Agroscope

This study examines how Common Agricultural Policy (CAP) payment intensity and national governance quality jointly shape technical efficiency in agricultural land use. The sample covers the 27 EU member states over 2005–2022, giving 486 balanced observations. We estimate a per-hectare panel stochastic frontier model. Per-hectare normalisation lowers the maximum variance inflation factor from 14.98 to 3.25. CAP payment intensity is the inverse hyperbolic sine of the implicit subsidy per hectare of utilised agricultural area (UAA). This measure correlates with directly reported product subsidies at r = 0.9994. The governance interaction is the central result. The CAP × WGI term is positive and significant across pooled, event-study, Mundlak, control-function, and no-dummy specifications (δ₃ between +0.008 and +0.018, p < 0.001 in five of six specifications). The stand-alone CAP effect weakens when subsidies are removed from the dependent variable, so we report it as secondary. A latent class model identifies two production technologies. Capital elasticity is positive in both classes, which resolves the negative aggregate capital coefficient. Efficiency rankings shift across specifications (Spearman ρ ≈ 0.4 between pooled and panel estimates), so we treat country rankings as indicative. The marginal CAP effect on inefficiency changes sign near a governance z-score of about 0.87. We interpret all results as conditional associations. They support governance-aware targeting of CAP payments rather than a prescriptive redesign.

Tillage greatly modifies soil structure, yet existing approaches to modelling tillage-induced soil structural change remain largely qualitative or over-simplified. Here, we present a quantitative, energy-based reformulation of the classical tillage equation that predicts soil fragmentation from the initial soil state and the applied energy. The new model partitions tillage energy into surface creation through fragmentation, displacement of existing soil fragments, and plastic deformation of the soil. Soil moisture and mechanical properties control the energy partitioning among these processes. As fragmentation progresses, an increasing proportion of tillage energy is dissipated through the displacement of existing fragments, whereas at elevated soil water contents energy is consumed by plastic deformation. Soil fragmentation resulting in the creation of new fragment surface area scales with the remaining energy. Literature-derived soil fragmentation data from drop-shatter tests and tillage experiments were used for model parameterisation. The model was subsequently evaluated against separate, independent literature datasets not used for parameterisation, covering tillage-induced soil fragmentation across a range of soil conditions. Illustrative applications demonstrate the model’s ability to capture (i) the texture-dependent soil workability range and (ii) the diminishing effectiveness of repeated tillage operations. We outline how the model-derived fragment surface area can be linked to fragment size distributions under simplifying assumptions. Assuming spherical fragment geometry and a Weibull distribution of fragment sizes allows an estimation of the tillage-induced pore size distribution. Altogether, our physically grounded model provides a basis for predicting tillage-induced soil structural change and can be incorporated into agroecosystem models. Further model refinement would benefit from datasets that jointly quantify tillage energy input, soil water status, as well as pre- and post-tillage soil structure and hydraulic properties, enabling a more mechanistic description of the transition from brittle fragmentation to plastic deformation.

In Amazonia pastures are usually established after deforestation by sowing Urochloa grasses. Productivity declines due to nitrogen (N) limitation. Integrating N2-fixing legumes and grasses with Biological Nitrification Inhibition (BNI) capacity are considered as options for improving pasture sustainability. We studied grass-alone (GA) and grass–legume (GL) pastures on seven farms in Colombian Amazonia. Each treatment included grass with either low (Urochloa brizantha), intermediate (U. decumbens), or high (U. humidicola) BNI capacity. Biomass yields were measured over six harvests. Total N and δ15N of grasses and legumes and mineral N in topsoil (0–0.1 m) were analyzed in the first harvest. GL pastures with high BNI U. humidicola showed a biomass yield benefit of 1.6 t ha−1 over six harvests and 9.3 kg N ha−1 higher N yield for the first harvest compared to GA pastures. More than 70

The traditional calibration approach for process-based models, such as DayCent, consists of the iterative adjustment of model parameters and comparison of the simulated total N2O flux to measured observations. However, the contributions of individual production pathways, namely nitrification and denitrification, are uncertain. Here, N2O emissions from the soil of sugar beet plots with control (Null) and mineral (NPK) fertilizer treatments were measured by a static chamber technique. The isotopic composition of emitted N2O was analyzed to identify the N2O production pathways. The latter showed that denitrification was the predominant source of N2O emissions at this site. The model’s default settings strongly overestimated the contributions of nitrification. This incorrect allocation of N2O emissions to nitrification could partly be explained by the model’s tendency to underestimate the soil water content during the growing season. DayCent model parameters were also manually adjusted to better represent the observation derived contributions of nitrification and denitrification. Although, this “expert-informed approach”, showed a slightly lower performance concerning the cumulative N2O flux (Null: RMSE = 0.37 kg N ha−1 yr−1, NPK: RMSE = 0.50 kg N ha−1 yr−1) than the traditional calibration (Null: RMSE = 0.15 kg N ha−1 yr−1, NPK: RMSE = 0.10 kg N ha−1 yr−1), it may be considered as more representative because it better reflected the higher contribution from denitrification shown by the isotope data. This study demonstrates that the inclusion of observational methods, such as isotope measurements, can provide important insight into model function and improve pathway-specific estimation of N2O emissions in DayCent.

Both policy-makers and agricultural economists place hope in result-based payment schemes for farmers, also in the realm of nitrate leaching to drinking water reserves. It is first shown that it is useful to refine this view, distinguishing between driver-, pressure- and state-related approaches. We then present four mitigation projects with different angles for financial transfers to farmers. Through interviews with project managers and additional document analysis it is determined that the trustworthiness of land managers, correct assumptions on causalities and hydrogeological areas of contribution, good group dynamics, fast hydrogeological responses and transaction costs are the factors determining which of the approaches, if any, is most effective. This should enable informed policy choices for projects where nitrate concentrations have to be reduced.