Agricultural commodities (corn, soybeans, rice, and wheat) make up a large portion of staple food and animal feed consumed worldwide . United States is the largest exporter of some of the staple commodities (36 percent of global corn, share close to 40 percent of global soybean, 16 percent of world wheat production). The export prices of these commodities are more volatile than overall U.S. export prices. This volatility is demonstrated in the below figure, which tracks price changes over time for corn,soybeans, and wheat exports, and crude oil imports.
During the period shown in the above chart, the graphs for export corn, soybeans, and wheat reveal prices subject to volatility—in other words, recording large price changes on 1- or 12-month bases.
This volatility occurs because agricultural goods prices are susceptible to weather events, crude oil price changes, and the value of the U.S. dollar. In addition, prices of some of these commodities ( corn, soybeans, and wheat) generally trend together because the crops share common pricedetermining factors such as substitutability, demand, biofuels, the value of the U.S. dollar, weather, and crude oil. The increased link between energy and non-energy commodity prices, strong demand by developing countries and changing weather patterns will be the dominant forces that are likely to shape developments in commodity markets. Application of Data, time series, and signal mining of common price determinants of the commodities would provide prognostic and/or predictive insight to farmers to achieve profit maximization. The commodities prices have direct influence on farm profitability and sustainability of small farmer and machine learning algorithms would enable farmers to sustain pricing shocks. Commodity Price prediction is vital to local, national, and global economy. It would better to regulate commodity pricing to consumers and helps small farmers to have a sustained agriculture. Inherently, commodity prices data is a time series data. Regression Models, Zero-Inflated Poisson Regression Models, Time Series AUF, and other ML Models help to predict commodity prices. Hanumayamma Data Science and Agriculture Analytics Platform apply series of Statistical, Machine Learning, and Mathematical Optimization Models to enable small farmers’ head of trends of changing commodity market swings and promise to preserve and protect farm inputs!
Forecasting commodity prices play an important role in terms of decision making of forecasted planted/harvested acreage of crops and financial wellbeing of small farmers. Expected agricultural commodity prices can influence production decisions of farmers and ranchers on planted/harvested acreage of crops or inventory of livestock and, thus, affect the supply of agricultural commodities.
Commodity price changes also affect farms’ financial wellbeing, for example sustained periods of low commodity prices reduce farm revenues and cause farmers to increasingly rely on credit, making them vulnerable to higher interest rates and other changes to economic conditions. Sustained periods of high commodity prices can contribute to increases in farm revenues and farm operator resilience to changes in economic conditions. Changes to commodity prices also have implications for food security: sustained low prices increase consumers' ability to purchase adequate quantities of food, while sustained high prices decrease their food security, particularly in developing countries.
The behavior of agricultural product prices is sufficiently unusual and requires special treatment. Agricultural Commodity Markets are sensitive to macroeconomic environment, oil prices, demand/supply, consumer tastes/preferences, adverse climatic conditions, biofuels, stock to use ratios, dollar exchange rates, speculation, food storage, speculative activity, financial markets, fertilizers, trade restrictions, wealth of nations, and other economic conditions. Economic growth, Money Supply, Weather, and Inflation have a positive impact on the commodity prices (bubbles), while effect of interest rate is negative.
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.