Remote sensing techniques to identify and characterize forage communities in a livestock system in the hyper-arid desert of San Juan (Argentina)

Abstract

Background and aims: The natural grasslands of arid zones cover 40% of the earth's surface and are a valuable source of forage for livestock. Inappropriate management and high livestock loads are among the factors responsible for their degradation. In this sense, a fast evaluation is essential to correct its use and promote conservation. The objective of the study was to identify and characterize, through satellite image processing and fieldwork, forage plant communities in a rainfed livestock system of San Juan.

M&M: Indicator variables of soil and vegetation were generated from a Landsat 8 OLI image. Subsequently, unsupervised kmeans classification was performed. On field, plant cover, mulch and percentage of bare soil were registered from linear transects. Finally, the livestock receptivity of the plant communities was estimated.

Results: 3 types of coverage were identified: coverage higher than 50%; higher than 20% and less than 50% and less than 20%. Also, two forage communities were identified, Lamaral and Zampal. In Lamaral, Prosopis alpataco var lamaro obtained a coverage of 48%, a receptivity of 2.21 hectare/goat equivalent. In Zampal, a 35% coverage of Atriplex undulata was registered and the receptivity was 1.80 hectare/goat equivalent.

Conclusions: The digital processing carried out was adequate for the purpose of the study and allowed recognizing, characterizing and mapping two forage communities. The richness of the species was low, with a predominance of shrubs and woody plants, limiting livestock in the area.