Examinando por Materia "Grasslands"

Mostrando 1 - 2 de 2

From rangelands to cropland, land-use change and its impact on soil organic carbon variables in a Peruvian Andean highlands: a machine learning modeling approach
(Springer, 2024-09-09) Carbajal, Mariella; Ramirez, David A.; Turin Canchaya, Cecilia Claudia; Schaeffer, Sean M.; Konkel, Julie; Ninanya, Johan; Rinza, Javier; De Mendiburu, Felipe; Zorogastua, Percy; Villaorduña, Liliana; Quiroz, Roberto
Andean highland soils contain significant quantities of soil organic carbon (SOC); however, more efforts still need to be made to understand the processes behind the accumulation and persistence of SOC and its fractions. This study modeled SOC variables—SOC, refractory SOC (RSOC), and the 13C isotope composition of SOC (d13CSOC)—using machine learning (ML) algorithms in the Central Andean Highlands of Peru, where grasslands and wetlands (‘‘bofedales’’) dominate the landscape surrounded by Junin National Reserve. A total of 198 soil samples (0.3 m depth) were collected to assess SOC variables. Four ML algorithms—random forest (RF), support vector machine (SVM), artificial neural networks (ANNs), and eXtreme gradient boosting (XGB)—were used to model SOC variablesusing remote sensing data, land-use and landcover (LULC, nine categories), climate topography, and sampled physical–chemical soil variables. RF was the best algorithm for SOC and d13CSOC prediction, whereas ANN was the best to model RSOC. ‘‘Bofedales’’ showed 2–3 times greater SOC (11.2 ± 1.60%) and RSOC (1.10 ± 0.23%) and more depleted d13CSOC (- 27.0 ± 0.44 &) than other LULC, which reflects high C persistent, turnover rates, and plant productivity. This highlights the importance of ‘‘bofedales’’ as SOC reservoirs. LULC and vegetation indices close to the near-infrared bands were the most critical environmental predictors to model C variables SOC and d13CSOC. In contrast, climatic indices were more important environmental predictors for RSOC. This study’s outcomes suggest the potential of ML methods, with a particular emphasis on RF, for mapping SOC and its fractions in the Andean highlands.
Secondary succession of mixed plantations established to rehabilitate abandoned pasture in the Peruvian Amazon
(Japan Soc Tropical Ecology, 2014-09-01) Kobayashi, Shigeo; Soudre Zambrano, Manuel Antonio; Ricse Tembladera, Auberto
Secondary succession or facilitation processes carried out at sites established for rehabilitating abandoned pastures and degraded forests (prurmas) are instrumental in their return to original forest status. An understanding of these secondary succession processes contributes to the rehabilitation of degraded forest ecosystems and to the livelihoods of local communities, and aids in conserving biodiversity. We studied secondary succession in mixed species plantations that were established to rehabilitate abandoned land. The initial vegetation in these abandoned pastures and croplands was grassland composed of three dominant species: Rottboellia exaltata, Imperata brasiliensis, and Brachyaria decumbens. After tree planting and weeding had been carried out, the site was first invaded by R. exaltata and Baccharis floribunda. These two species, which depend solely on sexual and not vegetative reproduction, facilitated secondary succession and elevated species diversity by enabling subsequent invasion by several species. By contrast, B. decumbens, I. brasiliensis, and Hyparrhenia rufa depend mainly on vegetative reproduction involving rhizomes and tillers, and subsequent invasion by other species was relatively less in stand types dominated by these three species. We found that further adequate rehabilitation techniques were necessary for the respective vegetation types.