HLI FORECAST SEASON 2011 - 2012...Page ii of iv Table 9 5 and 6 day forecast AHLU contingency tables...

final repport

Prepared by: Andrew Wiebe, Ella Castillo Anne Senabouth and Christine Killip

Katestone Environmental

Date published: May 2015

Heat Load Index Forecast Season 2014-2015

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Contents Abstract .... .......................................................................................................................................................... 1 Executive Summary ....................................................................................................................................................... 1 1. Project objectives ............................................................................................................................................. 3 2. Service upgrades for 2014-2015 season ......................................................................................................... 4 3. Methodology .................................................................................................................................................... 7

3.1 Overview ............................................................................................................................................................ 7 3.2 The weather model ............................................................................................................................................ 8 3.3 Heat Load .......................................................................................................................................................... 9 3.4 Delivery ............................................................................................................................................................ 11

3.4.1 Forecast generation ......................................................................................................................... 11 3.4.2 Website and database administration ............................................................................................... 12 3.4.3 Onsite AWS integration .................................................................................................................... 13 3.4.4 Alerts ................................................................................................................................................ 13

• AHLU event today: AHLU >50 units for today ................................................................................................ 13 3.4.5 Tools ................................................................................................................................................ 14

4. Season overview ........................................................................................................................................... 15 4.1 Climate ............................................................................................................................................................ 15

4.1.1 ENSO ............................................................................................................................................... 15 4.1.2 Tropical Cyclones ............................................................................................................................. 15

4.2 Heat Load ........................................................................................................................................................ 16 4.2.1 Heat Advisories ................................................................................................................................ 17 4.2.2 Automated alerts .............................................................................................................................. 18

4.3 Web site statistics ............................................................................................................................................ 19 5. Service performance ...................................................................................................................................... 22

5.1 Benchmark locations ........................................................................................................................................ 22 5.2 Forecast performance evaluation ..................................................................................................................... 23 5.3 Performance of 2014/15 season ...................................................................................................................... 24

5.3.1 Heat Load Index ............................................................................................................................... 24 5.3.2 Accumulated Heat Load Units .......................................................................................................... 27

6. Subscriber survey .......................................................................................................................................... 32 6.1 Users ............................................................................................................................................................... 32 6.2 Communication ................................................................................................................................................ 32 6.3 Alerts ............................................................................................................................................................... 32 6.4 Performance .................................................................................................................................................... 34 6.5 HLDN ............................................................................................................................................................... 34 6.6 Overall response .............................................................................................................................................. 35

7. Discussion ..................................................................................................................................................... 36 7.1 Future improvements ....................................................................................................................................... 36

8. Conclusion ..................................................................................................................................................... 37 9. Bibliography ................................................................................................................................................... 38 Appendix A - Evaluation parameters ............................................................................................................................ 39 Appendix B - AHLU Performance ................................................................................................................................. 48

B1. Day 1 ............................................................................................................................................................... 48 B2. Day 2 ............................................................................................................................................................... 59 B3. Day 3 ............................................................................................................................................................... 70 B4. Day 4 ............................................................................................................................................................... 81 B5. Day 5 ............................................................................................................................................................... 91 B6. Day 6 ..............................................................................................................................................................103 B7. Day 7 ..............................................................................................................................................................114

Appendix C - Survey Results ..................................................................................................................................... 125 C1. CHLT website .................................................................................................................................................127 C2. Major Town Forecasts .....................................................................................................................................129 C3. Forecast Accuracy ..........................................................................................................................................132 C4. Site management practices .............................................................................................................................133 C5. Alerts ..............................................................................................................................................................137 C6. Why Register? ................................................................................................................................................141

Tables Table 1 Southern Oscillation Index 30 day rolling average .............................................................................. 15 Table 2 Difference in monthly average daily maximum HLI from 2014/15 and 2013/14 .................................. 16 Table 3 Monthly average daily maximum HLI from 2014/15 ............................................................................ 17 Table 4 Heat Load Advisories issued between October 2013 and March 2014 ............................................... 18 Table 5 Web site traffic for 2012-13 to 2014-15 season .................................................................................. 19 Table 6 Forecast benchmark locations ............................................................................................................ 22 Table 7 1 and 2 day forecast AHLU contingency tables for all locations for the forecast season (forecast is

on the horizontal and observed on the vertical) .................................................................................. 28 Table 8 3 and 4 day forecast AHLU contingency tables for all locations for the forecast season .................... 29

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Table 9 5 and 6 day forecast AHLU contingency tables for all locations for the forecast season .................... 30 Table 10 7 day forecast AHLU contingency tables for all locations for the forecast season .............................. 31 Table 11 State and feedlot size (#SCU) distribution of respondents .................................................................. 32 Table A1 Performance benchmark guidelines .................................................................................................... 40 Table A2 Performance benchmarks by site 1 day ahead ................................................................................... 41 Table A3 Performance benchmarks by site 2 days ahead ................................................................................. 42 Table A4 Performance benchmarks by site 3 days ahead ................................................................................. 43 Table A5 Performance benchmarks by site 4 days ahead ................................................................................. 44 Table A6 Performance benchmarks by site 5 days ahead ................................................................................. 44 Table A7 Performance benchmarks by site 6 days ahead ................................................................................. 46 Table A8 Performance benchmarks by site 7 days ahead ................................................................................. 47 Table B1 Amberley ............................................................................................................................................. 48 Table B2 Emerald ............................................................................................................................................... 48 Table B3 Miles .................................................................................................................................................... 49 Table B4 Oakey .................................................................................................................................................. 50 Table B5 Roma .................................................................................................................................................. 50 Table B6 Warwick .............................................................................................................................................. 51 Table B7 Albury .................................................................................................................................................. 52 Table B8 Armidale .............................................................................................................................................. 52 Table B9 Cessnock ............................................................................................................................................ 53 Table B10 Griffith ................................................................................................................................................. 54 Table B11 Hay ...................................................................................................................................................... 54 Table B12 Moree .................................................................................................................................................. 55 Table B13 Tamworth ............................................................................................................................................ 56 Table B14 Yanco .................................................................................................................................................. 56 Table B15 Clare ................................................................................................................................................... 57 Table B16 Katanning ............................................................................................................................................ 58 Table B17 Charlton .............................................................................................................................................. 58 Table B18 Amberley ............................................................................................................................................. 59 Table B19 Emerald ............................................................................................................................................... 60 Table B20 Miles .................................................................................................................................................... 60 Table B21 Oakey .................................................................................................................................................. 61 Table B22 Roma .................................................................................................................................................. 62 Table B23 Warwick .............................................................................................................................................. 62 Table B24 Albury .................................................................................................................................................. 63 Table B25 Armidale .............................................................................................................................................. 63 Table B26 Cessnock ............................................................................................................................................ 64 Table B27 Griffith ................................................................................................................................................. 65 Table B28 Hay ...................................................................................................................................................... 65 Table B29 Moree .................................................................................................................................................. 66 Table B30 Tamworth ............................................................................................................................................ 67 Table B31 Yanco .................................................................................................................................................. 67 Table B32 Clare ................................................................................................................................................... 68 Table B33 Katanning ............................................................................................................................................ 68 Table B34 Charlton .............................................................................................................................................. 69 Table B35 Amberley ............................................................................................................................................. 70 Table B36 Emerald ............................................................................................................................................... 70 Table B37 Miles .................................................................................................................................................... 71 Table B38 Oakey .................................................................................................................................................. 72 Table B39 Roma .................................................................................................................................................. 72 Table B40 Warwick .............................................................................................................................................. 73 Table B41 Albury .................................................................................................................................................. 74 Table B42 Armidale .............................................................................................................................................. 74 Table B43 Cessnock ............................................................................................................................................ 75 Table B44 Griffith ................................................................................................................................................. 75 Table B45 Hay ...................................................................................................................................................... 76 Table B46 Moree .................................................................................................................................................. 77 Table B47 Tamworth ............................................................................................................................................ 77 Table B48 Yanco .................................................................................................................................................. 78 Table B49 Clare ................................................................................................................................................... 79 Table B50 Katanning ............................................................................................................................................ 79 Table B51 Charlton .............................................................................................................................................. 80 Table B52 Amberley ............................................................................................................................................. 81 Table B53 Emerald ............................................................................................................................................... 81

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Table B54 Miles .................................................................................................................................................... 82 Table B55 Oakey .................................................................................................................................................. 82 Table B56 Roma .................................................................................................................................................. 83 Table B57 Warwick .............................................................................................................................................. 84 Table B58 Albury .................................................................................................................................................. 84 Table B59 Armidale .............................................................................................................................................. 85 Table B60 Cessnock ............................................................................................................................................ 86 Table B61 Griffith ................................................................................................................................................. 86 Table B62 Hay ...................................................................................................................................................... 87 Table B63 Moree .................................................................................................................................................. 87 Table B64 Tamworth ............................................................................................................................................ 88 Table B65 Yanco .................................................................................................................................................. 89 Table B66 Clare ................................................................................................................................................... 89 Table B67 Katanning ............................................................................................................................................ 90 Table B68 Charlton .............................................................................................................................................. 91 Table B69 Amberley ............................................................................................................................................. 91 Table B70 Emerald ............................................................................................................................................... 92 Table B71 Miles .................................................................................................................................................... 92 Table B72 Oakey .................................................................................................................................................. 93 Table B73 Roma .................................................................................................................................................. 94 Table B74 Warwick .............................................................................................................................................. 94 Table B75 Albury .................................................................................................................................................. 95 Table B76 Armidale .............................................................................................................................................. 95 Table B77 Cessnock ............................................................................................................................................ 96 Table B78 Griffith ................................................................................................................................................. 97 Table B79 Hay ...................................................................................................................................................... 97 Table B80 Moree .................................................................................................................................................. 98 Table B81 Tamworth ............................................................................................................................................ 99 Table B82 Yanco .................................................................................................................................................. 99 Table B83 Clare ................................................................................................................................................. 100 Table B84 Katanning .......................................................................................................................................... 100 Table B85 Charlton ............................................................................................................................................ 101 Table B86 Amberley ........................................................................................................................................... 103 Table B87 Emerald ............................................................................................................................................. 103 Table B88 Miles .................................................................................................................................................. 104 Table B89 Oakey ................................................................................................................................................ 104 Table B90 Roma ................................................................................................................................................ 105 Table B91 Warwick ............................................................................................................................................ 106 Table B92 Albury ................................................................................................................................................ 106 Table B93 Armidale ............................................................................................................................................ 107 Table B94 Cessnock .......................................................................................................................................... 108 Table B95 Griffith ............................................................................................................................................... 108 Table B96 Hay .................................................................................................................................................... 109 Table B97 Moree ................................................................................................................................................ 109 Table B98 Tamworth .......................................................................................................................................... 110 Table B99 Yanco ................................................................................................................................................ 111 Table B100 Clare ................................................................................................................................................. 111 Table B101 Katanning .......................................................................................................................................... 112 Table B102 Charlton ............................................................................................................................................ 113 Table B103 Amberley ........................................................................................................................................... 114 Table B104 Emerald ............................................................................................................................................. 114 Table B105 Miles .................................................................................................................................................. 115 Table B106 Oakey ................................................................................................................................................ 115 Table B107 Roma ................................................................................................................................................ 116 Table B108 Warwick ............................................................................................................................................ 117 Table B109 Albury ................................................................................................................................................ 117 Table B110 Armidale ............................................................................................................................................ 118 Table B111 Cessnock .......................................................................................................................................... 119 Table B112 Griffith ............................................................................................................................................... 119 Table B113 Hay .................................................................................................................................................... 120 Table B114 Moree ................................................................................................................................................ 120 Table B115 Tamworth .......................................................................................................................................... 121 Table B116 Yanco ................................................................................................................................................ 122 Table B117 Clare ................................................................................................................................................. 122 Table B118 Katanning .......................................................................................................................................... 123 Table B119 Charlton ............................................................................................................................................ 124

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Table C1 If you were to number the following from 1-5 in terms of priority (with 1 being the preferred and 5 being the least preferred), what would be the best way to communicate with you? .......................... 125

Table C2 What other methods of communication would you like to receive information on the CHLT? ........... 125 Table C3 Are our current communications easy to understand?? .................................................................... 125 Table C4 Would you use a CHLT smart phone ‘app’ if one were available? .................................................... 126 Table C5 What other information would you like to receive? ............................................................................ 126 Table C6 Do the web pages load in an adequate time? ................................................................................... 127 Table C7 Do you like the new Site Summary page and find it helpful? ............................................................ 128 Table C8 What is more important to you in a Site Summary? .......................................................................... 128 Table C9 Do you currently view any of the major town forecasts? ................................................................... 129 Table C10 Would you like access to the major town forecasts in the future?? ................................................... 129 Table C11 Would you like to be able to view the major town forecasts on your My Site Summary page? ......... 129 Table C12 If Katestone were to remove the major town forecast information from the publicly accessible

section of the CHLT website, would you object? ............................................................................... 131 Table C13 How accurate do you perceive the weather forecast to be for your site? .......................................... 132 Table C14 How did you determine the accuracy of the forecast? ....................................................................... 132 Table C15 Do you have a weather station? ........................................................................................................ 133 Table C16 Is your weather station data being sent to Katestone for inclusion in your forecast? ........................ 134 Table C17 When your weather station is indicating your cattle will be carrying a heat load, do the cattle show

signs of heat stress? ......................................................................................................................... 135 Table C18 Do you have a daily heat load monitoring plan? ............................................................................... 135 Table C19 If the forecast indicates there is a heat event on its way, when do you take action to address the

issue (e.g. move to heat load ration)? ............................................................................................... 136 Table C20 Do you think the new alerts were an improvement over previous years? ......................................... 137 Table C21 The alerts were: ................................................................................................................................ 138 Table C22 (By State) The alerts were: ............................................................................................................... 139 Table C23 (By Feedlot Size) The alerts were: .................................................................................................... 140 Table C24 Not only were the type of alerts different from previous years, the level of risk was also set to a

value where the accumulated heat load was just in the high risk category (AHLU>50). In previous years you could select low, moderate or high risk category to receive an alert. Do you think the AHLU>50 indicated the appropriate level of risk of at your site? ....................................................... 141

Table C25 Where did you hear about CHLT? ..................................................................................................... 141 Table C26 How can the CHLT be improved? ..................................................................................................... 142 Table C27 Do you plan to use the CHLT into the future? ................................................................................... 143 Table C28 How can the uptake of the CHLT service among lot feeders be improved? ...................................... 143 Table C29 How can the uptake of the CHLT service among processors be improved? ..................................... 144 Figures Figure 1 Site Summary Page the Cattle Heat Load Toolbox ............................................................................... 3 Figure 2 Uptake of the CHLT service since its launch in 2010/11 ....................................................................... 4 Figure 3 Location of subscriber feedlots, colour coded by number of head of cattle ........................................... 5 Figure 4 Location of feedlots uploading to the HLDN, colour coded by head of cattle ........................................ 6 Figure 5 Schematic of the CHLT early warning system ...................................................................................... 7 Figure 6 K-WRF forecast domain ........................................................................................................................ 8 Figure 7 Schematic of the land surface scheme used in the K-WRF system ...................................................... 9 Figure 8 Overview of the current process to deliver a forecast to the CHLT ..................................................... 11 Figure 9 Example HLI Threshold Calculator and Alert level .............................................................................. 14 Figure 10 Difference in mean daily maximum HLI for 2013/14 and 2014/15 by month aggregated for the 17

benchmark locations ........................................................................................................................... 16 Figure 11 Breakdown of alerts sent by month during forecast period ................................................................. 18 Figure 12 Total number of alerts issued by type and month................................................................................ 19 Figure 13 Web site visits as a function of time of the day ................................................................................... 20 Figure 14 Web site visits as a function of day of the week .................................................................................. 20 Figure 15 Web site visits as a function of State ................................................................................................... 21 Figure 16 Benchmark locations ........................................................................................................................... 23 Figure 17 Mean HLI RMSE for the 17 benchmark sites since 2006 .................................................................... 24 Figure 18 Minimum, maximum and mean root mean square error aggregated for the 17 benchmark

locations .............................................................................................................................................. 25 Figure 19 Minimum, maximum and mean MAE aggregated for the 17 benchmark locations ............................. 25 Figure 20 Minimum, maximum and mean bias aggregated for the 17 benchmark locations ............................... 26 Figure 21 Minimum, maximum and mean IOA aggregated for the 17 benchmark locations ............................... 26 Figure 22 Perception of alerts by state ................................................................................................................ 33 Figure 23 Perception of alerts by feedlot size ..................................................................................................... 34

Heat Load Index Forecast Season 2014 - 2015

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Glossary Term Definition ACCESS Australian Community Climate and Earth-System Simulator AFWA Air Force Weather Agency AHLU Accumulated Heat Load Unit ARW Advanced Research and Weather AWS Automatic Weather Stations BOM Bureau of Meteorology CAPS Center for Analysis and Prediction of Storms CHTL Cattle Heat Load Toolbox DFI Digital Filter Initialisation ECMWF European Centre for Medium-Range Weather Forecasts ENSO The El Niño/Southern Oscillation ESRL Earth System Research Laboratory FAA Federal Aviation Administration GASP Global Analysis and Prediction model GFS Global Forecasting System HLI Heat Load Index HPC High performance computing IOA Index of Agreement km kilometre K-WRF Katestone Weather Research and Forecasting Model LAPS Local Area Prediction System LSM Land Surface Mocdel M metre m/s metres per second MAE mean absolute error MJO Madden-Julian Oscillation MMM Mesoscale and Microscale Meteorology MOS Model output statistic NCAR National Centre for Atmospheric Research NCEP National Centre for Environmental Prediction NMM Non-Hydrostatic Mesoscale Model NOAA National Oceanic and Atmospheric Administration NRL Naval Research Laboratory NWP Numerical weather prediction NWP Numerical weather prediction ºC degrees Celsius RMSE root mean squared error SOI Southern Oscillation Index SST sea surface temperature TC Tropical Cyclones VAR variational data-assimilation WMO World Meteorological Organisation WPS WRF Pre-processing System WRF Weather Research and Forecasting WSF WRF System Framework


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Abstract

Heat stress in feedlot cattle can have a deleterious effect on cattle performance and in extreme cases lead to cattle death. The National Feedlot Accreditation Scheme requires that feedlots have a heat stress management plan in place to cope with weather events that can lead to excessive heat loads. The Cattle Heat Load Toolbox, developed by Katestone, alerts feedlot operators of impending adverse weather conditions that could lead to excessive heat loads in feedlot cattle. The toolbox is web based and provides access to weather and heat load forecasts out one week and risk assessment programs. The service is underpinned by over 15 years of research into cattle heat load funded by MLA. The Cattle Heat Load Toolbox brings all this research together and uses a world class weather forecasting system to generate accurate forecasts across Australia. This service provides useful and practical information to help feedlot operators manage heat stress in cattle through advanced warning of adverse conditions. Thus allowing operators time to undertake appropriate actions to mitigate the risk of heat stress when alerted. Executive Summary

Management of heat stress in feedlot cattle is required as part of the National Feedlot Accreditation Scheme (NFAS). The Cattle Heat Load Toolbox (CHLT) provides an early warning system for impending adverse weather conditions that could lead to excessive heat loads in feedlot cattle. The toolbox is web based and provides access to weather and heat load forecasts out to one week along with risk assessment programs. Katestone has been providing this service for over ten years and in that time we have extended the service from 16 to 267 forecast locations. The forecast for the service is generated using the Weather Research & Forecasting model. The system is monitored by Katestone scientists throughout the summer season and assessed for performance in predicting the location, magnitude and duration of heat load events. The system has proven to accurately predict these key features and alert the relevant operators of the impending situation. There are currently 244 subscribers and 176 feedlots registered to use the forecasting service, covering nearly a million head of feedlot cattle across Australia. This season saw 26 new feedlots register for the service. Feedlot Operators subscribe to the service free of charge and request a forecast for their feedlot. Subscribers also define risk alert levels suitable to their feedlot management and cattle type and condition through the Risk Assessment Program. Alerts are sent daily by email or SMS to designated recipients (e.g. site managers, veterinarians). The service provides early warning of potential major heat load events and rapid changes in the HLI through the automated alerts system and the written Heat Load Advisory service. Warnings are issued at least 3 to 4 days in advance. A total of 8 written warnings were issued for the period October 2014 to March 2015 covering all the major heat load events across Australia. Overall the system performed well in predicting the HLI on an hour by hour basis for the majority of the forecast period. Feedback from the end of season survey indicates that there is a high level of satisfaction with the forecast accuracy, from most respondents in the survey. Some specific locations have expressed concerns with the accuracy of the forecast for their site. These locations will be reviewed during the off season to improve the performance for these users.


