Abstract: Soil water percolation is a key process in the life cycle of water in fields, watersheds, and river basins. The Environmental Policy Integrated Climate (EPIC) and the Agricultural Policy/Environmental eXtender (APEX) are continuous models developed for evaluating the environmental effects of agricultural management. Traditionally, these models have simulated soil water percolation processes using a tipping-bucket approach, with the rate of flow limited by the saturated hydraulic conductivity. This simple approach often leads to inaccuracy in simulating elevated soil water conditions where soil water content (SWC) levels may remain above field capacity under prolonged wet weather periods or limited drainage. To overcome this deficiency, a new sub-model, the variable saturation hydraulic conductivity (VSHC) method, was developed for simulating soil water percolation processes using a nonlinear equation to estimate the effective hydraulic conductivity as a function of the SWC and soil properties. The VSHC method was evaluated at three sites in the United States and two sites in Europe. In addition, a numerical solution of the Richards equation was used as a benchmark for SWC comparison. Results show that the VSHC method substantially improves the accuracy of the SWC simulation in long-term simulations, particularly during wet periods. At the watershed scale, results on the Riesel Y2 watershed indicate that the VSHC method enhances model performance in the high-flow regime of channel peak flows because of the improved estimation of SWC, which implies that the improved SWC simulation at the field scale is beneficial to hydrologic modeling at the watershed scale.

Abstract: LAI of winter wheat (Triticum aestivum L.) and soil water content of the topsoil (200 mm) and of the subsoil (500 mm) were considered as state variables of a dynamic soil-crop system. This system was assumed to progress according to a Bayesian probabilistic state space model, in which real values of LAI and soil water content were daily introduced in order to correct the model trajectory and reach better future evolution. The chosen crop model was mini STICS which can reduce the computing and execution times while ensuring the robustness of data processing and estimation. To predict simultaneously state variables and model parameters in this non-linear environment, three techniques were used: extended Kalman filtering (EKF), particle filtering (PF), and variational filtering (VF). The significantly improved performance of the VF method when compared to EKF and PF is demonstrated. The variational filter has a low computational complexity and the convergence speed of states and parameters estimation can be adjusted independently. Detailed case studies demonstrated that the root mean square error of the three estimated states (LAI and soil water content of two soil layers) was smaller and that the convergence of all considered parameters was ensured when using VF. Assimilating measurements in a crop model allows accurate prediction of LAI and soil water content at a local scale. As these biophysical properties are key parameters in the crop-plant system characterization, the system has the potential to be used in precision farming to aid farmers and decision makers in developing strategies for site-specific management of inputs, such as fertilizers and water irrigation.

Abstract: The diversity in farmers’ objectives and responses to external drivers is usually not considered in integrated assessment studies that investigate impacts and adaptation to climate and socio-economic change. Here, we present an approach to assess how farmers’ stated objectives relate to their currently implemented practices and to preferred adaptation options, and we discuss what this implies for assessments of future changes. We based our approach on a combination of multi-criteria decision-making methods. We consistently assessed the importance of farmers’ objectives and adaptation preferences from what farmers say (based on interviews), from what farmers actually do (by analysing current farm performance) and from what farmers want (through a selected alternative farm plan). Our study was performed for six arable farms in Flevoland, a province in the Netherlands. Based on interviews with farmers, we reduced the long list of possible objectives to the most important ones. The objectives we assessed included maximization of economic result and soil organic matter, and minimization of gross margin variance, working hours and nitrogen balance. In our sample, farmers’ stated preferences in objectives were often not fully reflected in realized farming practices. Adaptation preferences of farmers largely resembled their current performance, but generally involved a trend towards stated preferences. Our results suggest that in Flevoland, although farmers do have more objectives, in practical decision-making they focus on economic result maximization, while for strategic decision-making they account for objectives influencing long-term performance and indicators associated with sustainability, in this case soil organic matter.