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Approximately 65% of anthropogenic emissions of N2O, a potent greenhouse gas (GHG), originate from soils at a global scale, and particularly after N fertilisation of the main crops in Europe. Thanks to their capacity to fix atmospheric N2 through biological fixation, legumes can reduce N fertilizer use, and possibly N2O emissions. Nevertheless, the decomposition of crop organic matter during the crop cycle and residue decomposition, and possibly the N fixation process itself, could lead to N2O emissions. The objective of this study was to quantify N2O emissions from a dry pea crop (Pisum sativum, harvested at maturity) and from the subsequent crops in comparison with N2O emissions from wheat and oilseed rape crops, fertilized or not, in various rotations. A field experiment was conducted over 4 consecutive years to compare the emissions during the pea crop, in comparison with those during the wheat (fertilized or not) or oilseed rape crops, and after the pea crop, in comparison with other preceding crops. N2O fluxes were measured using static chambers. In spite of low N2O fluxes, mainly due to the site's soil characteristics, fluxes during the crop were significantly lower for pea and unfertilized wheat than for fertilized wheat and oilseed rape. The effect of the preceding crop was not significant, while soil mineral N at harvest was higher after the pea crop. These results should be confirmed over a wider range of soil types. Nevertheless, they demonstrate the absence of N2O emissions linked to the symbiotic N fixation process, and allow us to estimate the decrease in N2O emissions by 20–25% through including one pea crop in a three-year rotation. On a larger scale, this reduction of GHG emissions at field level has to be added to the decrease due to the reduced production and transport of the N fertilizer not applied to the pea crop.

The response of crops 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 the climate. To simulate the response of temperate crops to changing climate and [CO2], which accounts for the specific phenology of crops mediated by management practice, we describe 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 is tested here using 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 seven winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO2 fluxes (net ecosystem exchange, NEE), latent heat, and sensible heat fluxes. Additional measurements of leaf area index (LAI) and aboveground biomass and yield are used as well. Evaluation results revealed that ORCHIDEE-CROP (v0) reproduced the observed timing of crop development stages and the amplitude of the LAI changes. This is in contrast to ORCHIDEEv196 where, by default, crops have the same phenology as grass. A halving of the root mean square error for LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m2 m−2 was obtained when ORCHIDEEv196 and ORCHIDEE-CROP (v0) were compared across the seven study sites. Improved crop phenology and carbon allocation led to a good match between modeled and observed aboveground biomass (with a normalized root mean squared error (NRMSE) of 11.0–54.2 %), crop yield, daily carbon and energy fluxes (with a NRMSE of  ∼  9.0–20.1 and  ∼  9.4–22.3 % for NEE), and sensible and latent heat fluxes. The simulated yields for winter wheat and maize from ORCHIDEE-CROP (v0) showed a good match with the simulated results from STICS for three sites with available crop yield observations, where the average NRMSE was  ∼  8.8 %. The model data misfit for energy fluxes were within the uncertainties of the measurements, which themselves showed an incomplete energy balance closure within the range 80.6–86.3 %. The remaining discrepancies between the modeled and observed LAI and other variables at specific sites were partly attributable to unrealistic representations of management events by the model. ORCHIDEE-CROP (v0) has the ability to capture the spatial gradients of carbon and energy-related variables, such as gross primary productivity, NEE, and sensible and latent heat fluxes across the sites in Europe, which is an important requirement for future spatially explicit simulations. Further improvement of the model, with an explicit parameterization of nutritional dynamics and management, is expected to improve its predictive ability to simulate croplands in an Earth system model.

