# A Modeling Application for GHG Fluxes Estimates in Betel Nuts Plantations in Taiwan
###### Abstract  
Perennial woody crops could have a positive impact on carbon balance, absorbing carbon during growing season and storing it for several years, whereas annual crops do not have this particular effect. Usually, techniques for GHG (greenhouse gases) flux measurements have limited spatial representativeness, with some difficulties to extend leaf measurements to field scale. Models, especially if supported by remote sensing data, allow for upscaling the monitoring of these fluxes. The aim of this work was to evaluate the carbon fluxes (gross primary production, GPP; net ecosystem production, NEP) of the betel nut cultivars in Taiwan by a vegetation photosynthesis model (VPM). The model permitted estimating seasonal dynamics of GPP in a moist tropical evergreen forest. These plantations are very common in Taiwan and their role could be significant in environmental and development policies even though, until now, the consumption of the fruit of this tree is at the center of controversy due to their use and effects on the population. To obtain estimates of carbon fluxes on a large area that would appreciate its spatial variability, a model based on physiological processes was used. This model incorporated a series of procedures and monthly mean meteorological data, light use efficiency, and satellite enhanced vegetation index (EVI) were used as inputs. An additional purpose of this work was to compare the carbon uptake of different cultivars in Taiwan and Italy. Using a different model, always based on light use efficiency, a similar project was carried on Italian vineyards, with other climate conditions and different agricultural practices. G 202  
remote sensing represents a tool that can potentially upscale the spatial range provided by punctual measures. Due to the limited coverage of the ground measurements, the interest in remote sensing use for the estimation of GPP is increasing [4; 5]. Many models based on remote sensing application have been proposed to quantify the GPP; as they are simple, most of them are linear and efficient [6; 7; 8]. Remote sensing GPP models are grouped in four principal categories: light use efficiency (LUE) [9; 10], vegetation index (VI) driven [11; 12], process-based [7], and machine learning models [13; 14]. The approach of LUE models, introduced by Monteith [15], is based on the light use efficiency (LUE), which expresses the conversion efficiency of photosynthetically active radiation absorbed (aPAR) in gross primary production (GPP), assuming a linear dependence of GPP from aPAR, reduced by environmental stresses [16]. Many published studies focused on roles of forests, but a perennial woody crop can also contribute to carbon sequestration [10]. After the Kyoto Protocol, in the wake of the CO\({}_{2}\) quotas market--providing calculation of GHG emissions for each country and emission credits trade deriving from projects on absorption or, more generally, reduction of climate-changing gases--the number of studies investigating cultivars with a potential of atmospheric carbon sequestration is growing [17; 18]. This study focused on betel nuts plantations in Taiwan because they are widely spread in this country and under controversy for their role in human wellness and related bioethical implications [19; 20]. Another usually neglected aspect of that issue is the role of these plantations in gas exchanges due to their eco-physiological processes. Arboreal plants are able to absorb significant amounts of CO\({}_{2}\) and, in most cases, to fix the carbon in their tissues for many years, leading to a substantial removal of the atmospheric CO\({}_{2}\), mostly dependent on the growth phase and management [21; 22]. An approach able to estimate the role of betel nuts in the carbon cycle at a regional scale could surely help in the definition of ethical blind spots related to these plantations.