Abstract
Net primary productivity (NPP) is a key driver of ecosystem C balance. ScalingNPP up to larger areas requires indirectmethods: (a) for examble epsilon models based on light use efficiency (LUE = NPP/ APAR, where APAR is the absorbed photosynthetically active radiation by green elements of canopy, or else models based on water-use-efficiency (WUE = NPP/E, where E = evapo-transpiration); (b) remote sensing tools to estimate the fraction ofAPAR(fAPAR) from vegetation indexes, or to estimate E. However, LUE and WUE are suspected to vary in space (edaphoclimatic conditions, planting density) and time (seasonality, age), which needs to be documented before scaling up. Moreover, the application of this scaling approach to agroforestry systems with a stratified canopy may be difficult, since each layer contributes to the overall ecosystem light- and water-use efficiencies. The seasonal and inter-annual variabilities of LUE and WUE was assessed in a very simple bi-layer tropical coconut grove displaying minimum climatic and LAI variations, distinguishing the upper layer of coconuts, the herbaceous under-storey and the whole stand (subscripts C, H and S, respectively). We monitored NPP biometrically during 3 years above and below ground, together with microclimate and ES above the canopy (eddy-covariance), transpiration (TC) by sapflow, and fAPARC by LAI-2000 combined with canopy light absorption models. The partitioning of APAR, NPP and E was very close to the rule-of-thumb of canopy coverage by upper-layer (75%). Also the mean annual value of LUES (1.7 gDM MJPARi -1 ) or mean WUES (3.7 gDM kg_1 H2O) were mainly driven by the upper-layer of coconuts. However, the under-storey experienced around twice as much seasonal variations of NPP, E,LUE andWUE than the upper-storey. Given that NPPS varied by only 23% over the year, the high seasonal variations of WUES (240%) and LUES (250%) were mostly driven by the variations of APARS (230%) and were adjusted successfully using climate, age and density data, as a first step to estimate NPP on larger scales using climate, GIS and remotesensing.