Abstract: Organic agriculture aims to produce food while establishing an ecological balance to augment ecosystem services (ES) and has been rapidly expanding in the world since the 1980s. Recently, however, in several European countries, including Denmark, organic farmers have converted back to conventional farming. Hence, understanding how agricultural ES are affected by the number of years since conversion to organic farming is imperative for policy makers to guide future agricultural policy. In order to investigate the long-term effects of conversion to organic farming on ES we performed i) a model simulation case study by applying the Daisy model to simulate 14 different conversion scenarios for a Danish farm during a 65 year period with increasing number of years under organic farming, and ii) an on-farm case study in Denmark with one conventional farm, one organic farm under conversion, and three organic farms converted 10, 15 and 58 years ago, respectively. Both the model simulation case study and the on-farm case study showed that non-marketable ES values increased with increasing number of years under organic farming. Trade-offs between marketable and non-marketable ES were not evident, since also marketable ES values generally showed an increasing trend, except when the price difference between organic and conventional products in the model simulation study was the smallest, and when an alfalfa pre-crop in the on-farm case study resulted in a significantly higher level of plant available nitrogen, which boosted the yield and the associated marketable ES of the subsequent winter rye crop. These results indicate a possible benefit of preserving long-term organic farms and could be used to argue for agricultural policy interventions to offset further reduction in the number of organic farms or the land area under organic farming.

Abstract: We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

Abstract: This paper deals with the problem of predicting biomass and grain protein content using improved particle filtering (IPF) based on minimizing the Kullback-Leibler divergence. The performances of IPF are compared with those of the conventional particle filtering (PF) in two comparative studies. In the first one, we apply IPF and PF at a simple dynamic crop model with the aim to predict a single state variable, namely the winter wheat biomass, and to estimate several model parameters. In the second study, the proposed IPF and the PF are applied to a complex crop model (AZODYN) to predict a winter-wheat quality criterion, namely the grain protein content. The results of both comparative studies reveal that the IPF method provides a better estimation accuracy than the PF method. The benefit of the IPF method lies in its ability to provide accuracy related advantages over the PF method since, unlike the PF which depends on the choice of the sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of this sampling distribution, which also utilizes the observed data. The performance of the proposed method is evaluated in terms of estimation accuracy, root mean square error, mean absolute error and execution times.