Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Defalque G

PROVIDER: S-EPMC10904182 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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

Defalque Guilherme G Arfux Pedro P Pache Marcio M Franco Gumercindo G Santos Ricardo R

Data in brief 20240219

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 predic ...[more]

PMID: 38425873

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Project description:Passive acoustic monitoring (PAM) is a promising method for biodiversity assessment, which allows for longer and less intrusive sampling when compared to traditional methods (e.g., collecting specimens), by using sound recordings as the primary data source. Insects have great potential as models for the study and monitoring of acoustic assemblages due to their sensitivity to environmental changes. Nevertheless, ecoacoustic studies focused on insects are still scarce when compared to more charismatic groups. Insects' acoustic activity patterns respond to environmental factors, like temperature, moonlight, and precipitation, but community acoustic perspectives have been barely explored. Here, we provide an example of the usefulness of PAM to track temporal patterns of acoustic activity for a nocturnal assemblage of insects (Orthoptera). We integrate satellite remote sensing and astronomically measured environmental factors at a local scale in an Andean Forest of Colombia and evaluate the acoustic response of orthopterans through automated model detections of their songs for nine weeks (March and April of 2020). We describe the acoustic frequency range and diel period for the calling song of each representative species. Three species overlapped in frequency and diel acoustics but inhabit different strata: canopy, understory, and ground surface level. Based on the acoustic frequency and activity, we identified three trends: (i) both sampled cricket species call at lower frequency for shorter periods of time (dusk); (ii) all sampled katydid species call at higher frequency for longer time periods, including later hours at night; and (iii) the diel acoustic activity span window seems to increase proportionally with dominant acoustic frequency, but further research is required. We also identified a dusk chorus in which all the species sing at the same time. To quantify the acoustic response to environmental factors, we calculated a beta regression with the singing activity as a response variable and moon phase, surface temperature and daily precipitation as explanatory variables. The response to the moon phase was significant for the katydids but not for the crickets, possibly due to differences in diel activity periods. Crickets are active during dusk, thus the effects of moonlight on acoustic activity are negligible. The response to precipitation was significant for the two crickets and not for the katydids, possibly because of higher likelihood of rain interrupting crickets' shorter diel activity period. Our study shows how the local survey of orthopteran acoustic assemblages, with a species taxonomic resolution coupled with remote-sensing environmental measurements can reveal responses to environmental factors. In addition, we demonstrate how satellite data might prove to be a useful alternative source of environmental data for community studies with geographical, financial, or other constraints.

Dataset Information

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

Publications

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

Similar Datasets

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