• Energy Research

The intra-specific biodiversity of agricultural crops is very significant and likely to provide the single majoropportunity to cope with the effects of the changing climate on agricultural ecosystems. Assessment of adaptivecapacity must rely on quantitative descriptions of plant responses to environmental factors (e.g. soil wateravailability, temperature). Moreover climate scenario needs to be downscaled to the spatial scale relevant to cropand farm management. Distributed models of crop response to environmental forcing might be used for thispurpose, but severely constrained by the very scarce knowledge on variety-specific values of model parameters,thus limiting the potential exploitation of intra-specific biodiversity towards adaptation.We have developed an approach towards this objective that relies on two complementary elements:a)a distributed model of the soil [U+FFFD] plant [U+FFFD] atmosphere system to downscale climate scenariosThe intra-specific biodiversity of agricultural crops is very significant and likely to provide the single major opportunity to cope with the effects of the changing climate on agricultural ecosystems. Assessment of adaptive capacity must rely on quantitative descriptions of plant responses to environmental factors (e.g. soil water availability, temperature). Moreover climate scenario needs to be downscaled to the spatial scale relevant to crop and farm management. Distributed models of crop response to environmental forcing might be used for this purpose, but severely constrained by the very scarce knowledge on variety-specific values of model parameters, thus limiting the potential exploitation of intra-specific biodiversity towards adaptation. We have developed an approach towards this objective that relies on two complementary elements:a) a distributed model of the soil - plant - atmosphere system to downscale climate scenarios to landscape units, where generic model parameters for each species are used;b) a data base on climatic requirements of as many varieties as feasible for each species relevant to the agricultural production system of a given region.By means of this approach, the adaptability of some olive cultivars was evaluated in a composite (hills and plains) area of Southern Italy (Valle Telesina, Campania Region, about 20.000 ha). The yearly average temperature is 22.5 °C and rainfall ranges between 600 and 900 mm. Two different climate scenarios were considered: current climate (1961-1990) and future climate (2021-2050). Future climate scenarios at low spatial resolution were generated with general circulation models (AOGCM) and down-scaled by means of a statistical model (Tomozeiu et al., 2007). The climate was represented by daily observations of minimum, maximum temperature and precipitation on a regular grid with a spatial resolution of 35 km; 50 realizations were used for future climate.The soil water regime of 45 soil units was described for the two climate scenarios by using an hydrological distributed model (SWAP). For 11 olive cultivars, the yield response function to soil water regime was determined through the re-analysis of experimental data (unpublished or derived from scientific literature). According to these responses, cultivar-specific threshold values of soil water (or evapotranspiration) deficit were defined. The soil water regime calculated by the distributed model was compared with the threshold values to identify cultivars compatible with present and expected climates. The operation is repeated for a set of realizations of each climate scenario. This analysis is performed in a distributed manner, i.e. using the time series for each model grid to assess possible variations in the extent and spatial distribution of cultivated area of olive cultivars.In the study area future climate scenarios predict an increase of monthly minimum and maximum air temperature of about 2°C during the summer (June, July and August) and a reduction of rainfall in autumn.Spatial pattern of cultivars' distribution, according their threshold values and soil water regime, was determined in the present and future climate scenarios, thus assessing variations in cultivars' adaptability to future climate with respect to the present.

This is the 2024-1 release of the ICOS final quality ecosystem data product that includes eddy covariance fluxes, meteorological observations, ancillary and biometric data and full metadata at 73 labelled ICOS stations in the ecosystem domain. The archives contain more detailed description of the different data files contained in the archives. Measurements have been collected using the following instructions: ICOS Ecosystem Instructions for Air Meteorological Measurements (TA, RH, PA, WS, WD), https://doi.org/10.18160/NHEG-4KWW ICOS Ecosystem Instructions for Turbulent Flux Measurements of CO2, Energy and Momentum, https://doi.org/10.18160/QWV4-639G

