What is the true user-engagement of AgroMeteorological services in India?
No offense but are we listening to the farmers or just talking at them?
Image credits: Bernard Gagnon
In order to provide direct services to the farming community of the country an exclusive Division of Agricultural Meteorology was set up in 1932 under the umbrella of India Meteorological Department (IMD) at Pune with the objective to minimize the impact of adverse weather on crops and to make use of favorable weather to boost agricultural production.
- Official website of the Indian Meteorological Dept (website)
Jai Jawan, Jai Kisan! (All hail the soldier, all hail the farmer!)
- Late Sh. Lal Bahadur Shastri
My last newsletter talked about the climate risk from heat extremes in Northwest India. We’re talking about the “bread-basket” region crucial to the country’s food security, especially since we are the most populous country in the world now by some estimates (cheers?). This newsletter talks about the information machinery setup in India to convey the short- and long-term weather-related risks directly to farmers. As we go into an uncertain future with rapidly changing variability of extreme weather events, it’s necessary to ensure that we have a strong foundation of climate services in the country to cope with what lays ahead.
Overview of the AgroMet Service in India
The Meteorological Services for Agriculture in India (in short, AgroMet) is a Climate Service provided by the Indian Meteorological Department (IMD) for the benefits of minimizing weather-related crop yield losses and maximizing agricultural production through capitalizing on favorable windows of weather. “The main emphasis of the existing system, now known as Gramin Krishi Mausam Seva (GKMS), is to collect and organize climate/weather, soil and crop information, and to amalgamate them with weather forecast to assist farmers in taking management decisions.” The production of weather forecast is conducted solely by the IMD (through Gramin Krishi Mausam Seva) and the dissemination of weather advisories to farmers is performed by state agricultural universities through a network of AgroMeteorological Field Units (AMFUs). As of April 2023, agromet advisories are prepared on every Tuesday and Friday for all the agriculturally important districts (~700) and around 3100 blocks by 130 AMFUs and 199 DAMUs (Press Information Bureau of India). An expansion of the current system is underway “to extend up to sub-district/block level with dissemination up to village level to meet the end users requirements with establishment of District Agro-Meteorological Units (DAMUs) in each rural district of India” in the premises of Krishi Vigyan Kendras (KVKs) in collaboration with Indian Council of Agricultural Research (ICAR). Under the Public Private Partnership (PPP) mode, private players in the weather service space like Reuter Market light, IFFCO Kisan (IK), NOKIA-HCL, Handygo, Mahindra Samriddhi, and CAB International have started offering location-specific AgroMet services as well (MAUSAM Letters by Shirish Sharma, 2022). It’s not surprising because the total income gain in the agricultural sector due to the adoption of this service was estimated at Rs. 13,331 crore per annum (~$1.70 billion) in rain-fed districts; another study estimated that an investment of Rs 1000 crores will yield economic benefits of about Rs 50000 crores over a period of 5 years. That’s a no brainer, 50X-bagger investment, right? Right? Let’s cover some more details before we uncover the pitfalls and improvement avenues.
Network details
Total Agro-Met Field Units (AMFUs) – 130
Districts Served – 690
Farmers enrolled on mKisan portal – 43.7 million (or 4.37 crore)
Total planned District Agromet Units (DAMUs) – 530
Total budget – Less than $25 million/annum (best guess)
Services provided
Forecast level – District (or County)
Advisory frequency – Twice weekly (Tuesday and Friday)
Length of forecast – 5 days from date of issue
Forecast variables – Rainfall, Max/Min temperature, Humidity, Wind speed, Wind direction and Cloud cover
Sample 5-day advisory via phone text
Farmers are advised to not apply irrigation and any spray on the crops and also postponed of cotton sowing in view of upcoming adverse weather conditions.
- at Ganganagar District in Rajasthan issued on 2023-05-23
So, where’s the catch?
I’m gonna keep this part short and pose some basic questions that I’m unable to find good answers to.
Who is validating the accuracy of the advisories?
At the block level, the scope of responsibilities dictates a qualitative AND quantitative verification of forecast skill for every single issued advisory. I am searching for the verification archive as I write this. Additionally, I haven’t found any studies on the long-term (say 10 year) performance of the issued advisories across the country.
Are farmers using and benefitting from the issued advisories?
There’s a lot to unpack here. One doesn’t have to dig deeper to infer that the success (or failure) of the entire scheme relies on the end-user engagement. How is this feedback being collected, evaluated and incorporated up the chain? By definition, farmers are the primary and sole users of this service. I could show you the official feedback form used at the block level but that’s not gonna solve anything and I’m not looking to embarrass anyone. In the spirit of being productive, I’m going to borrow from Findlater et al. (2021) and pose broad questions that can hopefully guide my current research focus toward assessing the ground-level engagement of the behemoth GKMS scheme.
Is the Indian Agrimet service process-driven or product-driven?
Is the Indian Agrimet Service demand-driven or demand-relevant?
What do the farmers truly need and how does it affect their decision-making?
I’m gonna leave you a quote from the same paper so you have a better idea of the context of this problem. In the meantime, I’ll keep working on devising a plan to tease out the on-ground reality of the situation and how we can improve the tools in this impressive supply-chain of weather-related information. See you next time.
The production and delivery of climate services are still primarily considered a natural science endeavour, even though the use of climate information is primarily a social science problem. A transformational climate service requires that providers understand why and how decisions are made, by whom, in what diverse sociological contexts and with what non-climate constraints.