Data is changing the Kenyan agricultural landscape. It is making it possible for policy makers to see more dimensions of farming than was possible before.
Technology is replacing the role of agricultural extension officers, empowering farmers with information and freedom, and creating a greater impact than ever before.
A recent survey on I-Cow, an agricultural productivity application in Kenya, reveals startling statistics of big data.
These previously unavailable data are enabling farmers to improve on productivity. They have been made possible by the advent of technology tools that facilitate collection and dissemination of such big data.
The objectives of the survey were to determine how farmers rate the value, usefulness, and appropriateness of the information they receive on the iCow platform, and if there are any content gaps that will inform creation of better information in future.
In the past, farmers relied on agricultural extension officers for such information, which, even when it was available, was not accurate.
And when the Government withdrew the services of agricultural extension officers following the implementation of structural adjustment programs, only a handful of farmers could afford to hire private consultants.
The random survey selected a 100 farmers from 36 of the 47 counties in Kenya with a percent gender ratio of 34 females to 66 males. The sample selected had been on the iCow platform for 6 months and had been sent 72 agricultural rich content short messaging services (SMS).
Whereas iCow offers a variety of products to farmers, farmers for this particular survey were selected from a product on the iCow platform called Mashauri Mix (MX).
On joining the iCow platform farmers automatically are registered to MX, which delivers three agricultural content-rich SMS a week to their mobile phones.
The SMS’s include content over a wide variety of agricultural topics including crops, livestock, soils, human health and more. Registered farmers on MX thus receive 12 SMSs a month at a cost of $0.40 or Sh43.
Some of the key findings include greatly improved milk yields. Some farmers have seen improved yields of between 20 per cent and 200 per cent over a period of six months.
It also emerged that the average age of the farmer was 32 years, which is an indicator that the youth are increasingly becoming interested in farming.
This is positive news for Kenya considering an earlier report by titled, Cultivating Youth Entrepreneurship Through Agribusiness, which reported that the average age of Kenyan farmer at 60 years. The study also revealed that more than 34 percent use smart phones.
With improved feeding, even the ordinary cows’ yields improved. The report says, Cows (Local breeds) that were producing less than five litres a day increased yields by approximately one litre and those producing above five litres a day increase yields by approximately two litres or more.
For those increasing one litre extra a day this resulted in an average increase of 30 litres a month which translates into a monetary value of between Sh 900-Sh1,350 a month per animal where milk prices vary between Sh 30 -Sh45 depending on the location and season.
This evidence uncover many other opportunities that, if exploited, can lead to greater poverty reduction.
In Canada for example, technology and big data specifically, are making farming more attractive to young farmers, particularly in livestock, because such technologies are enabling farming to be considerably more profitable for those willingly to work at it.
A Canadian Livestock Blog, GenomeAlberta, show that big data is being used to rapidly analyze feed choices, grazing lands, and even recommend new ways for increasing animal yields.
Field mapping and animal tracking are also part of the data accessible to farmers. No more guessing how long grass supplies will last or where the cattle are grazing now.
Although ICow has not reached the capability of GenomeAlberta, information that gets to farmers is derived from research, hence the high yields being witnessed.
Big data will be not be a complex concept for long. Several efforts are underway to simplify big data so that the application of it would be as simple as using a mobile handset.
For example, it will be possible for a farmer to query data regarding what type of seed they need on their piece of land, the kind of fertilizer to be used and when would the seed be sawed to harvest the harvest optimal output.
In a similar manner, a livestock farmer will be able to query the gene pool of the livestock they intend to buy in order to yield the optimal milk output.
For this to happen, we must encourage collaboration between the academia and apps developers.
The writer is an associate professor at University of Nairobi’s Business School.