Weatherproofing Farmers Through Climate Services

Unpredictable weather variations and extreme events are now being seen as signs of the coming of climate change. This variability in climate, as also highlighted by the Intergovernmental Panel on Climate Change (IPCC), poses risks for food security. This calls for evaluation of various adaptation and mitigation options that can secure farmers’ livelihoods and provide food for all.

Our farmers’ presumptive analysis of the weather through traditional knowledge and age-old experience has long held them and their crops in good stead. Today, climate information and advisory services sent to farmers, on their mobile phones, are helping make agriculture more resilient towards the impacts of a varying climate.

The 2 Ts boost – technology and telecom

In India, these services have seen a giant leap of a change in last 10 years. The traditional “Farmers’ Weather Bulletin” and TV broadcasts including All India Radio have now evolved into sophisticated climate products and services delivered using ‘techno-social’ tools – smart phones and mobile apps.

The years 2006-07 saw a surge in agro-met services with companies like Nokia Life tools and Reuters Market Light (RML) entering the Indian market, which for a long time was served only by the Indian Meteorological Department (IMD). Today, weather information is also accompanied with market-related information, helping farmers get fair bargains for their produce.

The growth in number of farmer subscribers for climate services has been overwhelming. Over 50 lakh farmers have been reached in the state of Maharashtra only, while the number across India is in excess of 1.5 crore.

Technologies for effective dissemination and outreach are kicking in and are being implemented at scale by IMD’s Agro Meteorology Programme, GKMS (Grameen Krishi Mausam Seva). Innovations at local levels are also being experimented with. For e.g. Watershed Organisation Trust’s (WOTR) Agro-Meteorology program uses Automated Weather Systems (AWS) to improve the effectiveness and accuracy of local weather information. The farmers are informed about their local weather conditions almost real time through AWS, allowing them take more weather informed decisions.

Scaling and Downscaling

Scaling up of such experiments is a must but it poses several challenges. Since India has diverse topography and climatic conditions, the extent of village-level, farmer -specific data available is very limited. Also, there are limitations for downscaling district level or block level weather forecasts right up to the village-level.

What makes scaling further complicated are the institutional challenges that arise due to the amount of coordination required for generating and delivering advisories. The climate services sector in India is an example of a consortium of knowledge networks made up of private, public and not-for-profit institutions, including universities. This means that every advisory service requires collaboration between at least 3-4 different institutions!

Bottom up Responses

Farmers at their end are also using technology to battle the forces of weather variations. Using their smart phones they have formed crop-specific Whatsapp groups, which act as hyper-local communication platforms for and by farmers. This is an example of a bottom-up process of development and implementation of adaptation measures. Farmers can self-advise and readily share information among peers, such as response to pest attacks, differences in market prices etc.

TERI has been studying climate services system in India through its Indo-Norwegian Research Project on Governance of Climate Services. The project is a three-year study that analyses conditions for effective governance of climate services in India. It compares 4 Indian agro-meteorological service systems, both public and private to study how they are governed and if they provide rural farmers with tailored and participatory services in Maharashtra.

The project’s findings would be up for discussion at this year’s World Sustainable Development Summit from 15th to 17th February 2018. Do join us!

For more information visit –


Challenge of Policy Making for Climate Change Adaptation

Farmers in India and across the world are witnessing new variations in weather and seasonal changes. The challenge to take decisions under these variations gets compounded because often there is no precedent to it. What decisions work best can be known through experimentation and mostly in hindsight. This makes adaptation to climate change a complex process. The cause-effect conundrum, i.e. which solution gives what result is almost impossible to predict with certainty. Thus, human decision making under such unforeseen situations needs to be aided by additional information or decision support systems. Climate Services, the delivery of weather based agriculture advisories using ICT, help aid farmer’s decision making process by providing timely weather forecasts and corresponding advisories on agricultural practices.

The information farmers receive on climate services provides them with an option of incorporating it into their agriculture decision making. But it is almost impossible to measure with certainty how much of this information do they incorporate, in what form and when. This makes impact evaluation of adaptation solutions, like climate services, a very challenging exercise. At times even the farmers are unable to clearly demarcate the important variables they use for their decision making process. This is so because in order to cope with weather variations there are many possible actions and solutions to be experimented with. But the most effective solutions may not be known to them at the early stages and thus their decision making keeps evolving as they experiment with a set of solutions. Through this process of iterative decision making they learn to adapt to weather variations. This makes adaptation a highly localized and continuous process with no clear traces of solution impact pathways. But a set of good practices evolve over time.