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The majority of respondents said they would use a smart phone app if one were available. Overall the respondents were satisfied with the changes made to the service for the 2014-2015 season.


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1. Project objectives

The Cattle Heat Load Toolbox (see Figure 1) was developed to assist in warning feedlot operators of impending adverse weather conditions that could lead to excessive heat loads (and potential mortality) for feedlot cattle. The objectives of the project are to:

• Provide heat load forecasts for feedlot operators across Australia

• Deliver warnings of impending heat load events

• Provide access to forecasts and the Risk Analysis Program over the internet

• Provide guidance to feedlot operators to minimise losses due to heat load

Figure 1 Site Summary Page the Cattle Heat Load Toolbox

http://chlt.katestone.com.au/


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2. Service upgrades for 2014-2015 season

A total of 244 subscribers and 176 feedlots are currently registered for the CHLT (Figure 2). The CHLT service now provides heat load forecasts and tools that assist in the management of over 1,000,000 head of cattle across all states and territories in Australia.

Figure 2 Uptake of the CHLT service since its launch in 2010/11 The service is provided to any NFAS accredited feedlot and caters to large feedlots with greater than 10,000 head of cattle to smaller operations with less than 1,000 (see Figure 3). The 2014-2015 service saw some changes including:

• Introduction of the Heat Load Data Network, a repository for feedlots to upload their onsite weather stations to the CHLT service.

o Once the data is received it is formatted and injected into the forecast as the starting point for the AHLU calculation.

o A total of 50 feedlots expressed an interest in joining the network and to date 28 are currently uploading (Figure 4).

o Katestone is pleased to announce that the majority of feedlots with greater than 10,000 head of cattle are uploading their data to the network.

• Generation of a seven day forecast by Katestone's high resolution Numerical Weather Forecast (K-WRF). K-WRF previously only ran for 4 days with the final 3 days filled out by the global forecast.

• New site summary page to allow users to view an easy to read summary of the daily forecast (or observations, if uploaded)

• Updated help files


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• New "Tools" page with access to various checklists, the RAP, cattle observations chart and the Managing Summer Heat Workbook

• Modified alerts defining the type of event • Implementation of the forecast cycling method, where the land surface model

parameters from the previous forecast are re-initialised for the current forecast thereby maintaining continuity of the surface heat and moisture.

• Physical relocation of the forecasting, database and web server computers to a secure computing centre

Figure 3 Location of subscriber feedlots, colour coded by number of head of cattle


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Figure 4 Location of feedlots uploading to the HLDN, colour coded by head of cattle


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3. Methodology

3.1 Overview

There are three parts to a successful early warning system:

1. Accurate weather forecast

2. Appropriate triggers that are relevant to the local climate and represent conditions that are conducive to heat stress in lot fed cattle

3. Communication of the warnings via an appropriate media

The following schematic presents an overview of the CHLT system (Figure 5). The blue areas represent the global input from weather stations and models. These data are not gathered or generated directly by Katestone. The purple represents the local weather forecast, generated by Katestone every day. The red box indicates the areas of research that need to go into developing a robust system. The grey box represents the input from feedlot weather stations. And finally the delivery of the information is represented in green and shows the web site and alerts.

Figure 5 Schematic of the CHLT early warning system


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3.2 The weather model

The Weather Research and Forecasting – Advanced Research and Weather (WRF-ARW) model, or simply WRF is a numerical weather prediction (NWP) system used the world over by national weather service’s and militaries for mission critical applications. Katestone’s implementation of the WRF model (K-WRF) is initialised twice daily producing a 4 days at an hourly time step. The modelling domain extends from 105°E to 160°E and 8°S to 45°S encompassing a significant portion of the oceans to better resolve the generation of tropical weather systems (Figure 6). The resolution of the model is 12 km, meaning that data is generated at 12 km spacing over a 23,436,000 km2 area. K-WRF receives its initial conditions from the GFS model, which already contains data assimilated from the Australian WMO monitoring sites. The model also incorporates a detailed land surface model that accounts for soil type, moisture content, porosity and vegetation type and density (Figure 7).

Figure 6 K-WRF forecast domain


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Figure 7 Schematic of the land surface scheme used in the K-WRF system 3.3 Heat Load

There are a number of climatic conditions that may predispose feedlot cattle to high body heat loads, including:

• A recent rain event • A high ongoing minimum and maximum ambient temperature • A high ongoing relative humidity • An absence of cloud cover with a high solar radiation level • Minimal air movement over an extended period (4-5 days) • A sudden change to adverse climatic conditions

It is usually a combination of some or all of these conditions that leads to an excessive heat load event, which may result in cattle deaths if conditions persist for a number of days.

The calculation of HLI requires Relative Humidity (RH) expressed as a percentage, Wind Speed (WS) in m/s and Black Globe Temperature (BGT) in °C. HLI is calculated as a composite of HLILOW and HLIHIGH, with a weighting factor determined as a function of the difference in the calculated BGT and a threshold of 25 °C. A blending function was introduced as a result of an analysis of data over time, wherein it was evident that large jumps in HLI could occur under the some circumstances when the BGT passes through 25°C – for example from 24.9°C to 25.1°C.

In equation form, HLILOW and HLIHIGH are calculated as follows, noting that exp is the exponentiation function:

• HLILOW = 1.3 x BGT + 0.28 x RH - WS + 10.66 • HLIHIGH = 1.55 x BGT + 0.38 x RH - 0.5 x WS + exp (2.4 - WS) + 8.62

The weighting factor is calculated and used as:


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• FRACTIONHIGH = 1.0 / (1.0 + exp(-((BGT-25.0)/2.25))) • HLI = (FRACTIONHIGH * HLIHIGH) + ((1 – FRACTIONHIGH) * HLILOW)

It is also worth noting that if any calculation of HLI yields a value less than 50, this value should be set to 50, as the dissipation of heat does not increase below this point.

The use of BGT in calculating the HLI, rather than ambient temperature, takes into account radiation effects as well as air temperature. Although sensors for measuring BGT exist, these are not included as part of the standard weather station and must be ordered from a suitable supplier. In the absence of measured BGT, a quantified relationship between BGT, ambient temperature (T) and solar radiation (SR) can be used. Here solar radiation can either be a measured value or a calculated value.

BGT can be calculated from T and SR using the following equation (noting that log is the logarithm function using base-10):

• BGT = 1.33 x T - 2.65 x √T + 3.21 x log (SR + 1) + 3.5

Accumulated Heat Load Units (AHLU) has been developed to give some indication of the amount of heat that is accumulated by an animal when it is exposed to environmental conditions that are above its ability to maintain thermoneutral conditions.

For every hour that an animal is above its threshold HLI value, it will gain heat. This additional heat load accumulates over time and is reflected as an increase in body temperature. It is a normal physiological response for animals to gain heat during the day and dissipate this accumulated heat to the environment at night. If the animal cannot dissipate this accumulated heat overnight, the animal carries a heat load into the following day.

This makes the animal more susceptible to the effects of subsequent heat load. The three aspects that determine the potential for excessive heat load in feedlot cattle include time, intensity, and the opportunity to dissipate heat.

In order to calculate the AHLU, the HLI, upper (UL) and lower (LL) limit of the thermal neutral zones, and interval (in hours) between successive HLI estimates (Int) are required. LL is fixed at 77, while UL is a variable dependent on the HLI value at which stock begins to accumulate heat. This depends on the stock characteristics, location, and management practices including mitigation measures. The equation for calculating AHLU is as follows:

• AHLUCURRENT = AHLUPREVIOUS + BALANCE If the HLI is less than LL (77), then the heat is dissipated at half the rate of accumulation (the difference between HLI and LL). If the HLI falls between the LL and UL, then heat is neither dissipated nor accumulated. If the HLI is greater than UL, heat is accumulated. In equation form, the AHLUCURRENT is calculated as:

• HLI <= 77 AHLUCURRENT = AHLUPREVIOUS – Int * (77 - HLI) / 2 • 77 < HLI < UL AHLUCURRENT = AHLUPREVIOUS • HLI >= UL AHLUCURRENT = AHLUPREVIOUS + Int * (HLI – UL)

http://chlt.katestone.com.au/wp-admin/post.php?post=1164&action=edit#bgt-def


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AHLU values do not go below zero. If any calculation results in an AHLU value below zero, it is set to zero. Sites connected to the HLDN are initialised from AHLU calculated from data collected at local AWS, which theoretically would result in a more accurate AHLU forecast. The same holds true for BOM sites. Sites which do not have an integrated AWS are initialised from the previous day’s AHLU forecast. 3.4 Delivery

3.4.1 Forecast generation

The sequence of steps that must be completed for the forecast to be delivered (as outlined in Figure 8); from data retrieval and pre-processing to forecast computation and post processing is currently monitored by Katestone between the hours of 5 am and 9 pm, 7 days a week. In the event there is a system failure between these hours Katestone will work through the issue until it is resolved.

Figure 8 Overview of the current process to deliver a forecast to the CHLT

Once the forecast is generated a daily checklist is completed by Katestone’s meteorologist. These checks include but are not limited to:

1. K-WRF, GFS, ACCESS, ECMWF comparison

2. Weather systems analysis and comparison to satellite observations


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3. Hot spot identification and analysis

4. Performance analysis versus observations (magnitude and timing)

Under extreme situations that have the potential to adversely affect a significant number of locations a Heat Load Advisory will be issued by a Katestone meteorologist.

During these periods the lead meteorologist is on call to ensure the system is available and functioning. Catastrophic events such as complete severance of all telecommunications in and out of Brisbane are outside of our control.

3.4.2 Website and database administration

The CHLT system is administered and maintained by a Katestone system administrator. The system administrator maintains the integrity and security of the HPC facility within the data centre. There are three nodes within the HPC facility that require administration and maintenance:

1. Computational node - Core activities are data retrieval, pre-processing and forecast computation

2. Database node - Core activities are post processing, data storage and data availability to the web server

3. Web node - Core activities are website delivery, user information management, web security

The system administrator also maintains the CHLT website and includes:

• Registering new subscribers

o checks their coordinates are valid

o configures site specific forecast in the model

• Maintenance of the Cattle Heat Load Toolbox web site and associated databases

• E-mail and SMS alert functions

• Daily monitoring and maintenance of computer systems including weekends and holidays

• Online and phone support for registered users during regular office hours (9 am to 5 pm)

• Maintenance and update of the FAQ page


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3.4.3 Onsite AWS integration

The preceding season saw a total of 50 feedlots request to have their onsite AWS integrated into the CHLT service through the Heat Load Data Network (HLDN). To date 28 sites are operational, with a further 22 in varying stages of deployment and communication. The AWS integration requires continuous monitoring of data quality, as spurious data entering the system can adversely impact the prediction of risk and degrade confidence in the system.

Katestone provides an automated data quality check at the integration step that flags spurious data and issues an internal alert to manually quality assure the offending dataset. Our experience indicates that the spurious data is either due to damage to the sensor, i.e. lightning strike or changes to the data format following a system update by the AWS provider.

Katestone custom builds the data integration step for each sites specific AWS configuration, changes or updates to this configuration can impact the integration step and cause severe data quality issues further down the line. In the event spurious data is flagged Katestone will initiate the following process:

1. Lead meteorologist/Senior Data Analyst alerted

2. Data extracted for manually QA

3. AWS owner notified of issue and solution if known

4. If solution is unknown

5. AWS taken offline

6. AWS owner contacted and solution identified

7. If solution is still unknown

8. AWS supplier contacted and solution indentified

9. AWS reinstated to operations

3.4.4 Alerts

Katestone participated in a series of Heat Load workshops in the lead up to the 2014-15 forecast period. One of the major outcomes of the workshops was the standardisation of the alert thresholds used to warn lot feeders of impending adverse conditions. The consensus from the workshops was that the alert thresholds were to low and too many alerts were being issued when the cattle were not experiencing heat load. This led to alert fatigue, where lot feeders were not reacting to the alerts. The standardised alerts (for a user selected HLI Threshold value) now set in the system are:

• AHLU event today: AHLU >50 units for today • AHLU event tomorrow: AHLU >50 for tomorrow and AHLU = 0 for less than 6 hours • Extended AHLU event: AHLU >50 units for more than 3 consecutive days


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Another outcome of the workshops was to trial two new alert thresholds. These new thresholds are designed to capture events that normally would not have triggered an alert and to bring the alerts service more in-line with existing management practices. The new alerts are:

• Incomplete night time recovery: AHLU = 0 for less than 6 hours for more than 3 consecutive days in 7 day forecast period

• Rapid HLI change: change in HLI >40 units over 4 hours Feedback from survey regarding the new alerts is presented in Section 6.3. 3.4.5 Tools

Katestone added a “Tools” page to the CHLT website where previous reports, pre-summer checklists, the Managing Summer Heat Workbook and various other resources were made available to users. Katestone also added the “HLI Threshold Calculator” in the sidebar of all pages. The Calculator allows users to quickly calculate the HLI threshold for quick review of the correct forecast, based on the current status of their cattle and site. This is different from the RAP as it only takes into account the cattle and site details as these variables can change on a daily basis. For example as the cattle’s days on feed increases, their risk of experiencing heat load also increases and their alert threshold should therefore decrease overtime. The calculator allows the user to determine whether their current alert threshold is too high or too low for a given cattle population. To assist with this process the current alert level is displayed on the sidebar for each site (Figure 9).

Figure 9 Example HLI Threshold Calculator and Alert level


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4. Season overview

4.1 Climate

2014-15 was the fifth hottest summer on record for Australia, with both the national maximum and minimum temperatures being well above average. The regional maximum and minimum temperatures were also above average, except for regions in central Australia where the maximum temperature was below average. Spring 2014 (September to November) was the hottest on record, edging out the 2013 spring period by 0.1°C. Numerous highest on record maximum daily temperatures were recorded across QLD and NSW. The high temperatures were driven by a pocket of hot air that formed in Central Australia in October. This pocket moved east in association with a slow moving elongated trough that extended from Northern NSW to the Top End of the NT. These extreme temperatures persisted across NSW and QLD until the trough moved offshore near the end of November. Rainfall was 7% above average for Australia as a whole, this was skewed by the NT receiving 37% above average rainfall. WA and QLD both experienced below average rainfall at -8% and -9% respectively. 4.1.1 ENSO

The Southern Oscillation Index (SOI) remained neutral over the 2014/15 summer season (Table 1) although the BOM issued an El Niňo Watch due to warmer than average sea surface temperatures in the tropical Pacific Ocean. This summer was similar to the 2013/14 summer that was punctuated by swings from very hot and dry conditions to warm and wet, interspersed with Tropical Cyclones and localised flooding.

Table 1 Southern Oscillation Index 30 day rolling average

Date Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 SOI -8.0 -10.0 -5.5 -7.8 0.6 -11.2 Source: Bureau of Meteorology 4.1.2 Tropical Cyclones

Seven tropical cyclones (TC) were reported in the Australian region, two made landfall in Western Australia (WA), two made landfall in Queensland (QLD) and the Northern Territory (NT). TC Quang and TC Olwyn both crossed near Exmouth in WA, with Olwyn persisting as Low pressure system until Geraldton. TC Nathan persisted after making landfall on the QLD coast and strengthened in the Gulf of Carpentaria before making landfall again near Nhulunbuy in the NT. Nhulunguy was also near where TC Lam made land fall. Tropical Cyclone Marcia crossed the QLD coast near Yeppoon and tracked south as a low pressure system, emerging again along the coast north of Brisbane.


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4.2 Heat Load

On average the daily maximum HLI was higher in November and December and lower in February and March, compared to the same period last season across the 17 benchmark sites (Figure 10). Moree and Armidale saw a 5 HLI unit increase in the average daily maximum HLI during November 2014, while Clare and Albury saw a decrease in the average daily maximum HLI of 8 and 9 units during March (Table 2). Five sites (Moree, Tamworth, Armidale, Roma and Miles) experienced their highest average daily maximum during the month of December 2014 all other sites peaked in January or February (Table 3). Compared to last season (2013/14) these five sites all reached their highest average daily maximum HLI in February, except for Roma which peaked in January.

