Forage Mass Estimation in Silvopastoral and Full Sun Systems: Evaluation through Proximal Remote Sensing Applied to the SAFER Model

Author:

Luns Hatum de Almeida Samira1,Brunno Costa Souza Jarlyson1,Furlan Nogueira Sandra2ORCID,Ricardo Macedo Pezzopane José3ORCID,Heriberto de Castro Teixeira Antônio4,Bosi Cristiam3ORCID,Adami Marcos5ORCID,Zerbato Cristiano1,Carlos de Campos Bernardi Alberto3,Bayma Gustavo2ORCID,Pereira da Silva Rouverson1ORCID

Affiliation:

1. Department of Engineering and Mathematical Sciences, São Paulo State University (Unesp), Jaboticabal 14884900, SP, Brazil

2. Brazilian Agricultural Research Corporation, Embrapa Environment, Campinas 13918110, SP, Brazil

3. Brazilian Agricultural Research Corporation, Embrapa Southeast Livestock, São Carlos 13560970, SP, Brazil

4. Water Resources Program (PRORH), Federal University of Sergipe (UFS), São Cristóvão 49100000, SE, Brazil

5. Amazon Spatial Coordination, National Institute for Space Research (INPE), São José dos Campos 12227900, SP, Brazil

Abstract

The operational slowness in the execution of direct methods for estimating forage mass, an important variable for defining the animal stocking rate, gave rise to the need for methods with faster responses and greater territorial coverage. In this context, the aim of this study was to evaluate a method to estimate the mass of Urochloa brizantha cv. BRS Piatã in shaded and full sun systems, through proximal sensing applied to the Simple Algorithm for Evapotranspiration Retrieving (SAFER) model, applied with the Monteith Radiation Use Efficiency (RUE) model. The study was carried out in the experimental area of Fazenda Canchim, a research center of Embrapa Pecuária Sudeste, São Carlos, SP, Brazil (21°57′S, 47°50′W, 860 m), with collections of forage mass and reflectance in the silvopastoral systems animal production and full sun. Reflectance data, as well as meteorological data obtained by a weather station installed in the study area, were used as input for the SAFER model and, later, for the radiation use efficiency model to calculate the fresh mass of forage. The forage collected in the field was sent to the laboratory, separated, weighed and dried, generating the variables of pasture total dry mass), total leaf dry mass, leaf and stalk dry mass and leaf area index. With the variables of pasture, in situ, and fresh mass, obtained from SAFER, the training regression model, in which 80% were used for training and 20% for testing the models. The SAFER was able to promisingly express the behavior of forage variables, with a significant correlation with all of them. The variables that obtained the best estimation performance model were the dry mass of leaves and stems and the dry mass of leaves in silvopastoral and full sun systems, respectively. It was concluded that the association of the SAFER model with the proximal sensor allowed us to obtain a fast, precise and accurate forage estimation method.

Funder

Animal production in the future: tools to support decision making in pasture management and transfer of technology

Sustainable Rural Project – Cerrado

Coordination of Superior Level Staff Improvement

The National Council for Scientific and Technological Development

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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