Leaf nitrogen and leaf surface area influence the exchange of gases between terrestrial ecosystems and the atmosphere, and play a significant role in the global cycles of carbon, nitrogen and water. The purpose of this study is to use field-based and satellite remote-sensing-based methods to assess leaf nitrogen pools in five diverse European agricultural landscapes located in Denmark, Scotland (United Kingdom), Poland, the Netherlands and Italy. REGFLEC (REGularized canopy reFLECtance) is an advanced image-based inverse canopy radiative transfer modelling system which has shown proficiency for regional mapping of leaf area index (LAI) and leaf chlorophyll (CHLl) using remote sensing data. In this study, high spatial resolution (10–20 m) remote sensing images acquired from the multispectral sensors aboard the SPOT (Satellite For Observation of Earth) satellites were used to assess the capability of REGFLEC for mapping spatial variations in LAI, CHLland the relation to leaf nitrogen (Nl) data in five diverse European agricultural landscapes. REGFLEC is based on physical laws and includes an automatic model parameterization scheme which makes the tool independent of field data for model calibration. In this study, REGFLEC performance was evaluated using LAI measurements and non-destructive measurements (using a SPAD meter) of leaf-scale CHLl and Nl concentrations in 93 fields representing crop- and grasslands of the five landscapes. Furthermore, empirical relationships between field measurements (LAI, CHLl and Nl and five spectral vegetation indices (the Normalized Difference Vegetation Index, the Simple Ratio, the Enhanced Vegetation Index-2, the Green Normalized Difference Vegetation Index, and the green chlorophyll index) were used to assess field data coherence and to serve as a comparison basis for assessing REGFLEC model performance. The field measurements showed strong vertical CHLl gradient profiles in 26% of fields which affected REGFLEC performance as well as the relationships between spectral vegetation indices (SVIs) and field measurements. When the range of surface types increased, the REGFLEC results were in better agreement with field data than the empirical SVI regression models. Selecting only homogeneous canopies with uniform CHLl distributions as reference data for evaluation, REGFLEC was able to explain 69% of LAI observations (rmse = 0.76), 46% of measured canopy chlorophyll contents (rmse = 719 mg m−2) and 51% of measured canopy nitrogen contents (rmse = 2.7 g m−2). Better results were obtained for individual landscapes, except for Italy, where REGFLEC performed poorly due to a lack of dense vegetation canopies at the time of satellite recording. Presence of vegetation is needed to parameterize the REGFLEC model. Combining REGFLEC- and SVI-based model results to minimize errors for a \"snap-shot\" assessment of total leaf nitrogen pools in the five landscapes, results varied from 0.6 to 4.0 t km−2. Differences in leaf nitrogen pools between landscapes are attributed to seasonal variations, extents of agricultural area, species variations, and spatial variations in nutrient availability. In order to facilitate a substantial assessment of variations in Nl pools and their relation to landscape based nitrogen and carbon cycling processes, time series of satellite data are needed. The upcoming Sentinel-2 satellite mission will provide new multiple narrowband data opportunities at high spatio-temporal resolution which are expected to further improve remote sensing capabilities for mapping LAI, CHLl and Nl.

Improved management of nitrogen (N) in agriculture is necessary to achieve a sustainable balance between the production of food and other biomass, and the unwanted effects of N on water pollution, greenhouse gas emissions, biodiversity deterioration and human health. To analyse farm N-losses and the complex interactions within farming systems, efficient methods for identifying emissions hotspots and evaluating mitigation measures are therefore needed. The present paper aims to fill this gap at the farm and landscape scales. Six agricultural landscapes in Poland (PL), the Netherlands (NL), France (FR), Italy (IT), Scotland (UK) and Denmark (DK) were studied, and a common method was developed for undertaking farm inventories and the derivation of farm N balances, N surpluses and for evaluating uncertainty for the 222 farms and 11 440 ha of farmland included in the study. In all landscapes, a large variation in the farm N surplus was found, and thereby a large potential for reductions. The highest average N surpluses were found in the most livestock-intensive landscapes of IT, FR, and NL; on average 202 ± 28, 179 ± 63 and 178 ± 20 kg N ha−1 yr−1, respectively. All landscapes showed hotspots, especially from livestock farms, including a special UK case with large-scale landless poultry farming. Overall, the average N surplus from the land-based UK farms dominated by extensive sheep and cattle grazing was only 31 ± 10 kg N ha−1 yr−1, but was similar to the N surplus of PL and DK (122 ± 20 and 146 ± 55 kg N ha−1 yr−1, respectively) when landless poultry farming was included. We found farm N balances to be a useful indicator for N losses and the potential for improving N management. Significant correlations to N surplus were found, both with ammonia air concentrations and nitrate concentrations in soils and groundwater, measured during the period of N management data collection in the landscapes from 2007–2009. This indicates that farm N surpluses may be used as an independent dataset for validation of measured and modelled N emissions in agricultural landscapes. No significant correlation was found with N measured in surface waters, probably because of spatial and temporal variations in groundwater buffering and biogeochemical reactions affecting N flows from farm to surface waters. A case study of the development in N surplus from the landscape in DK from 1998–2008 showed a 22% reduction related to measures targeted at N emissions from livestock farms. Based on the large differences in N surplus between average N management farms and the most modern and N-efficient farms, it was concluded that additional N-surplus reductions of 25–50%, as compared to the present level, were realistic in all landscapes. The implemented N-surplus method was thus effective for comparing and synthesizing results on farm N emissions and the potentials of mitigation options. It is recommended for use in combination with other methods for the assessment of landscape N emissions and farm N efficiency, including more detailed N source and N sink hotspot mapping, measurements and modelling.