Figure 10 Difference in mean daily maximum HLI for 2013/14 and 2014/15 by month

aggregated for the 17 benchmark locations

Table 2 Difference in monthly average daily maximum HLI from 2014/15 and 2013/14

Benchmark Site Monthly Average Daily Maximum HLI (2014/15 – 2013/14) Oct Nov Dec Jan Feb Mar

Hay - 2 0 -3 0 -7 Moree - 5 2 0 -5 -1 Griffith - 3 0 -2 0 -6 Yanco - 3 0 -2 0 -7 Tamworth - 4 2 0 -5 -1 Cessnock - 4 4 4 0 -2 Armidale - 5 2 2 -4 -1


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Benchmark Site Monthly Average Daily Maximum HLI (2014/15 – 2013/14) Oct Nov Dec Jan Feb Mar

Albury - 4 0 -2 0 -8 Emerald - -1 3 3 -1 2 Roma - 3 3 1 0 0 Oakey - 3 3 3 -2 2 Warwick - 3 4 2 -3 4 Amberley - 1 2 4 -2 3 Miles - 1 3 2 -2 0 Clare - 3 -3 -3 -1 -9 Charlton - 2 1 -8 -1 -6 Katanning - -1 -4 -2 2 -1

Table 3 Monthly average daily maximum HLI from 2014/15

Benchmark Site Monthly Average Maximum HLI Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15

Hay 68 74 77 80 82 72 Moree 76 84 85 84 84 82 Griffith 69 74 77 81 82 72 Yanco 70 75 78 82 84 74 Tamworth 71 80 84 83 79 80 Cessnock 72 80 84 87 85 82 Armidale 63 73 76 76 74 73 Albury 69 75 78 81 83 72 Emerald 79 86 89 91 86 88 Roma 76 83 86 86 84 84 Oakey 74 82 84 85 80 82 Warwick 74 83 86 87 82 87 Amberley 77 85 89 93 87 91 Miles 77 84 88 88 84 85 Clare 67 70 72 76 78 69 Charlton 67 71 77 73 79 70 Katanning 67 69 70 75 77 72 4.2.1 Heat Advisories

Katestone issued Heat Load Advisories when heat load events were likely to impact a region and persist for several days. Eight advisories were issued between October 2014 and March 2015. These are summarised in Table 4 detailing the issue date, projected date of peak heat load and regions affected.


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Table 4 Heat Load Advisories issued between October 2013 and March 2014 Date

Issued Peak heat

event Regions Type 20/11/2014 22/11/2014 NSW, Southern QLD, VIC, SA Heat Load 10/01/2015 14/01/2015 Central and Northern NSW, Central Coast

QLD Rapid change in HLI

10/01/2015 15/01/2015 Southwest and Central QLD Rapid change in HLI

16/01/2015 17/01/2015 WA, Central and Northern NSW, Central QLD Heat Load 16/01/2015 17/01/2015 Central QLD Rapid change in

HLI 19/01/2015 22/01/2015 WA Heat Load 23/01/2015 24/01/2015 Northern NSW and QLD Rapid change in

HLI 18/02/2015 19/02/2015 NSW and QLD Rapid change in

HLI 4.2.2 Automated alerts

A total of 1,808 emails and 984 SMS alerts were issue during the 2014/15 summer forecast period, with a peak number of email and sms alerts sent in January (Figure 11). The breakdown alerts by type in Figure 12 shows that; the rapid change in HLI alert and the incomplete overnight recovery alert where the major contributor to the total number of alerts issued in November. It also shows that the November was the only time the incomplete recovery alert were issued.

Figure 11 Breakdown of alerts sent by month during forecast period


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Figure 12 Total number of alerts issued by type and month 4.3 Web site statistics

The web site was well visited with 11,100 sessions from over 2,300 users over the six month period October to March. The traffic has increased by almost 30% from the same period last year, with 30% more users. Most notably, the number of users accessing the website from a mobile device has increased by 350% (Table 5).

Table 5 Web site traffic for 2012-13 to 2014-15 season Season Page

Views Site

Users Sessions Mobiles

Sessions Average time on site

(mins) 2012-13 21,784 1,296 7,143 161 2.38 2013-14 28,350 1,683 8,515 524 2.57 2014-15 42,019 2,362 11,134 2,368 2.54 The following figures present the breakdown of the web site visits as a function of time of day (Figure 13) and day of the week (Figure 14) and State (Figure 15). The time of day and day of week breakdowns indicate that the site is most frequently used in the morning 6-11 am, 7 days a week. There is a slight drop off of page visits on the weekends, but this is probably due to the reduced number of staff onsite. Queensland is by far the most active state with 63% of visits followed by NSW (16%) and Victoria (5%), WA (3%), SA (2%) and the NT (1%). The remaining 10% of page visits are form overseas.


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Figure 13 Web site visits as a function of time of the day

Figure 14 Web site visits as a function of day of the week


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Figure 15 Web site visits as a function of State

0 1000 2000 3000 4000 5000 6000 7000 8000

ACT

NT

SA

WA

VIC

NSW

QLD

Visitors

Sta

te/T

errit

ory

Visits by state


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5. Service performance

5.1 Benchmark locations

The performance of the forecasting service has been assessed against 17 benchmark locations. Most of these sites have been included in the forecast service since its inception and provide a good measure of the forecasts performance over the years (Figure 16). Table 6 describes the benchmark locations.

Table 6 Forecast benchmark locations

Site Name Lat Lon WMO code State Hay -34.54 144.83 94702 NSW Moree -29.48 149.84 95527 NSW Griffith -34.24 146.06 95704 NSW Yanco -34.62 146.43 95705 NSW Tamworth -31.07 150.83 95762 NSW Cessnock -32.78 151.33 95771 NSW Armidale -30.52 151.61 95773 NSW Albury Airport -36.07 146.95 95896 NSW Emerald -23.56 148.17 94363 QLD Roma -26.54 148.77 94515 QLD Oakey -27.4 151.74 94552 QLD Warwick -28.2 152.1 94555 QLD RAAF Amberley -27.62 152.71 94568 QLD Miles -26.65 150.18 95529 QLD Clare High School -33.82 138.59 95667 SA Charlton -36.28 143.33 94839 VIC Katanning -33.68 117.6 94641 WA


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Figure 16 Benchmark locations 5.2 Forecast performance evaluation

The performance of the forecast is reviewed at the AWS sites identified above. These locations are Bureau of Meteorology sites and as such do not have Black Globe Temperature (BGT) measurements. Temperature, humidity and wind speed are reviewed for forecasting ability as is the HLI (and AHLUs) calculated assuming the clear skies assumption for solar radiation, as no measurements are available. The HLI and AHLU performance analysis is presented here. The performance analysis of the meteorological variables that are used to derive the HLI and AHLU values are presented in Appendix A. We have received some feedback from sites this summer indicating that BGT observations at site can be significantly different to those calculated using solar radiation assumptions. These issues have been taken into consideration in the future development path of the service, but unfortunately, cannot be addressed here. Katestone reviews the models performance in real time and noticed that as the monsoon was initiated some spurious meteorological events where being generated in the forecast. It was discovered that the assimilation of observed sea surface temperatures (SST) was causing spurious development of low pressure systems in the later stages of the forecast. The SST assimilation step was removed in early January and the system was monitored for improvement. Initial results were promising however further spurious events over the land surface were observed. By early February it was apparent that the cycling method (newly implemented at the commencement of the season) was introducing small variations in the seven day forecast that aggregated over time. These behaviours were not observed in the previous season of SST assimilation or in the pre-season testing of the new cycling system. On removal of the SST assimilation and the cycling this issue was resolved.


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5.3 Performance of 2014/15 season

5.3.1 Heat Load Index

Figure 17 shows the progression of the forecasts performance since 2006 for the 17 benchmark locations. The parameters that are used to assess the performance of the system are in Appendix A. The root mean square error (RMSE) for the one day out forecast of HLI is 4.7, the RMSE for the three day out forecast of HLI is 4.9, which is comparable to previous years and still below the benchmark RMSE of 5. While the system did not perform as well as last season it still outperformed all other forecast periods.

Figure 17 Mean HLI RMSE for the 17 benchmark sites since 2006 The RMSE is a good benchmark for assessing the performance of the forecast as it places a higher weighting on large errors. The result is a statistic that defines the largest error that can be expected on average. Figure 18 shows the aggregated RMSE for the 17 benchmark locations for the forecast period October 1 204 to April 1 2015 for a 7 day forecast horizon. The average RMSE remains below 5 until the 5th day of the forecast and only just exceeds 6 by the 7th day. The minimum and maximum values are the sites that had the lowest and highest RMSE for that forecast day. The distance between the crossbars in Figure 18 remain relatively the same throughout each day of the forecast, this indicates that the errors in the forecast are internally consistent and most likely the result of the sensitivity of the equations to small variations in the input parameters rather than the omission or misrepresentation of the environmental conditions.


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Figure 18 Minimum, maximum and mean root mean square error aggregated for the

17 benchmark locations The Mean Absolute Error (MAE) and Bias are also useful statistics when used in conjunction with the RMSE. Where the RMSE emphasises the few large errors in a dataset the MEA defines magnitude of the average deviation of the prediction from the observed. This is shown in Figure 19 where the average error in the HLI is about 3 units for the first 4 days of the forecast, only just reaching above 4 by the 7th day. The closer the MAE and the RMSE are to each other indicates a similar magnitude error in the forecast is of at all times. In this case the RMSE is higher than the MAE by about 2 HLI units. This indicates there are a small proportion of large errors in the data.

Figure 19 Minimum, maximum and mean MAE aggregated for the 17 benchmark

locations The Bias can now be used to define whether average errors in the forecast are positive (over predictions) or negative (under predictions). This is shown in Figure 20, where it is evident


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that on average the forecast has positive bias and tends to over predict the HLI at the 17 benchmark locations.

Figure 20 Minimum, maximum and mean bias aggregated for the 17 benchmark

locations The IOA is a good measure of model performance in that it compares each individual hour against the mean of the observations, such that if the predicted and observed values vary at different scales or rates a poor agreement will result (< 0.6), where an IOA of 1 indicates perfect agreement. Figure 21 shows the IOA aggregated for the 17 benchmark locations. The IOA shows good agreement on average across all 7 forecast days; with the minimum IOA only dropping below 0.90 on the 5th day of the forecast.

Figure 21 Minimum, maximum and mean IOA aggregated for the 17 benchmark

locations


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Overall the system performed well in predicting the HLI on an hour by hour basis for the majority of the forecast period. Feedback from the end of season survey (Section 6) indicates that there is a high level of satisfaction with the forecast accuracy, from most respondents in the survey. Some respondents indicated that the forecast was not accurate at all for their location. Katestone has flagged these sites for further analysis in the offseason and will make contact with the sites to during this process. It is also worth noting that as the data is paired in time the forecast can be an hour or two behind or ahead of the environment, causing a disparity in the dataset where the observed HLI is higher than predicted at any given hour. This can be caused by the movement of weather features, such as a trough, across the monitoring point. Where the model may move the trough through a region at 7 AM, where as in reality the trough crossed that point at 9 AM. These small variations at the hourly scale can cause large variations in the HLI and this is then displayed in the statistics on reanalysis. A review of daily AHLU via the contingency tables (as presented in the following section) overcomes some of the minor discrepancies by interpreting hourly data. 5.3.2 Accumulated Heat Load Units

Table 7, Table 8, Table 9 and Table 10 show the 1 through to 7 day forecast AHLU contingency tables for all locations combined for the forecast season. Appendix B contains AHLU contingency tables for the benchmark forecast locations. In the contingency tables, the Vertical represents the forecast and the horizontal represents the observed AHLU category. The tables summarise the accuracy of the forecast in predicting the correct AHLU risk level as well as the frequency each risk level was predicted. For example in Table 7 for day 1 of the forecast the Extreme AHLU 80 level was observed on average 5.6% of the time and was correctly predicted on average 4.1% of the time. The forecast period covers 185 data points, so a frequency of 5.6% translates to roughly 10 days and 4.1% to 7.5 days. The remaining 1.5% or 2.5 days is spread over the predictions for High, Medium and Low AHLU 80 risk levels. As expected the error in the forecast gradually increases the further out the forecast goes. Such that by day 7 the frequency of predicting a Low AHLU 80 risk level when an Extreme AHLU 80 risk level was observed has doubled from less than 1% to 2.3%. Overall the service correctly predicted the AHLU risk level the majority of the time.


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Table 7 1 and 2 day forecast AHLU contingency tables for all locations for the forecast season (forecast is on the horizontal and observed on the vertical)

1 Day

2 Day

AHLU 80

AHLU 80

Extreme 0.0 0.1 0.2 4.1

Extreme 0 0 0.4 3.3

High 0.2 0.6 1.4 0.9

High 0.3 0.8 1.2 0.8

Medium 0.7 2.5 2.2 0.5

Medium 0.7 2.5 2.1 0.6

Low 72.7 10.6 3.2 0.1

Low 72.7 10.3 3.3 0.9

Low Medium High Extreme


AHLU 83

AHLU 83

Extreme 0.0 0.0 0.2 1.7

Extreme 0 0 0.2 1.2

High 0.3 0.6 0.8 0.3

High 0.4 0.5 0.7 0.5

Medium 1.9 1.9 0.7 0.1

Medium 2 2 0.5 0.1

Low 85.7 5.1 0.8 0.0

Low 85.5 5.1 1 0.4



AHLU 86

AHLU 86

Extreme 0.0 0.0 0.1 0.2

Extreme 0 0 0.1 0.2

High 0.1 0.3 0.2 0.1

High 0.1 0.3 0.2 0

Medium 1.2 0.7 0.5 0.0

Medium 1.4 0.6 0.3 0.1

Low 94.3 1.9 0.4 0.0

Low 94.1 1.9 0.6 0.1



AHLU 89

AHLU 89

Extreme 0.0 0.0 0.0 0.0

Extreme 0 0 0 0

High 0.0 0.1 0.0 0.0

High 0.1 0.1 0 0

Medium 0.5 0.2 0.0 0.0

Medium 0.6 0.1 0 0

Low 98.1 1.0 0.1 0.0

Low 98 0.9 0.1 0



AHLU 92

AHLU 92

Extreme 0.0 0.0 0.0 0.0

Extreme 0 0 0 0

High 0.0 0.0 0.0 0.0

High 0 0 0 0

Medium 0.1 0.1 0.0 0.0

Medium 0.3 0 0 0

Low 99.6 0.3 0.0 0.0

Low 99.5 0.2 0 0



AHLU 95

AHLU 95

Extreme 0.0 0.0 0.0 0.0

Extreme 0 0 0 0

High 0.0 0.0 0.0 0.0

High 0 0 0 0

Medium 0.0 0.0 0.0 0.0

Medium 0 0 0 0

Low 100.0 0.0 0.0 0.0

Low 100 0 0 0




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Table 8 3 and 4 day forecast AHLU contingency tables for all locations for the forecast season

3 Day

4 Day

AHLU 80

AHLU_80

Extreme 0 0.2 0.5 2.9

Extreme 0.1 0.2 0.6 2.4

High 0.2 1 1.3 0.7

High 0.4 1 0.9 0.9

Medium 1 2.1 1.4 0.8

Medium 0.9 2.2 2.1 0.7

Low 72.1 10.5 4 1.2

Low 72 10.3 3.7 1.4



AHLU 83

AHLU_83

Extreme 0 0.1 0.3 0.8

Extreme 0.1 0.1 0.3 0.7

High 0.5 0.6 0.5 0.3

High 0.6 0.7 0.5 0.4

Medium 2.3 1.8 0.6 0.3

Medium 2.5 1.8 0.7 0.3

Low 84.9 5.2 1.1 0.7

Low 84.6 5.1 1 0.7



AHLU 86

AHLU_86

Extreme 0 0 0.1 0.2

Extreme 0.1 0 0.2 0.2

High 0.2 0.4 0.2 0

High 0.3 0.3 0.1 0

Medium 1.8 0.5 0.2 0

Medium 1.8 0.5 0.3 0

Low 93.6 2.1 0.6 0.2

Low 93.5 2.1 0.6 0.1



AHLU 89

AHLU_89

Extreme 0 0.1 0 0

Extreme 0 0.1 0 0

High 0.2 0.1 0 0

High 0.1 0.1 0 0

Medium 0.7 0.1 0 0

Medium 0.7 0.1 0 0

Low 97.8 0.9 0.1 0

Low 97.8 1.1 0.1 0



AHLU 92

AHLU_92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0.1 0 0 0

Medium 0.1 0 0 0

Medium 0 0.1 0 0

Low 99.5 0.3 0 0

Low 99.6 0.2 0 0



AHLU 95

AHLU_95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




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Table 9 5 and 6 day forecast AHLU contingency tables for all locations for the forecast season

5 Day

6 Day

AHLU 80

AHLU_80

Extreme 0.2 0.2 0.5 2.4

Extreme 0.1 0.2 0.3 2.1

High 0.4 1 1.3 0.6

High 0.5 0.8 1.3 1

Medium 1.1 2.5 1.4 0.8

Medium 1.2 1.7 1 0.5

Low 71.7 10.1 3.9 1.8

Low 71.6 11.2 4.4 2



AHLU 83

AHLU_83

Extreme 0.1 0.1 0.2 0.7

Extreme 0.1 0.1 0.2 0.6

High 0.6 0.6 0.5 0.3

High 0.5 0.6 0.4 0.4

Medium 3 1.7 0.6 0.3

Medium 2.2 1.4 0.6 0.3

Low 84 5.4 1.1 0.8

Low 85.1 5.5 1.1 0.9



AHLU 86

AHLU_86

Extreme 0.1 0 0.1 0.2

Extreme 0.1 0 0.1 0.2

High 0.3 0.3 0.2 0

High 0.2 0.2 0.1 0

Medium 1.7 0.6 0.3 0

Medium 1.6 0.7 0.4 0

Low 93.5 2 0.7 0.1

Low 93.7 2 0.7 0.2



AHLU 89

AHLU_89

Extreme 0 0.1 0 0

Extreme 0 0.1 0 0

High 0.1 0.1 0 0

High 0.1 0.1 0 0

Medium 0.7 0.1 0.1 0

Medium 0.5 0 0 0

Low 97.8 1.1 0 0

Low 98 1 0.1 0



AHLU 92

AHLU_92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.1 0.1 0 0

Medium 0.1 0 0 0

Low 99.6 0.2 0 0

Low 99.5 0.3 0 0



AHLU 95

AHLU_95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.1 0 0 0

Medium 0 0 0 0

Low 99.9 0 0 0

Low 100 0 0 0




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Table 10 7 day forecast AHLU contingency tables for all locations for the forecast season

7 Day

AHLU 80

Extreme 0.1 0.1 0.4 1.9

High 0.3 0.9 0.9 0.7

Medium 1.4 1.6 1.5 0.6

Low 71.7 11 4.5 2.3


AHLU 83

Extreme 0 0.1 0.1 0.6

High 0.4 0.5 0.4 0.2

Medium 2.1 1.1 0.6 0.3

Low 85.3 5.9 1.3 1


AHLU 86

Extreme 0.1 0 0.1 0.1

High 0.2 0.1 0.1 0.1

Medium 1.1 0.7 0.2 0

Low 94.4 1.9 0.8 0.1


AHLU 89

Extreme 0 0 0 0

High 0.1 0.1 0 0

Medium 0.5 0.2 0 0

Low 98 0.9 0.1 0


AHLU 92

Extreme 0 0 0 0

High 0 0 0 0

Medium 0.1 0.1 0 0

Low 99.6 0.3 0 0


AHLU 95

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0



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6. Subscriber survey

At the end of this season, a survey was sent out to all CHLT subscribers, asking them to rate the service and provide comment on where the service could be improved. The survey was also made available on the website, for visitors to the CHLT website service who are not registered. Detailed results are shown in Appendix C. 6.1 Users

The survey received a total of 56 responses, 29 from QLD, 19 from NSW, 5 from WA, 2 from SA and 1 from Victoria. Most of the respondents are from feedlots with over 10,000 heads of cattle (SCU), a breakdown on the respondents by size and state and shown in (Table 11)

Table 11 State and feedlot size (#SCU) distribution of respondents

State No cattle data

<1000 1,000-5,000

5,000-10,000

>10,000 Total

QLD 4 6 7 2 10 29 NSW 2 4 3 10 19 WA 2 2 1 5 SA 2 2 VIC 1 1

Total 4 10 16 6 20 56 6.2 Communication

While the respondents had a variety of preference for receiving communication, majority of subscribers believe that current communications are easy to understand. 84% percent of respondents also stated that they would use a CHLT smart phone APP if one were available.

