Forage mass production in integrated, extensive and intensive livestock systems in the central region of the State of São Paulo (Massa de forragem em sistemas pecuários integrados, extensivos e intensivos na região central do Estado de São Paulo)
The forage mass is information of great importance in the management of pastures and animals in productive systems. Unfortunately, there are still no well-established and well-founded methodologies for the estimation of forage mass values on a large scale, which allow its wide adoption in livestock systems and improve decision-making and management processes. In this database, we present three spreadsheets with information on the availability of forage mass in three different livestock systems: integration crop-livestock, extensive and intensive. Heights and masses (dry and fresh) are measurements taken in the field and estimates are made using the SAFER algorithm. Methods and data collection and estimates for the project study areas are described in Bayma et al. (2019) and Nogueira et al. (2021). The spreadsheet "Forage_Mass_Data_LIV_FUT" has six tabs: 1) "Sampling_points_ICLS" tab with a high resolution image of the evaluated Integrated crop-livestock systems (5 and 6) and the data sampling points (A01 to A50); 2) "Forrage_Mass_ICLS" tab is composed of columns with the following information: Sampiling data (January 2018 to November 2019), Livestock system (ICL), ICL System Repetition (repetitions 5 and 6 of the ICL System), Paddock number (6 paddocks in each repetition - from 1 to 6), Rotation management (paddocks can be in Grazing, pre-grazing, post-grazing or Grass growth), Sampling points (A01 to A50), Latitude, Longitude, Grass height (mean of 5 heights per point - in cm), Total Dry Mass (kg ha-1), Dry Green Mass (kg ha-1), Total Fresh Mass (kg ha-1), Fresh Green Mass (kg ha-1), Fresh Dead Mass (kg ha-1), Fresh Green Mass Estimation by Safer Model (kg ha-1 day-1), Forage growing days in rotation cycle and Forage growth days and Fresh Green Mass Estimation by Safer Model (kg ha-1 month-1); 3) "Sampling_points_Extensive" tab with a high resolution image of the extensive livestock systems evaluated (7 and 8) and the data sampling points (B01 to B40); 4) "Forage_Mass_Extensive" tab is composed of columns with the following information: Sampiling data (January 2018 to November 2019), Livestock system (Extensive), Extensive System Repetition (repetitions 7 and 8 of the Extensive System), Sampling points (B01 to B40), Latitude, Longitude, Grass height (mean of 5 heights per point - in cm), Total Dry Mass (kg ha-1), Dry Green Mass (kg ha-1), Total Fresh Mass (kg ha-1), Fresh Green Mass (kg ha-1), Fresh Dead Mass (kg ha-1), Fresh Green Mass Estimation by Safer Model (kg ha-1 day-1), Forage growing days in rotation cycle and Forage growth days and Fresh Green Mass Estimation by Safer Model (kg ha-1 month-1); 5) "Sampling_points_Intensive" tab with a high resolution image of the Intensive livestock systems evaluated (9 and 10) and the data collection points (C01 to C45) and 6) "Forage_Mass_Intensive" tab is composed of columns with the following information : Sampiling data (January 2018 to November 2019), Livestock system (Intensive), Intensive System Repetition (repetitions 9 and 10 of the Intensive System), Paddock number (6 paddocks in each repetition - from 1 to 6), Rotation management (paddocks can be in Grazing, pre-grazing, post-grazing or Grass growth), Sampling points (C01 to C45), Latitude, Longitude, Grass height (mean of 5 heights per point - in cm), Total Dry Mass (kg ha-1), Dry Green Mass (kg ha-1), Total Fresh Mass (kg ha-1), Fresh Green Mass (kg ha-1), Fresh Dead Mass (kg ha-1), Fresh Green Mass Estimation by Safer Model (kg ha-1 day-1), Forage growing days in rotation cycle and Forage growth days and Fresh Green Mass Estimation by Safer Model (kg ha-1 month-1).
| Main Authors: | , , , , |
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| Format: | Survey data biblioteca |
| Language: | English |
| Published: |
Redape
2022
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| Subjects: | Agricultural Sciences, Earth and Environmental Sciences, Forage, Forragem, Forage mass, Satellite imagery, Safer agrometeorological model, Evapotranspiration, Remote sensing, Sensor remoto, Digital Agriculture, Agricultura digital, Agrometeorology, Agrometeorologia, |
| Online Access: | https://doi.org/10.48432/1VJD45 |
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