Agroforestry systems may play a critical role in reducing the vulnerability of farmers' livelihood to droughts as tree-based systems provide several mechanisms that can mitigate the impacts from extreme weather events. Here, we use a replicated throughfall reduction experiment to study the drought response of a cacao/Gliricidia stand over a 13-month period. Soil water content was successfully reduced down to a soil depth of at least 2.5 m. Contrary to our expectations we measured only relatively small nonsignificant changes in cacao (À11%) and Gliricidia (À12%) sap flux densities, cacao leaf litterfall ( 1 8%), Gliricidia leaf litterfall (À2%), soil carbon dioxide efflux (À14%), and cacao yield (À10%) during roof closure. However, cacao bean yield in roof plots was substantially lower (À45%) compared with control plots during the main harvest following the period when soil water content was lowest. This indicates that cacao bean yield was more sensitive to drought than other ecosystem functions. We found evidence in this agroforest that there is complementary use of soil water resources through vertical partitioning of water uptake between cacao and Gliricidia. This, in combination with acclimation may have helped cacao trees to cope with the induced drought. Cacao agroforests may thus play an important role as a drought-tolerant land use in those (sub-) tropical regions where the frequency and severity of droughts is projected to increase.

Abstract. Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and model spatiotemporal variability in ecosystem properties and processes that affect terrestrial GPP. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FLUXNET to assess Published by Copernicus Publications on behalf of the European Geosciences Union. M. Verma et al.: Remote sensing of annual terrestrial gross primary productivity from MODIShow well four metrics derived from remotely sensed vegetation indices (hereafter referred to as proxies) and six remote sensing-based models capture spatial and temporal variations in annual GPP. Specifically, we used the FLUXNET La Thuile data set, which includes several times more sites (144) and site years (422) than previous studies have used. Our results show that remotely sensed proxies and modeled GPP are able to capture significant spatial variation in mean annual GPP in every biome except croplands, but that the percentage of explained variance differed substantially across biomes (10-80 %). The ability of remotely sensed proxies and models to explain interannual variability in GPP was even more limited. Remotely sensed proxies explained 40-60 % of interannual variance in annual GPP in moisturelimited biomes, including grasslands and shrublands. However, none of the models or remotely sensed proxies explained statistically significant amounts of interannual variation in GPP in croplands, evergreen needleleaf forests, or deciduous broadleaf forests. Robust and repeatable characterization of spatiotemporal variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. Our analyses highlight the power of remote sensing-based models, but also provide bounds on the uncertainties associated with these models. Uncertainty in flux tower GPP, and difference between the footprints of MODIS pixels and flux tower measurements are acknowledged as unresolved challenges.

ABSTRACT. We analyzed inter-annual trends in annual and seasonal vegetation activities in Central Asia from 1982 to 2003 and their correlation to climate variability using the NOAA/AVHRR Normalized Difference Vegetation Index (NDVI) dataset and a gridded climate dataset. The results indicate a significant increase in NDVI with a value of 11.35% over the growing season during the 22-year period. Totalled over the entire vegetated area, about 35% of all pixels exhibited significant upward trend in growing season NDVI. We found that NDVI increase in spring was the main contributor to the general upward trend, the spring NDVI increased in more than 50% of all pixels and showed an average value of 13.58%. Correlation analysis indicated a gradual rise in temperature as the only factor controlling trend in spring NDVI. Significant increase in vegetation activity was also identified for summer season, but its amplitude (9.23%) and comprising area (25.13% of all vegetated pixels) were less than for spring. Downward trends in growing season NDVI occurred in 2.17% of the total vegetated area. The greening trends of spring, growing season and summer NDVI strongly related with the climatic parameters: for each land cover type, we found significant correlation with spring temperature and total precipitation; 75% of all upward trends in growing season NDVI were explained by the combination of these both variables. We found that the NDVI trends and their climatic correlates demonstrate great spatial variability at the scale of individual land cover types and at per-pixel scale and proofed that the land use change caused by the constitutional change in the 1991 has substantial control on the vegetation trends. Increased vegetation growth indicated through the analysis of NOAA AVHRR NDVI time-series suggests an increasing carbon stock in biomass of ecosystems in Central Asia.

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