Approximately half of respondents believe that the CHLT web pages load in adequate time, while 35% believe that they don’t. The remaining respondents have stated that while the web pages are slow to load, they are used to it. Most respondents find the new Site Summary page useful. There are 32 subscribers who prefer more information displayed for a shorter period (1 day) compared to 21 subscribers who prefer less information displayed for 7 days.

A large number of respondents view the major town forecasts, for a number of reasons, such as transport of cattle, as reference, and for access to other employees and producers who are not set up with as CHLT subscribers. Most of the respondents would like to be able to access the major town forecasts, but would not object to this being removed from the publicly-accessible section of the CHLT website.

Most of the respondents heard about CHLT from Austmeat Auditor and ALFA comms. Some heard about CHLT from their nutritionists, word of mouth, MLA comms, or a combination of any of these.

6.3 Alerts

Most respondents (46 of 52) believe that the changes in alerts were an improvement compared to previous years. Three of those who did not agree were new users to the site. One respondent stated a preference for less detail. One respondent believes that the alerts are useless, while another states that the site crashed prior to a predicted event. Most respondents believe that the alerts received were sufficient, while others state that it’s good


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to know the forecast, the accuracy influenced their perception of the alerts. One user stated that they received irrelevant alerts. Most of the respondents (33 out of 53) believe that the alerts received were just right. Six respondents believed that the alerts were not received often, while 2 believe that they were received too often. One respondent finds that the alerts were too confusing to understand. An analysis of the breakdown by state is shown in Figure 22.

Figure 22 Perception of alerts by state


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Figure 23 shows that the most of the sizable feedlots were satisfied with the alerts. Approximately a third of the feedlots ranging from 1000-5000 SCU were satisfied. However, approximately a third believes that the alerts received were not enough.

Figure 23 Perception of alerts by feedlot size

Thirty-seven users believe the AHLU level of 50 was appropriate for their site. Eleven respondents believe that this is too low, while 4 respondents believe that this is too high. 6.4 Performance

Most respondents (83%) believe that the site forecast ranges from accurate most of the time to very accurate most of the time. There are 9 respondents who believe that the site forecast is inaccurate some or most of the time. Two users believe that the limitations of the forecast are due to difficulty in predicting extreme heat load events for their area. However, there are a number of users who believe that the temperature, HLI and/or AHLU forecasted is significantly different from what’s observed.

Most users base their assessment of accuracy by comparing the forecast to weather conditions on the day, BoM forecast for the closest town, on-site weather station, the behaviour of cattle, or a combination of all methods.

One user states that the forecast is useless for their site.

6.5 HLDN

Twenty-seven out of 55 respondents have weather stations, with approximately half (14) sending data to Katestone for inclusion in the forecast. Another five respondents are in the process of connecting their weather station. The reasons for not uploading data from weather stations seem to be mostly due to the difficulty in setting this up. Other reasons for not uploading include not realising that it could be connected, and problems with connection.


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When the weather station indicates a heat load event, most of the respondents believe that the cattle show signs of heat stress, thought sometimes tends the overestimate the heat load.

Most respondents have a daily heat load monitoring plan, used either everyday when it is hot or during the summer. A small number of respondents do not have a daily heat load monitoring plan, while a small number of those who have it do not use it.

Most of the respondents seem to take proactive action to address issues related to heat stress when issued with an early warning. A small number wait until cattle shows sign of heat stress before acting.

Three of the respondents also stated that the forecast does not affect activities. A number of respondents take into account other factors such as duration, extent, or assessment of whether the forecast could be influenced by meteorological events that are more difficult to predict; or other factors that are not inherent in the forecast. One respondent believes that the forecast is not applicable to his/her local conditions.

6.6 Overall response

Most respondents seem satisfied about the CHLT forecasting service; suggestions for improving the CHLT include improved accuracy, reliability, and accessibility. Other suggestions include making the forecast available earlier, simplifying the forecast, and making it easier to get onsite weather stations online. However, one respondent does not agree and has stated that they would prefer not to receive the forecast for their site.

Fifty-one out of 52 respondents stated that they plan to continue using CHLT in the future. One respondent does not plan on continuing to use the CHLT service due to concerns about accuracy.

Respondents believe that the uptake of the CHLT service among lot feeders can be improved through improvements in communications and promotions and improving accuracy of the CHLT service, including increasing the number of weather stations connected. In addition, one respondent suggested facilitating coupling the CHLT service with processor facilities.

Respondents also believe that increase in communication (through workshops or visiting processing plants), as well as marketing to processing facilities will increase the uptake of the CHLT service among processors.


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7. Discussion

The CHLT service has become an integral part of heat load management at Australian feedlots. The number of subscribers and feedlots that are registering for the service continues to grow every year. Overall the user base is satisfied with the delivery and performance of the service with some notable areas for improvement; in particular service availability and accuracy at a few select locations. Katestone has already taken steps to ensure maximum uptime of the service, by relocating the HPC facility to a secure off-site data centre. It is important to note that during the peak season the service is monitored between 5AM and 10PM every day; weekend and public holidays alike. While the system is automated, there are still components of the service that can fail and are completely outside of our control. Options for providing a complete backup system have been investigated and provided in the quotation for next year’s service. Forecast performance is generally considered to be accurate most of the time. There are some locations that forecast does not perform well. These locations are being investigated during the offseason to ascertain how the forecast can be improved. While statistics help to determine the direction of the forecast performance, i.e. improving or degrading, the more important test is a subjective one of:

“Is the forecast telling me what I need to know?” Thus question has been answered by those who completed the end of season survey. Generally, most users are happy with the service. There are a couple of very small feedlots in South Australia and Western Australia who are not happy with the service. It is not clear exactly what aspects of the service they don't like, but there appears to be some question as to the appropriate threshold to set the alerts. The project underway for next season to collect information on a daily basis on management practices, status of cattle and cattle behaviours will go a long way to helping answer this question. 7.1 Future improvements

The end of season survey indicates that 35% of respondents experienced slow loading times of the website. This is primarily due to the size of the images displayed on the site. Katestone can optimise the load times for low connectivity regions by decreasing the amount and size of images displayed on each page. This will significantly improve the load times of the web site. The site summary display was also well received with 70% approval amongst the respondents. 25% found the page useful sometimes while 5% did not find it useful. Of the negative responses we found that 61% of respondents would like to see more information for 1 day while 39% would like to see less information but for the full 7 day forecast period. Katestone recommends that the Site Summary page is updated to accommodate this by providing a toggle button to select a 1 day detailed forecasts or a 7 day outlook. The user can then set their preference while maintaining the ability to view the data in either format. The new alert thresholds were generally well received by all respondents. Some sites expressed concerns that the alerts were received too often or not often enough and that the alert thresholds were too low or too high. Further investigations into the how these sites interpret the alert thresholds for their cattle are required to determine if the alert or it application to a cattle population is appropriate.


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8. Conclusion

A heat load forecast service was provided for the 2014/15 summer period to over 176 feedlots and 91 publicly accessible sites. The service delivered 2,792 automated alerts via sms and e-mail and eight written advisories for major heat load events. The numerical weather prediction system, K-WRF, performed well in simulating the meteorological conditions that drive heat load events and in predicting the HLI and AHLU risk levels. The HLI and AHLU thresholds need to be investigated to determine regionally appropriate thresholds. Feedback from the survey indicated that sites in SA, VIC and WA felt they did not receive sufficient alerts. The majority of the users of the CHLT are very happy with the service and will be using it again in the future. The survey feedback also highlights areas for improvement. These have either been addressed in the service scope provided for next season, or are currently the focus of a research project.


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9. Bibliography

http://www.metoffice.gov.uk/research/modelling-systems/unified-model/weather-forecasting

http://www.bom.gov.au/australia/charts/about/about_access.shtml

http://www.ecmwf.int/products/forecasts/guide/The_ECMWF_global_atmospheric_model.html

http://www.emc.ncep.noaa.gov/index.php?branch=GFS

EA Systems (2004), “FLOT. 327 – Development of a Heat Load Risk Assessment Process for the Australian feedlot industry”. Gaughan J, Goopy J and Spark J (2002), “Excessive Heat Load Index for Feedlot Cattle”, University of Queensland. Liu, Y. and co-authors, 2008: The operational mesogammascale analysis and forecast system of the U.S. Army Test and Evaluation Command. Part I: Overview of the modeling system, the forecast products, and how the products are used. J. of Appl. Meteor. and Clim., 47, 1077-1092. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M.G. Duda, X-Y. Huang, W. Wang and J. G. Powers, 2005: A description of the Advanced Research WRF Version 3. NCAR Technical Note, NCAR/TN-475+(STR), National Center for Atmospheric Research, Boulder, CO, 125 pp.

http://www.metoffice.gov.uk/research/modelling-systems/unified-model/weather-forecasting

http://www.bom.gov.au/australia/charts/about/about_access.shtml



http://www.emc.ncep.noaa.gov/index.php?branch=GFS


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Appendix A - Evaluation parameters

The measures used to evaluate performance are the Index of Agreement (IOA), the Mean Absolute Error (MAE), Bias and the Root Mean Square Error (RMSE). For example:

• an IOA of 1 implies a perfect forecast with no difference between the observed and the predicted at all points in time

• an MAE of 2 for temperature signifies an average error of 2°C over the entire period of the analysis

• a Bias of -1 for temperature indicates that on average the model under predicts the temperature over the entire period of the analysis

• a RMSE of 2 for temperature signifies an average squared error of 2°C, the statistic exposes large errors and should be considered in relation to the MAE

• the closer the RMSE is the to the MAE indicates that most of the errors of the same magnitude and that they are consistent, this is preferable.

The IOA is the ratio of the total RMSE to the sum of two differences: the difference between each prediction and the observed mean and the difference between each observation and observed mean. The IOA is a good measure of model performance in that it compares each individual hour against the mean of the observations, such that if the predicted and observed values vary at different scales or rates a poor agreement will result (< 0.6). Where an IOA of 1 indicates perfect agreement or both the predicted and observed vary about the mean at the same scale and rate. The equation for the IOA is:

2

1

2

1

|)||(|

)(1IOA

meani

N

imeani

N

iii

OOOP

OP

−+−

−−=

∑

∑

=

=

The root mean squared error (RMSE) can be described as the standard deviation of the difference for hourly prediction and observation pairings at a specific point which measures the average magnitude of the error. The difference between forecast and corresponding observed values are each squared and then averaged over the sample. Finally, the square root of the average is taken. Since the errors are squared before they are averaged, the RMSE gives a relatively high weight to large errors. This means the RMSE is most useful when large errors are particularly undesirable. Overall, the RSME is a good overall measure of model performance. The equation for the RMSE is:

2

1)(1RMSE ∑

=

−=N

iii OP

N

The mean absolute error (MAE) measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables. The equation is given in the library references. Expressed in words, the MAE is the average over the verification sample of the absolute values of the differences between forecast and the corresponding observation. The MAE is a linear score which means that all the individual differences are weighted equally in the average. The equation for the MAE is:

)(MAE1

OP i

N

iiabs −=∑

−


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The MAE and the RMSE can be used together to diagnose the variation in the errors in a set of forecasts. The RMSE will always be larger or equal to the MAE; the greater difference between them, the greater the variance in the individual errors in the sample. If the RMSE=MAE, then all the errors are of the same magnitude. The Bias is simply the mean difference between the observed and the predicted. It provides a measure of the average sign and magnitude of the error; such that a negative value is an under prediction and a positive value an over prediction. The equation for Bias is:

)(BIAS1

OP i

N

ii −=∑

−

There are no defined standards for NWP model performance; statistical scores simply provide a means to quantify the magnitude of the difference between predictions and observations. Table A1 is a useful guide to performance benchmarks of what should be expected from a model. These values are guidelines and not absolute values of pass or fail. They are based on hind cast model evaluation techniques; where a hind cast is initialised by re-analysis data and corrected observations are assimilated (Emery et al 2001).

Table A1 Performance benchmark guidelines

Measure HLI T RH WS IOA >0.9 >0.7 >0.7 >0.6 MAE <6 <2 <10 <2 BIAS ±2 ±1 ±5 ±1 RMSE <6 <2 <15 <2


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Table A2 Performance benchmarks by site 1 day ahead HLI Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.96 0.95 0.95 0.97 0.97 0.96 0.97 0.95 0.96 0.97 0.97 0.97 0.97 0.97 0.96 0.97 0.91 MAE 1.48 2.02 2.99 1.06 1.57 2.56 1.46 0.79 1.08 0.02 0.55 0.48 -0.55 0.95 0.79 -0.35 1.36 BIAS 3.75 3.83 4.10 3.02 3.26 3.68 2.73 2.40 3.50 2.53 2.49 3.16 2.86 2.47 2.23 2.07 3.63 RMSE 5.35 5.72 5.66 4.35 4.68 5.21 4.31 4.40 5.02 4.09 4.20 4.77 4.26 4.01 3.99 3.43 6.38

BGT Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.97 0.97 0.96 0.97 0.97 0.97 0.97 0.97 0.96 0.97 0.97 0.96 0.96 0.96 0.97 0.98 0.88 MAE 1.43 1.28 1.78 1.66 1.05 1.39 0.83 1.43 2.04 1.47 1.43 1.61 2.22 1.49 0.68 0.96 2.05 BIAS 2.57 2.38 2.89 2.57 2.74 2.48 2.36 2.48 2.91 2.82 2.70 2.73 3.07 2.83 2.47 2.12 4.20 RMSE 3.28 3.09 3.56 3.25 3.48 3.16 3.15 3.15 3.65 3.69 3.59 3.53 3.81 3.75 3.39 2.90 6.78

T Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton

IOA 0.92 0.94 0.92 0.93 0.93 0.93 0.96 0.93 0.92 0.94 0.95 0.93 0.92 0.94 0.96 0.97 0.86 MAE 0.98 0.91 1.36 1.24 0.71 0.98 0.52 1.09 1.53 1.20 1.12 1.28 1.88 1.22 0.31 0.57 1.82 BIAS 2.26 2.01 2.52 2.23 2.40 2.18 2.05 2.27 2.51 2.53 2.31 2.41 2.78 2.52 2.11 1.73 3.18 RMSE 2.86 2.60 3.11 2.79 3.04 2.77 2.67 2.88 3.11 3.26 3.01 3.09 3.41 3.31 2.86 2.31 4.95

RH Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.89 0.88 0.85 0.89 0.81 0.89 0.86 0.88 0.84 0.81 0.85 0.84 0.81 0.81 0.91 0.92 0.77 MAE -3.16 1.82 -2.24 -3.99 2.04 -1.52 2.64 -1.82 -5.71 -4.52 -5.13 -4.43 -10.97 -3.22 -1.44 -8.23 -6.81 BIAS 10.99 10.74 13.82 11.75 13.85 11.08 10.81 11.97 13.78 13.27 11.45 12.47 15.21 12.79 11.33 11.78 14.86 RMSE 14.36 15.29 18.44 15.35 19.22 14.95 14.67 16.78 17.70 17.67 15.67 17.01 18.68 17.40 15.35 14.43 21.51

WS Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.67 0.62 0.54 0.67 0.61 0.56 0.75 0.73 0.63 0.70 0.74 0.65 0.66 0.75 0.56 0.81 0.72 MAE 0.10 -0.01 -1.71 -0.25 -0.20 -1.42 -0.07 0.51 0.11 0.53 0.30 0.75 0.61 -0.33 -1.92 -0.33 -0.90 BIAS 1.75 1.66 2.13 1.95 1.81 2.29 1.36 1.54 1.39 1.64 1.66 1.84 1.66 1.42 2.29 1.25 1.69 RMSE 2.30 2.16 2.64 2.50 2.33 2.86 1.74 1.96 1.83 2.13 2.22 2.39 2.17 1.90 2.86 1.65 2.13


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Table A3 Performance benchmarks by site 2 days ahead HLI Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.96 0.95 0.95 0.97 0.97 0.96 0.96 0.95 0.96 0.97 0.97 0.97 0.97 0.97 0.96 0.97 0.91 MAE 1.38 2.00 2.85 0.98 1.45 2.44 1.35 0.78 0.99 -0.03 0.52 0.46 -0.69 0.94 0.70 -0.50 1.38 BIAS 3.68 3.88 4.11 3.08 3.35 3.68 2.73 2.52 3.48 2.58 2.53 3.21 2.94 2.56 2.25 2.12 3.69 RMSE 5.23 5.78 5.66 4.41 4.77 5.18 4.33 4.45 5.02 4.17 4.20 4.78 4.36 4.10 4.04 3.49 6.38



IOA 0.93 0.94 0.92 0.93 0.93 0.93 0.95 0.92 0.92 0.94 0.95 0.92 0.92 0.93 0.95 0.97 0.86 MAE 0.79 0.70 1.07 1.05 0.58 0.85 0.17 1.02 1.50 1.09 0.97 1.03 1.64 1.17 0.18 0.30 1.71 BIAS 2.14 1.93 2.42 2.16 2.40 2.13 2.07 2.33 2.51 2.62 2.34 2.44 2.74 2.63 2.22 1.74 3.17 RMSE 2.73 2.56 2.98 2.72 3.03 2.72 2.74 2.96 3.12 3.37 3.09 3.14 3.38 3.42 3.02 2.37 4.87




