Data Science – the defender of the last resort

Across the world, small farmers are facing unprecedented challenges – a fact that can be witnessed through increasing rural poverty rates and the rapid decline of small farmers in the national US and other places. The numbers speak for themselves: In Europe, agriculture is the major employment provider with about 10.5 million agricultural holdings in 2016. Despite this, farm numbers, ironically, have been in steep decline for many years. Most of the EU’s farms are small in nature; two thirds were less than 5 hectares in size in 2016. These small farms play an important role in reducing the risk of rural poverty, by providing additional income and food. Like Europe, agriculture and animal husbandry are the major sources of income for the people in Asia. Agriculture is important for all countries of Asia and the Pacific, with more than 2.2 billion people relying190 on agriculture for their livelihood. Small and marginal farmers hold 86% of the agricultural landholding in India191.
Agriculture is by far the single most important economic activity in Africa193. It provides employment for about two-thirds of the continent’s working population and for each country contributes an average of 30 to 60 percent of gross domestic product and about 30 percent of the value of exports. While Africa holds more than 60% of the worlds arable land194, the continent’s share in global agricultural production remains low.
Agriculture is largest employer in the Latin America and Caribbean (LAC) region. In 2018, 14.1% of total labor force in the LAC region was employed in agriculture. Countries. The LAC region experiences high incidence of poverty and extreme poverty in rural areas (48.6% and 22.5%, respectively) since 2017 and widening gap between rural and urban poor with high undernourished people in the region (39.3 million).

Artificial Intelligence play an important role in improving productivity of small farmers across the world and improve standard of living. Through Data Science and Machine Learning Techniques, a small increase in productivity improves financial wellbeing of small farmers and reduce the widen gap & rural poverty. Importantly, Artificial Intelligence is the defender of the last resort in protecting small farmers from the onslaught on their survival - increasing "industrialization" of agriculture by large industrial houses and the application of automation at the expense of economic stability & sustainability of small farmers. Put it simply, the competitive market landscape is no longer plane and equal. In the "David and Goliath” fight, the small farmers need the capabilities of advanced machine learning and data science that can help them to prepare onslaught of “industrialization”.


Food Security

The World is at a critical juncture.In 2020, nearly one in three people did not have access to adequate food - between 720 and 811 million people faced hunger. Compared with 2019 , 46 million more people in Africa, almost 57 million more in Asia, and about 14 million more in Latin America and the Caribbean were affected by hunger. Based on for ecasts of global population growth, current deficit to feed people around the world, and increased demand for greener fuel & biodiesel, food security will remain an important economic development issue over the next several decades. As food-versus-fuel tension becomes more intense , the day will come when more agricultural products will be used for energy than food. Adding to the conundrum, the COVID-19 pandemic has changed the face of the earth in terms of supply chain, resource availability, and human labor and has exposed our vulnerabilities in food security to an even greater extent. In essence, humanity is at a critical juncture and what this unprecedented movement in our lives has thrusted upon us -- the practitioners of the agriculture and technologists of the world -- is to innovate and become more productive to address the multi-pronged food security challenges.

Hanumayamma Data Science platform, Agriculture Analytics and Dairy Analytics, collects data from world economic, agricultural, climate, and weather model datasets. It collects data from statistics handbooks across 98 national governments data. The data science platform perpetually applies time series, regressive, cluster, and forecast models to assess the food security!



More on Democratization of Artificial Intelligence for creating a sustainable food future, please check our published thought leadership paper: png
More on the role of combinatorial mathematical optimization and Heuristics, please check our published thought leadership paper: png