Citas bibligráficas
Cabrejo, C., (2019). Evaluación del estado de salud de pastizales altoandinos empleando técnicas multivariadas [Tesis, Universidad Nacional Agraria La Molina]. https://hdl.handle.net/20.500.12996/4100
Cabrejo, C., Evaluación del estado de salud de pastizales altoandinos empleando técnicas multivariadas [Tesis]. : Universidad Nacional Agraria La Molina; 2019. https://hdl.handle.net/20.500.12996/4100
@mastersthesis{renati/1113324,
title = "Evaluación del estado de salud de pastizales altoandinos empleando técnicas multivariadas",
author = "Cabrejo Sánchez, Cynthia Patricia",
publisher = "Universidad Nacional Agraria La Molina",
year = "2019"
}
Rangeland ecosystems provide a wide diversity of ecosystem services to society benefit, including a huge number of rural families worldwide. However, they are at risk by degradation and desertification of lands caused by biophysical and socioeconomic factors. Therefore, there is a necessity of more accurate and less subjective tools for studies that can provide appropriate information about the state of rangeland health. The main objective of this study was to reduce the observational bias of Pyke or State of Rangeland Health (Interpreting Indicators of Rangeland Health – IIRH) method through the addition of quantitative variables that are able to be measure with precision implements and less complex methods. In addition, the most sensitive variables to changes between states of rangeland health were identified and a mathematical model was generated by multivariate techniques as logistic regression and linear discriminant analysis in order to predict rangeland health and validate it accordingly to Pyke’s estimation. The evaluation zones were located in High Andean grasslands in different states of health in Huancavelica, Ancash, Lima and Pasco regions. In these areas 17 quantitative variables associated to rangeland attributes were evaluated and used to generate a mathematical model with the multivariate techniques before mentioned. Validation was performed in areas of rangelands located in the same regions of the initial evaluation. The linear discriminant analysis was the multivariate technique which generated the model with the highest accurate, according to results of Pyke method, during training and validation phase. In conclusion, the variables that were the most sensitive to changes between states of rangeland health and the most influential in the predictability of the model were basal vegetation cover, erosion pavement and bare soil. Pyke method precisely estimates rangeland health through the evaluation of ecological processes, but it cannot determine trend because of its qualitative form of evaluation. Multivariate techniques, which require quantitative data, permit estimate trend with an ecological approach, so they should be used in combination with Pyke method.
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