Digital technologies already serve an important role in the delivery and communication of agricultural information, complementing and expanding the reach of conventional extension services. However, sophisticated digital platforms and their applications in learning environments offer new opportunities which may significantly enhance agricultural knowledge exchange. This paper reports on a project that uses cutting-edge advances in virtual world technologies to develop web-based virtual ‘discussion-support’ tools for the rapid sharing of targeted climate information. These tools are designed to provide a stimulus for discussion, enhanced decision-making and improved climate risk management on farms. The project uses the Second Life virtual world environment to create customized scripted video clips (machinima). These feature real world settings and lifelike avatar actors who model conversations about climate risk and key farm operational decisions relevant to the lives and practices of specific groups of farmers. The system has been trialed with Indian cotton farmers and Australian sugarcane farmers. Further large scale evaluation in a range of agricultural systems will inform continual improvement of the approach. With improved internet access and uptake of mobile technologies, these tools have potential to provide new cost-effective options for real-time information exchange at local, regional, national and even global scales. Such tools may enhance rapid and effective needs-based knowledge sharing, capacity building and online learning opportunities within the agricultural sector; provide increasing opportunity for discussion around risk, decision-making and implementation of sustainable farming practices; and enable agricultural industries to become lead innovators in blended digital and ‘in person’ extension and outreach. Improved climate risk decision-making and management in agriculture is critical to the well-being and long-term sustainability of farming communities and future global food security.

1. Virtual world technologies & new
tools for supporting climate risk
decision making in agriculture
Digital Rural Futures Conference 2014
25th-27th June 2014, University of Southern Queensland, Toowoomba QLD
Dr Kate Reardon-Smith
Research Fellow (Climate Risk Management)
Digital Futures-CRN (Collaborative Research Network)
University of Southern Queensland, Toowoomba AUSTRALIA

2. Co-authors
Helen Farley2, Neil Cliffe1,2, Shahbaz Mushtaq1, Roger Stone1,
Joanne Doyle2, Neil Martin2, Jenny Ostini2, Tek Maraseni1, Torben
Marcussen1, Adam Loch3, Janette Lindesay4
1. International Centre for Applied Climate Sciences (ICACS), University of
Southern Queensland (USQ), Toowoomba QLD Australia
2. Australian Digital Futures Institute (ADFI), University of Southern
Queensland (USQ), Toowoomba QLD Australia
3. School of Commerce, University of South Australia (UniSA), Adelaide SA
Australia
4. Fenner School of Environment and Society, Australian National
University (ANU), Canberra ACT Australia

3. Digital Futures-Collaborative Research
Network (DF-CRN) Project 3
“Investigating the impact of a web-based
discussion-support agricultural-climate
information system on Australian farmers’
operational decision making”

4. Objective
• To develop digital tools for cost-effective delivery of timely,
targeted, contextualised agri-climate information and
knowledge services
Strategy
• Create and trial a virtual discussion-support system that integrates climate
information with farm management decision-making for the.
• Assess the effectiveness of the virtual discussion-support system in building
capacity for improved decision-making and effective climate change
response in a target group of farmers
“… climate information has no value
unless it changes a management
decision.”

5. Australian climate & climate risk
• Highest level of year-to-year rainfall variability globally

6. “… droughts and flooding rains …”
Source: Australia Bureau of Meteorology, December 2006
http://www.bom.gov.au/climate/drought/archive/20061204.shtml
Millennium Drought, 1995-2012
Australia Day, 2013

7. Impacts on agriculture

8. Predictions for future climate in Qld

9. Predictions for future climate in Qld
General temperature projections suggest an average
shift of ~1.0C by 2030 (medium emission scenario) &
up to 2.9C by 2070 (high emission scenario).

11. General rainfall projections suggest an increase in
summer precipitation (Jan to March) but general
decrease in other seasons.

12. Historical rainfall trends ..
Rainfall in eastern Queensland has declined
(reduction in duration and frequency of events)
but rainfall intensity has increased (Crimp).
Rainfall duration Rainfall intensity

13. Source: Webster et al. (Science, 2005)
Intensity of hurricanes according to the Saffir-Simpson scale
(categories 1 to 5):
100% increase in Category 4 and 5 systems since 1970.
Wind speeds
> 209 kph

14. Decision-making under uncertainty
• Increasing demands on science to provide information for
complex decision making to manage climate and related risk
• How can science best support complex decision making?
─ Good scientific knowledge
─ Community/stakeholder involvement
─ Adaptive management
─ Models that enable scenario testing
─ Evidence-based policy making and investment strategies

15. Climate information for
agricultural production systems
• Using seasonal climate forecasts (statistical and dynamic
coupled ocean/atmosphere models) to support adaptation
• Link to agricultural systems
- real time, downscaled regionally-targeted climate
information (increasing skill)
- relevant climate variables (e.g. temperature extremes)
- analysis of potential impacts of climate change and
possible solutions for effectively adapting practices to a
changing environment

16. Sources of climate variability
Climate phenomena Frequency/Time scale
Weather patterns Day/week
Madden-Julian Oscillation Month/s
SOI phases based on El Nino-Southern Oscillation (ENSO) Seasonal to interannual
Quasi-biennial Oscillation (QBO) 1-2 years
Antarctic Circumpolar Wave Interannual (3-5 years)
Latitude of Subtropical Ridge 10.6 years
Interdecadal Pacific Oscillation (IPO) 13+ years
Decadal Pacific Oscillation (DPO) 13-18 years
Multidecadal rainfall variability 18-39 years
Interhemispheric thermal contrast (secular climate signal) 50 years
Climate change

