Presentation at the 5th Global Science Conference on Climate-Smart Agriculture.
Title: Contrasting approaches to developing digital tools for enabling climate adaptation
Speaker: Julie Ingram
Energy is the beat of life irrespective of the domains. ATP- the energy curre...
Day1_Theme2_Julie Ingram
1. Contrasting approaches to developing
digital tools for enabling climate adaptation:
insights from Australia & Indonesia
Dr Julie Ingram, CCRI, University of Gloucestershire, UK
Yunita Winarto, Rhino Ariefiansyah, Adlinanur Prihandiani, Ghina
Ulaya, Ezra M. Choesin, University of Indonesia
Sue Walker, ARC, South Africa
OECD Research Fellowship 2018-19
2. Contrasting approaches to developing digital tools
Australia- northern
grain growing areas
Indonesia-
Science Field
Shops
Rainfall Observers Club
collect and interpret their
own rainfall data
Digital technologies -model
based DSS and aggregate
data (apps)
Supporting farmer learning and adaptation in high risk environments
Different cultures and contexts but same high risk environment
requiring useful information for decision making
To what extent do these support farmers’ learning?
3. Science Field Shops (SFS) in Indonesia
Supporting farmers to adapt their farming activities
to cope with increasing climate variability –
agrometeorological learning
SFS are coordinated by Professor Winarto and her research team at University
of Indonesia in Java- Indramayu (2009), Sumedang, and East Lombok
4. Rainfall variability
Local meteorological data not seen as relevant due to local
variability, while Climate Field School’s modular approach
does not engender learning
Both irrigated and rainfed ecosystems in Indonesia are affected by
the consequences of climate change and El Niño events.
Late onset and/or false start of the rainy season, long “dry spells”
in the midst of the rainy season, and shorter duration of
consecutive rainy days significantly affects the productivity of rice
and can lead led to an outbreak of pests and diseases & reduced
yield
5. Science Field Shops
• Farmers record their own daily rainfall and
agroecosystem measurements, observations and on-
farm experiments
• They meet monthly to discuss differences in yields in
relation to rainfall and other agroecological inputs
• Scientists support them with seasonal rainfall
scenarios- a monthly summary of the rainfall expected
over the next three months according to El Niño
predictions
• Based on farmer rainfall measurement and
complemented seasonal rainfall scenarios, farmers
improve their anticipation capability
6. Science Field Shops (SFS) in Indonesia
• Each farmer creates a graphs showing 10 day totals and
patterns to share at monthly meetings
• These often reveal long dry spells in the rainy season, shorter
duration of consecutive rainy days
• Monthly rainfall distribution graphs are particularly useful
during the transition period from the dry season to the rainy
season because of “false start of the rainy season”
• This data helps with planting decisions in rainfed areas and
discussion helps share adaptation strategies: dry nursery beds,
short maturing varieties, maize
7. Digitisation – aims to create a learning tool
which complements the other activities
• Manual recording in a book - valuable source but risk of loss
• Farmers can upload their data via an online application, or offline
excel sheet
• Farmers can download graphs processed from their data to get a
visual picture of the monthly and annual rainfall distribution charts
using a smartphone
• Using these graphs they can compare the average monthly
precipitation over several years which supports adaptation decisions
8. Australia: Decision support systems
• Dryland farming systems of northeast Australia
• Extreme variability in seasonal rainfall –highly
uncertain cropping prospects
What are the chances of getting rain?
What are the current soil moisture and
N conditions?
What inputs are needed?
What are the implications for yield?
9. Decision support systems and tools
Model-based decision support tools to analyze risk and support decision-making
using historical meteorological data - well-established in agricultural research and
practice in Australia
Model based DST like- YieldProphet translate local weather data into
probabilistic yields- offer scenarios, ‘what ifs’
Bespoke or generic software, email/text alerts, online calculators or guidance,
phone apps, and paper-based guidance - aggregate data to enhance the decision-
making power available to farmers - proliferation
DST something that takes away uncertainty either for immediate agronomic
decisions or for longer term learning and adaptation
Optimism
high but
uptake low
10. Digital tools – different levels of processing
to turn data into actionable knowledge
Data Information Simulation
monitoring scenarios
Level of processing: aggregation, analysis
from a quick calculator through to a step wise model
(APSIM) - suits different users
11. Participatory DSS
Low uptake but participatory approaches to model
based DSS:
• Benefits from user input, dialogue, co-design
• Shows tools are used for learning – not decisions
12. Participatory DSS: Learning not decision
support with ‘What if?’ questions
• What if we changed crop or rotation sequence?
• What if we changed the planting date?
