How Data Analytics will Transform the Agricultural Sector

How Data Analytics will Transform the Agricultural Sector

Agriculture is one of society's most important production areas. Agriculture contributes 26% of the Gross Domestic Product (GDP) of Kenya’s economy. The sector is the main source of employment to Kenyans with more than 40 % of the total population and more than 70% of Kenya’s rural people. Hence very crucial to economic growth not only in Kenya but the world’s economy too.

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Data Analytics in Agricultural Sector

Just like any other sector, Agriculture is facing challenges some of which are more complex and very urgent in nature, like the changing climate and land scarcity. With the growing population, it means there is a need to improve our crop production significantly to cater to this population. Other challenges in the sector include disease and pests invasions, use of outdated technology by farmers, poor infrastructure, reduced soil fertility, Agricultural research and development, and agricultural extension, etc. Farmers and companies in the Agricultural sector need to adopt big data analytics in order to become more productive. Below are some of the ways data analytics benefits the Agricultural sector.


It is now easier to identify which crop grows best during certain times of the year, season, and other specifics. The use of the historical data from agriculture could uncover all the trends with great efficiency and with minimal errors unlike traditionally where farmers relied on guesswork based on fellow farmers' experiences.


Data analytics can help farmers and companies within the agricultural sector to be much aware of the risk. Data could provide insights to detect this.


Some diseases for example on livestock can spread very easily and faster without Famers realizing the problem. These gadgets can help in collecting data on behavioral change which could be the first sign of illness and this could help farmers to take the right precautions on time.


The supply of Agricultural products presents a number of challenges for both farmers and distributors since food products are perishable. Data analytics can play a key role in the entire supply chain. Using data distributors can identify inefficiencies in their supply chains, retailers can monitor sales and inventory data, customer behavior, hence minimize on waste and stay updated on market demands.


Using data analytics farmers can have a piece of clear information from rainfall patterns to farm inputs hence enabling them to make smart decisions on what crops to plant to maximize yields and make profits.


How companies have leveraged big data effectively.

  1. Bayer: They developed an application that uses machine learning and artificial intelligence (AI) in weed identification. Helps farmers to identify the species of the weed by uploading photos on their app which matches it against Bayer database.
  2. Digital Transmission Network (DTN): A division of Schneider Electric, provides agricultural information solutions and market intelligence to its customers. Using DTN, farmers and commodity traders can access up-to-date weather and pricing data to better manage their business.


Generally, the agricultural sector has not been associated with data analytics, hence Most farmers have not embraced this new revolution yet. With the current changes experienced globally, farmers need to be up to date with them. With a clear understanding of a problem, farmers can get a solution easily. This is possible through big data analytics, it ca n give insights that can help farmers adjust accordingly to maximize on production hence higher profit.


Alphaeus, Nakala