Challenges to measure or generate evidence of adaptation further hinder the uptake and popularity of good practices. There is also a theoretical difficulty in establishing units for measuring adaptation and establish monitoring systems for its evaluation. This makes communicating adaptation through evidence a very difficult task. It also challenges the imagination of policy makers who mostly rely on numbers for estimating impacts. For example, the climate mitigation negotiations use the 2°C limit of temperature rise as the reference for determining how much emissions need to be reduced to achieve this climate goal. But in case of adaptation there is a dearth of quantifiable numbers which could guide the policy planning process. Thus, policy making for adaptation requires a shift of two kinds

1)   Moving away from relying only on numbers, and

2)   Decentralization of policy making to account for localized adaptation processes.

This shift further brings up two challenges

1)   How to measure what is intangible or un-measured, and

2)   At what scale should the policy making process be localized.

Unless research on climate change adaptation focuses to find answer to these two challenges, policy making for adaptation to climate change would remain a very challenging task.

Note: This article was first posted on my linkedin on October 3, 2016 for TERI’s World Sustainable Development Summit 2016. Link:

My Journey of Systems Thinking – Part IV

Many people have been asking me how to use systems thinking, how to make models, what are the steps that I follow, how does the development process unfold? To be honest, there is not set template that one can follow and reach the end. As John Sterman says, “It is inherently a creative process.” But there are steps which can guide the model building process. Sterman in his book, Business Dynamics, mentions these steps in detail and I recommend everyone interested to go through them. I myself has used them to write proposals on research using systems thinking and modeling. But over time I realized that applying them required some degree of adaptation of these steps. Since in India the degree of complexity is relatively more as compared to other developed countries where data is available and reliable under most circumstances, we need to be very creative while trying to understand complex systems using systems thinking. To give an example, while working in rural areas one realizes how poor is the data availability even for critical resources such as ground water. I remember once on a water wind mill assessment trip we were struggling to identify water hand pumps which are dry and which are still pumping water, what is their depth, when they were installed etc. People in villages had some vague idea about their date or year of installation, depth etc. But all that was not good enough for us to take a call on whether to implement a water windmill or not. The only parameters for which data was available and reliable (to an extent) was the meteorological conditions i.e. the wind speed, direction, precipitation etc. That too because WOTR had Automated Weather Stations installed there. So this goes to show that while working in India data availability and reliability is a big constraint. The second constraint is the information and knowledge available to understand the local dynamics. Another example being, people in villages find it very difficult to estimate their household numbers be it water, finance, energy or livestock productivity. For eg. their income and expenses figures rarely tally. Giving them the benefit of doubt that they take short term loans, receive some money through family, friends etc. even then all these numbers would often not add up. I have had a first hand experience on this issue too. So modeling social, ecological and economic systems in informal setting in India is a hard, hard work. Lot of assumptions and (educated) guesstimates have to be done to complete any sort of modeling process.

All this relates to how one then applies systems thinking and modeling in dynamically complex situations and systems in India? Does John’s modeling process and steps help our cause? In my opinion they do, albeit to a limited extent. One needs to creatively adapt them and use his/her sensing to move ahead. In my experience serendipity also plays an important role. Almost every time I have been badly stuck in some of the most challenging modeling projects and then I have found a way out either by someone willfully joining the work or through some support. All of it being not part of original plan.

So after doing some modeling work in very un-ordered settings I have adapted John’s modeling steps and have penned them down.  The pre requirements before these steps are that the problem has been identified, modelers have familiarized themselves with it and proper scoping exercise has been done with the end user and experts.  The modeling steps then are:

  1. Define purpose of the model and read about the issue or system of interest
  2. List down sectors, sub systems and determine model boundary
  3. Create Dynamic Hypothesis (DH)
  4. Develop questions based on DH
  5. Show DH to community (end user) and experts
  6. Review the questions and DH after step 5
  7. Collect information and data using questions developed in step 6
  8. Develop initial versions of simulation model
  9. Review DH based on simulation model
  10. Show revised DH to community (end users) and experts
  11. Show initial simulation results to community (end users) and experts
  12. (Incorporate suggestions) Revise the model, its boundaries and develop questions to fill in information and data gaps
  13. Collect data and information to fill the gaps
  14. Develop progressive versions of model (eg. v2, 3…… v17….  and repeat step 13
  15. Perform model calibration, sensitivity runs, extreme conditions test and finalize the model (sometimes back-casting also helps in model evaluation)
  16. Share the results with community for mobilization and aiding action
  17. Make model readable and fit for publication and dissemination
  18. Get the model reviewed by an expert and incorporate suggestions
  19. Publish the model and results
  20. Plan for next phase and how to go deeper into the issue to initiate change process, then start from Step 12 or any other appropriate step as suited

The most important thing to remember is to celebrate learning at each step and document the learning process. You would often be surprised that the learning process is an equally important and influential outcome of the modeling process as much as the end simulation results are.