Converting simulated total dry matter to fresh marketable yield for field vegetables at a range of nitrogen supply levels

Simultaneous analysis of economic and environmental performance of horticultural crop production requires qualified assumptions on the effect of management options, and particularly of nitrogen (N) fertilisation, on the net returns of the farm. Dynamic soil-plant-environment simulation models for agro-ecosystems are frequently applied to predict crop yield, generally as dry matter per area, and the environmental impact of production. Economic analysis requires conversion of yields to fresh marketable weight, which is not easy to calculate for vegetables, since different species have different properties and special market requirements. Furthermore, the marketable part of many vegetables is dependent on N availability during growth, which may lead to complete crop failure under sub-optimal N supply in tightly calculated N fertiliser regimes or low-input systems. In this paper we present two methods for converting simulated total dry matter to marketable fresh matter yield for various vegetables and European growth conditions, taking into consideration the effect of N supply: (i) a regression based function for vegetables sold as bulk or bunching ware and (ii) a population approach for piecewise sold row crops. For both methods, to be used in the context of a dynamic simulation model, parameter values were compiled from a literature survey. Implemented in such a model, both algorithms were tested against experimental field data, yielding an Index of Agreement of 0.80 for the regression strategy and 0.90 for the population strategy. Furthermore, the population strategy was capable of reflecting rather well the effect of crop spacing on yield and the effect of N supply on product grading

Wildlife vulnerability and risk maps for combined pollutants

Ecological risk and vulnerability maps can be used to improve the analysis of pollutant risks and communication to stakeholders. Often, such maps are made for one pollutant at the time. We used the results of wildlife vulnerability analysis, a novel trait-based risk assessment approach, to map overall vulnerability of habitats in Denmark to various metals and one insecticide. These maps were combined with maps of estimated soil concentrations for the same compounds divided by their Maximum Permissible Concentrations. This combination yielded relative risk maps that can be used to assess where the highest risk conditions to wildlife from these individual pollutants in Denmark occur (hot spot identification). In order to show how cumulative risk maps can be made, the maps of the individual pollutants were combined assuming different mechanisms of joint toxicity: no addition, concentration addition, antagonism and synergism. The study demonstrated that with an accurate set of geographical and ecological data one can use the results of vulnerability analysis to make relevant ecological risk maps that show hot spot areas for risks of single or cumulative risks from soil pollutant