Nitrous oxide (N2O) emissions from agricultural land are variable at the landscape scale due to variability in land use, management, soil type, and topography. A field experiment was carried out in a typical mixed farming landscape in Denmark, to investigate the main drivers of variations in N2O emissions, measured using static chambers. Measurements were made over a period of 20 months, and sampling was intensified during two weeks in spring 2009 when chambers were installed at ten locations or fields to cover different crops and topography and slurry was applied to three of the fields. N2O emissions during spring 2009 were relatively low, with maximum values below 20 ng N m−2 s−1. This applied to all land use types including winter grain crops, grasslands, meadows, and wetlands. Slurry application to wheat fields resulted in short-lived two-fold increases in emissions. The moderate N2O fluxes and their moderate response to slurry application were attributed to dry soil conditions due to the absence of rain during the four previous weeks. Cumulative annual emissions from two arable fields that were both fertilized with mineral fertilizer and manure were large (17 kg N2O-N ha−1 yr−1 and 5.5 kg N2O-N ha−1 yr−1) during the previous year when soil water conditions were favourable for N2O production during the first month following fertilizer application. Our findings confirm the importance of weather conditions as well as nitrogen management on N2O fluxes.

We analyze how biases of meteorological drivers impact the calculation of ecosystem CO2, water and energy fluxes by models. To do so, we drive the same ecosystem model by meteorology from gridded products and by meteorology from local observation at eddy-covariance flux sites. The study is focused on six flux tower sites in France spanning across a climate gradient of 7–14 °C annual mean surface air temperature and 600–1040 mm mean annual rainfall, with forest, grassland and cropland ecosystems. We evaluate the results of the ORCHIDEE process-based model driven by meteorology from four different analysis data sets against the same model driven by site-observed meteorology. The evaluation is decomposed into characteristic time scales. The main result is that there are significant differences in meteorology between analysis data sets and local observation. The phase of seasonal cycle of air temperature, humidity and shortwave downward radiation is reproduced correctly by all meteorological models (average R2 = 0.90). At sites located in altitude, the misfit of meteorological drivers from analysis data sets and tower meteorology is the largest. We show that day-to-day variations in weather are not completely well reproduced by meteorological models, with R2 between analysis data sets and measured local meteorology going from 0.35 to 0.70. The bias of meteorological driver impacts the flux simulation by ORCHIDEE, and thus would have an effect on regional and global budgets. The forcing error, defined by the simulated flux difference resulting from prescribing modeled instead of observed local meteorology drivers to ORCHIDEE, is quantified for the six studied sites at different time scales. The magnitude of this forcing error is compared to that of the model error defined as the modeled-minus-observed flux, thus containing uncertain parameterizations, parameter values, and initialization. The forcing error is on average smaller than but still comparable to model error, with the ratio of forcing error to model error being the largest on daily time scale (86%) and annual time scales (80%). The forcing error incurred from using a gridded meteorological data set to drive vegetation models is therefore an important component of the uncertainty budget of regional CO2, water and energy fluxes simulations, and should be taken into consideration in up-scaling studies.

