Small Farmers form major workforce across the agricultural industry and they make major economic force in terms spending money & improving overall rural and national economy. Profit maximization of crop yields is possible through predict crop prices in advance and to ensure farmers receive maximum price on their crops. High crop prices translated quickly into needed income for farmers. This extra income meant that farmers could buy new equipment, more food, clothing, and so on. The net multiplier effect is increase in Gross Domestic Product (GDP) and it underscores the fact that the GDP as a measure of economic well-being . In-time crop price prediction would have a huge impact on overall economy of the world. To augment small scale farmers with information and data services that enable them better plan crops with higher market price yields and reduce the risk of economic uncertainty and market shocks, we need machine learning enabled agricultural economic information services that provide real-time gauge into commodity price market and that provide advanced prognostic view to course correct, if any, unintended consequences. The goal is to put more money into the hands of small-scale farmers and reduce their overall agricultural debt and thus make them more prosperous.
Fertilizer Price Predict Model is based on the World Bank Pink Sheet Data. Please note that the Machine Learning model developed considers the variance of Fertilizer Price depend on the demand for Commodities. For instance, more demand of Commodities for example Rice or Wheat exert pressure on production side. This indeed increases the consumption of Fertilizers. Second, on the Supply side, in order to make Fertilizer one of the important component is Crude Oil or Natural Gas (depend on the manufacture technique). By inputting both demand and supply side, we can at least provide Small Farmer on the trend of Fertilizer prices. As per the World Bank pink sheet data, the following are common Fertilizers used:
Many of the agricultural datasets (for instance, the World Bank, Food And Agriculture Organization of the United Nations, the United States Department of Agriculture, and other agricultural research institutions) are derived from census and Survey Data Sources. The inclusion of Internet of Things (IoT) enabled data sources is a major game changer and helps to develop a closed loop back system with near teal-time or real-time information insights. Hanumayamma Dairy IoT Sensors provide such advantage to small farmers across the world. Dissecting the interplay of macro level economics, commodity prices, demand factors, environmental factors, transportation & storage factors, and weather patterns is complex. Enabling the small-scale farmer with this information on a constant basis would provide a better planning tool, information services, and enable small scale farmer to join information & Information Communication Technologies (ICT). Application of advanced data science techniques, Natural Language Processing, and Econometric models to deliver easily digestible form would bring big data revolution to small scale farmer. .
Hanumayamma Analytics platform analyzes the credit to Agriculture from over 120 countries on the amount of loans provided by the private/commercial banking sector to producers in agriculture, forestry and fisheries, including household producers, cooperatives, and agro-businesses. Predicting commodity prices is a challenge and includes data science & econometrics overlay with macroeconomic factors, governmental regulations, subsidies, import/export, production, and demand356 . The application of econometric analysis, advanced statistics techniques, and machine learning and artificial intelligence techniques provide understanding of price transmission, correlation, and causality.
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 relying on agriculture for their livelihood. Small and marginal farmers hold 86% of the agricultural landholding in India. Agriculture is by far the single most important economic activity in Africa. 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 land, 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”.