sheep untreated at summer drench 0.6 Don't summer drench 0.6 Give pre-lambing drench 1.3 Use 'smart drenching' (~12 hrs off feed) 1.3 Rotate chemicals 3.2 Monitor egg counts before drench 1.9 Assess when to drench visually (appearance of the sheep) 1.9 Only drench tail of mob 0.6 Don't drench much/ worms not a problem 2.6 Only drench weaners or lambs/ don't drench adult sheep 2.6 Use some form of genetic strategy 1.9 Cull daggy sheep 0.6 Select low worm count sheep 0.6 Flock structure limits other control measures 1.3 Disaster & chaos - no other control possible 3.2 n=153