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Table A4 Performance benchmarks by site 3 days ahead

HLI Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.95 0.95 0.94 0.96 0.96 0.95 0.97 0.95 0.96 0.97 0.97 0.96 0.97 0.97 0.96 0.97 0.90 MAE 1.39 2.17 2.89 1.03 1.62 2.46 1.21 0.72 0.84 -0.04 0.40 0.49 -0.71 0.92 0.65 -0.52 1.26 BIAS 3.86 4.05 4.33 3.29 3.54 3.88 2.73 2.58 3.49 2.60 2.58 3.37 3.08 2.60 2.32 2.21 3.92 RMSE 5.49 5.92 5.99 4.69 4.99 5.50 4.31 4.34 4.94 4.13 4.23 4.98 4.55 4.14 4.14 3.56 6.75



IOA 0.92 0.93 0.91 0.92 0.91 0.92 0.95 0.92 0.91 0.93 0.94 0.92 0.92 0.93 0.94 0.97 0.86 MAE 0.82 0.73 1.12 1.10 0.68 0.83 0.10 1.09 1.50 1.09 0.89 1.12 1.66 1.22 0.16 0.22 1.67 BIAS 2.18 2.01 2.54 2.25 2.56 2.23 2.16 2.44 2.52 2.70 2.41 2.52 2.78 2.75 2.36 1.81 3.25 RMSE 2.77 2.68 3.18 2.86 3.28 2.86 2.88 3.07 3.17 3.52 3.20 3.24 3.43 3.62 3.27 2.46 5.00




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Table A5 Performance benchmarks by site 4 days ahead HLI Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.95 0.94 0.94 0.96 0.95 0.95 0.96 0.96 0.96 0.97 0.96 0.96 0.97 0.97 0.95 0.96 0.90 MAE 1.39 2.27 2.92 1.02 1.73 2.47 1.28 0.69 0.87 -0.10 0.38 0.47 -0.67 0.90 0.85 -0.53 1.34 BIAS 3.95 4.23 4.32 3.41 3.85 4.08 2.90 2.63 3.60 2.81 2.75 3.41 3.08 2.74 2.48 2.43 3.98 RMSE 5.58 6.15 5.96 4.80 5.52 5.73 4.48 4.14 5.09 4.44 4.46 4.99 4.64 4.31 4.29 4.00 6.81



IOA 0.92 0.92 0.90 0.92 0.90 0.92 0.94 0.91 0.91 0.92 0.93 0.91 0.91 0.91 0.93 0.96 0.85 MAE 0.82 0.82 1.18 1.11 0.82 0.85 -0.07 1.13 1.55 1.02 0.83 1.14 1.68 1.12 0.24 0.19 1.68 BIAS 2.27 2.12 2.61 2.31 2.75 2.25 2.27 2.49 2.64 2.89 2.61 2.58 2.79 2.94 2.59 2.02 3.42 RMSE 2.88 2.79 3.28 2.93 3.54 2.89 2.99 3.14 3.30 3.76 3.46 3.30 3.47 3.83 3.55 2.73 5.05



Table A6 Performance benchmarks by site 5 days ahead HLI Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.95 0.93 0.94 0.96 0.95 0.94 0.96 0.94 0.95 0.96 0.95 0.96 0.96 0.96 0.94 0.96 0.88 MAE 1.46 2.59 2.90 1.04 1.62 2.60 1.47 0.78 1.13 0.21 0.67 0.58 -0.64 1.13 0.93 -0.49 1.63 BIAS 4.23 4.42 4.43 3.56 3.89 4.30 3.16 2.90 3.89 3.16 3.07 3.66 3.19 3.16 2.77 2.56 4.20 RMSE 5.93 6.47 6.03 5.02 5.49 5.96 4.91 4.71 5.56 5.03 5.00 5.32 4.82 5.00 4.84 4.14 7.05


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IOA 0.91 0.91 0.90 0.91 0.89 0.91 0.93 0.90 0.90 0.91 0.92 0.91 0.90 0.91 0.91 0.95 0.84 MAE 0.86 0.83 1.10 1.10 0.72 0.86 0.08 1.08 1.57 1.21 1.01 1.10 1.58 1.32 0.20 0.16 1.94 BIAS 2.37 2.20 2.65 2.46 2.77 2.41 2.44 2.64 2.70 2.97 2.79 2.64 2.93 3.01 2.86 2.27 3.61 RMSE 2.99 2.97 3.38 3.17 3.57 3.12 3.25 3.35 3.36 3.94 3.81 3.43 3.64 4.01 3.94 3.08 5.27

RH Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.86 0.84 0.79 0.85 0.75 0.84 0.80 0.82 0.80 0.75 0.78 0.79 0.79 0.75 0.83 0.87 0.74 MAE -3.07 2.87 -1.64 -3.90 1.40 -1.60 5.45 -2.11 -6.64 -3.50 -3.34 -3.40 -10.08 -2.71 0.60 -7.49 -6.48 BIAS 11.91 12.25 15.82 13.45 16.21 12.76 12.66 14.66 15.00 14.87 12.69 14.10 15.70 14.89 14.69 13.59 15.88 RMSE 15.90 17.64 21.01 17.87 22.03 17.44 17.18 20.19 19.57 20.27 18.44 18.95 19.58 20.21 20.58 17.47 22.17

WS Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.64 0.52 0.47 0.57 0.50 0.49 0.68 0.60 0.59 0.62 0.64 0.55 0.59 0.62 0.52 0.71 0.68 MAE -0.05 -0.29 -1.82 -0.41 -0.35 -1.65 -0.21 0.39 0.07 0.57 0.36 0.66 0.52 -0.30 -1.78 -0.47 -0.84 BIAS 1.89 1.90 2.35 2.28 2.12 2.64 1.53 1.85 1.52 1.86 1.95 2.14 1.87 1.78 2.28 1.60 1.78 RMSE 2.50 2.59 3.07 3.00 2.77 3.35 1.93 2.46 2.02 2.41 2.58 2.78 2.44 2.33 2.82 2.07 2.24


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IOA 0.90 0.91 0.89 0.90 0.88 0.89 0.92 0.88 0.88 0.89 0.90 0.90 0.90 0.89 0.90 0.94 0.83 MAE 0.93 0.82 1.11 1.15 0.80 0.97 0.21 1.24 1.78 1.31 1.03 1.23 1.72 1.40 0.22 0.20 1.89 BIAS 2.47 2.23 2.83 2.57 2.92 2.49 2.52 2.76 2.90 3.23 2.98 2.71 2.94 3.22 3.12 2.38 3.70 RMSE 3.14 2.98 3.52 3.34 3.75 3.30 3.42 3.58 3.65 4.32 4.06 3.59 3.81 4.31 4.16 3.24 5.33

RH Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.86 0.83 0.78 0.83 0.73 0.84 0.77 0.82 0.78 0.72 0.75 0.78 0.80 0.72 0.82 0.88 0.75 MAE -2.17 3.43 -0.27 -2.89 2.12 -0.98 5.55 -2.24 -6.37 -2.94 -2.72 -2.89 -9.58 -2.22 0.50 -7.96 -6.14 BIAS 12.02 13.07 16.16 13.81 16.56 12.74 13.25 14.68 15.69 15.35 13.51 13.95 15.17 15.41 15.19 13.67 15.75 RMSE 16.13 18.55 21.31 18.38 22.44 17.70 18.01 20.26 20.54 20.90 19.19 19.00 18.96 20.80 20.54 17.32 21.44

WS Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.63 0.56 0.47 0.56 0.49 0.47 0.58 0.59 0.56 0.56 0.59 0.53 0.55 0.58 0.50 0.67 0.62 MAE -0.21 -0.23 -1.76 -0.59 -0.21 -1.81 -0.16 0.26 0.15 0.51 0.39 0.74 0.54 -0.35 -1.77 -0.50 -0.75 BIAS 1.96 1.84 2.31 2.37 2.09 2.82 1.76 1.85 1.51 2.01 2.12 2.18 1.97 1.89 2.34 1.73 1.91 RMSE 2.58 2.38 2.99 3.08 2.70 3.54 2.24 2.44 2.00 2.61 2.82 2.82 2.57 2.44 2.92 2.19 2.48


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IOA 0.90 0.91 0.89 0.90 0.88 0.89 0.92 0.88 0.88 0.89 0.89 0.90 0.90 0.89 0.89 0.93 0.84 MAE 0.87 0.87 1.17 1.20 0.85 0.99 0.23 1.17 1.62 1.36 1.13 1.13 1.60 1.44 0.33 0.20 1.74 BIAS 2.48 2.32 2.78 2.54 2.88 2.54 2.64 2.77 2.97 3.33 3.17 2.75 2.98 3.29 3.35 2.74 3.81 RMSE 3.24 3.08 3.52 3.32 3.74 3.36 3.49 3.55 3.70 4.39 4.25 3.60 3.79 4.32 4.32 3.68 5.17

RH Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton

IOA 0.84 0.82 0.78 0.83 0.73 0.83 0.80 0.81 0.78 0.75 0.76 0.78 0.80 0.74 0.82 0.86 0.74 MAE -1.29 3.39 -0.75 -2.01 1.76 -0.27 4.69 -1.79 -5.84 -4.80 -4.33 -2.59 -9.15 -3.69 0.17 -8.27 -6.22 BIAS 12.73 13.77 16.36 14.02 16.50 13.47 12.82 15.33 16.02 15.49 14.25 14.13 15.08 15.40 15.54 14.80 16.14 RMSE 17.10 19.02 21.74 18.52 22.40 18.19 17.15 20.77 20.45 20.68 19.21 19.06 18.74 20.56 20.78 18.74 22.16

WS Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.61 0.56 0.47 0.54 0.50 0.47 0.60 0.55 0.58 0.56 0.56 0.51 0.52 0.56 0.49 0.61 0.58

MAE -0.27 -0.33 -1.83 -0.77 -0.26 -1.92 0.04 0.23 0.09 0.56 0.44 0.81 0.45 -0.30 -1.82 -0.44 -0.80 BIAS 2.02 1.87 2.35 2.51 2.06 2.90 1.62 1.94 1.51 1.99 2.20 2.22 1.98 1.90 2.41 1.86 1.97 RMSE 2.62 2.42 3.01 3.18 2.66 3.64 2.07 2.58 1.99 2.59 2.87 2.89 2.67 2.47 2.99 2.40 2.54


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Appendix B - AHLU Performance

B1. Day 1

Table B1 Amberley AHLU 80

Extreme 0 0 0 28.1 High 0.5 0 4.9 4.9

Medium 1.1 3.8 8.6 1.1 Low 32.4 10.8 3.2 0.5

Low Medium High Extreme AHLU 83

Extreme 0 0 1.6 11.9 High 0.5 1.1 5.4 2.2

Medium 2.2 6.5 3.8 0.5 Low 51.9 9.2 3.2 0


Extreme 0 0 0.5 0.5 High 0 0.5 2.2 0

Medium 3.8 2.7 5.4 0.5 Low 72.4 7.6 3.8 0


Extreme 0 0 0 0 High 0 1.6 0 0

Medium 1.1 1.1 0 0 Low 85.4 9.7 1.1 0


Extreme 0 0 0 0 High 0 0 0 0

Medium 0.5 1.1 0 0 Low 94.1 4.3 0 0


Extreme 0 0 0 0 High 0 0 0 0

Medium 0 0 0 0 Low 100 0 0 0


Table B2 Emerald AHLU 80

Extreme 0 0 1.1 33 High 0 0 4.3 3.8

Medium 0 3.8 9.7 1.1 Low 25.9 14.1 3.2 0


AHLU 83 Extreme 0 0 1.6 15.1

High 0 3.2 2.7 2.2 Medium 1.6 5.9 2.7 1.1

Low 53 9.7 1.1 0


AHLU 86 Extreme 0 0 0.5 2.7

High 0.5 3.2 0.5 2.2 Medium 2.2 2.2 1.6 0

Low 75.7 7.6 1.1 0


AHLU 89


Page 49 of 144

Extreme 0 0 0 0 High 0.5 0 0 0

Medium 3.8 1.1 0.5 0 Low 90.3 3.2 0.5 0


AHLU 92 Extreme 0 0 0 0

High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B3 Miles AHLU 80

Extreme 0 0 0.5 4.9 High 0 0 2.2 3.2

Medium 0 2.2 2.7 3.2 Low 50.8 20.5 9.2 0.5


AHLU 83 Extreme 0 0 0 0.5

High 0.5 1.6 0.5 0.5 Medium 0 3.8 0.5 0.5

Low 74.1 11.9 4.9 0.5



High 0 0 0 0 Medium 2.2 0.5 1.1 0

Low 88.1 7.6 0.5 0



High 0 0 0 0 Medium 0 0 0 0

Low 98.4 1.6 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 50 of 144

Table B4 Oakey AHLU 80

Extreme 0 0 0.5 0 High 0.5 0.5 1.1 0

Medium 0.5 2.2 1.1 0 Low 73 16.2 4.3 0



High 0.5 0.5 0.5 0 Medium 1.6 0.5 0 0

Low 89.7 6.5 0 0



High 0 0 0 0 Medium 2.2 0.5 0 0

Low 97.3 0 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B5 Roma AHLU 80

Extreme 0 0 0 0.5 High 0 1.6 3.8 1.1

Medium 0 3.2 1.1 0.5 Low 63.8 18.9 5.4 0



High 0 0.5 1.1 0 Medium 2.7 2.7 1.1 0

Low 84.3 5.4 2.2 0



High 0 0 0 0 Medium 2.2 2.7 0 0

Low 92.4 2.7 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 98.9 0.5 0 0


Page 51 of 144



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B6 Warwick AHLU 80

Extreme 0 0.5 0 0.5 High 0 0 2.2 0

Medium 0.5 1.1 3.2 0.5 Low 65.4 17.3 8.6 0



High 0.5 0 1.6 0 Medium 0.5 2.2 0.5 0

Low 82.2 11.9 0.5 0



High 0 0 0 0 Medium 1.1 2.2 0 0

Low 94.6 1.6 0.5 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 98.9 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 52 of 144

Table B7 Albury AHLU 80

Extreme 0 0 0 0 High 0 0 0 0

Medium 0.5 2.7 0 0 Low 85.4 9.7 1.6 0



High 0 0 0 0 Medium 1.6 0 0 0

Low 96.8 1.6 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B8 Armidale AHLU 80

Extreme 0 0 0 0 High 0 0 0 0

Medium 0 0 0 0 Low 99.5 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0


Page 53 of 144

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B9 Cessnock AHLU 80

Extreme 0 0 1.1 0 High 0 1.6 3.8 0.5

Medium 2.2 4.9 5.4 0 Low 64.9 13 2.7 0



High 0 0.5 0.5 0 Medium 4.9 5.4 1.1 0

Low 82.2 5.4 0 0



High 0.5 0.5 0 0 Medium 1.1 0.5 0 0

Low 96.8 0 0.5 0



High 0 0 0 0 Medium 1.1 0 0 0

Low 98.4 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 54 of 144

Table B10 Griffith AHLU 80

Extreme 0 0 0 0 High 0.5 0.5 0 0

Medium 2.2 5.4 1.1 0.5 Low 82.7 4.3 2.7 0



High 0.5 0 0 0 Medium 4.3 1.1 0.5 0

Low 90.8 2.7 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 98.9 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B11 Hay AHLU 80

Extreme 0 0 0 0 High 0 0 0 0

Medium 0.5 1.1 1.1 0 Low 85.9 7.6 3.8 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 94.6 4.3 0.5 0



High 0 0 0 0 Medium 0 0 0 0

Low 99.5 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0


Page 55 of 144

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B12 Moree AHLU 80

Extreme 0.5 0.5 0.5 0.5 High 0 1.6 1.1 1.1

Medium 1.6 3.8 1.6 0 Low 68.9 15.3 2.7 0



High 1.6 1.6 0.5 0 Medium 3.3 1.1 1.1 0

Low 85.2 5.5 0 0



High 1.1 0.5 0 0 Medium 2.2 0 0 0

Low 94 1.6 0.5 0



High 0 0 0 0 Medium 0.5 0.5 0 0

Low 98.4 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 56 of 144

Table B13 Tamworth AHLU 80

Extreme 0 0 0 0 High 1.1 3.3 0 0

Medium 1.1 1.6 0.5 0 Low 81.4 10.4 0.5 0



High 0.5 0 0 0 Medium 6 0.5 0 0

Low 90.7 2.2 0 0



High 0 0 0 0 Medium 2.7 0 0 0

Low 97.3 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B14 Yanco AHLU 80

Extreme 0 0 0 0 High 0 0.5 0 0

Medium 1.1 4.3 1.1 0.5 Low 80 8.6 3.8 0


Extreme 0 0 0 0 High 0 0.5 0 0

Medium 1.6 1.6 0 0 Low 90.8 4.9 0.5 0


Extreme 0 0 0 0 High 0 0 0 0

Medium 0.5 0 0 0 Low 98.4 1.1 0 0


Extreme 0 0 0 0 High 0 0 0 0

Medium 0 0 0 0


Page 57 of 144

Low 100 0 0 0


Extreme 0 0 0 0 High 0 0 0 0

Medium 0 0 0 0 Low 100 0 0 0


Extreme 0 0 0 0 High 0 0 0 0

Medium 0 0 0 0 Low 100 0 0 0


Table B15 Clare AHLU 80

Extreme 0 0 0 0 High 0 0 0 0

Medium 0 1.1 0 0 Low 96.2 2.2 0.5 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 98.9 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 58 of 144

Table B16 Katanning AHLU 80

Extreme 0 0 0 0 High 0 0 0 0

Medium 0.5 0.5 0 0 Low 95.7 3.2 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 99.5 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B17 Charlton AHLU 80

Extreme 0 0 0 0 High 0 0 0 0

Medium 0.8 0.8 0 0 Low 91.6 5 1.7 0



High 0 0 0 0 Medium 0.8 0 0 0

Low 95.8 3.4 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 99.2 0.8 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Page 59 of 144



