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Olesen, J. E., Jabloun, M., & Schelde, K. (2014). Reconciling estimates of climate change effects on nitrate leaching from agricultural crops. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Nitrate leaching from agricultural systems constitutes a severe environmental effect in regions with valuable groundwater resources and vulnerable aquatic ecosystems. Therefore cropping systems should in many parts of Europe reduce the amount of nitrate leached from the root zone. Since soil nitrogen transformation and loss processes are highly influenced by climate, including temperature and precipitation, estimates of climate change effects on nitrate leaching is in high demand for evaluating future groundwater and surface water protection policies. Modelling studies using both the FASSET and Daisy models for cereal crops as well as arable crop rotations in Denmark have shown increased nitrate leaching under projected climate change. Sensitivity analyses using these models have shown a higher response to changes in temperature than to precipitation, although in particular precipitation responses differ between soil types. Simulations for crop rotations show that current catch crop management may not be sufficient to maintain low nitrate leaching levels in future. These effects of temperature and precipitation as well as crop management are confirmed in an empirical analysis of nitrate leaching from a long-term cropping system experiment in Denmark. The main uncertainties on climate change effects on future nitrate leaching appears to be related to effects of climate change on soil organic matter and thus on the amount of soil total N available for mineralization as well as the effects of enhanced atmospheric CO2 concentration on crop residue quality and N mineralization.
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Sharif, B., Olesen, J. E., & Schelde, K. (2014). Statistical learning approach for modelling the effects of climate change on oilseed rape yield. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Statistical learning is a fairly new term referring to a set of supervised and unsupervised modelling and prediction techniques. It is based on traditional statistics but has been highly enhanced inspired by developments in machine learning and data mining. The main focus of statistical learning is to estimate the functions that quantify relations between several parameters and observed responses. These functions are further used for prediction, inference or a combination of both. For a particular case of quantitative responses, regularization techniques in regression are developed to overcome the weaknesses of ordinary least square (OLS) regression in prediction. These new shrinkage methods outperform OLS for prediction, especially in large datasets. In this study, a large dataset of field experiments on winter oilseed rape in Denmark for 22 years (1992 to 2013) was collected. Biweekly climatic data along with sowing date, harvest date, soil type and previous crop are considered as the explanatory variables. Yield of winter oilseed rape is considered as response variable. LASSO and Elastic Nets are the regularization techniques used to estimate the functions. Hold-one-out cross validation method for testing the prediction power reveals that these techniques are much useful in both prediction and inference. Since these techniques are included in recent versions of some software packages (e.g. R), they can be easily implemented by users at any level. The estimated function (model) is further used to predict the oilseed rape yield responses to climate change for several scenarios using representative weather data produced by a weather generator.
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Cammarano, D., Rötter, P., Ewert, F., Palosuo, T., Bindi, M., Kersebaum, K. C., et al. (2013). Challenges for Agro-Ecosystem Modelling in Climate Change Risk Assessment for major European Crops and Farming systems. (pp. 555–564).
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Yin, X., Kersebaum, K. C., Kollas, C., Armas-Herrera, C. M., Baby, S., Beaudoin, N., et al. (2016). Uncertainty in simulating N uptakes, N leaching and N use efficiency in crop rotation systems across Europe.. Berlin (Germany).
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Nendel, C., Thorburn, P., Melzer, D., Cerri, C. E. P., Claessens, L., Aggarwal, P. K., et al. (2016). Soil nitrogen mineralisation simulated by crop models across different environments and the consequences for model improvement.. Berlin (Germany).
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