Journal of Forest and Environmental Science

Institute of Forest Science, kangwon National University (강원대학교 산림과학연구소)
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Aboveground Net Primary Productivity and Spatial Distribution of Chaco Semi-Arid Forest in Copo National Park, Santiago del Estero, Argentina

  • Jose Luis Tiedemann (National University of Santiago del Estero, Faculty of Forestry Sciences)
  • Received : 2024.02.14
  • Accepted : 2024.06.02
  • Published : 2024.06.30
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Abstract

According to the REDD+ program, it is necessary to monitor, quantify, and report forest conditions in protected land areas. The objectives of this work were to quantify the average monthly aerial net primary productivity (ANPPMONTH) of semi-arid Chaco Forest at Copo National Park (CNP), Santiago del Estero, Argentina, during the period 2000-2023, as well as its spatial distribution and relationship, and its use efficiency (RUE) of average monthly rainfall (AMR). The ANPPMONTH model accounted for 90% of the seasonal variability from October to May, the average seasonal ANPPMONTH was 145 tons of dry matter per hectare (t dm/ha), being the maximum in January with 192 t dm/ha and the minimum in May with 91 t dm/ha. The surface area covered by ANPPMONTH exhibited a consistent positive trend from October to May (t test=15.65, p<0.01). Strong and significant direct relationships were found between ANPPMONTH and AMRs, linear models explaining 90% and 96% of the variability, respectively. The results obtained become reference values for assessing the capacity of the forest systems to stock carbon for global warming mitigation and for monitoring and controlling their response to extreme climatic adversities. The average ANPPMONTH reduces uncertainty when defining the thresholds to monitor and quantify ANPP and forest area, thus facilitating the detection of negative changes in land use in CNP. Such results evidence the National Parks Administration's high effectiveness for the maintenance of protected area and for the high ANPP of the FCHS of CNP in the period 2000-2023.

Keywords

Acknowledgement

To the Temporal-Vegetation Analysis System of the Space Research Institute /dsr.inpe.br/laf/series/. To the LP-DAAC/EOS-NASA Project. To the NASA Prediction of Worldwide Energy Resource Project- https://power.larc.nasa.gov/. To Sociedad Rural del Noreste Santiagueño (Mr. Silvio Vicente) for his contribution on rainfall data-sociedadruralquimili@hotmail.com

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