Application of the DNDC model to the Rodale Institute Farming Systems Trial: challenges for the validation of drainage and nitrate leaching in agroecosystem models
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- Tonitto, C., Li, C., Seidel, R. et al. Nutr Cycl Agroecosyst (2010) 87: 483. doi:10.1007/s10705-010-9354-8
Ecosystem models are increasingly used to guide natural resource management policy decisions. In this study, we build on available agroecosystem policy modeling tools by testing two methodologies for applying the Denitrification-Decomposition (DNDC) model to naturally-drained, temperate grain cropping systems. We used long-term observations from the Rodale Institute Farming Systems Trial (FST) to validate the DNDC model for application to grain cropping systems on silty clay loam soils typical of mid-Atlantic farmlands. Based on modeling efficiency (EF), Theil’s Inequality (U2), and correlation coefficient (r) metrics, the DNDC model showed moderate fit between observations and simulations at annual time scales for drainage (EF = 0.34, U2 = 0.12, r = 0.74) and nitrate leaching (EF = −0.05, U2 = 0.4, r = 0.86). Replication of observed seasonal water flux and nitrate leaching trends were difficult to capture in model simulations, resulting in a weak fit between observations and simulations for drainage (EF = −1.2, U2 = 0.89, r = 0.28) and nitrate leaching (EF = −2.5, U2 = 2.1, r = 0.3). Our comparison of observations and model outcomes highlights the challenge of scaling up belowground fluxes to farm or watershed scales. Ecosystem model representation of water transport generally assumes highly homogeneous soil conditions. In contrast, data from lysimeter sampling represents a small percentage of the total study area and is unlikely to capture average soil field properties. Additionally, our Rodale work highlights the limitation of biogeochemistry models which use vertical mass movement to describe water drainage and nitrate leaching. The application of the DNDC model to the Rodale FST demonstrates that model studies are not a simple substitute for field observation. The predictive utility of model outcomes can only be broadened through rigorous testing against long-term field observations.