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:
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 forecasts 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!
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 [8]. The application of econometric analysis, advanced statistics techniques, and machine learning and artificial intelligence techniques provide understanding of price transmission, correlation, and causality.
