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 (CHL_l) 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, CHL_land the relation to leaf nitrogen (N_l) 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 CHL_l and N_l concentrations in 93 fields representing crop- and grasslands of the five landscapes. Furthermore, empirical relationships between field measurements (LAI, CHL_l and N_l 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 CHL_l 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 CHL_l 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 N_l 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, CHL_l and N_l.

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.

Given the problem of climate change and the requirements laid down by the European Union in the field of gradual decarbonization of production, it is necessary to implement solutions of reducing greenhouse gas (GHG) emissions into agricultural practice. This research paper aimed to evaluate the carbon footprint and life-cycle costs of grain maize production in various tillage systems. The material for the analyses was data from 2015–2017 collected on 15 farms located in the Wielkopolska region (Poland) and growing maize for grain in three tillage systems: conventional, reduced, and no-tillage. The life-cycle assessment and life-cycle costing methodologies were applied to assess the GHG emissions and costs associated with the grain maize production in the stages from “cradle-to-farm gate”, i.e., from obtaining raw materials and producing means for agricultural production, through the processes of maize cultivation to grain harvesting. The calculated values of the carbon footprint indicator for maize production in conventional, reduced, and no-tillage systems were 2347.4, 2353.4, and 1868.7 CO₂ eq. ha⁻¹, respectively. The largest source of GHG emissions was the use of nitrogen fertilizers. Non-inversion tillage with cover crops and leaving a large amount of crop residues in the field increased the sequestration of organic carbon and contributed to a significant reduction of the carbon footprint in maize production. The conventional tillage system demonstrated the highest overall life-cycle costs per hectare.

Agriculture is one of the main factors with a direct impact on the natural environment (soil, water and air). An increased interest in the environmental impact of agricultural production results is due to—among other factors—significant human interference in the natural circulation of nutrients, posing a potential threat to the balance of ecosystems. Under current conditions, it is necessary to develop comprehensive diagnostic methods to control production processes in a way that would reduce costs and environmental burden throughout the product’s life cycle. Only a holistic approach that integrates environmental and economic analysis meets the criteria of analysis complexity, which is one of the main goals of methodical analysis of sustainable development. The article presents the results of the integrated environmental and economic assessment of selected crops. Maize and rapeseed production were assessed using the life cycle assessment (LCA) and life cycle costing (LCC) methodologies. The analysis was carried out on farms representing plant- and animal-based farming types. The conclusion presented in the study was based on the data from a study group consisting of 69 private commercial farms located in two regions of Poland. The calculated carbon footprint of both of winter rape and grain maize production was found to be higher in animal farming types. Pig farming type presented the highest overall costs of these crops, based on the approach of the LCC. Inclusion of carbon sequestration to the assessment of greenhouse warming potential allowed for the reduction of the net global warming potential (GWP) impact associated with the production of the analyzed crops. In both crops, mineral fertilization was the main factor influencing both the total carbon footprint and the LCC.