牧业牧场产肉量预测模型数据

可以用于牧场动物产肉量预测，输入为产奶量，出生率，牧场大小，动物数量，动物死亡率，其他费用，人工费用，设备维护费用，市场价格，水消耗量，饲料消耗，药品费用，总成本。输出为动物产肉量。该模型帮助解决了动物产肉量和牧场状况的关系建模的问题。通过调查采集牧场收支成本数据及牧场畜牧数据，并使用传统算法和多元线性回归算法预测牧场动物产肉量。该模型的输入变量包括产奶量，出生率，牧场大小，动物数量，动物死亡率，其他费用，人工费用，设备维护费用，市场价格，水消耗量，饲料消耗，药品费用，总成本。多元线性回归算法通过分析这些输入变量与动物产肉量之间的线性关系，确定每个变量的权重系数。在模型训练过程中，算法会利用历史数据进行优化，调整权重系数以最小化预测误差。模型通过最小二乘法等技术，根据输入的数据计算预测的动物产肉量，从而得出最终结果。通过这样的过程，模型能够将多个输入变量综合考虑，准确预测动物产肉量。

This dataset is designed for predicting the meat yield of pasture-raised animals. Its input features cover milk yield, birth rate, pasture size, number of livestock, animal mortality rate, other expenses, labor cost, equipment maintenance cost, market price, water consumption, feed consumption, medication cost, and total cost, while the output is animal meat yield. This model solves the problem of modeling the correlation between animal meat yield and pasture operational conditions. Data on pasture revenue, expenditure costs and on-site livestock farming records were collected via surveys, and traditional algorithms as well as multiple linear regression algorithms were adopted to predict the meat yield of pasture-raised animals. The input variables of the model include all the aforementioned features. The multiple linear regression algorithm identifies the weight coefficient for each variable by analyzing the linear correlation between these input variables and animal meat yield. In the model training phase, the algorithm optimizes itself using historical data, adjusting the weight coefficients to minimize prediction errors. The model computes the predicted animal meat yield based on the input data through techniques such as the least squares method, yielding the final prediction result. Through this workflow, the model can comprehensively incorporate multiple input variables to accurately predict animal meat yield.

搜集汇总

数据集介绍

特点

该数据集是一个用于预测牧场动物产肉量的模型数据，包含3674条记录，每年更新一次。数据涵盖了多个牧场运营相关的输入变量，如牧场大小、动物数量、饲料消耗等，输出为动物产肉量。模型采用多元线性回归算法，能够综合考虑多个变量，准确预测产肉量。