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


B2. Day 2


Extreme 0 0 0 22.7 High 0.5 1.1 3.2 5.9

Medium 1.1 2.2 8.6 3.8 Low 31.9 11.9 4.9 2.2



High 1.1 0.5 5.4 3.2 Medium 2.7 7 3.2 0.5

Low 50.8 9.7 3.8 4.9



High 0.5 0.5 1.6 0 Medium 4.3 3.2 2.2 0

Low 71.9 6.5 7 0.5


AHLU 89 Extreme 0 0.5 0 0

High 0.5 0.5 0 0 Medium 1.1 1.1 0.5 0

Low 85.4 9.7 0.5 0



High 0 0.5 0 0 Medium 0 0.5 0 0

Low 95.7 3.2 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



Page 60 of 144


Extreme 0 0 1.1 28.6 High 0 0.5 5.4 5.9

Medium 0 1.6 5.4 1.6 Low 25.4 16.2 5.9 2.2


AHLU 83 Extreme 0 0 1.1 12.4

High 0 4.3 0.5 4.3 Medium 1.6 4.9 3.2 1.1

Low 53.5 9.7 3.2 0



High 0.5 2.7 0.5 0 Medium 3.2 2.7 1.6 1.1

Low 74.1 8.1 1.1 1.6



High 1.1 1.1 0.5 0 Medium 4.3 0 0 0

Low 90.3 2.2 0.5 0



High 0 0 0 0 Medium 2.7 0 0 0

Low 97.3 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 1.1 2.2 High 0 0 2.2 0

Medium 0 2.2 1.6 2.2 Low 50.3 21.6 8.6 8.1


AHLU 83 Extreme 0 0.5 0 1.1

High 0.5 0.5 0.5 0 Medium 0 3.8 0.5 0

Low 74.1 12.4 4.9 1.1



High 0.5 0 0.5 0 Medium 1.1 0.5 0.5 0

Low 88.6 7.6 0.5 0



High 0 0 0 0 Medium 0.5 0.5 0 0


Page 61 of 144

Low 97.8 1.1 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0.5 0 High 0.5 0 0.5 0

Medium 0 4.3 1.1 0 Low 74.1 14.1 4.9 0



High 0 0.5 0.5 0 Medium 1.6 1.1 0 0

Low 90.3 5.9 0 0



High 0 0.5 0 0 Medium 0.5 0 0 0

Low 98.9 0 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 62 of 144


Extreme 0 0 1.1 0.5 High 0 2.7 2.2 0

Medium 0 2.2 2.2 0.5 Low 63.2 18.9 5.4 1.1


AHLU 83 Extreme 0 0 0.5 0

High 0.5 1.1 0.5 0 Medium 2.7 2.7 0.5 0

Low 83.2 5.9 2.2 0



High 0 0.5 0 0 Medium 3.8 1.6 0 0

Low 91.4 2.7 0 0



High 0.5 0 0 0 Medium 0.5 0 0 0

Low 98.4 0.5 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0.5 0 0.5 High 0 0.5 2.2 0

Medium 0.5 1.6 1.1 0 Low 65.9 15.7 10.8 0.5



High 1.1 0 1.1 0 Medium 1.6 2.2 0 0

Low 81.1 11.9 1.1 0



High 0 0.5 0 0 Medium 1.6 0.5 0 0

Low 94.6 2.2 0.5 0



High 0 0 0 0 Medium 1.1 0 0 0


Page 63 of 144

Low 98.4 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 0.5 0 0

Medium 0 3.8 0 0 Low 85.4 8.6 1.6 0



High 0 0 0 0 Medium 1.6 0 0 0

Low 96.8 1.6 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 0 0 0

Medium 0 0 0 0 Low 99.5 0.5 0 0



Page 64 of 144


High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 1.6 0 High 1.1 1.6 2.2 0.5

Medium 2.7 4.9 5.9 0 Low 62.7 14.1 2.7 0



High 1.1 0.5 1.1 0 Medium 3.8 5.4 0.5 0

Low 82.7 4.9 0 0



High 0.5 0 0 0 Medium 2.2 1.1 0.5 0

Low 95.7 0 0 0



High 0 0 0 0 Medium 1.1 0 0 0

Low 98.4 0.5 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



Page 65 of 144


High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0.5 1.6 0.5 0

Medium 1.1 3.8 2.2 0.5 Low 83.8 4.9 1.1 0



High 0.5 0 0 0 Medium 4.3 2.2 0 0

Low 90.3 2.2 0.5 0



High 0 0 0 0 Medium 1.1 0 0 0

Low 98.4 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 0 0 0

Medium 0 2.7 0.5 0 Low 87 5.4 4.3 0



High 0 0 0 0 Medium 1.1 0 0 0

Low 94.1 4.3 0.5 0



High 0 0 0 0


Page 66 of 144

Medium 0 0 0 0 Low 99.5 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 1.1 0.5 High 1.1 1.6 0.5 0.5

Medium 1.6 2.7 2.2 0.5 Low 68.3 16.4 2.7 0



High 1.1 1.1 1.1 0 Medium 2.7 1.6 0 0

Low 86.3 5.5 0.5 0



High 0 0.5 0 0 Medium 2.7 0.5 0 0

Low 94.5 1.1 0.5 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 98.4 1.1 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 67 of 144


Extreme 0 0 0 0 High 0.5 3.3 0.5 0

Medium 3.3 3.3 0.5 0 Low 80.3 8.2 0 0



High 1.1 0 0 0 Medium 5.5 0.5 0 0

Low 90.7 2.2 0 0



High 0 0 0 0 Medium 2.7 0 0 0

Low 97.3 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 0 0.5 0

Medium 0.5 3.8 3.2 0 Low 80.5 9.2 1.6 0.5



High 0 0 0 0 Medium 2.2 1.6 0 0

Low 89.7 5.9 0.5 0



High 0 0 0 0 Medium 0 0 0 0

Low 98.9 1.1 0 0



High 0 0 0 0 Medium 0 0 0 0


Page 68 of 144

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 0 0 0

Medium 0.5 0.5 0 0 Low 96.2 2.2 0.5 0



High 0 0 0 0 Medium 1.1 0 0 0

Low 98.4 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 0 0 0

Medium 0 1.1 0 0 Low 96.8 2.2 0 0



Page 69 of 144


High 0 0 0 0 Medium 0.5 0 0 0

Low 98.9 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 0 0 0

Medium 0.8 1.7 0.8 0 Low 92.4 3.4 0.8 0



High 0 0 0 0 Medium 0.8 0.8 0 0

Low 95.8 2.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 99.2 0.8 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 70 of 144


High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


B3. Day 3


Extreme 0 0 0.5 17.8 High 0 2.7 3.2 5.9

Medium 2.7 0.5 5.4 4.3 Low 30.3 11.4 7 8.1


AHLU 83 Extreme 0.5 0 3.2 4.3

High 0.5 0 2.7 2.2 Medium 4.3 7.6 3.2 1.6

Low 48.1 9.7 5.4 6.5


AHLU 86 Extreme 0.5 0 1.1 0.5

High 0 2.2 1.1 0 Medium 4.9 1.1 3.2 0

Low 70.3 8.6 5.9 0.5



High 1.1 0.5 0 0 Medium 2.7 1.1 0 0

Low 83.2 9.7 1.1 0



High 0 0.5 0 0 Medium 0.5 0.5 0 0

Low 94.1 4.3 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0.5 0 26.5 High 0 0.5 5.4 4.9

Medium 0 0.5 6.5 3.8 Low 25.4 16.2 6.5 3.2



High 0.5 2.7 1.1 3.2 Medium 1.1 5.4 3.2 3.2

Low 52.4 11.4 2.7 3.8


Page 71 of 144



High 1.1 2.2 0.5 0 Medium 2.7 3.2 0.5 0.5

Low 73.5 8.1 2.2 2.2



High 1.6 0.5 0.5 0 Medium 3.2 0 0.5 0

Low 90.3 2.7 0 0



High 0.5 0 0 0 Medium 1.6 0 0 0

Low 97.8 0 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



Extreme 0 0 1.1 2.2 High 0 0 1.1 1.1

Medium 0.5 1.1 1.1 1.6 Low 49.7 21.1 11.9 7.6


AHLU 83 Extreme 0 0.5 0.5 1.1

High 0 0.5 0 0 Medium 0.5 2.7 0.5 0

Low 73 14.6 4.9 1.1



High 0.5 0.5 1.1 0 Medium 0.5 0 0 0

Low 88.6 8.1 0.5 0



High 0 0 0 0 Medium 0.5 1.1 0 0

Low 97.8 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Page 72 of 144



Extreme 0 0 1.1 0 High 1.1 1.1 0.5 0

Medium 0.5 2.2 0 0 Low 72.4 15.7 5.4 0



High 0 0.5 0.5 0 Medium 3.2 0.5 0 0

Low 88.6 6.5 0 0



High 0 0 0 0 Medium 1.1 0.5 0 0

Low 98.4 0 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 1.6 0.5 High 0 2.2 1.1 0

Medium 0 3.2 1.1 1.1 Low 62.7 18.4 7.6 0.5


AHLU 83 Extreme 0 0.5 1.1 0

High 0 1.1 0 0 Medium 3.8 1.6 1.1 0

Low 82.2 6.5 2.2 0



High 0.5 1.1 0 0 Medium 1.6 1.1 0 0

Low 92.4 3.2 0 0


AHLU 89


Page 73 of 144

Extreme 0 0 0 0 High 0 0 0 0

Medium 1.6 0 0 0 Low 97.8 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 1.1 0.5 0.5 High 0 0.5 1.6 0

Medium 0.5 1.1 2.2 0 Low 65.4 16.2 9.7 0.5



High 1.1 0.5 1.1 0 Medium 1.6 1.6 1.1 0

Low 80.5 11.9 0.5 0



High 0 0.5 0 0 Medium 2.2 0.5 0 0

Low 93.5 2.7 0.5 0



High 0 0 0 0 Medium 1.6 0 0 0

Low 97.8 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 74 of 144


Extreme 0 0 0 0 High 0 1.1 0.5 0

Medium 1.1 4.3 0.5 0 Low 84.3 7.6 0.5 0



High 1.1 0 0 0 Medium 1.6 0.5 0 0

Low 95.7 1.1 0 0



High 0 0 0 0 Medium 1.6 0 0 0

Low 98.4 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 0 0 0

Medium 0 0 0 0 Low 99.5 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0


Page 75 of 144

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 1.6 0 High 0.5 1.1 2.7 0

Medium 3.2 3.8 2.2 0.5 Low 62.2 16.2 5.9 0



High 0.5 0.5 1.1 0 Medium 2.7 3.2 0 0

Low 84.3 7 0.5 0



High 0.5 0.5 0 0 Medium 1.6 0.5 0 0

Low 96.2 0 0.5 0



High 0 0 0 0 Medium 1.6 0 0 0

Low 97.8 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0.5 0 0 High 0.5 0.5 1.6 0

Medium 1.6 5.4 0.5 0.5 Low 82.7 4.3 1.6 0



Page 76 of 144


High 0.5 0 0 0 Medium 5.4 2.2 0.5 0

Low 89.7 1.6 0 0



High 0 0 0 0 Medium 2.2 0 0 0

Low 97.3 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B45 Hay AHLU_80

Extreme 0 0 0 0 High 0.5 1.1 0.5 0

Medium 0.5 2.2 2.2 0 Low 84.9 5.9 2.2 0



High 0.5 0 0 0 Medium 1.6 1.6 0 0

Low 93 2.7 0.5 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 98.9 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 77 of 144


High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0.5 1.6 0.5 High 0.5 2.2 0 0.5

Medium 2.2 3.3 1.1 0.5 Low 68.3 15.3 3.3 0



High 1.6 2.2 1.1 0 Medium 2.7 0 0 0

Low 85.8 6 0.5 0



High 0 0 0 0 Medium 3.8 1.1 0 0

Low 93.4 1.1 0.5 0



High 0 0 0 0 Medium 0 0 0 0

Low 98.9 1.1 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 1.1 3.3 1.1 0

Medium 2.7 2.2 0 0 Low 80.3 9.3 0 0



High 1.6 0.5 0 0 Medium 5.5 0.5 0 0

Low 90.2 1.6 0 0




Page 78 of 144

High 0 0 0 0 Medium 3.8 0 0 0

Low 96.2 0 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0.5 0 0 High 0 1.6 2.2 0

Medium 0 3.2 1.6 0.5 Low 80.5 8.6 1.1 0



High 0 1.1 0 0 Medium 2.2 3.2 0 0

Low 90.3 2.7 0.5 0



High 0 0 0 0 Medium 3.2 0 0 0

Low 95.7 1.1 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 79 of 144


Extreme 0 0 0 0 High 0 0 0 0

Medium 0.5 1.1 0 0 Low 95.7 2.2 0.5 0



High 0 0 0 0 Medium 1.1 0 0 0

Low 98.4 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 0.5 0 0

Medium 0 0.5 0 0 Low 96.2 2.7 0 0



High 0 0 0 0 Medium 1.1 0 0 0

Low 98.4 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0


Page 80 of 144

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 0 0 0

Medium 0.8 0.8 0 0 Low 91.6 5 1.7 0



High 0 0 0 0 Medium 0 0.8 0 0

Low 96.6 2.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 99.2 0.8 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 81 of 144

B4. Day 4

Table B52 Amberley AHLU_80

Extreme 0.5 0 1.1 16.2 High 0.5 1.6 2.7 5.9

Medium 0.5 2.2 5.9 4.3 Low 31.4 10.3 8.1 8.6


AHLU 83 Extreme 0.5 1.1 2.2 2.7

High 1.1 0.5 2.7 3.8 Medium 3.8 6.5 5.4 1.1

Low 48.6 8.6 4.3 7


AHLU 86 Extreme 0.5 0 1.6 0.5

High 1.1 1.1 0 0 Medium 4.9 1.6 4.3 0

Low 69.2 9.2 5.4 0.5



High 0.5 1.1 0 0 Medium 2.7 0 0 0

Low 82.7 11.4 1.1 0



High 0 0 0 0 Medium 0 1.6 0 0

Low 94.6 3.8 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0

Table B53 Emerald AHLU_80

Extreme 0 0.5 2.2 20.5 High 0 0 4.3 7

Medium 0 3.2 5.9 3.2 Low 24.9 14.6 6.5 7



High 0.5 3.8 1.1 3.2 Medium 2.2 4.3 2.7 3.2

Low 51.4 10.8 3.8 4.9


AHLU 86 Extreme 0.5 0 1.1 2.2

High 1.1 2.7 0.5 0.5 Medium 4.9 1.1 1.1 0.5

Low 71.9 8.6 1.6 1.6


AHLU 89 Extreme 0.5 1.1 0 0

High 0.5 0 0.5 0


Page 82 of 144

Medium 3.2 0 0 0 Low 90.3 3.2 0.5 0



High 1.1 0 0 0 Medium 0.5 0 0 0

Low 98.4 0 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0


Table B54 Miles AHLU_80

Extreme 0 0 0.5 2.2 High 0.5 0 1.1 1.1

Medium 0.5 2.2 3.8 2.7 Low 49.7 18.9 10.3 6.5


AHLU 83 Extreme 0.5 0 0.5 1.1

High 0 0.5 0.5 0 Medium 1.6 3.2 0.5 0.5

Low 71.9 14.1 4.3 0.5



High 0 0.5 0.5 0 Medium 1.6 0 0 0

Low 88.6 7.6 0.5 0



High 0 0 0 0 Medium 0.5 1.1 0 0

Low 97.8 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B55 Oakey AHLU_80

Extreme 0.5 0 1.1 0 High 0.5 1.1 0 0

Medium 1.1 1.1 1.1 0 Low 72.4 16.2 4.9 0


Page 83 of 144



High 1.1 0.5 0 0 Medium 2.2 0.5 0 0

Low 88.6 6.5 0 0



High 0 0 0 0 Medium 2.2 0.5 0 0

Low 97.3 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B56 Roma AHLU_80

Extreme 0 0 1.6 0.5 High 0.5 2.2 0 0

Medium 0.5 2.7 3.2 1.1 Low 61.6 18.9 6.5 0.5



High 0 0.5 0 0 Medium 3.2 2.7 0 0

Low 83.2 6.5 2.7 0



High 0.5 0.5 0 0 Medium 0.5 0.5 0 0

Low 94.1 3.8 0 0



High 0 0 0 0 Medium 1.1 0 0 0

Low 98.4 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Page 84 of 144



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B57 Warwick AHLU_80

Extreme 0 1.1 0 0.5 High 1.1 2.2 2.2 0

Medium 1.6 1.1 2.2 0 Low 63.2 14.6 9.7 0.5



High 1.1 1.1 1.1 0 Medium 3.2 1.6 0.5 0

Low 78.9 11.4 1.1 0



High 0.5 0 0 0 Medium 2.7 1.1 0 0

Low 92.4 2.7 0.5 0



High 0 0 0 0 Medium 1.1 0 0 0

Low 98.4 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B58 Albury AHLU_80

Extreme 0 0 0 0 High 0 0.5 0 0

Medium 1.1 3.8 1.1 0 Low 83.8 9.2 0.5 0


AHLU_83 Extreme 0 0 0 0

High 0.5 0 0 0 Medium 2.2 0.5 0 0

Low 95.7 1.1 0 0


AHLU_86


Page 85 of 144

Extreme 0 0 0 0 High 0 0 0 0

Medium 0.5 0 0 0 Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B59 Armidale AHLU_80

Extreme 0 0 0 0 High 0 0 0 0

Medium 0 0 0 0 Low 99.5 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 86 of 144

Table B60 Cessnock AHLU_80

Extreme 0.5 0 0.5 0 High 0.5 1.6 3.8 0

Medium 2.7 4.3 4.3 0 Low 63.2 14.1 3.8 0.5



High 1.6 1.6 1.1 0 Medium 6.5 3.8 0 0

Low 79.5 5.4 0.5 0



High 0.5 0 0 0 Medium 3.2 1.1 0 0

Low 94.6 0 0.5 0



High 0 0 0 0 Medium 1.1 0 0 0

Low 98.4 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B61 Griffith AHLU_80

Extreme 0 0.5 0.5 0 High 1.1 1.6 0 0.5

Medium 1.1 3.8 1.6 0 Low 82.7 4.9 1.6 0



High 0.5 0.5 0 0 Medium 4.3 1.6 0.5 0

Low 90.3 2.2 0 0



High 0.5 0 0 0 Medium 1.1 0.5 0 0

Low 97.8 0 0 0



High 0 0 0 0 Medium 0.5 0 0 0


Page 87 of 144

Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B62 Hay AHLU_80

Extreme 0 0 0 0 High 0.5 1.1 0.5 0

Medium 0.5 2.2 2.2 0 Low 84.9 5.9 2.2 0



High 0.5 0 0 0 Medium 1.6 1.6 0 0

Low 93 2.7 0.5 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 98.9 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B63 Moree AHLU_80

Extreme 1.1 1.1 1.1 0.5 High 0.5 1.6 0 0.5

Medium 1.1 3.8 0.5 0.5 Low 68.3 13.7 5.5 0



Page 88 of 144


High 1.1 1.1 0.5 0 Medium 4.4 0 1.1 0

Low 84.2 7.7 0 0



High 0 0 0 0 Medium 3.3 1.1 0 0

Low 94 1.1 0.5 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 98.4 1.1 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B64 Tamworth AHLU_80

