Lamb & Goat Market Forecast 1.4

Angelo State University ❘ Freeware ❘
iOS
out of 11 votes

This application is designed to assist Texas sheep and goat producers in predicting the fluctuation in value of light-weight slaughter lambs and kid goats. Users are prompted to specify the intended month of sale, animal weight, and the cost of maintaining ownership. Utilizing market data sourced from the Producers Livestock Auction Co. in San Angelo, TX, spanning as far back as 2010, the tool then calculates the forecasted value change (Marginal Revenue) based on variables such as month, weight gain, and cost of maintenance.

It is important to note that the application does not provide exact price predictions but rather approximates the expected change in value per animal that is typically observed within the market context considering factors like time of year and market weight. Additionally, the tool displays the current value ($/Hd) and market price ($/Cwt.) while offering a link to USDA-Agricultural Marketing Service reports for quick assessment of prevailing market conditions by producers.

Overview

Lamb & Goat Market Forecast is a Freeware software in the category System Utilities developed by Angelo State University.

The latest version of Lamb & Goat Market Forecast is 1.4, released on 06/01/2024. It was initially added to our database on 06/01/2024.

Lamb & Goat Market Forecast runs on the following operating systems: iOS.

Users of Lamb & Goat Market Forecast gave it a rating of 5 out of 5 stars.

Pros

Comprehensive market analysis based on historical data and trends.
Expert insights from a reputable academic institution.
Useful for farmers, investors, and stakeholders in the meat industry.
Incorporates factors such as supply and demand, pricing, and seasonal trends.
Access to expert predictions to make informed decisions about production and sales.

Cons

May not cover specific regional markets in detail.
Forecasts are estimates and can be affected by unpredictable variables like weather or disease outbreaks.
Limited accessibility to non-academic audiences due to technical jargon.
Potential lag in data representation depending on publication frequency.
Dependence on historical data may not always reflect real-time changes in the market.

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