@article{fdi:010053589,
  title = {{M}apping organic carbon stocks in eucalyptus plantations of the central highlands of {M}adagascar : a multiple regression approach},
  author = {{R}azakamanarivo, {R}. {H}. and {G}rinand, {C}lovis and {R}azafindrakoto, {M}. {A}. and {B}ernoux, {M}artial and {A}lbrecht, {A}lain},
  editor = {},
  language = {{ENG}},
  abstract = {{R}ecent concerns about global warming have resulted in more concerted studies on quantification and modeling of carbon ({C}) storage in different ecosystems. {T}he aim of this study was to assess and map the carbon stocks in above ({ABG}), below-ground ({BLG}) biomass and soil organic carbon contained in the 30 centimeter top-layer ({SOC}) in coppices of eucalyptus plantations in the central highlands of {M}adagascar in an area of 1590 ha. {R}elationships between {C} stock and various biophysical (stool or shoot stockings and ages, circumferences) and spatial (elevation, slope, and soil type) factors that may affect {C} storage within each pool were investigated. {T}hree different modeling techniques were tested and compared for various factor sets: (i) simple linear regression ({SLM}), (ii) multiple linear ({MLM}) models and, (iii) boosted regression tree ({BRT}) models. {W}eights of the factors in the respective model were analyzed for the three pool-specific models that produced the highest accuracy measurement. {A} regional spatial prediction of carbon stocks was performed using spatial layers derived from a digital elevation model, remote sensing imagery and expert knowledge. {R}esults showed that {BRT} had the best predictive capacity for {C} stocks compared with the linear regression models. {E}levation and slope were found to be the most relevant predictors for modeling {C} stock in each pool, and mainly for the {SOC}. {A} factor representing circumferences of stools and their stocking (stools.ha(-1)) largely influenced {BLG}. {S}hoot circumference at breast height and shoot age were the best factors for {ABG} fitting. {A}ccuracy assessment carried out using coefficient of determination ({R}-2) and ratio of standard deviation to prediction error ({RPD}) showed satisfactory results, with 0.74 and 1.95 for {AGB}, 0.85 and 2.59 for {BLG}, and 0.61 and 1.6 for {SOC} respectively. {A}pplication of the best fitted models with spatial explanatory factors allowed to map and estimate {C} contained within each pool : 32 +/- 13 {G}g {C} for {ABG}, 67 +/- 15 {G}g {C} for {BLG} and, 139 +/- 36 {G}g {C} for {SOC} (1 {G}g=10(9) g). {A} total of 238 +/- 40 {G}g {C} was obtained for the entire study area by combining the three {C} maps. {D}espite their relatively low predictive quality, models and {C} maps produced herein provided relevant reference values of {C} storage under plantation ecosystems in {M}adagascar. {T}his study contributed to the reducing of uncertainty related to {C} monitoring and baseline definition in managed terrestrial ecosystem.},
  keywords = {{A}bove-and below-ground biomass ; {S}oil organic carbon ; {S}patial ; variability ; {B}oosted regression tree ; {S}hort rotation forestry ; {E}ucalyptus robusta},
  booktitle = {},
  journal = {{G}eoderma},
  volume = {162},
  numero = {3-4},
  pages = {335--346},
  ISSN = {0016-7061},
  year = {2011},
  DOI = {10.1016/j.geoderma.2011.03.006},
  URL = {https://www.documentation.ird.fr/hor/fdi:010053589},
}