Day1_Theme2_Julie Ingram
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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
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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