In agriculture, short-term weather forecasts tend to be more accurate due to advancements in meteorology. Types of forecasting in agriculture include weather forecasts, crop yield forecasts, pest and disease forecasts, and market price forecasts. Each type serves to help farmers make informed decisions for better planning and management.

Dr Rebaz abdulrahman thank you for your contribution to the discussion

While quantitative methods, which rely on historical data, are typically the most accurate forecasting methods, they don't work well for long-term predictions. If you're planning to forecast over several years, try qualitative forecasting methods, which rely on expert opinions instead of company-specific data. The most commonly used models in crop forecasting are Empirical Statistical models. In this approach, one or several variables (representing weather or climate, soil characteristics or a time trend) are related to crop responses such as yield. The forecast is usually restricted to only essential variables like temperature and precipitation. Additionally, long-range weather forecasting is used for seasonal planning for the crop type variety, farm input redistribution, harvest arrangement, etc. Output method is used to measure agricultural income in India. This method is also called as production method and consists of three stages. Trend projection is the most straightforward way to estimate production forecasting. This method uses your company's past sales data to predict future production. The idea is that the factors responsible for past trends and spikes in demand will continue at the roughly same rate. By crop forecasting we mean estimate or predicting the yield of certain crop during its growth period and sufficiently ahead of the time of its harvest. Weather has a significant impact on the prevalence of pests and diseases, the availability of water, and the amount of fertilizer needed to grow crops. Farmers rely on climate patterns and weather forecasting in agriculture to determine which crops to cultivate and when to sow them. Weather Forecasting is crucial since it helps to determine future climate changes. With the use of latitude, we can determine the probability of snow and hail reaching the surface. We are able to identify the thermal energy from the sun that is exposed to a region.

Rebaz Abdulrahman Can you share some dataset or link to historical market prices for different Crops around the world (finer resolution) from 1950/1970 to current?

Also the forecasted Market prices?

Dr Mohammad Umer thank you for your contribution to the discussion

The forecast is usually restricted to only essential variables like temperature and precipitation. Additionally, long-range weather forecasting is used for seasonal planning for the crop type variety, farm input redistribution, harvest arrangement, etc. The most commonly used models in crop forecasting are Empirical Statistical models. In this approach, one or several variables (representing weather or climate, soil characteristics or a time trend) are related to crop responses such as yield. By crop forecasting we mean estimate or predicting the yield of certain crop during its growth period and sufficiently ahead of the time of its harvest. Based on crop weather studies, crop yield forecast models are prepared for estimating yield much before actual harvest of the crops. By use of empirical- statistical models using correlation and regression technique crops yield are forecast on an operational basis for the country. Strategic decisions can be made based on what is working and not working. Forecasts help businesses anticipate change, reduce uncertainty and identify the best ways to achieve their goals. Some important features or characteristics of forecasting are as follows: Forecasting is strictly concerned with future events only. It analysis the probability of a future event or transaction occurring or happening. It involves analysis of data from the past and the present. Forecasting is essentially a process of analyzing the past and present business movements and trends to obtain some idea or clues regarding future trends and business movements. Forecasting is looking into the future so that we can accordingly plan for it. However, forecasting is not a haywire process.Forecasting involves making predictions about the future. In finance, forecasting is used by companies to estimate earnings or other data for subsequent periods. Traders and analysts use forecasts in valuation models, to time trades, and to identify trends. Forecasts are often predicated on historical data. Fluctuations in agricultural commodity prices affect the supply and demand of agricultural commodities and have a significant impact on consumers. Accurate prediction of agricultural commodity prices would facilitate the reduction of risk caused by price fluctuations. Forecasts for groups of items tend to be more accurate than forecasts for individual items because forecasting errors among items in a group usually have a canceling effect. While quantitative methods, which rely on historical data, are typically the most accurate forecasting methods, they don't work well for long-term predictions. If you're planning to forecast over several years, try qualitative forecasting methods, which rely on expert opinions instead of company-specific data. Both qualitative and Quantitative methods are often complemented each other to arrive a better conclusion, through each of them have their own limitation. There are no globally best or accurate forecasting methods or techniques. As the same time, there are no forecasting techniques without limitations or pitfalls. Aggregate forecasts are more accurate than individual forecasts. Longer term forecasts are more accurate because a business has more time to adjust the forecast. Seasonality and cycles are examples of forecasting data types. A horizontal data pattern typically occurs with demand patterns for a new product.