Fruit load governs transpiration of olive trees

We tested the hypothesis that whole-tree water consumption of olives (Olea europaea L.) is fruit load-dependent and investigated the driving physiological mechanisms. Fruit load was manipulated in mature olives grown in weighing-drainage lysimeters. Fruit was thinned or entirely removed from trees at three separate stages of growth: early, mid and late in the season. Tree-scale transpiration, calculated from lysimeter water balance, was found to be a function of fruit load, canopy size and weather conditions. Fruit removal caused an immediate decline in water consumption, measured as whole-plant transpiration normalized to tree size, which persisted until the end of the season. The later the execution of fruit removal, the greater was the response. The amount of water transpired by a fruit-loaded tree was found to be roughly 30% greater than that of an equivalent low- or nonyielding tree. The tree-scale response to fruit was reflected in stem water potential but was not mirrored in leaf-scale physiological measurements of stomatal conductance or photosynthesis. Trees with low or no fruit load had higher vegetative growth rates. However, no significant difference was observed in the overall aboveground dry biomass among groups, when fruit was included. This case, where carbon sources and sinks were both not limiting, suggests that the role of fruit on water consumption involves signaling and alterations in hydraulic properties of vascular tissues and tree organs.</p

COSMOS-Europe : a European network of cosmic-ray neutron soil moisture sensors

We thank TERENO (Terrestrial Environmental Observatories), funded by the Helmholtz-Gemeinschaft for the financing and maintenance of CRNS stations. We acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) of the research unit FOR 2694 Cosmic Sense (grant no. 357874777) and by the German Federal Ministry of Education of the Research BioökonomieREVIER, Digitales Geosystem – Rheinisches Revier project (grant no. 031B0918A). COSMOS-UK has been supported financially by the UK’s Natural Environment Research Council (grant no. NE/R016429/1). The Olocau experimental watershed is partially supported by the Spanish Ministry of Science and Innovation through the research project TETISCHANGE (grant no. RTI2018-093717-BI00). The Calderona experimental site is partially supported by the Spanish Ministry of Science and Innovation through the research projects CEHYRFO-MED (grant no. CGL2017-86839- C3-2-R) and SILVADAPT.NET (grant no. RED2018-102719-T) and the LIFE project RESILIENT FORESTS (grant no. LIFE17 CCA/ES/000063). The University of Bristol’s Sheepdrove sites have been supported by the UK’s Natural Environment Research Council through a number of projects (grant nos. NE/M003086/1, NE/R004897/1, and NE/T005645/1) and by the International Atomic Energy Agency of the United Nations (grant no. CRP D12014). Acknowledgements. We thank Peter Strauss and Gerhab Rab from the Institute for Land and Water Management Research, Federal Agency for Water Management Austria, Petzenkirchen, Austria. We thank Trenton Franz from the School of Natural Resources, University of Nebraska–Lincoln, Lincoln, NE, United States. We also thank Carmen Zengerle, Mandy Kasner, Felix Pohl, and Solveig Landmark, UFZ Leipzig, for supporting field calibration, lab analysis, and data processing. We furthermore thank Daniel Dolfus, Marius Schmidt, Ansgar Weuthen, and Bernd Schilling, Forschungszentrum Jülich, Germany. The COSMOS-UK project team is thanked for making its data available to COSMOS-Europe. Luca Stevanato is thanked for the technical details about the Finapp sensor. The stations at Cunnersdorf, Lindenberg, and Harzgerode have been supported by Falk Böttcher, Frank Beyrich, and Petra Fude, German Weather Service (DWD). The Zerbst site has been supported by Getec Green Energy GmbH and Jörg Kachelmann (Meteologix AG). The CESBIO sites have been supported by the CNES TOSCA program. The ERA5-Land data are provided by ECMWF (Muñoz Sabater, 2021). The Jena dataset was retrieved at the site of The Jena Experiment, operated by DFG research unit FOR 1451.Peer reviewedPublisher PD

Effects of starvation and subsequent refeeding on the size and nutrient content of the hepatopancreas of Cherax destructor (Decapoda: Parastacidae)

At very high resolution scale (i.e. grid cells of 1 km2), land surface model parameters can be calibrated with eddy-covariance flux data and point-scale soil moisture data. However, measurement scales of eddy-covariance and point-scale data differ substantially. In our study, we investigated the impact of reducing the scale mismatch between surface energy flux and soil moisture observations by replacing point-scale soil moisture data with observations derived from Cosmic-Ray Neutron Sensors (CRNSs) made at larger spatial scales. Five soil and evapotranspiration parameters of the Joint UK Land Environment Simulator (JULES) were calibrated against point-scale and Cosmic-Ray Neutron Sensor soil moisture data separately. We calibrated the model for 12 sites in the USA representing a range of climatic, soil, and vegetation conditions. The improvement in latent heat flux estimation for the two calibration solutions was assessed by comparison to eddy-covariance flux data and to JULES simulations with default parameter values. Calibrations against the two soil moisture products alone did show an advantage for the cosmic-ray technique. However, further analyses of two-objective calibrations with soil moisture and latent heat flux showed no substantial differences between both calibration strategies. This was mainly caused by the limited effect of calibrating soil parameters on soil moisture dynamics and surface energy fluxes. Other factors that played a role were limited spatial variability in surface fluxes implied by soil moisture spatio-temporal stability, and data quality issues

Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity

In the last few years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture data, averaged over several hectares and tens of decimeters in depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutron intensity and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed one to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically based weighting approach. In this work, a revised sensitivity function is used to calculate weighted averages of point data. The function is different from the simple exponential convention by the extraordinary sensitivity to the first few meters around the probe, and by dependencies on air pressure, air humidity, soil moisture, and vegetation. The approach is extensively tested at six distinct monitoring sites: two sites with multiple calibration datasets and four sites with continuous time series datasets. In all cases, the revised averaging method improved the performance of the CRNS products. The revised approach further helped to reveal hidden hydrological processes which otherwise remained unexplained in the data or were lost in the process of overcalibration. The presented weighting approach increases the overall accuracy of CRNS products and will have an impact on all their applications in agriculture, hydrology, and modeling