17. Time frames for
agricultural management decisions
Decision Type (eg. only)
Logistics (eg. scheduling of planting / harvest operations)
Tactical crop management (eg. fertiliser / pesticide use)
Crop type (eg. wheat or chickpeas)
Crop sequence (eg. long or short fallows)
Crop rotations (eg. winter or summer crops)
Crop industry (eg. grain or cotton, phase farming)
Agricultural industry (eg. crops or pastures)
Landuse (eg. agriculture or natural systems)
Landuse and adaptation of current systems
Decision Type (eg. only)
Logistics (eg. scheduling of planting / harvest operations)
Tactical crop management (eg. fertiliser / pesticide use)
Crop type (eg. wheat or chickpeas)
Crop sequence (eg. long or short fallows)
Crop rotations (eg. winter or summer crops)
Crop industry (eg. grain or cotton, phase farming)
Agricultural industry (eg. crops or pastures)
Landuse (eg. agriculture or natural systems)
Landuse and adaptation of current systems
Frequency (years)
Intraseasonal (> 0.2)
Intraseasonal (0.2 – 0.5)
Seasonal (0.5 – 1.0)
Interannual (0.5 – 2.0)
Annual / biennial (1 – 2)
Decadal (~ 10)
Interdecadal (10 – 20)
Multidecadal (20 +)
Climate change
Frequency (years)
Intraseasonal (> 0.2)
Intraseasonal (0.2 – 0.5)
Seasonal (0.5 – 1.0)
Interannual (0.5 – 2.0)
Annual / biennial (1 – 2)
Decadal (~ 10)
Interdecadal (10 – 20)
Multidecadal (20 +)
Climate change

18. Seasonal climate forecasting
http://www.usq.edu.au/acsc

19. Farming systems science
• crop and pasture agronomy
• grazing management
• soil nutrient and water cycles
• precision agriculture
• Crop simulation modelling
• resource economics
 Decision Support Systems
e.g. Yield Prophet (R)

20. Decision Support Systems (DSS)
• Aimed at supporting farming decisions to optimise
yield and profitability
• However, slow/limited uptake of DSS by farmers (Lynch et al.
2000, Nguyen et al. 2006, Hochman et al. 2009)
• Issues identified include:
 fear of using computers
 time constraints
 poor marketing
 complexity
 lack of local relevance
 lack of end-user involvement
 mismatched objectives
between developers & users
(Nguyen et al. 2006)
 Problems associated with implementation of DSS arise largely
from concentrating too much on technologies and not enough
on the users

21. • Farming involves tradeoffs between risks and gains resulting
from management decisions in the face of future uncertainty
• Most problems in agriculture have a large solution space
• Farmers’ main problem is knowing what the future will be, not
how to respond to it (Stone, P. & Hochman 2004)
• So, need to:
─ focus on human rather than technical elements of decision-making
─ provide information to inform/complement existing decision-making
processes
Decision-making in agriculture

22. Targeted support for
on-farm decision-making
Farming systems
science
Seasonal forecast
modelling
Understanding decision-making and
adoption behaviour

23. Second Life
• A virtual world
• User-created content and virtual marketplace
• Avatars which can be customised & manipulated
• Machinima (animated video clips) created
─ scripted conversations
─ recorded soundtracks
─ folio (background sounds)
─ storyboarding
─ screen capture software (e.g. FRAPS)

24. Prototype machinima:
Indian cotton farmers, 2010

25. Sweet success, 2013
Customised avatars – Australian sugar farmers
Back stories
Scripted conversations

26. Contextualized settings
- Queensland sugar cane farm and landscape

27. Pilot ‘Sweet Success’ machinima
• Harvesting (v1) – pilot evaluation
17 semi-structured
Interviews to
evaluate
prototype machinima
2013
• Machinima: a useful tool to support
discussions around climate risk
• Audio: scripts appropriately targeted to
discussion topics
• Visual: avatar ‘look’ was extremely
important
• Technical challenge: seamless link between
climate forecasts and discussions

28. • Four machinima now developed:
─ Harvesting (v2)
─ Fertiliser application
─ Irrigation
─ Planning
Sweet Success scenarios, 2014

29. Evaluation
1. Workshops (4), group discussions and
20-24 semi-structured interviews (pre and post
workshop; qualitative analysis)
2. Online surveys – 300-400 canegrowers
─ Responses to machinima
─ Farming background
─ Approach to risk
─ Decision-making style

30. Research questions/themes
• Potential for machinima to provide a relevant engaging
technology rich learning environment?
• Readily adapted for different farming systems and locations
by using culturally appropriate clothing, language and
settings?
• Able to be disseminated widely and cost-effectively?
• Effectiveness as a discussion support and capacity building
tool?
• Contribution to sustainable land management?

31. Impact
Relevant engaging learning environment
+
Wide and cost-effective reach
+
Capacity building & improved decision
making
Enhanced climate risk management
Sustainable productive farming systems
Environmental security

32. Future challenges
• Availability of suitable technology to enable this system to be
easily/effectively extended into rural areas in Australia and
elsewhere, including developing countries
• Ensuring the relevance of the system to diverse cultures,
traditions, farming systems.
• Customising, in conjunction with stakeholders, to ensure
acceptance by Australian & international farming communities
• Investigating whether such discussion support systems influence
decision-making and result in measurable changes in terms of on-
ground outcomes
• How best to roll this out in the real world (e.g. WAMIS)

33. Acknowledgements
• This project is supported through the Australian Government’s
Collaborative Research Networks (CRN) program.
• Research partners:
─ Noel Jacobson and Amanda Hassett (Top Dingo),
─ Matt Kealley (CANEGROWERS Australia)
─ Jeff Coutts (USQ Adjunct)

34. Thank you