Decoding the Virtuous Crop Cycles in India
Story of combining telematics and ground truth data to predict the farm mechanization demand
In this story, we discuss how crop cycle data served as a string that connected our geographic, demographic, hotspot, and mechanization data, to produce several insights of vital importance for the agricultural ecosystem and related industries.
The annual cycle of agriculture consists of several activities performed throughout the year for each crop. These activities include land preparation, inter culture, sowing, harvesting, etc. Based on the activities performed by the farmer, there will be different mechanization requirements, and thus, different machine and implement requirements. If one can predict beforehand which activities are going to be performed in which region at what time, a lot of value can be provided to all players involved in the agricultural industry. This can be viewed as a demand planning and forecasting problem, where we analyze the crop cycle data and figure out the demand of different implements/ tractors/ harvesters/ transplanters throughout the year for different crops in different regions.
Crop Calendar for major Indian Crops
We had geographic and demographic data for the farmers on our platform. The task was to get the crop cycles for the major Indian crops, for different regions. We sourced this data from several trusted open databases. We went a step ahead, and even integrated the mechanization requirements at each step of the crop cycle. As of now, we considered only a few major Indian crops, namely, Rice, Wheat, Rabi, Maize, and Chilli. The crop cycle for a particular crop spanned 6–7 months. Also, we must understand that these cycles are different in different states of India. For example, a task like land preparation for Kharif potato begins in May in Andhra Pradesh meanwhile the land preparation in the state of Bihar would start around August or September.
We organized the crop cycle + mechanization data in a structured way as shown in the figure below.
Putting it all together — Crop Cycle, Geographic and Demographic data
The compiled crop cycles were then combined with the geographic and demographic data that we had for a farmer, and voila! The entire calendar of activities to be performed by the farmer and his implement requirements was available to us.
This can be easily extended to all the farmers in our database, and then farmers with similar calendars can be clubbed together.
Telematics Based Location Intelligence
Even after knowing the clusters of farmers with similar crop calendars, we needed to find certain location hotspots that would aid in campaign planning. Using the telematics data available to us from the various tractors, we had already identified important location hotspots in each region, where the farmers visited frequently or spent a lot of time. A glimpse of our detailed hotspot analysis can be found in this post exploring Meerut from a data lens. We analyzed geographic hotspot data points from West Bengal, Madhya Pradesh, and Maharashtra for the purpose of the pilot. Below is an example of visualization for a village in West Bengal: Bhatar. Bhatar is an important rice belt in West Bengal. Some of the relevant location spots found in these regions were fertilizer suppliers, farm areas, highway meeting points, and fish farming spots.
Thus, this post explains how the combination of crop cycle with the several types of data that we already had (geographic, demographic, etc.), created valuable insights for nearly all players in the agricultural ecosystem.
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