Extreme 0 0.5 0.5 0 High 1.1 2.2 0 0

Medium 2.2 2.7 0 0 Low 80.9 9.3 0.5 0



High 1.1 0.5 0 0 Medium 5.5 0.5 0 0

Low 90.7 1.6 0 0



High 0.5 0 0 0 Medium 2.2 0 0 0

Low 97.3 0 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 89 of 144


High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B65 Yanco AHLU_80

Extreme 0 0 0.5 0 High 0 1.6 0.5 0.5

Medium 1.1 3.3 2.2 0 Low 79.3 8.7 2.2 0



High 0 1.1 0 0 Medium 2.2 2.2 0.5 0

Low 89.7 4.3 0 0



High 0 0 0 0 Medium 1.6 0 0 0

Low 97.3 1.1 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B66 Clare AHLU_80

Extreme 0 0 0 0 High 0 0 0 0

Medium 0 1.1 0 0 Low 96.2 2.2 0.5 0



High 0 0 0 0 Medium 1.1 0 0 0

Low 98.4 0.5 0 0




Page 90 of 144

High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B67 Katanning AHLU_80

Extreme 0 0 0 0 High 0 0 0 0

Medium 0 1.6 0 0 Low 96.2 2.2 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 99.5 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 91 of 144

Table B68 Charlton AHLU_80

Extreme 0 0 0 0 High 0 0 0 0

Medium 0.8 0 0 0 Low 91.5 5.9 1.7 0



High 0 0 0 0 Medium 0 0 0 0

Low 96.6 3.4 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 99.2 0.8 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


B5. Day 5


Extreme 0.5 0 2.2 15.7 High 0.5 1.1 2.2 5.4

Medium 2.2 1.6 4.9 4.9 Low 28.6 13 7.6 9.7


AHLU 83 Extreme 0.5 1.1 1.6 2.2

High 2.2 2.2 3.2 2.7 Medium 2.7 3.2 4.9 2.2

Low 48.1 10.8 4.9 7.6


AHLU 86 Extreme 0.5 0 1.1 0.5

High 3.2 0.5 1.1 0 Medium 4.9 2.2 3.2 0

Low 67 8.6 6.5 0.5




Page 92 of 144

High 1.1 1.1 0 0 Medium 3.2 0.5 0.5 0

Low 81.6 10.8 0.5 0



High 0 0.5 0 0 Medium 0.5 1.1 0 0

Low 94.1 3.8 0 0



High 0 0 0 0 Medium 1.1 0 0 0

Low 98.9 0 0 0



Extreme 0 0 1.6 20 High 0 2.2 5.4 3.2

Medium 0 2.2 5.4 4.9 Low 24.9 14.1 5.9 10.3



High 1.1 3.2 1.6 2.2 Medium 2.2 5.9 1.6 2.7

Low 50.8 9.7 4.9 5.9


AHLU 86 Extreme 1.1 0 0.5 2.2

High 0 2.7 1.1 0.5 Medium 7 1.1 0.5 0

Low 69.7 9.2 2.7 1.6


AHLU 89 Extreme 0.5 0.5 0 0

High 0 0.5 0 0 Medium 3.8 0.5 1.1 0

Low 90.3 2.7 0 0



High 0 0 0 0 Medium 1.1 0 0 0

Low 98.9 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0.5 2.2 High 0 0.5 2.7 1.1

Medium 0.5 2.7 2.2 2.2 Low 50.8 18.4 8.1 8.1


AHLU 83


Page 93 of 144

Extreme 0.5 0 0.5 1.1 High 0.5 0 0 0.5

Medium 2.2 4.3 0.5 0 Low 70.3 14.1 4.9 0.5



High 0 0.5 0.5 0 Medium 1.1 0.5 0.5 0

Low 88.6 7.6 0.5 0



High 0 0 0 0 Medium 0 0 0 0

Low 98.4 1.6 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 1.1 0 0.5 0 High 0 0.5 1.1 0

Medium 3.2 2.2 0.5 0 Low 69.7 16.2 4.9 0



High 0 0 0.5 0 Medium 4.9 1.1 0 0

Low 87 6.5 0 0



High 0 0 0 0 Medium 1.1 0.5 0 0

Low 98.4 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0


Page 94 of 144

Medium 0 0 0 0 Low 100 0 0 0



Extreme 0 0 1.6 0.5 High 1.1 1.6 2.2 0

Medium 2.2 3.2 1.6 0.5 Low 61.1 17.3 5.9 1.1



High 0.5 0.5 0 0 Medium 4.3 3.8 0.5 0

Low 81.1 4.9 2.7 0



High 0.5 1.1 0 0 Medium 1.6 0.5 0 0

Low 92.4 3.8 0 0



High 0 0 0 0 Medium 1.1 0 0 0

Low 98.4 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0.5 0.5 0.5 0.5 High 1.1 1.6 2.2 0

Medium 1.1 2.2 1.1 0 Low 63.2 15.1 9.7 0.5



High 0.5 1.1 1.1 0 Medium 5.4 1.1 0.5 0

Low 77.8 11.9 0.5 0



High 0 0 0 0 Medium 1.6 1.6 0 0

Low 94.6 1.6 0.5 0


AHLU 89


Page 95 of 144

Extreme 0 0 0 0 High 0 0 0 0

Medium 0 0 0 0 Low 99.5 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 1.1 0 0

Medium 0.5 1.6 0.5 0 Low 84.3 10.8 1.1 0



High 0 0 0 0 Medium 2.2 0.5 0 0

Low 96.2 1.1 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 0 0 0

Medium 0 0 0 0 Low 99.5 0.5 0 0


Page 96 of 144



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0.5 0 High 1.1 1.1 3.2 0

Medium 1.6 5.9 3.8 0 Low 63.2 13.5 5.4 0.5



High 1.6 1.1 1.1 0 Medium 3.2 2.7 0 0

Low 82.2 7.6 0.5 0



High 0 0 0 0 Medium 4.3 1.1 0 0

Low 94.1 0 0.5 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 98.9 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Page 97 of 144



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0.5 0 0.5 High 1.1 1.1 0.5 0

Medium 2.2 4.3 0.5 0 Low 81.1 5.4 2.7 0



High 1.1 0 0 0 Medium 6.5 1.1 0.5 0

Low 87.6 3.2 0 0



High 0.5 0 0 0 Medium 1.1 0.5 0 0

Low 97.8 0 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 0 0 0

Medium 0.5 3.8 1.6 0 Low 85.9 4.9 3.2 0



High 0 0 0 0 Medium 2.2 1.6 0 0

Low 93 2.7 0.5 0


AHLU 86


Page 98 of 144

Extreme 0 0 0 0 High 0 0 0 0

Medium 0 0 0 0 Low 99.5 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0.5 2.2 High 0 0.5 2.7 1.1

Medium 0.5 2.7 2.2 2.2 Low 50.8 18.4 8.1 8.1


AHLU 83 Extreme 0.5 0 0.5 1.1

High 0.5 0 0 0.5 Medium 2.2 4.3 0.5 0

Low 70.3 14.1 4.9 0.5



High 0 0.5 0.5 0 Medium 1.1 0.5 0.5 0

Low 88.6 7.6 0.5 0



High 0 0 0 0 Medium 0 0 0 0

Low 98.4 1.6 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 99 of 144


Extreme 0 0.5 0 0 High 0.5 1.6 0.5 0

Medium 1.6 3.8 0 0 Low 82.5 8.2 0.5 0



High 0.5 0 0 0 Medium 3.8 0.5 0 0

Low 92.9 2.2 0 0



High 0.5 0 0 0 Medium 0.5 0 0 0

Low 98.9 0 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0.5 High 0 2.2 1.1 0

Medium 1.1 2.7 1.1 0 Low 78.9 9.2 3.2 0



High 0.5 0.5 0 0 Medium 3.2 2.2 0.5 0

Low 88.1 4.9 0 0



High 0 0 0 0 Medium 1.6 0 0 0

Low 97.3 1.1 0 0



High 0 0 0 0 Medium 0.5 0 0 0


Page 100 of 144

Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 0 0 0

Medium 1.1 1.1 0 0 Low 95.1 2.2 0.5 0



High 0 0 0 0 Medium 1.6 0 0 0

Low 97.8 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 0 0 0

Medium 0 0.5 0 0 Low 96.8 2.7 0 0



Page 101 of 144


High 0 0 0 0 Medium 0.5 0 0 0

Low 98.9 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 0 0 0

Medium 0 0.8 0 0 Low 92.4 5 1.7 0



High 0 0 0 0 Medium 0.8 0 0 0

Low 95.8 3.4 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 99.2 0.8 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 102 of 144


High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 103 of 144

B6. Day 6

Table B86 Amberley AHLU_80

Extreme 1.6 0 1.1 13 High 0 0.5 3.2 8.6

Medium 2.2 3.2 2.7 2.2 Low 28.1 12.4 9.7 11.4


AHLU_83 Extreme 0.5 0.5 1.1 2.2

High 1.6 2.7 3.2 2.2 Medium 4.9 2.7 3.2 2.2

Low 47.6 11.4 5.9 8.1


AHLU_86 Extreme 1.1 0 1.1 0.5

High 1.1 1.1 0 0 Medium 6.5 2.2 3.8 0

Low 68.1 7.6 6.5 0.5


AHLU_89 Extreme 0 0.5 0 0

High 0.5 0.5 0 0 Medium 3.2 0.5 0 0

Low 83.2 10.3 1.1 0



High 0 0 0 0 Medium 0 0.5 0 0

Low 94.6 4.9 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B87 Emerald AHLU_80

Extreme 0 0.5 0.5 18.9 High 0 0.5 6.5 3.8

Medium 0 3.8 3.8 3.2 Low 24.3 13.5 8.1 12.4


AHLU_83 Extreme 0 0 0.5 7

High 1.1 2.7 0.5 3.8 Medium 1.6 5.9 2.7 2.2

Low 50.8 11.4 3.8 5.9


AHLU_86 Extreme 1.1 0 0 2.7

High 1.1 1.6 1.6 0 Medium 3.2 2.7 1.6 0

Low 72.4 8.1 1.6 2.2


AHLU_89


Page 104 of 144

Extreme 0 0.5 0 0 High 0.5 1.1 0 0

Medium 2.7 0 0.5 0 Low 91.4 2.7 0.5 0



High 0 0 0 0 Medium 1.6 0 0 0

Low 98.4 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B88 Miles

AHLU_80 Extreme 0.5 0 0.5 1.6

High 0 0 1.6 2.2 Medium 2.2 2.7 1.6 2.2

Low 49.2 18.4 9.7 7.6


AHLU 83 Extreme 0.5 0 0 1.1

High 0 0 0 0 Medium 2.2 4.3 2.2 0.5

Low 72.4 12.4 3.8 0.5



High 0 0 0 0 Medium 1.1 1.6 0.5 0

Low 88.6 7 1.1 0



High 0 0 0 0 Medium 0 0 0 0

Low 98.4 1.6 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B89 Oakey AHLU_80

Extreme 0 0 0 0 High 1.6 0.5 1.1 0

Medium 1.6 1.6 1.1 0


Page 105 of 144

Low 71.9 15.7 4.9 0



High 0 0 0.5 0 Medium 2.7 1.1 0 0

Low 89.7 5.9 0 0



High 0 0 0 0 Medium 0.5 0.5 0 0

Low 98.9 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B90 Roma

AHLU_80 Extreme 0 0.5 1.1 0.5

High 2.2 1.6 2.2 0.5 Medium 1.6 1.6 0.5 0

Low 60 18.9 7.6 1.1



High 1.1 0.5 0.5 0 Medium 4.9 2.7 0.5 0

Low 80 5.9 2.7 0



High 0.5 0 0 0 Medium 3.2 1.1 0 0

Low 90.8 4.3 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 98.9 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0


Page 106 of 144

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B91 Warwick AHLU_80

Extreme 0 0 0 0 High 1.1 2.2 0.5 0.5

Medium 2.2 0 1.6 0 Low 62.2 18.4 10.8 0.5



High 0.5 0.5 0.5 0 Medium 1.6 0.5 0.5 0

Low 82.2 12.4 1.1 0



High 0 0 0 0 Medium 2.2 0.5 0 0

Low 94.1 2.7 0.5 0



High 0 0 0 0 Medium 0 0 0 0

Low 99.5 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B92 Albury AHLU_80

Extreme 0 0 0 0 High 0 0.5 0 0

Medium 0.5 2.7 0.5 0 Low 84.3 10.3 1.1 0



High 0 0 0 0 Medium 2.2 0.5 0 0

Low 96.2 1.1 0 0




Page 107 of 144

High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B93 Armidale AHLU_80

Extreme 0 0 0 0 High 0 0 0 0

Medium 0 0 0 0 Low 99.5 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 108 of 144

Table B94 Cessnock AHLU_80

Extreme 0 0 1.1 0 High 2.2 0 2.2 0

Medium 2.2 5.9 3.2 0 Low 64.3 14.1 4.3 0.5


AHLU_83 Extreme 0 0 0.5 0

High 0.5 1.1 0.5 0 Medium 4.9 2.2 0 0

Low 83.8 5.9 0.5 0



High 1.1 0 0 0 Medium 2.7 1.1 0 0

Low 94.6 0 0.5 0



High 0.5 0 0 0 Medium 0.5 0 0 0

Low 98.4 0.5 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0


Table B95 Griffith AHLU_80

Extreme 0 0.5 0 0 High 0 2.2 0.5 0.5

Medium 0.5 1.1 1.6 0 Low 83.8 7.6 1.6 0



High 1.1 0 0 0 Medium 2.2 0.5 0.5 0

Low 91.9 3.8 0 0



High 0 0 0 0 Medium 1.6 0 0 0

Low 97.8 0.5 0 0



High 0 0 0 0 Medium 0.5 0 0 0


Page 109 of 144

Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B96 Hay 6 Day


High 0 0.5 1.1 0 Medium 0 0.5 0 0

Low 86.5 7.6 3.8 0



High 0.5 0.5 0 0 Medium 1.1 0 0 0

Low 93.5 3.8 0.5 0



High 0 0 0 0 Medium 1.1 0 0 0

Low 98.4 0.5 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B97 Moree AHLU_80

Extreme 0 1.1 1.1 0.5 High 0.5 1.1 0.5 0.5

Medium 3.3 1.6 0 0 Low 66.7 17.5 4.9 0.5


Page 110 of 144



High 0.5 0.5 1.1 0 Medium 3.3 0.5 0 0

Low 86.3 7.1 0.5 0



High 0 0 0 0 Medium 1.6 1.1 0 0

Low 95.6 1.1 0.5 0



High 0 0 0 0 Medium 0 0 0 0

Low 98.9 1.1 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B98 Tamworth AHLU_80

Extreme 0 0.5 0 0 High 0.5 1.1 0.5 0

Medium 1.1 1.6 0 0 Low 83.1 10.9 0.5 0



High 1.6 0 0 0 Medium 2.2 0.5 0 0

Low 93.4 2.2 0 0



High 0 0 0 0 Medium 2.2 0 0 0

Low 97.8 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Page 111 of 144



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B99 Yanco AHLU_80

Extreme 0 0 0 0 High 0 1.6 1.1 0.5

Medium 1.1 1.6 0.5 0 Low 78.9 10.8 3.8 0



High 0 0.5 0 0 Medium 1.6 1.6 0.5 0

Low 90.3 5.4 0 0



High 0 0 0 0 Medium 1.1 0 0 0

Low 97.8 1.1 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B100 Clare AHLU_80

Extreme 0 0 0 0 High 0.5 0.5 0 0

Medium 0.5 0 0 0 Low 95.1 2.7 0.5 0



High 0 0 0 0 Medium 1.6 0 0 0

Low 97.8 0.5 0 0


AHLU_86


Page 112 of 144

Extreme 0 0 0 0 High 0 0 0 0

Medium 0 0 0 0 Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0


Table B101 Katanning AHLU_80

Extreme 0 0 0 0 High 0 0 0 0

Medium 0.5 0.5 0 0 Low 95.7 3.2 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 99.5 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 113 of 144

Table B102 Charlton AHLU_80

Extreme 0 0 0 0 High 0 0 0 0

Medium 1.7 0 0 0 Low 90.8 5.9 1.7 0



High 0 0 0 0 Medium 0.8 0 0 0

Low 95.8 3.4 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 99.2 0.8 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



Page 114 of 144

B7. Day 7


Extreme 1.1 0 1.6 11.4 High 1.6 0.5 4.3 5.4

Medium 1.6 3.8 3.2 4.3 Low 27.6 10.8 9.2 13.5


AHLU 83 Extreme 0.5 1.1 1.6 2.2

High 1.1 1.6 2.2 2.2 Medium 3.8 3.8 4.3 1.6

Low 48.1 11.4 6.5 8.1


AHLU 86 Extreme 1.1 0.5 1.1 0.5

High 1.6 0.5 0 0 Medium 3.2 3.8 1.6 0

Low 70.8 5.9 8.6 0.5



High 0.5 0 0 0 Medium 2.7 2.7 0 0

Low 83.8 8.6 1.1 0



High 0 0 0 0 Medium 0.5 1.1 0 0

Low 94.1 4.3 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



Extreme 0 0.5 1.6 17.8 High 0 1.6 2.7 4.3

Medium 0 1.6 4.9 2.2 Low 24.9 14.1 10.3 13.5



High 1.1 2.2 0.5 1.6 Medium 2.7 3.8 1.1 2.7

Low 50.8 13 5.4 8.1



High 0 0.5 1.6 1.6 Medium 2.2 3.2 0.5 0

Low 76.8 7.6 2.2 1.6



High 1.1 1.1 0 0


Page 115 of 144

Medium 2.7 0.5 0.5 0 Low 90.8 2.7 0.5 0



High 0 0 0 0 Medium 1.1 0 0 0

Low 98.9 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0.5 1.1 High 0 0.5 0.5 1.6

Medium 2.2 1.1 3.2 2.7 Low 49.7 19.5 9.7 7.6


AHLU 83 Extreme 0 0 0 0.5

High 0.5 0 1.1 0 Medium 2.7 3.2 1.1 0.5

Low 70.8 14.6 3.8 1.1



High 0 0 0 0 Medium 0.5 0.5 0.5 0

Low 89.7 7.6 1.1 0



High 0 0 0 0 Medium 0 0 0 0

Low 98.4 1.6 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0.5 0.5 1.6 0

Medium 1.1 0.5 1.1 0 Low 74.1 16.2 4.3 0




Page 116 of 144

High 0 0.5 0 0 Medium 1.6 0.5 0.5 0

Low 90.3 6.5 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 98.9 0.5 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0.5 0.5 1.1 0 High 0.5 0 0.5 0