Abstract. The responses of crop functioning to changing climate and atmospheric CO2 concentration ([CO2]) could have large effects on food production, and impact carbon, water and energy fluxes, causing feedbacks to climate. To simulate the responses of temperate crops to changing climate and [CO2], accounting for the specific phenology of crops mediated by management practice, we present here the development of a process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), which integrates a generic crop phenology and harvest module and a very simple parameterization of nitrogen fertilization, into the land surface model (LSM) ORCHIDEEv196, in order to simulate biophysical and biochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but it is tested here for maize and winter wheat, with the phenological parameterizations of two European varieties originating from the STICS agronomical model. We evaluate the ORCHIDEE-CROP (v0) model against eddy covariance and biometric measurements at 7 winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO2 fluxes (NEE), latent heat and sensible heat fluxes. Additional measurements of leaf area index (LAI), aboveground biomass and yield are used as well. Evaluation results reveal that ORCHIDEE-CROP (v0) reproduces the observed timing of crop development stages and the amplitude of pertaining LAI changes in contrast to ORCHIDEEv196 in which by default crops have the same phenology than grass. A near-halving of the root mean square error of LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m2 m−2 is obtained between ORCHIDEEv196 and ORCHIDEE-CROP (v0) across the 7 study sites. Improved crop phenology and carbon allocation lead to a general good match between modelled and observed aboveground biomass (with a normalized root mean squared error (NRMSE) of 11.0–54.2 %), crop yield, as well as of the daily carbon and energy fluxes with NRMSE of ~9.0–20.1 and ~9.4–22.3 % for NEE, and sensible and latent heat fluxes, respectively. The model data mistfit for energy fluxes are within uncertainties of the measurements, which themselves show an incomplete energy balance closure within the range 80.6–86.3 %. The remaining discrepancies between modelled and observed LAI and other variables at specific sites are partly attributable to unrealistic representation of management events. In addition, ORCHIDEE-CROP (v0) is shown to have the ability to capture the spatial gradients of carbon and energy-related variables, such as gross primary productivity, NEE, sensible heat fluxes and latent heat fluxes, across the sites in Europe, an important requirement for future spatially explicit simulations. Further improvement of the model with an explicit parameterization of nutrition dynamics and of management, is expected to improve its predictive ability to simulate croplands in an Earth System Model.

This is the 2023-2 INTERIM release of the ICOS final quality ecosystem data product that includes eddy covariance fluxes, meteorological observations, ancillary and biometric data and full metadata at 39 labelled ICOS stations in the ecosystem domain. The archives contain more detailed description of the different data files contained in the archives. Please note that the 2023 measurements included in this interim release don't cover the full year, but do essentially cover the growing season. Measurements have been collected using the following instructions: ICOS Ecosystem Instructions for Air Meteorological Measurements (TA, RH, PA, WS, WD), https://doi.org/10.18160/NHEG-4KWW ICOS Ecosystem Instructions for Turbulent Flux Measurements of CO2, Energy and Momentum, https://doi.org/10.18160/QWV4-639G

The responses of crop plants to changing climate and CO2 could have large effects on food production, and impact carbon, water and energy fluxes, causing feedbacks to climate. To simulate the responses of temperate crops to changing climate and CO2, accounting for the specific phenology of crops mediated by management practice, we develop a process-oriented terrestrial biogeochemical model ORCHIDEE-CROP, which integrates a generic crop phenology and harvest module and a very simple parameterization of nitrogen fertilization, into the DGVM ORCHIDEEv196, in order to simulate biophysical and biogeochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but it is tested here for maize and winter wheat, with the phenological parameterizations of two European varieties. We evaluate this new model against eddy covariance and biometric measurements at 7 winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are Net Ecosystem Exchange (NEE), latent heat and sensible heat fluxes. Site additional measurements of leaf area index (LAI), aboveground biomass and yield are used as well. Evaluation results reveal that ORCHIDEE-CROP reproduces the observed timing of crop development stages and the amplitude of pertaining LAI changes in contrast to ORCHIDEEv196, that by default applies to crops the same phenology of grass. A near-halving of the root mean square error of LAI from 2.38±0.77 m2 m-2 to 1.08±0.34 m2 m-2 is obtained between ORCHIDEEv196 and ORCHIDEE-CROP across the 7 study sites. Improved crop phenology and carbon allocation lead to a general good match between modelled and observed aboveground biomass [with a normalized root mean squared error (NRMSE) of 11.0%-54.2%], crop yield, as well as of carbon and energy fluxes with NRMSE of ~9.0-20.1% and ~9.4-22.3% for NEE, and sensible and latent heat fluxes, respectively. The model data misfits for energy fluxes are within uncertainties of the measurements, which show an incomplete energy balance closure within the range 80.6-86.3%. The remaining discrepancies between modelled and observed LAI and other variables at specific sites are partly attributable to unrealistic representation of human management. In addition, ORCHIDEE-CROP is shown to have the ability to capture the spatial gradients of both biogeochemical (gross primary productivity and NEE) and biophysical (sensible and latent heat fluxes) variables across the sites in Europe, an important requirement for future spatially explicit simulations. Further improvement of the model with an explicit parameterization of nutrition dynamics and management, is expected to improve its predictive ability to simulate croplands in an Earth System Model

The prediction of crop phenology in the future climate is useful to identify options of agriculture adaptation to climate change.In this work, thermal sums were used to estimate phenological stages of a maize crop in the Sele Plain (Campania, Italy)under different climate scenarios, taking into account different sowing date options. Risks of heat spells during floweringwere also evaluated. This analysis indicates a trend towards an earlier optimal sowing date and shorter growing seasons for 3maize varieties of different maturity classes. It shows, moreover, that earlier sowing will not avoid risks of critical temperaturesduring flowering.