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 brachiaria Decumbens forage. The experiment was performed in the Midwest region of Brazil (20°26'37.7″S 54°50'58.5″W) and according to the Köppen climate classification system, the region has a tropical wet and dry climate (Aw). This climate type is typically associated with high temperatures throughout the year. The herd had free access to water and the forage. The PAF supplemented the herd three times a day and collected data such as the frequency and time spent in the feeder of each animal. The experiment data started in December 2022 up to October 2023. The animals were weighed every 13-28 days, and the animals' Average Daily Gain (ADG) was registered. In addition, spectral and weather parameters were acquired via geoprocessing and meteorological (Application Programming Interfaces - APIs). This dataset can support stakeholders in understanding the bovines' collective behaviour at the supplementation process and designing and developing machine-learning models to estimate the quantity of supplementation ingested by the herd.

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. Nevertheless, datasets with pasture yield and nutritional parameters, remote sensing and weather information are scarce and rarely available, limiting the design of prediction models. This paper presents a dataset with more than 300 samples of pasture laboratory analyses collected over nearly twelve months from two paddocks. Latitude and longitude coordinates were collected for each sample using GPS coordinates, and this data helped acquire multispectral band signals and eight vegetation index values extracted from Google Earth Engine (Sentinel-2 satellite) for each pixel of each sample. Furthermore, the dataset has weather data from APIs and a meteorological station. These data can also motivate new studies that aim determine pasture behaviour, joining this dataset with larger datasets that have similar information.