• What if we changed the N fertiliser?
FARMSCAPE (Legacy-
YieldProphet generates
probability curves)
“It’s like doing 10 years of harvest or
experiments on your farm”
Comment from farmer to consultant
13. How do DSS help learning?
You might try 2-3 different scenarios- and very rapidly will have
level of confidence to support what you thought anyway or tweak
your own rules of thumb so quickly you’ve got your learning and
you don’t have to go back to it (consultant)
On our farm we’ve been using YP since 2013, barely a year goes by
when it doesn’t correct my intuition. In my view it has a place each
year to help you overcome things you haven’t thought about or
experienced…also it can remember further back than we can (Farmer,
BCG)
You have a 100 years farming experience in an afternoon
People without experience really valued the tools because it gives
them experience, but those older ones who were comfortable about
intuitive decisions are less inclined to want to use them
14. • Proliferation of tools
• New players
• Big data
opportunities
• New generation of
models
• Mobile phone usage
“Ten farming apps you
should download”
The Land Jan 2017
Digital disruption to learning or
opportunity?
15. Digital disruption to learning?
Experiences with DSS have
shown that users can benefit
most by learning due to:
• Asking ‘What If’
• Resetting rules of thumb
• User input -group
dialogue & co design
• Networks of support
Proliferation of apps, software
tools, big data
• Simple and quick
• Requires little processing
• But where is this learning?
• Turning the what? But what
about the why?
• Data rich but knowledge poor?
Digital tools need to build the growers’ capability to
be not only data users but also co-creators
Involve growers and advisory networks in the
cooperative development and testing of these
digital tools
16. Concluding remarks
Australia- DSS and
apps
Indonesia-
Science Field
Shops
Self recorded and digitised
archive of farmers own rainfall
and agro ecology data –visual
analysis helps adaptation –
experiential processing
DSS -analytical with
experiential processing leads to
learning. Proliferation of
‘simple’ digital tools - will it
disrupt learning?
Different institutional and cultural contexts but show that:
• Combining analytical processing with farmers’ experiential processing
is important for learning
• In Indonesia social networks are a key dimension
• Participatory input in DSS in Australia provided benefits
17. Digital tools: different levels of processing
WISDOM
KNOWLEDGE
INFORMATION
DATA
ADD
VALUE
Monitoring Sensors GPSExperiment data
MODELS
DATA
Ackoff, 1989; Eastwood et al., 2012
Level of
tool
processing
Experiential
processing
Analytical
processing
18. Thank you
Many thanks to
Professor Winarto and her research
team at University of Indonesia and to
the farmers and facilitators of the
Science Field Shops in Indramayu,
Sumedang and Lombok
Professor Helen Ross University of
Queensland and many industry and
institutional interviewees
OECD for the CRP research fellowship
funding opportunity
19. Selected Refs
Winarto Y.T., Stigter C.J. 2016. Incremental Learning and Gradual Changes:
"Science Field Shops" as an Educational Approach to Coping Better with
Climate Change in Agriculture. In: L. Wilson and C. Stevenson (Eds.).
Promoting Climate Change Awareness through Environmental Education. IGI
Global (Information Science Reference), Hershey, PA, USA, pp. 59—93.
Winarto, M.Y.T. and Stigter, K., 2011. Agrometeorological learning: coping
better with climate change. Saarbrücken: LAP Lambert Academic Publishing.
Rhino Ariefiansyah, Ezra M. Choesin, Adlinanur F. Prihandiani, Dea Rifia Bella,
Jefri Pakpahan, Fernan & KahardityoTransforming Farmers' Empirical
Observations into Scientific Products: Agromet Data Application
Development as a Means of Storing and Processing of Farmers's
Agrometeorology Data. Not yet published
Editor's Notes
Digital technologies are set to play an increasing role supporting farmers’ learning and knowledge development. Web and blog sites, social media, mobile applications, decision support tools, email discussions and e-learning products are growing across the web to support rural learning. However developing digital tools that convert data into useful information for decision making remains a challenge
This paper looks at 2 approaches (loosely top down vs bottom up)and asks To what extent do these support farmers’ learning?
SFS aim to help increase the adaptive capacity of the farmers, - prompted by limitations of existing extension service, and government run Climate Field Schools (CFS) to build capacity. See refs at end of ppt
UI introduced agrometeorological learning processes among farmers in other regencies, namely Indramayu in West Java in 2009 and East Lombok in West Nusatenggara in late 2014.
Science Field Shops (SFS) are equipping smallholder farmers to undertake their own rainfall and agroecosystem measurements, observations and on-farm experiments, and supporting them with seasonal rainfall scenarios. Together these enable farmers to adapt to, and build resilience for climate change.