Process based models have been developed to simulate ammonia (NH3) volatilization after surface slurry spreading, but none of them have managed to realistically represent slurry infiltration into the soil and the development of a specific slurry interface that modifies surface temperature and humidity conditions. To account for these physical effects, it is proposed to add to the model a layer of slurry on the surface of the soil. It is hypothesized that the slurry layer can be regarded as a soil layer from the hydrological point of view, characterized by its specific hydraulic parameters, and whose thickness depends on the quantity and bulk density of the slurry dry matter. An assessment was performed by comparisons with measurements of NH3 volatilization fluxes from two specific datasets. The addition of the slurry layer clearly improved the simulations of both the NH3 volatilization flux and ammoniacal nitrogen (NHX-N) distribution in the soil. This concept together with its parameterization proved to be relevant for accounting for effect of the slurry application rates on volatilization, and subsequent abatement obtained with band spreading. The efficiency factor was improved from -0.35 to 0.71. This concept also succeeded in describing the slurry layer state over the whole range from slurry ponding at the surface to the dried-out state, via a slurry in an intermediate drying state. This was actually a prerequisite for the good simulation of NH3 volatilization over the whole volatilization event. This approach is promising for extension to various organic materials present at the soil surface.

EORTC trial 22921 examined the addition of preoperative or postoperative chemotherapy to preoperative radiotherapy in patients with rectal cancer. After a median follow-up of 5 years, chemotherapy—irrespective of timing—significantly improved local control. Adjuvant chemotherapy did not improve survival, but the Kaplan-Meier curves diverged, suggesting possible delayed benefit. Here, we report the updated long-term results. We randomly assigned patients with clinical stage T3 or T4 resectable rectal cancer to receive preoperative radiotherapy with or without concomitant chemotherapy before surgery followed by either adjuvant chemotherapy or surveillance. Randomisation was done using minimisation with factors of institution, sex, T stage, and distance from the tumour to the anal verge. Study coordinators, clinicians, and patients were aware of assignment. Radiotherapy consisted of 45 Gy to the posterior pelvis in 25 fractions of 1·8 Gy over 5 weeks. Each course of chemotherapy consisted of fluorouracil (350 mg/m2 per day intravenous bolus) and folinic acid (leucovorin; 20 mg/m2 per day intravenous bolus). For preoperative chemotherapy, two courses were given (during weeks 1 and 5 of radiotherapy). Adjuvant chemotherapy was given in four cycles, every 3 weeks. The primary endpoint was overall survival. This analysis was done by intention to treat. The trial is registered with ClinicalTrials.gov, number NCT00002523. 1011 patients were randomly assigned to treatment between April, 1993, and March, 2003 (252 to preoperative radiotherapy and 253 to each of the other three groups). After a median follow-up of 10·4 years (IQR 7·8–13·1), 10-year overall survival was 49·4% (95% CI 44·6–54·1) for the preoperative radiotherapy group and 50·7% (45·9–55·2) for the preoperative radiotherapy and chemotherapy group (HR 0·99, 95% CI 0·83–1·18; p=0·91). 10-year overall survival was 51·8% (95% CI 47·0–56·4) for the adjuvant chemotherapy group and 48·4% (43·6–53·0) for the surveillance group (HR 0·91, 95% CI 0·77–1·09, p=0·32). 10-year disease-free survival was 44·2% (95% CI 39·5–48·8) for the preoperative radiotherapy group and 46·4% (41·7–50·9) for the preoperative radiotherapy and chemotherapy group (HR 0·93, 95% CI 0·79–1·10; p=0·38). 10-year disease-free survival was 47·0% (95% CI 42·2–51·6) for the adjuvant chemotherapy group and 43·7% (39·1–48·2) for the surveillance group (HR 0·91, 95% CI 0·77–1·08, p=0·29). At 10 years, cumulative incidence of local relapse was 22·4% (95% CI 17·1–27·6) with radiotherapy alone, 11·8% (7·8–15·8) with neoadjuvant radiotherapy and chemotherapy, 14·5% (10·1–18·9) with radiotherapy and adjuvant chemotherapy and 11·7% (7·7–15·6) with both adjuvant and neoadjuvant chemotherapy (p=0·0017). There was no difference in cumulative incidence of distant metastases (p=0·52). The frequency of long-term side-effects did not differ between the four groups (p=0·22). Adjuvant fluorouracil-based chemotherapy after preoperative radiotherapy (with or without chemotherapy) does not affect disease-free survival or overall survival. Our trial does not support the current practice of adjuvant chemotherapy after preoperative radiotherapy with or without chemotherapy. New treatment strategies incorporating neoadjuvant chemotherapy are required. EORTC, US National Cancer Institute, Programme Hospitalier de Recherche Clinique, Ligue contre le Cancer Comité du Doubs.

