Potassium (K) is an important limiting factor of tree growth, but little is known of the effects of K supply on the long-distance transport of photosynthetic carbon (C) in the phloem and of the interaction between K fertilization and drought. We pulse-labelled 2-year-old Eucalyptus grandis L. trees grown in a field trial combining K fertilization (+K and −K) and throughfall exclusion (+W and −W), and we estimated the velocity of C transfer by comparing time lags between the uptake of ¹³CO₂ and its recovery in trunk CO₂ efflux recorded at different heights. We also analysed the dynamics of the labelled photosynthates recovered in the foliage and in the phloem sap (inner bark extract). The mean residence time of labelled C in the foliage was short (21–31 h). The time series of ¹³C in excess in the foliage was affected by the level of fertilization, whereas the effect of throughfall exclusion was not significant. The velocity of C transfer in the trunk (0.20–0.82 m h⁻¹) was twice as high in +K trees than in −K trees, with no significant effect of throughfall exclusion except for one +K −W tree labelled in the middle of the drought season that was exposed to a more pronounced water stress (midday leaf water potential of −2.2 MPa). Our results suggest that besides reductions in photosynthetic C supply and in C demand by sink organs, the lower velocity under K deficiency is due to a lower cross-sectional area of the sieve tubes, whereas an increase in phloem sap viscosity is more likely limiting phloem transport under drought. In all treatments, 10 times less ¹³C was recovered in inner bark extracts at the bottom of the trunk when compared with the base of the crown, suggesting that a large part of the labelled assimilates has been exported out of the phloem and replaced by unlabelled C. This supports the ‘leakage-retrieval mechanism’ that may play a role in maintaining the pressure gradient between source and sink organs required to sustain high velocity of phloem transport in tall trees.

Many remote sensing‐based evapotranspiration (RSBET) algorithms have been proposed in the past decades and evaluated using flux tower data, mainly over North America and Europe. Model evaluation across South America has been done locally or using only a single algorithm at a time. Here, we provide the first evaluation of multiple RSBET models, at a daily scale, across a wide variety of biomes, climate zones, and land uses in South America. We used meteorological data from 25 flux towers to force four RSBET models: Priestley–Taylor Jet Propulsion Laboratory (PT‐JPL), Global Land Evaporation Amsterdam Model (GLEAM), Penman–Monteith Mu model (PM‐MOD), and Penman–Monteith Nagler model (PM‐VI). was predicted satisfactorily by all four models, with correlations consistently higher () for GLEAM and PT‐JPL, and PM‐MOD and PM‐VI presenting overall better responses in terms of percent bias (%). As for PM‐VI, this outcome is expected, given that the model requires calibration with local data. Model skill seems to be unrelated to land‐use but instead presented some dependency on biome and climate, with the models producing the best results for wet to moderately wet environments. Our findings show the suitability of individual models for a number of combinations of land cover types, biomes, and climates. At the same time, no model outperformed the others for all conditions, which emphasizes the need for adapting individual algorithms to take into account intrinsic characteristics of climates and ecosystems in South America.

Sugarcane (complex hybrids of Saccharum spp., C4 plant) croplands provide cane stalk feedstock for sugar and biofuel (ethanol) production. It is critical for us to analyze the phenology and gross primary production (GPP) of sugarcane croplands, which would help us to better understand and monitor the sugarcane growing condition and the carbon cycle. In this study, we combined the data from two sugarcane EC flux tower sites in Brazil and the USA, images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and data-driven models to study the phenology and GPP of sugarcane croplands. The seasonal dynamics of climate, vegetation indices from MODIS images, and GPP from two sugarcane flux tower sites (GPP_EC) reveal the temporal consistency in sugarcane phenology (crop calendar: green-up dates and harvesting dates) as estimated by the vegetation indices and GPP_EC data. The Land Surface Water Index (LSWI) is shown to be useful to delineate the phenology of sugarcane croplands. The relationship between the sugarcane GPP_EC and the Enhanced Vegetation Index (EVI) is stronger than the relationship between the GPP_EC and the Normalized Difference Vegetation Index (NDVI). We ran the Vegetation Photosynthesis Model (VPM), which uses the light use efficiency (LUE) concept and is driven by climate data and MODIS images, to estimate the daily GPP at the two sugarcane sites (GPP_VPM). The seasonal dynamics of the GPP_VPM and GPP_EC at the two sites agreed reasonably well with each other, which indicates that VPM is a powerful tool for estimating the GPP of sugarcane croplands in Brazil and the USA. This study clearly highlights the potential of combining eddy covariance technology, satellite-based remote sensing technology, and data-driven models for better understanding and monitoring the phenology and GPP of sugarcane croplands under different climate and management practices.

Bioenergy from sugarcane production is considered a key mitigation strategy for global warming. Improving its representation in land surface models is important to further understand the interactions between climate and bioenergy production, supporting accurate climate projections and decision‐making. This study aimed to calibrate and evaluate the Joint UK Land Environment Simulator (JULES) for climate impact assessments in sugarcane. A dataset composed of soil moisture, water and carbon fluxes and biomass measurements from field experiments across Brazil was used to calibrate and evaluate JULES‐crop and JULES‐BE parametrizations. The ability to predict the spatiotemporal variability of sugarcane yields and the impact of climate change was also tested at five Brazilian microregions. Parameters related to sugarcane allometry, crop growth and development were derived and tested for JULES‐crop and JULES‐BE, together with the response to atmospheric carbon dioxide, temperature and low‐water availability (CTW‐response). Both parametrizations showed comparable performance to other sugarcane dynamic models, with a root mean squared error of 6.75 and 6.05 t ha⁻¹ for stalk dry matter for JULES‐crop and JULES‐BE, respectively. The parametrizations were also able to replicate the average yield patterns observed in the five microregions over 30 years when the yield gap factors were taken into account, with the correlation (r) between simulated and observed interannual variability of yields ranging from 0.33 to 0.56. An overall small positive trend was found in future yield projections of sugarcane using the new calibrations, with exception of the Jataí mesoregion which showed a clear negative trend for the SSP5 scenario from the years 2070 to 2100. Our simulations showed that an abrupt negative impact on sugarcane yields is expected if daytime temperatures above 35°C become more frequent. The newly calibrated version of JULES can be applied regionally and globally to help understand the interactions between climate and bioenergy production.