Medium 3.2 1.6 3.2 1.1 Low 58.4 21.6 6.5 1.1



High 1.1 1.1 0.5 0 Medium 2.7 0.5 0.5 0

Low 82.7 8.1 2.7 0



High 0 0 0 0 Medium 1.1 1.1 0 0

Low 94.1 3.8 0 0



High 0 0 0 0 Medium 1.1 0 0 0

Low 98.4 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0




Page 117 of 144

High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 1.6 1.1 0.5

Medium 1.1 1.1 2.2 0 Low 64.3 17.8 9.7 0.5



High 1.1 0.5 0.5 0 Medium 1.6 1.1 0.5 0

Low 82.2 10.8 1.6 0



High 0 0 0 0 Medium 1.6 1.1 0 0

Low 94.1 2.7 0.5 0



High 0 0 0 0 Medium 0 0 0 0

Low 99.5 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 0.5 0 0

Medium 0 2.2 0 0 Low 85.4 10.3 1.6 0



High 0 0 0 0 Medium 1.6 0 0 0

Low 96.8 1.6 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



Page 118 of 144


High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 1.1 0 1.6 11.4 High 1.6 0.5 4.3 5.4

Medium 1.6 3.8 3.2 4.3 Low 27.6 10.8 9.2 13.5


AHLU 83 Extreme 0.5 1.1 1.6 2.2

High 1.1 1.6 2.2 2.2 Medium 3.8 3.8 4.3 1.6

Low 48.1 11.4 6.5 8.1


AHLU 86 Extreme 1.1 0.5 1.1 0.5

High 1.6 0.5 0 0 Medium 3.2 3.8 1.6 0

Low 70.8 5.9 8.6 0.5



High 0.5 0 0 0 Medium 2.7 2.7 0 0

Low 83.8 8.6 1.1 0



High 0 0 0 0 Medium 0.5 1.1 0 0

Low 94.1 4.3 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



Page 119 of 144


Extreme 0 0 1.1 0 High 1.1 1.1 1.6 0

Medium 2.7 3.2 3.8 0 Low 63.8 14.6 6.5 0.5



High 0 0.5 1.1 0 Medium 5.4 1.6 0 0

Low 81.6 9.2 0.5 0



High 0 0 0 0 Medium 2.7 0.5 0 0

Low 95.7 0.5 0.5 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 98.9 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0.5 0 0 High 0 1.1 0 0

Medium 1.6 2.2 0.5 0 Low 83.2 7 3.2 0.5



High 1.1 0 0 0 Medium 2.2 0.5 0 0

Low 91.9 3.8 0.5 0



High 0.5 0 0 0 Medium 0.5 0 0 0

Low 98.4 0.5 0 0



High 0 0 0 0 Medium 0.5 0 0 0


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Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Table B113 Hay 7 Day


High 0.5 0.5 1.1 0 Medium 1.6 1.1 1.1 0

Low 84.3 7 2.7 0



High 0 0 0 0 Medium 1.1 1.1 0.5 0

Low 94.1 3.2 0 0



High 0 0 0 0 Medium 0.5 0.5 0 0

Low 98.9 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0.5 0 0.5 0.5 High 0 2.7 1.6 0.5

Medium 2.2 3.3 0 0 Low 68.7 14.3 4.4 0.5


Page 121 of 144



High 0.5 1.1 0.5 0 Medium 3.3 1.6 0.5 0

Low 85.7 6 0.5 0



High 0 0 0 0 Medium 3.3 1.1 0 0

Low 94 1.1 0.5 0



High 0 0 0 0 Medium 0 0.5 0 0

Low 98.9 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0.5 2.7 0 0

Medium 3.8 2.2 0.5 0 Low 80.3 9.3 0.5 0



High 0 0.5 0 0 Medium 4.9 0.5 0 0

Low 92.3 1.6 0 0



High 0 0 0 0 Medium 1.6 0 0 0

Low 98.4 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0


Page 122 of 144



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0.5 0 0 High 0 1.1 0 0

Medium 0.5 1.6 0.5 0 Low 80 10.3 4.9 0.5



High 0 0.5 0 0 Medium 1.6 0.5 0 0

Low 90.8 5.9 0.5 0



High 0.5 0 0 0 Medium 0.5 0 0 0

Low 97.8 1.1 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 99.5 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 0 0 0

Medium 0 0.5 0 0 Low 96.2 2.7 0.5 0



High 0 0 0 0 Medium 0 0 0 0

Low 99.5 0.5 0 0


AHLU 86


Page 123 of 144

Extreme 0 0 0 0 High 0 0 0 0

Medium 0 0 0 0 Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Extreme 0 0 0 0 High 0 0 0 0

Medium 2.2 0.5 0 0 Low 93.5 3.8 0 0



High 0 0 0 0 Medium 0.5 0 0 0

Low 98.9 0.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 124 of 144


Extreme 0 0 0 0 High 0 0 0 0

Medium 0 0 0 0 Low 92.4 5.9 1.7 0



High 0 0 0 0 Medium 0 0 0 0

Low 97.5 2.5 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 99.2 0.8 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



High 0 0 0 0 Medium 0 0 0 0

Low 100 0 0 0



Page 125 of 144

Appendix C - Survey Results

Table C1 If you were to number the following from 1-5 in terms of priority (with 1 being the preferred and 5 being the least preferred), what would be the best way to communicate with you?

Rank 1 2 3 4 5 Total Responses

Through workshops/training sessions 9 6 14 7 11 47 CHLT e-mail newsletters 23 16 3 1 1 44 ALFA e-mail newsletters 10 12 10 6 4 42 Notices on the CHLT web site 6 6 14 14 10 50 Short webinars to explain CHLT updates 4 7 4 14 18 47

Total Responses 52 47 45 42 44

Table C2 What other methods of communication would you like to receive

information on the CHLT? Email 8 SMS 3 All of the above / Above is enough 2 Nil / N/A / None 5 Total Responses 18

Table C3 Are our current communications easy to understand??

Yes 54 No 2 Total Responses 56


Page 126 of 144

Table C4 Would you use a CHLT smart phone ‘app’ if one were available?


Table C5 What other information would you like to receive? Rainfall alert 1 Weather tracking 1 Total Responses 2


Page 127 of 144

C1. CHLT website

Table C6 Do the web pages load in an adequate time?

Yes 30 Not always 19 Slow but I'm used to it 5 Total Responses 54


Page 128 of 144

Table C7 Do you like the new Site Summary page and find it helpful?

Yes 37 Sometimes 14 No. Why?

• I don't like the animated loop (slow internet connection). For each day I'm really only interested in the HLI max number, but I can only view 1 day at a time. If the number is high, then I want to see the hourly detail. I generally go straight to the hourly detail page without even bothering to look at the site summary page as the top of the hourly details page has what I'm most looking for.

• Not used it • Irrelevant to our conditions

3

Total Responses 54

Table C8 What is more important to you in a Site Summary?

More information displayed for 1 day 32 Less information displayed for 7 days 21 Total Responses 52


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C2. Major Town Forecasts

Table C9 Do you currently view any of the major town forecasts?


Table C10 Would you like access to the major town forecasts in the future??


Table C11 Would you like to be able to view the major town forecasts on your My Site Summary page?


Page 130 of 144



Page 131 of 144

Table C12 If Katestone were to remove the major town forecast information from the publicly accessible section of the CHLT website, would you object?

No 39 Yes. Why?

• we dont have a site forecast set up yet • Transport of cattle through major towns is an issue. • When you are trucking cattle it is very good to be able to look at what forecast conditions

cattle could potentially be arriving in. • On site data is not always reliable so town data is a backup/reference point • SOMETIMES YOU LIKE TO SEE INFORMATION ON OTHER AREAS • It helps planning when transferring cattle to kill locations and transferring feeders • It is useful information esp for the big picture • I advise other producers and employees to look at website in my absence • We don't have an onsite weather station. • In case people within the team wanted information but did not have the password. • Need to see how other regions are faring • informative • Often good quick reference to use • Don't have a weather station

14

Total Responses 53


Page 132 of 144

C3. Forecast Accuracy

Table C13 How accurate do you perceive the weather forecast to be for your site?

Very accurate most of the time 13 Very accurate some of the time 2 Accurate most of the time 29 Inaccurate some of the time 6 Inaccurate most of the time 3 Total Responses 53 Additional comments:

• It's OK. During the storm season, it seems to regularly predict rainfall (& associated EHL events) when BOM is only forecasting isolated showers for our area. As the feedlot is located in a bit of a rain shadow (due to surrounding terrain), these EHL events don't occur in reality. Unfortunately, a mass over-prediction of EHL events has led to staff complacency at times.

• breezes off the edge of storms can make a lot of difference but are impossable to predict • there can be variations on actual HLI • OFTEN MAX TEMP IS 4-8 DEGREES DIFFERENT TO WHAT IS EXPERIENCED • The major centre is some distance from my actual site, and therefore less relevant than actual onsite

data • Totally useless • the actual AHLU calculated form our weather station and the forecast are out significantly

Table C14 How did you determine the accuracy of the forecast?


Page 133 of 144

By comparing the weather conditions on the day against the earlier CHLT forecast 14 By comparing my site forecast to the BOM forecasts for the closest town 11 By comparing it to my on site weather station 12 By comparing it to the behaviour of my cattle 12 Just a feeling 1 Other

• Combination of 1, 2 & 4 • comparing to site weather station and behaviour of cattle • weather conditons at the time and what our weather station is telling us

3

Total Responses 53

C4. Site management practices

Table C15 Do you have a weather station?



Page 134 of 144

Table C16 Is your weather station data being sent to Katestone for inclusion in your forecast?

Yes 14 In Progress 5 No. Why not?

• difficult to retreive and "just another job for a one man band to do" • in the process • Not sure • I have started to do it, but it became too difficult... i need to do it in the off season • Not set up to do so • Didnt realise it could be • Problems with connection • Consultant

8

Total Responses 55


Page 135 of 144

Table C17 When your weather station is indicating your cattle will be carrying a heat load, do the cattle show signs of heat stress?

Yes always 3 Most of the time 11 Sometimes but tends to overestimate the heat load 11 Sometimes but tends to underestimate the heat load 1 Sometimes overestimates and sometimes underestimates 1 Total Responses 27

Table C18 Do you have a daily heat load monitoring plan?

Yes we use it every day when it is hot 30 Yes, we use it every day during the summer 15 Yes but we don't really use it 6 Don't have one 2 Total Responses 53


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Table C19 If the forecast indicates there is a heat event on its way, when do you take action to address the issue (e.g. move to heat load ration)?

Heat load event predicted for three – five days out 22 Heat load event predicted for tomorrow 8 When I get sent an alert 5 Heat load event predicted for five – seven days out 2 Don't change any activities based on the forecast 3 Wait until cattle show signs of heat stress before acting 3 Heat load event predicted for today 3 Other (please specify)

• Our Heat Load Action Plan contains many steps - some are only undertaken when signs of heat stress are evident (eg sprinklering), others are based on daily forecasts (e. g. monitoring frequencies), others are implemented about 3 days prior (eg heat load ration). Note that for the heat load ration, it depends on how severe the heat load forecast is (we don't act unless forecast AHLU > 20), how long the forecast event is to last for (if only 1 day, we don't act) & how likely I think it is that the forecast is accurate (ie if forecast EHL event is mostly due to a wet weather event that BOM doesn't really forecast for our area, then we don't act at that time)

• depends on the day, and what the cattle have been like recently • Take into consideration the local climate, pen conditions, forecast weather conditions and

the windspeed. Generally the duration of the expected heat load period, humidity and what the cattle are actually indicated upon inspection trigger the move to heat ration or alternate action to mitigate the heat.

• When we reach 2 of the 3 indicators as outlined in the heat stress plan • evaluate to length and severity of the event, If the event depending on severity is only

forecast to be no more than 2 days we don’t bother to change, or we will only change high risk cattle.

• Forecast is never applicable to our conditions • A combination of forecast, current cattle behaviour and our own weather station.

7

Total Responses 53


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C5. Alerts

The alerts were changed at the beginning of this summer as follows:

• AHLU event today: AHLU >50 units for today • AHLU event tomorrow: AHLU >50 for tomorrow and AHLU = 0 for less than 6 hours • Extended AHLU event: AHLU >50 units for more than 3 consecutive days • Incomplete night-time recovery: AHLU = 0 for less than 6 hours for more than 3

consecutive days in 7 day forecast period • Rapid HLI change*: change in HLI >40 units over 4 hours.

(Please Note: This alert is likely to change when more research becomes available)

Table C20 Do you think the new alerts were an improvement over previous years?

Yes 46 No. Why not?

• N/A - we are new users • 1st year we have used Katestone • Less detail would be better - just an alert for a heads up is all that’s required - I can work out

the rest • I'm new to this site • Totally useless • The site crashed just prior to a predicted event

6

Total Responses 52


Page 138 of 144

Table C21 The alerts were:

Just right 33 Not received often enough 6 Received too often 2 Too confusing to understand 1 Other (please specify)

• not accurate for our location • This year the AHLU in the Riverina was relatively low but the cattle expressed higher level of

heat load due to the humidity. • Sometimes ok, sometimes inaccurate • they seemed to be right but that was just our experience • Never for this area • good to know the forecast but not really accurate so hard to take seriously • never recieved any alerts • WA was bombarded with eastern states alerts

8

Total Responses 53


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Table C22 (By State) The alerts were:

Other (please specify)

• QLD o Sometimes ok, sometimes inaccurate o they seemed to be right but that was just our experience o not accurate for our location

• NSW o This year the AHLU in the Riverina was relatively low but the cattle expressed higher level of

heat load due to the humidity. o good to know the forecast but not really accurate so hard to take seriously

• WA o WA was bombarded with eastern states alerts o Never for this area

• VIC o never received any alerts


Page 140 of 144

Table C23 (By Feedlot Size) The alerts were:

Other (please specify)

• <1000 SCU o not accurate for our location o Never for this area

• 1000 – 5000 SCU o Sometimes ok, sometimes inaccurate o they seemed to be right but that was just our experience o never received any alerts o WA was bombarded with eastern states alerts

• >= 10000 SCU o This year the AHLU in the Riverina was relatively low but the cattle expressed higher level of

heat load due to the humidity. o good to know the forecast but not really accurate so hard to take seriously


Page 141 of 144

Table C24 Not only were the type of alerts different from previous years, the level of risk was also set to a value where the accumulated heat load was just in the high risk category (AHLU>50). In previous years you could select low, moderate or high risk category to receive an alert. Do you think the AHLU>50 indicated the appropriate level of risk of at your site?

Yes 37 No, too low 11 No, too high 4 Total Responses 52

C6. Why Register?

Table C25 Where did you hear about CHLT?

Austmeat Auditor 21 ALFA comms 13 Word of mouth 4 MLA comms 3 Other 9

Nutritionist 5 ALFA workshop 1 Work 1 As part of heat load management program 1 Original Katestone site 1 Early contributor to risk modelling 1

Total Responses 50


Page 142 of 144

Table C26 How can the CHLT be improved? No improvement required

• Information was good, so not sure how it can be improved at this stage. • Have the daily forecast available early in the morning • good as is • Keep up the good work!

Improve accuracy of forecast

• Keep looking at improving the accuracy of the forecasting. • need to work on the forecast vs. the actual data from on site weather station to see if the prediction can

be more accurate otherwise there is no confidence to make management decisions

Improve reliability of forecast

• Not sure of the cause, but there were quite a few times, during hot weather, that the website was not able to be accessed. This needs to be fixed as a matter of urgency.

• Being available for access and not broken down all the time as it was this summer • Get a backup plan to get the info out. This last season there was a critical failure of communication at

the only real moment of the year when we needed info to manage the coming situation of heat and humidity. As it transpired where we were, deadly conditions did not develop but it was only luck. This past year was simply NOT GOOD ENOUGH!

• Reliability. Site was inoperable during several heat periods. Further focus on 2 - 3 days prior to heat event accuracy.

Greater accessibility

• a phone app would be handy sometimes • Be more interactive and more info available while out in the pens or away off site.

Earlier forecast

• It would be great if forecasts could be updated earlier in the day (so that planning can occur before 7am), but I realise that you are probably waiting for weather forecast updates first. During summer, I like to look at forecasts around 6-6:30am QLD time, but they are rarely available that early. Presumably due to the upload time of the new forecast information, even the previous day's forecast for today is typically unavailable at that time.

• Have the daily forecast available early in the morning

Simplify the forecast

• Keep it simple and not connected to a whole lot of record keeping and data collection. • Be more region specific , WA doesn't need eastern states alerts

Other comments:

• For our site, just scrap the idea! • I want to be able to see the forecast HLI for the next 7 days at the end of the afternoon. I quite often

check at night to see what is coming but would find that no forecast would load until the next morning when I'm busy doing other stuff.

• Link to weather station • BECOME MORE SITE SPECIFIC • Make it easier to get onsite weather stations online. I believe that once CHLT are aware of an onsite

weather station, it would be in their interest to get the data... this can only improve the overall data provided to all subscribers.

• Maybe a yearly on farm visit to some of the feedlots in different states during summer months.


Page 143 of 144

Table C27 Do you plan to use the CHLT into the future?

Yes 48 No. Why not?

• Not ever anywhere near accurate 1

Total Responses 49

Table C28 How can the uptake of the CHLT service among lot feeders be improved?

• Communication • Maybe communication between feedlots to let others in the industry know certain ways of dealing with

heat events. I don't think feedlotters tell the whole truth about how bad or good they got through a heat event

• Word of mouth • Continue site promotion • Through promotion by Jeff House • Workshops • accurate information needs to be circulated for specific locations instead of general areas • Providing good predictions • Continue to provide accurate service and keep being proactive through ALFA website and newsletters. • It needs to be one of several tools and managers need to understand how to evaluate the warnings • CONTINUE TO MAKE USER FRIENDLY APPLICATIONS • Increase the number of sites, either major centres and / or onsite weather stations. • Making on site weather stations mandatory • At least every second year do a national circuit of workshops to train new people coming in and refresh

we old and broken down ones. • Facilitate coupling with processor facilities


Page 144 of 144

Table C29 How can the uptake of the CHLT service among processors be improved?

• Communication. • Marketing to processors? • Getting into the processing industry through workshops. lairage conditions at sites needs to be

monitored as well as feedlots. • The more information the better • By collaboration and sharing of knowledge • Workshops or visiting plants • Make appointments and visit processor establishments to hear their concerns and outline the heat

issues for cattle suppliers and clarify the expectations.

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HLI FORECAST SEASON 2011 - 2012...Page ii of iv Table 9 5 and 6 day forecast AHLU contingency tables...

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