Spatial interactions within a landscape may lead to large inputs of reactive nitrogen (Nr) transferred from cultivated areas and farms to oligotrophic ecosystems and induce environmental threats such as acidification, nitric pollution or eutrophication of protected areas. The paper presents a new methodology to estimate Nr fluxes at the landscape scale by taking into account spatial interactions between landscape elements. This methodology includes estimates of indirect Nr emissions due to short-range atmospheric and hydrological transfers. We used the NitroScape model which integrates processes of Nr transformation and short-range transfer in a dynamic and spatially distributed way to simulate Nr fluxes and budgets at the landscape scale. Four configurations of NitroScape were implemented by taking into account or not the atmospheric, hydrological or both pathways of Nr transfer. We simulated Nr fluxes, especially direct and indirect Nr emissions, within a test landscape including pig farms, croplands and unmanaged ecosystems. Simulation results showed the ability of NitroScape to simulate patterns of Nr emissions and recapture for each landscape element and the whole landscape. NitroScape made it possible to quantify the contribution of both atmospheric and hydrological transfers to Nr fluxes, budgets and indirect Nr emissions. For instance, indirect N2O emissions were estimated at around 21% of the total N2O emissions. They varied within the landscape according to land use, meteorological and soil conditions as well as topography. This first attempt proved that the NitroScape model is a useful tool to estimate the effect of spatial interactions on Nr fluxes and budgets as well as indirect Nr emissions within landscapes. Our approach needs to be further tested by applying NitroScape to several spatial arrangements of agro-ecosystems within the landscape and to real and larger landscapes.

Ammonia concentration and fluxes were measured above a growing triticale field for two months during May and June 2010 at the NitroEurope crop site in Grignon (Fr-Gri) near Paris, France. The measurement campaign started 15 days following a 40 kg N ha−1 application of an ammonium nitrate solution. A new mini-wedd (Wet Effluent Denuder) flow injection analyser with three channels (ROSAA, RObust and Sensitive Ammonia Analyser) was used to measure NH3 fluxes using the aerodynamic gradient method. The measured ammonia concentrations varied from 0.01 to 39 μg NH3 m−3 and were largely influenced by advection from the nearby farm. The ammonia fluxes ranged from -560 to 220 ng NH3 m−2 s−1 and averaged -29 ng NH3 m−2 s−1. During some periods the large deposition fluxes could only be explained by a very small surface resistance, which may be partly due to the high concentrations of certain acid gases (HNO3 and SO2) observed in this suburban area. Ammonia emissions were also observed. The canopy compensation point Cc was around 1.5 μg NH3 m−3 on average. The canopy emission potential Γc (Cc normalised for the temperature response of the Henry equilibrium) decreased over the course of the measurement campaign from Γc = 2200 to Γc = 450, the latter value being close to the median stomatal emission potential (Γs) and lower than the median ground emission potential (Γg) for managed ecosystems reported in the literature. The temporal dynamics of the measured NH3 flux compared well with the Surfatm-NH3 model using fitted parameters. The subjectivity of the model fitting is discussed based on a sensitivity analysis.