A dataset on Nelore Cattle supplementation in the midwest region of brazil for integrated analysis of supplementation, satellite remote sensing, and weather variables.

Monitoring the herd during supplementation is essential to understanding animals' ingestive activities, making decisions when choosing the supplement parameters, and correctly managing the livestock and agriculture processes. Programmable Automatic Feeders (PAFs) are important tools that support stakeholders in the treatment process, decreasing the time and cost compared to traditional supplementation methods. This paper presents a dataset that consists of data acquired from a supplementation experiment using a PAF with a Nelore herd in a paddock of 16 ha with Decumbens forage.

A dataset for pasture parameter estimation based on satellite remote sensing and weather variables.

Estimating pasture parameters is essential for decision-making in the management of livestock and agriculture. Despite that, the time-consuming acquisition of outdoor forage samples and the high cost of laboratory analysis make it infeasible to predict parameters of quality and quantity forage recurrently and with great accuracy. Previous work has shown that multispectral and weather data have correlation with forage parameters, enabling the design of supervised machine learning models to predict forage conditions.