In these shops, farmers, scientists and local extension officers meet to discuss consequences of vulnerabilities based on the farmers’ own discoveries from rainfall measurements and agroecosystem observations, and to contribute to solving actual local problems expressed by farmers
Based on farmer rainfall measurement and complemented seasonal rainfall scenarios, farmers would be able to improve their anticipation capability and make decisions accordingly
Photo acknowledgement- Professor Winarto’s team
interannual variations in precipitation severely affect agricultural activities in Indonesia,
- increasing irregularity and intensity of rainfall is a concern
Climate services, (1) measuring, recording, and documenting daily rainfall for every participant; (2) observing, recording, and documenting agroecosystem data; (3) comparing and analyzing yields at the end of the farming season by considering rainfall conditions, availability, and use of intake, methods of farming, and other related factors; (4) organizing SFS activities; (5) compiling, translating, and disseminating climate forecast information in the form of seasonal rainfall scenarios (every three months and updated monthly); (6) exchanging knowledge about the above aspects; and finally, (7) providing guidelines for implementing field experiments to answer practical problems faced by farmers
Their data are valuable for farmers as they act as a reference when dealing with similar climate or agroecological events. During a weather-related event such as drought, flood, or pest outbreaks, farmers consult their records to learn the appropriate strategies to avoid loss or failure
Farmers are sent monthly climate scenarios in order to provide them with new knowledge that can be combined and discussed with their gathered data. The terminology of the climate scenarios is explained in advance so that farmers know how to interpret the data.
This “seasonal scenario” is a monthly summary of the rainfall expected over the next three months according to predictions from El Niño - Southern Oscillation (ENSO) indicators of sea surface temperatures and Southern Oscillation Index (SOI) pressure differences (Walker, 2017)
Group of rainfall observers discussing their agrometeorological observations. (photo by Yunita T. Winarto).
The learning process was expanded to include visualisation of the rainfall data and observations. The idea of graphs came from the farmers,
Not yet published: Transforming Farmers' Empirical Observations into Scientific Products: Agromet Data Application Development as a Means of Storing and Processing of Farmers's Agrometeorology Data by Rhino Ariefiansyah, Ezra M. Choesin, Adlinanur F. Prihandiani, Dea Rifia Bella, Jefri Pakpahan, Fernan & Kahardityo Digitisation of 10 years data- website launch and visualisation
Crop yield is strongly influenced by extreme variability in seasonal rainfall and so the prospects
for any cropping season are often highly uncertain. This high risk environment makes many
investment decisions such as fertiliser application difficult, and many agronomic
decisions encompass trade-offs between options of low risk and return versus those
with higher returns but commensurately higher risks. Farmers can offset the risk of
low in-season rainfall through reduced cropping frequency and storing soil water
during fallow periods prior to planting.
Analyses of historical meteorological records enable farmers and
other decision-makers to better understand and adapt to the
variability
Translating “raw” climate information into agricultural impacts and management advisories increases its relevance for farmer decision-making
DST
DSS for farmers/advisers- computer-aided management systems are based on scientific models developed with the purpose of enhancing farmer decision-making
Bespoke or generic software, email/text alerts, online calculators or guidance, phone apps, and paper-based guidance - aggregate data to enhance the decision-making power available to farmers
Agricultural DS tools are typically software applications, commonly based on models describing various biophysical processes in farming systems and the response to varying management practices (Jakku & Thorburn, 2010;
McCown, R.L., 2002a. Locating agricultural decision support systems in the troubled past and sociotechnical
complexity of ‘models for management’.Agricultura l Systems 74, 11–26.
McCown, R.L., 2002b. Changing systems for supporting farmers decisions: paradigms, problems, and
prospects.Agricul tural Systems 74, 179–220.
Decision support systems (DSS) are interactive and usually computer-based systems
that help decision-makers utilise data and models to solve unstructured problems
Although a lack of widespread adoption by farmers. Where researchers, advisers and farmers have collaborated in developing DSS to pursue improved farm management practice, benefits have been realised.
Quotes from yield prophet users (2019)
Hansen, J.W., Vaughan, C., Kagabo, D.M., Dinku, T., Carr, E.R., Körner, J. and Zougmoré, R.B., 2019. Climate Services Can Support African Farmers' Context-Specific Adaptation Needs at Scale. Frontiers in Sustainable Food Systems, 3(21), pp.1-16.
Shared problem of the usability gap DST and climate services
Forecasts – dealing with probabilistic information – not context specific
Ackoff (1989) data is the basis of the data-information-knowledge-wisdom hierarchy